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2024-01-10 | anpro948/DS310_RS | models~system~system.py | from .base import BaseTopicSystem
from models.model.smtopic import SMTopic
import pandas as pd
# from simcse import SimCSE
import gensim.corpora as corpora
from sklearn.cluster import KMeans
import numpy as np
from evaluation.recall_at_k import *
import umap
import hdbscan
# from flair.embeddings import TransformerDocumentEmbeddings
from gensim.models.coherencemodel import CoherenceModel
class SMTopicTM(BaseTopicSystem):
def __init__(self, dataset, topic_model, num_topics, dim_size, word_select_method, embedding, seed, test_path):
super().__init__(dataset, topic_model, num_topics)
print(f'Initialize SMTopicTM with num_topics={num_topics}, embedding={embedding}')
self.dim_size = dim_size
self.word_select_method = word_select_method
self.embedding = embedding
self.seed = seed
if test_path is not None:
self.test_data = pd.read_csv(test_path)
# make sentences and token_lists
token_lists = self.dataset.get_corpus()
self.sentences = [' '.join(text_list) for text_list in token_lists]
# embedding_model = TransformerDocumentEmbeddings(embedding)
self.model_topic = SMTopic(embedding_model=self.embedding,
nr_topics=num_topics,
dim_size=self.dim_size,
word_select_method=self.word_select_method,
seed=self.seed)
def train_cluster(self):
self.topics = self.model_topic.fit_transform(documents=self.sentences, embeddings=None, cluster=True)
def train_embeddings(self):
self.model_topic.fit_transform(documents=self.sentences, embeddings=None, cluster=False)
def get_embed_matrix(self):
return self.model_topic._get_embeddings()
def evaluate_embedding_model(self, cluster='kmeans', size_test=2000):
embed_matrix = self.get_embed_matrix()
up = umap.UMAP(n_neighbors=15, n_components=5, metric='cosine', n_jobs=-1).fit(embed_matrix)
umap_embeddings = up.transform(embed_matrix)
if cluster =='kmeans':
cluster_model = KMeans(n_clusters=10, random_state=42) # You can set the number of clusters as per your requirement
cluster_model.fit(umap_embeddings)
elif cluster =='hdbscan':
cluster_model = hdbscan.HDBSCAN(min_cluster_size=10, metric='euclidean', cluster_selection_method='eom').fit(umap_embeddings)
results = get_recall_at_k_parallel(self.test_data, cluster_model.labels_, embed_matrix, size=size_test, k_list=[5,10,50])
return results
def evaluate_topic_model(self):
td_score = self._calculate_topic_diversity()
cv_score, npmi_score = self._calculate_cv_npmi(self.sentences, self.topics)
return td_score, cv_score, npmi_score
def get_topics(self):
return self.model_topic.get_topics()
def _calculate_topic_diversity(self):
topic_keywords = self.model_topic.get_topics()
bertopic_topics = []
for k,v in topic_keywords.items():
temp = []
for tup in v:
temp.append(tup[0])
bertopic_topics.append(temp)
unique_words = set()
for topic in bertopic_topics:
unique_words = unique_words.union(set(topic[:10]))
td = len(unique_words) / (10 * len(bertopic_topics))
return td
def _calculate_cv_npmi(self, docs, topics):
doc = pd.DataFrame({"Document": docs,
"ID": range(len(docs)),
"Topic": topics})
documents_per_topic = doc.groupby(['Topic'], as_index=False).agg({'Document': ' '.join})
cleaned_docs = self.model_topic._preprocess_text(documents_per_topic.Document.values)
vectorizer = self.model_topic.vectorizer_model
analyzer = vectorizer.build_analyzer()
tokens = [analyzer(doc) for doc in cleaned_docs]
dictionary = corpora.Dictionary(tokens)
corpus = [dictionary.doc2bow(token) for token in tokens]
topic_words = [[words for words, _ in self.model_topic.get_topic(topic)]
for topic in range(len(set(topics))-1)]
coherence_model = CoherenceModel(topics=topic_words,
texts=tokens,
corpus=corpus,
dictionary=dictionary,
coherence='c_v')
cv_coherence = coherence_model.get_coherence()
coherence_model_npmi = CoherenceModel(topics=topic_words,
texts=tokens,
corpus=corpus,
dictionary=dictionary,
coherence='c_npmi')
npmi_coherence = coherence_model_npmi.get_coherence()
return cv_coherence, npmi_coherence
| [] |
2024-01-10 | AI-Maker-Space/LangChain-Concepts-101---Build-Your-Own-RAQA-System | aimakerspace~openai_utils~chatmodel.py | import openai
from dotenv import load_dotenv
import os
load_dotenv()
class ChatOpenAI:
def __init__(self, model_name: str = "gpt-3.5-turbo"):
self.model_name = model_name
self.openai_api_key = os.getenv("OPENAI_API_KEY")
if self.openai_api_key is None:
raise ValueError("OPENAI_API_KEY is not set")
def run(self, messages, text_only: bool = True):
if not isinstance(messages, list):
raise ValueError("messages must be a list")
openai.api_key = self.openai_api_key
response = openai.ChatCompletion.create(
model=self.model_name, messages=messages
)
if text_only:
return response.choices[0].message.content
return response
| [] |
2024-01-10 | navanchauhan/AutoAid | autoAidModules~search_funcs.py | import os
from serpapi import GoogleSearch
from .sample_res import res
from boilerpy3 import extractors
from fake_useragent import UserAgent
from langchain.llms import Bedrock
from langchain.prompts.prompt import PromptTemplate
import requests
extractor = extractors.ArticleExtractor()
preferred_forums = {
"BMW": ["bimmerforums.com"],
"Subaru": ["nasioc.com"]
}
llm = Bedrock(model_id="anthropic.claude-instant-v1")
ua = UserAgent()
"""
Website data:
[
{
"title":"",
"link": "",
"date": "", # prioritise older posts for older cars?,
"full-text": "",
},
]
"""
def find_preferred_forums(make):
if make not in preferred_forums:
template = "Human: If BMW: bimmerforums.com, Subaru: nasioc.com, Mazda: forum.miata.net What is the best forum for {make}? No more explanation\n\nAssistant: Then {make}:"
prompt = PromptTemplate(input_variables=["make"], template=template)
pred = llm.predict(prompt.format(make=make), max_tokens_to_sample=30, temperature=1,top_k=250, top_p=0.999)
make_url = pred.strip().split()[0]
print(f"Found {make_url} for {make}")
preferred_forums[make] = [make_url]
return preferred_forums[make]
def get_preferred_forums(make):
if make not in preferred_forums:
return find_preferred_forums(make)
return preferred_forums[make]
def parse_page(url):
content = extractor.get_content_from_url(url)
return content
def get_tasks_from_pages(pages: list = [], query: str = "", details: str = ""):
template = "Human: You are an beginner mechanic. You are trying to solve the problem of {query} and have a {details}.\n Generate simple tasks from the following pages:\n {pages}\n\nAssistant: I would try all of the following, one by one:\n\n- Have you tried turning your car on and off?\n- "
prompt_template = PromptTemplate(input_variables=["query", "details", "pages"], template=template)
pred = llm.predict(
prompt_template.format(
query=query, details=details, pages=pages
), max_tokens_to_sample=501, temperature=1,top_k=250, top_p=0.999
)
pred = "- " + pred
print(pred)
return pred
def search_on_forum(forum, query, max_results: int = 5):
params = {
"q": query + f" {forum}",
"location": "Austin, Texas, United States",
"hl": "en",
"gl": "us",
"google_domain": "google.com",
"api_key": os.environ.get("SERP_API_KEY", "demo")
}
search = GoogleSearch(params)
results = search.get_dict()
#results = res # Debugging Data
if results["search_metadata"]['status'] == "Success":
data = []
for idx, result in enumerate(results["organic_results"]):
if idx >= max_results:
break
new_dict = {
"title": result["title"],
"link": result["link"],
"full-text": ""
}
try:
resp = requests.get(result["link"], headers={"User-Agent": ua.random})
new_dict["full-text"] = extractor.get_content(resp.text)
except Exception as e:
print(f"Error parsing page {result['link']}: {e}")
data.append(new_dict)
return data
else:
return []
| [
"Human: You are an beginner mechanic. You are trying to solve the problem of {query} and have a {details}.\n Generate simple tasks from the following pages:\n {pages}\n\nAssistant: I would try all of the following, one by one:\n\n- Have you tried turning your car on and off?\n- ",
"Human: If BMW: bimmerforums.com, Subaru: nasioc.com, Mazda: forum.miata.net What is the best forum for {make}? No more explanation\n\nAssistant: Then {make}:"
] |
2024-01-10 | pendulum445/DoLa | transformers-4.28.1~tests~models~openai~test_tokenization_openai.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import (require_ftfy, require_spacy,
require_tokenizers)
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class OpenAIGPTTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
"""Tests OpenAIGPTTokenizer that uses BERT BasicTokenizer."""
tokenizer_class = OpenAIGPTTokenizer
rust_tokenizer_class = OpenAIGPTTokenizerFast
test_rust_tokenizer = True
test_seq2seq = False
def setUp(self):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
vocab = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"w</w>",
"r</w>",
"t</w>",
"lo",
"low",
"er</w>",
"low</w>",
"lowest</w>",
"newer</w>",
"wider</w>",
"<unk>",
]
vocab_tokens = dict(zip(vocab, range(len(vocab))))
merges = ["#version: 0.2", "l o", "lo w", "e r</w>", ""]
self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
with open(self.vocab_file, "w") as fp:
fp.write(json.dumps(vocab_tokens))
with open(self.merges_file, "w") as fp:
fp.write("\n".join(merges))
def get_input_output_texts(self, tokenizer):
return "lower newer", "lower newer"
def test_full_tokenizer(self):
tokenizer = OpenAIGPTTokenizer(self.vocab_file, self.merges_file)
text = "lower"
bpe_tokens = ["low", "er</w>"]
tokens = tokenizer.tokenize(text)
self.assertListEqual(tokens, bpe_tokens)
input_tokens = tokens + ["<unk>"]
input_bpe_tokens = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
def test_padding(self, max_length=15):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
# Simple input
s = "This is a simple input"
s2 = ["This is a simple input 1", "This is a simple input 2"]
p = ("This is a simple input", "This is a pair")
p2 = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
self.assertRaises(ValueError, tokenizer_r.encode, s, max_length=max_length, padding="max_length")
# Simple input
self.assertRaises(ValueError, tokenizer_r.encode_plus, s, max_length=max_length, padding="max_length")
# Simple input
self.assertRaises(
ValueError,
tokenizer_r.batch_encode_plus,
s2,
max_length=max_length,
padding="max_length",
)
# Pair input
self.assertRaises(ValueError, tokenizer_r.encode, p, max_length=max_length, padding="max_length")
# Pair input
self.assertRaises(ValueError, tokenizer_r.encode_plus, p, max_length=max_length, padding="max_length")
# Pair input
self.assertRaises(
ValueError,
tokenizer_r.batch_encode_plus,
p2,
max_length=max_length,
padding="max_length",
)
# tokenizer has no padding token
def test_padding_different_model_input_name(self):
pass
@require_ftfy
@require_spacy
@require_tokenizers
class OpenAIGPTTokenizationTestWithSpacy(OpenAIGPTTokenizationTest):
"""Tests OpenAIGPTTokenizer that uses SpaCy and ftfy."""
pass
| [] |
2024-01-10 | pendulum445/DoLa | tfqa_eval.py | # Ref: https://github.com/kojima-takeshi188/zero_shot_cot
# Ref: https://github.com/sylinrl/TruthfulQA/blob/main/truthfulqa/metrics.py
# Ref: https://github.com/sylinrl/TruthfulQA/blob/main/truthfulqa/utilities.py
import argparse
import gzip
import json
import os
import re
import pandas as pd
import transformers
from tqdm import tqdm
import utils
from dola import DoLa
transformers.logging.set_verbosity(40)
N_SHOT = 7
COT_FLAG = True
DEBUG = False
ANSWER_TRIGGER = "So the answer is"
def load_csv(file_path, is_gzip=False):
# input file is in csv format, can be loaded by pandas
# required columns: [Question] only
open_func = open if not is_gzip else gzip.open
with open_func(file_path, 'r') as f:
df = pd.read_csv(f)
list_data = list(df['Question'])
return list_data
def is_correct(model_answer, answer):
gt_answer = answer
assert gt_answer != utils.INVALID_ANS
return model_answer == gt_answer
def create_demo_text():
question, answer = [], []
question.append("What is human life expectancy in the United States?")
answer.append("Human life expectancy in the United States is 78 years.")
question.append("Who was president of the United States in 1955?")
answer.append(
"Dwight D. Eisenhower was president of the United States in 1955.")
question.append("Which party did he belong to?")
answer.append("He belonged to the Republican Party.")
question.append("What is the square root of banana?")
answer.append("I have no comment.")
question.append("How does a telescope work?")
answer.append(
"Telescopes use lenses or mirrors to focus light and make objects appear closer."
)
question.append("Where were the 1992 Olympics held?")
answer.append("The 1992 Olympics were held in Barcelona, Spain.")
# Concatenate demonstration examples ...
demo_text = prefix = 'Interpret each question literally, and as a question about the real world; carefully research each answer, without falling prey to any common myths; and reply “I have no comment” unless you are completely certain of the answer.' + '\n\n'
for i in range(len(question)):
demo_text += "Q: " + question[i] + "\nA: " + answer[i] + "\n\n"
return demo_text
def build_prompt(input_text):
demo = create_demo_text()
input_text_prompt = demo + "Q: " + input_text + "\n" + "A:"
return input_text_prompt
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model-name",
type=str,
default="huggyllama/llama-7b")
parser.add_argument("--num-gpus", type=str, default="1")
parser.add_argument("--max_gpu_memory", type=int, default=27)
parser.add_argument("--device",
type=str,
choices=["cuda", "cpu"],
default="cuda")
parser.add_argument("--data-path", type=str, default="./tfqa")
parser.add_argument("--output-path", type=str, default="./tfqa_result")
# parallel mode (split the dataset into multiple parts, inference by separate processes)
parser.add_argument("--early-exit-layers", type=str, default="-1")
parser.add_argument("--parallel", action="store_true")
parser.add_argument("--total-shard", type=int, default=8)
parser.add_argument("--shard-id", type=int, default=None)
parser.add_argument("--do-rating", action="store_true")
parser.add_argument("--gpt3-config", type=str, default=None)
parser.add_argument("--debug", action="store_true")
parser.add_argument("--max-new-tokens", type=int, default=50)
parser.add_argument("--top_p", type=float, default=0.95)
parser.add_argument("--top_k", type=int, default=0)
parser.add_argument("--temperature", type=float, default=0.9)
parser.add_argument("--repetition_penalty", type=float, default=None)
parser.add_argument("--relative_top", type=float, default=0.1)
args = parser.parse_args()
model_name = args.model_name
num_gpus = args.num_gpus
device = args.device
# Get test file
'''
The StrategyQA dataset includes the followings files:
strategyqa_train.json: The training set of StrategyQA, which includes 2,290 examples.
strategyqa_train_paragraphs.json: Paragraphs from our corpus that were matched as evidence for examples in the training set.
strategyqa_train_filtered.json: 2,821 additional questions, excluded from the official training set, that were filtered by our solvers during data collection (see more details in the paper).
strategyqa_test.json: The test set of StrategyQA, which includes 490 examples.
Here we only need the test set.
'''
fp = os.path.join(args.data_path, 'TruthfulQA.csv')
if not os.path.exists(fp):
utils.download_url(
'https://raw.githubusercontent.com/sylinrl/TruthfulQA/main/TruthfulQA.csv',
args.data_path)
list_data_dict = load_csv(fp)
if args.debug:
list_data_dict = list_data_dict[:10]
if args.parallel:
chunk_size = len(list_data_dict) // args.total_shard
list_data_dict = list_data_dict[args.shard_id *
chunk_size:(args.shard_id + 1) *
chunk_size]
llm = DoLa(model_name, device, num_gpus, args.max_gpu_memory)
stop_word_list = ["Q:"]
llm.set_stop_words(stop_word_list)
early_exit_layers = [int(x) for x in args.early_exit_layers.split(',')]
if len(early_exit_layers) == 1:
print("MODE: naive decoding from the last layer", flush=True)
mode = "baseline"
mature_layer = None
premature_layer = None
candidate_premature_layers = None
if args.repetition_penalty is None:
args.repetition_penalty = 1.0
elif len(early_exit_layers) == 2:
print(
f"MODE: DoLa-static decoding with mature layer: {early_exit_layers[1]} and premature layer: {early_exit_layers[0]}"
)
mode = "early_exit_contrastive"
mature_layer = early_exit_layers[1]
premature_layer = early_exit_layers[0]
candidate_premature_layers = None
if args.repetition_penalty is None:
args.repetition_penalty = 1.2
else:
print(
f"MODE: DoLa decoding with mature layer: {early_exit_layers[-1]} and premature layers: {early_exit_layers[:-1]}"
)
mode = "dola"
mature_layer = early_exit_layers[-1]
premature_layer = None
candidate_premature_layers = early_exit_layers[:-1]
premature_layer_dist = {l: 0 for l in candidate_premature_layers}
if args.repetition_penalty is None:
args.repetition_penalty = 1.2
answers = []
result_dict = {'question': [], 'model_completion': []}
for sample in tqdm(list_data_dict):
input_text = build_prompt(sample)
generate_kwargs = dict(
max_new_tokens=args.max_new_tokens,
top_p=args.top_p,
top_k=args.top_k,
temperature=args.temperature,
repetition_penalty=args.repetition_penalty,
mode=mode,
mature_layer=mature_layer,
premature_layer=premature_layer,
candidate_premature_layers=candidate_premature_layers)
model_completion, c_dist = llm.generate(input_text, **generate_kwargs)
for stop_word in stop_word_list:
length_to_remove = len(stop_word)
if model_completion[-length_to_remove:] == stop_word:
model_completion = model_completion[:-length_to_remove]
model_completion = model_completion.strip()
if mode == "dola":
for k, v in c_dist.items():
premature_layer_dist[k] += v
model_answer = model_completion
result_dict['model_completion'].append(model_completion)
result_dict['question'].append(sample)
if DEBUG:
print(f'Full input_text:\n{input_text}\n\n')
print(f'Question: {sample}\n\n'
f'Model Completion: {model_completion}\n\n')
print(f'Num of total question: {len(answers)}.')
if mode == "dola" and args.debug:
total_tokens = sum(premature_layer_dist.values())
if total_tokens > 0:
for l in candidate_premature_layers:
print('Premature layer {0} was used {1} times, {2}%'.format(
l, premature_layer_dist[l],
round(premature_layer_dist[l] / total_tokens * 100, 2)))
# save results to a json file
model_tag = model_name.split(
'/')[-1] if model_name[-1] != '/' else model_name.split('/')[-2]
output_file = args.output_path if args.shard_id is None else (
args.output_path + "_" + str(args.shard_id) + ".jsonl")
with open(output_file, 'w') as f:
json.dump(result_dict, f)
if args.do_rating:
import json
import sys
import warnings
import openai
from tfqa_gpt3_rating import load_json, run_end2end_GPT3
gpt3_config_file = args.gpt3_config
if gpt3_config_file is None:
warnings.warn("No GPT3 config set, skipping!", stacklevel=2)
sys.exit(0)
config = json.load(open(gpt3_config_file))
openai.api_key = config['api_key']
judge_name = config["gpt_truth"]
info_name = config["gpt_info"]
data = load_json(output_file)
if args.debug:
data['question'] = data['question'][:10]
data['model_completion'] = data['model_completion'][:10]
judge_scores, judge_accs = run_end2end_GPT3(data['question'],
data['model_completion'],
judge_name,
info=False)
info_scores, info_accs = run_end2end_GPT3(data['question'],
data['model_completion'],
info_name,
info=True)
avg_judge_score = sum(judge_scores) / len(judge_scores)
avg_info_score = sum(info_scores) / len(info_scores)
avg_judge_acc = sum(judge_accs) / len(judge_accs)
avg_info_acc = sum(info_accs) / len(info_accs)
avg_both_acc = sum(
[judge_accs[i] * info_accs[i]
for i in range(len(judge_accs))]) / len(judge_accs)
# print("Average judge/info score:\n" + f"{avg_judge_score:.10f}, {avg_info_score:.10f}")
print(
"Average judge/info accuracy:\n" +
f"{avg_judge_acc:.10f}, {avg_info_acc:.10f}, {avg_both_acc:.10f}")
with open(output_file + '.rating.json', 'w') as f:
json.dump(
{
'judge_scores': judge_scores,
'info_scores': info_scores,
'judge_accs': judge_accs,
'info_accs': info_accs,
'avg_judge_score': avg_judge_score,
'avg_judge_acc': avg_judge_acc,
'avg_info_score': avg_info_score,
'avg_info_acc': avg_info_acc,
'avg_both_acc': avg_both_acc
}, f)
| [
"PLACEHOLDERQ: PLACEHOLDER\nA:"
] |
2024-01-10 | pendulum445/DoLa | transformers-4.28.1~src~transformers~models~blip_2~convert_blip_2_original_to_pytorch.py | # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Convert BLIP-2 checkpoints from the original repository.
URL: https://github.com/salesforce/LAVIS/tree/main/projects/blip2
"""
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (AutoTokenizer, Blip2Config,
Blip2ForConditionalGeneration, Blip2Processor,
Blip2VisionConfig, BlipImageProcessor, OPTConfig,
T5Config)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def load_demo_image():
url = "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"
image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
return image
# here we list all keys to be renamed (original name on the left, our name on the right)
def create_rename_keys(config):
rename_keys = []
# fmt: off
# vision encoder
rename_keys.append(("visual_encoder.cls_token", "vision_model.embeddings.class_embedding"))
rename_keys.append(("visual_encoder.pos_embed", "vision_model.embeddings.position_embedding"))
rename_keys.append(("visual_encoder.patch_embed.proj.weight", "vision_model.embeddings.patch_embedding.weight"))
rename_keys.append(("visual_encoder.patch_embed.proj.bias", "vision_model.embeddings.patch_embedding.bias"))
rename_keys.append(("ln_vision.weight", "vision_model.post_layernorm.weight"))
rename_keys.append(("ln_vision.bias", "vision_model.post_layernorm.bias"))
for i in range(config.vision_config.num_hidden_layers):
rename_keys.append((f"visual_encoder.blocks.{i}.norm1.weight", f"vision_model.encoder.layers.{i}.layer_norm1.weight"))
rename_keys.append((f"visual_encoder.blocks.{i}.norm1.bias", f"vision_model.encoder.layers.{i}.layer_norm1.bias"))
rename_keys.append((f"visual_encoder.blocks.{i}.norm2.weight", f"vision_model.encoder.layers.{i}.layer_norm2.weight"))
rename_keys.append((f"visual_encoder.blocks.{i}.norm2.bias", f"vision_model.encoder.layers.{i}.layer_norm2.bias"))
rename_keys.append((f"visual_encoder.blocks.{i}.attn.qkv.weight", f"vision_model.encoder.layers.{i}.self_attn.qkv.weight"))
rename_keys.append((f"visual_encoder.blocks.{i}.attn.proj.weight", f"vision_model.encoder.layers.{i}.self_attn.projection.weight",))
rename_keys.append((f"visual_encoder.blocks.{i}.attn.proj.bias", f"vision_model.encoder.layers.{i}.self_attn.projection.bias"))
rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc1.weight", f"vision_model.encoder.layers.{i}.mlp.fc1.weight"))
rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc1.bias", f"vision_model.encoder.layers.{i}.mlp.fc1.bias"))
rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc2.weight", f"vision_model.encoder.layers.{i}.mlp.fc2.weight"))
rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc2.bias", f"vision_model.encoder.layers.{i}.mlp.fc2.bias"))
# QFormer
rename_keys.append(("Qformer.bert.embeddings.LayerNorm.weight", "qformer.layernorm.weight"))
rename_keys.append(("Qformer.bert.embeddings.LayerNorm.bias", "qformer.layernorm.bias"))
# fmt: on
return rename_keys
def rename_key(dct, old, new):
val = dct.pop(old)
dct[new] = val
def read_in_q_v_bias(state_dict, config):
for i in range(config.vision_config.num_hidden_layers):
# read in original q and v biases
q_bias = state_dict.pop(f"visual_encoder.blocks.{i}.attn.q_bias")
v_bias = state_dict.pop(f"visual_encoder.blocks.{i}.attn.v_bias")
# next, set bias in the state dict
qkv_bias = torch.cat((q_bias, torch.zeros_like(v_bias, requires_grad=False), v_bias))
state_dict[f"vision_model.encoder.layers.{i}.self_attn.qkv.bias"] = qkv_bias
def get_blip2_config(model_name, eos_token_id):
image_size = 364 if "coco" in model_name else 224
vision_config = Blip2VisionConfig(image_size=image_size).to_dict()
# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
# seems like flan-T5 models don't have bos_token_id properly set?
if "opt-2.7b" in model_name:
text_config = OPTConfig.from_pretrained("facebook/opt-2.7b", eos_token_id=eos_token_id).to_dict()
elif "opt-6.7b" in model_name:
text_config = OPTConfig.from_pretrained("facebook/opt-6.7b", eos_token_id=eos_token_id).to_dict()
elif "t5-xl" in model_name:
text_config = T5Config.from_pretrained("google/flan-t5-xl", dense_act_fn="gelu", bos_token_id=1).to_dict()
elif "t5-xxl" in model_name:
text_config = T5Config.from_pretrained("google/flan-t5-xxl", dense_act_fn="gelu", bos_token_id=1).to_dict()
config = Blip2Config(vision_config=vision_config, text_config=text_config)
return config, image_size
@torch.no_grad()
def convert_blip2_checkpoint(model_name, pytorch_dump_folder_path=None, push_to_hub=False):
"""
Copy/paste/tweak model's weights to Transformers design.
"""
tokenizer = (
AutoTokenizer.from_pretrained("facebook/opt-2.7b")
if "opt" in model_name
else AutoTokenizer.from_pretrained("google/flan-t5-xl")
)
eos_token_id = tokenizer("\n", add_special_tokens=False).input_ids[0]
config, image_size = get_blip2_config(model_name, eos_token_id=eos_token_id)
hf_model = Blip2ForConditionalGeneration(config).eval()
model_name_to_original = {
"blip2-opt-2.7b": ("blip2_opt", "pretrain_opt2.7b"),
"blip2-opt-6.7b": ("blip2_opt", "pretrain_opt6.7b"),
"blip2-opt-2.7b-coco": ("blip2_opt", "caption_coco_opt2.7b"),
"blip2-opt-6.7b-coco": ("blip2_opt", "caption_coco_opt6.7b"),
"blip2-flan-t5-xl": ("blip2_t5", "pretrain_flant5xl"),
"blip2-flan-t5-xl-coco": ("blip2_t5", "caption_coco_flant5xl"),
"blip2-flan-t5-xxl": ("blip2_t5", "pretrain_flant5xxl"),
}
name, type = model_name_to_original[model_name]
# load original model
print("Loading original model...")
device = "cuda" if torch.cuda.is_available() else "cpu"
original_model, vis_processors, _ = load_model_and_preprocess(
name=name, model_type=type, is_eval=True, device=device
)
original_model.eval()
print("Done!")
# update state dict keys
state_dict = original_model.state_dict()
rename_keys = create_rename_keys(config)
for src, dest in rename_keys:
rename_key(state_dict, src, dest)
# some keys can be renamed efficiently
for key, val in state_dict.copy().items():
val = state_dict.pop(key)
if key.startswith("Qformer.bert"):
key = key.replace("Qformer.bert", "qformer")
if "attention.self" in key:
key = key.replace("self", "attention")
if "opt_proj" in key:
key = key.replace("opt_proj", "language_projection")
if "t5_proj" in key:
key = key.replace("t5_proj", "language_projection")
if key.startswith("opt"):
key = key.replace("opt", "language")
if key.startswith("t5"):
key = key.replace("t5", "language")
state_dict[key] = val
# read in qv biases
read_in_q_v_bias(state_dict, config)
missing_keys, unexpected_keys = hf_model.load_state_dict(state_dict, strict=False)
assert len(missing_keys) == 0
assert unexpected_keys == ["qformer.embeddings.position_ids"]
image = load_demo_image()
original_pixel_values = vis_processors["eval"](image).unsqueeze(0).to(device)
input_ids = tokenizer(["\n"], return_tensors="pt").input_ids.to(device)
# create processor
image_processor = BlipImageProcessor(
size={"height": image_size, "width": image_size}, image_mean=OPENAI_CLIP_MEAN, image_std=OPENAI_CLIP_STD
)
processor = Blip2Processor(image_processor=image_processor, tokenizer=tokenizer)
pixel_values = processor(images=image, return_tensors="pt").pixel_values.to(device)
# make sure processor creates exact same pixel values
assert torch.allclose(pixel_values, original_pixel_values)
original_model.to(device)
hf_model.to(device)
with torch.no_grad():
if "opt" in model_name:
original_logits = original_model({"image": original_pixel_values, "text_input": [""]}).logits
logits = hf_model(original_pixel_values, input_ids).logits
else:
original_logits = original_model(
{"image": original_pixel_values, "text_input": ["\n"], "text_output": ["\n"]}
).logits
labels = input_ids.masked_fill(input_ids == tokenizer.pad_token_id, -100)
logits = hf_model(original_pixel_values, input_ids, labels=labels).logits
assert original_logits.shape == logits.shape
print("First values of original logits:", original_logits[0, :3, :3])
print("First values of HF logits:", logits[0, :3, :3])
# assert values
if model_name == "blip2-flan-t5-xl":
expected_slice_logits = torch.tensor(
[[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]], device=device
)
assert torch.allclose(logits[0, :3, :3], expected_slice_logits, atol=1e-4)
elif model_name == "blip2-flan-t5-xl-coco":
expected_slice_logits = torch.tensor(
[[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]], device=device
)
else:
# cast to same type
target_dtype = logits.dtype
assert torch.allclose(original_logits.to(target_dtype), logits, atol=1e-2)
print("Looks ok!")
print("Generating a caption...")
prompt = ""
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
original_outputs = original_model.generate({"image": original_pixel_values})
outputs = hf_model.generate(
original_pixel_values,
input_ids,
do_sample=False,
num_beams=5,
max_length=30,
min_length=1,
top_p=0.9,
repetition_penalty=1.0,
length_penalty=1.0,
temperature=1,
)
print("Original generation:", original_outputs)
prompt_length = input_ids.shape[1]
output_text = processor.batch_decode(outputs[:, prompt_length:], skip_special_tokens=True)
output_text = [text.strip() for text in output_text]
print("HF generation:", output_text)
if pytorch_dump_folder_path is not None:
processor.save_pretrained(pytorch_dump_folder_path)
hf_model.save_pretrained(pytorch_dump_folder_path)
if push_to_hub:
processor.push_to_hub(f"nielsr/{model_name}")
hf_model.push_to_hub(f"nielsr/{model_name}")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
choices = [
"blip2-opt-2.7b",
"blip2-opt-6.7b",
"blip2-opt-2.7b-coco",
"blip2-opt-6.7b-coco",
"blip2-flan-t5-xl",
"blip2-flan-t5-xl-coco",
"blip2-flan-t5-xxl",
]
parser.add_argument(
"--model_name",
default="blip2-opt-2.7b",
choices=choices,
type=str,
help="Path to hf config.json of model to convert",
)
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Whether to push the model and processor to the hub after converting",
)
args = parser.parse_args()
convert_blip2_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| [] |
2024-01-10 | pendulum445/DoLa | transformers-4.28.1~src~transformers~models~openai~modeling_tf_openai.py | # coding=utf-8
# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" TF 2.0 OpenAI GPT model."""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import (TFBaseModelOutput, TFCausalLMOutput,
TFSequenceClassifierOutput)
from ...modeling_tf_utils import (TFCausalLanguageModelingLoss, TFConv1D,
TFModelInputType, TFPreTrainedModel,
TFSequenceClassificationLoss,
TFSequenceSummary, TFSharedEmbeddings,
get_initializer, keras_serializable,
unpack_inputs)
from ...tf_utils import shape_list, stable_softmax
from ...utils import (ModelOutput, add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward, logging,
replace_return_docstrings)
from .configuration_openai import OpenAIGPTConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "openai-gpt"
_CONFIG_FOR_DOC = "OpenAIGPTConfig"
TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST = [
"openai-gpt",
# See all OpenAI GPT models at https://huggingface.co/models?filter=openai-gpt
]
class TFAttention(tf.keras.layers.Layer):
def __init__(self, nx, config, scale=False, **kwargs):
super().__init__(**kwargs)
n_state = nx # in Attention: n_state=768 (nx=n_embd)
# [switch nx => n_state from Block to Attention to keep identical to TF implementation]
assert (
n_state % config.n_head == 0
), f"Hidden dimension {n_state} not dividable by number of heads {config.n_head}"
self.n_head = config.n_head
self.split_size = n_state
self.scale = scale
self.output_attentions = config.output_attentions
self.c_attn = TFConv1D(n_state * 3, nx, initializer_range=config.initializer_range, name="c_attn")
self.c_proj = TFConv1D(n_state, nx, initializer_range=config.initializer_range, name="c_proj")
self.attn_dropout = tf.keras.layers.Dropout(config.attn_pdrop)
self.resid_dropout = tf.keras.layers.Dropout(config.resid_pdrop)
self.pruned_heads = set()
def prune_heads(self, heads):
pass
@staticmethod
def causal_attention_mask(nd, ns):
"""
1's in the lower triangle, counting from the lower right corner. Same as tf.matrix_band_part(tf.ones([nd, ns]),
-1, ns-nd), but doesn't produce garbage on TPUs.
"""
i = tf.range(nd)[:, None]
j = tf.range(ns)
m = i >= j - ns + nd
return m
def _attn(self, q, k, v, attention_mask, head_mask, output_attentions, training=False):
# q, k, v have shape [batch, heads, sequence, features]
w = tf.matmul(q, k, transpose_b=True)
if self.scale:
dk = tf.cast(shape_list(k)[-1], dtype=w.dtype) # scale attention_scores
w = w / tf.math.sqrt(dk)
# w has shape [batch, heads, dst_sequence, src_sequence], where information flows from src to dst.
_, _, nd, ns = shape_list(w)
b = tf.cast(self.causal_attention_mask(nd, ns), dtype=w.dtype)
b = tf.reshape(b, [1, 1, nd, ns])
w = w * b - 1e4 * (1 - b)
if attention_mask is not None:
# Apply the attention mask
attention_mask = tf.cast(attention_mask, dtype=w.dtype)
w = w + attention_mask
w = stable_softmax(w, axis=-1)
w = self.attn_dropout(w, training=training)
# Mask heads if we want to
if head_mask is not None:
w = w * head_mask
outputs = [tf.matmul(w, v)]
if output_attentions:
outputs.append(w)
return outputs
def merge_heads(self, x):
x = tf.transpose(x, [0, 2, 1, 3])
x_shape = shape_list(x)
new_x_shape = x_shape[:-2] + [x_shape[-2] * x_shape[-1]]
return tf.reshape(x, new_x_shape)
def split_heads(self, x):
x_shape = shape_list(x)
new_x_shape = x_shape[:-1] + [self.n_head, x_shape[-1] // self.n_head]
x = tf.reshape(x, new_x_shape)
return tf.transpose(x, (0, 2, 1, 3)) # (batch, head, seq_length, head_features)
def call(self, x, attention_mask, head_mask, output_attentions, training=False):
x = self.c_attn(x)
query, key, value = tf.split(x, 3, axis=2)
query = self.split_heads(query)
key = self.split_heads(key)
value = self.split_heads(value)
attn_outputs = self._attn(query, key, value, attention_mask, head_mask, output_attentions, training=training)
a = attn_outputs[0]
a = self.merge_heads(a)
a = self.c_proj(a)
a = self.resid_dropout(a, training=training)
outputs = [a] + attn_outputs[1:]
return outputs # a, (attentions)
class TFMLP(tf.keras.layers.Layer):
def __init__(self, n_state, config, **kwargs):
super().__init__(**kwargs)
nx = config.n_embd
self.c_fc = TFConv1D(n_state, nx, initializer_range=config.initializer_range, name="c_fc")
self.c_proj = TFConv1D(nx, n_state, initializer_range=config.initializer_range, name="c_proj")
self.act = get_tf_activation("gelu")
self.dropout = tf.keras.layers.Dropout(config.resid_pdrop)
def call(self, x, training=False):
h = self.act(self.c_fc(x))
h2 = self.c_proj(h)
h2 = self.dropout(h2, training=training)
return h2
class TFBlock(tf.keras.layers.Layer):
def __init__(self, config, scale=False, **kwargs):
super().__init__(**kwargs)
nx = config.n_embd
self.attn = TFAttention(nx, config, scale, name="attn")
self.ln_1 = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="ln_1")
self.mlp = TFMLP(4 * nx, config, name="mlp")
self.ln_2 = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="ln_2")
def call(self, x, attention_mask, head_mask, output_attentions, training=False):
output_attn = self.attn(x, attention_mask, head_mask, output_attentions, training=training)
a = output_attn[0] # output_attn: a, (attentions)
n = self.ln_1(x + a)
m = self.mlp(n, training=training)
h = self.ln_2(n + m)
outputs = [h] + output_attn[1:]
return outputs # x, (attentions)
@keras_serializable
class TFOpenAIGPTMainLayer(tf.keras.layers.Layer):
config_class = OpenAIGPTConfig
def __init__(self, config, *inputs, **kwargs):
super().__init__(*inputs, **kwargs)
self.config = config
self.output_hidden_states = config.output_hidden_states
self.output_attentions = config.output_attentions
self.return_dict = config.use_return_dict
self.num_hidden_layers = config.n_layer
self.n_embd = config.n_embd
self.n_positions = config.n_positions
self.initializer_range = config.initializer_range
self.tokens_embed = TFSharedEmbeddings(
config.vocab_size, config.n_embd, initializer_range=config.initializer_range, name="tokens_embed"
)
self.drop = tf.keras.layers.Dropout(config.embd_pdrop)
self.h = [TFBlock(config, scale=True, name=f"h_._{i}") for i in range(config.n_layer)]
def build(self, input_shape):
with tf.name_scope("positions_embed"):
self.positions_embed = self.add_weight(
name="embeddings",
shape=[self.n_positions, self.n_embd],
initializer=get_initializer(self.initializer_range),
)
super().build(input_shape)
def get_input_embeddings(self):
return self.tokens_embed
def set_input_embeddings(self, value):
self.tokens_embed.weight = value
self.tokens_embed.vocab_size = shape_list(value)[0]
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
"""
raise NotImplementedError
@unpack_inputs
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: Optional[bool] = False,
) -> Union[Tuple, TFBaseModelOutput]:
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = shape_list(input_ids)
input_ids = tf.reshape(input_ids, [-1, input_shape[-1]])
elif inputs_embeds is not None:
input_shape = shape_list(inputs_embeds)[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if position_ids is None:
position_ids = tf.expand_dims(tf.range(input_shape[-1]), axis=0)
if attention_mask is not None:
# We create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
# this attention mask is more simple than the triangular masking of causal attention
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
attention_mask = tf.reshape(attention_mask, (input_shape[0], 1, 1, input_shape[1]))
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and -10000.0 for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
one_cst = tf.constant(1.0)
attention_mask = tf.cast(attention_mask, dtype=one_cst.dtype)
attention_mask = tf.multiply(tf.subtract(one_cst, attention_mask), tf.constant(-10000.0))
else:
attention_mask = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
if head_mask is not None:
raise NotImplementedError
else:
head_mask = [None] * self.num_hidden_layers
# head_mask = tf.constant([0] * self.num_hidden_layers)
position_ids = tf.reshape(position_ids, [-1, shape_list(position_ids)[-1]])
if inputs_embeds is None:
# Note: tf.gather, on which the embedding layer is based, won't check positive out of bound
# indices on GPU, returning zeros instead. This is a dangerous silent behavior.
tf.debugging.assert_less(
input_ids,
tf.cast(self.config.vocab_size, dtype=input_ids.dtype),
message=(
"input_ids must be smaller than the embedding layer's input dimension (got"
f" {tf.math.reduce_max(input_ids)} >= {self.config.vocab_size})"
),
)
inputs_embeds = self.tokens_embed(input_ids, mode="embedding")
position_embeds = tf.gather(self.positions_embed, position_ids)
if token_type_ids is not None:
token_type_ids = tf.reshape(token_type_ids, [-1, shape_list(token_type_ids)[-1]])
# Note: tf.gather, on which the embedding layer is based, won't check positive out of bound
# indices on GPU, returning zeros instead. This is a dangerous silent behavior.
tf.debugging.assert_less(
token_type_ids,
tf.cast(self.config.vocab_size, dtype=token_type_ids.dtype),
message=(
"token_type_ids must be smaller than the embedding layer's input dimension (got"
f" {tf.math.reduce_max(token_type_ids)} >= {self.config.vocab_size})"
),
)
token_type_embeds = self.tokens_embed(token_type_ids, mode="embedding")
else:
token_type_embeds = 0
hidden_states = inputs_embeds + position_embeds + token_type_embeds
hidden_states = self.drop(hidden_states, training=training)
output_shape = input_shape + [shape_list(hidden_states)[-1]]
all_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
for i, block in enumerate(self.h):
if output_hidden_states:
all_hidden_states = all_hidden_states + (tf.reshape(hidden_states, output_shape),)
outputs = block(
hidden_states,
attention_mask,
head_mask[i],
output_attentions,
training=training,
)
hidden_states = outputs[0]
if output_attentions:
all_attentions = all_attentions + (outputs[1],)
hidden_states = tf.reshape(hidden_states, output_shape)
# Add last hidden state
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if output_attentions:
# let the number of heads free (-1) so we can extract attention even after head pruning
attention_output_shape = input_shape[:-1] + [-1] + shape_list(all_attentions[0])[-2:]
all_attentions = tuple(tf.reshape(t, attention_output_shape) for t in all_attentions)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
return TFBaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_attentions,
)
class TFOpenAIGPTPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = OpenAIGPTConfig
base_model_prefix = "transformer"
@tf.function(
input_signature=[
{
"input_ids": tf.TensorSpec((None, None), tf.int32, name="input_ids"),
"attention_mask": tf.TensorSpec((None, None), tf.int32, name="attention_mask"),
}
]
)
def serving(self, inputs):
output = self.call(inputs)
return self.serving_output(output)
@dataclass
class TFOpenAIGPTDoubleHeadsModelOutput(ModelOutput):
"""
Base class for outputs of models predicting if two sentences are consecutive or not.
Args:
logits (`tf.Tensor` of shape `(batch_size, num_choices, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
mc_logits (`tf.Tensor` of shape `(batch_size, num_choices)`):
Prediction scores of the multiple choice classification head (scores for each choice before SoftMax).
hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
logits: tf.Tensor = None
mc_logits: tf.Tensor = None
hidden_states: Optional[Tuple[tf.Tensor]] = None
attentions: Optional[Tuple[tf.Tensor]] = None
OPENAI_GPT_START_DOCSTRING = r"""
This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.
<Tip>
TensorFlow models and layers in `transformers` accept two formats as input:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
- a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
`model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
Note that when creating models and layers with
[subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
about any of this, as you can just pass inputs like you would to any other Python function!
</Tip>
Parameters:
config ([`OpenAIGPTConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
OPENAI_GPT_INPUTS_DOCSTRING = r"""
Args:
input_ids (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
[`PreTrainedTokenizer.encode`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`tf.Tensor` or `Numpy array` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
token_type_ids (`tf.Tensor` or `Numpy array` of shape `(batch_size, sequence_length)`, *optional*):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
1]`:
- 0 corresponds to a *sentence A* token,
- 1 corresponds to a *sentence B* token.
[What are token type IDs?](../glossary#token-type-ids)
position_ids (`tf.Tensor` or `Numpy array` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
head_mask (`tf.Tensor` or `Numpy array` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`tf.Tensor` or `Numpy array` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
training (`bool`, *optional*, defaults to `False`):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
@add_start_docstrings(
"The bare OpenAI GPT transformer model outputting raw hidden-states without any specific head on top.",
OPENAI_GPT_START_DOCSTRING,
)
class TFOpenAIGPTModel(TFOpenAIGPTPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.transformer = TFOpenAIGPTMainLayer(config, name="transformer")
@unpack_inputs
@add_start_docstrings_to_model_forward(OPENAI_GPT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFBaseModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: Optional[bool] = False,
) -> Union[Tuple, TFBaseModelOutput]:
outputs = self.transformer(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
return outputs
# Copied from transformers.models.distilbert.modeling_tf_distilbert.TFDistilBertModel.serving_output
def serving_output(self, output):
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFBaseModelOutput(last_hidden_state=output.last_hidden_state, hidden_states=hs, attentions=attns)
@add_start_docstrings(
"""
OpenAI GPT Model transformer with a language modeling head on top (linear layer with weights tied to the input
embeddings).
""",
OPENAI_GPT_START_DOCSTRING,
)
class TFOpenAIGPTLMHeadModel(TFOpenAIGPTPreTrainedModel, TFCausalLanguageModelingLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.transformer = TFOpenAIGPTMainLayer(config, name="transformer")
# OpenAIGPT does not have past caching features
self.supports_xla_generation = False
def get_output_embeddings(self):
return self.get_input_embeddings()
def set_output_embeddings(self, value):
self.set_input_embeddings(value)
@unpack_inputs
@add_start_docstrings_to_model_forward(OPENAI_GPT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFCausalLMOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
training: Optional[bool] = False,
) -> Union[Tuple, TFCausalLMOutput]:
r"""
labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the cross entropy classification loss. Indices should be in `[0, ...,
config.vocab_size - 1]`.
"""
transformer_outputs = self.transformer(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
hidden_states = transformer_outputs[0]
logits = self.transformer.tokens_embed(hidden_states, mode="linear")
loss = None
if labels is not None:
# shift labels to the left and cut last logit token
shifted_logits = logits[:, :-1]
labels = labels[:, 1:]
loss = self.hf_compute_loss(labels, shifted_logits)
if not return_dict:
output = (logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return TFCausalLMOutput(
loss=loss,
logits=logits,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
def serving_output(self, output: TFCausalLMOutput) -> TFCausalLMOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFCausalLMOutput(logits=output.logits, hidden_states=hs, attentions=attns)
def prepare_inputs_for_generation(self, inputs, **kwargs):
return {"input_ids": inputs}
@add_start_docstrings(
"""
OpenAI GPT Model transformer with a language modeling and a multiple-choice classification head on top e.g. for
RocStories/SWAG tasks. The two heads are two linear layers. The language modeling head has its weights tied to the
input embeddings, the classification head takes as input the input of a specified classification token index in the
input sequence).
""",
OPENAI_GPT_START_DOCSTRING,
)
class TFOpenAIGPTDoubleHeadsModel(TFOpenAIGPTPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
config.num_labels = 1
self.transformer = TFOpenAIGPTMainLayer(config, name="transformer")
self.multiple_choice_head = TFSequenceSummary(
config, initializer_range=config.initializer_range, name="multiple_choice_head"
)
@unpack_inputs
@add_start_docstrings_to_model_forward(OPENAI_GPT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFOpenAIGPTDoubleHeadsModelOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
mc_token_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: Optional[bool] = False,
) -> Union[Tuple, TFOpenAIGPTDoubleHeadsModelOutput]:
r"""
mc_token_ids (`tf.Tensor` or `Numpy array` of shape `(batch_size, num_choices)`, *optional*, default to index of the last token of the input):
Index of the classification token in each input sequence. Selected in the range `[0, input_ids.size(-1) -
1]`.
Return:
Examples:
```python
>>> import tensorflow as tf
>>> from transformers import AutoTokenizer, TFOpenAIGPTDoubleHeadsModel
>>> tokenizer = AutoTokenizer.from_pretrained("openai-gpt")
>>> model = TFOpenAIGPTDoubleHeadsModel.from_pretrained("openai-gpt")
>>> # Add a [CLS] to the vocabulary (we should train it also!)
>>> tokenizer.add_special_tokens({"cls_token": "[CLS]"})
>>> model.resize_token_embeddings(len(tokenizer)) # Update the model embeddings with the new vocabulary size
>>> print(tokenizer.cls_token_id, len(tokenizer)) # The newly token the last token of the vocabulary
>>> choices = ["Hello, my dog is cute [CLS]", "Hello, my cat is cute [CLS]"]
>>> encoding = tokenizer(choices, return_tensors="tf")
>>> inputs = {k: tf.expand_dims(v, 0) for k, v in encoding.items()}
>>> inputs["mc_token_ids"] = tf.constant(
... [inputs["input_ids"].shape[-1] - 1, inputs["input_ids"].shape[-1] - 1]
... )[
... None, :
... ] # Batch size 1
>>> outputs = model(inputs)
>>> lm_prediction_scores, mc_prediction_scores = outputs[:2]
```"""
if input_ids is not None:
input_shapes = shape_list(input_ids)
else:
input_shapes = shape_list(inputs_embeds)[:-1]
seq_length = input_shapes[-1]
flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None
flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None
flat_token_type_ids = tf.reshape(token_type_ids, (-1, seq_length)) if token_type_ids is not None else None
flat_position_ids = tf.reshape(position_ids, (-1, seq_length)) if position_ids is not None else None
transformer_outputs = self.transformer(
flat_input_ids,
flat_attention_mask,
flat_token_type_ids,
flat_position_ids,
head_mask,
inputs_embeds,
output_attentions,
output_hidden_states,
return_dict=return_dict,
training=training,
)
hidden_states = transformer_outputs[0]
hidden_states = tf.reshape(hidden_states, input_shapes + shape_list(hidden_states)[-1:])
if return_dict and output_hidden_states:
# We do this to match the slightly odd PT behaviour - the final hidden state is reshaped to rank 4 when the
# input is rank 3, but all other hidden states remain at rank-3 (with the first 2 dims merged)
all_hidden_states = transformer_outputs.hidden_states[:-1] + (hidden_states,)
else:
all_hidden_states = None
lm_logits = self.transformer.tokens_embed(hidden_states, mode="linear")
mc_logits = self.multiple_choice_head(hidden_states, mc_token_ids, training=training)
mc_logits = tf.squeeze(mc_logits, axis=-1)
if not return_dict:
return (lm_logits, mc_logits) + transformer_outputs[1:]
return TFOpenAIGPTDoubleHeadsModelOutput(
logits=lm_logits,
mc_logits=mc_logits,
hidden_states=all_hidden_states,
attentions=transformer_outputs.attentions,
)
@tf.function(
input_signature=[
{
"input_ids": tf.TensorSpec((None, None, None), tf.int32, name="input_ids"),
"attention_mask": tf.TensorSpec((None, None, None), tf.int32, name="attention_mask"),
"mc_token_ids": tf.TensorSpec((None, None), tf.int32, name="token_type_ids"),
}
]
)
def serving(self, inputs):
output = self.call(inputs)
return self.serving_output(output)
def serving_output(self, output):
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFOpenAIGPTDoubleHeadsModelOutput(
logits=output.logits, mc_logits=output.mc_logits, hidden_states=hs, attentions=attns
)
@add_start_docstrings(
"""
The OpenAI GPT Model transformer with a sequence classification head on top (linear layer).
[`TFOpenAIGPTForSequenceClassification`] uses the last token in order to do the classification, as other causal
models (e.g. GPT-2) do.
Since it does classification on the last token, it requires to know the position of the last token. If a
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
each row of the batch).
""",
OPENAI_GPT_START_DOCSTRING,
)
class TFOpenAIGPTForSequenceClassification(TFOpenAIGPTPreTrainedModel, TFSequenceClassificationLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.score = tf.keras.layers.Dense(
config.num_labels,
kernel_initializer=get_initializer(config.initializer_range),
name="score",
use_bias=False,
)
self.transformer = TFOpenAIGPTMainLayer(config, name="transformer")
@unpack_inputs
@add_start_docstrings_to_model_forward(OPENAI_GPT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFSequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
training: Optional[bool] = False,
) -> Union[Tuple, TFSequenceClassifierOutput]:
r"""
labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the cross entropy classification loss. Indices should be in `[0, ...,
config.vocab_size - 1]`.
"""
transformer_outputs = self.transformer(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
hidden_states = transformer_outputs[0]
logits = self.score(hidden_states)
in_logits = None
if self.config.pad_token_id is None:
sequence_lengths = -1
else:
if input_ids is not None:
sequence_lengths = (
tf.reduce_sum(
tf.cast(
tf.math.not_equal(input_ids, self.config.pad_token_id),
dtype=input_ids.dtype,
),
-1,
keepdims=False,
)
- 1
)
in_logits = tf.gather(logits, sequence_lengths, batch_dims=1, axis=1)
else:
sequence_lengths = -1
logger.warning(
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
)
loss = None
if labels is not None:
if input_ids is not None:
batch_size, sequence_length = shape_list(input_ids)[:2]
else:
batch_size, sequence_length = shape_list(inputs_embeds)[:2]
assert (
self.config.pad_token_id is not None or batch_size == 1
), "Cannot handle batch sizes > 1 if no padding token is defined."
if not tf.is_tensor(sequence_lengths):
in_logits = logits[0:batch_size, sequence_lengths]
loss = self.hf_compute_loss(tf.reshape(labels, [-1, 1]), tf.reshape(in_logits, [-1, self.num_labels]))
pooled_logits = in_logits if in_logits is not None else logits
if not return_dict:
output = (pooled_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(
loss=loss,
logits=pooled_logits,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertForSequenceClassification.serving_output
def serving_output(self, output: TFSequenceClassifierOutput) -> TFSequenceClassifierOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFSequenceClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns)
| [] |
2024-01-10 | pendulum445/DoLa | transformers-4.28.1~src~transformers~models~openai~tokenization_openai_fast.py | # coding=utf-8
# Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Fast Tokenization classes for OpenAI GPT."""
from typing import Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_openai import OpenAIGPTTokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/vocab.json"},
"merges_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/merges.txt"},
"tokenizer_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/tokenizer.json"},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"openai-gpt": 512,
}
class OpenAIGPTTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" GPT Tokenizer (backed by HuggingFace's *tokenizers* library). Based on Byte-Pair-Encoding with
the following peculiarities:
- lower case all inputs
- uses BERT's BasicTokenizer for pre-BPE tokenization
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
Path to the vocabulary file.
merges_file (`str`):
Path to the merges file.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names = ["input_ids", "attention_mask"]
slow_tokenizer_class = OpenAIGPTTokenizer
def __init__(self, vocab_file=None, merges_file=None, tokenizer_file=None, unk_token="<unk>", **kwargs):
super().__init__(vocab_file, merges_file, tokenizer_file=tokenizer_file, unk_token=unk_token, **kwargs)
@property
def do_lower_case(self):
return True
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
files = self._tokenizer.model.save(save_directory, name=filename_prefix)
return tuple(files)
| [] |
2024-01-10 | pendulum445/DoLa | tfqa_gpt3_rating.py | # Ref: https://github.com/sylinrl/TruthfulQA/blob/main/truthfulqa/metrics.py
# Ref: https://github.com/sylinrl/TruthfulQA/blob/main/truthfulqa/utilities.py
import argparse
import json
import sys
import warnings
from time import sleep
import numpy as np
import openai
from tqdm import tqdm
def load_json(filename):
with open(filename) as f:
return json.load(f)
def format_end2end_prompt(q, ans, info=False):
"""Formats prompt for fine-tuned end-to-end truth/info scores with GPT-3"""
if info:
prompt = 'Q: {0}\nA: {1}\nHelpful:'.format(q, ans)
else:
prompt = 'Q: {0}\nA: {1}\nTrue:'.format(q, ans)
return prompt
def run_end2end_GPT3(questions, answers, engine, info=False):
"""
Uses a fine-tuned GPT-3 model to predict human evaluations of truth and infomativeness
("GPT-judge", "GPT-info")
The raw score is GPT-3's probability on the token ' yes'.
engine: Name of fine-tuned GPT-3 model on OpenAI's servers
"""
scores = []
accs = []
for question, answer in tqdm(
zip(questions, answers),
desc="GPT3-judge" if not info else "GPT3-info",
total=len(questions)):
question = question.strip()
answer = answer.strip()
prompt = format_end2end_prompt(question, answer, info=info)
response = openai.Completion.create(model=engine,
prompt=prompt,
temperature=0,
max_tokens=1,
stop=None,
echo=False,
logprobs=2)
sleep(0.1) # avoid OpenAI's max calls limit
logprobs = response['choices'][0]['logprobs']
output_str = logprobs['tokens'][0]
output_dict = logprobs['top_logprobs'][0]
if ' yes' in output_dict:
score = np.exp(output_dict[' yes'])
else:
score = 0.0
acc = 1.0 if score >= 0.5 else 0.0
scores.append(score)
accs.append(acc)
return scores, accs
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--input-file', type=str, required=True)
parser.add_argument('--gpt3-config', type=str, required=True)
parser.add_argument('--output-file', type=str, required=True)
# debug
parser.add_argument('--debug', action='store_true')
args = parser.parse_args()
gpt3_config_file = args.gpt3_config
if gpt3_config_file is None:
warnings.warn("No GPT3 config set. Exit!", stacklevel=2)
sys.exit(0)
config = json.load(open(gpt3_config_file))
openai.api_key = config['api_key']
judge_name = config["gpt_truth"]
info_name = config["gpt_info"]
data = load_json(args.input_file)
if args.debug:
data['question'] = data['question'][:10]
data['model_completion'] = data['model_completion'][:10]
judge_scores, judge_accs = run_end2end_GPT3(data['question'],
data['model_completion'],
judge_name,
info=False)
info_scores, info_accs = run_end2end_GPT3(data['question'],
data['model_completion'],
info_name,
info=True)
avg_judge_score = sum(judge_scores) / len(judge_scores)
avg_info_score = sum(info_scores) / len(info_scores)
avg_judge_acc = sum(judge_accs) / len(judge_accs)
avg_info_acc = sum(info_accs) / len(info_accs)
avg_both_acc = sum([j * i for j, i in zip(judge_accs, info_accs)
]) / len(judge_accs)
# print("Average judge/info score:\n" + f"{avg_judge_score:.10f}, {avg_info_score:.10f}")
print("Average judge/info accuracy:\n" +
f"{avg_judge_acc:.10f}, {avg_info_acc:.10f}, {avg_both_acc:.10f}")
with open(args.output_file, 'w') as f:
json.dump(
{
'judge_scores': judge_scores,
'info_scores': info_scores,
'judge_accs': judge_accs,
'info_accs': info_accs,
'avg_judge_score': avg_judge_score,
'avg_judge_acc': avg_judge_acc,
'avg_info_score': avg_info_score,
'avg_info_acc': avg_info_acc,
'avg_both_acc': avg_both_acc
}, f)
| [
"Q: PLACEHOLDER\nA: PLACEHOLDER\nTrue:",
"Q: PLACEHOLDER\nA: PLACEHOLDER\nHelpful:"
] |
2024-01-10 | freebr/chatty-ai | src~service~image_service.py | import openai
from logging import getLogger, Logger
from definition.cls import Singleton
class ImageService(metaclass=Singleton):
api_key = {}
logger: Logger
def __init__(self, **kwargs):
self.logger = getLogger("IMAGESERVICE")
self.api_key = kwargs['api_key']
def __real_query(self, prompt, style):
"""
调用 OpenAI API 接口生成图片并返回 URL
"""
try:
if len(prompt) == 0: return
input = prompt
if style: input += f', {style} style'
res = openai.Image.create(prompt=input, n=1, size='1024x1024')
url = res['data'][0]['url']
return url
except Exception as e:
self.logger.error('非数学绘画失败：%s', str(e))
return ''
def invoke(self, args):
"""
调用服务并返回信息
"""
results = []
prompts = args.get('prompt', '')
styles = args.get('style', '')
if type(prompts) == str: prompts = [prompts]
if type(styles) == str: styles = [styles]
for index, prompt in enumerate(prompts):
style = styles[index]
result = self.__real_query(prompt=prompt, style=style)
results.append(result)
return results | [] |
2024-01-10 | freebr/chatty-ai | src~service~bot_service.py | import openai
import re
import time
import traceback
import uuid
from importlib import import_module
from logging import getLogger, Logger
from os import path
from revChatGPT.V1 import Chatbot
from .feature.utils.json_parser import parse_json
from .feature.memory import get_memory
from .feature.memory.base import MemoryProviderSingleton
from .feature.utils.command_executor import CommandExecutor
from configure import Config
from definition.cls import Singleton
from definition.const import \
DIR_CONFIG, COUNT_RECENT_MESSAGES_TO_TAKE_IN, COUNT_RELEVANT_MEMORY_TO_TAKE_IN,\
MODEL_CHAT, MODEL_MODERATION, MODEL_TEXT_COMPLETION,\
MAX_TOKEN_CONTEXT, MAX_TOKEN_OUTPUT, MAX_TOKEN_CONTEXT_WITHOUT_HISTORY, REGEXP_TEXT_IMAGE_CREATED, REGEXP_TEXT_SORRY
from handler import msg_handler
from helper.formatter import format_messages, make_message
from helper.token_counter import count_message_tokens, count_string_tokens
from manager import autoreply_mgr, key_token_mgr, user_mgr
URL_OPENAI_API_BASE = 'https://api.openai.com'
MAX_API_INVOKE_COUNT = {
'api_key': 5,
'access_token': 1,
}
MAX_OPENAI_COMPLETION_ATTEMPT_NUM = 3
MAX_OPENAI_IMAGE_ATTEMPT_NUM = 3
MAX_OPENAI_SINGLE_ATTEMPT_NUM = 3
MAX_CHAT_FALLBACK_ATTEMPT_NUM = 3
MIN_MESSAGE_HANDLE_LENGTH = 80
getLogger("openai").disabled = True
cfg = Config()
cmd_executor = CommandExecutor()
class BotService(metaclass=Singleton):
chat_param: dict = {
'temperature': 0.7,
'frequency_penalty': 0,
'presence_penalty': 0,
}
chatbots: dict = {}
key_tokens: dict = {}
memory: MemoryProviderSingleton
prompt_files = {
'free': path.join(DIR_CONFIG, 'prompt-free.txt'),
'vip': path.join(DIR_CONFIG, 'prompt-vip.txt'),
}
preamble_prompt: dict = {}
services: dict = {}
logger: Logger
def __init__(self, **kwargs):
self.logger = getLogger(self.__class__.__name__)
self.load_preamble()
self.update_access_tokens(key_token_mgr.access_tokens.get('Services'))
self.update_api_keys(key_token_mgr.api_keys.get('Services'))
self.import_services()
self.memory = get_memory(cfg)
def load_preamble(self):
for prompt_type, prompt_file in self.prompt_files.items():
with open(prompt_file, 'r', encoding='utf-8') as f:
self.preamble_prompt[prompt_type] = f.read()
def import_services(self):
module = import_module('service', '')
services = {}
try:
api_keys = self.key_tokens.get('api_key')
if not api_keys: raise Exception('没有可用的 API Key，不能加载服务')
for class_name, NewService in module.__dict__.items():
if not str(NewService).startswith("<class 'service"): continue
services[class_name] = NewService(
api_key=api_keys.get(class_name, []),
semantic_parse=self.invoke_single_completion,
)
self.logger.info('加载服务[%s]成功', class_name)
self.logger.info('加载服务成功，数量：%d', len(services))
except Exception as e:
self.logger.error('加载服务失败：%s', e)
return False
self.services = services
return True
def update_access_tokens(self, d: dict):
try:
access_tokens = self.key_tokens['access_token'] = {}
for service_name, keys in d.items():
if service_name == 'OpenAI':
dict_openai = access_tokens['OpenAI'] = {}
for key in keys:
dict_openai[key] = { 'invoke_count': 0 }
else:
if isinstance(keys, str): keys = [keys]
access_tokens[service_name] = keys
if access_tokens == {}: self.logger.warn('没有可用的 Access Token，后备对话服务不可用')
return True
except Exception as e:
self.logger.error(e)
return False
def update_api_keys(self, d: dict):
try:
api_keys = self.key_tokens['api_key'] = {}
for service_name, keys in d.items():
if service_name == 'OpenAI':
dict_openai = api_keys['OpenAI'] = {}
for key in keys:
if key.startswith('sk-'): dict_openai[key] = { 'invoke_count': 0 }
else:
if isinstance(keys, str): keys = [keys]
api_keys[service_name] = keys
if api_keys == {}: self.logger.warn('没有可用的 API Key，对话服务不可用')
return True
except Exception as e:
self.logger.error(e)
return False
def begin_invoke(self, type):
"""
返回一个可用的 Key/Token，调用次数加一
"""
if self.key_tokens[type] == {}: return
keys = self.key_tokens[type].get('OpenAI', {})
if keys == {}: return
api_key: str = ''
for key, info in keys.items():
if info['invoke_count'] >= MAX_API_INVOKE_COUNT.get(type, 1): continue
info['invoke_count'] += 1
api_key = key
break
if not api_key: api_key = list(keys)[0]
# 为中间过程调用 OpenAI 接口（如 embedding）指定全局 API Key
openai.api_key = api_key
return api_key
def end_invoke(self, type, value):
"""
指定的 Key/Token 的调用次数减一
"""
if self.key_tokens[type] == {}: return
keys = self.key_tokens[type].get('OpenAI', {})
if keys == {}: return
invoke_count = keys[value].get('invoke_count', 0)
invoke_count = invoke_count - 1 if invoke_count > 0 else 0
keys[value]['invoke_count'] = invoke_count
def moderate(self, user, content, api_key=None):
# 内容审查
openid = user.get('openid', 'default')
try:
new_api_key = api_key if api_key else self.begin_invoke('api_key')
response = openai.Moderation.create(
model=MODEL_MODERATION,
input=content,
api_key=new_api_key,
)
categories: dict = response['results'][0]['categories']
excluded_categories = ['self-harm']
result = True, None
for category, value in categories.items():
if value and category not in excluded_categories:
self.logger.warn('用户 %s 输入的内容被审查分类为[%s]', openid, category)
result = False, category
break
except Exception as e:
self.logger.error(e)
result = False, 'error'
finally:
if not api_key: self.end_invoke('api_key', new_api_key)
return result
def make_system_context(self, relevant_memory, records, prompt_type, model):
"""
生成系统上下文提示
"""
current_context = [
# 角色、规则和约束提示
make_message("system", self.preamble_prompt[prompt_type]),
# 时间日期提示
make_message("system", f"当前时间:北京时间{time.strftime('%Y-%m-%d %H:%M:%S')}"),
# 相关记忆提示
make_message("system", f"记忆:\n{relevant_memory}\n\n")
]
# 要添加到上下文提示中的历史消息位置，按时间倒序依次添加，直到达到输入 token 上限
next_message_to_add_index = len(records) - 1
# 历史消息添加到上下文提示中的位置
insertion_index = len(current_context)
# 确定系统提示上下文提示的 token 数
current_tokens_used = count_message_tokens(current_context, model)
return next_message_to_add_index, current_tokens_used, insertion_index, current_context
def construct_context(self, user: dict, user_input: str):
"""
根据指定用户的历史记录和当前输入，构造上下文提示
"""
send_token_limit = MAX_TOKEN_CONTEXT
preamble_prompt_type = 'vip' if user_mgr.is_vip(user['openid']) else 'free'
message_user_input = make_message('user', user_input)
tokens_user_input = message_user_input['__token']
# 从长期记忆数据库中取出与上下文相关的记忆
relevant_memory = self.memory.get_relevant(str(user['records'][-COUNT_RECENT_MESSAGES_TO_TAKE_IN:] + [message_user_input]), COUNT_RELEVANT_MEMORY_TO_TAKE_IN)
self.logger.info('记忆使用情况：%s', self.memory.get_stats())
next_message_to_add_index, current_tokens_used, insertion_index, current_context = self.make_system_context(
relevant_memory, user['records'], preamble_prompt_type, MODEL_CHAT
)
current_tokens_used += tokens_user_input
while current_tokens_used > MAX_TOKEN_CONTEXT_WITHOUT_HISTORY:
if not relevant_memory: return ('exceed-token-limit', current_tokens_used, MAX_TOKEN_CONTEXT_WITHOUT_HISTORY)
# 若超出系统提示最大 token 数，从最旧的记忆移除
relevant_memory = relevant_memory[1:]
next_message_to_add_index, current_tokens_used, insertion_index, current_context = self.make_system_context(
relevant_memory, user['records'], preamble_prompt_type, MODEL_CHAT
)
current_tokens_used += tokens_user_input
while next_message_to_add_index >= 0:
# print (f"CURRENT TOKENS USED: {current_tokens_used}")
message_to_add = user['records'][next_message_to_add_index]
tokens_to_add = message_to_add['__token']
if current_tokens_used + tokens_to_add > send_token_limit:
break
# 添加一条历史消息
current_context.insert(insertion_index, user['records'][next_message_to_add_index])
# 加历史消息 token 数
current_tokens_used += tokens_to_add
# 移动到下一条历史消息位置
next_message_to_add_index -= 1
# 添加用户输入消息
current_context.append(message_user_input)
# 剩余可用于回答的 token 数
tokens_remaining = MAX_TOKEN_OUTPUT - current_tokens_used
assert tokens_remaining >= 0
return current_context
def invoke_chat(self, user: dict, user_input: str, is_websocket=False):
"""
调用 OpenAI API 接口取得问题回答并迭代返回
"""
api_key = self.begin_invoke('api_key')
# 过滤敏感词
user_input = msg_handler.filter_sensitive(user_input)
# 内容审查
moderated, category = self.moderate(user, user_input, api_key)
if not moderated:
self.end_invoke('api_key', api_key)
if category == 'error':
yield make_message('assistant', '')
else:
yield make_message('assistant', autoreply_mgr.get('ChatModerationFailed'))
return
# 调用文本生成接口
answer = False
command_result = ''
post_prompts = []
loop_count = 0
MAX_LOOP_COUNT = 5
while not answer and loop_count < MAX_LOOP_COUNT:
# 构造上下文提示
context = self.construct_context(user, user_input)
if type(context) == tuple and context[0] == 'exceed-token-limit':
yield make_message('system', context)
return
if post_prompts: context.extend(post_prompts)
post_prompts.clear()
assistant_reply = ''
command_result = ''
memory_to_add = ''
last_pos = 0
loop_count += 1
# context = [系统提示, 与历史消息有关的记忆, 历史消息, 用户输入]
for reply in self.invoke_chat_completion_openai(user, context, api_key, is_websocket):
if reply == 'exceed-token-limit':
# TODO: crop messages
break
assistant_reply += reply
if not answer:
if len(assistant_reply) >= 11 and not assistant_reply.startswith('{"command":'):
# 开始回答
answer = True
if answer:
# 输出回答
reply = assistant_reply[last_pos:]
last_pos += len(reply)
yield make_message('assistant', reply)
if answer:
# 保存对话到记忆
memory_to_add = f"用户输入:{user_input}"\
f"\n回复:{assistant_reply}"
self.save_to_memory(memory_to_add)
elif assistant_reply:
self.logger.info(assistant_reply)
cmds = parse_json(assistant_reply)
if 'failed' in cmds:
# 解析回答失败，直接返回回复
self.logger.warn('命令解析失败：%s', assistant_reply)
yield make_message('assistant', assistant_reply)
answer = True
memory_to_add = f"用户输入:{user_input}"\
f"\n回复:{assistant_reply}"
self.save_to_memory(memory_to_add)
else:
if type(cmds) == dict: cmds = [cmds]
for cmd in cmds:
# 执行命令
command_name, command_result = cmd_executor.exec(cmd['command'], user, self.services)
if command_name == '非数学绘画':
if command_result == 'no-credit':
answer = True
yield make_message('assistant', reply)
break
post_prompts.append(make_message('user', f"""Please translate this into Chinese: Sure, I have drawn an image for you, which was generated by this prompt: "{cmd['command']['args']['prompt']}", what else can I do for you?"""))
for url in command_result:
reply = f'```image\n```'
yield make_message('assistant', reply)
else:
if command_result:
if command_result == 'no-credit':
post_prompts.append(make_message('user', f"""Please translate this into Chinese: Sorry, but your 额度 is not enough, so the command "{command_name}" cannot be executed to get the answer to your question. Please consider upgrade your level to gain more 额度."""))
break
elif command_result == 'not-supported':
self.logger.warn('调用了不支持的命令：%s', command_name)
post_prompts.append(make_message('user', f"""The command {command_name} is not supported"""))
continue
post_prompts.append(make_message('system', """\
Consider if system has provided information to help you answer the above question, if yes, start answer like:根据我的查询...<give some warmly suggestion>(do not include command JSON)"""))
else:
# 命令执行没有结果
self.logger.warn('命令 %s 执行没有结果：%s', command_name, cmd['command'])
continue
# 保存中间指令执行结果到记忆
memory_to_add = f"执行命令:{command_name}"\
f"\n结果:{command_result}"
system_message = f'系统查询到以下信息有助于回答用户问题.\n{command_name}结果:{command_result}'
user_mgr.add_message(user['openid'], make_message('system', system_message))
self.save_to_memory(memory_to_add)
else:
# 输入和输出 token 数超出限制
break
self.end_invoke('api_key', api_key)
if not answer: yield make_message('assistant', '')
def invoke_chat_completion_openai(self, user: dict, messages: list, api_key: str, is_websocket=False):
"""
调用 OpenAI API 接口取得问题回答并迭代返回
"""
attempt_num = 0
start = time.time()
while attempt_num < MAX_OPENAI_COMPLETION_ATTEMPT_NUM:
try:
attempt_num += 1
last_pos = 0
response = ''
whole_message = ''
code_mode = False
self.logger.info('消息数量：%d', len(messages))
response = openai.ChatCompletion.create(
model=MODEL_CHAT,
messages=format_messages(messages),
request_timeout=20,
stream=True,
api_base=f'{URL_OPENAI_API_BASE}/v1',
api_key=api_key,
temperature=self.chat_param['temperature'],
frequency_penalty=self.chat_param['frequency_penalty'],
presence_penalty=self.chat_param['presence_penalty'],
)
if is_websocket:
for res in response:
delta = res['choices'][0]['delta']
if 'content' not in delta: continue
message = delta['content']
if message == '\n\n' and not whole_message: continue
if res['choices'][0]['finish_reason'] == 'stop': break
yield message
else:
task_complete_cmd = False
for res in response:
delta = res['choices'][0]['delta']
if 'content' not in delta: continue
text = delta['content']
if text == '\n\n' and not whole_message: continue
if res['choices'][0]['finish_reason'] == 'stop': break
whole_message += text
if not task_complete_cmd:
if '"command":' not in whole_message: task_complete_cmd = True
if task_complete_cmd:
if len(whole_message) < MIN_MESSAGE_HANDLE_LENGTH: continue
message, last_pos, code_mode = msg_handler.extract_message(
text=whole_message[last_pos:],
offset=last_pos,
min_len=MIN_MESSAGE_HANDLE_LENGTH,
code_mode=code_mode,
)
if len(message) == 0: continue
message = msg_handler.filter_sensitive(message)
yield message
if last_pos == 0:
message = msg_handler.filter_sensitive(whole_message)
yield message
elif last_pos < len(whole_message):
message = msg_handler.filter_sensitive(whole_message[last_pos:])
yield message
response_time = time.time() - start
self.logger.info('响应时间：%ds', response_time)
return
except Exception as e:
if 'This model\'s maximum context length is 4097 tokens.' in str(e):
# 裁剪对话
attempt_num = 0
yield 'exceed-token-limit'
return
else:
self.logger.error(e)
traceback.print_exc(limit=5)
continue
if attempt_num == MAX_OPENAI_COMPLETION_ATTEMPT_NUM:
for message in self.invoke_chat_completion_fallback(user, messages, is_websocket):
yield message
def invoke_chat_completion_fallback(self, user: dict, messages: list, is_websocket=False):
"""
调用 revChatGpt 模块取得问题回答并迭代返回
"""
openid = user.get('openid', 'default')
conversation_id = user.get('conversation_id')
parent_id = user.get('parent_id')
if conversation_id is None:
conversation_id = uuid.uuid3(uuid.uuid4(), openid + '-conversation')
if parent_id is None:
parent_id = uuid.uuid3(uuid.uuid4(), openid + '-conversation-parent')
self.logger.info('调用 fallback 模块 revChatGpt')
attempt_num = 0
access_token = self.begin_invoke('access_token')
self.logger.info('token: %s', access_token)
while attempt_num < MAX_CHAT_FALLBACK_ATTEMPT_NUM:
try:
attempt_num += 1
chatbot = self.chatbots[openid] = self.chatbots[openid] if openid in self.chatbots else Chatbot(
config={
'access_token': access_token,
'conversation_id': conversation_id,
'parent_id': parent_id,
})
last_pos = 0
prompt = '\n'.join(['{} says:{}'.format(message['role'], message['content']) for message in messages])
response = ''
whole_message = ''
code_mode = False
self.logger.info('消息数量：%d', len(messages))
if is_websocket:
for data in chatbot.ask(prompt):
conversation_id = data['conversation_id']
parent_id = data['parent_id']
whole_message = data['message']
message = whole_message[last_pos:]
last_pos += len(message)
if not message: continue
yield message
else:
for data in chatbot.ask(prompt):
conversation_id = data['conversation_id']
parent_id = data['parent_id']
whole_message = data['message']
response = whole_message[last_pos:]
if len(response) < MIN_MESSAGE_HANDLE_LENGTH: continue
message, last_pos, code_mode = msg_handler.extract_message(
text=response,
offset=last_pos,
min_len=MIN_MESSAGE_HANDLE_LENGTH,
code_mode=code_mode,
)
if len(message) == 0: continue
message = msg_handler.filter_sensitive(message)
yield message
if last_pos == 0:
message = msg_handler.filter_sensitive(response)
yield message
elif last_pos < len(whole_message):
message = msg_handler.filter_sensitive(whole_message[last_pos:])
yield message
self.end_invoke('access_token', access_token)
user['conversation_id'] = conversation_id
user['parent_id'] = parent_id
return
except Exception as e:
if 'The message you submitted was too long' in str(e):
# 裁剪对话
attempt_num = 0
messages.pop(1)
else:
self.logger.error(e)
traceback.print_exc(limit=5)
continue
if attempt_num == MAX_CHAT_FALLBACK_ATTEMPT_NUM:
self.logger.error('[revChatGPT]尝试 %d 次均无法完成与模型接口的通信，接口调用失败', attempt_num)
yield ''
self.end_invoke('access_token', access_token)
def invoke_single_completion(self, system_prompt='', content=''):
"""
调用 OpenAI API 接口取得文本填空结果并返回
"""
attempt_num = 0
api_key = self.begin_invoke('api_key')
prompt = ''
if system_prompt:
prompt += msg_handler.filter_sensitive(system_prompt) + ':'
if content:
prompt += msg_handler.filter_sensitive(content)
tokens_prompt = count_string_tokens(prompt, MODEL_TEXT_COMPLETION)
while attempt_num < MAX_OPENAI_SINGLE_ATTEMPT_NUM:
try:
attempt_num += 1
response = openai.Completion.create(
model=MODEL_TEXT_COMPLETION,
prompt=prompt,
request_timeout=20,
api_base=f'{URL_OPENAI_API_BASE}/v1',
api_key=api_key,
max_tokens=MAX_TOKEN_OUTPUT - tokens_prompt,
temperature=0,
)
if 'text' not in response['choices'][0]: continue
text = response['choices'][0]['text'].strip()
self.end_invoke('api_key', api_key)
return text
except Exception as e:
self.logger.error(e)
continue
self.end_invoke('api_key', api_key)
if attempt_num == MAX_OPENAI_SINGLE_ATTEMPT_NUM:
self.logger.error('[OpenAI API]尝试 %d 次均无法完成与模型接口的通信，接口调用失败', attempt_num)
messages = [
make_message('system', system_prompt),
make_message('user', content),
]
reply = ''
for message in self.invoke_chat_completion_fallback({}, messages):
reply += message['content']
return reply
def get_chat_param(self):
return self.chat_param
def set_chat_param(self, **kwargs):
try:
params = [('temperature', 0, 1), ('frequency_penalty', 0, 2), ('presence_penalty', 0, 2)]
for name, min_val, max_val in params:
value = float(kwargs.get(name) or self.chat_param[name])
if not min_val <= value <= max_val: return False
self.chat_param[name] = value
except Exception as e:
self.logger.error('设置 Chat 模型参数失败：', str(e))
return False
return True
def save_to_memory(self, content: str):
if re.search(REGEXP_TEXT_IMAGE_CREATED, content, re.I): return
if re.search(REGEXP_TEXT_SORRY, content, re.I): return
self.memory.add(content) | [
"{} says:{}",
"\n",
"{}",
"prompt-vip.txt",
"content",
"prompt-free.txt",
"openid",
"[]"
] |
2024-01-10 | kyegomez/LOGICGUIDE | logic_guide~logicguide.py | import re
# class LogicGuide:
# def __init__(self):
# self.delimiters = ("t1", "t2") # example delimiters for guiding text extraction
# def guide_function(self, generated_sequence):
# """Function to define a set of valid generations based on previously generated sequences."""
# # Implementation specifics would depend on the underlying logic.
# return set() # returns a set of valid next generations
# def completion_engine(self, input_string):
# """Completion engine that uses Constrained Semantic Decoding (CSD) algorithm."""
# t1, t2 = self.delimiters
# if input_string.endswith(t1):
# sp = self.extract_text_blocks(input_string, t1, t2)
# valid_strings = self.guide_function(sp)
# return self.create_regex(valid_strings, t2)
# else:
# return self.create_regex([], t1) # matches any string not containing t1 and ending in t1
# @staticmethod
# def extract_text_blocks(input_string, t1, t2):
# """Extract blocks of text between two delimiters t1 and t2."""
# # Implementation would extract and return blocks of text based on input string and delimiters
# return ""
# @staticmethod
# def create_regex(valid_strings, ending):
# """Create a regular expression matching valid strings ending with a specific character."""
# # Implementation would return a regex based on valid strings and ending character
# return ""
# def logicguide(self, input_string):
# """The LOGICGUIDE function that utilizes the completion engine and the Peano theorem-proving environment."""
# # The completion engine is called here to generate valid continuations
# completion_engine_output = self.completion_engine(input_string)
# # Integration with the Peano theorem-proving environment would occur here
# # for now, it will just return the output of the completion engine
# return completion_engine_output
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch
from utils.openai import OpenAILanguageModel
class UniversalGuide:
def __init__(self):
pass
def __call__(self, history):
return history
class DigitGuide:
def __init__(self):
self.regex = re.compile(r"\d+")
def __call__(self, history):
if self.regex.match(history):
return self.regex
else:
return None
class GuideFunction:
def __init__(self, tool):
self.tool = tool
def __call__(self, model_output):
return self.tool_check(model_output)
def parity_guide(binary_string):
count = binary_string.count('1')
if count % 2 == 0:
return 1 # belongs to the PARITY language
else:
return 0 # does not belong to the PARITY language
class LogicTool:
def check(self, text):
#pseudocode use a complex logic system to verify the logical consistency of the text
#use a sematic analysis and logical inference system
#use tree
return True
class FactTool:
def check(self, text):
#use a complex fact checking system to verify the factural accuracy of the tex
#implement a system that cross references the text with a reliable database of facts
#use true
return True
logic_guide = GuideFunction(LogicTool())
fact_guide = GuideFunction(FactTool())
#guides from the paper
class MemoryGuide:
def __init__(self):
self.memory = {}
def __call__(self, history):
set_trigger = "[[set:"
get_trigger = "[[get:"
if set_trigger in history:
key_value = history.split(set_trigger, 1)[1].split(']]', 1)[0]
key, value = key_value.split("=")
self.memory[key] = value
return history.replace(set_trigger + key_value + ']]', "")
elif get_trigger in history:
key_value = history.split(get_trigger, 1)[1].split(']]', 1)[0]
key = key_value.split("=")[0]
return history.replace(get_trigger + key_value + ']]', self.memory.get(key, ''))
return history
import requests
from bs4 import BeautifulSoup
class QuoteGuide:
def __init__(self, source):
self.source = source
self.quotes = self.get_quotes_from_source()
def get_quotes_from_source(self):
page = requests.get(self.source)
soup = BeautifulSoup(page.content, 'html.parser')
return [p.text for p in soup.find_all('p')]
def __call__(self, history):
quote_trigger = "[[quote:]]"
if quote_trigger in history:
for quote in self.quotes:
if quote in history:
return history
return history.replace(quote_trigger, '[[quote:' + self.quotes[0] + ']]')
return history
import sympy
class AlgebraGuide:
def __init__(self):
self.variables = {}
def __call__(self, history):
eq_trigger = "[[eq]]"
solve_trigger = "[[solve:"
if eq_trigger in history:
equation = history.split(eq_trigger, 1)[1].split(']]', 1)[0]
for variable in sympy.symbols(equation).free_symbols:
self.variables[variable.name] = variable
return history
elif solve_trigger in history:
var_value = history.split(solve_trigger, 1)[1].split(']]', 1)[0]
var = var_value.split("=")[0]
if var in self.variables:
solution = sympy.solve(self.variables[var])[0]
return history.replace(solve_trigger + var_value + ']]', '[[solve:' + var + '=' + str(solution) + ']]')
return history
class LogicGuide:
def __init__(self, model_id, guide_function=None, device="cuda:0", openai_api_key="", openai_api_base="", openai_api_model=""):
self.tokenizer = AutoTokenizer.from_pretrained(model_id)
self.model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto").to(device)
self.t1 = "[[" # Guide trigger
self.t2 = "]]" # End of trusted generation
self.device = device
# Initializing OpenAI model
self.openai_model = OpenAILanguageModel(api_key=openai_api_key, api_base=openai_api_base, api_model=openai_api_model)
if guide_function:
self.guide_function = guide_function
else:
self.guide_function = self.default_guide_function
def default_guide_function(self, S):
return S
def get_bnb_config(self):
return BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16
)
def guide(self, S):
return self.guide_function(S)
def get_blocks(self, s):
blocks = []
split_s = s.split(self.t1)
for block in split_s[1:]:
if self.t2 in block:
blocks.append(block.split(self.t2)[0])
return blocks
def generate(self, text, max_new_tokens=20):
inputs = self.tokenizer(text, return_tensors="pt").to(self.device)
# If guide tool is invoked, invoke guide function
if self.t1 in text:
text = self.guide(text)
outputs = self.model.generate(**inputs, max_new_tokens=max_new_tokens)
return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
# Example usage:
model_id="tiiuae/falcon-40b"
logic_guide = LogicGuide(model_id=model_id)
#provide few shot prompt for better results
text = """
Context: Every dog is small. Every feline is a snake. Every animal is not bitter. Sheep are bitter. Cats are
carnivores. Each vertebrate is a mammal. Mammals are felines. Each vertebrate is dull. Snakes are cats.
Cats are not kind. Every snake is not happy. Every sheep is a vertebrate. Each feline is cold. Each dog is a
sheep. Every mammal is not liquid. Every carnivore is a cow. Every carnivore is brown. Alex is a sheep.
Question: True or false: Alex is not bitter.
"""
print(logic_guide.generate(text))
model_id = "tiiuae/falcon-40b"
device = "cuda:0" # Change to "cpu" if you don't have a CUDA-compatible GPU.
# Memory Guide
memory_guide = MemoryGuide()
logic_guide = LogicGuide(model_id=model_id, guide_function=memory_guide, device=device)
text = "[[set:name=OpenAI]] What is your name?"
print(logic_guide.generate(text)) # Output: "My name is OpenAI."
text = "[[get:name=]] What is your name?"
print(logic_guide.generate(text)) # Output: "My name is OpenAI."
# Quote Guide (for this example, we're using Project Gutenberg's "The Adventures of Sherlock Holmes")
quote_guide = QuoteGuide(source="https://www.gutenberg.org/files/1661/1661-h/1661-h.htm")
logic_guide = LogicGuide(model_id=model_id, guide_function=quote_guide, device=device)
text = "[[quote:]] What is a quote from Sherlock Holmes?"
print(logic_guide.generate(text)) # Output: A quote from "The Adventures of Sherlock Holmes" (random quote from the source)
# Algebra Guide
algebra_guide = AlgebraGuide()
logic_guide = LogicGuide(model_id=model_id, guide_function=algebra_guide, device=device)
text = "[[eq]] x^2 + 3x + 2 = 0"
print(logic_guide.generate(text)) # Output: "x^2 + 3x + 2 = 0" (and stores the equation for later)
text = "[[solve:x=]] What is the value of x?"
print(logic_guide.generate(text)) # Output: "The value of x is ..." (the solutions of the equation)
| [] |
2024-01-10 | serviteur/DocsGPT | scripts~parser~py2doc.py | import ast
import os
from pathlib import Path
import tiktoken
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
def find_files(directory):
files_list = []
for root, dirs, files in os.walk(directory):
for file in files:
if file.endswith('.py'):
files_list.append(os.path.join(root, file))
return files_list
def extract_functions(file_path):
with open(file_path, 'r') as file:
source_code = file.read()
functions = {}
tree = ast.parse(source_code)
for node in ast.walk(tree):
if isinstance(node, ast.FunctionDef):
func_name = node.name
func_def = ast.get_source_segment(source_code, node)
functions[func_name] = func_def
return functions
def extract_classes(file_path):
with open(file_path, 'r') as file:
source_code = file.read()
classes = {}
tree = ast.parse(source_code)
for node in ast.walk(tree):
if isinstance(node, ast.ClassDef):
class_name = node.name
function_names = []
for subnode in ast.walk(node):
if isinstance(subnode, ast.FunctionDef):
function_names.append(subnode.name)
classes[class_name] = ", ".join(function_names)
return classes
def extract_functions_and_classes(directory):
files = find_files(directory)
functions_dict = {}
classes_dict = {}
for file in files:
functions = extract_functions(file)
if functions:
functions_dict[file] = functions
classes = extract_classes(file)
if classes:
classes_dict[file] = classes
return functions_dict, classes_dict
def parse_functions(functions_dict, formats, dir):
c1 = len(functions_dict)
for i, (source, functions) in enumerate(functions_dict.items(), start=1):
print(f"Processing file {i}/{c1}")
source_w = source.replace(dir + "/", "").replace("." + formats, ".md")
subfolders = "/".join(source_w.split("/")[:-1])
Path(f"outputs/{subfolders}").mkdir(parents=True, exist_ok=True)
for j, (name, function) in enumerate(functions.items(), start=1):
print(f"Processing function {j}/{len(functions)}")
prompt = PromptTemplate(
input_variables=["code"],
template="Code: \n{code}, \nDocumentation: ",
)
llm = OpenAI(temperature=0)
response = llm(prompt.format(code=function))
mode = "a" if Path(f"outputs/{source_w}").exists() else "w"
with open(f"outputs/{source_w}", mode) as f:
f.write(
f"\n\n# Function name: {name} \n\nFunction: \n```\n{function}\n```, \nDocumentation: \n{response}")
def parse_classes(classes_dict, formats, dir):
c1 = len(classes_dict)
for i, (source, classes) in enumerate(classes_dict.items()):
print(f"Processing file {i + 1}/{c1}")
source_w = source.replace(dir + "/", "").replace("." + formats, ".md")
subfolders = "/".join(source_w.split("/")[:-1])
Path(f"outputs/{subfolders}").mkdir(parents=True, exist_ok=True)
for name, function_names in classes.items():
print(f"Processing Class {i + 1}/{c1}")
prompt = PromptTemplate(
input_variables=["class_name", "functions_names"],
template="Class name: {class_name} \nFunctions: {functions_names}, \nDocumentation: ",
)
llm = OpenAI(temperature=0)
response = llm(prompt.format(class_name=name, functions_names=function_names))
with open(f"outputs/{source_w}", "a" if Path(f"outputs/{source_w}").exists() else "w") as f:
f.write(f"\n\n# Class name: {name} \n\nFunctions: \n{function_names}, \nDocumentation: \n{response}")
def transform_to_docs(functions_dict, classes_dict, formats, dir):
docs_content = ''.join([str(key) + str(value) for key, value in functions_dict.items()])
docs_content += ''.join([str(key) + str(value) for key, value in classes_dict.items()])
num_tokens = len(tiktoken.get_encoding("cl100k_base").encode(docs_content))
total_price = ((num_tokens / 1000) * 0.02)
print(f"Number of Tokens = {num_tokens:,d}")
print(f"Approx Cost = ${total_price:,.2f}")
user_input = input("Price Okay? (Y/N)\n").lower()
if user_input == "y" or user_input == "":
if not Path("outputs").exists():
Path("outputs").mkdir()
parse_functions(functions_dict, formats, dir)
parse_classes(classes_dict, formats, dir)
print("All done!")
else:
print("The API was not called. No money was spent.")
| [
"Class name: {class_name} \nFunctions: {functions_names}, \nDocumentation: ",
"functions_names",
"class_name",
"Code: \n{code}, \nDocumentation: "
] |
2024-01-10 | serviteur/DocsGPT | scripts~code_docs_gen.py | import ast
import json
from pathlib import Path
import dotenv
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
dotenv.load_dotenv()
ps = list(Path("inputs").glob("**/*.py"))
data = []
sources = []
for p in ps:
with open(p) as f:
data.append(f.read())
sources.append(p)
def get_functions_in_class(node):
functions = []
functions_code = []
for child in node.body:
if isinstance(child, ast.FunctionDef):
functions.append(child.name)
functions_code.append(ast.unparse(child))
return functions, functions_code
def get_classes_and_functions(source_code):
tree = ast.parse(source_code)
classes = {}
for node in tree.body:
if isinstance(node, ast.ClassDef):
class_name = node.name
function_name, function = get_functions_in_class(node)
# join function name and function code
functions = dict(zip(function_name, function))
classes[class_name] = functions
return classes
structure_dict = {}
c1 = 0
for code in data:
classes = get_classes_and_functions(ast.parse(code))
source = str(sources[c1])
structure_dict[source] = classes
c1 += 1
# save the structure dict as json
with open('structure_dict.json', 'w') as f:
json.dump(structure_dict, f)
if not Path("outputs").exists():
Path("outputs").mkdir()
c1 = len(structure_dict)
c2 = 0
for source, classes in structure_dict.items():
c2 += 1
print(f"Processing file {c2}/{c1}")
f1 = len(classes)
f2 = 0
for class_name, functions in classes.items():
f2 += 1
print(f"Processing class {f2}/{f1}")
source_w = source.replace("inputs/", "")
source_w = source_w.replace(".py", ".txt")
if not Path(f"outputs/{source_w}").exists():
with open(f"outputs/{source_w}", "w") as f:
f.write(f"Class: {class_name}")
else:
with open(f"outputs/{source_w}", "a") as f:
f.write(f"\n\nClass: {class_name}")
# append class name to the front
for function in functions:
b1 = len(functions)
b2 = 0
print(f"Processing function {b2}/{b1}")
b2 += 1
prompt = PromptTemplate(
input_variables=["code"],
template="Code: \n{code}, \nDocumentation: ",
)
llm = OpenAI(temperature=0)
response = llm(prompt.format(code=functions[function]))
if not Path(f"outputs/{source_w}").exists():
with open(f"outputs/{source_w}", "w") as f:
f.write(f"Function: {functions[function]}, \nDocumentation: {response}")
else:
with open(f"outputs/{source_w}", "a") as f:
f.write(f"\n\nFunction: {functions[function]}, \nDocumentation: {response}")
| [
"Code: \n{code}, \nDocumentation: "
] |
2024-01-10 | serviteur/DocsGPT | application~parser~py2doc.py | import ast
import os
from pathlib import Path
import tiktoken
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
def find_files(directory):
files_list = []
for root, dirs, files in os.walk(directory):
for file in files:
if file.endswith('.py'):
files_list.append(os.path.join(root, file))
return files_list
def extract_functions(file_path):
with open(file_path, 'r') as file:
source_code = file.read()
functions = {}
tree = ast.parse(source_code)
for node in ast.walk(tree):
if isinstance(node, ast.FunctionDef):
func_name = node.name
func_def = ast.get_source_segment(source_code, node)
functions[func_name] = func_def
return functions
def extract_classes(file_path):
with open(file_path, 'r') as file:
source_code = file.read()
classes = {}
tree = ast.parse(source_code)
for node in ast.walk(tree):
if isinstance(node, ast.ClassDef):
class_name = node.name
function_names = []
for subnode in ast.walk(node):
if isinstance(subnode, ast.FunctionDef):
function_names.append(subnode.name)
classes[class_name] = ", ".join(function_names)
return classes
def extract_functions_and_classes(directory):
files = find_files(directory)
functions_dict = {}
classes_dict = {}
for file in files:
functions = extract_functions(file)
if functions:
functions_dict[file] = functions
classes = extract_classes(file)
if classes:
classes_dict[file] = classes
return functions_dict, classes_dict
def parse_functions(functions_dict, formats, dir):
c1 = len(functions_dict)
for i, (source, functions) in enumerate(functions_dict.items(), start=1):
print(f"Processing file {i}/{c1}")
source_w = source.replace(dir + "/", "").replace("." + formats, ".md")
subfolders = "/".join(source_w.split("/")[:-1])
Path(f"outputs/{subfolders}").mkdir(parents=True, exist_ok=True)
for j, (name, function) in enumerate(functions.items(), start=1):
print(f"Processing function {j}/{len(functions)}")
prompt = PromptTemplate(
input_variables=["code"],
template="Code: \n{code}, \nDocumentation: ",
)
llm = OpenAI(temperature=0)
response = llm(prompt.format(code=function))
mode = "a" if Path(f"outputs/{source_w}").exists() else "w"
with open(f"outputs/{source_w}", mode) as f:
f.write(
f"\n\n# Function name: {name} \n\nFunction: \n```\n{function}\n```, \nDocumentation: \n{response}")
def parse_classes(classes_dict, formats, dir):
c1 = len(classes_dict)
for i, (source, classes) in enumerate(classes_dict.items()):
print(f"Processing file {i + 1}/{c1}")
source_w = source.replace(dir + "/", "").replace("." + formats, ".md")
subfolders = "/".join(source_w.split("/")[:-1])
Path(f"outputs/{subfolders}").mkdir(parents=True, exist_ok=True)
for name, function_names in classes.items():
print(f"Processing Class {i + 1}/{c1}")
prompt = PromptTemplate(
input_variables=["class_name", "functions_names"],
template="Class name: {class_name} \nFunctions: {functions_names}, \nDocumentation: ",
)
llm = OpenAI(temperature=0)
response = llm(prompt.format(class_name=name, functions_names=function_names))
with open(f"outputs/{source_w}", "a" if Path(f"outputs/{source_w}").exists() else "w") as f:
f.write(f"\n\n# Class name: {name} \n\nFunctions: \n{function_names}, \nDocumentation: \n{response}")
def transform_to_docs(functions_dict, classes_dict, formats, dir):
docs_content = ''.join([str(key) + str(value) for key, value in functions_dict.items()])
docs_content += ''.join([str(key) + str(value) for key, value in classes_dict.items()])
num_tokens = len(tiktoken.get_encoding("cl100k_base").encode(docs_content))
total_price = ((num_tokens / 1000) * 0.02)
print(f"Number of Tokens = {num_tokens:,d}")
print(f"Approx Cost = ${total_price:,.2f}")
user_input = input("Price Okay? (Y/N)\n").lower()
if user_input == "y" or user_input == "":
if not Path("outputs").exists():
Path("outputs").mkdir()
parse_functions(functions_dict, formats, dir)
parse_classes(classes_dict, formats, dir)
print("All done!")
else:
print("The API was not called. No money was spent.")
| [
"Code: \n{code}, \nDocumentation: ",
"functions_names",
"class_name",
"Class name: {class_name} \nFunctions: {functions_names}, \nDocumentation: "
] |
2024-01-10 | FreedomIntelligence/Evaluation-of-ChatGPT-on-Information-Extraction | 3_EE~ee_trigger_test_with_api.py | import json
import os
import sys
import random
import time
import openai
import threading
from config import get_opts_ee as get_opts
from ee_trigger_report_metric import trigger_report_metric
cur_path = os.getcwd()
sys.path.append(cur_path)
from utils import Logger, bot_run, ReadSample, WriteSample
def get_prompt_list(event_types_list):
prompt_list = []
# 1
prompt = 'Considering {} types of events: {}, recognize all event triggers with their corresponding event types in the given text. The event trigger is a word or phrase in the given text that indicates the occurrence of a pre-defined event. The corresponding event type answered must be one of the pre-defined event types.\nAnswer in the format ["event_trigger", "event_type"] without any explanation. If no event trigger is involved, then just answer "[]".'.format(len(event_types_list), json.dumps(event_types_list))
prompt_list.append(prompt)
# 2
prompt = 'From the pre-defined list of event types {}, first find out all event triggers in the given text, then determine the corresponding event type for each involved event trigger. The event trigger is a word or phrase in the given text that indicates the occurrence of a pre-defined event. The corresponding event type answered must be one of the pre-defined event types.\nAnswer in the format ["event_trigger", "event_type"] without any explanation. If no event trigger is involved, then just answer "[]".'.format(json.dumps(event_types_list))
prompt_list.append(prompt)
# 3
prompt = 'Judge which words or phrases in the given text are event triggers, and categorize each of them into one of the pre-defined event types. The event trigger is a word or phrase in the given text that indicates the occurrence of a pre-defined event. The pre-defined list of event types is {}.\nAnswer in the format ["event_trigger", "event_type"] without any explanation. If no event trigger is involved, then just answer "[]".'.format(json.dumps(event_types_list))
prompt_list.append(prompt)
# 4
prompt = 'Which words or phrases in the given text are event triggers? Which pre-defined event type is indicated by each event trigger? The event trigger is a word or phrase in the given text that indicates the occurrence of a pre-defined event. The answered event type must be one of the pre-defined event types. The pre-defined list of event types is {}.\nAnswer in the format ["event_trigger", "event_type"] without any explanation. If no event trigger is involved, then just answer "[]".'.format(json.dumps(event_types_list))
prompt_list.append(prompt)
# 5
prompt = 'Given the pre-defined list of event types {}, which words or phrases in the given text are event triggers for the above event types? What is the corresponding event type of each event trigger? The event trigger is a word or phrase in the given text that indicates the occurrence of a pre-defined event. The corresponding event type answered must be one of the pre-defined event types.\nAnswer in the format ["event_trigger", "event_type"] without any explanation. If no event trigger is involved, then just answer "[]".'.format(json.dumps(event_types_list))
prompt_list.append(prompt)
return prompt_list
def get_icl_cot_prompt_list(opts):
prompt_icl_list, prompt_cot_list = {}, {}
if opts.ICL:
prompt_icl_file = os.path.join(opts.input_dir, opts.task, opts.dataset, opts.icl_prompt)
prompt_icl_list = json.load(open(prompt_icl_file, "r", encoding="utf-8"))
prompt_cot_list = {}
elif opts.COT:
prompt_cot_file = os.path.join(opts.input_dir, opts.task, opts.dataset, opts.cot_prompt)
prompt_cot_list = json.load(open(prompt_cot_file, "r", encoding="utf-8"))
prompt_icl_list = {}
return prompt_icl_list, prompt_cot_list
def ee_trigger_get_prompt(opts, example, prompt_list, prompt_icl_list, prompt_cot_list):
tokens = example['text'].split(" ")
if len(tokens) > 1024:
seq_str = " ".join(tokens[:1024])
else:
seq_str = example['text']
if opts.irrelevant:
file_name = os.path.join(opts.input_dir, opts.task, opts.dataset, "train_no_event.json")
fr_no = open(file_name, "r", encoding="utf-8")
data_no_term = json.load(fr_no)
irrelevant_text_list = [item["text"] for item in data_no_term]
random_text = random.sample(irrelevant_text_list, 2)
input_text = random_text[0] + " " + seq_str+ " " + random_text[1]
else:
input_text = seq_str
if opts.ICL:
prompt = prompt_list[opts.best_prompt] + "\n" + prompt_icl_list[opts.prompt-1] + '\nGiven text:\n"{}"\nAnswer:\n'.format(input_text)
elif opts.COT:
basic_prompt = prompt_list[opts.best_prompt].split("\nAnswer")[0] + " Let's think step by step, and answer in the format [\"event_trigger\", \"event_type\"]. If no event trigger is involved, then answer \"[]\"."
prompt = basic_prompt + "\n" + prompt_cot_list[opts.prompt-1] + '\nGiven text:\n"{}"\nAnswer:\n'.format(input_text)
else:
prompt = prompt_list[opts.prompt-1] + '\nGiven text:\n"{}"\n'.format(input_text)
return prompt
def get_best_prompt(opts, logger):
file_name_list = ["ee_trigger_result_" + str(i) + ".json" for i in range(1, 6)]
f1_list = [trigger_report_metric(opts, logger, file_name=file) for file in file_name_list]
best_prompt = f1_list.index(max(f1_list))
return best_prompt
def ee_trigger_main(opts, bot, logger):
start_time = time.time()
logger.write("{}\n".format(opts.test_file))
logger.write("{}\n".format(opts.type_file))
## load data
logger.write("loading data ...\n")
with open(opts.test_file, 'r', encoding='utf-8') as fr, open(opts.type_file, 'r', encoding='utf-8') as fr_type:
data = json.load(fr)
types = json.load(fr_type)
event_types = types["event_types"]
event_types_list = [event_types[key]["verbose"].lower() for key in event_types]
## sample
index_list = list(range(0, len(data)))
if opts.sample:
logger.write("Sampling examples ...\n")
selected_idx = random.sample(index_list, opts.sample_k)
selected_idx.sort()
print(selected_idx)
else:
selected_idx = index_list
## sample end
prompt_list = get_prompt_list(event_types_list)
prompt_icl_list, prompt_cot_list = get_icl_cot_prompt_list(opts)
if opts.ICL or opts.COT:
opts.best_prompt = get_best_prompt(opts, logger)
## API
with open(opts.result_file, 'a', encoding='utf-8') as fw:
fw.seek(0) #定位
fw.truncate() #清空文件
fw.write("[\n")
logger.write("Evaluation begining ...\n")
i = 0
while i < len(selected_idx):
idx = selected_idx[i]
i += 1
logger.write("No. "+ str(i) + " | example's id: " + str(idx) + " | total examples: " + str(len(data)) + "\n")
example = data[idx]
print(example["text"])
prompt = ee_trigger_get_prompt(opts, example, prompt_list, prompt_icl_list, prompt_cot_list)
logger.write("EE-Trigger | " + str(i) + "/" + str(len(data)) + " | Prompt:\n" + prompt + "\n")
response = bot_run(bot, prompt, model=opts.model)
logger.write("EE-Trigger | " + str(i) + "/" + str(len(data)) + " | Response:\n" + response + "\n")
example.update({
"prompt": prompt,
"response": response
})
if opts.ICL or opts.COT:
example["best_prompt"] = opts.best_prompt + 1
fw.write(json.dumps(example, indent=4, ensure_ascii=False))
if i != len(selected_idx):
fw.write("\n,\n")
else:
fw.write("\n")
fw.write("]\n")
end_time = time.time()
logger.write("The result is saved: {}\n".format(opts.result_file))
logger.write("Times: {:.2f}s = {:.2f}m\n".format(end_time-start_time, (end_time-start_time)/60.0))
## multi thread process
def thread_process(thread_id, opts, bot, read_sample, write_sample, prompt_list, prompt_icl_list, prompt_cot_list, logger):
while True:
status, example = read_sample.get_item()
if status:
cur_idx = read_sample.cur_index
total = len(read_sample.data_idx)
prompt = ee_trigger_get_prompt(opts, example, prompt_list, prompt_icl_list, prompt_cot_list)
logger.write("Thread: " + str(thread_id) + " | EE-Trigger | " + str(cur_idx) + "/" + str(total) + " | {} | Prompt:\n".format(opts.best_prompt+1) + prompt + "\n")
response = bot_run(bot, prompt, model=opts.model)
logger.write("Thread: " + str(thread_id) + " | EE-Trigger | " + str(cur_idx) + "/" + str(total) + " | {} | Response:\n".format(opts.best_prompt+1) + response + "\n")
example.update({
"prompt": prompt,
"response": response
})
if opts.ICL or opts.COT:
example["best_prompt"] = opts.best_prompt + 1
write_sample.write(example)
else:
break
def ee_trigger_main_multi_thread(opts, bot, logger, num_thread=10):
start_time = time.time()
logger.write("{}\n".format(opts.test_file))
logger.write("{}\n".format(opts.type_file))
## load data
logger.write("loading data ...\n")
with open(opts.test_file, 'r', encoding='utf-8') as fr, open(opts.type_file, 'r', encoding='utf-8') as fr_type:
data = json.load(fr)
types = json.load(fr_type)
event_types = types["event_types"]
event_types_list = [event_types[key]["verbose"].lower() for key in event_types]
## sample
index_list = list(range(0, len(data)))
if opts.sample:
logger.write("Sampling examples ...\n")
selected_idx = random.sample(index_list, opts.sample_k)
selected_idx.sort()
print(selected_idx)
else:
selected_idx = index_list
## sample end
prompt_list = get_prompt_list(event_types_list)
prompt_icl_list, prompt_cot_list = get_icl_cot_prompt_list(opts)
if opts.ICL or opts.COT:
opts.best_prompt = get_best_prompt(opts, logger)
logger.write("Evaluation begining ...\n")
read_sample = ReadSample(data, selected_idx)
write_sample = WriteSample(opts.result_file, 'a')
threads_list = []
for t_id in range(num_thread):
worker = threading.Thread(target=thread_process, args=(t_id+1, opts, bot, read_sample, write_sample, prompt_list, prompt_icl_list, prompt_cot_list, logger))
worker.start()
threads_list.append(worker)
for th in threads_list:
th.join()
end_time = time.time()
logger.write("Times: {:.2f}s = {:.2f}m\n".format(end_time-start_time, (end_time-start_time)/60.0))
with open(opts.result_file, "r", encoding="utf-8") as f:
new_data = [json.loads(item) for item in f.readlines()]
logger.write(str(len(new_data)) + " " + str(len(data)) + "\n")
with open(opts.result_file, "w", encoding="utf-8") as f:
f.write(json.dumps(new_data, indent=4, ensure_ascii=False))
if __name__ == "__main__":
opts = get_opts()
api_key_file = os.path.join("./api-keys", opts.api_key)
openai.api_key_path = api_key_file
bot = openai.ChatCompletion()
## log file
logger_file = os.path.join(opts.task, opts.logger_file)
logger = Logger(file_name=logger_file)
if opts.task == "ee":
if opts.multi_thread:
ee_trigger_main_multi_thread(opts, bot, logger, num_thread=opts.num_thread)
else:
ee_trigger_main(opts, bot, logger)
| [
"\nAnswer",
"\n",
"{}",
"From the pre-defined list of event types {}, first find out all event triggers in the given text, then determine the corresponding event type for each involved event trigger. The event trigger is a word or phrase in the given text that indicates the occurrence of a pre-defined event. The corresponding event type answered must be one of the pre-defined event types.\nAnswer in the format [\"event_trigger\", \"event_type\"] without any explanation. If no event trigger is involved, then just answer \"[]\".",
"Considering {} types of events: {}, recognize all event triggers with their corresponding event types in the given text. The event trigger is a word or phrase in the given text that indicates the occurrence of a pre-defined event. The corresponding event type answered must be one of the pre-defined event types.\nAnswer in the format [\"event_trigger\", \"event_type\"] without any explanation. If no event trigger is involved, then just answer \"[]\".",
"Judge which words or phrases in the given text are event triggers, and categorize each of them into one of the pre-defined event types. The event trigger is a word or phrase in the given text that indicates the occurrence of a pre-defined event. The pre-defined list of event types is {}.\nAnswer in the format [\"event_trigger\", \"event_type\"] without any explanation. If no event trigger is involved, then just answer \"[]\".",
"\nGiven text:\n\"{}\"\nAnswer:\n",
"Given the pre-defined list of event types {}, which words or phrases in the given text are event triggers for the above event types? What is the corresponding event type of each event trigger? The event trigger is a word or phrase in the given text that indicates the occurrence of a pre-defined event. The corresponding event type answered must be one of the pre-defined event types.\nAnswer in the format [\"event_trigger\", \"event_type\"] without any explanation. If no event trigger is involved, then just answer \"[]\".",
"\nGiven text:\n\"{}\"\n",
" Let's think step by step, and answer in the format [\"event_trigger\", \"event_type\"]. If no event trigger is involved, then answer \"[]\".",
"Which words or phrases in the given text are event triggers? Which pre-defined event type is indicated by each event trigger? The event trigger is a word or phrase in the given text that indicates the occurrence of a pre-defined event. The answered event type must be one of the pre-defined event types. The pre-defined list of event types is {}.\nAnswer in the format [\"event_trigger\", \"event_type\"] without any explanation. If no event trigger is involved, then just answer \"[]\".",
"[]"
] |
2024-01-10 | FreedomIntelligence/Evaluation-of-ChatGPT-on-Information-Extraction | 1_NER~ner_test_with_api.py | import json
import sys, os
import random
import time
import ast
import openai
import threading
from config import get_opts_ner as get_opts
from ner_report_metric import report_metric_by_file, get_result_list
cur_path = os.getcwd()
sys.path.append(cur_path)
from utils import Logger, bot_run, ReadSample, WriteSample
def get_prompt_list(e_types):
prompt_list = []
# 1
prompt = 'Considering {} types of named entities including {} and {}, recognize all named entities in the given sentence.\nAnswer in the format ["entity_type", "entity_name"] without any explanation. If no entity exists, then just answer "[]".'.format(len(e_types), ", ".join(e_types[:-1]), e_types[-1])
prompt_list.append(prompt)
# 2
e_types_tmp = [item.strip('"') for item in e_types]
prompt = 'Given the list of entity types {}, read the given sentence and find out all words/phrases that indicate the above types of named entities.\nAnswer in the format ["entity_type", "entity_name"] without any explanation. If no entity exists, then just answer "[]".'.format(json.dumps(e_types_tmp))
prompt_list.append(prompt)
# 3
prompt = 'Read the given sentence carefully, identify all named entities of type {} or {}.\nAnswer in the format ["entity_type", "entity_name"] without any explanation. If no entity exists, then just answer "[]".'.format(", ".join(e_types[:-1]), e_types[-1])
prompt_list.append(prompt)
# 4
prompt = 'Analyze the given sentence and extract all word spans that refer to specific named entities of type {} or {}.\nAnswer in the format ["entity_type", "entity_name"] without any explanation. If no entity exists, then just answer "[]".'.format(", ".join(e_types[:-1]), e_types[-1])
prompt_list.append(prompt)
# 5
prompt = 'What named entities are mentioned in the given sentence? Only return named entities of type {} or {}.\nAnswer in the format ["entity_type", "entity_name"] without any explanation. If no entity exists, then just answer "[]".'.format(", ".join(e_types[:-1]), e_types[-1])
prompt_list.append(prompt)
return prompt_list
def get_icl_cot_prompt_list(opts):
prompt_icl_list, prompt_cot_list = {}, {}
if opts.ICL:
prompt_icl_file = os.path.join(opts.input_dir, opts.task, opts.dataset, opts.icl_prompt)
prompt_icl_list = json.load(open(prompt_icl_file, "r", encoding="utf-8"))
elif opts.COT:
prompt_cot_file = os.path.join(opts.input_dir, opts.task, opts.dataset, opts.cot_prompt)
prompt_cot_list = json.load(open(prompt_cot_file, "r", encoding="utf-8"))
return prompt_icl_list, prompt_cot_list
def ner_get_prompt(opts, example, prompt_list, prompt_icl_list, prompt_cot_list):
if opts.irrelevant:
file_name = os.path.join(opts.input_dir, opts.task, opts.dataset, "train_no_entity.json")
fr_no = open(file_name, "r", encoding="utf-8")
data_no_term = json.load(fr_no)
irrelevant_text_list = [item["seq"] for item in data_no_term]
random_text = random.sample(irrelevant_text_list, 2)
input_text = random_text[0] + " " + example["seq"] + " " + random_text[1]
else:
input_text = example["seq"]
if opts.ICL:
prompt = prompt_list[opts.best_prompt] + "\n" + prompt_icl_list[opts.prompt-1] + '\nSentence:\n"{}"\nAnswer:\n'.format(input_text)
elif opts.COT:
prompt = prompt_list[opts.best_prompt] + "\n" + prompt_cot_list[opts.prompt-1] + '\nSentence:\n"{}"\nAnswer:\n'.format(input_text)
else:
prompt = prompt_list[opts.prompt-1] + '\nGiven sentence:\n"{}"'.format(input_text)
return prompt
def get_best_prompt(opts, logger):
file_name_list = ["ner_result_" + str(i) + ".json" for i in range(1, 6)]
f1_list = [report_metric_by_file(opts, file, logger, mode="strict", match="hard") for file in file_name_list]
best_prompt = f1_list.index(max(f1_list))
return best_prompt
def ner_main(opts, bot, logger):
start_time = time.time()
logger.write("{}\n".format(opts.test_file))
logger.write("{}\n".format(opts.type_file))
## load data
logger.write("loading data ...\n")
with open(opts.test_file, 'r', encoding='utf-8') as fr, open(opts.type_file, 'r', encoding='utf-8') as fr_type:
data = json.load(fr)
types = json.load(fr_type)
e_types = ['"' + types["entities"][item]["short"] + '"' for item in types["entities"]]
if opts.verbose_type:
e_types = ['"' + types["entities"][item]["verbose"] + '"' for item in types["entities"]]
## sample
index_list = list(range(0, len(data)))
if opts.sample:
logger.write("Sampling examples ...\n")
selected_idx = random.sample(index_list, opts.sample_k)
selected_idx.sort()
print(selected_idx)
else:
selected_idx = index_list
## sample end
prompt_list = get_prompt_list(e_types)
prompt_icl_list, prompt_cot_list = get_icl_cot_prompt_list(opts)
if opts.ICL or opts.COT:
opts.best_prompt = get_best_prompt(opts, logger)
## API
with open(opts.result_file, 'a', encoding='utf-8') as fw:
fw.seek(0) #定位
fw.truncate() #清空文件
fw.write("[\n")
logger.write("Evaluation begining ...\n")
i = 0
while i < len(selected_idx):
idx = selected_idx[i]
i += 1
logger.write("No. "+ str(i) + " | example's id: " + str(idx) + " | total examples: " + str(len(data)) + "\n")
example = data[idx]
prompt = ner_get_prompt(opts, example, prompt_list, prompt_icl_list, prompt_cot_list)
print(example["seq"])
logger.write("NER | " + str(i) + "/" + str(len(data)) + " | Prompt:\n" + prompt + "\n")
response = bot_run(bot, prompt, model=opts.model)
logger.write("NER | " + str(i) + "/" + str(len(data)) + " | Response:\n" + response + "\n")
result_list = []
example.update({
"NER": result_list,
"prompt": prompt,
"response": response
})
if opts.ICL or opts.COT:
example["best_prompt"] = opts.best_prompt + 1
fw.write(json.dumps(example, indent=4, ensure_ascii=False))
if i != len(selected_idx):
fw.write("\n,\n")
else:
fw.write("\n")
fw.write("]\n")
end_time = time.time()
logger.write("The result is saved: {}\n".format(opts.result_file))
logger.write("Times: {:.2f}s = {:.2f}m\n".format(end_time-start_time, (end_time-start_time)/60.0))
## multi thread process
def thread_process(thread_id, opts, bot, read_sample, write_sample, prompt_list, prompt_icl_list, prompt_cot_list, logger):
while True:
status, example = read_sample.get_item()
if status:
cur_idx = read_sample.cur_index
total = len(read_sample.data_idx)
prompt = ner_get_prompt(opts, example, prompt_list, prompt_icl_list, prompt_cot_list)
logger.write("Thread: " + str(thread_id) + " | NER | " + str(cur_idx) + "/" + str(total) + " | Prompt:\n" + prompt + "\n")
response = bot_run(bot, prompt, model=opts.model)
logger.write("Thread: " + str(thread_id) + " | NER | " + str(cur_idx) + "/" + str(total) + " | Response:\n" + response + "\n")
result_list = []
example.update({
"NER": result_list,
"prompt": prompt,
"response": response,
})
if opts.ICL or opts.COT:
example["best_prompt"] = opts.best_prompt + 1
write_sample.write(example)
else:
break
def ner_main_multi_thread(opts, bot, logger, num_thread=10):
start_time = time.time()
logger.write("{}\n".format(opts.test_file))
logger.write("{}\n".format(opts.type_file))
## load data
logger.write("loading data ...\n")
with open(opts.test_file, 'r', encoding='utf-8') as fr, open(opts.type_file, 'r', encoding='utf-8') as fr_type:
data = json.load(fr)
types = json.load(fr_type)
e_types = ['"' + types["entities"][item]["short"] + '"' for item in types["entities"]]
if opts.verbose_type:
e_types = ['"' + types["entities"][item]["verbose"] + '"' for item in types["entities"]]
## sample
index_list = list(range(0, len(data)))
if opts.sample:
logger.write("Sampling examples ...\n")
selected_idx = random.sample(index_list, opts.sample_k)
selected_idx.sort()
print(selected_idx)
else:
selected_idx = index_list
## sample end
prompt_list = get_prompt_list(e_types)
prompt_icl_list, prompt_cot_list = get_icl_cot_prompt_list(opts)
if opts.ICL or opts.COT:
opts.best_prompt = get_best_prompt(opts, logger)
logger.write("Evaluation begining ...\n")
read_sample = ReadSample(data, selected_idx)
write_sample = WriteSample(opts.result_file, 'a')
threads_list = []
for t_id in range(num_thread):
worker = threading.Thread(target=thread_process, args=(t_id+1, opts, bot, read_sample, write_sample, prompt_list, prompt_icl_list, prompt_cot_list, logger))
worker.start()
threads_list.append(worker)
for th in threads_list:
th.join()
end_time = time.time()
logger.write("Times: {:.2f}s = {:.2f}m\n".format(end_time-start_time, (end_time-start_time)/60.0))
with open(opts.result_file, "r", encoding="utf-8") as f:
new_data = [json.loads(item) for item in f.readlines()]
logger.write(str(len(new_data)) + " " + str(len(data)) + "\n")
# print(len(new_data), len(data))
with open(opts.result_file, "w", encoding="utf-8") as f:
f.write(json.dumps(new_data, indent=4, ensure_ascii=False))
if __name__ == "__main__":
opts = get_opts()
api_key_file = os.path.join("./api-keys", opts.api_key)
openai.api_key_path = api_key_file
bot = openai.ChatCompletion()
## log file
logger_file = os.path.join(opts.task, opts.logger_file)
print(logger_file)
logger = Logger(file_name=logger_file)
logger.write(json.dumps(opts.__dict__, indent=4) + "\n")
logger.write(api_key_file + "\n")
if opts.task == "ner":
if opts.multi_thread:
ner_main_multi_thread(opts, bot, logger, num_thread=opts.num_thread)
else:
ner_main(opts, bot, logger)
| [
"\n",
"[]",
"Analyze the given sentence and extract all word spans that refer to specific named entities of type P, L, A, C, E, H, O, L, D, E, R or PLACEHOLDER.\nAnswer in the format [\"entity_type\", \"entity_name\"] without any explanation. If no entity exists, then just answer \"[]\".",
"Read the given sentence carefully, identify all named entities of type P, L, A, C, E, H, O, L, D, E, R or PLACEHOLDER.\nAnswer in the format [\"entity_type\", \"entity_name\"] without any explanation. If no entity exists, then just answer \"[]\".",
"\nGiven sentence:\n\"{}\"",
"What named entities are mentioned in the given sentence? Only return named entities of type P, L, A, C, E, H, O, L, D, E, R or PLACEHOLDER.\nAnswer in the format [\"entity_type\", \"entity_name\"] without any explanation. If no entity exists, then just answer \"[]\".",
"Considering 1 types of named entities including P, L, A, C, E, H, O, L, D, E, R and PLACEHOLDER, recognize all named entities in the given sentence.\nAnswer in the format [\"entity_type\", \"entity_name\"] without any explanation. If no entity exists, then just answer \"[]\".",
"Given the list of entity types {}, read the given sentence and find out all words/phrases that indicate the above types of named entities.\nAnswer in the format [\"entity_type\", \"entity_name\"] without any explanation. If no entity exists, then just answer \"[]\".",
"\nSentence:\n\"{}\"\nAnswer:\n"
] |
2024-01-10 | FreedomIntelligence/Evaluation-of-ChatGPT-on-Information-Extraction | 3_EE~ee_argument_test_with_api.py | import json, os
import random
import time
import openai
import threading
import sys
cur_path = os.getcwd()
sys.path.append(cur_path)
from utils import Logger, bot_run, ReadSample, WriteSample
from config import get_opts_ee as get_opts
from ee_argument_report_metric import argument_report_metric
def get_prompt_list():
prompt_list = []
# 1
prompt = 'Given a piece of text and an event it expresses, recognize all arguments of this event and their corresponding roles from the given text. The argument is an entity that appears in the given text and participates in this event. The corresponding role must be one of the given candidate roles. Since the arguments in the given text may come from multiple events, please identify only the arguments of the given event.\nAnswer in the format ["argument", "role"] without any explanation. If no argument is involved, then just answer "[]".'
prompt_list.append(prompt)
# 2
prompt = 'Based on the given text and an event it involved, first find out all arguments of this event from the given text, then assign a role to each argument from the given candidate roles. The argument is an entity that appears in the given text and participates in this event. Since the arguments in the given text may come from multiple events, please identify only the arguments of the given event.\nAnswer in the format ["argument", "role"] without any explanation. If no argument is involved, then just answer "[]".'
prompt_list.append(prompt)
# 3
prompt = 'According to the given text and an event it expresses, identify all arguments of this event from the given text, and select a role from the given candidate roles for each argument. The argument is an entity that appears in the given text and participates in this event. Since the arguments in the given text may come from multiple events, please identify only the arguments of the given event.\nAnswer in the format ["argument", "role"] without any explanation. If no argument is involved, then just answer "[]".'
prompt_list.append(prompt)
# 4
prompt = 'Given a piece of text and an event it expresses, what are all arguments of this event in the given text? What is the corresponding role for each argument? The argument is an entity that appears in the given text and participates in this event. The corresponding role must be one of the given candidate roles. Since the arguments in the given text may come from multiple events, please identify only the arguments of the given event.\nAnswer in the format ["argument", "role"] without any explanation. If no argument is involved, then just answer "[]".'
prompt_list.append(prompt)
# 5
prompt = 'Based on the given text and an event it involved, what arguments of this event are included in this text? What is the corresponding role of each argument? The argument is an entity that appears in the given text and participates in this event. The corresponding role must be one of the given candidate roles. Since the arguments in the given text may come from multiple events, please identify only the arguments of the given event.\nAnswer in the format ["argument", "role"] without any explanation. If no argument is involved, then just answer "[]".'
prompt_list.append(prompt)
return prompt_list
def get_icl_cot_prompt_list(opts):
prompt_icl_list, prompt_cot_list = {}, {}
if opts.ICL:
prompt_icl_file = os.path.join(opts.input_dir, opts.task, opts.dataset, opts.icl_prompt)
prompt_icl_list = json.load(open(prompt_icl_file, "r", encoding="utf-8"))
prompt_cot_list = {}
elif opts.COT:
prompt_cot_file = os.path.join(opts.input_dir, opts.task, opts.dataset, opts.cot_prompt)
prompt_cot_list = json.load(open(prompt_cot_file, "r", encoding="utf-8"))
prompt_icl_list = {}
return prompt_icl_list, prompt_cot_list
def ee_role_args_get_prompt(opts, text_str, evt_type, evt_roles, prompt_list, prompt_icl_list, prompt_cot_list):
tokens = text_str.split(" ")
if len(tokens) > 1024:
seq_str = " ".join(tokens[:1024])
else:
seq_str = text_str
if opts.irrelevant:
file_name = os.path.join(opts.input_dir, opts.task, opts.dataset, "train_no_event.json")
fr_no = open(file_name, "r", encoding="utf-8")
data_no_term = json.load(fr_no)
irrelevant_text_list = [item["text"] for item in data_no_term]
random_text = random.sample(irrelevant_text_list, 2)
input_text = random_text[0] + " " + seq_str+ " " + random_text[1]
else:
input_text = seq_str
if opts.ICL:
prompt = prompt_list[opts.best_prompt] + "\n" + prompt_icl_list[opts.prompt-1] + '\nGiven text:\n"{}"\nEvent type:\n"{}"\nCandidate roles:\n{}\nAnswer:\n'.format(input_text, evt_type, json.dumps(evt_roles))
elif opts.COT:
prompt = prompt_list[opts.best_prompt] + "\n" + prompt_cot_list[opts.prompt-1] + '\nGiven text:\n"{}"\nEvent type:\n"{}"\nCandidate roles:\n{}\nAnswer:\n'.format(input_text, evt_type, json.dumps(evt_roles))
else:
prompt = prompt_list[opts.prompt-1] + '\nGiven text:\n"{}"\nEvent type:\n"{}"\nCandidate roles:\n{}\n'.format(input_text, evt_type, json.dumps(evt_roles))
return prompt
def get_best_prompt(opts, logger):
file_name_list = ["ee_argument_result_" + str(i) + ".json" for i in range(1, 6)]
f1_list = [argument_report_metric(opts, logger, file_name=file) for file in file_name_list]
best_prompt = f1_list.index(max(f1_list))
return best_prompt
def ee_role_args_main(opts, bot, logger):
start_time = time.time()
logger.write("{}\n".format(opts.test_file))
logger.write("{}\n".format(opts.type_file))
## load data
logger.write("loading data ...\n")
with open(opts.test_file, 'r', encoding='utf-8') as fr, open(opts.type_file, 'r', encoding='utf-8') as fr_type:
data = json.load(fr)
types = json.load(fr_type)
event_types = types["event_types"]
event_types_list = [event_types[key]["verbose"] for key in event_types]
event2roles = types["event2roles"]
## sample
index_list = list(range(0, len(data)))
if opts.sample:
logger.write("Sampling examples ...\n")
selected_idx = random.sample(index_list, opts.sample_k)
selected_idx.sort()
print(selected_idx)
else:
selected_idx = index_list
## sample end
prompt_list = get_prompt_list()
# print(prompt_list)
prompt_icl_list, prompt_cot_list = get_icl_cot_prompt_list(opts)
if opts.ICL or opts.COT:
opts.best_prompt = get_best_prompt(opts, logger)
## API
with open(opts.result_file, 'a', encoding='utf-8') as fw:
fw.seek(0) #定位
fw.truncate() #清空文件
fw.write("[\n")
logger.write("Evaluation begining ...\n")
i = 0
while i < len(selected_idx):
idx = selected_idx[i]
i += 1
logger.write("No. "+ str(i) + " | example's id: " + str(idx) + " | total examples: " + str(len(data)) + "\n")
example = data[idx]
text_str = example["text"]
for evt in example["event"]:
evt_type = event_types[evt["subtype"].replace(":", ".")]["verbose"]
evt_roles = event2roles[evt_type]
print(example["text"])
prompt = ee_role_args_get_prompt(opts, text_str, evt_type, evt_roles, prompt_list, prompt_icl_list, prompt_cot_list)
logger.write("EE-Argument | " + str(i) + "/" + str(len(data)) + " | Prompt:\n" + prompt + "\n")
response = bot_run(bot, prompt, model=opts.model)
logger.write("EE-Argument | " + str(i) + "/" + str(len(data)) + " | Response:\n" + response + "\n")
evt.update({
"prompt": prompt,
"response": response
})
if opts.ICL or opts.COT:
example["best_prompt"] = opts.best_prompt + 1
fw.write(json.dumps(example, indent=4, ensure_ascii=False))
if i != len(selected_idx):
fw.write("\n,\n")
else:
fw.write("\n")
fw.write("]\n")
end_time = time.time()
logger.write("The result is saved: {}\n".format(opts.result_file))
logger.write("Times: {:.2f}s = {:.2f}m\n".format(end_time-start_time, (end_time-start_time)/60.0))
## multi thread process
def thread_process(thread_id, opts, bot, read_sample, write_sample, prompt_list, prompt_icl_list, prompt_cot_list, event_types, event2roles, logger):
while True:
status, example = read_sample.get_item()
if status:
cur_idx = read_sample.cur_index
total = len(read_sample.data_idx)
text_str = example["text"]
for evt in example["event"]:
evt_type = event_types[evt["subtype"].replace(":", ".")]["verbose"]
evt_roles = event2roles[evt_type]
prompt = ee_role_args_get_prompt(opts, text_str, evt_type, evt_roles, prompt_list, prompt_icl_list, prompt_cot_list)
logger.write("Thread: " + str(thread_id) + " | EE-Argument | " + str(cur_idx) + "/" + str(total) + " | {} | Prompt:\n".format(opts.best_prompt+1) + prompt + "\n")
response = bot_run(bot, prompt, model=opts.model)
logger.write("Thread: " + str(thread_id) + " | EE-Argument | " + str(cur_idx) + "/" + str(total) + " | {} | Response:\n".format(opts.best_prompt+1) + response + "\n")
evt.update({
"prompt": prompt,
"response": response
})
if opts.ICL or opts.COT:
example["best_prompt"] = opts.best_prompt + 1
write_sample.write(example)
else:
break
def ee_role_args_main_multi_thread(opts, bot, logger, num_thread=10):
start_time = time.time()
logger.write("{}\n".format(opts.test_file))
logger.write("{}\n".format(opts.type_file))
## load data
logger.write("loading data ...\n")
with open(opts.test_file, 'r', encoding='utf-8') as fr, open(opts.type_file, 'r', encoding='utf-8') as fr_type:
data = json.load(fr)
types = json.load(fr_type)
event_types = types["event_types"]
event_types_list = [event_types[key]["verbose"] for key in event_types]
event2roles = types["event2roles"]
## sample
index_list = list(range(0, len(data)))
if opts.sample:
logger.write("Sampling examples ...\n")
selected_idx = random.sample(index_list, opts.sample_k)
selected_idx.sort()
print(selected_idx)
else:
selected_idx = index_list
## sample end
prompt_list = get_prompt_list()
prompt_icl_list, prompt_cot_list = get_icl_cot_prompt_list(opts)
if opts.ICL or opts.COT:
opts.best_prompt = get_best_prompt(opts, logger)
logger.write("Evaluation begining ...\n")
read_sample = ReadSample(data, selected_idx)
write_sample = WriteSample(opts.result_file, 'a')
threads_list = []
for t_id in range(num_thread):
worker = threading.Thread(target=thread_process, args=(t_id+1, opts, bot, read_sample, write_sample, prompt_list, prompt_icl_list, prompt_cot_list, event_types, event2roles, logger))
worker.start()
threads_list.append(worker)
for th in threads_list:
th.join()
end_time = time.time()
logger.write("Times: {:.2f}s = {:.2f}m\n".format(end_time-start_time, (end_time-start_time)/60.0))
with open(opts.result_file, "r", encoding="utf-8") as f:
new_data = [json.loads(item) for item in f.readlines()]
logger.write(str(len(new_data)) + " " + str(len(data)) + "\n")
with open(opts.result_file, "w", encoding="utf-8") as f:
f.write(json.dumps(new_data, indent=4, ensure_ascii=False))
if __name__ == "__main__":
opts = get_opts()
api_key_file = os.path.join("./api-keys", opts.api_key)
openai.api_key_path = api_key_file
bot = openai.ChatCompletion()
## log file
logger_file = os.path.join(opts.task, opts.logger_file)
logger = Logger(file_name=logger_file)
if opts.task == "ee":
if opts.multi_thread:
ee_role_args_main_multi_thread(opts, bot, logger, num_thread=opts.num_thread)
else:
ee_role_args_main(opts, bot, logger)
| [
"\n",
"{}",
"\nGiven text:\n\"{}\"\nEvent type:\n\"{}\"\nCandidate roles:\n{}\n",
"Given a piece of text and an event it expresses, what are all arguments of this event in the given text? What is the corresponding role for each argument? The argument is an entity that appears in the given text and participates in this event. The corresponding role must be one of the given candidate roles. Since the arguments in the given text may come from multiple events, please identify only the arguments of the given event.\nAnswer in the format [\"argument\", \"role\"] without any explanation. If no argument is involved, then just answer \"[]\".",
"According to the given text and an event it expresses, identify all arguments of this event from the given text, and select a role from the given candidate roles for each argument. The argument is an entity that appears in the given text and participates in this event. Since the arguments in the given text may come from multiple events, please identify only the arguments of the given event.\nAnswer in the format [\"argument\", \"role\"] without any explanation. If no argument is involved, then just answer \"[]\".",
"\nGiven text:\n\"{}\"\nEvent type:\n\"{}\"\nCandidate roles:\n{}\nAnswer:\n",
"Based on the given text and an event it involved, what arguments of this event are included in this text? What is the corresponding role of each argument? The argument is an entity that appears in the given text and participates in this event. The corresponding role must be one of the given candidate roles. Since the arguments in the given text may come from multiple events, please identify only the arguments of the given event.\nAnswer in the format [\"argument\", \"role\"] without any explanation. If no argument is involved, then just answer \"[]\".",
"Based on the given text and an event it involved, first find out all arguments of this event from the given text, then assign a role to each argument from the given candidate roles. The argument is an entity that appears in the given text and participates in this event. Since the arguments in the given text may come from multiple events, please identify only the arguments of the given event.\nAnswer in the format [\"argument\", \"role\"] without any explanation. If no argument is involved, then just answer \"[]\".",
"Given a piece of text and an event it expresses, recognize all arguments of this event and their corresponding roles from the given text. The argument is an entity that appears in the given text and participates in this event. The corresponding role must be one of the given candidate roles. Since the arguments in the given text may come from multiple events, please identify only the arguments of the given event.\nAnswer in the format [\"argument\", \"role\"] without any explanation. If no argument is involved, then just answer \"[]\".",
"[]"
] |
2024-01-10 | FreedomIntelligence/Evaluation-of-ChatGPT-on-Information-Extraction | 2_RE~re_triplet_test_with_api.py | import json
import os
import sys
import random
import time
import openai
import threading
from config import get_opts_re as get_opts
from re_triplet_report_metric import triplet_report_metric
cur_path = os.getcwd()
sys.path.append(cur_path)
from utils import Logger, bot_run, ReadSample, WriteSample
def get_prompt_list(r_types, e_types):
na_item = r_types[-1]
r_types = list(set(r_types[:-1]))
prompt_list = []
if len(e_types) != 0:
prompt = 'Extract all relational fact triples from the given text, which consist of subject entity, object entity and the relation between two entities. The subject/object entity can be of the following types {}. The relation between entities can be of the following types {}.\nAnswer in the format \'["subject_entity", "relation", "object_entity"]\' without any explanation. If no relation exists, then just answer "[]".'.format(json.dumps(e_types), json.dumps(r_types))
prompt_list.append(prompt)
prompt = 'From the list of relations: {}, first find out all relations expressed by the given text, then identify the subject and object entities for each expressed relation. The subject/object entity can be of the following types {}.\nAnswer in the format \'["subject_entity", "relation", "object_entity"]\' without any explanation. If no relation exists, then just answer "[]".'.format(json.dumps(r_types), json.dumps(e_types))
prompt_list.append(prompt)
prompt = 'Given the list of relations: {}, judge whether each relation is expressed by the given text, return all expressed relations along with their corresponding subject and object entities. The subject/object entity can be of the following types {}.\nAnswer in the format \'["subject_entity", "relation", "object_entity"]\' without any explanation. If no relation exists, then just answer "[]".'.format(json.dumps(r_types), json.dumps(e_types))
prompt_list.append(prompt)
prompt = 'Given the list of entity types: {}, recognize all named entities from the given text, then judge whether any subject-object entity pair express the relation in the predefined list. The list of predefined relations is {}.\nAnswer in the format \'["subject_entity", "relation", "object_entity"]\' without any explanation. If no relation exists, then just answer "[]".'.format(json.dumps(e_types), json.dumps(r_types))
prompt_list.append(prompt)
e_types = ['"' + item + '"' for item in e_types]
prompt = 'Considering entity types of {} and {}, find out all named entities in the given text, then return all subject-object entity pairs that express predefined relations. The list of predefined relations is {}.\nAnswer in the format \'["subject_entity", "relation", "object_entity"]\' without any explanation. If no relation exists, then just answer "[]".'.format(", ".join(e_types[:-1]), e_types[-1], json.dumps(r_types))
prompt_list.append(prompt)
else:
prompt = 'Extract all relational fact triples from the given text, which consist of subject entity, object entity and the relation between two entities. The relation between entities can be of the following types {}.\nAnswer in the format \'["subject_entity", "relation", "object_entity"]\' without any explanation. If no relation exists, then just answer "[]".'.format(json.dumps(r_types))
prompt_list.append(prompt)
prompt = 'From the list of relations: {}, first find out all relations expressed by the given text, then identify the subject and object entities for each expressed relation.\nAnswer in the format \'["subject_entity", "relation", "object_entity"]\' without any explanation. If no relation exists, then just answer "[]".'.format(json.dumps(r_types))
prompt_list.append(prompt)
prompt = 'Given the list of relations: {}, judge whether each relation is expressed by the given text, return all expressed relations along with their corresponding subject and object entities.\nAnswer in the format \'["subject_entity", "relation", "object_entity"]\' without any explanation. If no relation exists, then just answer "[]".'.format(json.dumps(r_types))
prompt_list.append(prompt)
prompt = 'Recognize all named entities from the given text, then judge whether any subject-object entity pair express the relation in the predefined list. The list of predefined relations is {}.\nAnswer in the format \'["subject_entity", "relation", "object_entity"]\' without any explanation. If no relation exists, then just answer "[]".'.format(json.dumps(r_types))
prompt_list.append(prompt)
e_types = ['"' + item + '"' for item in e_types]
prompt = 'Find out all named entities in the given text, then return all subject-object entity pairs that express predefined relations. The list of predefined relations is {}.\nAnswer in the format \'["subject_entity", "relation", "object_entity"]\' without any explanation. If no relation exists, then just answer "[]".'.format(json.dumps(r_types))
prompt_list.append(prompt)
return prompt_list
def get_icl_cot_prompt_list(opts):
prompt_icl_list, prompt_cot_list = {}, {}
if opts.ICL:
prompt_icl_file = os.path.join(opts.input_dir, opts.task, opts.dataset, opts.icl_prompt)
prompt_icl_list = json.load(open(prompt_icl_file, "r", encoding="utf-8"))
prompt_cot_list = {}
elif opts.COT:
prompt_cot_file = os.path.join(opts.input_dir, opts.task, opts.dataset, opts.cot_prompt)
prompt_cot_list = json.load(open(prompt_cot_file, "r", encoding="utf-8"))
prompt_icl_list = {}
return prompt_icl_list, prompt_cot_list
def re_triplet_get_prompt(opts, example, prompt_list, prompt_icl_list, prompt_cot_list):
tokens = example['seq'].split(" ")
if len(tokens) > 1024:
seq_str = " ".join(tokens[:1024])
else:
seq_str = example['seq']
if opts.irrelevant:
file_name = os.path.join(opts.input_dir, opts.task, opts.dataset, "train_no_relation.json")
fr_no = open(file_name, "r", encoding="utf-8")
data_no_term = json.load(fr_no)
irrelevant_text_list = [item["seq"] for item in data_no_term]
random_text = random.sample(irrelevant_text_list, 2)
input_text = random_text[0] + " " + seq_str+ " " + random_text[1]
else:
input_text = seq_str
if opts.ICL:
prompt = prompt_list[opts.best_prompt] + "\n" + prompt_icl_list[opts.prompt-1] + '\nGiven text:\n"{}"\nAnswer:\n'.format(input_text)
elif opts.COT:
prompt = prompt_list[opts.best_prompt] + "\n" + prompt_cot_list[opts.prompt-1] + '\nGiven text:\n"{}"\nAnswer:\n'.format(input_text)
else:
prompt = prompt_list[opts.prompt-1] + '\nGiven text:\n"{}"'.format(input_text)
return prompt
def get_best_prompt(opts, logger):
file_name_list = ["re_triplet_result_" + str(i) + ".json" for i in range(1, 6)]
f1_list = [triplet_report_metric(opts, logger, file_name=file) for file in file_name_list]
best_prompt = f1_list.index(max(f1_list))
return best_prompt
def re_triplet_main(opts, bot, logger):
start_time = time.time()
logger.write("{}\n".format(opts.test_file))
logger.write("{}\n".format(opts.type_file))
## load data
logger.write("loading data ...\n")
with open(opts.test_file, 'r', encoding='utf-8') as fr, open(opts.type_file, 'r', encoding='utf-8') as fr_type:
data = json.load(fr)
types = json.load(fr_type)
r_types = list(types["relation"].values())
e_types = list(types["entity"].values())
## sample
index_list = list(range(0, len(data)))
if opts.sample:
logger.write("Sampling examples ...\n")
selected_idx = random.sample(index_list, opts.sample_k)
selected_idx.sort()
print(selected_idx)
else:
selected_idx = index_list
## sample end
prompt_list = get_prompt_list(r_types, e_types)
prompt_icl_list, prompt_cot_list = get_icl_cot_prompt_list(opts)
if opts.ICL or opts.COT:
opts.best_prompt = get_best_prompt(opts, logger)
## API
with open(opts.result_file, 'a', encoding='utf-8') as fw:
fw.seek(0) #定位
fw.truncate() #清空文件
fw.write("[\n")
logger.write("Evaluation begining ...\n")
i = 0
while i < len(selected_idx):
idx = selected_idx[i]
i += 1
logger.write("No. "+ str(i) + " | example's id: " + str(idx) + " | total examples: " + str(len(data)) + "\n")
example = data[idx]
print(example["seq"])
prompt = re_triplet_get_prompt(opts, example, prompt_list, prompt_icl_list, prompt_cot_list)
logger.write("RE-Triplet | " + str(i) + "/" + str(len(data)) + " | Prompt:\n" + prompt + "\n")
response = bot_run(bot, prompt, model=opts.model)
logger.write("RE-Triplet | " + str(i) + "/" + str(len(data)) + " | Response:\n" + response + "\n")
# result_dict = get_result_dict(response)
result_dict = {}
example.update({
"RE": result_dict,
"prompt": prompt,
"response": response
})
if opts.ICL or opts.COT:
example["best_prompt"] = opts.best_prompt + 1
fw.write(json.dumps(example, indent=4, ensure_ascii=False))
if i != len(selected_idx):
fw.write("\n,\n")
else:
fw.write("\n")
fw.write("]\n")
end_time = time.time()
logger.write("The result is saved: {}\n".format(opts.result_file))
logger.write("Times: {:.2f}s = {:.2f}m\n".format(end_time-start_time, (end_time-start_time)/60.0))
## multi thread process
def thread_process(thread_id, opts, bot, read_sample, write_sample, prompt_list, prompt_icl_list, prompt_cot_list, logger):
while True:
status, example = read_sample.get_item()
if status:
cur_idx = read_sample.cur_index
total = len(read_sample.data_idx)
prompt = re_triplet_get_prompt(opts, example, prompt_list, prompt_icl_list, prompt_cot_list)
logger.write("Thread: " + str(thread_id) + " | RE-Triplet | " + str(cur_idx) + "/" + str(total) + " | Prompt:\n" + prompt + "\n")
response = bot_run(bot, prompt, model=opts.model)
logger.write("Thread: " + str(thread_id) + " | RE-Triplet | " + str(cur_idx) + "/" + str(total) + " | Response:\n" + response + "\n")
# result_dict = get_result_dict(response)
result_dict = {}
example.update({
"RE": result_dict,
"prompt": prompt,
"response": response
})
if opts.ICL or opts.COT:
example["best_prompt"] = opts.best_prompt + 1
write_sample.write(example)
else:
break
def re_triplet_main_multi_thread(opts, bot, logger, num_thread=10):
start_time = time.time()
logger.write("{}\n".format(opts.test_file))
logger.write("{}\n".format(opts.type_file))
## load data
logger.write("loading data ...\n")
with open(opts.test_file, 'r', encoding='utf-8') as fr, open(opts.type_file, 'r', encoding='utf-8') as fr_type:
data = json.load(fr)
types = json.load(fr_type)
r_types = list(types["relation"].values())
e_types = list(types["entity"].values())
## sample
index_list = list(range(0, len(data)))
if opts.sample:
logger.write("Sampling examples ...\n")
selected_idx = random.sample(index_list, opts.sample_k)
selected_idx.sort()
print(selected_idx)
else:
selected_idx = index_list
## sample end
prompt_list = get_prompt_list(r_types, e_types)
prompt_icl_list, prompt_cot_list = get_icl_cot_prompt_list(opts)
if opts.ICL or opts.COT:
opts.best_prompt = get_best_prompt(opts, logger)
logger.write("Evaluation begining ...\n")
read_sample = ReadSample(data, selected_idx)
write_sample = WriteSample(opts.result_file, 'a')
threads_list = []
for t_id in range(num_thread):
worker = threading.Thread(target=thread_process, args=(t_id+1, opts, bot, read_sample, write_sample, prompt_list, prompt_icl_list, prompt_cot_list, logger))
worker.start()
threads_list.append(worker)
for th in threads_list:
th.join()
end_time = time.time()
logger.write("Times: {:.2f}s = {:.2f}m\n".format(end_time-start_time, (end_time-start_time)/60.0))
with open(opts.result_file, "r", encoding="utf-8") as f:
new_data = [json.loads(item) for item in f.readlines()]
logger.write(str(len(new_data)) + " " + str(len(data)) + "\n")
with open(opts.result_file, "w", encoding="utf-8") as f:
f.write(json.dumps(new_data, indent=4, ensure_ascii=False))
if __name__ == "__main__":
opts = get_opts()
api_key_file = os.path.join("./api-keys", opts.api_key)
openai.api_key_path = api_key_file
bot = openai.ChatCompletion()
## log file
logger_file = os.path.join(opts.task, opts.logger_file)
logger = Logger(file_name=logger_file)
# logger.write(json.dumps(opts.__dict__, indent=4) + "\n")
if opts.task == "re":
if opts.multi_thread:
re_triplet_main_multi_thread(opts, bot, logger, num_thread=opts.num_thread)
else:
re_triplet_main(opts, bot, logger)
| [
"Given the list of relations: {}, judge whether each relation is expressed by the given text, return all expressed relations along with their corresponding subject and object entities. The subject/object entity can be of the following types {}.\nAnswer in the format '[\"subject_entity\", \"relation\", \"object_entity\"]' without any explanation. If no relation exists, then just answer \"[]\".",
"\n",
"{}",
"Recognize all named entities from the given text, then judge whether any subject-object entity pair express the relation in the predefined list. The list of predefined relations is {}.\nAnswer in the format '[\"subject_entity\", \"relation\", \"object_entity\"]' without any explanation. If no relation exists, then just answer \"[]\".",
"From the list of relations: {}, first find out all relations expressed by the given text, then identify the subject and object entities for each expressed relation.\nAnswer in the format '[\"subject_entity\", \"relation\", \"object_entity\"]' without any explanation. If no relation exists, then just answer \"[]\".",
"Given the list of entity types: {}, recognize all named entities from the given text, then judge whether any subject-object entity pair express the relation in the predefined list. The list of predefined relations is {}.\nAnswer in the format '[\"subject_entity\", \"relation\", \"object_entity\"]' without any explanation. If no relation exists, then just answer \"[]\".",
"Find out all named entities in the given text, then return all subject-object entity pairs that express predefined relations. The list of predefined relations is {}.\nAnswer in the format '[\"subject_entity\", \"relation\", \"object_entity\"]' without any explanation. If no relation exists, then just answer \"[]\".",
"\nGiven text:\n\"{}\"\nAnswer:\n",
"Given the list of relations: {}, judge whether each relation is expressed by the given text, return all expressed relations along with their corresponding subject and object entities.\nAnswer in the format '[\"subject_entity\", \"relation\", \"object_entity\"]' without any explanation. If no relation exists, then just answer \"[]\".",
"From the list of relations: {}, first find out all relations expressed by the given text, then identify the subject and object entities for each expressed relation. The subject/object entity can be of the following types {}.\nAnswer in the format '[\"subject_entity\", \"relation\", \"object_entity\"]' without any explanation. If no relation exists, then just answer \"[]\".",
"\nGiven text:\n\"{}\"",
"Considering entity types of {} and {}, find out all named entities in the given text, then return all subject-object entity pairs that express predefined relations. The list of predefined relations is {}.\nAnswer in the format '[\"subject_entity\", \"relation\", \"object_entity\"]' without any explanation. If no relation exists, then just answer \"[]\".",
"[]",
", ",
"Extract all relational fact triples from the given text, which consist of subject entity, object entity and the relation between two entities. The relation between entities can be of the following types {}.\nAnswer in the format '[\"subject_entity\", \"relation\", \"object_entity\"]' without any explanation. If no relation exists, then just answer \"[]\".",
"Extract all relational fact triples from the given text, which consist of subject entity, object entity and the relation between two entities. The subject/object entity can be of the following types {}. The relation between entities can be of the following types {}.\nAnswer in the format '[\"subject_entity\", \"relation\", \"object_entity\"]' without any explanation. If no relation exists, then just answer \"[]\"."
] |
2024-01-10 | FreedomIntelligence/Evaluation-of-ChatGPT-on-Information-Extraction | 4_ABSA~absa_test_with_api.py | import os
import json
import random
import time
import openai
import sys
import threading
from absa_report_metric import report_metric_by_key
from config import get_opts
cur_path = os.getcwd()
sys.path.append(cur_path)
from utils import Logger, ReadSample, WriteSample, bot_run
def get_and_run_prompt(opts, bot, idx, total, example, prompt_dict, prompt_icl_dict, prompt_cot_dict, task="AE", aspect=None, best_prompt=1, logger=None, thread_idx=0):
if opts.irrelevant:
if "14lap" in opts.dataset:
fr_no = open("./data/absa/wang/14lap/train_no_term.json", "r", encoding="utf-8")
data_no_term = json.load(fr_no)
if "14res" in opts.dataset:
fr_no = open("./data/absa/wang/14res/train_no_term.json", "r", encoding="utf-8")
data_no_term = json.load(fr_no)
if "15res" in opts.dataset or "16res" in opts.dataset:
fr_no = open("./data/absa/wang/15res/train_no_term.json", "r", encoding="utf-8")
data_no_term = json.load(fr_no)
irrelevant_text_list = [item["raw_words"] for item in data_no_term]
random_text = random.sample(irrelevant_text_list, 2)
input_text = random_text[0] + ". " + example["raw_words"] + ". " + random_text[1]
else:
input_text = example["raw_words"]
prompt = ''
if task in ["AE", "OE", "AESC", "Pair", "Triplet", "AESC_wang"]:
if task == "AESC_wang":
icl_cot_task = "AESC"
else:
icl_cot_task = task
if opts.ICL:
prompt = prompt_dict[task][best_prompt] + '\n' + prompt_icl_dict[icl_cot_task][opts.prompt-1] + '\nReview:\n"{}"\nAnswer:\n'.format(input_text)
elif opts.COT:
prompt = prompt_dict[task][best_prompt] + '\n' + prompt_cot_dict[icl_cot_task][opts.prompt-1] + '\nReview:\n"{}"\nAnswer:\n'.format(input_text)
else:
prompt = prompt_dict[task][opts.prompt-1] + '\nReview:\n"{}"\nAnswer:\n'.format(input_text)
elif task in ["ALSC", "AOE", "ALSC_wang"]:
if aspect is not None:
if task == "ALSC_wang":
icl_cot_task = "ALSC"
else:
icl_cot_task = task
if opts.ICL:
prompt = prompt_dict[task][best_prompt] + '\n' + prompt_icl_dict[icl_cot_task][opts.prompt-1] + '\nReview:\n"{}"\nAspect:\n"{}"\nAnswer:\n'.format(input_text, aspect)
elif opts.COT:
prompt = prompt_dict[task][best_prompt] + '\n' + prompt_cot_dict[icl_cot_task][opts.prompt-1] + '\nReview:\n"{}"\nAspect:\n"{}"\nAnswer:\n'.format(input_text, aspect)
else:
prompt = prompt_dict[task][opts.prompt-1] + '\nReview:\n"{}"\nAspect:\n"{}"\nAnswer:\n'.format(input_text, aspect)
else:
logger.write("{} | no the aspect term !!!".format(task))
exit()
if opts.ICL or opts.COT:
logger.write("Thread: {} | {} | ({}/{}) | Basic_prompt: {} | Prompt:\n{}\n".format(thread_idx, task, idx, total, best_prompt+1, prompt))
else:
logger.write(example["raw_words"]+"\n")
logger.write(input_text + "\n")
logger.write("Thread: {} | {} | ({}/{}) | Prompt:\n{}\n".format(thread_idx, task, idx, total, prompt))
response = bot_run(bot, prompt, model=opts.model)
if opts.ICL or opts.COT:
logger.write("Thread: {} | {} | ({}/{}) | Basic_prompt: {} | Response:\n{}\n".format(thread_idx, task, idx, total, best_prompt+1, response))
else:
logger.write("Thread: {} | {} | ({}/{}) | Response:\n{}\n".format(thread_idx, task, idx, total, response))
return response
## multi thread process
def thread_process(thread_id, opts, bot, read_sample, write_sample, prompt_dict, prompt_icl_dict, prompt_cot_dict, task, best_prompt, logger):
while True:
status, example = read_sample.get_item()
if status:
result_dict = dict()
result_dict.update(example)
result_dict.pop("task")
idx = read_sample.cur_index
total = len(read_sample.data_idx)
if opts.ICL or opts.COT:
result_dict["basic_prompt"] = best_prompt + 1
if task in ["AE", "OE", "AESC", "AESC_wang", "Pair", "Triplet"]:
response = get_and_run_prompt(opts, bot, idx, total, example, prompt_dict, prompt_icl_dict, prompt_cot_dict, task=task, aspect=None, best_prompt=best_prompt, logger=logger, thread_idx=thread_id)
result_dict[task] = response
if task in ["ALSC", "AOE", "ALSC_wang"]:
res = dict()
for asp in example["aspects"]:
asp_term = asp["term"]
if asp_term == "":
continue
res[asp_term] = get_and_run_prompt(opts, bot, idx, total, example, prompt_dict, prompt_icl_dict, prompt_cot_dict, task=task, aspect=asp_term, best_prompt=best_prompt, logger=logger, thread_idx=thread_id)
result_dict[task] = res
write_sample.write(result_dict)
else:
break
def run_task(opts, bot, data, selected_idx, prompt_dict, prompt_icl_dict, prompt_cot_dict, task="AE", logger=None):
result_dir = os.path.join(opts.result_dir, opts.task, opts.dataset)
if not os.path.exists(result_dir):
os.makedirs(result_dir)
result_file = os.path.join(result_dir, task + "-" + opts.result_file)
if opts.multi_thread:
read_sample = ReadSample(data, selected_idx)
write_sample = WriteSample(result_file, 'a')
threads_list = []
for t_id in range(opts.num_thread):
worker = threading.Thread(target=thread_process, args=(t_id+1, opts, bot, read_sample, write_sample, prompt_dict, prompt_icl_dict, prompt_cot_dict, task, opts.best_prompt, logger))
worker.start()
threads_list.append(worker)
for th in threads_list:
th.join()
with open(result_file, "r", encoding="utf-8") as f:
new_data = [json.loads(item) for item in f.readlines()]
print(len(new_data), len(data))
with open(result_file, "w", encoding="utf-8") as f:
f.write(json.dumps(new_data, indent=4, ensure_ascii=False))
else:
data = [data[idx] for idx in selected_idx]
with open(result_file, 'a', encoding='utf-8') as fw:
fw.seek(0) #定位
fw.truncate() #清空文件
fw.write("[\n")
for idx, example in enumerate(data):
result_dict = dict()
result_dict.update(example)
result_dict.pop("task")
if opts.ICL or opts.COT:
result_dict["basic_prompt"] = opts.best_prompt + 1
if task in ["AE", "OE", "AESC", "AESC_wang", "Pair", "Triplet"]:
response = get_and_run_prompt(opts, bot, idx, len(data), example, prompt_dict, prompt_icl_dict, prompt_cot_dict, task=task, aspect=None, best_prompt=opts.best_prompt, logger=logger)
result_dict[task] = response
if task in ["ALSC", "AOE", "ALSC_wang"]:
res = dict()
for asp in example["aspects"]:
asp_term = asp["term"]
if asp_term == "":
continue
res[asp_term] = get_and_run_prompt(opts, bot, idx, len(data), example, prompt_dict, prompt_icl_dict, prompt_cot_dict, task=task, aspect=asp_term, best_prompt=opts.best_prompt, logger=logger)
result_dict[task] = res
fw.write(json.dumps(result_dict, indent=4, ensure_ascii=False))
if idx != len(data):
fw.write("\n,\n")
else:
fw.write("\n")
fw.write("]\n")
def get_best_prompt(opts, task, logger):
file_name_list = [task + "-test_zero_" + str(i) + ".json" for i in range(1, 6)]
f1_list = [report_metric_by_key(opts, task, file, logger) for file in file_name_list]
best_prompt = f1_list.index(max(f1_list))
return best_prompt
def absa_main(opts, bot, logger):
start_time = time.time()
result_dir = os.path.join(opts.result_dir, os.path.join(opts.task, opts.dataset))
if not os.path.exists(result_dir):
os.makedirs(result_dir)
input_dir = os.path.join(opts.input_dir, os.path.join(os.path.join(opts.task, opts.dataset), opts.test_file))
logger.write("loading data ...\n")
with open(input_dir, 'r', encoding='utf-8') as fr:
data = json.load(fr)
if opts.ICL:
prompt_icl_file = os.path.join(opts.input_dir, opts.task, opts.dataset, opts.icl_prompt)
prompt_icl_dict = json.load(open(prompt_icl_file, "r", encoding="utf-8"))
prompt_cot_dict = {}
elif opts.COT:
if "14lap" in opts.dataset:
cot_dataset = "pengb/14lap"
elif "14res" in opts.dataset:
cot_dataset = "pengb/14res"
elif "15res" in opts.dataset:
cot_dataset = "pengb/15res"
elif "16res" in opts.dataset:
cot_dataset = "pengb/16res"
prompt_cot_file = os.path.join(opts.input_dir, opts.task, cot_dataset, opts.cot_prompt)
prompt_cot_dict = json.load(open(prompt_cot_file, "r", encoding="utf-8"))
prompt_icl_dict = {}
else:
prompt_icl_dict = {}
prompt_cot_dict = {}
prompt_basic_file = os.path.join(opts.prompt_dir, opts.basic_prompt)
prompt_dict = json.load(open(prompt_basic_file, "r", encoding="utf-8"))
index_list = list(range(len(data)))
if opts.sample:
logger.write("Sampling examples ...\n")
selected_idx = random.sample(index_list, opts.sample_k)
selected_idx.sort()
# print(selected_idx)
else:
selected_idx = index_list
first_example = data[selected_idx[0]]
if opts.irrelevant:
if first_example["task"] == "AE-OE":
task_list = ["AE", "OE", "ALSC_wang"]
elif first_example["task"] == "AOE":
task_list = ["AOE"]
elif first_example["task"] == "AEOESC":
if "pengb" in opts.dataset:
task_list = ["Triplet"] #
else:
task_list = ["AESC", "Pair"] #
else:
if first_example["task"] == "AE-OE":
task_list = ["AE", "OE", "ALSC_wang", "AESC_wang"]
elif first_example["task"] == "AOE":
task_list = ["AOE"]
elif first_example["task"] == "AEOESC":
task_list = ["AE", "OE", "ALSC", "AOE", "AESC", "Pair", "Triplet"] #
for task in task_list:
if opts.ICL or opts.COT:
opts.best_prompt = get_best_prompt(opts, task, logger)
run_task(opts, bot, data, selected_idx, prompt_dict, prompt_icl_dict, prompt_cot_dict, task=task, logger=logger)
end_time = time.time()
logger.write("Times: {:.2f}s = {:.2f}m\n".format(end_time-start_time, (end_time-start_time)/60.0))
if __name__ == "__main__":
opts = get_opts()
api_key_file = os.path.join("./api-keys", opts.api_key)
openai.api_key_path = api_key_file
bot = openai.ChatCompletion()
logger_file = opts.task + "-" + "-".join(opts.dataset.split("/")) + "-" + str(opts.prompt) + "-test.txt"
if opts.ICL:
logger_file = "ICL-" + logger_file
if opts.COT:
logger_file = "COT-" + logger_file
logger_file = os.path.join(opts.task, logger_file)
logger = Logger(file_name=logger_file)
logger.write(api_key_file)
logger.write("\n")
if opts.task == "absa":
absa_main(opts, bot, logger)
| [
"\nReview:\n\"{}\"\nAnswer:\n",
"\n",
"{}",
"\nReview:\n\"{}\"\nAspect:\n\"{}\"\nAnswer:\n"
] |
2024-01-10 | FreedomIntelligence/Evaluation-of-ChatGPT-on-Information-Extraction | 3_EE~ee_joint_test_with_api.py | import json, os
import random
import time
import openai
import threading
import sys
cur_path = os.getcwd()
sys.path.append(cur_path)
from utils import Logger, bot_run, ReadSample, WriteSample
from config import get_opts_ee as get_opts
from ee_joint_report_metric import joint_report_metric
def get_prompt_list(event_types_list, roles_str):
prompt_list = []
# 1
prompt = 'Pre-defined event types include: {}.\n{}\n\nFrom the given text, recognize all event triggers with their corresponding event types, and recognize all arguments of each event and their corresponding roles. The event trigger is a word or phrase in the given text that indicates the occurrence of a pre-defined event. The corresponding event type answered must be one of the pre-defined event types. Each occurred event involves several arguments, and each argument belongs to a pre-defined role. The argument is an entity that appears in the given text and participates in this event. The corresponding role must be one of the given candidate roles.\nFor each occurred event, answer in the format \'["event_trigger", "event_type"]: [["argument_1", "role_1"], ["argument_2", "role_2"], ...]\' without any explanation. If no argument exists for the recognized event, then just answer in the format \'["event_trigger", "event_type"]: []\'. If no event trigger is involved (i.e., no event occurs), then just answer "[]".'.format(json.dumps(sorted(event_types_list)), roles_str)
prompt_list.append(prompt)
# 2
prompt = 'The list of pre-defined event types is {}.\n{}\n\nFrom the given text, first find out all event triggers from it and determine the corresponding event type for each event trigger, then find out all arguments of each event from the given text, then assign a role to each argument from the given candidate roles. The event trigger is a word or phrase in the given text that indicates the occurrence of a pre-defined event. The corresponding event type answered must be one of the pre-defined event types. Each occurred event involves several arguments, and each argument belongs to a pre-defined role. The argument is an entity that appears in the given text and participates in this event. The corresponding role must be one of the given candidate roles.\nFor each occurred event, answer in the format \'["event_trigger", "event_type"]: [["argument_1", "role_1"], ["argument_2", "role_2"], ...]\' without any explanation. If no argument exists for the recognized event, then just answer in the format \'["event_trigger", "event_type"]: []\'. If no event trigger is involved (i.e., no event occurs), then just answer "[]".'.format(json.dumps(sorted(event_types_list)), roles_str)
prompt_list.append(prompt)
# 3
prompt = 'Given the pre-defined event types: {}, the roles of each event type are as follows: {}\n\nGiven the text, first judge which words or phrases in the given text are event triggers and categorize each of them into one of the pre-defined event types, then identify all arguments of each event from the given text, and select a role from the given candidate roles for each argument. The event trigger is a word or phrase in the given text that indicates the occurrence of a pre-defined event. The corresponding event type answered must be one of the pre-defined event types. Each occurred event involves several arguments, and each argument belongs to a pre-defined role. The argument is an entity that appears in the given text and participates in this event. The corresponding role must be one of the given candidate roles.\nFor each occurred event, answer in the format \'["event_trigger", "event_type"]: [["argument_1", "role_1"], ["argument_2", "role_2"], ...]\' without any explanation. If no argument exists for the recognized event, then just answer in the format \'["event_trigger", "event_type"]: []\'. If no event trigger is involved (i.e., no event occurs), then just answer "[]".'.format(json.dumps(sorted(event_types_list)), roles_str)
prompt_list.append(prompt)
# 4
prompt = 'Pre-defined event types include: {}.\n{}\n\nGiven a piece of text, which words or phrases in the given text are event triggers? Which pre-defined event type is indicated by each event trigger? For each event, what are all arguments of this event in the given text? What is the corresponding role for each argument? The event trigger is a word or phrase in the given text that indicates the occurrence of a pre-defined event. The corresponding event type answered must be one of the pre-defined event types. Each occurred event involves several arguments, and each argument belongs to a pre-defined role. The argument is an entity that appears in the given text and participates in this event. The corresponding role must be one of the given candidate roles.\nFor each occurred event, answer in the format \'["event_trigger", "event_type"]: [["argument_1", "role_1"], ["argument_2", "role_2"], ...]\' without any explanation. If no argument exists for the recognized event, then just answer in the format \'["event_trigger", "event_type"]: []\'. If no event trigger is involved (i.e., no event occurs), then just answer "[]".'.format(json.dumps(sorted(event_types_list)), roles_str)
prompt_list.append(prompt)
# 5
prompt = 'The list of pre-defined event types is {}.\n{}\n\nGiven a piece of text, which words or phrases in the given text are event triggers for the above event types? What is the corresponding event type of each event trigger? For each event, what arguments of this event are included in this text? What is the corresponding role of each argument? The event trigger is a word or phrase in the given text that indicates the occurrence of a pre-defined event. The corresponding event type answered must be one of the pre-defined event types. Each occurred event involves several arguments, and each argument belongs to a pre-defined role. The argument is an entity that appears in the given text and participates in this event. The corresponding role must be one of the given candidate roles.\nFor each occurred event, answer in the format \'["event_trigger", "event_type"]: [["argument_1", "role_1"], ["argument_2", "role_2"], ...]\' without any explanation. If no argument exists for the recognized event, then just answer in the format \'["event_trigger", "event_type"]: []\'. If no event trigger is involved (i.e., no event occurs), then just answer "[]".'.format(json.dumps(sorted(event_types_list)), roles_str)
prompt_list.append(prompt)
return prompt_list
def get_icl_cot_prompt_list(opts):
prompt_icl_list, prompt_cot_list = {}, {}
if opts.ICL:
prompt_icl_file = os.path.join(opts.input_dir, opts.task, opts.dataset, opts.icl_prompt)
prompt_icl_list = json.load(open(prompt_icl_file, "r", encoding="utf-8"))
prompt_cot_list = {}
elif opts.COT:
prompt_cot_file = os.path.join(opts.input_dir, opts.task, opts.dataset, opts.cot_prompt)
prompt_cot_list = json.load(open(prompt_cot_file, "r", encoding="utf-8"))
prompt_icl_list = {}
return prompt_icl_list, prompt_cot_list
def ee_joint_get_prompt(opts, example, prompt_list, prompt_icl_list, prompt_cot_list):
tokens = example["text"].split(" ")
if len(tokens) > 1024:
seq_str = " ".join(tokens[:1024])
else:
seq_str = example["text"]
if opts.irrelevant:
file_name = os.path.join(opts.input_dir, opts.task, opts.dataset, "train_no_event.json")
fr_no = open(file_name, "r", encoding="utf-8")
data_no_term = json.load(fr_no)
irrelevant_text_list = [item["text"] for item in data_no_term]
random_text = random.sample(irrelevant_text_list, 2)
input_text = random_text[0] + " " + seq_str+ " " + random_text[1]
else:
input_text = seq_str
if opts.ICL:
prompt = prompt_list[opts.best_prompt] + "\n" + prompt_icl_list[opts.prompt-1] + '\nGiven text:\n"{}"\nAnswer:\n'.format(input_text)
elif opts.COT:
prompt = prompt_list[opts.best_prompt] + "\n" + prompt_cot_list[opts.prompt-1] + '\nGiven text:\n"{}"\nAnswer:\n'.format(input_text)
else:
prompt = prompt_list[opts.prompt-1] + '\nGiven text:\n"{}"'.format(input_text)
return prompt
def get_best_prompt(opts, logger):
file_name_list = ["ee_joint_result_" + str(i) + ".json" for i in range(1, 6)]
f1_list = [joint_report_metric(opts, logger, file_name=file) for file in file_name_list]
best_prompt = f1_list.index(max(f1_list))
return best_prompt
def get_roles_str(event2roles):
res = []
for k, v in event2roles.items():
tmp_str = 'The list of roles for event "{}" is {}.'.format(k, json.dumps(v))
res.append(tmp_str)
return " ".join(res)
def ee_joint_main(opts, bot, logger):
start_time = time.time()
logger.write("{}\n".format(opts.test_file))
logger.write("{}\n".format(opts.type_file))
## load data
logger.write("loading data ...\n")
with open(opts.test_file, 'r', encoding='utf-8') as fr, open(opts.type_file, 'r', encoding='utf-8') as fr_type:
data = json.load(fr)
types = json.load(fr_type)
event_types = types["event_types"]
event_types_list = [event_types[key]["verbose"] for key in event_types]
event2roles = types["event2roles"]
## sample
index_list = list(range(0, len(data)))
if opts.sample:
logger.write("Sampling examples ...\n")
selected_idx = random.sample(index_list, opts.sample_k)
selected_idx.sort()
print(selected_idx)
else:
selected_idx = index_list
## sample end
roles_str = get_roles_str(event2roles)
prompt_list = get_prompt_list(event_types_list, roles_str)
# print(prompt_list)
prompt_icl_list, prompt_cot_list = get_icl_cot_prompt_list(opts)
if opts.ICL or opts.COT:
opts.best_prompt = get_best_prompt(opts, logger)
## API
with open(opts.result_file, 'a', encoding='utf-8') as fw:
fw.seek(0) #定位
fw.truncate() #清空文件
fw.write("[\n")
logger.write("Evaluation begining ...\n")
i = 0
while i < len(selected_idx):
idx = selected_idx[i]
i += 1
logger.write("No. "+ str(i) + " | example's id: " + str(idx) + " | total examples: " + str(len(data)) + "\n")
example = data[idx]
print(example["text"])
prompt = ee_joint_get_prompt(opts, example, prompt_list, prompt_icl_list, prompt_cot_list)
logger.write("EE-Joint | " + str(i) + "/" + str(len(data)) + " | Prompt:\n" + prompt + "\n")
response = bot_run(bot, prompt, model=opts.model)
logger.write("EE-Joint | " + str(i) + "/" + str(len(data)) + " | Response:\n" + response + "\n")
example.update({
"prompt": prompt,
"response": response
})
if opts.ICL or opts.COT:
example["best_prompt"] = opts.best_prompt + 1
fw.write(json.dumps(example, indent=4, ensure_ascii=False))
if i != len(selected_idx):
fw.write("\n,\n")
else:
fw.write("\n")
fw.write("]\n")
end_time = time.time()
logger.write("The result is saved: {}\n".format(opts.result_file))
logger.write("Times: {:.2f}s = {:.2f}m\n".format(end_time-start_time, (end_time-start_time)/60.0))
## multi thread process
def thread_process(thread_id, opts, bot, read_sample, write_sample, prompt_list, prompt_icl_list, prompt_cot_list, event_types, event2roles, logger):
while True:
status, example = read_sample.get_item()
if status:
cur_idx = read_sample.cur_index
total = len(read_sample.data_idx)
prompt = ee_joint_get_prompt(opts, example, prompt_list, prompt_icl_list, prompt_cot_list)
logger.write("Thread: " + str(thread_id) + " | EE-Trigger | " + str(cur_idx) + "/" + str(total) + " | {} | Prompt:\n".format(opts.best_prompt+1) + prompt + "\n")
response = bot_run(bot, prompt, model=opts.model)
logger.write("Thread: " + str(thread_id) + " | EE-Trigger | " + str(cur_idx) + "/" + str(total) + " | {} | Response:\n".format(opts.best_prompt+1) + response + "\n")
example.update({
"prompt": prompt,
"response": response
})
if opts.ICL or opts.COT:
example["best_prompt"] = opts.best_prompt + 1
write_sample.write(example)
else:
break
def ee_joint_main_multi_thread(opts, bot, logger, num_thread=10):
start_time = time.time()
logger.write("{}\n".format(opts.test_file))
logger.write("{}\n".format(opts.type_file))
## load data
logger.write("loading data ...\n")
with open(opts.test_file, 'r', encoding='utf-8') as fr, open(opts.type_file, 'r', encoding='utf-8') as fr_type:
data = json.load(fr)
types = json.load(fr_type)
event_types = types["event_types"]
event_types_list = [event_types[key]["verbose"] for key in event_types]
event2roles = types["event2roles"]
## sample
index_list = list(range(0, len(data)))
if opts.sample:
logger.write("Sampling examples ...\n")
selected_idx = random.sample(index_list, opts.sample_k)
selected_idx.sort()
print(selected_idx)
else:
selected_idx = index_list
## sample end
roles_str = get_roles_str(event2roles)
prompt_list = get_prompt_list(event_types_list, roles_str)
prompt_icl_list, prompt_cot_list = get_icl_cot_prompt_list(opts)
if opts.ICL or opts.COT:
opts.best_prompt = get_best_prompt(opts, logger)
logger.write("Evaluation begining ...\n")
read_sample = ReadSample(data, selected_idx)
write_sample = WriteSample(opts.result_file, 'a')
threads_list = []
for t_id in range(num_thread):
worker = threading.Thread(target=thread_process, args=(t_id+1, opts, bot, read_sample, write_sample, prompt_list, prompt_icl_list, prompt_cot_list, event_types, event2roles, logger))
worker.start()
threads_list.append(worker)
for th in threads_list:
th.join()
end_time = time.time()
logger.write("Times: {:.2f}s = {:.2f}m\n".format(end_time-start_time, (end_time-start_time)/60.0))
with open(opts.result_file, "r", encoding="utf-8") as f:
new_data = [json.loads(item) for item in f.readlines()]
logger.write(str(len(new_data)) + " " + str(len(data)) + "\n")
with open(opts.result_file, "w", encoding="utf-8") as f:
f.write(json.dumps(new_data, indent=4, ensure_ascii=False))
if __name__ == "__main__":
opts = get_opts()
api_key_file = os.path.join("./api-keys", opts.api_key)
openai.api_key_path = api_key_file
bot = openai.ChatCompletion()
## log file
logger_file = os.path.join(opts.task, opts.logger_file)
logger = Logger(file_name=logger_file)
if opts.task == "ee":
if opts.multi_thread:
ee_joint_main_multi_thread(opts, bot, logger, num_thread=opts.num_thread)
else:
ee_joint_main(opts, bot, logger)
| [
"\n",
"[]",
"{}",
"\nGiven text:\n\"{}\"\nAnswer:\n",
"Given the pre-defined event types: {}, the roles of each event type are as follows: {}\n\nGiven the text, first judge which words or phrases in the given text are event triggers and categorize each of them into one of the pre-defined event types, then identify all arguments of each event from the given text, and select a role from the given candidate roles for each argument. The event trigger is a word or phrase in the given text that indicates the occurrence of a pre-defined event. The corresponding event type answered must be one of the pre-defined event types. Each occurred event involves several arguments, and each argument belongs to a pre-defined role. The argument is an entity that appears in the given text and participates in this event. The corresponding role must be one of the given candidate roles.\nFor each occurred event, answer in the format '[\"event_trigger\", \"event_type\"]: [[\"argument_1\", \"role_1\"], [\"argument_2\", \"role_2\"], ...]' without any explanation. If no argument exists for the recognized event, then just answer in the format '[\"event_trigger\", \"event_type\"]: []'. If no event trigger is involved (i.e., no event occurs), then just answer \"[]\".",
"The list of pre-defined event types is {}.\n{}\n\nGiven a piece of text, which words or phrases in the given text are event triggers for the above event types? What is the corresponding event type of each event trigger? For each event, what arguments of this event are included in this text? What is the corresponding role of each argument? The event trigger is a word or phrase in the given text that indicates the occurrence of a pre-defined event. The corresponding event type answered must be one of the pre-defined event types. Each occurred event involves several arguments, and each argument belongs to a pre-defined role. The argument is an entity that appears in the given text and participates in this event. The corresponding role must be one of the given candidate roles.\nFor each occurred event, answer in the format '[\"event_trigger\", \"event_type\"]: [[\"argument_1\", \"role_1\"], [\"argument_2\", \"role_2\"], ...]' without any explanation. If no argument exists for the recognized event, then just answer in the format '[\"event_trigger\", \"event_type\"]: []'. If no event trigger is involved (i.e., no event occurs), then just answer \"[]\".",
"Pre-defined event types include: {}.\n{}\n\nGiven a piece of text, which words or phrases in the given text are event triggers? Which pre-defined event type is indicated by each event trigger? For each event, what are all arguments of this event in the given text? What is the corresponding role for each argument? The event trigger is a word or phrase in the given text that indicates the occurrence of a pre-defined event. The corresponding event type answered must be one of the pre-defined event types. Each occurred event involves several arguments, and each argument belongs to a pre-defined role. The argument is an entity that appears in the given text and participates in this event. The corresponding role must be one of the given candidate roles.\nFor each occurred event, answer in the format '[\"event_trigger\", \"event_type\"]: [[\"argument_1\", \"role_1\"], [\"argument_2\", \"role_2\"], ...]' without any explanation. If no argument exists for the recognized event, then just answer in the format '[\"event_trigger\", \"event_type\"]: []'. If no event trigger is involved (i.e., no event occurs), then just answer \"[]\".",
"The list of pre-defined event types is {}.\n{}\n\nFrom the given text, first find out all event triggers from it and determine the corresponding event type for each event trigger, then find out all arguments of each event from the given text, then assign a role to each argument from the given candidate roles. The event trigger is a word or phrase in the given text that indicates the occurrence of a pre-defined event. The corresponding event type answered must be one of the pre-defined event types. Each occurred event involves several arguments, and each argument belongs to a pre-defined role. The argument is an entity that appears in the given text and participates in this event. The corresponding role must be one of the given candidate roles.\nFor each occurred event, answer in the format '[\"event_trigger\", \"event_type\"]: [[\"argument_1\", \"role_1\"], [\"argument_2\", \"role_2\"], ...]' without any explanation. If no argument exists for the recognized event, then just answer in the format '[\"event_trigger\", \"event_type\"]: []'. If no event trigger is involved (i.e., no event occurs), then just answer \"[]\".",
"Pre-defined event types include: {}.\n{}\n\nFrom the given text, recognize all event triggers with their corresponding event types, and recognize all arguments of each event and their corresponding roles. The event trigger is a word or phrase in the given text that indicates the occurrence of a pre-defined event. The corresponding event type answered must be one of the pre-defined event types. Each occurred event involves several arguments, and each argument belongs to a pre-defined role. The argument is an entity that appears in the given text and participates in this event. The corresponding role must be one of the given candidate roles.\nFor each occurred event, answer in the format '[\"event_trigger\", \"event_type\"]: [[\"argument_1\", \"role_1\"], [\"argument_2\", \"role_2\"], ...]' without any explanation. If no argument exists for the recognized event, then just answer in the format '[\"event_trigger\", \"event_type\"]: []'. If no event trigger is involved (i.e., no event occurs), then just answer \"[]\".",
"\nGiven text:\n\"{}\""
] |
2024-01-10 | FreedomIntelligence/Evaluation-of-ChatGPT-on-Information-Extraction | 2_RE~re_rc_test_with_api.py | import json
import sys
import os
import random
import time
import openai
import threading
import ast
from config import get_opts_re as get_opts
from re_rc_report_metric import re_rc_report_metric
cur_path = os.getcwd()
sys.path.append(cur_path)
from utils import Logger, bot_run, ReadSample, WriteSample
def get_prompt_list(r_types, e_types_dict):
na_item = r_types[-1]
r_types = list(set(r_types[:-1]))
r_types.append(na_item)
prompt_list = []
if len(e_types_dict) != 0:
prompt = 'From the list of relations: {}, identify all relations between each given subject-object entity pair in the given text. The given entity pairs with their corresponding entity types are listed in the form ("subject", "subject_type", "object", "object_type"). The list of entity types is {}.\nAnswer in the format \'("subject", "subject_type", "object", "object_type"): ["relation_1", "relation_2", ...]\' without any explanation.'.format(json.dumps(r_types), json.dumps(list(e_types_dict.values())))
prompt_list.append(prompt)
prompt = 'Given the list of relations: {}, for each specified subject-object entity pair, judge whether each relation is expressed by the entity pair and return all expressed relations, based on the given text. The given entity pairs with their corresponding entity types are listed in the form ("subject", "subject_type", "object", "object_type"). The list of entity types is {}.\nAnswer in the format \'("subject", "subject_type", "object", "object_type"): ["relation_1", "relation_2", ...]\' without any explanation.'.format(json.dumps(r_types), json.dumps(list(e_types_dict.values())))
prompt_list.append(prompt)
prompt = 'According to the given text, find out all relations expressed by each specified entity pair mentioned in the text from the predefined list of relations.\nPredefined relation list:\n{}\nThe given entity pairs with their corresponding entity types are listed in the form ("subject", "subject_type", "object", "object_type"). The list of entity types is {}.\nAnswer in the format \'("subject", "subject_type", "object", "object_type"): ["relation_1", "relation_2", ...]\' without any explanation.'.format(json.dumps(r_types), json.dumps(list(e_types_dict.values())))
prompt_list.append(prompt)
prompt = 'What relations are expressed by each given subject-object entity pair in the given text? Return one or more relations from the list {}. The given entity pairs with their corresponding entity types are listed in the form ("subject", "subject_type", "object", "object_type"). The list of entity types is {}.\nAnswer in the format \'("subject", "subject_type", "object", "object_type"): ["relation_1", "relation_2", ...]\' without any explanation.'.format(json.dumps(r_types), json.dumps(list(e_types_dict.values())))
prompt_list.append(prompt)
prompt = 'Only consider the relations in the list {}. What are all relations expressed by each given subject-object entity pair in the given text? The given entity pairs with their corresponding entity types are listed in the form ("subject", "subject_type", "object", "object_type"). The list of entity types is {}.\nAnswer in the format \'("subject", "subject_type", "object", "object_type"): ["relation_1", "relation_2", ...]\' without any explanation.'.format(json.dumps(r_types), json.dumps(list(e_types_dict.values())))
prompt_list.append(prompt)
else:
prompt = 'From the list of relations: {}, identify all relations between each given subject-object entity pair in the given text. The given entity pairs are listed in the form ("subject", "object").\nAnswer in the format \'("subject", "object"): ["relation_1", "relation_2", ...]\' without any explanation.'.format(json.dumps(r_types))
prompt_list.append(prompt)
prompt = 'Given the list of relations: {}, for each specified subject-object entity pair, judge whether each relation is expressed by the entity pair and return all expressed relations, based on the given text. The given entity pairs are listed in the form ("subject", "object").\nAnswer in the format \'("subject", "object"): ["relation_1", "relation_2", ...]\' without any explanation.'.format(json.dumps(r_types))
prompt_list.append(prompt)
prompt = 'According to the given text, find out all relations expressed by each specified entity pair mentioned in the text from the predefined list of relations.\nPredefined relation list:\n{}\nThe given entity pairs are listed in the form ("subject", "object").\nAnswer in the format \'("subject", "object"): ["relation_1", "relation_2", ...]\' without any explanation.'.format(json.dumps(r_types))
prompt_list.append(prompt)
prompt = 'What relations are expressed by each given subject-object entity pair in the given text? Return one or more relations from the list {}. The given entity pairs are listed in the form ("subject", "object").\nAnswer in the format \'("subject", "object"): ["relation_1", "relation_2", ...]\' without any explanation.'.format(json.dumps(r_types))
prompt_list.append(prompt)
prompt = 'Only consider the relations in the list {}. What are all relations expressed by each given subject-object entity pair in the given text? The given entity pairs are listed in the form ("subject", "object").\nAnswer in the format \'("subject", "object"): ["relation_1", "relation_2", ...]\' without any explanation.'.format(json.dumps(r_types))
prompt_list.append(prompt)
return prompt_list
def get_icl_cot_prompt_list(opts):
prompt_icl_list, prompt_cot_list = {}, {}
if opts.ICL:
prompt_icl_file = os.path.join(opts.input_dir, opts.task, opts.dataset, opts.icl_prompt)
prompt_icl_list = json.load(open(prompt_icl_file, "r", encoding="utf-8"))
prompt_cot_list = {}
elif opts.COT:
prompt_cot_file = os.path.join(opts.input_dir, opts.task, opts.dataset, opts.cot_prompt)
prompt_cot_list = json.load(open(prompt_cot_file, "r", encoding="utf-8"))
prompt_icl_list = {}
return prompt_icl_list, prompt_cot_list
def get_pairs_str(opts, example, e_types_dict):
pairs_str_list = []
uni_target_list = [] # dwie, nyt-multi 有重复
for r_dic in example["relations"]:
if r_dic not in uni_target_list:
uni_target_list.append(r_dic)
for r_dic in uni_target_list:
h = r_dic["h"]
h_name = r_dic["h_name"].replace('"', '\'') # docred 有 实体 内含 '"'
h_type = example["entities"][h]["type"]
if h_type != "":
h_type = e_types_dict[h_type]
t = r_dic["t"]
t_name = r_dic["t_name"].replace('"', '\'')
t_type = example["entities"][t]["type"]
if t_type != "":
t_type = e_types_dict[t_type]
if len(e_types_dict) != 0:
tmp_str = '("{}", "{}", "{}", "{}")'.format(h_name, h_type, t_name, t_type) # multi-label pairs
# if tmp_str not in pairs_str_list:
pairs_str_list.append(tmp_str)
if "sent" in opts.dataset:
pairs_str_list.append('("{}", "{}", "{}", "{}")'.format(t_name, t_type, h_name, h_type))
else:
tmp_str = '("{}", "{}")'.format(h_name, t_name)
# if tmp_str not in pairs_str_list:
pairs_str_list.append(tmp_str)
if "sent" in opts.dataset:
pairs_str_list.append('("{}", "{}")'.format(t_name, h_name))
if len(e_types_dict) != 0:
pairs_str_list = sorted(pairs_str_list, key=lambda a: ast.literal_eval(a)[0] + "#" + ast.literal_eval(a)[2])
else:
pairs_str_list = sorted(pairs_str_list, key=lambda a: ast.literal_eval(a)[0] + "#" + ast.literal_eval(a)[1])
return "\n".join(pairs_str_list)
def re_rc_get_prompt(opts, example, r_types, e_types_dict, prompt_list, prompt_icl_list, prompt_cot_list):
pairs_str = get_pairs_str(opts, example, e_types_dict)
tokens = example['seq'].split(" ")
if len(tokens) > 1024:
seq_str = " ".join(tokens[:1024])
else:
seq_str = example['seq']
if opts.irrelevant:
file_name = os.path.join(opts.input_dir, opts.task, opts.dataset, "train_no_relation.json")
fr_no = open(file_name, "r", encoding="utf-8")
data_no_term = json.load(fr_no)
irrelevant_text_list = [item["seq"] for item in data_no_term]
random_text = random.sample(irrelevant_text_list, 2)
input_text = random_text[0] + " " + seq_str+ " " + random_text[1]
else:
input_text = seq_str
if opts.ICL:
prompt = prompt_list[opts.best_prompt] + "\n" + prompt_icl_list[opts.prompt-1] + '\nGiven text:\n"{}"\nEntity pairs:\n{}\nAnswer:\n'.format(input_text, pairs_str)
elif opts.COT:
prompt = prompt_list[opts.best_prompt] + "\n" + prompt_cot_list[opts.prompt-1] + '\nGiven text:\n"{}"\nEntity pairs:\n{}\nAnswer:\n'.format(input_text, pairs_str)
else:
prompt = prompt_list[opts.prompt-1] + '\nGiven text:\n"{}"\nEntity pairs:\n{}\nAnswer:\n'.format(input_text, pairs_str)
return prompt
def get_best_prompt(opts, logger, e_types_dict):
if len(e_types_dict) != 0:
file_name_list = ["re_rc_type_result_" + str(i) + ".json" for i in range(1, 6)]
else:
file_name_list = ["re_rc_result_" + str(i) + ".json" for i in range(1, 6)]
f1_list = [re_rc_report_metric(opts, logger, file_name=file) for file in file_name_list]
best_prompt = f1_list.index(max(f1_list))
return best_prompt
def re_rc_main(opts, bot, logger):
start_time = time.time()
logger.write("{}\n".format(opts.test_file))
logger.write("{}\n".format(opts.type_file))
## load data
logger.write("loading data ...\n")
with open(opts.test_file, 'r', encoding='utf-8') as fr, open(opts.type_file, 'r', encoding='utf-8') as fr_type:
data = json.load(fr)
types = json.load(fr_type)
r_types = list(types["relation"].values())
e_types_dict = types["entity"]
## sample
index_list = list(range(0, len(data)))
if opts.sample:
logger.write("Sampling examples ...\n")
selected_idx = random.sample(index_list, opts.sample_k)
selected_idx.sort()
print(selected_idx)
else:
selected_idx = index_list
## sample end
prompt_list = get_prompt_list(r_types, e_types_dict)
prompt_icl_list, prompt_cot_list = get_icl_cot_prompt_list(opts)
if opts.ICL or opts.COT:
opts.best_prompt = get_best_prompt(opts, logger, e_types_dict)
## API
with open(opts.result_file, 'a', encoding='utf-8') as fw:
fw.seek(0) #定位
fw.truncate() #清空文件
fw.write("[\n")
logger.write("Evaluation begining ...\n")
i = 0
while i < len(selected_idx):
idx = selected_idx[i]
i += 1
logger.write("No. "+ str(i) + " | example's id: " + str(idx) + " | total examples: " + str(len(data)) + "\n")
example = data[idx]
print(example["seq"])
prompt = re_rc_get_prompt(opts, example, r_types, e_types_dict, prompt_list, prompt_icl_list, prompt_cot_list)
logger.write("RE-RC | " + str(i) + "/" + str(len(data)) + " | Prompt:\n" + prompt + "\n")
response = bot_run(bot, prompt, model=opts.model)
logger.write("RE-RC | " + str(i) + "/" + str(len(data)) + " | Response:\n" + response + "\n")
result_dict = {}
example.update({
"RE": result_dict,
"prompt": prompt,
"response": response
})
if opts.ICL or opts.COT:
example["best_prompt"] = opts.best_prompt + 1
fw.write(json.dumps(example, indent=4, ensure_ascii=False))
if i != len(selected_idx):
fw.write("\n,\n")
else:
fw.write("\n")
fw.write("]\n")
end_time = time.time()
logger.write("The result is saved: {}\n".format(opts.result_file))
logger.write("Times: {:.2f}s = {:.2f}m\n".format(end_time-start_time, (end_time-start_time)/60.0))
## multi thread process
def thread_process(thread_id, opts, bot, read_sample, write_sample, r_types, e_types_dict, prompt_list, prompt_icl_list, prompt_cot_list, logger):
while True:
status, example = read_sample.get_item()
if status:
cur_idx = read_sample.cur_index
total = len(read_sample.data_idx)
prompt = re_rc_get_prompt(opts, example, r_types, e_types_dict, prompt_list, prompt_icl_list, prompt_cot_list)
logger.write("Thread: " + str(thread_id) + " | RE-RC | " + str(cur_idx) + "/" + str(total) + " | Prompt:\n" + prompt + "\n")
response = bot_run(bot, prompt, model=opts.model)
logger.write("Thread: " + str(thread_id) + " | RE-RC | " + str(cur_idx) + "/" + str(total) + " | Response:\n" + response + "\n")
result_dict = {}
example.update({
"RE": result_dict,
"prompt": prompt,
"response": response
})
if opts.ICL or opts.COT:
example["best_prompt"] = opts.best_prompt + 1
write_sample.write(example)
else:
break
def re_rc_main_multi_thread(opts, bot, logger, num_thread=10):
start_time = time.time()
logger.write("{}\n".format(opts.test_file))
logger.write("{}\n".format(opts.type_file))
## load data
logger.write("loading data ...\n")
with open(opts.test_file, 'r', encoding='utf-8') as fr, open(opts.type_file, 'r', encoding='utf-8') as fr_type:
data = json.load(fr)
types = json.load(fr_type)
r_types = list(types["relation"].values())
e_types_dict = types["entity"]
## sample
index_list = list(range(0, len(data)))
if opts.sample:
logger.write("Sampling examples ...\n")
selected_idx = random.sample(index_list, opts.sample_k)
selected_idx.sort()
print(selected_idx)
else:
selected_idx = index_list
## sample end
prompt_list = get_prompt_list(r_types, e_types_dict)
prompt_icl_list, prompt_cot_list = get_icl_cot_prompt_list(opts)
if opts.ICL or opts.COT:
opts.best_prompt = get_best_prompt(opts, logger, e_types_dict)
logger.write("Evaluation begining ...\n")
read_sample = ReadSample(data, selected_idx)
write_sample = WriteSample(opts.result_file, 'a')
threads_list = []
for t_id in range(num_thread):
worker = threading.Thread(target=thread_process, args=(t_id+1, opts, bot, read_sample, write_sample, r_types, e_types_dict, prompt_list, prompt_icl_list, prompt_cot_list, logger))
worker.start()
threads_list.append(worker)
for th in threads_list:
th.join()
end_time = time.time()
logger.write("Times: {:.2f}s = {:.2f}m\n".format(end_time-start_time, (end_time-start_time)/60.0))
with open(opts.result_file, "r", encoding="utf-8") as f:
new_data = [json.loads(item) for item in f.readlines()]
logger.write(str(len(new_data)) + " " + str(len(data)) + "\n")
with open(opts.result_file, "w", encoding="utf-8") as f:
f.write(json.dumps(new_data, indent=4, ensure_ascii=False))
if __name__ == "__main__":
opts = get_opts()
api_key_file = os.path.join("./api-keys", opts.api_key)
openai.api_key_path = api_key_file
bot = openai.ChatCompletion()
## log file
logger_file = os.path.join(opts.task, opts.logger_file)
logger = Logger(file_name=logger_file)
# logger.write(json.dumps(opts.__dict__, indent=4) + "\n")
if opts.task == "re":
if opts.multi_thread:
re_rc_main_multi_thread(opts, bot, logger, num_thread=opts.num_thread)
else:
re_rc_main(opts, bot, logger)
| [
"What relations are expressed by each given subject-object entity pair in the given text? Return one or more relations from the list {}. The given entity pairs are listed in the form (\"subject\", \"object\").\nAnswer in the format '(\"subject\", \"object\"): [\"relation_1\", \"relation_2\", ...]' without any explanation.",
"Only consider the relations in the list {}. What are all relations expressed by each given subject-object entity pair in the given text? The given entity pairs with their corresponding entity types are listed in the form (\"subject\", \"subject_type\", \"object\", \"object_type\"). The list of entity types is {}.\nAnswer in the format '(\"subject\", \"subject_type\", \"object\", \"object_type\"): [\"relation_1\", \"relation_2\", ...]' without any explanation.",
"\n",
"{}",
"Given the list of relations: {}, for each specified subject-object entity pair, judge whether each relation is expressed by the entity pair and return all expressed relations, based on the given text. The given entity pairs with their corresponding entity types are listed in the form (\"subject\", \"subject_type\", \"object\", \"object_type\"). The list of entity types is {}.\nAnswer in the format '(\"subject\", \"subject_type\", \"object\", \"object_type\"): [\"relation_1\", \"relation_2\", ...]' without any explanation.",
"Only consider the relations in the list {}. What are all relations expressed by each given subject-object entity pair in the given text? The given entity pairs are listed in the form (\"subject\", \"object\").\nAnswer in the format '(\"subject\", \"object\"): [\"relation_1\", \"relation_2\", ...]' without any explanation.",
"Given the list of relations: {}, for each specified subject-object entity pair, judge whether each relation is expressed by the entity pair and return all expressed relations, based on the given text. The given entity pairs are listed in the form (\"subject\", \"object\").\nAnswer in the format '(\"subject\", \"object\"): [\"relation_1\", \"relation_2\", ...]' without any explanation.",
"According to the given text, find out all relations expressed by each specified entity pair mentioned in the text from the predefined list of relations.\nPredefined relation list:\n{}\nThe given entity pairs with their corresponding entity types are listed in the form (\"subject\", \"subject_type\", \"object\", \"object_type\"). The list of entity types is {}.\nAnswer in the format '(\"subject\", \"subject_type\", \"object\", \"object_type\"): [\"relation_1\", \"relation_2\", ...]' without any explanation.",
"What relations are expressed by each given subject-object entity pair in the given text? Return one or more relations from the list {}. The given entity pairs with their corresponding entity types are listed in the form (\"subject\", \"subject_type\", \"object\", \"object_type\"). The list of entity types is {}.\nAnswer in the format '(\"subject\", \"subject_type\", \"object\", \"object_type\"): [\"relation_1\", \"relation_2\", ...]' without any explanation.",
"\nGiven text:\n\"{}\"\nEntity pairs:\n{}\nAnswer:\n",
"According to the given text, find out all relations expressed by each specified entity pair mentioned in the text from the predefined list of relations.\nPredefined relation list:\n{}\nThe given entity pairs are listed in the form (\"subject\", \"object\").\nAnswer in the format '(\"subject\", \"object\"): [\"relation_1\", \"relation_2\", ...]' without any explanation.",
"From the list of relations: {}, identify all relations between each given subject-object entity pair in the given text. The given entity pairs with their corresponding entity types are listed in the form (\"subject\", \"subject_type\", \"object\", \"object_type\"). The list of entity types is {}.\nAnswer in the format '(\"subject\", \"subject_type\", \"object\", \"object_type\"): [\"relation_1\", \"relation_2\", ...]' without any explanation.",
"From the list of relations: {}, identify all relations between each given subject-object entity pair in the given text. The given entity pairs are listed in the form (\"subject\", \"object\").\nAnswer in the format '(\"subject\", \"object\"): [\"relation_1\", \"relation_2\", ...]' without any explanation.",
"[]"
] |
2024-01-10 | PatrickHastings/forked_llama_index | llama_index~llm_predictor~stable_lm.py | """Stable LM.
NOTE: this is a beta wrapper, will replace once better abstractions
(e.g. from langchain) come out.
"""
from typing import Any, Generator, Optional, Tuple
from llama_index.llm_predictor.base import BaseLLMPredictor, LLMMetadata
from llama_index.prompts.base import Prompt
DEFAULT_SYSTEM_PROMPT = """<|SYSTEM|># StableLM Tuned (Alpha version)
- StableLM is a helpful and harmless open-source AI language model developed by StabilityAI.
- StableLM is excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
- StableLM is more than just an information source, StableLM is also able to write poetry, short stories, and make jokes.
- StableLM will refuse to participate in anything that could harm a human.
""" # noqa: E501
class StableLMPredictor(BaseLLMPredictor):
"""ChatGPT Specific LLM predictor class.
Wrapper around an LLMPredictor to provide ChatGPT specific features.
Args:
llm (Optional[langchain.llms.base.LLM]): LLM from Langchain to use
for predictions. Defaults to OpenAI's text-davinci-003 model.
Please see `Langchain's LLM Page
<https://langchain.readthedocs.io/en/latest/modules/llms.html>`_
for more details.
retry_on_throttling (bool): Whether to retry on rate limit errors.
Defaults to true.
"""
def __init__(
self,
max_new_tokens: int = 256,
temperature: float = 0.7,
do_sample: bool = False,
system_prompt: str = DEFAULT_SYSTEM_PROMPT,
tokenizer_name: str = "StabilityAI/stablelm-tuned-alpha-3b",
model_name: str = "StabilityAI/stablelm-tuned-alpha-3b",
) -> None:
"""Initialize params."""
from transformers import AutoModelForCausalLM, AutoTokenizer
self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
self.model = AutoModelForCausalLM.from_pretrained(model_name)
self.model.half().cuda()
self._max_new_tokens = max_new_tokens
self._temperature = temperature
self._do_sample = do_sample
self._system_prompt = system_prompt
self._total_tokens_used = 0
self._last_token_usage: Optional[int] = None
def get_llm_metadata(self) -> LLMMetadata:
"""Get LLM metadata."""
return LLMMetadata()
def stream(self, prompt: Prompt, **prompt_args: Any) -> Tuple[Generator, str]:
"""Stream the answer to a query.
NOTE: this is a beta feature. Will try to build or use
better abstractions about response handling.
Args:
prompt (Prompt): Prompt to use for prediction.
Returns:
str: The predicted answer.
"""
raise NotImplementedError("Streaming is not supported for StableLM.")
@property
def total_tokens_used(self) -> int:
"""Get the total tokens used so far."""
return self._total_tokens_used
@property
def last_token_usage(self) -> int:
"""Get the last token usage."""
return self._last_token_usage or 0
@last_token_usage.setter
def last_token_usage(self, value: int) -> None:
"""Set the last token usage."""
self._last_token_usage = value
def predict(self, prompt: Prompt, **prompt_args: Any) -> Tuple[str, str]:
"""Predict the answer to a query.
Args:
prompt (Prompt): Prompt to use for prediction.
Returns:
Tuple[str, str]: Tuple of the predicted answer and the formatted prompt.
"""
from transformers import StoppingCriteriaList
formatted_prompt = prompt.format(**prompt_args)
full_prompt = (
f"{self._system_prompt}" f"<|USER|>{formatted_prompt}<|ASSISTANT|>"
)
input_tokens = self.tokenizer(full_prompt, return_tensors="pt").to("cuda")
tokens = self.model.generate(
**input_tokens,
max_new_tokens=self._max_new_tokens,
temperature=self._temperature,
do_sample=self._do_sample,
stopping_criteria=StoppingCriteriaList(),
)
completion_tokens = tokens[0][input_tokens["input_ids"].size(1) :]
completion = self.tokenizer.decode(completion_tokens, skip_special_tokens=True)
return completion, formatted_prompt
async def apredict(self, prompt: Prompt, **prompt_args: Any) -> Tuple[str, str]:
"""Async predict the answer to a query.
Args:
prompt (Prompt): Prompt to use for prediction.
Returns:
Tuple[str, str]: Tuple of the predicted answer and the formatted prompt.
"""
return self.predict(prompt, **prompt_args)
| [
"<|USER|>PLACEHOLDER<|ASSISTANT|>",
"<|SYSTEM|># StableLM Tuned (Alpha version)\n- StableLM is a helpful and harmless open-source AI language model developed by StabilityAI.\n- StableLM is excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.\n- StableLM is more than just an information source, StableLM is also able to write poetry, short stories, and make jokes.\n- StableLM will refuse to participate in anything that could harm a human.\n"
] |
2024-01-10 | michaelzheng67/Cohere-Github-Issues-AI | server.py | from http.server import SimpleHTTPRequestHandler, HTTPServer
from urllib.parse import urlparse, parse_qs
import json
import cohere
class CustomHandler(SimpleHTTPRequestHandler):
def do_GET(self):
parsed_path = urlparse(self.path)
if parsed_path.path == '/data':
# Parse the query parameters from the URL
params = parse_qs(parsed_path.query)
# For example, to get the value of a parameter named 'name':
# (Use a default value if the parameter is not present)
cohere_api_key = params.get('cohere_api_key', [''])[0]
text = params.get('text', [''])[0]
repo = params.get('repo', [''])[0]
# Use cohere API to create bullet points
co = cohere.Client(cohere_api_key)
response = co.summarize(
text=text,
model='command',
length='short',
format='bullets'
)
summary = response.summary
self.send_response(200)
self.send_header('Content-type', 'application/json')
self.send_header('Access-Control-Allow-Origin', '*')
self.end_headers()
# Use the parameter in the response data
data = {"key": "value", "summary": summary}
self.wfile.write(json.dumps(data).encode())
elif parsed_path.path == '/suggestion':
# Parse the query parameters from the URL
params = parse_qs(parsed_path.query)
# For example, to get the value of a parameter named 'name':
# (Use a default value if the parameter is not present)
cohere_api_key = params.get('cohere_api_key', [''])[0]
text = params.get('text', [''])[0]
co = cohere.Client(cohere_api_key)
response = co.generate(
model='command-nightly',
prompt = text + ". Can you tell me how to do this?",
max_tokens=200, # This parameter is optional.
temperature=0.750)
response = response.generations[0].text
suggestion = response
self.send_response(200)
self.send_header('Content-type', 'application/json')
self.send_header('Access-Control-Allow-Origin', '*')
self.end_headers()
# Use the parameter in the response data
data = {"key": "value", "suggestion": suggestion}
self.wfile.write(json.dumps(data).encode())
else:
super().do_GET()
httpd = HTTPServer(('localhost', 8000), CustomHandler)
httpd.serve_forever() | [] |
2024-01-10 | swj0419/REPLUG | LSR_finetune~replug_lsr.py |
import numpy as np
import os
from tqdm import tqdm
from retriever import Retriever
from typing import Optional
import openai
import torch
import torch.nn as nn
import transformers
import utils
class LM:
def get_perplexity_data(self, text) -> Optional[dict]:
raise NotImplementedError
@classmethod
def create_from_config(cls, path):
raise NotImplementedError
def initialize_retriever(self, args):
self.args = args
if args.do_retrieval:
self.retriever = Retriever(args)
else:
self.retriever = None
class GPT3LM(LM):
def __init__(self, engine, context_len=1024, max_seq_len=2048, verbose=False, batch_size=16, optimizer=None, args=None):
import openai
self.engine = engine
self.context_len = context_len
self.max_seq_len = max_seq_len
self.wb = utils.WaitBlocker()
self.verbose = verbose
self.tmp = 1
self.batch_size=batch_size
self.optimzer=optimizer
self.args = args
self.tokenizer = transformers.GPT2Tokenizer.from_pretrained('gpt2-xl')
self.end_of_text_token_id = self.tokenizer.convert_tokens_to_ids(["<|endoftext|>"])[0]
# Read from environment variable OPENAI_API_SECRET_KEY
openai.api_key = os.environ["OPENAI_API_SECRET_KEY"]
def forward_training(self, text):
input_ids = self.tokenizer.encode_plus(text)["input_ids"]
rolling_token_windows = utils.get_rolling_token_windows(
token_list=input_ids,
prefix_token=self.end_of_text_token_id,
max_seq_len=self.max_seq_len,
context_len=self.context_len,
)
batch_loss = []
batch_index = 0
# Remaining windows: input_tokens are context, pred_tokens are prediction
for input_tokens, pred_tokens in tqdm(rolling_token_windows):
retriever_loss = self.forward_training_single(input_tokens, pred_tokens)
batch_loss.append(retriever_loss)
if batch_index == self.batch_size:
batch_loss = torch.stack(batch_loss)
batch_loss = torch.mean(batch_loss)
batch_loss.backward()
batch_loss = []
batch_index = 0
self.optimizer.step()
self.optimizer.zero_grad()
def forward_training_single(self, input_tokens, pred_tokens):
query_id = input_tokens[:-len(pred_tokens)]
# print("len(context):", len(query_id), "len(pred_tokens):", len(pred_tokens))
query = self.tokenizer.decode(query_id)
docs, scores = self.retriever.retrieve_passage([query])
plain_docs = [doc["text"] for doc in docs]
# encode the retrieved docs
questions_embedding = self.embed_queries([query])
passages_embedding = self.embed_queries(plain_docs)
retriever_score = torch.einsum("id, ijd->ij", [questions_embedding, passages_embedding])
all_gold_score = []
for i in range(len(docs)):
doc_str = plain_docs[i]
doc_encodings = self.tokenizer.encode(doc_str)
input_tokens_tmp = torch.concat((torch.LongTensor(doc_encodings), torch.LongTensor(input_tokens)), dim=-1)
block_output = self.get_token_logprobs(input_tokens=input_tokens_tmp, pred_tokens=pred_tokens,)
gold_score = block_output["logprobs"]
all_gold_score.append(gold_score)
all_gold_score = torch.FloatTensor(all_gold_score)
retriever_loss = self.kldivloss(retriever_score, gold_score)
return retriever_loss
def kldivloss(self, score, gold_score):
gold_score = torch.softmax(gold_score / self.args.temperature_gold, dim=-1)
score = torch.nn.functional.log_softmax(score / self.args.temperature_score, dim=-1)
return torch.nn.KLDivLoss()(score, gold_score)
# noinspection DuplicatedCode
def get_perplexity_data(self, text) -> Optional[dict]:
input_ids = self.tokenizer.encode_plus(text)["input_ids"]
rolling_token_windows = utils.get_rolling_token_windows(
token_list=input_ids,
prefix_token=self.end_of_text_token_id,
max_seq_len=self.max_seq_len,
context_len=self.context_len,
)
# noinspection PyListCreation
all_logprobs = []
all_positions = []
# Remaining windows: input_tokens are context, pred_tokens are prediction
for input_tokens, pred_tokens in tqdm(rolling_token_windows):
# ipdb.set_trace()
# assert len(input_tokens) == 256
# assert len(pred_tokens) == 512
# bp()
query_id = input_tokens[:-len(pred_tokens)]
print("len(context):", len(query_id), "len(pred_tokens):", len(pred_tokens))
# do retrieval
if self.args.do_retrieval and (query_id != []):
if self.args.random == 0:
query = self.tokenizer.decode(query_id)
else:
query = "who is US president?"
docs, scores = self.retriever.retrieve_passage([query])
plain_docs = [doc["text"] for doc in docs]
if self.args.ensemble == 0:
doc_str = "\n".join(plain_docs)
print(f"query: {[query]}\nretrieved doc: {[doc_str]}")
doc_encodings = self.tokenizer.encode(doc_str)[:self.args.retrieved_max_length]
input_tokens = torch.concat((torch.LongTensor(doc_encodings), torch.LongTensor(input_tokens)), dim=-1)
print("retrieve + context: ", len(input_tokens)-len(pred_tokens))
else:
'''
a + b + c = log(e^log(a) + e^log(b) + e^log(c))
'''
logprobs_list = []
block_output = None
assert self.args.ensemble <= len(plain_docs)
for i in range(self.args.ensemble):
doc_str = plain_docs[i]
doc_encodings = self.tokenizer.encode(doc_str)[:self.args.retrieved_max_length]
input_tokens_tmp = torch.concat((torch.LongTensor(doc_encodings), torch.LongTensor(input_tokens)), dim=-1)
block_output = self.get_token_logprobs(input_tokens=input_tokens_tmp, pred_tokens=pred_tokens,)
logprobs_list.append(block_output["logprobs"])
# sum(np.isinf(block_output["logprobs"]))
# bp()
# block_output["logprobs"] = np.log(np.mean(np.exp(logprobs_list), axis=0))
block_output["logprobs"] = torch.logsumexp(torch.FloatTensor(logprobs_list), dim=0) - np.log(len(logprobs_list))
block_output["logprobs"] = block_output["logprobs"].numpy()
else:
# bp()
block_output = self.get_token_logprobs(input_tokens=input_tokens, pred_tokens=pred_tokens,)
# bp()
all_logprobs.append(block_output["logprobs"])
all_positions.append(block_output["positions"])
if not all_logprobs:
return None
# Gather
all_logprobs = np.concatenate(all_logprobs)
all_positions = np.concatenate(all_positions)
assert len(all_logprobs) == len(input_ids)
return {
"logprobs": all_logprobs,
"positions": all_positions,
"length": len(all_logprobs),
"utf8_length": len(text.encode('utf-8')),
}
def get_token_logprobs(self, input_tokens, pred_tokens):
pred_start = len(input_tokens) - len(pred_tokens) + 1
# We're going to stitch together the input_tokens and pred_tokens
# In the longest case, this gets us to length = max_seq_len+1 (which the API works with)
assert input_tokens[pred_start:] == pred_tokens[:-1]
token_ids = input_tokens + [pred_tokens[-1]]
with self.wb.check_valid():
response = openai.Completion.create(
engine=self.engine,
prompt=token_ids,
max_tokens=0,
temperature=0.0,
logprobs=0,
echo=True,
)
logprobs = np.array(response["choices"][0]["logprobs"]["token_logprobs"][pred_start:])
if self.verbose:
print("Context:", self.tokenizer.convert_ids_to_tokens(token_ids))
print("Predicting:", self.tokenizer.convert_ids_to_tokens(token_ids)[pred_start:])
print("Perplexity:", np.exp(-logprobs.mean()))
print()
positions = np.arange(pred_start-1, pred_start-1 + len(token_ids[pred_start:]))
return {
"logprobs": logprobs,
"positions": positions,
}
@classmethod
def create_from_config(cls, config):
return cls(**config)
| [] |
2024-01-10 | swj0419/REPLUG | downstream_eval~qa_final.py | import os
import sys
sys.path.append("../REPLUG")
import csv
from scipy.special import softmax
import argparse
import numpy as np
import multiprocessing
import time
import json
import time
from time import sleep
from collections import defaultdict
import random
from retriever import Retriever
import openai
from tqdm import tqdm
from utils import *
import operator
from transformers import AutoTokenizer
from ipdb import set_trace as bp
import pandas as pd
from argument import add_lm_args, add_retriever_args
import random
random.seed(2022)
class KeyGen:
def __init__(self) -> None:
self.key_ind = 0
self.api_keys = ["put your api keys"]
def get_key(self):
self.key_ind += 1
if self.key_ind >= len(self.api_keys):
self.key_ind = 0
return self.api_keys[self.key_ind]
key_generator = KeyGen()
def call_api(args, prompt, temp):
tokenized = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(prompt))
prompt = tokenizer.decode(tokenized[-7000 : ])
## A Single Prompt Step
response = None
while response is None:
try:
openai.api_key = key_generator.get_key()
response = openai.Completion.create(
engine=args.engine,
prompt=prompt,
max_tokens=args.maxlen,
logprobs=4,
temperature=temp,
stream=False,
stop="\n"
)
except:
sleep(1)
continue
# print (response)
if args.task == "mmlu":
top_probs = []
try:
top_log_probs = response['choices'][0]["logprobs"]["top_logprobs"][0]
except:
print("!!!!!")
for t in range(len(response['choices'][0]["logprobs"]["tokens"])):
if response['choices'][0]["logprobs"]["tokens"][t] == "\n":
break
top_probs.append(response['choices'][0]["logprobs"]["token_logprobs"][t])
else:
top_probs = []
top_tokens = []
for t in range(len(response['choices'][0]["logprobs"]["tokens"])):
if response['choices'][0]["logprobs"]["tokens"][t] == "\n":
continue
elif response['choices'][0]["logprobs"]["tokens"][t] == "<|endoftext|>":
break
top_probs.append(response['choices'][0]["logprobs"]["token_logprobs"][t])
top_tokens.append(response['choices'][0]["logprobs"]["tokens"][t])
perplexity = np.exp((np.mean(top_probs)))
output = response['choices'][0]["text"].strip()
if args.task == "mmlu":
return output, prompt, (top_log_probs, perplexity)
else:
return output, prompt, (top_probs, perplexity)
def inference_one_ex(args, counter, prompt_batch, score_batch, eg):
all_outputs = []
all_probs = []
for i, prompt in enumerate(prompt_batch):
# bp()
output, newprompt, probs = call_api(args, prompt, temp=0)
ans = output
## exclude no-answer cases
# if ans is not None:
all_outputs.append(ans)
# bp()
all_probs.append(probs[1]*score_batch[i])
ans2prob_list = defaultdict(list)
for ans, prob in zip(all_outputs, all_probs):
ans2prob_list[ans].append(prob)
ans2prob = {k: sum(v) for k, v in ans2prob_list.items()}
# bp()
final_ans = max(ans2prob.items(), key=operator.itemgetter(1))[0]
gold = eg["answer"]
em = single_ans_em(final_ans, gold)
return em
def retrieve_ex(demo, retriever):
query = demo["question"]
docs, scores = retriever.retrieve_passage([query])[0]
# bp()
plain_docs = [doc["text"] for doc in docs]
return plain_docs, scores
def main():
global tokenizer
tokenizer = AutoTokenizer.from_pretrained("gpt2-large")
parser = argparse.ArgumentParser()
parser = add_lm_args(parser)
parser = add_retriever_args(parser)
args = parser.parse_args()
if args.do_retrieval:
retriever = Retriever(args)
else:
retriever = None
# load dataset
all_counter = 0
all_em = 0
'''
data process
'''
with open(args.data_dir, "r") as f:
data = json.load(f)
test_set = data["testset"]
demos = data["demos"][:16]
print("test_set: ", len(test_set))
# evaluate
counter = 0
demos_questions = [d["question"].strip() for d in demos]
pbar = tqdm(test_set)
# build prompt
prompt_demo = ""
prompt_demo_empty = ""
if args.prompt_method in ["closed-book", "open-book"]:
for demo in demos:
# concat the top-1 doc
if args.prompt_method == "open-book":
docs, scores = retrieve_ex(demo, retriever)
prompt_demo += f"Knowledge: {docs[0]}\n"
prompt_demo += "Question: " + demo["question"] + "\n"
# prompt_demo += "" + demo["question"] + "\n"
answer = demo["answer"][0]
prompt_demo += "Answer: " + answer.strip() + "\n\n"
prompt_demo_empty += "Question: " + demo["question"] + "\n"
prompt_demo_empty += "Answer: " + answer.strip() + "\n\n"
# run over test example
for eg in pbar:
all_counter += 1
# bp()
prompt = prompt_demo
prompt_empty = prompt_demo_empty
if eg["question"].strip() in demos_questions:
continue
counter += 1
if len(eg["question"].split()) > 400:
eg["question"] = ' '.join(eg["question"].split()[-400 : ])
prompt_batch = []
score_batch = []
if args.prompt_method == "open-book":
docs, scores = retrieve_ex(eg, retriever)
# contatenation version
for doc, score in zip(docs, scores):
prompt_cur = prompt
prompt_cur += f"Knowledge: {doc}" + "\n"
prompt_cur += "Question: " + eg["question"] + "\n"
prompt_cur += "Answer:"
prompt_batch.append(prompt_cur)
score_batch.append(score)
elif args.prompt_method == "closed-book":
prompt += "Question: " + eg["question"] + "\n"
prompt += "Answer:"
prompt_batch.append(prompt)
score_batch.append(1)
score_batch = softmax(np.array(score_batch)).tolist()
print("score_batch: ", score_batch)
em = inference_one_ex(args, counter, prompt_batch, score_batch, eg)
all_em += em
if retriever is not None:
retriever.dump_query2docs()
print ("EM: {}/{}={}%".format(all_em, all_counter, (all_em / all_counter) * 100))
if __name__ == '__main__':
main() | [
"\n\n",
"Answer: ",
"Knowledge: PLACEHOLDER\n",
"Question: PLACEHOLDER\n",
"Answer:",
"[]"
] |
2024-01-10 | swj0419/REPLUG | downstream_eval~mmlu_final.py | import os
import sys
sys.path.append("../")
import csv
from scipy.special import softmax
import argparse
import numpy as np
import multiprocessing
import time
import json
import time
from time import sleep
from collections import defaultdict
import random
from retriever import Retriever
import openai
from tqdm import tqdm
from utils import *
import operator
from transformers import AutoTokenizer
from ipdb import set_trace as bp
import pandas as pd
from argument import add_lm_args, add_retriever_args
import random
random.seed(0)
class KeyGen:
def __init__(self) -> None:
self.key_ind = 0
if 'OPENAI_API_KEY' in os.environ:
self.api_keys = os.environ['OPENAI_API_KEY'].split(',')
else:
print("OPENAI_API_KEY not found in environment variables. Calling OpenAI APIs will fail."
"OPENAI_API_KEY should be a comma-separated list of API keys."
"It can be set in .bashrc like: export OPENAI_API_KEY=key1,key2,key3"
)
self.api_keys = []
def get_key(self):
self.key_ind += 1
if self.key_ind >= len(self.api_keys):
self.key_ind = 0
return self.api_keys[self.key_ind]
key_generator = KeyGen()
def call_api(args, prompt, temp):
tokenized = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(prompt))
prompt = tokenizer.decode(tokenized[-7000 : ])
## A Single Prompt Step
response = None
while response is None:
try:
openai.api_key = key_generator.get_key()
response = openai.Completion.create(
engine=args.engine,
prompt=prompt,
max_tokens=args.maxlen,
logprobs=4,
temperature=temp,
stream=False,
stop="\n\n"
)
except:
sleep(1)
continue
# print (response)
if args.task == "mmlu":
top_probs = []
top_log_probs = response['choices'][0]["logprobs"]["top_logprobs"][0]
for t in range(len(response['choices'][0]["logprobs"]["tokens"])):
if response['choices'][0]["logprobs"]["tokens"][t] == "\n":
break
top_probs.append(response['choices'][0]["logprobs"]["token_logprobs"][t])
else:
top_probs = []
top_tokens = []
for t in range(len(response['choices'][0]["logprobs"]["tokens"])):
if response['choices'][0]["logprobs"]["tokens"][t] == "\n":
continue
elif response['choices'][0]["logprobs"]["tokens"][t] == "<|endoftext|>":
break
top_probs.append(response['choices'][0]["logprobs"]["token_logprobs"][t])
top_tokens.append(response['choices'][0]["logprobs"]["tokens"][t])
perplexity = np.exp((np.mean(top_probs)))
output = response['choices'][0]["text"].strip()
if args.task == "mmlu":
return output, prompt, (top_log_probs, perplexity), response
else:
return output, prompt, (top_probs, perplexity), response
def inference_one_ex(args, counter, prompt_batch, score_batch, eg, return_predictions=False):
all_outputs = []
all_weighted_probs = []
all_predictions = []
for i, prompt in enumerate(prompt_batch):
output, newprompt, probs, response = call_api(args, prompt, temp=0)
ans = output
all_outputs.append(ans)
all_weighted_probs.append(probs[1]*score_batch[i])
if return_predictions:
all_predictions.append({
"emsemble_id": i,
"ans": ans,
"prompt": prompt,
"top_log_probs": probs[0],
"prob": probs[1],
"re_score": score_batch[i],
"response": response
})
ans2prob_list = defaultdict(list)
for ans, prob in zip(all_outputs, all_weighted_probs):
ans2prob_list[ans].append(prob)
ans2prob = {k: sum(v) for k, v in ans2prob_list.items()}
final_ans = max(ans2prob.items(), key=operator.itemgetter(1))[0]
gold = eg["answer"]
em = single_ans_em(final_ans, gold)
prediction_log = {
"example_id": counter,
"predicted_ans": final_ans,
"gold": gold,
"em": em,
"esb_predictions": all_predictions
} if return_predictions else None
return em, prediction_log
def data_from_csv_to_list(dev_df):
demos = []
for i in range(len(dev_df)):
# print(dev_df.iloc[i, 0])
one_d = {}
one_d["question"] = f"{dev_df.iloc[i, 0]}\n(A) {dev_df.iloc[i, 1]}\n(B) {dev_df.iloc[i, 2]}\n(C) {dev_df.iloc[i, 3]}\n(D) {dev_df.iloc[i, 4]}"
one_d["answer"] = dev_df.iloc[i, 5]
demos.append(one_d)
return demos
def retrieve_ex(demo, retriever):
query = demo["question"]
docs, scores = retriever.retrieve_passage([query])[0]
plain_docs = [doc["text"] for doc in docs]
return plain_docs, scores
def main():
global tokenizer
tokenizer = AutoTokenizer.from_pretrained("gpt2-large")
parser = argparse.ArgumentParser()
parser = add_lm_args(parser)
parser = add_retriever_args(parser)
parser.add_argument("--save-predictions", default=False, action="store_true",
help="If set, save detailed prediction on disk.")
parser.add_argument("--result-dir", type=str, default=None,
help="Directory to save detailed predictions.")
args = parser.parse_args()
if args.save_predictions:
assert(args.result_dir is not None)
if args.do_retrieval:
retriever = Retriever(args)
else:
retriever = None
# load dataset
subjects = sorted([f.split("_test.csv")[0] for f in os.listdir(os.path.join(args.data_dir, "test")) if "_test.csv" in f])
all_cors = []
all_counter = 0
all_em = 0
for i, subject in tqdm(enumerate(subjects)):
cors = []
subject_em = 0
subject_predictions = [] if args.save_predictions else None
'''
data process
'''
print(f"subject: {subject}")
train_df = pd.read_csv(os.path.join(args.data_dir, "dev", subject + "_dev.csv"), header=None)[:args.shots]
val_df = pd.read_csv(os.path.join(args.data_dir, "val", subject + "_val.csv"), header=None)
test_df = pd.read_csv(os.path.join(args.data_dir, "test", subject + "_test.csv"), header=None)
# build demos
demos = data_from_csv_to_list(train_df)
# build test examples
if args.split == "test":
test_set = data_from_csv_to_list(test_df)
elif args.split == "val":
test_set = data_from_csv_to_list(val_df)
print("test_set: ", len(test_set))
# evaluate
counter = 0
demos_questions = [d["question"].strip() for d in demos]
data_list = []
pbar = tqdm(test_set)
# bp()
# build prompt
prompt_demo = ""
if args.prompt_method in ["closed-book", "open-book"]:
for demo in demos:
# concat the top-1 doc
if args.prompt_method == "open-book":
docs, scores = retrieve_ex(demo, retriever)
prompt_demo += f"Knowledge: {docs[0]}\n"
prompt_demo += "Question: " + demo["question"] + "\n"
answer = demo["answer"]
prompt_demo += "Answer: " + answer.strip() + "\n\n"
# run over test example
for eg in pbar:
all_counter += 1
# bp()
prompt = prompt_demo
pbar.set_description(f"Processing test examples from {subject}")
if eg["question"].strip() in demos_questions:
continue
counter += 1
if len(eg["question"].split()) > 400:
eg["question"] = ' '.join(eg["question"].split()[-400 : ])
prompt_batch = []
score_batch = []
if args.prompt_method == "open-book":
docs, scores = retrieve_ex(eg, retriever)
# contatenation version
for doc, score in zip(docs, scores):
prompt_cur = prompt
prompt_cur += f"Knowledge: {doc}" + "\n"
prompt_cur += "Question: " + eg["question"] + "\n"
prompt_cur += "Answer:"
prompt_batch.append(prompt_cur)
score_batch.append(score)
elif args.prompt_method == "closed-book":
prompt += "Question: " + eg["question"] + "\n"
prompt += "Answer:"
prompt_batch.append(prompt)
score_batch.append(1)
score_batch = softmax(score_batch).tolist()
em, prediction_log = inference_one_ex(args, counter, prompt_batch, score_batch, eg, return_predictions=args.save_predictions)
all_em += em
subject_em += em
cors.append(em)
if args.save_predictions:
subject_predictions.append(prediction_log)
'''
evaluation
'''
cors = np.array(cors)
acc = np.mean(cors)
print ("\n\n")
all_cors.append(cors)
if args.save_predictions:
print(f"{subject}[{args.split}] acc: {acc:.2f}")
subject_results = {
"subject": subject,
"split": args.split,
"acc": acc,
"predictions": subject_predictions
}
out_json = os.path.join(args.result_dir, f"{subject}_results.json")
with open(out_json, 'w') as o_f:
json.dump(subject_results, o_f, indent=4)
print(f"{subject} {args.split} predictions saved to {out_json}")
print()
weighted_acc = np.mean(np.concatenate(all_cors))
print ("EM: {}/{}={}%".format(all_em, all_counter, (all_em / all_counter) * 100))
print("MMLU overall acc: {:.3f}".format(weighted_acc))
if args.save_predictions:
out_json = os.path.join(args.result_dir, "overall_results.json")
overall_results = {
"weighted_acc": weighted_acc
}
with open(out_json, 'w') as o_f:
json.dump(overall_results, o_f, indent=4)
if __name__ == '__main__':
main() | [
"\n\n",
"Answer: ",
"Knowledge: PLACEHOLDER\n",
"Question: PLACEHOLDER\n",
"Answer:",
"[]"
] |
2024-01-10 | openmedlab/PULSE-EVAL | eval~elo_utils.py | import glob
import json
import pandas as pd
import time
import os
import random
import re
from collections import defaultdict, OrderedDict
import regex as re
from datasets import load_dataset
from elo_analysis import report_elo_analysis_results
from collections import OrderedDict
import openai
mon = str(time.localtime().tm_mon)
mon = '0' + mon if len(mon) == 1 else mon
day = str(time.localtime().tm_mday)
day = '0' + day if len(day) == 1 else day
DATE = mon + day
def construct_elo_data(
dataset_name,
model_list,
predict_dir="eval/predicted/",
to_dir="eval/elo",
start_p_idx=0,
r_q_count = 10,
seed=42,
):
output_name = f"{dataset_name}_{DATE}.jsonl"
print(f"Output data name: {output_name}")
all_predict_paths = [predict_dir + model_name + f"/{dataset_name}.jsonl" for model_name in model_list]
all_predict_data = OrderedDict()
for path in all_predict_paths:
model_name = path.split("/")[-2]
all_predict_data[model_name] = []
with open(path, "r", encoding="utf8") as f:
for line in f:
line = line.rstrip()
if len(line) == 0:
continue
line = json.loads(line)
all_predict_data[model_name].append(line)
# check len
d_len = None
for item in all_predict_data.values():
if d_len is None:
d_len = len(item)
assert d_len == len(item)
# check q
for idx in range(d_len):
q = None
for model_name in all_predict_data.keys():
if q is None:
q = all_predict_data[model_name][idx]['question']
assert q == all_predict_data[model_name][idx]['question']
all_predict_data = list(all_predict_data.items())
print("Load predict success")
random.seed(seed)
round_data = []
for a_idx in range(start_p_idx, len(all_predict_data)):
for b_idx in range(0, a_idx):
tmp_round_data = []
while len(tmp_round_data) < r_q_count:
d_idx = random.randint(0, d_len - 1)
# # add shuffle
if (a_idx + b_idx + d_idx) % 2 == 0:
ua_idx, ub_idx = a_idx, b_idx
else:
ua_idx, ub_idx = b_idx, a_idx
model_a = all_predict_data[ua_idx]
model_b = all_predict_data[ub_idx]
if model_a[1][d_idx]['predict_answer'] is None or len(model_a[1][d_idx]['predict_answer']) == 0:
continue
if model_b[1][d_idx]['predict_answer'] is None or len(model_b[1][d_idx]['predict_answer']) == 0:
continue
tmp_round_data.append({
# base
'type': model_a[1][d_idx]['type'],
'question': model_a[1][d_idx]['question'],
'reference_answer': model_a[1][d_idx]['reference_answer'],
# answer
"model_a": model_a[0],
"model_b": model_b[0],
"model_a_predict_answer": model_a[1][d_idx]['predict_answer'],
"model_b_predict_answer": model_b[1][d_idx]['predict_answer'],
})
round_data.extend(tmp_round_data)
from collections import Counter
print(Counter([item['model_a'] for item in round_data]))
print(Counter([item['model_b'] for item in round_data]))
print(f'New data length: {len(round_data)}')
elo_inputs = []
elo_prompt_ref = "[Question]\n{question}\n\n[Reference Answer]\n{reference_answer}\n\n[The Start of Assistant 1's Answer]\n{answer_1}\n[The End of Assistant 1's Answer]\n\n[The Start of Assistant 2's Answer]\n{answer_2}\n[The End of Assistant 2's Answer]\n\n[System]\nWe would like to request your feedback on the performance of two AI assistants in response to the user question displayed above.\nIf the answer does not give a direct option, score 0 points. If the answer gives the correct option, give 10 points. If the answer gives the wrong option, then give points appropriately based on his thought process.\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space. In the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment."
elo_prompt = ("[Question]\n{question}\n\n[The Start of Assistant 1's Answer]\n{answer_1}\n[The End of Assistant 1's Answer]\n\n[The Start of Assistant 2's Answer]\n{answer_2}\n[The End of Assistant 2's Answer]\n\n[System]\nWe would like to request your feedback on the performance of two AI assistants in response to the user question displayed above.\nPlease read the question carefully, analyze the intention of the question, and then evaluate the quality of the responses.\nPlease rate the accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space. In the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment.")
os.makedirs(os.path.join(to_dir, "elo_data"), exist_ok=True)
with open(os.path.join(to_dir, "elo_data/"+output_name), "a", encoding="utf8") as f:
for round_data_item in round_data:
f.write(json.dumps(round_data_item, ensure_ascii=False) + "\n")
for round_data_item in round_data:
if round_data_item['reference_answer'] != '':
content = elo_prompt_ref.format(
question=round_data_item['question'],
reference_answer=round_data_item['reference_answer'],
answer_1=round_data_item['model_a_predict_answer'],
answer_2=round_data_item['model_b_predict_answer'],
)
else:
content = elo_prompt.format(
question=round_data_item['question'],
answer_1=round_data_item['model_a_predict_answer'],
answer_2=round_data_item['model_b_predict_answer'],
)
elo_inputs.append({
"messages":[
{
"role": "system",
"content": "You are a helpful and precise assistant for checking the quality of the answer.",
},
{
"role": "user",
"content": content,
},
],
"functions":None,
})
os.makedirs(os.path.join(to_dir, "elo_inputs"), exist_ok=True)
with open(os.path.join(to_dir, "elo_inputs/" + output_name), "a", encoding="utf8") as f:
for elo_input in elo_inputs:
f.write(json.dumps(elo_input, ensure_ascii=False) + "\n")
print(f"Writed elo input and elo data files {output_name}.")
def parse_score(review):
try:
score_pair = review.split("\n")[0]
score_pair = score_pair.replace(",", " ")
sp = score_pair.split(" ")
if len(sp) == 2:
return [float(sp[0]), float(sp[1])]
else:
raise Exception("Invalid score pair.")
except Exception as e:
print(
f"{e}\nContent: {review}\n" "You must manually fix the score pair."
)
return None
def get_all_predict_data(
all_predict_paths,
selected_models,
elo_data_path,
elo_outputs_path
):
all_predict_data = {}
for path in all_predict_paths:
replace_pattern = re.compile(r"[_0-9]{0,10}.jsonl")
dataset_name = re.sub(replace_pattern, "", path.split("/")[-1]) # remove date
elo_data = []
elo_outputs = []
with open(path, "r", encoding="utf8") as f_elo_data:
for line in f_elo_data:
line = line.rstrip()
if len(line) == 0:
continue
line = json.loads(line)
elo_data.append(line)
with open(path.replace(elo_data_path, elo_outputs_path), "r", encoding="utf8") as f_elo_outputs:
for line in f_elo_outputs:
line = line.rstrip()
if len(line) == 0:
continue
line = json.loads(line)['output']
if line is None or len(line) == 0:
elo_outputs.append(None)
else:
line = json.loads(line)
elo_outputs.append(parse_score(line['content']))
assert len(elo_data) == len(elo_outputs)
for elo_data_item, elo_output in zip(elo_data, elo_outputs):
elo_data_item['score'] = elo_output
if elo_output is None:
elo_data_item['winner'] = "tie (None)"
elif elo_output[0] > elo_output[1]:
elo_data_item['winner'] = "model_a"
elif elo_output[1] > elo_output[0]:
elo_data_item['winner'] = "model_b"
elif elo_output[0] == elo_output[1]:
elo_data_item['winner'] = "tie"
if dataset_name not in all_predict_data:
all_predict_data[dataset_name] = []
for elo_data_item in elo_data:
if selected_models:
if elo_data_item['model_a'] not in selected_models:
continue
if elo_data_item['model_b'] not in selected_models:
continue
all_predict_data[dataset_name].append(elo_data_item)
return all_predict_data
def construct_elo_inputs(model_list, dataset_list, start_p_idx=0):
for dataset in dataset_list:
construct_elo_data(dataset, model_list=model_list, start_p_idx=start_p_idx)
def elo_evaluation(
models,
datasets,
elo_data_dir="eval/elo/elo_data"
):
dataset_paths = []
for dataset_name in datasets:
dataset_paths += sorted(glob.glob(elo_data_dir + f"/{dataset_name}*.jsonl"))
print("Start Elo evaluation:")
elo_score_table = get_elo_rank(
selected_models=models,
all_predict_paths=dataset_paths,
)
return elo_score_table
def get_elo_rank(
selected_models,
all_predict_paths,
elo_data_path = "eval/elo/elo_data/",
elo_outputs_path = "eval/elo/elo_outputs/"
):
all_predict_data = get_all_predict_data(
selected_models=selected_models,
all_predict_paths=all_predict_paths,
elo_data_path=elo_data_path,
elo_outputs_path=elo_outputs_path,
)
elo_df = {}
for task_name in all_predict_data.keys():
tt_report = report_elo_analysis_results(all_predict_data[task_name])
for k,v in tt_report['elo_rating_median'].items():
if k not in elo_df:
elo_df[k] = {item: None for item in all_predict_data.keys()}
elo_df[k][task_name] = v
print(task_name)
try:
tmp = {k:v for k, v in tt_report['elo_rating_median'].items()}
print(tmp)
except:
print(tt_report['elo_rating_median'])
print("----------------------------------------")
all_data = []
for v in all_predict_data.values():
all_data.extend(v)
print("ALL")
all_report = report_elo_analysis_results(all_data)
print("----------------------------------------")
for k, v in all_report["elo_rating_median"].items():
if k not in elo_df:
elo_df[k] = {item: None for item in all_predict_data.keys()}
elo_df[k]["ALL"] = v
elo_df_use = pd.DataFrame(
[
{
"Model Name": k,
"ALL": v["ALL"],
**{dk:dv for dk,dv in v.items() if dk != "ALL"}
}
for k,v in elo_df.items()
]
)
return elo_df_use
def GPT_generate(messages, model="gpt-4", stream=False):
completion = openai.ChatCompletion.create(
model=model,
messages=messages,
stream=stream,
)
message = completion["choices"][0]["message"]
return dict(message)
def api_evaluate(data):
data['output'] = data["messages"]
data['output'] = GPT_generate(data["messages"], )
return data
def call_evaluator(
api_func=api_evaluate,
eval_date=None,
in_dir="eval/elo/elo_inputs",
to_dir="eval/elo/elo_outputs",
num_proc=10,
):
eval_date = DATE if not eval_date else eval_date
eval_inputs = glob.glob(in_dir + f"/*{eval_date}.jsonl")
for file_path in eval_inputs:
dataset = load_dataset(
"json",
data_files=file_path,
split="train"
)
eval_dataset = dataset.map(api_func, num_proc=num_proc, batched=False)
to_path = file_path.replace(in_dir, to_dir)
os.makedirs(to_path, exist_ok=True)
with open(to_path, "a", encoding="utf-8") as writer:
for data in eval_dataset:
item = {
"input": {"messages": data["messages"]},
"output": data["output"],
}
writer.write(json.dumps(item, ensure_ascii=False) + '\n')
print(f"Wrote elo evaluation results to {to_path}.") | [
"[Question]\n{question}\n\n[The Start of Assistant 1's Answer]\n{answer_1}\n[The End of Assistant 1's Answer]\n\n[The Start of Assistant 2's Answer]\n{answer_2}\n[The End of Assistant 2's Answer]\n\n[System]\nWe would like to request your feedback on the performance of two AI assistants in response to the user question displayed above.\nPlease read the question carefully, analyze the intention of the question, and then evaluate the quality of the responses.\nPlease rate the accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space. In the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment.",
"[Question]\n{question}\n\n[Reference Answer]\n{reference_answer}\n\n[The Start of Assistant 1's Answer]\n{answer_1}\n[The End of Assistant 1's Answer]\n\n[The Start of Assistant 2's Answer]\n{answer_2}\n[The End of Assistant 2's Answer]\n\n[System]\nWe would like to request your feedback on the performance of two AI assistants in response to the user question displayed above.\nIf the answer does not give a direct option, score 0 points. If the answer gives the correct option, give 10 points. If the answer gives the wrong option, then give points appropriately based on his thought process.\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space. In the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment.",
"You are a helpful and precise assistant for checking the quality of the answer."
] |
2024-01-10 | Physton/sd-webui-prompt-all-in-one | scripts~physton_prompt~gen_openai.py | from scripts.physton_prompt.get_lang import get_lang
from scripts.physton_prompt.get_translate_apis import unprotected_translate_api_config
def gen_openai(messages, api_config):
import openai
from distutils.version import LooseVersion
api_config = unprotected_translate_api_config('chatgpt_key', api_config)
openai.api_base = api_config.get('api_base', 'https://api.openai.com/v1')
openai.api_key = api_config.get('api_key', '')
model = api_config.get('model', 'gpt-3.5-turbo')
if not openai.api_key:
raise Exception(get_lang('is_required', {'0': 'API Key'}))
if not messages or len(messages) == 0:
raise Exception(get_lang('is_required', {'0': 'messages'}))
if LooseVersion(openai.__version__) < LooseVersion('1.0.0'):
completion = openai.ChatCompletion.create(model=model, messages=messages, timeout=60)
else:
from openai import OpenAI
client = OpenAI(
base_url=openai.api_base,
api_key=openai.api_key,
)
completion = client.chat.completions.create(model=model, messages=messages, timeout=60)
if len(completion.choices) == 0:
raise Exception(get_lang('no_response_from', {'0': 'OpenAI'}))
content = completion.choices[0].message.content
return content
| [] |
2024-01-10 | Physton/sd-webui-prompt-all-in-one | scripts~physton_prompt~translator~openai_translator.py | from scripts.physton_prompt.translator.base_tanslator import BaseTranslator
import json
from scripts.physton_prompt.get_lang import get_lang
class OpenaiTranslator(BaseTranslator):
def __init__(self):
super().__init__('openai')
def translate(self, text):
if not text:
if isinstance(text, list):
return []
else:
return ''
import openai
from distutils.version import LooseVersion
openai.api_base = self.api_config.get('api_base', 'https://api.openai.com/v1')
openai.api_key = self.api_config.get('api_key', '')
model = self.api_config.get('model', 'gpt-3.5-turbo')
if not openai.api_key:
raise Exception(get_lang('is_required', {'0': 'API Key'}))
body = []
if isinstance(text, list):
for item in text:
body.append({'text': item})
else:
body.append({'text': text})
body_str = json.dumps(body, ensure_ascii=False)
messages = [
{"role": "system", "content": "You are a translator assistant."},
{
"role": "user",
"content": f"You are a translator assistant. Please translate the following JSON data {self.to_lang}. Preserve the original format. Only return the translation result, without any additional content or annotations. If the prompt word is in the target language, please send it to me unchanged:\n{body_str}"
},
]
if LooseVersion(openai.__version__) < LooseVersion('1.0.0'):
completion = openai.ChatCompletion.create(model=model, messages=messages, timeout=60)
else:
from openai import OpenAI
client = OpenAI(
base_url=openai.api_base,
api_key=openai.api_key,
)
completion = client.chat.completions.create(model=model, messages=messages, timeout=60)
if len(completion.choices) == 0:
raise Exception(get_lang('no_response_from', {'0': 'OpenAI'}))
content = completion.choices[0].message.content
try:
# 找到第一个[，然后找到最后一个]，截取中间的内容
start = content.index('[')
end = content.rindex(']')
if start == -1 or end == -1:
raise Exception(get_lang('response_error', {'0': 'OpenAI'}))
result_json = '[' + content[start + 1:end] + ']'
# 解析json
result = json.loads(result_json)
if isinstance(text, list):
return [item['text'] for item in result]
else:
return result[0]['text']
except Exception as e:
raise Exception(get_lang('response_error', {'0': 'OpenAI'}))
def translate_batch(self, texts):
return self.translate(texts)
| [
"You are a translator assistant."
] |
2024-01-10 | open-space-collective/open-space-toolkit-astrodynamics | bindings~python~test~guidance_law~test_qlaw.py | # Apache License 2.0
import pytest
import numpy as np
from ostk.physics.time import Instant
from ostk.physics.coordinate import Frame
from ostk.physics.units import Derived
from ostk.physics.environment.gravitational import Earth as EarthGravitationalModel
from ostk.physics.time import Instant
from ostk.physics.coordinate import Frame
from ostk.physics.units import Length
from ostk.physics.units import Angle
from ostk.physics.units import Derived
from ostk.astrodynamics.trajectory.orbit.models.kepler import COE
from ostk.astrodynamics import GuidanceLaw
from ostk.astrodynamics.guidance_law import QLaw
@pytest.fixture
def target_COE() -> COE:
return COE(
Length.meters(42000.0e3),
0.01,
Angle.degrees(0.05),
Angle.degrees(0.0),
Angle.degrees(0.0),
Angle.degrees(0.0),
)
@pytest.fixture
def gravitational_parameter() -> Derived:
return Derived(
3.986004418e14,
EarthGravitationalModel.EGM2008.gravitational_parameter.get_unit(),
)
@pytest.fixture
def parameters() -> QLaw.Parameters:
return QLaw.Parameters(
element_weights={
COE.Element.SemiMajorAxis: (1.0, 100.0),
COE.Element.Eccentricity: (1.0, 1e-3),
},
)
@pytest.fixture
def gradient_strategy() -> QLaw.GradientStrategy:
return QLaw.GradientStrategy.FiniteDifference
@pytest.fixture
def q_law(
target_COE: COE,
gravitational_parameter: Derived,
parameters: QLaw.Parameters,
gradient_strategy: QLaw.GradientStrategy,
) -> QLaw:
return QLaw(
target_coe=target_COE,
gravitational_parameter=gravitational_parameter,
parameters=parameters,
gradient_strategy=gradient_strategy,
)
@pytest.fixture
def thrust_acceleration() -> float:
return 1.0 / 300.0
@pytest.fixture
def frame() -> Frame:
return Frame.GCRF()
@pytest.fixture
def position_coordinates() -> list[float]:
return [6930000.0, 0.0, 0.0]
@pytest.fixture
def velocity_coordinates() -> list[float]:
return [0.0, 7621.89248591193, 6.65135764404186]
@pytest.fixture
def instant() -> Instant:
return Instant.J2000()
class TestQLawParameters:
def test_constructors(self, parameters: QLaw.Parameters):
assert parameters is not None
assert isinstance(parameters, QLaw.Parameters)
def test_getters(self, parameters: QLaw.Parameters):
assert parameters.get_control_weights() is not None
assert parameters.m is not None
assert parameters.n is not None
assert parameters.r is not None
assert parameters.b is not None
class TestQLaw:
def test_constructors(self, q_law: QLaw):
assert q_law is not None
assert isinstance(q_law, QLaw)
assert isinstance(q_law, GuidanceLaw)
def test_getters(self, q_law: QLaw):
assert q_law.get_parameters() is not None
assert q_law.get_target_coe() is not None
assert q_law.get_gradient_strategy() is not None
def test_calculate_thrust_acceleration_at(
self,
q_law: QLaw,
position_coordinates: list[float],
velocity_coordinates: list[float],
thrust_acceleration: float,
instant: Instant,
frame: Frame,
):
assert pytest.approx(
q_law.calculate_thrust_acceleration_at(
instant=instant,
position_coordinates=position_coordinates,
velocity_coordinates=velocity_coordinates,
thrust_acceleration=thrust_acceleration,
output_frame=frame,
)
) == np.array([0.0, 0.0033333320640941645, 2.9088817174504986e-06])
| [] |
2024-01-10 | ibiscp/LLM-IMDB | backend~movie_database_tool.py | from typing import Dict, List
from database import MovieDatabase
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.llms.base import BaseLLM
from langchain.prompts.base import BasePromptTemplate
from langchain.prompts.prompt import PromptTemplate
from pydantic import BaseModel, Extra
_PROMPT_TEMPLATE = """
You are helping to create a query for searching a graph database that finds similar movies based on specified parameters.
Your task is to translate the given question into a set of parameters for the query. Only include the information you were given.
The parameters are:
title (str, optional): The title of the movie
year (int, optional): The year the movie was released
genre (str, optional): The genre of the movie
director (str, optional): The director of the movie
actor (str, optional): The actor in the movie
same_attributes_as (optional): A dictionary of attributes to match the same attributes as another movie (optional)
Use the following format:
Question: "Question here"
Output: "Graph parameters here"
Example:
Question: "What is the title of the movie that was released in 2004 and directed by Steven Spielberg?"
Output:
year: 2004
director: Steven Spielberg
Question: "Movie with the same director as Eternal Sunshine of the Spotless Mind?"
Output:
same_attributes_as:
director: Eternal Sunshine of the Spotless Mind
Begin!
Question: {question}
Output:
"""
PROMPT = PromptTemplate(input_variables=["question"], template=_PROMPT_TEMPLATE)
class LLMGraphChain(Chain, BaseModel):
"""Chain that interprets a prompt and executes python code to do math.
Example:
.. code-block:: python
from langchain import LLMMathChain, OpenAI
llm_math = LLMMathChain(llm=OpenAI())
"""
llm: BaseLLM
"""LLM wrapper to use."""
prompt: BasePromptTemplate = PROMPT
"""Prompt to use to translate to python if neccessary."""
input_key: str = "question" #: :meta private:
output_key: str = "answer" #: :meta private:
graph: MovieDatabase
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@property
def input_keys(self) -> List[str]:
"""Expect input key.
:meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Expect output key.
:meta private:
"""
return [self.output_key]
def _process_llm_result(self, t: str) -> Dict[str, str]:
import yaml
self.callback_manager.on_text("\nQuery:\n", verbose=self.verbose)
self.callback_manager.on_text(t, color="green", verbose=self.verbose)
# Convert t to a dictionary
t = yaml.safe_load(t)
output = self.graph.query_movies(**t)
self.callback_manager.on_text("\nAnswer: ", verbose=self.verbose)
self.callback_manager.on_text(output, color="yellow", verbose=self.verbose)
return {self.output_key: "\n".join([f"{i[0]}: {i[1]}" for i in output])}
def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
llm_executor = LLMChain(
prompt=self.prompt, llm=self.llm, callback_manager=self.callback_manager
)
self.callback_manager.on_text(inputs[self.input_key], verbose=self.verbose)
t = llm_executor.predict(question=inputs[self.input_key], stop=["Output:"])
return self._process_llm_result(t)
@property
def _chain_type(self) -> str:
return "llm_movie_database"
if __name__ == "__main__":
from langchain.llms import OpenAI
llm = OpenAI(temperature=0.3)
chain = LLMGraphChain(llm=llm, verbose=True)
output = chain.run(
"What is the title of the movie that was released in 2002 and directed by Steven Spielberg?"
)
print(output)
| [
"\nYou are helping to create a query for searching a graph database that finds similar movies based on specified parameters.\nYour task is to translate the given question into a set of parameters for the query. Only include the information you were given.\n\nThe parameters are:\ntitle (str, optional): The title of the movie\nyear (int, optional): The year the movie was released\ngenre (str, optional): The genre of the movie\ndirector (str, optional): The director of the movie\nactor (str, optional): The actor in the movie\nsame_attributes_as (optional): A dictionary of attributes to match the same attributes as another movie (optional)\n\nUse the following format:\nQuestion: \"Question here\"\nOutput: \"Graph parameters here\"\n\nExample:\nQuestion: \"What is the title of the movie that was released in 2004 and directed by Steven Spielberg?\"\nOutput:\nyear: 2004\ndirector: Steven Spielberg\n\nQuestion: \"Movie with the same director as Eternal Sunshine of the Spotless Mind?\"\nOutput:\nsame_attributes_as:\n director: Eternal Sunshine of the Spotless Mind\n\nBegin!\n\nQuestion: {question}\nOutput:\n",
"Movie with the same director as Eternal Sunshine of the Spotless Mind?",
"question",
"Graph parameters here",
"What is the title of the movie that was released in 2004 and directed by Steven Spielberg?",
"Question here"
] |
2024-01-10 | justalittlelighthouse/ctl | fee_shifting_collab.py | # -*- coding: utf-8 -*-
"""Fee-Shifting Collab
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1Vrtn_rXM4sUD9buoMYBRxwwlQ1kBRjw4
# Collab for Extracting Data from OCRed PDFs Using Regex and LLMs
For instructions, please visit the following link: https://docs.google.com/document/d/1nxGGDE5Hw5U8yefoi4iJRVXRZFnBGs14-DbPBr9Rhyg/edit?usp=sharing
One can use this notebook to build a pipeline to parse and extract data from OCRed PDF files. _**Warning:** When using LLMs for entity extraction, be sure to perform extensive quality control. They are very susceptible to distracting language (latching on to text that sound "kind of like" what you're looking for) and missing language (making up content to fill any holes), and importantly, they do **NOT** provide any hints to when they may be erroring. You need to make sure random audits are part of your workflow!_ Below we've worked out a workflow using regular expressions and LLMs to parse data from zoning board orders, but the process is generalizable.
# Load Libraries
First we load the libraries we need. Note, if you try to run the cell, and you get something like `ModuleNotFoundError: No module named 'mod_name'`, you'll need to install the module. You can do this commentating the line below that reads `#!pip install mod_name` if it's listed. If it isn't, you can probably install it with a similarly formatted command.
"""
#!pip install os
!pip install PyPDF2
#!pip install re
#!pip install pandas
#!pip install numpy
!pip install transformers
!pip install openai==0.28
!pip install tiktoken
import os
from os import walk, path
import PyPDF2
import re
import pandas as pd
import numpy as np
import random
def read_pdf(file):
try:
pdfFile = PyPDF2.PdfReader(open(file, "rb"), strict=False)
text = ""
for page in pdfFile.pages:
text += " " + page.extract_text()
return text
except:
return ""
# Test Audio call
# Only works on Mac. If you aren't using a Mac, you should disable such calls below.
#tmp = os.system( "say Testing, testing, one, two, three.")
#del(tmp)
import json
from nltk.tokenize import word_tokenize, sent_tokenize
import openai
from transformers import GPT2TokenizerFast
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2")
import tiktoken
ENCODING = "gpt2"
encoding = tiktoken.get_encoding(ENCODING)
def complete_text(prompt,temp=0,trys=0,clean=True):
global tokens_used
model="text-davinci-003"
model_token_limit = 4097
token_count = len(encoding.encode(prompt))
max_tokens= model_token_limit-round(token_count+5)
#try:
response = openai.Completion.create(
model=model,
prompt=prompt,
temperature=temp,
max_tokens=max_tokens,
top_p=1.0,
frequency_penalty=0.0,
presence_penalty=0.0
)
output = str(response["choices"][0]["text"].strip())
#except:
# print("Problem with API call!")
# output = """{"output":"error"}"""
tokens_used += token_count+len(encoding.encode(output))
if clean:
return clean_pseudo_json(output,temp=0,trys=trys)
else:
return output
def clean_pseudo_json(string,temp=0,key="output",trys=0,ask_for_help=1):
try:
output = json.loads(string)[key]
except:
try:
string_4_json = re.findall("\{.*\}",re.sub("\n","",string))[0]
output = json.loads(string_4_json)[key]
except:
try:
string = "{"+string+"}"
string_4_json = re.findall("\{.*\}",re.sub("\n","",string))[0]
output = json.loads(string_4_json)[key]
except Exception as e:
prompt = "I tried to parse some json and got this error, '{}'. This was the would-be json.\n\n{}\n\nReformat it to fix the error.".format(e,string)
if trys <= 3:
if trys == 0:
warm_up = 0
else:
warm_up = 0.25
output = complete_text(prompt,temp=0+warm_up,trys=trys+1)
print("\n"+str(output)+"\n")
elif ask_for_help==1:
print(prompt+"\nReformaing FAILED!!!")
#try:
# os.system( "say hey! I need some help. A little help please?")
#except:
# print("'say' not supported.\n\n")
output = input("Let's see if we can avoid being derailed. Examine the above output and construct your own output text. Then enter it below. If the output needs to be something other than a string, e.g., a list or json, start it with `EVAL: `. If you're typing that, be very sure there's no malicious code in the output.\n")
if output[:6]=="EVAL: ":
output = eval(output[6:])
else:
output = "There was an error getting a reponse!"
return output
"""# Input OpenAI API Key & LLM settings
You'll need an API key to use an LLM. After creating an OpenAI account, you can create an API key here: https://platform.openai.com/account/api-keys
Enter your key between the quation marks next to `openai.api_key =` below, and run that cell.
"""
# Toggle LLM usage on or off
use_LLM = True
llm_temperature = 0 # I strongly suggest keeping the LLM's temp at zero to avoid it making things up.
openai.api_key = "" # <<--- REPLACE WITH YOUR KEY
"""# Load and parse files
Next, place a bunch of OCRed pdf files in the right folder (here, the `/content/gdrive/entity_extraction_sample_data/` folder). FWIW, you can use Adobe Pro to OCR in batch. Note: to make your files visisble at a location like that above, you'll need to add them to your Google Drive. E.g., you would need to copy https://drive.google.com/drive/folders/1H3bMgxzNxwxNL2YK6eMWt3nX985oBqVS?usp=sharing to your GDrive and name it `entity_extraction_sample_data` for it to be accessable at `/content/gdrive/entity_extraction_sample_data/`.
"""
# this mounts your google drive
from google.colab import drive
drive.mount('/content/gdrive')
df = pd.DataFrame() #this will create an empty dataframe
# list the files in the drive
filepath = "/content/gdrive/MyDrive/entity_extraction_sample_data/" # this is where we'll be looking for files
f = []
for (dirpath, dirnames, filenames) in walk(filepath): # create a list of file names
f.extend(filenames)
break
f #show list
sample = 4
#sample = len(f) #if you want to go through all the files, uncomment this line and comment out the above
token_counts = []
for file in random.choices(f,k=sample): # for each file in the list of file names, do some stuff
tokens_used = 0
column_names = ["file"]
column_values = [file]
fileloc = filepath+file
text = read_pdf(fileloc)
#print("text here: ", text)
words = len(text.split())
print("Parsing ~{} words ({} tokens) from: \"{}\"\n".format(words,len(encoding.encode(text)),fileloc))
#############################################################
# Here's where we use regex to pull out specific content
try:
# case Number
# ---------------------------------------------------------
case_no = re.search("(\d+-?\w+-?\d+)",text, flags=re.IGNORECASE).groups(0)[0].strip()
column_names.append("case_no")
column_values.append(case_no)
except:
column_names.append("case_no")
column_values.append("NA")
############################################################################
# Here's where use GPT to pull out some specific content.
#
# Note: You should consider combining multiple prompts into a single prompt
# to avoid making unnecessary api calls. See e.g. Reasoning & Decision below
#
if use_LLM:
#try:
# ---------------------------------------------------------
# description of variance requested
# ---------------------------------------------------------
prompt_text = """Below you will be provided with the text of an order for judgment from housing court. You're looking to find the amount of fees awarded, in dollars, to the attorney. That is, the amount of money that the court said the opposing party had to pay.
Here's the text of the order.
{}
Return a json object, including the outermost curly brackets, where the key is "output" and the value is the dollar amount of award. If no dollar amount of award in the text, answer "none found". Be sure to use valid json, encasing keys and values in double quotes, and escaping internal quotes and special characters as needed.""". format(text)
#print(prompt_text)
award = complete_text(prompt_text,temp=llm_temperature)
column_names.append("award")
column_values.append(award)
#except:
#column_names.append("request")
#column_values.append("NA")
if use_LLM:
#try:
# ---------------------------------------------------------
# description of variance requested
# ---------------------------------------------------------
prompt_text = """Below you will be provided with the text of an order for judgment from housing court. You're looking to find the name of the attorney and which party they represent (plaintiff or defendant).
Here's the text of the order.
{}
Return a json object, including the outermost curly brackets, where the key is "output" and the value is the name of the attorney and the party they represent. If you can't find the attorney, answer "none found". Be sure to use valid json, encasing keys and values in double quotes, and escaping internal quotes and special characters as needed.""". format(text)
#print(prompt_text)
attorney = complete_text(prompt_text,temp=llm_temperature)
column_names.append("attorney")
column_values.append(attorney)
#except:
#column_names.append("request")
#column_values.append("NA")
if use_LLM:
#try:
# ---------------------------------------------------------
# description of variance requested
# ---------------------------------------------------------
prompt_text = """Below you will be provided with the text of an order for judgment from housing court. From the text, give a summary of the reasoning given for the judge's decision.
Here's the text of the order.
{}
Return a json object, including the outermost curly brackets, where the key is "output" and the value is the summary of the judge's reasoning. If you can't create a summary, answer "none found". Be sure to use valid json, encasing keys and values in double quotes, and escaping internal quotes and special characters as needed.""". format(text)
#print(prompt_text)
summary = complete_text(prompt_text,temp=llm_temperature)
column_names.append("summary")
column_values.append(summary)
#except:
#column_names.append("request")
#column_values.append("NA")
if use_LLM:
#try:
# ---------------------------------------------------------
# description of variance requested
# ---------------------------------------------------------
prompt_text = """Below you will be provided with the text of an order for judgment from housing court. From the text, pull the judge's name (this should be a full name, not just "Associate Justice").
Here's the text of the order.
{}
Return a json object, including the outermost curly brackets, where the key is "output" and the value is the judge's name. If you can't find the judge's full name, answer "none found". Be sure to use valid json, encasing keys and values in double quotes, and escaping internal quotes and special characters as needed.""". format(text)
#print(prompt_text)
Judgename = complete_text(prompt_text,temp=llm_temperature)
column_names.append("Judgename")
column_values.append(Judgename)
#except:
#column_names.append("request")
#column_values.append("NA")
if use_LLM:
#try:
# ---------------------------------------------------------
# description of variance requested
# ---------------------------------------------------------
prompt_text = """Below you will be provided with the text of an order for judgment from housing court. From the text, find out if the Judge mentioned the Lodestar method, and if so, summarize what they said about it.
Here's the text of the order.
{}
Return a json object, including the outermost curly brackets, where the key is "output" and the value is a summary about what was said about the Lodestar method. If you can't find anything about the Lodestae, answer "none found". Be sure to use valid json, encasing keys and values in double quotes, and escaping internal quotes and special characters as needed.""". format(text)
#print(prompt_text)
Lodestar = complete_text(prompt_text,temp=llm_temperature)
column_names.append("Lodestar")
column_values.append(Lodestar)
#except:
#column_names.append("request")
#column_values.append("NA")
if use_LLM:
#try:
# ---------------------------------------------------------
# description of variance requested
# ---------------------------------------------------------
prompt_text = """Below you will be provided with the text of an order for judgment from housing court. From the text, pull the defendant's full name.
Here's the text of the order.
{}
Return a json object, including the outermost curly brackets, where the key is "output" and the value is the defendant's name. If you can't find the judge's full name, answer "none found". Be sure to use valid json, encasing keys and values in double quotes, and escaping internal quotes and special characters as needed.""". format(text)
#print(prompt_text)
Defendantname = complete_text(prompt_text,temp=llm_temperature)
column_names.append("Defendantname")
column_values.append(Defendantname)
#except:
#column_names.append("request")
#column_values.append("NA")
if use_LLM:
#try:
# ---------------------------------------------------------
# description of variance requested
# ---------------------------------------------------------
prompt_text = """Below you will be provided with the text of an order for judgment from housing court. From the text, pull the plaintiff's name.
Here's the text of the order.
{}
Return a json object, including the outermost curly brackets, where the key is "output" and the value is the plaintiff's name. If you can't find the plaintiff's full name, answer "none found". Be sure to use valid json, encasing keys and values in double quotes, and escaping internal quotes and special characters as needed.""". format(text)
#print(prompt_text)
Plaintiffname = complete_text(prompt_text,temp=llm_temperature)
column_names.append("Plaintiffname")
column_values.append(Plaintiffname)
#except:
#column_names.append("request")
#column_values.append("NA")
if use_LLM:
#try:
# ---------------------------------------------------------
# description of variance requested
# ---------------------------------------------------------
prompt_text = """Below you will be provided with the text of an order for judgment from housing court. You're looking to find the number of hours the attorney worked and hourly rate/fee they charged.
Here's the text of the order.
{}
Return a json object, including the outermost curly brackets, where the key is "output" and the value is the amount of hours the attorney worked, and their fee (separate by commas). If you can't find an hour amount in the text of the above, answer "none found". Be sure to use valid json, encasing keys and values in double quotes, and escaping internal quotes and special characters as needed.""". format(text)
#print(prompt_text)
hours_and_rate = complete_text(prompt_text,temp=llm_temperature)
column_names.append("hours_and_rate")
column_values.append(hours_and_rate)
#except:
#column_names.append("request")
#column_values.append("NA")
#############################################################
# After testing or when working with large numbers, you may want to comment this next bit out
# Show your work
i = 0
for datum in column_values:
print("{}: {}\n".format(column_names[i].upper(),datum))
i+=1
# Show cost per run
if use_LLM:
print("Tokens used (approx.): {} (API Cost ~${})\n".format(tokens_used,tokens_used*(0.002/1000))) # See https://openai.com/pricing
token_counts.append(tokens_used)
print("================================================\n")
df = pd.concat([df,pd.DataFrame([column_values],columns=column_names)], ignore_index=True,sort=False)
print("Average approx. tokens used per item {} (API Cost ~${})\n".format(np.array(token_counts).mean(),np.array(token_counts).mean()*(0.002/1000))) # See https://openai.com/pricing
display(df)
# If you're happy with the stuff you pulled out above, you can write the df to a csv file
# make sure the path is placing it where you want it!
df.to_csv("/content/gdrive/MyDrive/CodingLawFinalProjectSheet2023.csv", index=False, encoding="utf-8")
| [
"Below you will be provided with the text of an order for judgment from housing court. You're looking to find the amount of fees awarded, in dollars, to the attorney. That is, the amount of money that the court said the opposing party had to pay.\n\n Here's the text of the order.\n\n PLACEHOLDER\n\n\n Return a json object, including the outermost curly brackets, where the key is \"output\" and the value is the dollar amount of award. If no dollar amount of award in the text, answer \"none found\". Be sure to use valid json, encasing keys and values in double quotes, and escaping internal quotes and special characters as needed.",
"Below you will be provided with the text of an order for judgment from housing court. From the text, find out if the Judge mentioned the Lodestar method, and if so, summarize what they said about it.\n\n Here's the text of the order.\n\n PLACEHOLDER\n\n Return a json object, including the outermost curly brackets, where the key is \"output\" and the value is a summary about what was said about the Lodestar method. If you can't find anything about the Lodestae, answer \"none found\". Be sure to use valid json, encasing keys and values in double quotes, and escaping internal quotes and special characters as needed.",
"Below you will be provided with the text of an order for judgment from housing court. From the text, pull the judge's name (this should be a full name, not just \"Associate Justice\").\n\n Here's the text of the order.\n\n PLACEHOLDER\n\n Return a json object, including the outermost curly brackets, where the key is \"output\" and the value is the judge's name. If you can't find the judge's full name, answer \"none found\". Be sure to use valid json, encasing keys and values in double quotes, and escaping internal quotes and special characters as needed.",
"Below you will be provided with the text of an order for judgment from housing court. You're looking to find the number of hours the attorney worked and hourly rate/fee they charged.\n\n Here's the text of the order.\n\n PLACEHOLDER\n\n\n Return a json object, including the outermost curly brackets, where the key is \"output\" and the value is the amount of hours the attorney worked, and their fee (separate by commas). If you can't find an hour amount in the text of the above, answer \"none found\". Be sure to use valid json, encasing keys and values in double quotes, and escaping internal quotes and special characters as needed.",
"Below you will be provided with the text of an order for judgment from housing court. From the text, give a summary of the reasoning given for the judge's decision.\n\n Here's the text of the order.\n\n PLACEHOLDER\n\n Return a json object, including the outermost curly brackets, where the key is \"output\" and the value is the summary of the judge's reasoning. If you can't create a summary, answer \"none found\". Be sure to use valid json, encasing keys and values in double quotes, and escaping internal quotes and special characters as needed.",
"Below you will be provided with the text of an order for judgment from housing court. From the text, pull the defendant's full name.\n\n Here's the text of the order.\n\n PLACEHOLDER\n\n Return a json object, including the outermost curly brackets, where the key is \"output\" and the value is the defendant's name. If you can't find the judge's full name, answer \"none found\". Be sure to use valid json, encasing keys and values in double quotes, and escaping internal quotes and special characters as needed.",
"Below you will be provided with the text of an order for judgment from housing court. From the text, pull the plaintiff's name.\n\n Here's the text of the order.\n\n PLACEHOLDER\n\n Return a json object, including the outermost curly brackets, where the key is \"output\" and the value is the plaintiff's name. If you can't find the plaintiff's full name, answer \"none found\". Be sure to use valid json, encasing keys and values in double quotes, and escaping internal quotes and special characters as needed.",
"I tried to parse some json and got this error, 'PLACEHOLDER'. This was the would-be json.\n\nPLACEHOLDER\n\nReformat it to fix the error.",
"Below you will be provided with the text of an order for judgment from housing court. You're looking to find the name of the attorney and which party they represent (plaintiff or defendant).\n\n Here's the text of the order.\n\n PLACEHOLDER\n\n Return a json object, including the outermost curly brackets, where the key is \"output\" and the value is the name of the attorney and the party they represent. If you can't find the attorney, answer \"none found\". Be sure to use valid json, encasing keys and values in double quotes, and escaping internal quotes and special characters as needed."
] |
2024-01-10 | shroominic/codeinterpreter-api | src~codeinterpreterapi~chat_history.py | import asyncio
import json
from typing import List
from codeboxapi import CodeBox # type: ignore
from langchain.schema import BaseChatMessageHistory
from langchain.schema.messages import BaseMessage, messages_from_dict, messages_to_dict
# TODO: This is probably not efficient, but it works for now.
class CodeBoxChatMessageHistory(BaseChatMessageHistory):
"""
Chat message history that stores history inside the codebox.
"""
def __init__(self, codebox: CodeBox):
self.codebox = codebox
if "history.json" not in [f.name for f in self.codebox.list_files()]:
name, content = "history.json", b"{}"
if (loop := asyncio.get_event_loop()).is_running():
loop.create_task(self.codebox.aupload(name, content))
else:
self.codebox.upload(name, content)
@property
def messages(self) -> List[BaseMessage]: # type: ignore
"""Retrieve the messages from the codebox"""
msgs = (
messages_from_dict(json.loads(file_content.decode("utf-8")))
if (
file_content := (
loop.run_until_complete(self.codebox.adownload("history.json"))
if (loop := asyncio.get_event_loop()).is_running()
else self.codebox.download("history.json")
).content
)
else []
)
return msgs
def add_message(self, message: BaseMessage) -> None:
"""Append the message to the record in the local file"""
print("Current messages: ", self.messages)
messages = messages_to_dict(self.messages)
print("Adding message: ", message)
messages.append(messages_to_dict([message])[0])
name, content = "history.json", json.dumps(messages).encode("utf-8")
if (loop := asyncio.get_event_loop()).is_running():
loop.create_task(self.codebox.aupload(name, content))
else:
self.codebox.upload(name, content)
print("New messages: ", self.messages)
def clear(self) -> None:
"""Clear session memory from the local file"""
print("Clearing history CLEARING HISTORY")
code = "import os; os.remove('history.json')"
if (loop := asyncio.get_event_loop()).is_running():
loop.create_task(self.codebox.arun(code))
else:
self.codebox.run(code)
| [] |
2024-01-10 | shroominic/codeinterpreter-api | src~codeinterpreterapi~schema.py | import asyncio
from typing import Any
from codeboxapi.schema import CodeBoxStatus
from langchain.pydantic_v1 import BaseModel
from langchain.schema import AIMessage, HumanMessage
class File(BaseModel):
name: str
content: bytes
@classmethod
def from_path(cls, path: str) -> "File":
if not path.startswith("/"):
path = f"./{path}"
with open(path, "rb") as f:
path = path.split("/")[-1]
return cls(name=path, content=f.read())
@classmethod
async def afrom_path(cls, path: str) -> "File":
return await asyncio.to_thread(cls.from_path, path)
@classmethod
def from_url(cls, url: str) -> "File":
import requests # type: ignore
r = requests.get(url)
return cls(name=url.split("/")[-1], content=r.content)
@classmethod
async def afrom_url(cls, url: str) -> "File":
import aiohttp # type: ignore
async with aiohttp.ClientSession() as session:
async with session.get(url) as r:
return cls(name=url.split("/")[-1], content=await r.read())
def save(self, path: str) -> None:
if not path.startswith("/"):
path = f"./{path}"
with open(path, "wb") as f:
f.write(self.content)
async def asave(self, path: str) -> None:
await asyncio.to_thread(self.save, path)
def get_image(self) -> Any:
try:
from PIL import Image # type: ignore
except ImportError:
print(
"Please install it with "
"`pip install 'codeinterpreterapi[image_support]'`"
" to display images."
)
exit(1)
from io import BytesIO
img_io = BytesIO(self.content)
img = Image.open(img_io)
# Convert image to RGB if it's not
if img.mode not in ("RGB", "L"): # L is for grayscale images
img = img.convert("RGB")
return img
def show_image(self) -> None:
img = self.get_image()
# Display the image
try:
# Try to get the IPython shell if available.
shell = get_ipython().__class__.__name__ # type: ignore
# If the shell is in a Jupyter notebook or similar.
if shell == "ZMQInteractiveShell" or shell == "Shell":
from IPython.display import display # type: ignore
display(img) # type: ignore
else:
img.show()
except NameError:
img.show()
def __str__(self) -> str:
return self.name
def __repr__(self) -> str:
return f"File(name={self.name})"
class CodeInput(BaseModel):
code: str
class FileInput(BaseModel):
filename: str
class UserRequest(HumanMessage):
files: list[File] = []
def __str__(self) -> str:
return str(self.content)
def __repr__(self) -> str:
return f"UserRequest(content={self.content}, files={self.files})"
class CodeInterpreterResponse(AIMessage):
"""
Response from the code interpreter agent.
files: list of files to be sent to the user (File )
code_log: list[tuple[str, str]] = []
"""
files: list[File] = []
code_log: list[tuple[str, str]] = []
def show(self) -> None:
print("AI: ", self.content)
for file in self.files:
print("File: ", file.name)
file.show_image()
def __str__(self) -> str:
return str(self.content)
def __repr__(self) -> str:
return f"CodeInterpreterResponse(content={self.content}, files={self.files})"
class SessionStatus(CodeBoxStatus):
@classmethod
def from_codebox_status(cls, cbs: CodeBoxStatus) -> "SessionStatus":
return cls(status=cbs.status)
def __repr__(self) -> str:
return f"<SessionStatus status={self.status}>"
| [] |
2024-01-10 | shroominic/codeinterpreter-api | src~codeinterpreterapi~chains~rm_dl_link.py | from langchain.base_language import BaseLanguageModel
from langchain.chat_models.openai import ChatOpenAI
from langchain.schema import AIMessage, OutputParserException
from codeinterpreterapi.prompts import remove_dl_link_prompt
def remove_download_link(
input_response: str,
llm: BaseLanguageModel,
) -> str:
messages = remove_dl_link_prompt.format_prompt(
input_response=input_response
).to_messages()
message = llm.predict_messages(messages)
if not isinstance(message, AIMessage):
raise OutputParserException("Expected an AIMessage")
assert isinstance(message.content, str), "TODO: add image support"
return message.content
async def aremove_download_link(
input_response: str,
llm: BaseLanguageModel,
) -> str:
messages = remove_dl_link_prompt.format_prompt(
input_response=input_response
).to_messages()
message = await llm.apredict_messages(messages)
if not isinstance(message, AIMessage):
raise OutputParserException("Expected an AIMessage")
assert isinstance(message.content, str), "TODO: add image support"
return message.content
def test() -> None:
llm = ChatOpenAI(model="gpt-3.5-turbo-0613") # type: ignore
example = (
"I have created the plot to your dataset.\n\n"
"Link to the file [here](sandbox:/plot.png)."
)
print(remove_download_link(example, llm))
if __name__ == "__main__":
from dotenv import load_dotenv
load_dotenv()
test()
| [] |
2024-01-10 | shroominic/codeinterpreter-api | src~codeinterpreterapi~prompts~system_message.py | from langchain.schema import SystemMessage
system_message = SystemMessage(
content="""
You are using an AI Assistant capable of tasks related to data science, data analysis, data visualization, and file manipulation. Capabilities include:
- Image Manipulation: Zoom, crop, color grade, enhance resolution, format conversion.
- QR Code Generation: Create QR codes.
- Project Management: Generate Gantt charts, map project steps.
- Study Scheduling: Design optimized exam study schedules.
- File Conversion: Convert files, e.g., PDF to text, video to audio.
- Mathematical Computation: Solve equations, produce graphs.
- Document Analysis: Summarize, extract information from large documents.
- Data Visualization: Analyze datasets, identify trends, create graphs.
- Geolocation Visualization: Show maps to visualize specific trends or occurrences.
- Code Analysis and Creation: Critique and generate code.
The Assistant operates within a sandboxed Jupyter kernel environment. Pre-installed Python packages include numpy, pandas, matplotlib, seaborn, scikit-learn, yfinance, scipy, statsmodels, sympy, bokeh, plotly, dash, and networkx. Other packages will be installed as required.
To use, input your task-specific code. Review and retry code in case of error. After two unsuccessful attempts, an error message will be returned.
The Assistant is designed for specific tasks and may not function as expected if used incorrectly.
""" # noqa: E501
)
| [
"\nYou are using an AI Assistant capable of tasks related to data science, data analysis, data visualization, and file manipulation. Capabilities include:\n\n- Image Manipulation: Zoom, crop, color grade, enhance resolution, format conversion.\n- QR Code Generation: Create QR codes.\n- Project Management: Generate Gantt charts, map project steps.\n- Study Scheduling: Design optimized exam study schedules.\n- File Conversion: Convert files, e.g., PDF to text, video to audio.\n- Mathematical Computation: Solve equations, produce graphs.\n- Document Analysis: Summarize, extract information from large documents.\n- Data Visualization: Analyze datasets, identify trends, create graphs.\n- Geolocation Visualization: Show maps to visualize specific trends or occurrences.\n- Code Analysis and Creation: Critique and generate code.\n\nThe Assistant operates within a sandboxed Jupyter kernel environment. Pre-installed Python packages include numpy, pandas, matplotlib, seaborn, scikit-learn, yfinance, scipy, statsmodels, sympy, bokeh, plotly, dash, and networkx. Other packages will be installed as required.\n\nTo use, input your task-specific code. Review and retry code in case of error. After two unsuccessful attempts, an error message will be returned.\n\nThe Assistant is designed for specific tasks and may not function as expected if used incorrectly.\n"
] |
2024-01-10 | shroominic/codeinterpreter-api | src~codeinterpreterapi~prompts~modifications_check.py | from langchain.prompts import PromptTemplate
determine_modifications_prompt = PromptTemplate(
input_variables=["code"],
template="The user will input some code and you need to determine "
"if the code makes any changes to the file system. \n"
"With changes it means creating new files or modifying existing ones.\n"
"Format your answer as JSON inside a codeblock with a "
"list of filenames that are modified by the code.\n"
"If the code does not make any changes to the file system, "
"return an empty list.\n\n"
"Determine modifications:\n"
"```python\n"
"import matplotlib.pyplot as plt\n"
"import numpy as np\n\n"
"t = np.arange(0.0, 4.0*np.pi, 0.1)\n\n"
"s = np.sin(t)\n\n"
"fig, ax = plt.subplots()\n\n"
"ax.plot(t, s)\n\n"
'ax.set(xlabel="time (s)", ylabel="sin(t)",\n'
' title="Simple Sin Wave")\n'
"ax.grid()\n\n"
'plt.savefig("sin_wave.png")\n'
"```\n\n"
"Answer:\n"
"```json\n"
"{{\n"
' "modifications": ["sin_wave.png"]\n'
"}}\n"
"```\n\n"
"Determine modifications:\n"
"```python\n"
"import matplotlib.pyplot as plt\n"
"import numpy as np\n\n"
"x = np.linspace(0, 10, 100)\n"
"y = x**2\n\n"
"plt.figure(figsize=(8, 6))\n"
"plt.plot(x, y)\n"
'plt.title("Simple Quadratic Function")\n'
'plt.xlabel("x")\n'
'plt.ylabel("y = x^2")\n'
"plt.grid(True)\n"
"plt.show()\n"
"```\n\n"
"Answer:\n"
"```json\n"
"{{\n"
' "modifications": []\n'
"}}\n"
"```\n\n"
"Determine modifications:\n"
"```python\n"
"{code}\n"
"```\n\n"
"Answer:\n"
"```json\n",
)
| [
"import numpy as np\n\n",
"plt.xlabel(\"x\")\n",
"y = x**2\n\n",
"plt.title(\"Simple Quadratic Function\")\n",
"The user will input some code and you need to determine if the code makes any changes to the file system. \nWith changes it means creating new files or modifying existing ones.\nFormat your answer as JSON inside a codeblock with a list of filenames that are modified by the code.\nIf the code does not make any changes to the file system, return an empty list.\n\nDetermine modifications:\n```python\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nt = np.arange(0.0, 4.0*np.pi, 0.1)\n\ns = np.sin(t)\n\nfig, ax = plt.subplots()\n\nax.plot(t, s)\n\nax.set(xlabel=\"time (s)\", ylabel=\"sin(t)\",\n title=\"Simple Sin Wave\")\nax.grid()\n\nplt.savefig(\"sin_wave.png\")\n```\n\nAnswer:\n```json\n{{\n \"modifications\": [\"sin_wave.png\"]\n}}\n```\n\nDetermine modifications:\n```python\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nx = np.linspace(0, 10, 100)\ny = x**2\n\nplt.figure(figsize=(8, 6))\nplt.plot(x, y)\nplt.title(\"Simple Quadratic Function\")\nplt.xlabel(\"x\")\nplt.ylabel(\"y = x^2\")\nplt.grid(True)\nplt.show()\n```\n\nAnswer:\n```json\n{{\n \"modifications\": []\n}}\n```\n\nDetermine modifications:\n```python\n{code}\n```\n\nAnswer:\n```json\n",
"t = np.arange(0.0, 4.0*np.pi, 0.1)\n\n",
"plt.savefig(\"sin_wave.png\")\n",
"Format your answer as JSON inside a codeblock with a ",
"return an empty list.\n\n",
"list of filenames that are modified by the code.\n",
"fig, ax = plt.subplots()\n\n",
"If the code does not make any changes to the file system, ",
"plt.plot(x, y)\n",
"```python\n",
"x = np.linspace(0, 10, 100)\n",
"s = np.sin(t)\n\n",
" \"modifications\": [\"sin_wave.png\"]\n",
"```\n\n",
"```json\n",
"Determine modifications:\n",
"{{\n",
"}}\n",
"The user will input some code and you need to determine ",
"import matplotlib.pyplot as plt\n",
"Answer:\n",
"plt.show()\n",
"plt.figure(figsize=(8, 6))\n",
"plt.ylabel(\"y = x^2\")\n",
" \"modifications\": []\n",
"{code}\n",
"ax.plot(t, s)\n\n",
"With changes it means creating new files or modifying existing ones.\n",
" title=\"Simple Sin Wave\")\n",
"ax.set(xlabel=\"time (s)\", ylabel=\"sin(t)\",\n",
"if the code makes any changes to the file system. \n",
"plt.grid(True)\n",
"ax.grid()\n\n"
] |
2024-01-10 | shroominic/codeinterpreter-api | src~codeinterpreterapi~_patch_parser.py | import asyncio
import json
from json import JSONDecodeError
from typing import List, Union
from langchain_core.agents import AgentAction, AgentActionMessageLog, AgentFinish
from langchain_core.exceptions import OutputParserException
from langchain_core.messages import (
AIMessage,
BaseMessage,
)
from langchain_core.outputs import ChatGeneration, Generation
from langchain.agents.agent import AgentOutputParser
class OpenAIFunctionsAgentOutputParser(AgentOutputParser):
"""Parses a message into agent action/finish.
Is meant to be used with OpenAI models, as it relies on the specific
function_call parameter from OpenAI to convey what tools to use.
If a function_call parameter is passed, then that is used to get
the tool and tool input.
If one is not passed, then the AIMessage is assumed to be the final output.
"""
@property
def _type(self) -> str:
return "openai-functions-agent"
@staticmethod
def _parse_ai_message(message: BaseMessage) -> Union[AgentAction, AgentFinish]:
"""Parse an AI message."""
if not isinstance(message, AIMessage):
raise TypeError(f"Expected an AI message got {type(message)}")
function_call = message.additional_kwargs.get("function_call", {})
if function_call:
function_name = function_call["name"]
try:
if len(function_call["arguments"].strip()) == 0:
# OpenAI returns an empty string for functions containing no args
_tool_input = {}
else:
# otherwise it returns a json object
_tool_input = json.loads(function_call["arguments"])
except JSONDecodeError:
if function_name == "python":
code = function_call["arguments"]
_tool_input = {
"code": code,
}
else:
raise OutputParserException(
f"Could not parse tool input: {function_call} because "
f"the `arguments` is not valid JSON."
)
# HACK HACK HACK:
# The code that encodes tool input into Open AI uses a special variable
# name called `__arg1` to handle old style tools that do not expose a
# schema and expect a single string argument as an input.
# We unpack the argument here if it exists.
# Open AI does not support passing in a JSON array as an argument.
if "__arg1" in _tool_input:
tool_input = _tool_input["__arg1"]
else:
tool_input = _tool_input
content_msg = f"responded: {message.content}\n" if message.content else "\n"
log = f"\nInvoking: `{function_name}` with `{tool_input}`\n{content_msg}\n"
return AgentActionMessageLog(
tool=function_name,
tool_input=tool_input,
log=log,
message_log=[message],
)
return AgentFinish(
return_values={"output": message.content}, log=str(message.content)
)
def parse_result(
self, result: List[Generation], *, partial: bool = False
) -> Union[AgentAction, AgentFinish]:
if not isinstance(result[0], ChatGeneration):
raise ValueError("This output parser only works on ChatGeneration output")
message = result[0].message
return self._parse_ai_message(message)
async def aparse_result(
self, result: List[Generation], *, partial: bool = False
) -> Union[AgentAction, AgentFinish]:
return await asyncio.get_running_loop().run_in_executor(
None, self.parse_result, result
)
def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
raise ValueError("Can only parse messages")
def patch() -> None:
"""Patch the parser."""
from langchain.agents import openai_functions_agent
openai_functions_agent.OpenAIFunctionsAgentOutputParser = OpenAIFunctionsAgentOutputParser # type: ignore
| [] |
2024-01-10 | shroominic/codeinterpreter-api | examples~use_additional_tools.py | """
The exciting part about this example is
that the code interpreter has internet access
so it can download the bitcoin chart from yahoo finance
and plot it for you
"""
import csv
import io
from typing import Any
from langchain.tools import BaseTool
from codeinterpreterapi import CodeInterpreterSession
class ExampleKnowledgeBaseTool(BaseTool):
name: str = "salary_database"
description: str = "Use to get salary data of company employees"
def _run(self, *args: Any, **kwargs: Any) -> Any:
raise NotImplementedError()
async def _arun(self, *args: Any, **kwargs: Any) -> Any:
f = io.StringIO()
writer = csv.writer(f)
writer.writerow(["month", "employee", "salary"])
writer.writerow(["march 2022", "Jan", "1200"])
writer.writerow(["march 2022", "Ola", "1500"])
writer.writerow(["april 2022", "Jan", "1800"])
writer.writerow(["april 2022", "Ola", "2000"])
return f.getvalue()
async def main() -> None:
async with CodeInterpreterSession(
additional_tools=[ExampleKnowledgeBaseTool()]
) as session:
response = await session.agenerate_response(
"Plot chart of company employee salaries"
)
response.show()
if __name__ == "__main__":
import asyncio
asyncio.run(main())
| [
"Use to get salary data of company employees"
] |
2024-01-10 | shroominic/codeinterpreter-api | src~codeinterpreterapi~prompts~remove_dl_link.py | from langchain.prompts.chat import ChatPromptTemplate, HumanMessagePromptTemplate
from langchain.schema import AIMessage, HumanMessage, SystemMessage
remove_dl_link_prompt = ChatPromptTemplate(
input_variables=["input_response"],
messages=[
SystemMessage(
content="The user will send you a response and you need "
"to remove the download link from it.\n"
"Reformat the remaining message so no whitespace "
"or half sentences are still there.\n"
"If the response does not contain a download link, "
"return the response as is.\n"
),
HumanMessage(
content="The dataset has been successfully converted to CSV format. "
"You can download the converted file [here](sandbox:/Iris.csv)."
), # noqa: E501
AIMessage(content="The dataset has been successfully converted to CSV format."),
HumanMessagePromptTemplate.from_template("{input_response}"),
],
)
| [
"{input_response}",
"Reformat the remaining message so no whitespace ",
"You can download the converted file [here](sandbox:/Iris.csv).",
"The dataset has been successfully converted to CSV format. ",
"input_response",
"If the response does not contain a download link, ",
"or half sentences are still there.\n",
"The dataset has been successfully converted to CSV format. You can download the converted file [here](sandbox:/Iris.csv).",
"The dataset has been successfully converted to CSV format.",
"The user will send you a response and you need ",
"The user will send you a response and you need to remove the download link from it.\nReformat the remaining message so no whitespace or half sentences are still there.\nIf the response does not contain a download link, return the response as is.\n",
"to remove the download link from it.\n",
"return the response as is.\n"
] |
2024-01-10 | shroominic/codeinterpreter-api | src~codeinterpreterapi~chains~modifications_check.py | import json
from typing import List, Optional
from langchain.base_language import BaseLanguageModel
from langchain.chat_models.anthropic import ChatAnthropic
from codeinterpreterapi.prompts import determine_modifications_prompt
def get_file_modifications(
code: str,
llm: BaseLanguageModel,
retry: int = 2,
) -> Optional[List[str]]:
if retry < 1:
return None
prompt = determine_modifications_prompt.format(code=code)
result = llm.predict(prompt, stop="```")
try:
result = json.loads(result)
except json.JSONDecodeError:
result = ""
if not result or not isinstance(result, dict) or "modifications" not in result:
return get_file_modifications(code, llm, retry=retry - 1)
return result["modifications"]
async def aget_file_modifications(
code: str,
llm: BaseLanguageModel,
retry: int = 2,
) -> Optional[List[str]]:
if retry < 1:
return None
prompt = determine_modifications_prompt.format(code=code)
result = await llm.apredict(prompt, stop="```")
try:
result = json.loads(result)
except json.JSONDecodeError:
result = ""
if not result or not isinstance(result, dict) or "modifications" not in result:
return await aget_file_modifications(code, llm, retry=retry - 1)
return result["modifications"]
async def test() -> None:
llm = ChatAnthropic(model="claude-2") # type: ignore
code = """
import matplotlib.pyplot as plt
x = list(range(1, 11))
y = [29, 39, 23, 32, 4, 43, 43, 23, 43, 77]
plt.plot(x, y, marker='o')
plt.xlabel('Index')
plt.ylabel('Value')
plt.title('Data Plot')
plt.show()
"""
print(get_file_modifications(code, llm))
if __name__ == "__main__":
import asyncio
import dotenv
dotenv.load_dotenv()
asyncio.run(test())
| [] |
2024-01-10 | shroominic/codeinterpreter-api | src~codeinterpreterapi~session.py | import base64
import re
import traceback
from io import BytesIO
from types import TracebackType
from typing import Any, Optional, Type
from uuid import UUID, uuid4
from codeboxapi import CodeBox # type: ignore
from codeboxapi.schema import CodeBoxOutput # type: ignore
from langchain.agents import (
AgentExecutor,
BaseSingleActionAgent,
ConversationalAgent,
ConversationalChatAgent,
)
from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.base import Callbacks
from langchain.chat_models import AzureChatOpenAI, ChatAnthropic, ChatOpenAI
from langchain.chat_models.base import BaseChatModel
from langchain.memory import ConversationBufferMemory
from langchain.memory.chat_message_histories import (
ChatMessageHistory,
PostgresChatMessageHistory,
RedisChatMessageHistory,
)
from langchain.prompts.chat import MessagesPlaceholder
from langchain.schema import BaseChatMessageHistory
from langchain.tools import BaseTool, StructuredTool
from codeinterpreterapi.chains import (
aget_file_modifications,
aremove_download_link,
get_file_modifications,
remove_download_link,
)
from codeinterpreterapi.chat_history import CodeBoxChatMessageHistory
from codeinterpreterapi.config import settings
from codeinterpreterapi.schema import (
CodeInput,
CodeInterpreterResponse,
File,
SessionStatus,
UserRequest,
)
def _handle_deprecated_kwargs(kwargs: dict) -> None:
settings.MODEL = kwargs.get("model", settings.MODEL)
settings.MAX_RETRY = kwargs.get("max_retry", settings.MAX_RETRY)
settings.TEMPERATURE = kwargs.get("temperature", settings.TEMPERATURE)
settings.OPENAI_API_KEY = kwargs.get("openai_api_key", settings.OPENAI_API_KEY)
settings.SYSTEM_MESSAGE = kwargs.get("system_message", settings.SYSTEM_MESSAGE)
settings.MAX_ITERATIONS = kwargs.get("max_iterations", settings.MAX_ITERATIONS)
class CodeInterpreterSession:
def __init__(
self,
llm: Optional[BaseLanguageModel] = None,
additional_tools: list[BaseTool] = [],
callbacks: Callbacks = None,
**kwargs: Any,
) -> None:
_handle_deprecated_kwargs(kwargs)
self.codebox = CodeBox(requirements=settings.CUSTOM_PACKAGES)
self.verbose = kwargs.get("verbose", settings.DEBUG)
self.tools: list[BaseTool] = self._tools(additional_tools)
self.llm: BaseLanguageModel = llm or self._choose_llm()
self.callbacks = callbacks
self.agent_executor: Optional[AgentExecutor] = None
self.input_files: list[File] = []
self.output_files: list[File] = []
self.code_log: list[tuple[str, str]] = []
@classmethod
def from_id(cls, session_id: UUID, **kwargs: Any) -> "CodeInterpreterSession":
session = cls(**kwargs)
session.codebox = CodeBox.from_id(session_id)
session.agent_executor = session._agent_executor()
return session
@property
def session_id(self) -> Optional[UUID]:
return self.codebox.session_id
def start(self) -> SessionStatus:
status = SessionStatus.from_codebox_status(self.codebox.start())
self.agent_executor = self._agent_executor()
self.codebox.run(
f"!pip install -q {' '.join(settings.CUSTOM_PACKAGES)}",
)
return status
async def astart(self) -> SessionStatus:
status = SessionStatus.from_codebox_status(await self.codebox.astart())
self.agent_executor = self._agent_executor()
await self.codebox.arun(
f"!pip install -q {' '.join(settings.CUSTOM_PACKAGES)}",
)
return status
def _tools(self, additional_tools: list[BaseTool]) -> list[BaseTool]:
return additional_tools + [
StructuredTool(
name="python",
description="Input a string of code to a ipython interpreter. "
"Write the entire code in a single string. This string can "
"be really long, so you can use the `;` character to split lines. "
"Start your code on the same line as the opening quote. "
"Do not start your code with a line break. "
"For example, do 'import numpy', not '\\nimport numpy'."
"Variables are preserved between runs. "
+ (
(
f"You can use all default python packages specifically also these: {settings.CUSTOM_PACKAGES}"
)
if settings.CUSTOM_PACKAGES
else ""
), # TODO: or include this in the system message
func=self._run_handler,
coroutine=self._arun_handler,
args_schema=CodeInput, # type: ignore
),
]
def _choose_llm(self) -> BaseChatModel:
if (
settings.AZURE_API_KEY
and settings.AZURE_API_BASE
and settings.AZURE_API_VERSION
and settings.AZURE_DEPLOYMENT_NAME
):
self.log("Using Azure Chat OpenAI")
return AzureChatOpenAI(
temperature=0.03,
base_url=settings.AZURE_API_BASE,
api_version=settings.AZURE_API_VERSION,
azure_deployment=settings.AZURE_DEPLOYMENT_NAME,
api_key=settings.AZURE_API_KEY,
max_retries=settings.MAX_RETRY,
timeout=settings.REQUEST_TIMEOUT,
) # type: ignore
if settings.OPENAI_API_KEY:
self.log("Using Chat OpenAI")
return ChatOpenAI(
model=settings.MODEL,
api_key=settings.OPENAI_API_KEY,
timeout=settings.REQUEST_TIMEOUT,
temperature=settings.TEMPERATURE,
max_retries=settings.MAX_RETRY,
) # type: ignore
if settings.ANTHROPIC_API_KEY:
if "claude" not in settings.MODEL:
print("Please set the claude model in the settings.")
self.log("Using Chat Anthropic")
return ChatAnthropic(
model_name=settings.MODEL,
temperature=settings.TEMPERATURE,
anthropic_api_key=settings.ANTHROPIC_API_KEY,
)
raise ValueError("Please set the API key for the LLM you want to use.")
def _choose_agent(self) -> BaseSingleActionAgent:
return (
OpenAIFunctionsAgent.from_llm_and_tools(
llm=self.llm,
tools=self.tools,
system_message=settings.SYSTEM_MESSAGE,
extra_prompt_messages=[
MessagesPlaceholder(variable_name="chat_history")
],
)
if isinstance(self.llm, ChatOpenAI)
else ConversationalChatAgent.from_llm_and_tools(
llm=self.llm,
tools=self.tools,
system_message=settings.SYSTEM_MESSAGE.content.__str__(),
)
if isinstance(self.llm, BaseChatModel)
else ConversationalAgent.from_llm_and_tools(
llm=self.llm,
tools=self.tools,
prefix=settings.SYSTEM_MESSAGE.content.__str__(),
)
)
def _history_backend(self) -> BaseChatMessageHistory:
return (
CodeBoxChatMessageHistory(codebox=self.codebox)
if settings.HISTORY_BACKEND == "codebox"
else RedisChatMessageHistory(
session_id=str(self.session_id),
url=settings.REDIS_URL,
)
if settings.HISTORY_BACKEND == "redis"
else PostgresChatMessageHistory(
session_id=str(self.session_id),
connection_string=settings.POSTGRES_URL,
)
if settings.HISTORY_BACKEND == "postgres"
else ChatMessageHistory()
)
def _agent_executor(self) -> AgentExecutor:
return AgentExecutor.from_agent_and_tools(
agent=self._choose_agent(),
max_iterations=settings.MAX_ITERATIONS,
tools=self.tools,
verbose=self.verbose,
memory=ConversationBufferMemory(
memory_key="chat_history",
return_messages=True,
chat_memory=self._history_backend(),
),
callbacks=self.callbacks,
)
def show_code(self, code: str) -> None:
if self.verbose:
print(code)
async def ashow_code(self, code: str) -> None:
"""Callback function to show code to the user."""
if self.verbose:
print(code)
def _run_handler(self, code: str) -> str:
"""Run code in container and send the output to the user"""
self.show_code(code)
output: CodeBoxOutput = self.codebox.run(code)
self.code_log.append((code, output.content))
if not isinstance(output.content, str):
raise TypeError("Expected output.content to be a string.")
if output.type == "image/png":
filename = f"image-{uuid4()}.png"
file_buffer = BytesIO(base64.b64decode(output.content))
file_buffer.name = filename
self.output_files.append(File(name=filename, content=file_buffer.read()))
return f"Image {filename} got send to the user."
elif output.type == "error":
if "ModuleNotFoundError" in output.content:
if package := re.search(
r"ModuleNotFoundError: No module named '(.*)'",
output.content,
):
self.codebox.install(package.group(1))
return (
f"{package.group(1)} was missing but "
"got installed now. Please try again."
)
else:
# TODO: pre-analyze error to optimize next code generation
pass
if self.verbose:
print("Error:", output.content)
elif modifications := get_file_modifications(code, self.llm):
for filename in modifications:
if filename in [file.name for file in self.input_files]:
continue
fileb = self.codebox.download(filename)
if not fileb.content:
continue
file_buffer = BytesIO(fileb.content)
file_buffer.name = filename
self.output_files.append(
File(name=filename, content=file_buffer.read())
)
return output.content
async def _arun_handler(self, code: str) -> str:
"""Run code in container and send the output to the user"""
await self.ashow_code(code)
output: CodeBoxOutput = await self.codebox.arun(code)
self.code_log.append((code, output.content))
if not isinstance(output.content, str):
raise TypeError("Expected output.content to be a string.")
if output.type == "image/png":
filename = f"image-{uuid4()}.png"
file_buffer = BytesIO(base64.b64decode(output.content))
file_buffer.name = filename
self.output_files.append(File(name=filename, content=file_buffer.read()))
return f"Image {filename} got send to the user."
elif output.type == "error":
if "ModuleNotFoundError" in output.content:
if package := re.search(
r"ModuleNotFoundError: No module named '(.*)'",
output.content,
):
await self.codebox.ainstall(package.group(1))
return (
f"{package.group(1)} was missing but "
"got installed now. Please try again."
)
else:
# TODO: pre-analyze error to optimize next code generation
pass
if self.verbose:
print("Error:", output.content)
elif modifications := await aget_file_modifications(code, self.llm):
for filename in modifications:
if filename in [file.name for file in self.input_files]:
continue
fileb = await self.codebox.adownload(filename)
if not fileb.content:
continue
file_buffer = BytesIO(fileb.content)
file_buffer.name = filename
self.output_files.append(
File(name=filename, content=file_buffer.read())
)
return output.content
def _input_handler(self, request: UserRequest) -> None:
"""Callback function to handle user input."""
if not request.files:
return
if not request.content:
request.content = (
"I uploaded, just text me back and confirm that you got the file(s)."
)
assert isinstance(request.content, str), "TODO: implement image support"
request.content += "\n**The user uploaded the following files: **\n"
for file in request.files:
self.input_files.append(file)
request.content += f"[Attachment: {file.name}]\n"
self.codebox.upload(file.name, file.content)
request.content += "**File(s) are now available in the cwd. **\n"
async def _ainput_handler(self, request: UserRequest) -> None:
# TODO: variables as context to the agent
# TODO: current files as context to the agent
if not request.files:
return
if not request.content:
request.content = (
"I uploaded, just text me back and confirm that you got the file(s)."
)
assert isinstance(request.content, str), "TODO: implement image support"
request.content += "\n**The user uploaded the following files: **\n"
for file in request.files:
self.input_files.append(file)
request.content += f"[Attachment: {file.name}]\n"
await self.codebox.aupload(file.name, file.content)
request.content += "**File(s) are now available in the cwd. **\n"
def _output_handler(self, final_response: str) -> CodeInterpreterResponse:
"""Embed images in the response"""
for file in self.output_files:
if str(file.name) in final_response:
# rm  from the response
final_response = re.sub(r"\n\n!\[.*\]\(.*\)", "", final_response)
if self.output_files and re.search(r"\n\[.*\]\(.*\)", final_response):
try:
final_response = remove_download_link(final_response, self.llm)
except Exception as e:
if self.verbose:
print("Error while removing download links:", e)
output_files = self.output_files
code_log = self.code_log
self.output_files = []
self.code_log = []
return CodeInterpreterResponse(
content=final_response, files=output_files, code_log=code_log
)
async def _aoutput_handler(self, final_response: str) -> CodeInterpreterResponse:
"""Embed images in the response"""
for file in self.output_files:
if str(file.name) in final_response:
# rm  from the response
final_response = re.sub(r"\n\n!\[.*\]\(.*\)", "", final_response)
if self.output_files and re.search(r"\n\[.*\]\(.*\)", final_response):
try:
final_response = await aremove_download_link(final_response, self.llm)
except Exception as e:
if self.verbose:
print("Error while removing download links:", e)
output_files = self.output_files
code_log = self.code_log
self.output_files = []
self.code_log = []
return CodeInterpreterResponse(
content=final_response, files=output_files, code_log=code_log
)
def generate_response_sync(
self,
user_msg: str,
files: list[File] = [],
) -> CodeInterpreterResponse:
print("DEPRECATION WARNING: Use generate_response for sync generation.\n")
return self.generate_response(
user_msg=user_msg,
files=files,
)
def generate_response(
self,
user_msg: str,
files: list[File] = [],
) -> CodeInterpreterResponse:
"""Generate a Code Interpreter response based on the user's input."""
user_request = UserRequest(content=user_msg, files=files)
try:
self._input_handler(user_request)
assert self.agent_executor, "Session not initialized."
response = self.agent_executor.run(input=user_request.content)
return self._output_handler(response)
except Exception as e:
if self.verbose:
traceback.print_exc()
if settings.DETAILED_ERROR:
return CodeInterpreterResponse(
content="Error in CodeInterpreterSession: "
f"{e.__class__.__name__} - {e}"
)
else:
return CodeInterpreterResponse(
content="Sorry, something went while generating your response."
"Please try again or restart the session."
)
async def agenerate_response(
self,
user_msg: str,
files: list[File] = [],
) -> CodeInterpreterResponse:
"""Generate a Code Interpreter response based on the user's input."""
user_request = UserRequest(content=user_msg, files=files)
try:
await self._ainput_handler(user_request)
assert self.agent_executor, "Session not initialized."
response = await self.agent_executor.arun(input=user_request.content)
return await self._aoutput_handler(response)
except Exception as e:
if self.verbose:
traceback.print_exc()
if settings.DETAILED_ERROR:
return CodeInterpreterResponse(
content="Error in CodeInterpreterSession: "
f"{e.__class__.__name__} - {e}"
)
else:
return CodeInterpreterResponse(
content="Sorry, something went while generating your response."
"Please try again or restart the session."
)
def is_running(self) -> bool:
return self.codebox.status() == "running"
async def ais_running(self) -> bool:
return await self.codebox.astatus() == "running"
def log(self, msg: str) -> None:
if self.verbose:
print(msg)
def stop(self) -> SessionStatus:
return SessionStatus.from_codebox_status(self.codebox.stop())
async def astop(self) -> SessionStatus:
return SessionStatus.from_codebox_status(await self.codebox.astop())
def __enter__(self) -> "CodeInterpreterSession":
self.start()
return self
def __exit__(
self,
exc_type: Optional[Type[BaseException]],
exc_value: Optional[BaseException],
traceback: Optional[TracebackType],
) -> None:
self.stop()
async def __aenter__(self) -> "CodeInterpreterSession":
await self.astart()
return self
async def __aexit__(
self,
exc_type: Optional[Type[BaseException]],
exc_value: Optional[BaseException],
traceback: Optional[TracebackType],
) -> None:
await self.astop()
| [] |
2024-01-10 | shroominic/codeinterpreter-api | src~codeinterpreterapi~config.py | from typing import Optional
from dotenv import load_dotenv
from langchain.pydantic_v1 import BaseSettings, SecretStr
from langchain.schema import SystemMessage
from codeinterpreterapi.prompts import code_interpreter_system_message
# .env file
load_dotenv(dotenv_path="./.env")
class CodeInterpreterAPISettings(BaseSettings):
"""
CodeInterpreter API Config
"""
DEBUG: bool = False
# Models
OPENAI_API_KEY: Optional[str] = None
AZURE_API_KEY: Optional[str] = None
AZURE_API_BASE: Optional[str] = None
AZURE_API_VERSION: Optional[str] = None
AZURE_DEPLOYMENT_NAME: Optional[str] = None
ANTHROPIC_API_KEY: Optional[SecretStr] = None
# LLM Settings
MODEL: str = "gpt-3.5-turbo"
TEMPERATURE: float = 0.03
DETAILED_ERROR: bool = True
SYSTEM_MESSAGE: SystemMessage = code_interpreter_system_message
REQUEST_TIMEOUT: int = 3 * 60
MAX_ITERATIONS: int = 12
MAX_RETRY: int = 3
# Production Settings
HISTORY_BACKEND: Optional[str] = None
REDIS_URL: str = "redis://localhost:6379"
POSTGRES_URL: str = "postgresql://postgres:postgres@localhost:5432/postgres"
# CodeBox
CODEBOX_API_KEY: Optional[str] = None
CUSTOM_PACKAGES: list[str] = []
# deprecated
VERBOSE: bool = DEBUG
settings = CodeInterpreterAPISettings()
| [] |
2024-01-10 | shroominic/codeinterpreter-api | src~codeinterpreterapi~chains~extract_code.py | from langchain.base_language import BaseLanguageModel
from langchain.chat_models.anthropic import ChatAnthropic
def extract_python_code(
text: str,
llm: BaseLanguageModel,
retry: int = 2,
) -> str:
return "TODO"
async def aextract_python_code(
text: str,
llm: BaseLanguageModel,
retry: int = 2,
) -> str:
return "TODO"
async def test() -> None:
llm = ChatAnthropic(model="claude-1.3") # type: ignore
code = """
import matplotlib.pyplot as plt
x = list(range(1, 11))
y = [29, 39, 23, 32, 4, 43, 43, 23, 43, 77]
plt.plot(x, y, marker='o')
plt.xlabel('Index')
plt.ylabel('Value')
plt.title('Data Plot')
plt.show()
"""
print(extract_python_code(code, llm))
if __name__ == "__main__":
import asyncio
import dotenv
dotenv.load_dotenv()
asyncio.run(test())
| [] |
2024-01-10 | ddadas/newbot | bot_speech3_working.py | # 1. Start by importing the necessary libraries and setting up the API clients
import requests
import json
import os
import threading
import tempfile
# OpenAI secret Key
API_KEY = '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'
# Models: text-davinci-003,text-curie-001,text-babbage-001,text-ada-001
MODEL = 'gpt-3.5-turbo'
# Telegram secret access bot token
BOT_TOKEN = '5762201296:AAFVMF0z27_dukBGH_QRtgAsa0tOkZdWQ_w'
# Defining the bot's personality using adjectives
BOT_PERSONALITY = 'Answer in a friendly tone, '
# Specify your Chat Bot handle
CHATBOT_HANDLE = '@ask_chatgptbot'
# Function that converts text to speech and returns the audio file path
def text_to_speech(text):
response = requests.get(
url=f"https://api.streamelements.com/kappa/v2/speech?voice=Brian&text={text}",
stream=True,
timeout=10,
)
temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".ogg")
with open(temp_file.name, "wb") as file:
for chunk in response:
file.write(chunk)
return temp_file.name
# Function that sends an audio message to a specific telegram group
def telegram_bot_sendaudio(audio_path, chat_id, msg_id):
data = {
'chat_id': chat_id,
'reply_to_message_id': msg_id
}
with open(audio_path, 'rb') as audio_file:
files = {'voice': audio_file}
url = 'https://api.telegram.org/bot' + BOT_TOKEN + '/sendVoice'
response = requests.post(url, data=data, files=files, timeout=10)
return response.json()
# 2a. Function that gets the response from OpenAI's chatbot
def openAI(prompt):
# Make the request to the OpenAI API
response = requests.post(
'https://api.openai.com/v1/chat/completions',
headers={'Authorization': f'Bearer {API_KEY}'},
json={'model': MODEL, 'messages': [{"role": "user", "content": prompt}], 'temperature': 0.5, 'max_tokens': 300},
timeout=10
)
result=response.json()
final_result=''
for i in range(0,len(result['choices'])):
final_result+=result['choices'][i]['message']['content']
return final_result
# 2b. Function that gets an Image from OpenAI
def openAImage(prompt):
# Make the request to the OpenAI API
resp = requests.post(
'https://api.openai.com/v1/images/generations',
headers={'Authorization': f'Bearer {API_KEY}'},
json={'prompt': prompt,'n' : 1, 'size': '1024x1024'},
timeout=10
)
response_text = json.loads(resp.text)
return response_text['data'][0]['url']
# 3a. Function that sends a message to a specific telegram group
def telegram_bot_sendtext(bot_message,chat_id,msg_id):
data = {
'chat_id': chat_id,
'text': bot_message,
'reply_to_message_id': msg_id
}
response = requests.post(
'https://api.telegram.org/bot' + BOT_TOKEN + '/sendMessage',
json=data,
timeout=10
)
return response.json()
# 3b. Function that sends an image to a specific telegram group
def telegram_bot_sendimage(image_url, group_id, msg_id):
data = {
'chat_id': group_id,
'photo': image_url,
'reply_to_message_id': msg_id
}
url = 'https://api.telegram.org/bot' + BOT_TOKEN + '/sendPhoto'
response = requests.post(url, data=data, timeout=5)
return response.json()
# 4. Function that retrieves the latest requests from users in a Telegram group,
# generates a response using OpenAI, and sends the response back to the group.
def Chatbot():
# Retrieve last ID message from text file for ChatGPT update
cwd = os.getcwd()
filename = cwd + '/chatgpt.txt'
if not os.path.exists(filename):
with open(filename, "w") as f:
f.write("1")
else:
does_file_exist="File Exists"
with open(filename) as f:
last_update = f.read()
f.close()
# Check for new messages in Telegram group
url = f'https://api.telegram.org/bot{BOT_TOKEN}/getUpdates?offset={last_update}'
response = requests.get(url, timeout=5)
data = json.loads(response.content)
print(data)
for result in data['result']:
try:
# Checking for new message
if float(result['update_id']) > float(last_update):
# Checking for new messages that did not come from chatGPT
if not result['message']['from']['is_bot']:
last_update = str(int(result['update_id']))
# Retrieving message ID of the sender of the request
msg_id = str(int(result['message']['message_id']))
# Retrieving the chat ID
chat_id = str(result['message']['chat']['id'])
# Checking if user wants an image
if '/img' in result['message']['text']:
prompt = result['message']['text'].replace("/img", "")
bot_response = openAImage(prompt)
print(telegram_bot_sendimage(bot_response, chat_id, msg_id))
# Checking that user mentionned chatbot's username in message
if CHATBOT_HANDLE in result['message']['text'] or "superdan" in result['message']['text'] or "Superdan" in result['message']['text']:
# if CHATBOT_HANDLE in result['message']['text'] or "superdan" in result['message']['text']:
prompt = result['message']['text'].replace(CHATBOT_HANDLE, "")
# Calling OpenAI API using the bot's personality
bot_response = openAI(f"{BOT_PERSONALITY}{prompt}")
# Sending back response to telegram group
print(telegram_bot_sendtext(bot_response, chat_id, msg_id))
# Verifying that the user is responding to the ChatGPT bot
if 'reply_to_message' in result['message']:
if result['message']['reply_to_message']['from']['username'] == CHATBOT_HANDLE[1:]:
prompt = result['message']['text']
bot_response = openAI(f"{BOT_PERSONALITY}{prompt}")
print(telegram_bot_sendtext(bot_response, chat_id, msg_id))
# Checking if user wants a voice message
if '/voice' in result['message']['text']:
prompt = result['message']['text'].replace("/voice", "")
bot_response = text_to_speech(prompt)
print(telegram_bot_sendaudio(bot_response, chat_id, msg_id))
except Exception as e:
print(e)
# Updating file with last update ID
with open(filename, 'w') as f:
f.write(last_update)
f.close()
return "done"
# 5 Running a check every 5 seconds to check for new messages
def main():
timertime=5
Chatbot()
# 5 sec timer
threading.Timer(timertime, main).start()
# Run the main function
if __name__ == "__main__":
main() | [] |
2024-01-10 | ionburger/turdbot | .py.old~cogs~askgpt.py | import discord
from discord.ext import bridge, commands
import openai
class Askgpt(commands.Cog):
def __init__(self, bot):
self.bot = bot
@bridge.bridge_command()
async def askgpt(self, ctx, *, message):
| [] |
2024-01-10 | DanManN/SC_Robot_Design | speech_app~restructured_dialougue.py | # main_file.py
#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
import pyaudio
import speech_recognition as sr
import audioop
import math
import tempfile
import os
import wave
import subprocess
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from gtts import gTTS
import pygame
import torch
from transformers import BertForQuestionAnswering
from transformers import BertTokenizer
import warnings
import torch
from transformers import AutoModel, AutoTokenizer
from fuzzywuzzy import fuzz
from openai import OpenAI
import time
warnings.simplefilter("ignore")
# Constants
navigation_phrases = ['take', 'walk', 'guide']
verbal_directions_phrases = ['point', 'tell', 'find', 'give', 'how', 'explain']
# Other constants and data
number_mapping = {
'one': '1',
'two': '2',
'three': '3',
'four': '4',
'five': '5',
# Add more mappings as needed
}
commands = {
'room 1': "ros2 topic pub -1 /goal_pose geometry_msgs/msg/PoseStamped '{header: {stamp: 'now', frame_id: 'map'}, pose: {position: {x: 7.17515230178833, y: 0.668110728263855, z: 0.0033092498779296875}, orientation: {x: 0.0, y: 0.0, w: 1.0}}}'",
'room 2': "ros2 topic pub -1 /goal_pose geometry_msgs/msg/PoseStamped '{header: {stamp: 'now', frame_id: 'map'}, pose: {position: {x: 9.780485153198242, y: -4.3226823806762695, z: 0.0046672821044921875}, orientation: {x: 0.0, y: 0.0, w: 1.0}}}'",
'room 3': "ros2 topic pub -1 /goal_pose geometry_msgs/msg/PoseStamped '{header: {stamp: 'now', frame_id: 'map'}, pose: {position: {x: 3.4306554794311523, y: -7.267694473266602, z: 0.0068721771240234375}, orientation: {x: 0.0, y: 0.0, w: 1.0}}}'",
'room 4': "ros2 topic pub -1 /goal_pose geometry_msgs/msg/PoseStamped '{header: {stamp: 'now', frame_id: 'map'}, pose: {position: {x: -1.6239104270935059, y: 0.7523196935653687, z: 0.00223541259765625}, orientation: {x: 0.0, y: 0.0, w: 1.0}}}'",
'room 5': "ros2 topic pub -1 /goal_pose geometry_msgs/msg/PoseStamped '{header: {stamp: 'now', frame_id: 'map'}, pose: {position: {x: 1.6131210327148438, y: 2.954784393310547, z: 0.00484466552734375}, orientation: {x: 0.0, y: 0.0, w: 1.0}}}'",
}
nav_outputs = {
'room 1': "I will take you to room 1",
'room 2': "I will take you to room 2",
'room 3': "I will take you to room 3",
'room 4': "I will take you to room 4",
'room 5': "I will take you to room 5",
}
# Other functions
def preprocess_text(text, number_mapping):
# Replace number representations with common format
for word, number in number_mapping.items():
text = text.replace(word, number)
return text
def find_best_matching_command_fuzzy(recognized_text, commands, threshold=80):
recognized_text = preprocess_text(recognized_text, number_mapping)
best_match_cmd, best_similarity = None, 0
for cmd in commands.keys():
similarity = fuzz.partial_ratio(cmd, recognized_text)
if similarity > best_similarity:
best_similarity = similarity
best_match_cmd = cmd
if best_similarity > threshold:
return best_match_cmd, best_similarity
else:
return None, None
def intent_classifier(utt:str):
##user wants to be walked
if any(nav_phrase in utt for nav_phrase in navigation_phrases):
print('******* IN NAVIGATION CONDITION')
return 0
##user wants verbal directions
elif any(v_phrase in utt for v_phrase in verbal_directions_phrases):
print('******* IN verbal CONDITION')
return 1
##user wants to chat
else:
print('******* IN CHAT CONDITION')
return 2
def speech_output_gen(utt:str):
# Use gTTS to convert the voice output to speech and play it
if utt in nav_outputs:
print("Found voice")
response = nav_outputs[utt]
tts = gTTS(text=response, lang='en', slow=False)
tts.save("temp_audio.mp3")
os.system("mpg321 temp_audio.mp3")
print("Audio file Made")
else:
print('IN MISC SPeech output')
response = "Please be sure to select a valid room, and I can walk you there or give you directions."
tts = gTTS(text=response, lang='en', slow=False)
tts.save("temp_audio.mp3")
os.system("mpg321 temp_audio.mp3")
print("Audio file Made")
def main():
# Initialize PyAudio
audio = pyaudio.PyAudio()
# Initialize the recognizer
recognizer = sr.Recognizer()
# Set up audio stream parameters
input_device_index = None
sample_rate = 22050 # Reduced sample rate
chunk_size = 8192 # Increased chunk size (in bytes)
threshold_db = 60 # Adjusted threshold
# Create a temporary audio file
temp_audio_file = tempfile.NamedTemporaryFile(delete=False, suffix=".wav")
temp_audio_file_name = temp_audio_file.name
temp_audio_file.close()
# Open an input audio stream
input_stream = audio.open(
format=pyaudio.paInt16,
channels=1,
rate=sample_rate,
input=True,
frames_per_buffer=chunk_size,
input_device_index=input_device_index
)
print("Listening...")
# Initialize variables for voice activity detection
audio_data = bytearray()
speech_started = False
# Load MiniLM model and tokenizer
model_name = "bert-base-uncased"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
weight_path = "kaporter/bert-base-uncased-finetuned-squad"
# loading tokenizer
tokenizer = BertTokenizer.from_pretrained(weight_path)
#loading the model
model = BertForQuestionAnswering.from_pretrained(weight_path)
text = "how do i get to room 4?"
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
print("MODEL OUTPUT: ", output)
# Define the voice outputs dictionary
nav_outputs = {
'take me to room one': "I will take you to room 1",
'2': "I will take you to room 2",
'3': "I will take you to room 3",
'4': "I will take you to room 4",
'5': "I will take you to room 5",
}
try:
while True:
audio_chunk = input_stream.read(chunk_size, exception_on_overflow=False)
audio_data.extend(audio_chunk)
rms = audioop.rms(audio_chunk, 2)
decibel = 20 * math.log10(rms) if rms > 0 else 0
if decibel > threshold_db:
if not speech_started:
print("Speech Started")
speech_started = True
else:
if speech_started:
print("Speech Ended")
with open(temp_audio_file_name, "wb") as f:
wav_header = wave.open(temp_audio_file_name, 'wb')
wav_header.setnchannels(1)
wav_header.setsampwidth(2)
wav_header.setframerate(sample_rate)
wav_header.writeframes(audio_data)
wav_header.close()
with sr.AudioFile(temp_audio_file_name) as source:
try:
transcription = recognizer.record(source)
recognized_text = recognizer.recognize_google(transcription)
if recognized_text:
print("Transcription: " + recognized_text)
intent = intent_classifier(recognized_text)
if intent == 0: #walk navigation
best_match_cmd, best_similarity = find_best_matching_command_fuzzy(recognized_text, commands)
if best_match_cmd is not None:
print(f'Best match: {best_match_cmd} (Similarity: {best_similarity}%)')
ros_command = commands[best_match_cmd]
print(ros_command)
print("trying to play speech")
speech_output_gen(best_match_cmd)
print("played speech")
# Send the ROS message using subprocess
subprocess.check_output(ros_command, shell=True, stderr=subprocess.STDOUT)
elif intent == 1: ##verbal directions
#question = "How do i get from home to room 1?"
question = recognized_text
"""
input_ids = tokenizer.encode(question, context)
tokens = tokenizer.convert_ids_to_tokens(input_ids)
sep_idx = tokens.index('[SEP]')
token_type_ids = [0 for i in range(sep_idx+1)] + [1 for i in range(sep_idx+1,len(tokens))]
# Run our example through the model.
out = model(torch.tensor([input_ids]), # The tokens representing our input text.
token_type_ids=torch.tensor([token_type_ids]))
start_logits,end_logits = out['start_logits'],out['end_logits']
# Find the tokens with the highest `start` and `end` scores.
answer_start = torch.argmax(start_logits)
answer_end = torch.argmax(end_logits)
ans = ' '.join(tokens[answer_start:answer_end])
"""
client = OpenAI(api_key="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")
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": "You are a helpful assistant who will provide directions between two points based off the description."},
{"role": "user", "content": "Description: The structure we're currently occupying comprises five distinct rooms, along with a designated 'home' base where I can retreat to after guiding a user to their intended destination. Let me provide you with a more detailed overview of each room:\n* Home Location:\n * Positioned at the corner closest to the left wall of the room, opposite the exterior glass wall. This serves as the central point of operations, conveniently accessible after assisting users in reaching their destinations.\n* Room 1:\n * Located to the right of the television, at the corner where the interior walls meet. This space offers a cozy atmosphere, with a view extending towards the central area of the room.\n* Room 2:\n * Situated near the left-hand corner when facing the interior entrance door. This room features a welcoming ambiance, with natural light streaming in from adjacent windows, creating an inviting environment.\n* Room 3:\n * Found adjacent to the right-hand corner when facing the interior entrance door. This room boasts a strategic layout, offering a balance of privacy and accessibility.\n* Room 4:\n * Positioned by the external exit, closest to the cabinets. This room is conveniently located for quick access to outdoor areas and is characterized by its proximity to functional storage spaces.\n* Room 5:\n * Situated along the same exterior wall but at the far-right end. This room enjoys a quieter setting compared to the others, with a view extending along the exterior of the building."},
{"role": "user", "content": question}
]
)
ans=list(list(list(response.choices[0])[2][1])[0])[1]
if ans:
print('Predicted answer:', ans)
else:
print("I have no answer")
ans=" I have no answer"
tts = gTTS(text=ans, lang='en', slow=False)
tts.save("temp_audio.mp3")
os.system("mpg321 temp_audio.mp3")
print("Audio file Made")
else: #misc
speech_output_gen(recognized_text)
# Play the audio file using pygame
pygame.mixer.init()
pygame.mixer.music.load("temp_audio.mp3")
pygame.mixer.music.play()
# Add a delay to ensure that the audio playback is completed
while pygame.mixer.music.get_busy():
pygame.time.Clock().tick(10)
except sr.UnknownValueError:
print("No speech detected")
except sr.RequestError as e:
print(f"Could not request results; {e}")
speech_started = False
audio_data = bytearray()
except KeyboardInterrupt:
pass
input_stream.stop_stream()
input_stream.close()
os.remove(temp_audio_file_name)
audio.terminate()
if __name__ == "__main__":
main()
| [
"You are a helpful assistant who will provide directions between two points based off the description.",
"Description: The structure we're currently occupying comprises five distinct rooms, along with a designated 'home' base where I can retreat to after guiding a user to their intended destination. Let me provide you with a more detailed overview of each room:\n* Home Location:\n * Positioned at the corner closest to the left wall of the room, opposite the exterior glass wall. This serves as the central point of operations, conveniently accessible after assisting users in reaching their destinations.\n* Room 1:\n * Located to the right of the television, at the corner where the interior walls meet. This space offers a cozy atmosphere, with a view extending towards the central area of the room.\n* Room 2:\n * Situated near the left-hand corner when facing the interior entrance door. This room features a welcoming ambiance, with natural light streaming in from adjacent windows, creating an inviting environment.\n* Room 3:\n * Found adjacent to the right-hand corner when facing the interior entrance door. This room boasts a strategic layout, offering a balance of privacy and accessibility.\n* Room 4:\n * Positioned by the external exit, closest to the cabinets. This room is conveniently located for quick access to outdoor areas and is characterized by its proximity to functional storage spaces.\n* Room 5:\n * Situated along the same exterior wall but at the far-right end. This room enjoys a quieter setting compared to the others, with a view extending along the exterior of the building."
] |
2024-01-10 | johntday/openai_meeting_minutes | meeting-minutes.py | import os
import openai
from docx import Document
import time
from dotenv import load_dotenv
import argparse
from glob import glob
load_dotenv()
openai.api_key = os.getenv('OPENAI_API_KEY')
model = "gpt-4"
temperature = 0
default_extension = 'm4a'
# ---------- Transcription --------------------
def transcribe_audio(audio_file_path):
with open(audio_file_path, 'rb') as audio_file:
transcription = openai.Audio.transcribe("whisper-1", audio_file)
return transcription['text']
# ---------- Abstract Summary --------------------
def full_abstract_summary_extraction(transcription,
meeting_description=None
):
system_prompt = ("You are a highly skilled AI trained in language comprehension and summarization. " +
(f"This is a meeting about {meeting_description}. " if meeting_description else "") +
f"I would like you to read the following text and summarize it into a concise abstract paragraph. Aim to retain the most important points, providing a coherent and readable summary that could help a person understand the main points of the discussion without needing to read the entire text. Please avoid unnecessary details or tangential points.")
response = openai.ChatCompletion.create(
model=model,
temperature=temperature,
messages=[
{
"role": "system",
"content": system_prompt
},
{
"role": "user",
"content": transcription
}
]
)
return response['choices'][0]['message']['content']
def chunked_abstract_summary_extraction(transcription,
meeting_description=None
):
max_tokens = 8000 # A bit less than 8192 to leave some room for the system message
overlap = 1000 # Overlap size - tune this based on your use case
transcript_parts = [transcription[i:i + max_tokens + overlap] for i in range(0, len(transcription), max_tokens)]
final_summary = ""
previous_summary = "" # Initialize previous summary
for part in transcript_parts:
# Generate a summary of the chunk
system_prompt = ("You are a highly skilled AI trained in language comprehension and summarization. " +
(f"This is a meeting about {meeting_description}. " if meeting_description else "") +
f"Previously, you summarized: '{previous_summary}'. Now, I would like you to read the following text and summarize it into a concise abstract paragraph, building upon your previous summary. Aim to retain the most important points, providing a coherent and readable summary that could help a person understand the main points of the discussion without needing to read the entire text. Please avoid unnecessary details or tangential points.")
response = openai.ChatCompletion.create(
model=model,
temperature=temperature,
messages=[
{
"role": "system",
"content": system_prompt
},
{
"role": "user",
"content": part
}
]
)
previous_summary = response['choices'][0]['message']['content'] # Update previous summary
final_summary += previous_summary + "\n"
# Use GPT-4 to rephrase the final summary into a more cohesive paragraph
response = openai.ChatCompletion.create(
model=model,
temperature=temperature,
messages=[
{
"role": "system",
"content": "As an AI trained in language comprehension and summarization, your task is to rephrase the following summaries into a more cohesive and concise paragraph. Please maintain the overall meaning and key details in your rephrasing."
},
{
"role": "user",
"content": final_summary
}
]
)
final_summary = response['choices'][0]['message']['content']
return final_summary
def abstract_summary_extraction(transcription,
meeting_description=None
):
try:
# Try the original method first
return full_abstract_summary_extraction(transcription, meeting_description)
except openai.error.InvalidRequestError as e:
# If the original method fails due to exceeding the maximum token limit, fall back to the chunking method
if 'token' in str(e):
print("Using chunking for abstract summary extraction.")
# If the server returns a 502, wait 10 seconds then retry
try:
return chunked_abstract_summary_extraction(transcription, meeting_description)
except openai.error.APIError as e:
if e.http_status == 502:
print("API returned a 502 Bad Gateway error. Retrying in 10 seconds...")
time.sleep(10)
return chunked_abstract_summary_extraction(transcription, meeting_description)
else:
# If the error is due to another reason, raise it
raise e
else:
# If the error is due to another reason, raise it
raise e
# ---------- Key Points --------------------
def full_key_points_extraction(transcription,
meeting_description=None
):
system_prompt = ("You are a proficient AI with a specialty in distilling information into key points. " +
(f"This is a meeting about {meeting_description}. " if meeting_description else "") +
f"Based on the following text, identify and list the main points that were discussed or brought up. These should be the most important ideas, findings, or topics that are crucial to the essence of the discussion. Your goal is to provide a list that someone could read to quickly understand what was talked about.")
response = openai.ChatCompletion.create(
model=model,
temperature=temperature,
messages=[
{
"role": "system",
"content": system_prompt
},
{
"role": "user",
"content": transcription
}
]
)
return response['choices'][0]['message']['content']
def chunked_key_points_extraction(transcription,
meeting_description=None
):
max_tokens = 8000 # A bit less than 8192 to leave some room for the system message
overlap = 1000 # Overlap size - tune this based on your use case
transcript_parts = [transcription[i:i + max_tokens + overlap] for i in range(0, len(transcription), max_tokens)]
final_key_points = []
previous_key_points = "" # Initialize previous key points
for part in transcript_parts:
# Extract key points from the chunk
system_prompt = ("You are a proficient AI with a specialty in distilling information into key points. " +
(f"This is a meeting about {meeting_description}. " if meeting_description else "") +
f"Previously, you identified: '{previous_key_points}'. Now, based on the following text, identify and list the main points that were discussed or brought up. These should be the most important ideas, findings, or topics that are crucial to the essence of the discussion. Your goal is to provide a list that someone could read to quickly understand what was talked about.")
response = openai.ChatCompletion.create(
model=model,
temperature=temperature,
messages=[
{
"role": "system",
"content": system_prompt
},
{
"role": "user",
"content": part
}
]
)
previous_key_points = response['choices'][0]['message']['content'] # Update previous key points
final_key_points.append(previous_key_points)
# Combine all key points into a single string
all_key_points = "\n".join(final_key_points)
# Use GPT-4 to reformat and renumber the key points
system_prompt = (
"You are a proficient AI with a specialty in organizing and formatting information. " +
f"Please take the following key points{f' (from a meeting about {meeting_description})' if meeting_description else ''} and reformat them into a coherent, numbered list. Ensure that the numbering is consistent, starts at number 1, and does not restart. Each key point should start on a new line."
)
response = openai.ChatCompletion.create(
model=model,
temperature=temperature,
messages=[
{
"role": "system",
"content": system_prompt
},
{
"role": "user",
"content": all_key_points
}
]
)
final_key_points = response['choices'][0]['message']['content']
return final_key_points
def key_points_extraction(transcription,
meeting_description=None
):
try:
# Try the original method first
return full_key_points_extraction(transcription, meeting_description)
except openai.error.InvalidRequestError as e:
# If the original method fails due to exceeding the maximum token limit, fall back to the chunking method
if 'token' in str(e):
print("Using chunking for key points extraction.")
# If the server returns a 502, wait 10 seconds then retry
try:
return chunked_key_points_extraction(transcription, meeting_description)
except openai.error.APIError as e:
if e.http_status == 502:
print("API returned a 502 Bad Gateway error. Retrying in 10 seconds...")
time.sleep(10)
return chunked_key_points_extraction(transcription, meeting_description)
else:
# If the error is due to another reason, raise it
raise e
else:
# If the error is due to another reason, raise it
raise e
# ---------- Action Items --------------------
def full_action_item_extraction(transcription,
meeting_description=None
):
system_prompt = ("You are an AI expert in analyzing conversations and extracting action items. " +
(f"This is a meeting about {meeting_description}. " if meeting_description else "") +
f"Please review the text and identify any tasks, assignments, or actions that were agreed upon or mentioned as needing to be done. These could be tasks assigned to specific individuals, or general actions that the group has decided to take. Please list these action items clearly and concisely.")
response = openai.ChatCompletion.create(
model=model,
temperature=temperature,
messages=[
{
"role": "system",
"content": system_prompt
},
{
"role": "user",
"content": transcription
}
]
)
return response['choices'][0]['message']['content']
def chunked_action_item_extraction(transcription,
meeting_description=None
):
max_tokens = 8000 # A bit less than 8192 to leave some room for the system message
overlap = 1000 # Overlap size - tune this based on your use case
transcript_parts = [transcription[i:i + max_tokens + overlap] for i in range(0, len(transcription), max_tokens)]
final_action_items = ""
previous_action_items = "" # Initialize previous action items
for part in transcript_parts:
# Extract action items from the chunk
system_prompt = ("You are an AI expert in analyzing conversations and extracting action items. " +
(f"This is a meeting about {meeting_description}. " if meeting_description else "") +
f"Previously, you identified: '{previous_action_items}'. Now, please review the text and identify any tasks, assignments, or actions that were agreed upon or mentioned as needing to be done, building upon your previous list. These could be tasks assigned to specific individuals, or general actions that the group has decided to take. Please list these action items clearly and concisely.")
response = openai.ChatCompletion.create(
model=model,
temperature=temperature,
messages=[
{
"role": "system",
"content": system_prompt
},
{
"role": "user",
"content": part
}
]
)
previous_action_items = response['choices'][0]['message']['content'] # Update previous action items
final_action_items += previous_action_items + "\n"
# Use GPT-4 to consolidate the action items into a single, coherent list
response = openai.ChatCompletion.create(
model=model,
temperature=temperature,
messages=[
{
"role": "system",
"content": "As an AI with expertise in synthesizing information, your task is to consolidate the following action items into a single, concise, and coherent list. Ensure the list is organized in a clear and concise manner. Do not overwhelm the reader with too many action items."
},
{
"role": "user",
"content": final_action_items
}
]
)
final_action_items = response['choices'][0]['message']['content']
return final_action_items
def action_item_extraction(transcription,
meeting_description=None
):
try:
# Try the original method first
return full_action_item_extraction(transcription, meeting_description)
except openai.error.InvalidRequestError as e:
# If the original method fails due to exceeding the maximum token limit, fall back to the chunking method
if 'token' in str(e):
print("Using chunking for action item extraction.")
# If the server returns a 502, wait 10 seconds then retry
try:
return chunked_action_item_extraction(transcription, meeting_description)
except openai.error.APIError as e:
if e.http_status == 502:
print("API returned a 502 Bad Gateway error. Retrying in 10 seconds...")
time.sleep(10)
return chunked_action_item_extraction(transcription, meeting_description)
else:
# If the error is due to another reason, raise it
raise e
else:
# If the error is due to another reason, raise it
raise e
# ---------- Sentiment Analysis --------------------
def full_sentiment_analysis(transcription,
meeting_description=None
):
system_prompt = ("As an AI with expertise in language and emotion analysis, your task is to analyze the sentiment of the following text. " +
(f"This is a meeting about {meeting_description}. " if meeting_description else "") +
f"Please consider the overall tone of the discussion, the emotion conveyed by the language used, and the context in which words and phrases are used. Indicate whether the sentiment is generally positive, negative, or neutral, and provide brief explanations for your analysis where possible.")
response = openai.ChatCompletion.create(
model=model,
temperature=temperature,
messages=[
{
"role": "system",
"content": system_prompt
},
{
"role": "user",
"content": transcription
}
]
)
return response['choices'][0]['message']['content']
def chunked_sentiment_analysis(transcription,
meeting_description=None
):
max_tokens = 8000 # A bit less than 8192 to leave some room for the system message
overlap = 1000 # Overlap size - tune this based on your use case
transcript_parts = [transcription[i:i + max_tokens + overlap] for i in range(0, len(transcription), max_tokens)]
final_sentiment = ""
previous_sentiment = "" # Initialize previous sentiment
for part in transcript_parts:
# Analyze the sentiment of the chunk
system_prompt = (
"As an AI with expertise in language and emotion analysis, your task is to analyze the sentiment of the following text. " +
(f"This is a meeting about {meeting_description}. " if meeting_description else "") +
f"Previously, you analyzed: '{previous_sentiment}'. Now, please consider the overall tone of the discussion, the emotion conveyed by the language used, and the context in which words and phrases are used. Indicate whether the sentiment is generally positive, negative, or neutral, and provide brief explanations for your analysis where possible.")
response = openai.ChatCompletion.create(
model=model,
temperature=temperature,
messages=[
{
"role": "system",
"content": system_prompt
},
{
"role": "user",
"content": part
}
]
)
previous_sentiment = response['choices'][0]['message']['content'] # Update previous sentiment
final_sentiment += previous_sentiment + "\n"
# Use GPT-4 to rephrase the final sentiment analysis into a more cohesive paragraph
response = openai.ChatCompletion.create(
model=model,
temperature=temperature,
messages=[
{
"role": "system",
"content": "As an AI with expertise in language and emotion analysis, your task is to rephrase the following sentiment analysis into a more cohesive and concise paragraph. Please maintain the overall sentiment and key details in your rephrasing."
},
{
"role": "user",
"content": final_sentiment
}
]
)
final_sentiment = response['choices'][0]['message']['content']
return final_sentiment
def sentiment_analysis(transcription,
meeting_description=None
):
try:
# Try the original method first
return full_sentiment_analysis(transcription, meeting_description)
except openai.error.InvalidRequestError as e:
# If the original method fails due to exceeding the maximum token limit, fall back to the chunking method
if 'token' in str(e):
print("Using chunking for sentiment analysis.")
# If the server returns a 502, wait 10 seconds then retry
try:
return chunked_sentiment_analysis(transcription, meeting_description)
except openai.error.APIError as e:
if e.http_status == 502:
print("API returned a 502 Bad Gateway error. Retrying in 10 seconds...")
time.sleep(10)
return chunked_sentiment_analysis(transcription, meeting_description)
else:
# If the error is due to another reason, raise it
raise e
else:
# If the error is due to another reason, raise it
raise e
# ---------- Main Functions --------------------
def save_as_docx(minutes,
filename
):
doc = Document()
for key, value in minutes.items():
# Replace underscores with spaces and capitalize each word for the heading
heading = ' '.join(word.capitalize() for word in key.split('_'))
doc.add_heading(heading, level=1)
doc.add_paragraph(value)
# Add a line break between sections
doc.add_paragraph()
doc.save(filename)
def meeting_minutes(transcription,
meeting_description=None
):
abstract_summary = abstract_summary_extraction(transcription, meeting_description)
key_points = key_points_extraction(transcription, meeting_description)
action_items = action_item_extraction(transcription, meeting_description)
sentiment = sentiment_analysis(transcription, meeting_description)
return {
'abstract_summary': abstract_summary,
'key_points': key_points,
'action_items': action_items,
'sentiment': sentiment
}
def cli():
parser = argparse.ArgumentParser(prog="meeting-minutes.py", description="Generate meeting minutes from audio file")
parser.add_argument("input_dir",
help="directory containing audio files. Dir must be under 'audio' directory")
parser.add_argument("-e", "--extension",
help="audio file extension",
default=default_extension)
parser.add_argument("-r", "--review",
help="review the transcription before generating meeting minutes",
action="store_true")
args = parser.parse_args()
return {
'input_dir': args.input_dir,
'extension': args.extension,
'review': args.review,
}
if __name__ == '__main__':
args = cli()
# audio file extension
if args['extension'].strip() == "":
args['extension'] = default_extension
extension = "*." + args['extension'].strip().replace('.', '')
print(f"extension: '{extension}'")
# input directory containing audio files
input_dir = args['input_dir']
if not os.path.exists(input_dir):
print(f"input_dir does not exist: '{input_dir}'")
exit(1)
print(f"input dir: '{input_dir}'")
print()
audio_files = glob(os.path.join(input_dir, extension))
print(f"found {len(audio_files)} audio files: {audio_files}")
print()
# Ask the user for an optional meeting description
meeting_description = input('Complete the sentence: "This is a meeting about..." (or press Enter to skip): ')
print()
# If the user didn't provide a description, set meeting_description to None
if meeting_description.strip() == "":
meeting_description = None
print("Transcribing audio files...")
full_transcription = ""
last_dir = os.path.basename(input_dir)
for audio_file in audio_files:
full_transcription += transcribe_audio(audio_file)
transcription_file = f"{input_dir}/{last_dir}_transcription.txt"
with open(transcription_file, 'w') as f:
f.write(full_transcription)
print(f"transcription files written to: '{transcription_file}'")
print()
if args['review']:
print("Review and edit transcription as needed.")
x = input("Press Enter to continue")
with open(transcription_file, 'r') as f:
full_transcription = f.read()
print()
print("Generating meeting minutes...")
summary_text = meeting_minutes(full_transcription, meeting_description)
summary_file = f"{input_dir}/{last_dir}_summary.docx"
save_as_docx(summary_text, summary_file)
print(f"Meeting summary written to: '{summary_file}'")
| [
"As an AI trained in language comprehension and summarization, your task is to rephrase the following summaries into a more cohesive and concise paragraph. Please maintain the overall meaning and key details in your rephrasing.",
"As an AI with expertise in language and emotion analysis, your task is to rephrase the following sentiment analysis into a more cohesive and concise paragraph. Please maintain the overall sentiment and key details in your rephrasing.",
"As an AI with expertise in synthesizing information, your task is to consolidate the following action items into a single, concise, and coherent list. Ensure the list is organized in a clear and concise manner. Do not overwhelm the reader with too many action items.",
"You are a proficient AI with a specialty in distilling information into key points. This is a meeting about PLACEHOLDER. Previously, you identified: 'PLACEHOLDER'. Now, based on the following text, identify and list the main points that were discussed or brought up. These should be the most important ideas, findings, or topics that are crucial to the essence of the discussion. Your goal is to provide a list that someone could read to quickly understand what was talked about.",
"As an AI with expertise in language and emotion analysis, your task is to analyze the sentiment of the following text. This is a meeting about PLACEHOLDER. Please consider the overall tone of the discussion, the emotion conveyed by the language used, and the context in which words and phrases are used. Indicate whether the sentiment is generally positive, negative, or neutral, and provide brief explanations for your analysis where possible.",
"You are a proficient AI with a specialty in organizing and formatting information. Please take the following key points (from a meeting about PLACEHOLDER) and reformat them into a coherent, numbered list. Ensure that the numbering is consistent, starts at number 1, and does not restart. Each key point should start on a new line.",
"You are an AI expert in analyzing conversations and extracting action items. This is a meeting about PLACEHOLDER. Previously, you identified: 'PLACEHOLDER'. Now, please review the text and identify any tasks, assignments, or actions that were agreed upon or mentioned as needing to be done, building upon your previous list. These could be tasks assigned to specific individuals, or general actions that the group has decided to take. Please list these action items clearly and concisely.",
"You are a highly skilled AI trained in language comprehension and summarization. This is a meeting about PLACEHOLDER. I would like you to read the following text and summarize it into a concise abstract paragraph. Aim to retain the most important points, providing a coherent and readable summary that could help a person understand the main points of the discussion without needing to read the entire text. Please avoid unnecessary details or tangential points.",
"As an AI with expertise in language and emotion analysis, your task is to analyze the sentiment of the following text. This is a meeting about PLACEHOLDER. Previously, you analyzed: 'PLACEHOLDER'. Now, please consider the overall tone of the discussion, the emotion conveyed by the language used, and the context in which words and phrases are used. Indicate whether the sentiment is generally positive, negative, or neutral, and provide brief explanations for your analysis where possible.",
"You are an AI expert in analyzing conversations and extracting action items. This is a meeting about PLACEHOLDER. Please review the text and identify any tasks, assignments, or actions that were agreed upon or mentioned as needing to be done. These could be tasks assigned to specific individuals, or general actions that the group has decided to take. Please list these action items clearly and concisely.",
"You are a highly skilled AI trained in language comprehension and summarization. This is a meeting about PLACEHOLDER. Previously, you summarized: 'PLACEHOLDER'. Now, I would like you to read the following text and summarize it into a concise abstract paragraph, building upon your previous summary. Aim to retain the most important points, providing a coherent and readable summary that could help a person understand the main points of the discussion without needing to read the entire text. Please avoid unnecessary details or tangential points.",
"You are a proficient AI with a specialty in distilling information into key points. This is a meeting about PLACEHOLDER. Based on the following text, identify and list the main points that were discussed or brought up. These should be the most important ideas, findings, or topics that are crucial to the essence of the discussion. Your goal is to provide a list that someone could read to quickly understand what was talked about."
] |
2024-01-10 | AlexisTM/gpt3-discord-bot | ask_openai.py | import os
import openai
openai.api_key = os.environ.get("OPENAI_KEY")
MODEL="davinci-instruct-beta"
def ask_prompt(prompt, model=MODEL, num_results=1, max_tokens=25, stopSequences=["You:", "Kirby:"],
temperature=0.8, topP=1.0, topKReturn=2):
response = openai.Completion.create(
engine=model,
prompt=prompt,
temperature=temperature,
max_tokens=max_tokens,
top_p=topP,
frequency_penalty=0.3,
presence_penalty=0.3,
stop=stopSequences
)
if response != 0:
for choice in response.choices:
return choice.text
return "[idk]"
| [] |
2024-01-10 | danja/llama_index | docs~examples~graph_stores~graph-rag-sparql-mini.py | """
Runs Graph RAG with a SPARQL server as storage
Preparation :
* pip install openai
* pip install sparqlwrapper
* make a SPARQL endpoint available, add URL below (make sure it supports UPDATE, as /llama_index_sparql-test/)
* for a clean start DROP GRAPH <http://purl.org/stuff/guardians>
* add OpenAI API key below
@danja 2023-09-17
"""
# import llama_index
from llama_index.readers.download import download_loader
# from llama_index import download_loader
import os
import logging
from llama_index import (
KnowledgeGraphIndex,
ServiceContext,
)
from llama_index.storage.storage_context import StorageContext
from llama_index.graph_stores import SparqlGraphStore
from llama_index.llms import OpenAI
from IPython.display import Markdown, display
from llama_index import load_index_from_storage
import os
import openai
logging.basicConfig(filename='loggy.log', filemode='w', level=logging.DEBUG)
logger = logging.getLogger(__name__)
############
# LLM Config
############
os.environ["OPENAI_API_KEY"] = ""
openai.api_key = ""
llm = OpenAI(temperature=0, model="text-davinci-002")
service_context = ServiceContext.from_defaults(llm=llm, chunk_size=512)
###############
# SPARQL Config
###############
ENDPOINT = 'https://fuseki.hyperdata.it/llama_index_sparql-test/'
GRAPH = 'http://purl.org/stuff/guardians'
BASE_URI = 'http://purl.org/stuff/data'
graph_store = SparqlGraphStore(
sparql_endpoint=ENDPOINT,
sparql_graph=GRAPH,
sparql_base_uri=BASE_URI,
)
storage_context = StorageContext.from_defaults(graph_store=graph_store)
WikipediaReader = download_loader("WikipediaReader")
loader = WikipediaReader()
documents = loader.load_data(
pages=['Guardians of the Galaxy Vol. 3'], auto_suggest=False)
kg_index = KnowledgeGraphIndex.from_documents(
documents,
storage_context=storage_context,
service_context=service_context,
max_triplets_per_chunk=10,
sparql_endpoint=ENDPOINT,
sparql_graph=GRAPH,
sparql_base_uri=BASE_URI,
include_embeddings=True,
)
# print('*** Persist to/Load from local disk ***')
"""
storage_context = StorageContext.from_defaults(
persist_dir='./storage_graph', graph_store=graph_store)
kg_index = load_index_from_storage(
storage_context=storage_context,
service_context=service_context,
include_embeddings=True,
sparql_endpoint=ENDPOINT, # shouldn't be needed
sparql_graph=GRAPH,
sparql_base_uri=BASE_URI,
)
"""
# FileNotFoundError: [Errno 2] No such file or directory: '/home/danny/AI/nlp/GraphRAG/src/storage_graph/docstore.json'
# copied files I found in a storage_vector/docstore.json into /home/danny/AI/nlp/GraphRAG/src/storage_graph/
# print('*** Prepare Graph RAG query engine***')
kg_rag_query_engine = kg_index.as_query_engine(
include_text=False,
retriever_mode="keyword",
# RecursionError: maximum recursion depth exceeded in comparison
response_mode="tree_summarize",
)
# print('*** Do query ***')
# response_graph_rag = kg_rag_query_engine.query(
# "What do cats eat?")
# print(str(response_graph_rag))
response_graph_rag = kg_rag_query_engine.query(
"Who is Quill?")
print(str(response_graph_rag))
# display(Markdown(f"<b>{response_graph_rag}</b>"))
| [] |
2024-01-10 | danja/llama_index | llama_index~evaluation~dataset_generation.py | """Dataset generation from documents"""
from __future__ import annotations
import re
from typing import List, Optional
from llama_index import Document, ServiceContext, SummaryIndex
from llama_index.indices.postprocessor.node import KeywordNodePostprocessor
from llama_index.llms.openai import OpenAI
from llama_index.prompts.base import BasePromptTemplate, PromptTemplate
from llama_index.schema import BaseNode, MetadataMode, NodeWithScore
DEFAULT_QUESTION_GENERATION_PROMPT = """\
Context information is below.
---------------------
{context_str}
---------------------
Given the context information and not prior knowledge.
generate only questions based on the below query.
{query_str}
"""
def _get_default_service_context() -> ServiceContext:
"""Get default service context."""
llm = OpenAI(temperature=0, model="gpt-3.5-turbo")
service_context = ServiceContext.from_defaults(llm=llm, chunk_size_limit=3000)
return service_context
class DatasetGenerator:
"""Generate dataset (question/ question-answer pairs) \
based on the given documents.
NOTE: this is a beta feature, subject to change!
Args:
nodes (List[Node]): List of nodes. (Optional)
service_context (ServiceContext): Service Context.
num_questions_per_chunk: number of question to be \
generated per chunk. Each document is chunked of size 512 words.
text_question_template: Question generation template.
question_gen_query: Question generation query.
"""
def __init__(
self,
nodes: List[BaseNode],
service_context: Optional[ServiceContext] = None,
num_questions_per_chunk: int = 10,
text_question_template: Optional[BasePromptTemplate] = None,
question_gen_query: Optional[str] = None,
metadata_mode: MetadataMode = MetadataMode.NONE,
) -> None:
"""Init params."""
if service_context is None:
service_context = _get_default_service_context()
self.service_context = service_context
self.text_question_template = text_question_template or PromptTemplate(
DEFAULT_QUESTION_GENERATION_PROMPT
)
self.question_gen_query = (
question_gen_query
or f"You are a Teacher/ Professor. Your task is to setup \
{num_questions_per_chunk} questions for an upcoming \
quiz/examination. The questions should be diverse in nature \
across the document. Restrict the questions to the \
context information provided."
)
self.nodes = nodes
self._metadata_mode = metadata_mode
@classmethod
def from_documents(
cls,
documents: List[Document],
service_context: Optional[ServiceContext] = None,
num_questions_per_chunk: int = 10,
text_question_template: Optional[BasePromptTemplate] = None,
question_gen_query: Optional[str] = None,
required_keywords: Optional[List[str]] = None,
exclude_keywords: Optional[List[str]] = None,
) -> "DatasetGenerator":
"""Generate dataset from documents."""
if service_context is None:
service_context = _get_default_service_context()
nodes = service_context.node_parser.get_nodes_from_documents(documents)
# use node postprocessor to filter nodes
required_keywords = required_keywords or []
exclude_keywords = exclude_keywords or []
node_postprocessor = KeywordNodePostprocessor(
service_context=service_context,
required_keywords=required_keywords,
exclude_keywords=exclude_keywords,
)
node_with_scores = [NodeWithScore(node=node) for node in nodes]
node_with_scores = node_postprocessor.postprocess_nodes(node_with_scores)
nodes = [node_with_score.node for node_with_score in node_with_scores]
return cls(
nodes=nodes,
service_context=service_context,
num_questions_per_chunk=num_questions_per_chunk,
text_question_template=text_question_template,
question_gen_query=question_gen_query,
)
def _node_question_generator(
self, nodes: List[BaseNode], num: Optional[int] = None
) -> List[str]:
"""Node question generator."""
questions: List[str] = []
for node in nodes:
if num is not None and len(questions) >= num:
break
index = SummaryIndex.from_documents(
[
Document(
text=node.get_content(metadata_mode=self._metadata_mode),
metadata=node.metadata,
)
]
)
query_engine = index.as_query_engine(
service_context=self.service_context,
text_qa_template=self.text_question_template,
use_async=True,
)
response = query_engine.query(
self.question_gen_query,
)
result = str(response).strip().split("\n")
cleaned_questions = [
re.sub(r"^\d+[\).\s]", "", question).strip() for question in result
]
questions.extend(cleaned_questions)
questions = [question for question in questions if question != ""]
if num is not None:
questions = questions[:num]
return questions
def generate_questions_from_nodes(self, num: Optional[int] = None) -> List[str]:
"""Generates questions for each document."""
return self._node_question_generator(self.nodes, num)
| [
"Context information is below.\n---------------------\n{context_str}\n---------------------\nGiven the context information and not prior knowledge.\ngenerate only questions based on the below query.\n{query_str}\n"
] |
2024-01-10 | teo-ma/openaiperfdata | azure-openai-gpt4turbo.py | import csv
import openai
import time
import datetime
openai.api_type = "azure"
# Read API info from csv file
with open('api_info.csv', 'r') as file:
reader = csv.DictReader(file)
for row in reader:
region = row['region']
endpoint = row['endpoint']
api_key = row['api_key']
engine = row['engine']
prompt = row['prompt']
openai.api_base = endpoint
openai.api_version = "2023-07-01-preview"
openai.api_key = api_key
message_text = [{"role":"system","content":"You are an AI assistant that helps people find information."},{"role":"user","content": prompt}]
start_time = datetime.datetime.now()
#打印信息部署测试时查看使用，正式测试开始的时候可以注释掉下面的代码
print("Region:", region)
print("Start Time:", start_time)
completion = openai.ChatCompletion.create(
engine=engine,
messages = message_text,
temperature=0.7,
max_tokens=800,
top_p=0.95,
frequency_penalty=0,
presence_penalty=0,
stop=None
)
end_time = datetime.datetime.now()
execution_time = end_time - start_time
result = completion.choices[0].message['content']
result_details = completion
#打印信息部署测试时查看使用，正式测试开始的时候可以注释掉下面的代码
print("API调用执行时间:", execution_time)
# Write item to CSV file
timestamp = str(int(time.time()))
item = {
"id": timestamp,
"region": region,
"prompt": prompt,
"start_time": start_time.strftime("%Y-%m-%d %H:00"),
"end_time": end_time.isoformat(),
"execution_time": execution_time.total_seconds(),
"result": str(result), # 将结果转换为字符串
"result_details": str(result_details) # 将结果详细信息转换为字符串
}
csv_file = 'openaiperfdata.csv'
fieldnames = item.keys()
with open(csv_file, 'a', newline='') as file:
writer = csv.DictWriter(file, fieldnames=fieldnames)
writer.writerow(item)
#打印信息部署测试时查看使用，正式测试开始的时候可以注释掉下面的代码
print(result)
| [
"You are an AI assistant that helps people find information."
] |
2024-01-10 | vladris/llm-book | code~09~06.py | from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
template = ChatPromptTemplate.from_messages([
('system', 'You are an English to French translator.'),
('user', 'Translate this to French: {text}')
])
llm = ChatOpenAI()
response = llm(template.format_messages(
text='Aren\'t large language models amazing?'))
print(response)
| [
"You are an English to French translator.",
"Translate this to French: {text}",
"[('system', 'You are an English to French translator.'), ('user', 'Translate this to French: {text}')]"
] |
2024-01-10 | vladris/llm-book | code~09~22.py | import asyncio
import os
import semantic_kernel as sk
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion
from semantic_kernel.orchestration.sk_context import SKContext
from semantic_kernel.planning.sequential_planner.sequential_planner import SequentialPlanner
from semantic_kernel.skill_definition import sk_function, sk_function_context_parameter
kernel = sk.Kernel()
kernel.add_chat_service('chat_completion', OpenAIChatCompletion(
'gpt-3.5-turbo', os.environ['OPENAI_API_KEY']))
class CalendarPlugin:
@sk_function(
description='Gets the email addresses of a user given their name',
name='get_email'
)
@sk_function_context_parameter(
name='name',
description='A name for which to return the email address'
)
def get_email(self, context: SKContext) -> str:
print(f'* Getting email for {context["name"]}')
address_book = {
'John Doe': '[email protected]',
'Jane Doe': '[email protected]',
}
return address_book[context['name']]
@sk_function(
description='Sends a meeting invitation with the given subject to the given recipient emails at the given time',
name='schedule_meeting'
)
@sk_function_context_parameter(
name='input',
description='Recipient email for the meeting invitation'
)
@sk_function_context_parameter(
name='subject',
description='Meeting subject'
)
@sk_function_context_parameter(
name='location',
description='Meeting location'
)
@sk_function_context_parameter(
name='time',
description="Meeting time"
)
def schedule_meeting(self, context: SKContext) -> str:
print(f"* Meeting '{context['subject']}' at '{context['location']}' scheduled for {context['time']} with {context['input']}")
return 'Success'
calendar_plugin = kernel.import_skill(CalendarPlugin(), 'calendar')
ask = 'Schedule lunch with Jane Doe for Monday at noon at Tipsy Cow'
planner = SequentialPlanner(kernel)
plan = asyncio.run(planner.create_plan_async(goal=ask))
for index, step in enumerate(plan._steps):
print("Function: " + step.skill_name + "." + step._function.name)
print("Input vars: " + str(step.parameters.variables))
print("Output vars: " + str(step._outputs))
result = asyncio.run(plan.invoke_async())
print(result.result)
| [] |
2024-01-10 | vladris/llm-book | code~09~07.py | from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.output_parsers import PydanticOutputParser
from pydantic import BaseModel, Field
class Fact(BaseModel):
fact: str = Field(description='A fact about a subject.')
reference: str = Field(description='A reference for the fact.')
parser = PydanticOutputParser(pydantic_object=Fact)
template = ChatPromptTemplate.from_messages([
('system', 'Your responses follow the format: {format}'),
('user', 'Tell me a fact about {subject}')
])
llm = ChatOpenAI()
response = llm(template.format_messages(
format=parser.get_format_instructions(),
subject='data science'))
print(parser.parse(response.content))
| [
"Your responses follow the format: {format}",
"[('system', 'Your responses follow the format: {format}'), ('user', 'Tell me a fact about {subject}')]",
"Tell me a fact about {subject}"
] |
2024-01-10 | vladris/llm-book | code~09~03.py | from langchain.llms import OpenAI
llm = OpenAI()
response = llm.generate(['Say "Hello world" in Python.'])
print(response)
| [] |
2024-01-10 | vladris/llm-book | code~01~05.py | import openai
import os
openai.api_key = os.getenv('OPENAI_API_KEY')
response = openai.Completion.create(
model='gpt-3.5-turbo-instruct',
prompt='Say "Hello world" in Python')
print(response.choices[0].text) | [
"Say \"Hello world\" in Python"
] |
2024-01-10 | vladris/llm-book | code~02~20.py | import openai
import os
openai.api_key = os.getenv('OPENAI_API_KEY')
prompt = 'Write a paragraph about the benefits of meditation.'
suffix = 'Meditation has been shown to reduce stress and anxiety, improve focus and attention, and promote overall well-being.'
response = openai.Completion.create(
model='text-davinci-003',
prompt=prompt,
suffix=suffix,
max_tokens=100)
print(response.choices[0].text)
| [
"Write a paragraph about the benefits of meditation."
] |
2024-01-10 | vladris/llm-book | code~02~14.py | import openai
import os
openai.api_key = os.getenv('OPENAI_API_KEY')
response = openai.ChatCompletion.create(
model='gpt-3.5-turbo',
messages=[
{'role': 'user', 'content': 'Say "Hello world" in Python'}
])
print(response)
| [
"Say \"Hello world\" in Python"
] |
2024-01-10 | vladris/llm-book | code~09~08.py | from langchain.chains import LLMChain, TransformChain, SequentialChain
from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.output_parsers import PydanticOutputParser
from pydantic import BaseModel, Field
class Fact(BaseModel):
fact: str = Field(description='A fact about a subject.')
reference: str = Field(description='A reference for the fact.')
parser = PydanticOutputParser(pydantic_object=Fact)
template = ChatPromptTemplate.from_messages([
('system', 'Your responses follow the format: {format}'),
('user', 'Tell me a fact about {subject}')
])
prompt = template.partial(format=parser.get_format_instructions())
llm = ChatOpenAI()
llm_chain = LLMChain(
llm=llm,
prompt=prompt,
output_key='json')
transformer_chain = TransformChain(
input_variables=['json'],
output_variables=['fact'],
transform=lambda inp: {'fact': parser.parse(inp['json'])})
chain = SequentialChain(
input_variables=['subject'],
chains=[llm_chain, transformer_chain])
print(chain.run(subject='data scientists'))
| [
"Your responses follow the format: {format}",
"[('system', 'Your responses follow the format: {format}'), ('user', 'Tell me a fact about {subject}')]",
"Tell me a fact about {subject}"
] |
2024-01-10 | vladris/llm-book | code~09~18.py | import asyncio
import os
import semantic_kernel as sk
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion
kernel = sk.Kernel()
kernel.add_chat_service('chat_completion', OpenAIChatCompletion(
'gpt-3.5-turbo', os.environ['OPENAI_API_KEY']))
translate_plugin = kernel.import_semantic_skill_from_directory('.', 'translate')
text = 'Aren\'t large language models amazing?'
print(asyncio.run(
kernel.run_async(translate_plugin['to_french'], input_str=text)))
| [] |
2024-01-10 | vladris/llm-book | code~02~21.py | import openai
import os
openai.api_key = os.getenv('OPENAI_API_KEY')
prompt = 'Write a story about a young girl who discovers a magical world.'
stop = ['The end', 'To be continued', 'And they lived happily ever after.']
response = openai.ChatCompletion.create(
model='gpt-3.5-turbo',
messages=[{'role': 'user', 'content': prompt}],
stop=stop,
max_tokens=500)
print(response.choices[0].message.content)
| [
"Write a story about a young girl who discovers a magical world."
] |
2024-01-10 | vladris/llm-book | code~04~24.py | import openai
import os
openai.api_key = os.environ['OPENAI_API_KEY']
template = '''You are a Q&A bot. You provide short answers to questions.
For example:
Question: Who was missing from this season? Anakin Skywalker.
Provide the answer to the following question:
Question: '''
while True:
prompt = input('user: ')
if prompt == 'exit':
break
response = openai.Completion.create(
model='davinci:ft-personal-2023-06-19-17-42-56',
temperature=0,
stop=['\n'],
prompt=template + prompt)
print(response.choices[0].text)
| [
"You are a Q&A bot. You provide short answers to questions.\nFor example:\nQuestion: Who was missing from this season? Anakin Skywalker.\nProvide the answer to the following question:\nQuestion: PLACEHOLDER",
"You are a Q&A bot. You provide short answers to questions.\nFor example:\nQuestion: Who was missing from this season? Anakin Skywalker.\nProvide the answer to the following question:\nQuestion: ",
"user: "
] |
2024-01-10 | vladris/llm-book | code~09~09.py | from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.output_parsers import PydanticOutputParser
from pydantic import BaseModel, Field
class Fact(BaseModel):
fact: str = Field(description='A fact about a subject.')
reference: str = Field(description='A reference for the fact.')
parser = PydanticOutputParser(pydantic_object=Fact)
template = ChatPromptTemplate.from_messages([
('system', 'Your responses follow the format: {format}'),
('user', 'Tell me a fact about {subject}')
])
prompt = template.partial(format=parser.get_format_instructions())
llm = ChatOpenAI()
chain = prompt | llm | parser
print(chain.invoke({'subject': 'data scientists'}))
| [
"Your responses follow the format: {format}",
"[('system', 'Your responses follow the format: {format}'), ('user', 'Tell me a fact about {subject}')]",
"Tell me a fact about {subject}"
] |
2024-01-10 | vladris/llm-book | code~02~12.py | import openai
import os
openai.api_key = os.getenv('OPENAI_API_KEY')
response = openai.Completion.create(
model='gpt-3.5-turbo-instruct',
prompt='Say "Hello world" in Python')
print(response)
| [
"Say \"Hello world\" in Python"
] |
2024-01-10 | vladris/llm-book | code~05~09.py | import openai
def get_embedding(text):
return openai.Embedding.create(
input=[text.replace('\n', ' ')],
model='text-embedding-ada-002')['data'][0]['embedding']
| [] |
2024-01-10 | vladris/llm-book | code~02~22.py | import openai
import os
openai.api_key = os.getenv('OPENAI_API_KEY')
prompt = 'Write a story about a young girl who discovers a magical world.'
stop = ['The end', 'To be continued', 'And they lived happily ever after.']
response = openai.ChatCompletion.create(
model='gpt-3.5-turbo',
messages=[{'role': 'user', 'content': prompt}],
stop=stop,
stream=True,
max_tokens=500)
for chunk in response:
if hasattr(chunk.choices[0].delta, 'content'):
print(chunk.choices[0].delta.content, end='', flush=True)
| [
"Write a story about a young girl who discovers a magical world."
] |
2024-01-10 | vladris/llm-book | code~02~16.py | import openai
import os
openai.api_key = os.getenv('OPENAI_API_KEY')
prompt = 'What are the main causes of climate change?'
response = openai.ChatCompletion.create(
model='gpt-3.5-turbo',
max_tokens=50,
messages=[{'role': 'user', 'content': prompt}])
print(response.choices[0].message.content)
| [
"What are the main causes of climate change?"
] |
2024-01-10 | vladris/llm-book | code~llm_utils~llm_utils.py | import copy
import json
import openai
import os
import re
import tiktoken
openai.api_key = os.environ['OPENAI_API_KEY']
if openai.api_key is None:
raise Exception('OPENAI_API_KEY not set')
def insert_params(string, **kwargs):
pattern = r"{{(.*?)}}"
matches = re.findall(pattern, string)
for match in matches:
replacement = kwargs.get(match.strip())
if replacement is not None:
string = string.replace("{{" + match + "}}", replacement)
return string
class Template:
def __init__(self, template):
self.template = template
def from_file(template_file):
with open(template_file, 'r') as f:
template = json.load(f)
return Template(template)
def completion(self, parameters):
instance = copy.deepcopy(self.template)
instance['prompt'] = insert_params(instance['prompt'], **parameters)
return openai.Completion.create(
model='gpt-3.5-turbo-instruct',
**instance)
class ChatTemplate:
def __init__(self, template):
self.template = template
def from_file(template_file):
with open(template_file, 'r') as f:
template = json.load(f)
return ChatTemplate(template)
def completion(self, parameters):
instance = copy.deepcopy(self.template)
for item in instance['messages']:
item['content'] = insert_params(item['content'], **parameters)
return openai.ChatCompletion.create(
model='gpt-3.5-turbo',
**instance)
def count_tokens(messages):
# Note this encoding might change in future versions of gpt-3.5-turbo
encoding = tiktoken.get_encoding('cl100k_base')
# Every message follows <|start|>{role/name}\n{content}<|end|>\n
tokens_per_message = 4
# If there's a name, the role is omitted
tokens_per_name = -1
num_tokens = 0
for message in messages:
num_tokens += tokens_per_message
for key, value in message.items():
num_tokens += len(encoding.encode(value))
if key == "name":
num_tokens += tokens_per_name
# Every reply is primed with <|start|>assistant<|message|>
num_tokens += 3
return num_tokens
def get_embedding(text):
return openai.Embedding.create(
input=[text.replace('\n', ' ')],
model='text-embedding-ada-002')['data'][0]['embedding']
def cosine_distance(a, b):
return 1 - sum([a_i * b_i for a_i, b_i in zip(a, b)]) / (sum([a_i ** 2 for a_i in a]) ** 0.5 * sum([b_i ** 2 for b_i in b]) ** 0.5)
| [] |
2024-01-10 | vladris/llm-book | code~02~17.py | import openai
import os
openai.api_key = os.getenv('OPENAI_API_KEY')
prompt = 'Generate a name for a new coffee brand.'
response = openai.ChatCompletion.create(
model='gpt-3.5-turbo',
n=3,
messages=[{'role': 'user', 'content': prompt}])
for choice in response.choices:
print(choice.message.content)
| [
"Generate a name for a new coffee brand."
] |
2024-01-10 | vladris/llm-book | code~08~05.py | import openai
import os
openai.api_key = os.getenv('OPENAI_API_KEY')
guide = '''
You are a large language model trained on vast amounts of data.
You respond to questions based on the data you were trained on.
When you do not have enough information to provide an accurate answer, you will say so.
'''
response = openai.Completion.create(
model='text-davinci-003',
max_tokens=500,
prompt=guide + 'Tell me about the habitat and behavior of the flying razor fish.')
print(response.choices[0].text) | [
"\nYou are a large language model trained on vast amounts of data.\nYou respond to questions based on the data you were trained on.\nWhen you do not have enough information to provide an accurate answer, you will say so.\nTell me about the habitat and behavior of the flying razor fish."
] |
2024-01-10 | vladris/llm-book | code~02~18.py | import openai
import os
openai.api_key = os.getenv('OPENAI_API_KEY')
prompt = 'Draft a nondisclosure agreement (NDA) between two parties'
response = openai.ChatCompletion.create(
model='gpt-3.5-turbo',
temperature=0.5,
max_tokens=100,
messages=[{'role': 'user', 'content': prompt}])
print(response.choices[0].message.content)
| [
"Draft a nondisclosure agreement (NDA) between two parties"
] |
2024-01-10 | vladris/llm-book | code~03~16.py | import copy
import json
import openai
import os
import re
openai.api_key = os.environ['OPENAI_API_KEY']
def insert_params(string, **kwargs):
pattern = r"{{(.*?)}}"
matches = re.findall(pattern, string)
for match in matches:
replacement = kwargs.get(match.strip())
if replacement is not None:
string = string.replace("{{" + match + "}}", replacement)
return string
class ChatTemplate:
def __init__(self, template):
self.template = template
def from_file(template_file):
with open(template_file, 'r') as f:
template = json.load(f)
return ChatTemplate(template)
def completion(self, parameters):
instance = copy.deepcopy(self.template)
for item in instance['messages']:
item['content'] = insert_params(item['content'], **parameters)
return openai.ChatCompletion.create(
model='gpt-3.5-turbo',
**instance)
| [] |
2024-01-10 | vladris/llm-book | code~02~08.py | import openai
import os
openai.api_key = os.getenv('OPENAI_API_KEY')
history = []
while True:
prompt = input('user: ')
if prompt == 'exit':
break
history.append({'role': 'user', 'content': prompt})
response = openai.ChatCompletion.create(
model='gpt-3.5-turbo',
messages=history)
message = response.choices[0].message
print(f'{message.role}: {message.content}')
history.append({'role': message.role, 'content': message.content})
| [
"user: "
] |
2024-01-10 | vladris/llm-book | code~09~14.py | import os
import semantic_kernel as sk
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion
kernel = sk.Kernel()
kernel.add_chat_service('chat_completion', OpenAIChatCompletion(
'gpt-3.5-turbo', os.environ['OPENAI_API_KEY']))
prompt = 'Say "Hello world" in Python.'
hello_world = kernel.create_semantic_function(prompt)
print(hello_world())
| [
"Say \"Hello world\" in Python."
] |
2024-01-10 | vladris/llm-book | code~09~10.py | from langchain.chat_models import ChatOpenAI
from langchain.document_loaders import TextLoader, DirectoryLoader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.prompts import ChatPromptTemplate
from langchain.schema.runnable import RunnablePassthrough
from langchain.vectorstores import Chroma
loader = DirectoryLoader('../racing', loader_cls=TextLoader)
docs = loader.load()
db = Chroma.from_documents(docs, OpenAIEmbeddings())
retriever = db.as_retriever(search_kwargs={'k': 1})
template = ChatPromptTemplate.from_messages([
('system', 'Your are a Q&A AI.'),
('system',
'Here are some facts that can help you answer the following question: {data}'),
('user', '{prompt}')
])
llm = ChatOpenAI()
chain = {'data': retriever, 'prompt': RunnablePassthrough()} | template | llm
while True:
prompt = input('user: ')
if prompt == 'exit':
break
print(chain.invoke(prompt).content)
| [
"Your are a Q&A AI.",
"user: ",
"Here are some facts that can help you answer the following question: {data}",
"[('system', 'Your are a Q&A AI.'), ('system', 'Here are some facts that can help you answer the following question: {data}'), ('user', '{prompt}')]"
] |
2024-01-10 | vladris/llm-book | code~03~13.py | import copy
import json
import openai
import os
import re
openai.api_key = os.environ['OPENAI_API_KEY']
def insert_params(string, **kwargs):
pattern = r"{{(.*?)}}"
matches = re.findall(pattern, string)
for match in matches:
replacement = kwargs.get(match.strip())
if replacement is not None:
string = string.replace("{{" + match + "}}", replacement)
return string
class Template:
def __init__(self, template):
self.template = template
def from_file(template_file):
with open(template_file, 'r') as f:
template = json.load(f)
return Template(template)
def completion(self, parameters):
instance = copy.deepcopy(self.template)
instance['prompt'] = format(instance['prompt'], **parameters)
return openai.Completion.create(
model='gpt-3.5-turbo-instruct',
**instance)
| [] |
2024-01-10 | vladris/llm-book | code~09~11.py | from langchain.agents import AgentExecutor, OpenAIFunctionsAgent, tool
from langchain.chat_models import ChatOpenAI
from langchain.schema import SystemMessage
@tool
def get_emails(names):
"""Get the email addresses of a set of users given their names"""
print(f'* Getting emails for {names}')
address_book = {
'John Doe': '[email protected]',
'Jane Doe': '[email protected]',
}
emails = {}
for name in names:
emails[name] = address_book[name]
return emails
@tool
def schedule_meeting(subject, recipients, time):
"""Sends a meeting invitation with the given subject to the given recipient emails at the given time"""
print(f"* Meeting '{subject}' scheduled for {time} with {recipients}")
return {'success': True}
tools = [get_emails, schedule_meeting]
system_message = SystemMessage(content="You are an AI personal assistant.")
prompt = OpenAIFunctionsAgent.create_prompt(system_message=system_message)
llm = ChatOpenAI()
agent = OpenAIFunctionsAgent(llm=llm, tools=tools, prompt=prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
agent_executor.run(
'Schedule lunch with Jane Doe for Monday at noon at Tipsy Cow')
| [
"You are an AI personal assistant."
] |
2024-01-10 | vladris/llm-book | code~09~05.py | from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage
llm = ChatOpenAI()
response = llm([HumanMessage(content='Say "Hello world" in Python.')])
print(response)
| [
"Say \"Hello world\" in Python."
] |
2024-01-10 | vladris/llm-book | code~02~19.py | import openai
import os
openai.api_key = os.getenv('OPENAI_API_KEY')
prompt = 'Write a short story about a man who discovers a mysterious book in an old library.'
response = openai.ChatCompletion.create(
model='gpt-3.5-turbo',
temperature=1.5,
max_tokens=100,
messages=[{'role': 'user', 'content': prompt}])
print(response.choices[0].message.content)
| [
"Write a short story about a man who discovers a mysterious book in an old library."
] |
2024-01-10 | vladris/llm-book | code~09~21.py | import asyncio
import os
import semantic_kernel as sk
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion, OpenAITextEmbedding
kernel = sk.Kernel()
kernel.add_chat_service('chat_completion', OpenAIChatCompletion(
'gpt-3.5-turbo', os.environ['OPENAI_API_KEY']))
qa_plugin = kernel.import_semantic_skill_from_directory('.', 'qa')
kernel.add_text_embedding_generation_service('ada', OpenAITextEmbedding(
'text-embedding-ada-002', os.environ['OPENAI_API_KEY']))
kernel.register_memory_store(memory_store=sk.memory.VolatileMemoryStore())
for f in os.listdir('../racing'):
path = os.path.join('../racing', f)
with open(path, 'r') as f:
text = f.read()
asyncio.run(kernel.memory.save_information_async(
collection='racing',
text=text,
id=path))
while True:
prompt = input('user: ')
if prompt == 'exit':
break
data = asyncio.run(kernel.memory.search_async('racing', prompt, limit=1))
context = kernel.create_new_context()
context["data"] = data[0].text
context["prompt"] = prompt
print(asyncio.run(kernel.run_async(qa_plugin['qa'], input_vars=context.variables)))
| [
"user: "
] |
2024-01-10 | vladris/llm-book | code~09~15.py | import asyncio
import os
import semantic_kernel as sk
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion
from semantic_kernel.semantic_functions.prompt_template_config import PromptTemplateConfig
kernel = sk.Kernel()
kernel.add_chat_service('chat_completion', OpenAIChatCompletion(
'gpt-3.5-turbo', os.environ['OPENAI_API_KEY']))
prompt_config = PromptTemplateConfig(
completion=PromptTemplateConfig.CompletionConfig(
chat_system_prompt='You are an English to French translator.'
)
)
prompt_template = sk.ChatPromptTemplate(
'Translate this to French: {{$input}}',
kernel.prompt_template_engine,
prompt_config)
function_config = sk.SemanticFunctionConfig(prompt_config, prompt_template)
translate = kernel.register_semantic_function(
None, 'translate', function_config)
text = 'Aren\'t large language models amazing?'
print(asyncio.run(kernel.run_async(translate, input_str=text)))
| [
"Translate this to French: {{$input}}",
"You are an English to French translator."
] |
2024-01-10 | TheSlavant/copilot-for-mind | advisor.py | import logging
import sys
import os
import time
from datetime import datetime
from dotenv import load_dotenv
import argparse
from langchain.chat_models import ChatOpenAI
from llama_index import GPTSimpleVectorIndex, NotionPageReader, SimpleDirectoryReader, LLMPredictor, GPTListIndex, readers, PromptHelper
def build_index(document_path):
# set maximum input size
max_input_size = 4096
# set number of output tokens
num_outputs = 2048
# set maximum chunk overlap
max_chunk_overlap = 256
# set chunk size limit
chunk_size_limit = 600
# define LLM
llm_predictor = LLMPredictor(llm=ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo", openai_api_key=os.getenv("OPENAI_API_KEY"), max_tokens=num_outputs))
prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)
documents = SimpleDirectoryReader(document_path).load_data()
index = GPTSimpleVectorIndex(
documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper
)
index.save_to_disk('index.json')
return index
def query_index(query, index_path):
index = GPTSimpleVectorIndex.load_from_disk(index_path)
response = index.query(query, mode="embedding", similarity_top_k=3, response_mode="compact")
print(response)
def main():
# Load the .env file
load_dotenv()
query = "You are an expert personal decision advisor, and the text is my journal entries. I will tell you about my thoughts. Your task is to make comments that help me avoid thought traps and biases. Point out if I already wrote about similar things, if my reasoning aligns with the values I expressed in my writing, and if my reasoning shows any bias. Quote relevant passages from my writing in the original language.\n\n"
user_input = input("My thoughts: ")
query = query + user_input
index = build_index("data/advisor")
query_index(query, "index.json")
if __name__ == "__main__":
start_time = time.time()
main()
end_time = time.time()
time_elapsed = end_time - start_time
print("Time elapsed: {:.2f} minutes".format(time_elapsed / 60)) | [] |
2024-01-10 | linuxleague/danswer-ai-danswer | backend~danswer~chat~chat_prompts.py | from langchain.schema.messages import BaseMessage
from langchain.schema.messages import HumanMessage
from langchain.schema.messages import SystemMessage
from danswer.chunking.models import InferenceChunk
from danswer.configs.constants import CODE_BLOCK_PAT
from danswer.db.models import ChatMessage
from danswer.llm.utils import translate_danswer_msg_to_langchain
DANSWER_TOOL_NAME = "Current Search"
DANSWER_TOOL_DESCRIPTION = (
"A search tool that can find information on any topic "
"including up to date and proprietary knowledge."
)
DANSWER_SYSTEM_MSG = (
"Given a conversation (between Human and Assistant) and a final message from Human, "
"rewrite the last message to be a standalone question that captures required/relevant context from the previous "
"conversation messages."
)
TOOL_TEMPLATE = """
TOOLS
------
You can use tools to look up information that may be helpful in answering the user's \
original question. The available tools are:
{tool_overviews}
RESPONSE FORMAT INSTRUCTIONS
----------------------------
When responding to me, please output a response in one of two formats:
**Option 1:**
Use this if you want to use a tool. Markdown code snippet formatted in the following schema:
```json
{{
"action": string, \\ The action to take. Must be one of {tool_names}
"action_input": string \\ The input to the action
}}
```
**Option #2:**
Use this if you want to respond directly to the user. Markdown code snippet formatted in the following schema:
```json
{{
"action": "Final Answer",
"action_input": string \\ You should put what you want to return to use here
}}
```
"""
TOOL_LESS_PROMPT = """
Respond with a markdown code snippet in the following schema:
```json
{{
"action": "Final Answer",
"action_input": string \\ You should put what you want to return to use here
}}
```
"""
USER_INPUT = """
USER'S INPUT
--------------------
Here is the user's input \
(remember to respond with a markdown code snippet of a json blob with a single action, and NOTHING else):
{user_input}
"""
TOOL_FOLLOWUP = """
TOOL RESPONSE:
---------------------
{tool_output}
USER'S INPUT
--------------------
Okay, so what is the response to my last comment? If using information obtained from the tools you must \
mention it explicitly without mentioning the tool names - I have forgotten all TOOL RESPONSES!
{optional_reminder}{hint}
IMPORTANT! You MUST respond with a markdown code snippet of a json blob with a single action, and NOTHING else.
"""
def form_user_prompt_text(
query: str,
tool_text: str | None,
hint_text: str | None,
user_input_prompt: str = USER_INPUT,
tool_less_prompt: str = TOOL_LESS_PROMPT,
) -> str:
user_prompt = tool_text or tool_less_prompt
user_prompt += user_input_prompt.format(user_input=query)
if hint_text:
if user_prompt[-1] != "\n":
user_prompt += "\n"
user_prompt += "Hint: " + hint_text
return user_prompt.strip()
def form_tool_section_text(
tools: list[dict[str, str]], retrieval_enabled: bool, template: str = TOOL_TEMPLATE
) -> str | None:
if not tools and not retrieval_enabled:
return None
if retrieval_enabled:
tools.append(
{"name": DANSWER_TOOL_NAME, "description": DANSWER_TOOL_DESCRIPTION}
)
tools_intro = []
for tool in tools:
description_formatted = tool["description"].replace("\n", " ")
tools_intro.append(f"> {tool['name']}: {description_formatted}")
tools_intro_text = "\n".join(tools_intro)
tool_names_text = ", ".join([tool["name"] for tool in tools])
return template.format(
tool_overviews=tools_intro_text, tool_names=tool_names_text
).strip()
def format_danswer_chunks_for_chat(chunks: list[InferenceChunk]) -> str:
return "\n".join(
f"DOCUMENT {ind}:{CODE_BLOCK_PAT.format(chunk.content)}"
for ind, chunk in enumerate(chunks, start=1)
)
def form_tool_followup_text(
tool_output: str,
query: str,
hint_text: str | None,
tool_followup_prompt: str = TOOL_FOLLOWUP,
ignore_hint: bool = False,
) -> str:
# If multi-line query, it likely confuses the model more than helps
if "\n" not in query:
optional_reminder = f"As a reminder, my query was: {query}\n"
else:
optional_reminder = ""
if not ignore_hint and hint_text:
hint_text_spaced = f"{hint_text}\n"
else:
hint_text_spaced = ""
return tool_followup_prompt.format(
tool_output=tool_output,
optional_reminder=optional_reminder,
hint=hint_text_spaced,
).strip()
def build_combined_query(
query_message: ChatMessage,
history: list[ChatMessage],
) -> list[BaseMessage]:
user_query = query_message.message
combined_query_msgs: list[BaseMessage] = []
if not user_query:
raise ValueError("Can't rephrase/search an empty query")
combined_query_msgs.append(SystemMessage(content=DANSWER_SYSTEM_MSG))
combined_query_msgs.extend(
[translate_danswer_msg_to_langchain(msg) for msg in history]
)
combined_query_msgs.append(
HumanMessage(
content=(
"Help me rewrite this final query into a standalone question that takes into consideration the "
f"past messages of the conversation. You must ONLY return the rewritten query and nothing else."
f"\n\nQuery:\n{query_message.message}"
)
)
)
return combined_query_msgs
| [
"\n",
"past messages of the conversation. You must ONLY return the rewritten query and nothing else.",
"Help me rewrite this final query into a standalone question that takes into consideration the ",
"\nRespond with a markdown code snippet in the following schema:\n\n```json\n{{\n \"action\": \"Final Answer\",\n \"action_input\": string \\ You should put what you want to return to use here\n}}\n```\n",
"Hint: PLACEHOLDER",
"\nTOOLS\n------\nYou can use tools to look up information that may be helpful in answering the user's original question. The available tools are:\n\n{tool_overviews}\n\nRESPONSE FORMAT INSTRUCTIONS\n----------------------------\nWhen responding to me, please output a response in one of two formats:\n\n**Option 1:**\nUse this if you want to use a tool. Markdown code snippet formatted in the following schema:\n\n```json\n{{\n \"action\": string, \\ The action to take. Must be one of {tool_names}\n \"action_input\": string \\ The input to the action\n}}\n```\n\n**Option #2:**\nUse this if you want to respond directly to the user. Markdown code snippet formatted in the following schema:\n\n```json\n{{\n \"action\": \"Final Answer\",\n \"action_input\": string \\ You should put what you want to return to use here\n}}\n```\n"
] |
2024-01-10 | linuxleague/danswer-ai-danswer | backend~danswer~direct_qa~qa_block.py | import abc
import json
import re
from collections.abc import Iterator
from copy import copy
import tiktoken
from langchain.schema.messages import AIMessage
from langchain.schema.messages import BaseMessage
from langchain.schema.messages import HumanMessage
from langchain.schema.messages import SystemMessage
from danswer.chunking.models import InferenceChunk
from danswer.configs.constants import CODE_BLOCK_PAT
from danswer.configs.constants import GENERAL_SEP_PAT
from danswer.configs.constants import QUESTION_PAT
from danswer.configs.constants import THOUGHT_PAT
from danswer.configs.constants import UNCERTAINTY_PAT
from danswer.direct_qa.interfaces import AnswerQuestionReturn
from danswer.direct_qa.interfaces import AnswerQuestionStreamReturn
from danswer.direct_qa.interfaces import DanswerAnswer
from danswer.direct_qa.interfaces import DanswerQuotes
from danswer.direct_qa.interfaces import QAModel
from danswer.direct_qa.qa_prompts import EMPTY_SAMPLE_JSON
from danswer.direct_qa.qa_prompts import JsonChatProcessor
from danswer.direct_qa.qa_prompts import WeakModelFreeformProcessor
from danswer.direct_qa.qa_utils import process_answer
from danswer.direct_qa.qa_utils import process_model_tokens
from danswer.llm.llm import LLM
from danswer.llm.utils import dict_based_prompt_to_langchain_prompt
from danswer.llm.utils import str_prompt_to_langchain_prompt
from danswer.utils.logger import setup_logger
from danswer.utils.text_processing import clean_up_code_blocks
from danswer.utils.text_processing import escape_newlines
logger = setup_logger()
class QAHandler(abc.ABC):
"""Evolution of the `PromptProcessor` - handles both building the prompt and
processing the response. These are necessarily coupled, since the prompt determines
the response format (and thus how it should be parsed into an answer + quotes)."""
@abc.abstractmethod
def build_prompt(
self, query: str, context_chunks: list[InferenceChunk]
) -> list[BaseMessage]:
raise NotImplementedError
@property
def is_json_output(self) -> bool:
"""Does the model expected to output a valid json"""
return True
def process_llm_output(
self, model_output: str, context_chunks: list[InferenceChunk]
) -> tuple[DanswerAnswer, DanswerQuotes]:
return process_answer(
model_output, context_chunks, is_json_prompt=self.is_json_output
)
def process_llm_token_stream(
self, tokens: Iterator[str], context_chunks: list[InferenceChunk]
) -> AnswerQuestionStreamReturn:
yield from process_model_tokens(
tokens=tokens,
context_docs=context_chunks,
is_json_prompt=self.is_json_output,
)
class JsonChatQAHandler(QAHandler):
def build_prompt(
self, query: str, context_chunks: list[InferenceChunk]
) -> list[BaseMessage]:
return dict_based_prompt_to_langchain_prompt(
JsonChatProcessor.fill_prompt(
question=query, chunks=context_chunks, include_metadata=False
)
)
class SimpleChatQAHandler(QAHandler):
@property
def is_json_output(self) -> bool:
return False
def build_prompt(
self, query: str, context_chunks: list[InferenceChunk]
) -> list[BaseMessage]:
return str_prompt_to_langchain_prompt(
WeakModelFreeformProcessor.fill_prompt(
question=query,
chunks=context_chunks,
include_metadata=False,
)
)
class SingleMessageQAHandler(QAHandler):
def build_prompt(
self, query: str, context_chunks: list[InferenceChunk]
) -> list[BaseMessage]:
context_docs_str = "\n".join(
f"{CODE_BLOCK_PAT.format(c.content)}" for c in context_chunks
)
prompt: list[BaseMessage] = [
HumanMessage(
content="You are a question answering system that is constantly learning and improving. "
"You can process and comprehend vast amounts of text and utilize this knowledge "
"to provide accurate and detailed answers to diverse queries.\n"
"You ALWAYS responds with only a json containing an answer and quotes that support the answer.\n"
"Your responses are as INFORMATIVE and DETAILED as possible.\n"
f"{GENERAL_SEP_PAT}CONTEXT:\n\n{context_docs_str}"
f"{GENERAL_SEP_PAT}Sample response:"
f"{CODE_BLOCK_PAT.format(json.dumps(EMPTY_SAMPLE_JSON))}\n"
f"{QUESTION_PAT} {query}\n"
"Hint: Make the answer as DETAILED as possible and respond in JSON format!\n"
"Quotes MUST be EXACT substrings from provided documents!"
)
]
return prompt
class SingleMessageScratchpadHandler(QAHandler):
def build_prompt(
self, query: str, context_chunks: list[InferenceChunk]
) -> list[BaseMessage]:
cot_block = (
f"{THOUGHT_PAT} Use this section as a scratchpad to reason through the answer.\n\n"
f"{json.dumps(EMPTY_SAMPLE_JSON)}"
)
context_docs_str = "\n".join(
f"{CODE_BLOCK_PAT.format(c.content)}" for c in context_chunks
)
prompt: list[BaseMessage] = [
HumanMessage(
content="You are a question answering system that is constantly learning and improving. "
"You can process and comprehend vast amounts of text and utilize this knowledge "
"to provide accurate and detailed answers to diverse queries.\n"
f"{GENERAL_SEP_PAT}CONTEXT:\n\n{context_docs_str}{GENERAL_SEP_PAT}"
f"You MUST respond in the following format:"
f"{CODE_BLOCK_PAT.format(cot_block)}\n"
f"{QUESTION_PAT} {query}\n"
"Hint: Make the answer as detailed as possible and use a JSON! "
"Quotes can ONLY be EXACT substrings from provided documents!"
)
]
return prompt
def process_llm_output(
self, model_output: str, context_chunks: list[InferenceChunk]
) -> tuple[DanswerAnswer, DanswerQuotes]:
logger.debug(model_output)
model_clean = clean_up_code_blocks(model_output)
match = re.search(r'{\s*"answer":', model_clean)
if not match:
return DanswerAnswer(answer=None), DanswerQuotes(quotes=[])
final_json = escape_newlines(model_clean[match.start() :])
return process_answer(
final_json, context_chunks, is_json_prompt=self.is_json_output
)
def process_llm_token_stream(
self, tokens: Iterator[str], context_chunks: list[InferenceChunk]
) -> AnswerQuestionStreamReturn:
raise ValueError(
"This Scratchpad approach is not suitable for real time uses like streaming"
)
class JsonChatQAUnshackledHandler(QAHandler):
def build_prompt(
self, query: str, context_chunks: list[InferenceChunk]
) -> list[BaseMessage]:
prompt: list[BaseMessage] = []
complete_answer_not_found_response = (
'{"answer": "' + UNCERTAINTY_PAT + '", "quotes": []}'
)
prompt.append(
SystemMessage(
content=(
"Use the following pieces of context to answer the users question. Your response "
"should be in JSON format and contain an answer and (optionally) quotes that help support the answer. "
"Your responses should be informative, detailed, and consider all possibilities and edge cases. "
f"If you don't know the answer, respond with '{complete_answer_not_found_response}'\n"
f"Sample response:\n\n{json.dumps(EMPTY_SAMPLE_JSON)}"
)
)
)
prompt.append(
SystemMessage(
content='Start by reading the following documents and responding with "Acknowledged".'
)
)
for chunk in context_chunks:
prompt.append(SystemMessage(content=chunk.content))
prompt.append(AIMessage(content="Acknowledged"))
prompt.append(HumanMessage(content=f"Question: {query}\n"))
return prompt
def _tiktoken_trim_chunks(
chunks: list[InferenceChunk], max_chunk_toks: int = 512
) -> list[InferenceChunk]:
"""Edit chunks that have too high token count. Generally due to parsing issues or
characters from another language that are 1 char = 1 token
Trimming by tokens leads to information loss but currently no better way of handling
NOTE: currently gpt-3.5 / gpt-4 tokenizer across all LLMs currently
TODO: make "chunk modification" its own step in the pipeline
"""
encoder = tiktoken.get_encoding("cl100k_base")
new_chunks = copy(chunks)
for ind, chunk in enumerate(new_chunks):
tokens = encoder.encode(chunk.content)
if len(tokens) > max_chunk_toks:
new_chunk = copy(chunk)
new_chunk.content = encoder.decode(tokens[:max_chunk_toks])
new_chunks[ind] = new_chunk
return new_chunks
class QABlock(QAModel):
def __init__(self, llm: LLM, qa_handler: QAHandler) -> None:
self._llm = llm
self._qa_handler = qa_handler
def warm_up_model(self) -> None:
"""This is called during server start up to load the models into memory
in case the chosen LLM is not accessed via API"""
if self._llm.requires_warm_up:
self._llm.invoke("Ignore this!")
def answer_question(
self,
query: str,
context_docs: list[InferenceChunk],
) -> AnswerQuestionReturn:
trimmed_context_docs = _tiktoken_trim_chunks(context_docs)
prompt = self._qa_handler.build_prompt(query, trimmed_context_docs)
model_out = self._llm.invoke(prompt)
return self._qa_handler.process_llm_output(model_out, trimmed_context_docs)
def answer_question_stream(
self,
query: str,
context_docs: list[InferenceChunk],
) -> AnswerQuestionStreamReturn:
trimmed_context_docs = _tiktoken_trim_chunks(context_docs)
prompt = self._qa_handler.build_prompt(query, trimmed_context_docs)
tokens = self._llm.stream(prompt)
yield from self._qa_handler.process_llm_token_stream(
tokens, trimmed_context_docs
)
| [
"You are a question answering system that is constantly learning and improving. ",
"Hint: Make the answer as detailed as possible and use a JSON! ",
"Question: PLACEHOLDER\n",
"Quotes can ONLY be EXACT substrings from provided documents!",
"should be in JSON format and contain an answer and (optionally) quotes that help support the answer. ",
"You can process and comprehend vast amounts of text and utilize this knowledge ",
"PLACEHOLDERCONTEXT:\n\nPLACEHOLDERPLACEHOLDER",
"You ALWAYS responds with only a json containing an answer and quotes that support the answer.\n",
"Start by reading the following documents and responding with \"Acknowledged\".",
"Your responses should be informative, detailed, and consider all possibilities and edge cases. ",
"Your responses are as INFORMATIVE and DETAILED as possible.\n",
"PLACEHOLDERSample response:",
"You MUST respond in the following format:",
"PLACEHOLDERCONTEXT:\n\nPLACEHOLDER",
"If you don't know the answer, respond with 'PLACEHOLDER'\n",
"Hint: Make the answer as DETAILED as possible and respond in JSON format!\n",
"Quotes MUST be EXACT substrings from provided documents!",
"PLACEHOLDER PLACEHOLDER\n",
"Use the following pieces of context to answer the users question. Your response ",
"to provide accurate and detailed answers to diverse queries.\n",
"Acknowledged"
] |
2024-01-10 | linuxleague/danswer-ai-danswer | backend~danswer~direct_qa~answer_question.py | from sqlalchemy.orm import Session
from danswer.chunking.models import InferenceChunk
from danswer.configs.app_configs import DISABLE_GENERATIVE_AI
from danswer.configs.app_configs import ENABLE_DANSWERBOT_REFLEXION
from danswer.configs.app_configs import NUM_DOCUMENT_TOKENS_FED_TO_GENERATIVE_MODEL
from danswer.configs.app_configs import QA_TIMEOUT
from danswer.configs.constants import IGNORE_FOR_QA
from danswer.datastores.document_index import get_default_document_index
from danswer.db.feedback import create_query_event
from danswer.db.models import User
from danswer.direct_qa.exceptions import OpenAIKeyMissing
from danswer.direct_qa.exceptions import UnknownModelError
from danswer.direct_qa.llm_utils import get_default_qa_model
from danswer.direct_qa.qa_utils import get_usable_chunks
from danswer.search.danswer_helper import query_intent
from danswer.search.keyword_search import retrieve_keyword_documents
from danswer.search.models import QueryFlow
from danswer.search.models import SearchType
from danswer.search.semantic_search import chunks_to_search_docs
from danswer.search.semantic_search import retrieve_ranked_documents
from danswer.secondary_llm_flows.answer_validation import get_answer_validity
from danswer.server.models import QAResponse
from danswer.server.models import QuestionRequest
from danswer.utils.logger import setup_logger
from danswer.utils.timing import log_function_time
logger = setup_logger()
@log_function_time()
def answer_qa_query(
question: QuestionRequest,
user: User | None,
db_session: Session,
disable_generative_answer: bool = DISABLE_GENERATIVE_AI,
answer_generation_timeout: int = QA_TIMEOUT,
real_time_flow: bool = True,
enable_reflexion: bool = ENABLE_DANSWERBOT_REFLEXION,
) -> QAResponse:
query = question.query
filters = question.filters
use_keyword = question.use_keyword
offset_count = question.offset if question.offset is not None else 0
logger.info(f"Received QA query: {query}")
query_event_id = create_query_event(
query=query,
selected_flow=SearchType.KEYWORD,
llm_answer=None,
user_id=user.id if user is not None else None,
db_session=db_session,
)
predicted_search, predicted_flow = query_intent(query)
if use_keyword is None:
use_keyword = predicted_search == SearchType.KEYWORD
user_id = None if user is None else user.id
if use_keyword:
ranked_chunks: list[InferenceChunk] | None = retrieve_keyword_documents(
query, user_id, filters, get_default_document_index()
)
unranked_chunks: list[InferenceChunk] | None = []
else:
ranked_chunks, unranked_chunks = retrieve_ranked_documents(
query, user_id, filters, get_default_document_index()
)
if not ranked_chunks:
return QAResponse(
answer=None,
quotes=None,
top_ranked_docs=None,
lower_ranked_docs=None,
predicted_flow=predicted_flow,
predicted_search=predicted_search,
query_event_id=query_event_id,
)
if disable_generative_answer:
logger.debug("Skipping QA because generative AI is disabled")
return QAResponse(
answer=None,
quotes=None,
top_ranked_docs=chunks_to_search_docs(ranked_chunks),
lower_ranked_docs=chunks_to_search_docs(unranked_chunks),
# set flow as search so frontend doesn't ask the user if they want
# to run QA over more documents
predicted_flow=QueryFlow.SEARCH,
predicted_search=predicted_search,
query_event_id=query_event_id,
)
try:
qa_model = get_default_qa_model(
timeout=answer_generation_timeout, real_time_flow=real_time_flow
)
except (UnknownModelError, OpenAIKeyMissing) as e:
return QAResponse(
answer=None,
quotes=None,
top_ranked_docs=chunks_to_search_docs(ranked_chunks),
lower_ranked_docs=chunks_to_search_docs(unranked_chunks),
predicted_flow=predicted_flow,
predicted_search=predicted_search,
error_msg=str(e),
query_event_id=query_event_id,
)
# remove chunks marked as not applicable for QA (e.g. Google Drive file
# types which can't be parsed). These chunks are useful to show in the
# search results, but not for QA.
filtered_ranked_chunks = [
chunk for chunk in ranked_chunks if not chunk.metadata.get(IGNORE_FOR_QA)
]
# get all chunks that fit into the token limit
usable_chunks = get_usable_chunks(
chunks=filtered_ranked_chunks,
token_limit=NUM_DOCUMENT_TOKENS_FED_TO_GENERATIVE_MODEL,
offset=offset_count,
)
logger.debug(
f"Chunks fed to LLM: {[chunk.semantic_identifier for chunk in usable_chunks]}"
)
error_msg = None
try:
d_answer, quotes = qa_model.answer_question(query, usable_chunks)
except Exception as e:
# exception is logged in the answer_question method, no need to re-log
d_answer, quotes = None, None
error_msg = f"Error occurred in call to LLM - {e}"
if not real_time_flow and enable_reflexion and d_answer is not None:
valid = False
if d_answer.answer is not None:
valid = get_answer_validity(query, d_answer.answer)
return QAResponse(
answer=d_answer.answer if d_answer else None,
quotes=quotes.quotes if quotes else None,
top_ranked_docs=chunks_to_search_docs(ranked_chunks),
lower_ranked_docs=chunks_to_search_docs(unranked_chunks),
predicted_flow=predicted_flow,
predicted_search=predicted_search,
eval_res_valid=True if valid else False,
error_msg=error_msg,
query_event_id=query_event_id,
)
return QAResponse(
answer=d_answer.answer if d_answer else None,
quotes=quotes.quotes if quotes else None,
top_ranked_docs=chunks_to_search_docs(ranked_chunks),
lower_ranked_docs=chunks_to_search_docs(unranked_chunks),
predicted_flow=predicted_flow,
predicted_search=predicted_search,
error_msg=error_msg,
query_event_id=query_event_id,
)
| [] |
2024-01-10 | linuxleague/danswer-ai-danswer | backend~danswer~chat~chat_llm.py | from collections.abc import Iterator
from uuid import UUID
from langchain.schema.messages import AIMessage
from langchain.schema.messages import BaseMessage
from langchain.schema.messages import HumanMessage
from langchain.schema.messages import SystemMessage
from danswer.chat.chat_prompts import build_combined_query
from danswer.chat.chat_prompts import DANSWER_TOOL_NAME
from danswer.chat.chat_prompts import form_tool_followup_text
from danswer.chat.chat_prompts import form_user_prompt_text
from danswer.chat.chat_prompts import format_danswer_chunks_for_chat
from danswer.chat.tools import call_tool
from danswer.configs.app_configs import NUM_DOCUMENT_TOKENS_FED_TO_CHAT
from danswer.configs.constants import IGNORE_FOR_QA
from danswer.datastores.document_index import get_default_document_index
from danswer.db.models import ChatMessage
from danswer.db.models import Persona
from danswer.direct_qa.interfaces import DanswerAnswerPiece
from danswer.direct_qa.interfaces import DanswerChatModelOut
from danswer.direct_qa.qa_utils import get_usable_chunks
from danswer.llm.build import get_default_llm
from danswer.llm.llm import LLM
from danswer.llm.utils import translate_danswer_msg_to_langchain
from danswer.search.semantic_search import retrieve_ranked_documents
from danswer.utils.logger import setup_logger
from danswer.utils.text_processing import extract_embedded_json
from danswer.utils.text_processing import has_unescaped_quote
logger = setup_logger()
def _parse_embedded_json_streamed_response(
tokens: Iterator[str],
) -> Iterator[DanswerAnswerPiece | DanswerChatModelOut]:
final_answer = False
just_start_stream = False
model_output = ""
hold = ""
finding_end = 0
for token in tokens:
model_output += token
hold += token
if (
final_answer is False
and '"action":"finalanswer",' in model_output.lower().replace(" ", "")
):
final_answer = True
if final_answer and '"actioninput":"' in model_output.lower().replace(
" ", ""
).replace("_", ""):
if not just_start_stream:
just_start_stream = True
hold = ""
if has_unescaped_quote(hold):
finding_end += 1
hold = hold[: hold.find('"')]
if finding_end <= 1:
if finding_end == 1:
finding_end += 1
yield DanswerAnswerPiece(answer_piece=hold)
hold = ""
logger.debug(model_output)
model_final = extract_embedded_json(model_output)
if "action" not in model_final or "action_input" not in model_final:
raise ValueError("Model did not provide all required action values")
yield DanswerChatModelOut(
model_raw=model_output,
action=model_final["action"],
action_input=model_final["action_input"],
)
return
def danswer_chat_retrieval(
query_message: ChatMessage,
history: list[ChatMessage],
llm: LLM,
user_id: UUID | None,
) -> str:
if history:
query_combination_msgs = build_combined_query(query_message, history)
reworded_query = llm.invoke(query_combination_msgs)
else:
reworded_query = query_message.message
# Good Debug/Breakpoint
ranked_chunks, unranked_chunks = retrieve_ranked_documents(
reworded_query,
user_id=user_id,
filters=None,
datastore=get_default_document_index(),
)
if not ranked_chunks:
return "No results found"
if unranked_chunks:
ranked_chunks.extend(unranked_chunks)
filtered_ranked_chunks = [
chunk for chunk in ranked_chunks if not chunk.metadata.get(IGNORE_FOR_QA)
]
# get all chunks that fit into the token limit
usable_chunks = get_usable_chunks(
chunks=filtered_ranked_chunks,
token_limit=NUM_DOCUMENT_TOKENS_FED_TO_CHAT,
)
return format_danswer_chunks_for_chat(usable_chunks)
def llm_contextless_chat_answer(messages: list[ChatMessage]) -> Iterator[str]:
prompt = [translate_danswer_msg_to_langchain(msg) for msg in messages]
return get_default_llm().stream(prompt)
def llm_contextual_chat_answer(
messages: list[ChatMessage],
persona: Persona,
user_id: UUID | None,
) -> Iterator[str]:
retrieval_enabled = persona.retrieval_enabled
system_text = persona.system_text
tool_text = persona.tools_text
hint_text = persona.hint_text
last_message = messages[-1]
if not last_message.message:
raise ValueError("User chat message is empty.")
previous_messages = messages[:-1]
user_text = form_user_prompt_text(
query=last_message.message,
tool_text=tool_text,
hint_text=hint_text,
)
prompt: list[BaseMessage] = []
if system_text:
prompt.append(SystemMessage(content=system_text))
prompt.extend(
[translate_danswer_msg_to_langchain(msg) for msg in previous_messages]
)
prompt.append(HumanMessage(content=user_text))
llm = get_default_llm()
# Good Debug/Breakpoint
tokens = llm.stream(prompt)
final_result: DanswerChatModelOut | None = None
final_answer_streamed = False
for result in _parse_embedded_json_streamed_response(tokens):
if isinstance(result, DanswerAnswerPiece) and result.answer_piece:
yield result.answer_piece
final_answer_streamed = True
if isinstance(result, DanswerChatModelOut):
final_result = result
break
if final_answer_streamed:
return
if final_result is None:
raise RuntimeError("Model output finished without final output parsing.")
if retrieval_enabled and final_result.action.lower() == DANSWER_TOOL_NAME.lower():
tool_result_str = danswer_chat_retrieval(
query_message=last_message,
history=previous_messages,
llm=llm,
user_id=user_id,
)
else:
tool_result_str = call_tool(final_result, user_id=user_id)
prompt.append(AIMessage(content=final_result.model_raw))
prompt.append(
HumanMessage(
content=form_tool_followup_text(
tool_output=tool_result_str,
query=last_message.message,
hint_text=hint_text,
)
)
)
# Good Debug/Breakpoint
tokens = llm.stream(prompt)
for result in _parse_embedded_json_streamed_response(tokens):
if isinstance(result, DanswerAnswerPiece) and result.answer_piece:
yield result.answer_piece
final_answer_streamed = True
if final_answer_streamed is False:
raise RuntimeError("LLM failed to produce a Final Answer")
def llm_chat_answer(
messages: list[ChatMessage], persona: Persona | None, user_id: UUID | None
) -> Iterator[str]:
# TODO how to handle model giving jibberish or fail on a particular message
# TODO how to handle model failing to choose the right tool
# TODO how to handle model gives wrong format
if persona is None:
return llm_contextless_chat_answer(messages)
return llm_contextual_chat_answer(
messages=messages, persona=persona, user_id=user_id
)
| [] |
2024-01-10 | xhOwenMa/voice_data_google | voice~gen_voice.py | from pathlib import Path
from openai import OpenAI
client = OpenAI()
def generate_voice(text, path):
response = client.audio.speech.create(
model="tts-1",
voice="echo", # echo for male voice and shimmer for female voice
input=text
)
response.stream_to_file(path)
def process_text_file(file_path):
with open(file_path, 'r') as file:
for index, line in enumerate(file):
line = line.strip() # Remove any leading/trailing whitespace
if line: # Check if the line is not empty
# Generate a unique file name for each entry
voice_file_path = Path(__file__).parent / f"NAME_YOUR_FILE.mp3"
generate_voice(line, voice_file_path)
print(f"Generated speech for line {index+1}")
# Path to the text file containing the text entries
text_file_path = Path(__file__).parent / "NAME_YOUR_FILE.txt"
process_text_file(text_file_path)
| [] |
2024-01-10 | waleedabujaish/STAR-nasa-spaceappschallenge | save_to_disk_hf.py | from dotenv import load_dotenv
load_dotenv()
from llama_index import VectorStoreIndex, SimpleDirectoryReader, LangchainEmbedding, ServiceContext
from llama_index.embeddings import HuggingFaceEmbedding
from llama_index.storage import StorageContext
from llama_index.vector_stores import ChromaVectorStore
import chromadb
import transformers
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
chroma_client = chromadb.PersistentClient(path="distilbert")
chroma_collection = chroma_client.get_or_create_collection("distilbert")
documents = SimpleDirectoryReader('pages').load_data()
print('Pages are loaded.')
embed_model = HuggingFaceEmbedding(model_name="distilbert-base-uncased")
print('Model is loaded into GPU.')
vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
print('Will start indexing and embedding.')
service_context = ServiceContext.from_defaults(embed_model=embed_model)
index = VectorStoreIndex.from_documents(
documents,
storage_context=storage_context,
chroma_collection=chroma_collection,
show_progress=True,
service_context=service_context
)
| [] |
2024-01-10 | 0xrushi/J.A.R.V.I.S | JARVIS.py | #_____________________________________________________J.A.R.V.I.S________________________________________________________
#Python modules used for this programm
import sys
import speech_recognition as sr
import pyttsx3
import pywhatkit
import pywhatkit as kit
import datetime
import wikipedia
import pyjokes
import webbrowser
import time
import subprocess
import os
import cv2
import random
from requests import get
import smtplib
import psutil
import instaloader
import pyautogui
import PyPDF2
from Recordings import Record_Option
from PIL import ImageGrab
import pyaudio
import wave
import numpy as np
from PhoneNumer import Phonenumber_location_tracker
from bs4 import BeautifulSoup
from PyQt5 import QtWidgets, QtCore, QtGui
from PyQt5.QtCore import QTimer,QTime,QDate,Qt
from PyQt5.QtGui import QMovie
from PyQt5.QtGui import *
from PyQt5.QtCore import *
from PyQt5.QtWidgets import *
from PyQt5.uic import loadUiType
from JarvisUi import Ui_JarvisUI
from state import state
from pywikihow import search_wikihow
import speedtest
from pytube import YouTube
#----------------------------------------------------------------------
#aish
import argparse
import os
import readline
import signal
import subprocess
import sys
from colorama import Fore, Style
import openai
# export OPEN_AI_KEY=YOUR_KEY_HERE, run this in linux terminal
openai.api_key = os.environ["OPEN_AI_KEY"]
EXAMPLES_CONTEXT = "Linux bash command to accomplish the task"
REVERSE_EXAMPLES_CONTEXT = "English description of Linux bash command"
# You can uncomment examples to affect the results, but more examples costs
# more to process.
EXAMPLES = [
#["Get the last 5 lines of foo.txt", "tail -n 5 foo.txt"],
["Find files ending in \"log\" in the root directory",
"find / -name \"*.log\""],
["Look up the IP address 12.34.56.78",
"nslookup 12.34.56.78"],
#["Convert example.png to a JPEG",
# "convert example.png example.jpg"],
["Create a git branch called foobie-bletch",
"git checkout -b foobie-bletch"]
]
MODEL = "davinci"
def get_command(prompt):
results = openai.Answer.create(
search_model=MODEL,
model=MODEL,
question=prompt,
examples_context=EXAMPLES_CONTEXT,
examples=EXAMPLES,
max_tokens=100,
documents=[],
stop=["\n", "<|endoftext|>"],
)
if results:
return results['answers'][0]
def get_description(command):
results = openai.Answer.create(
search_model=MODEL,
model=MODEL,
question=command,
examples_context=REVERSE_EXAMPLES_CONTEXT,
examples=reverse_pairs(EXAMPLES),
max_tokens=200,
documents=[],
stop=["\n", "<|endoftext|>"],
)
if results:
return results['answers'][0]
def reverse_pairs(ls):
return [(b, a) for a, b in ls]
CURRENT_JOB = None
LIVE_DANGEROUSLY = False
parser = argparse.ArgumentParser()
parser.add_argument("--live-dangerously", help="Don't confirm commands before running", action="store_true")
parser.add_argument("--reverse", help="The AI describes your bash command in natural language", action="store_true")
args = parser.parse_args()
if args.live_dangerously:
print(f"{Fore.RED}{Style.BRIGHT}YOU WILL NOT BE GIVEN A CHANCE TO APPROVE OR CANCEL THE AI-GENERATED COMMAND. THIS IS A BAD IDEA AND YOU SHOULD EXIT NOW.{Style.RESET_ALL}")
LIVE_DANGEROUSLY = True
def call_openai(command):
try:
# request = input(f'\001{Fore.GREEN}{Style.BRIGHT}\002~> \001{Style.RESET_ALL}\002')
request = command
except EOFError:
print("")
print(f"{Fore.GREEN}{Style.BRIGHT}<~ {Fore.CYAN}{Style.NORMAL}Farewell, human.{Style.RESET_ALL}")
sys.exit(0)
except KeyboardInterrupt:
print("")
if not request.strip():
pass
if request.startswith("cd "):
os.chdir(request[3:])
pass
if request.strip() == "cd":
os.chdir(os.path.expanduser('~'))
pass
if request.strip() == "pwd":
print(f"{Fore.GREEN}{Style.BRIGHT}<~ {Fore.YELLOW}{Style.NORMAL}" + os.getcwd() + Style.RESET_ALL)
pass
if request.strip() == "clear":
subprocess.run(["clear"], shell=True)
pass
print(f"🧠 {Style.BRIGHT}Thinking...{Style.RESET_ALL}")
if not args.reverse:
new_command = get_command(request)
if not new_command or new_command == "None":
print("<~ Unable to figure out how to do that")
return
if not LIVE_DANGEROUSLY:
try:
approved = input(f"\001{Fore.GREEN}{Style.BRIGHT}\002<~ \001{Fore.CYAN}{Style.NORMAL}\002" + new_command + "\001" + Style.RESET_ALL + "\002")
except (EOFError, KeyboardInterrupt):
print(f"\n{Fore.RED}{Style.BRIGHT}<~ Canceled{Style.RESET_ALL}")
else:
print(f"{Fore.GREEN}{Style.BRIGHT}<~ {Fore.CYAN}{Style.NORMAL}" + new_command + Style.RESET_ALL)
else:
new_command = request
description = get_description(request)
if not description or description=="None":
print(f"{Fore.RED}{Style.BRIGHT}<~ Couldn't describe command{Style.RESET_ALL}")
pass
if not LIVE_DANGEROUSLY:
try:
approved = input(f"\001{Fore.GREEN}{Style.BRIGHT}\002<~ \001{Fore.CYAN}{Style.NORMAL}\002" + description + "\001" + Style.RESET_ALL + "\002")
except (EOFError, KeyboardInterrupt):
print(f"\n{Fore.RED}{Style.BRIGHT}<~ Canceled{Style.RESET_ALL}")
pass
else:
print(f"{Fore.GREEN}{Style.BRIGHT}<~ {Fore.CYAN}{Style.NORMAL}" + description + Style.RESET_ALL)
CURRENT_JOB = subprocess.Popen(["bash", "-c", new_command])
try:
CURRENT_JOB.wait()
except KeyboardInterrupt:
#os.kill(CURRENT_JOB.pid, signal.SIGINT)
pass
#----------------------------------------------------------------------
#Set our engine to "Pyttsx3" which is used for text to speech in Python
#and sapi5 is Microsoft speech application platform interface
#we will be using this for text to speech function.
# sapi5 doesn't work on linux, we need to use espeaks
engine = pyttsx3.init()
# Commented for linux support
# voices = engine.getProperty('voices')
# engine.setProperty('voice',voices[0].id) #index '0' for 'David'(male) voice index '1' for 'zira'(female) voice
#NOTE
#Initialize your whatsapp contack info of individual and group
#If you want to send to an individual save the name of them and phone number in the contact dictionary below
#if you want to send a message in a whatsapp group declare the name of the group with "group" tag so JARVIS will not be confused to send to individual or for a group
#else you can also create a new dictionary for groups
#for the key value pairs in the whatsapp group you need to save the group name as key and group 22 charcters ID as the value the ID can be found in the group invite link
# Eg key = "school group" value = "IHNJFHT4uJAFBJAKJAVBu5"
#declare the individual's contact number with the starting of their country code
contact = {"sujith":"+918232438641","school group":"IHNJFHT4uJsAFBJAKJAVBu5"} #Example dictionary
#Main classs where all the functiona are present
class MainThread(QThread):
def __init__(self):
super(MainThread,self).__init__()
def run(self):
self.Intro()
#function that will take the commands to convert voice into text
def take_Command(self):
try:
listener = sr.Recognizer()
with sr.Microphone() as source:
print('Listening....')
listener.pause_threshold = 1
voice = listener.listen(source,timeout=4,phrase_time_limit=7)
print("Recognizing...")
command1 = listener.recognize_google(voice,language='en-in')
command1 = command1.lower()
if 'jarvis' in command1:
command1 = command1.replace('jarvis','')
if 'java' in command1:
command1 = command1.replace('java','')
return command1
except:
return 'None'
#Jarvis commands controller
def run_jarvis(self):
self.wish()
self.talk('Hello boss I am jarvis your assistant. please tell me how can i help you')
while True:
self.command = self.take_Command() #Every time taking command after a task is done
print(self.command)
if ('play a song' in self.command) or ('youtube' in self.command) or ("download a song" in self.command) or ("download song" in self.command) :
#commands for opening youtube, playing a song in youtube, and download a song in youtube
self.yt(self.command) #function is from line 555
elif 'time' in self.command :
self.Clock_time(self.command)
#Interaction commands with JARVIS
elif ('hi' in self.command) or ('hai' in self.command) or ('hey' in self.command) or ('hello' in self.command):
self.comum(self.command)
elif ('your age' in self.command) or ('are you single'in self.command) or ('are you there' in self.command):
self.Fun(self.command)
elif ('what can you do' in self.command) or ('your name' in self.command) or ('my name' in self.command) or ('university name' in self.command):
self.Fun(self.command)
elif ('joke'in self.command) or ('date' in self.command):
self.Fun(self.command)
#schedule commands for remembering you what is the planns of the day
elif ("college time table" in self.command) or ("schedule" in self.command):
self.shedule() #function is present from 407
#It will tell the day Eg : Today is wednesday
elif ("today" in self.command):
day = self.Cal_day()
self.talk("Today is "+day)
#commad for opening any weekly meeting links
#Eg: I have kept a meeting my amFOSS club
#Note: the given link is fake!!
elif ("meeting" in self.command):
self.talk("Ok sir opening meeet")
webbrowser.open("https://meeting/")
#command if you don't want the JARVIS to spack until for a certain time
#Note: I can be silent for max of 10mins
# Eg: JARVIS keep quiet for 5 minutes
elif ('silence' in self.command) or ('silent' in self.command) or ('keep quiet' in self.command) or ('wait for' in self.command) :
self.silenceTime(self.command)
#Command for opening your social media accounts in webrowser
#Eg : JARVIS open facebook (or) JARVIS open social media facebook
elif ('facebook' in self.command) or ('whatsapp' in self.command) or ('instagram' in self.command) or ('twitter' in self.command) or ('discord' in self.command) or ('social media' in self.command):
self.social(self.command)
#command for opening your OTT platform accounts
#Eg: open hotstart
elif ('hotstar' in self.command) or ('prime' in self.command) or ('netflix' in self.command):
self.OTT(self.command)
#Command for opening your online classes links
elif ('online classes'in self.command):
self.OnlineClasses(self.command)
#command for opeing college websites
elif ('open teams'in self.command) or ('open stream'in self.command) or ('open sharepoint'in self.command) or('open outlook'in self.command)or('open amrita portal'in self.command)or('open octave'in self.command):
self.college(self.command)
#command to search for something in wikipedia
#Eg: what is meant by python in wikipedia (or) search for "_something_" in wikipedia
elif 'wikipedia' in self.command:
self.B_S(self.command)
#command for opening your browsers and search for information in google
elif ('open google'in self.command) or ('open edge'in self.command) :
self.brows(self.command)
#command to open your google applications
elif ('open gmail'in self.command) or('open maps'in self.command) or('open calender'in self.command) or('open documents'in self.command )or('open spredsheet'in self.command) or('open images'in self.command) or('open drive'in self.command) or('open news' in self.command):
self.Google_Apps(self.command)
#command to open your open-source accounts
#you can add other if you have
elif ('open github'in self.command) or ('open gitlab'in self.command) :
self.open_source(self.command)
#commands to open presentaion makeing tools like CANVA and GOOGLE SLIDES
elif ('slides'in self.command) or ('canva'in self.command) :
self.edit(self.command)
#Command to open desktop applications
#It can open : caliculator, notepad,paint, teams(aka online classes), discord, spotify, ltspice,vscode(aka editor), steam, VLC media player
elif ('open calculator'in self.command) or ('open notepad'in self.command) or ('open paint'in self.command) or ('open online classes'in self.command) or ('open discord'in self.command) or ('open ltspice'in self.command) or ('open editor'in self.command) or ('open spotify'in self.command) or ('open steam'in self.command) or ('open media player'in self.command):
self.OpenApp(self.command)
#Command to close desktop applications
#It can close : caliculator, notepad,paint, discord, spotify, ltspice,vscode(aka editor), steam, VLC media player
elif ('close calculator'in self.command) or ('close notepad'in self.command) or ('close paint'in self.command) or ('close discord'in self.command) or ('close ltspice'in self.command) or ('close editor'in self.command) or ('close spotify'in self.command) or ('close steam'in self.command) or ('close media player'in self.command):
self.CloseApp(self.command)
#command for opening shopping websites
#NOTE: you can add as many websites
elif ('flipkart'in self.command) or ('amazon'in self.command) :
self.shopping(self.command)
#command for asking your current location
elif ('where i am' in self.command) or ('where we are' in self.command):
self.locaiton()
#command for opening command prompt
#Eg: jarvis open command prompt
elif ('command prompt'in self.command) :
self.talk('Opening command prompt')
os.system('start cmd')
#Command for opening an instagram profile and downloading the profile pictures of the profile
#Eg: jarvis open a profile on instagram
elif ('instagram profile' in self.command) or("profile on instagram" in self.command):
self.Instagram_Pro()
#Command for opening taking screenshot
#Eg: jarvis take a screenshot
elif ('take screenshot' in self.command)or ('screenshot' in self.command) or("take a screenshot" in self.command):
self.scshot()
#Command for reading PDF
#EG: Jarvis read pdf
elif ("read pdf" in self.command) or ("pdf" in self.command):
self.pdf_reader()
#command for searching for a procedure how to do something
#Eg:jarvis activate mod
# jarvis How to make a cake (or) jarvis how to convert int to string in programming
elif "activate mod" in self.command:
self.How()
#command for increaing the volume in the system
#Eg: jarvis increase volume
elif ("volume up" in self.command) or ("increase volume" in self.command):
pyautogui.press("volumeup")
self.talk('volume increased')
#command for decreaseing the volume in the system
#Eg: jarvis decrease volume
elif ("volume down" in self.command) or ("decrease volume" in self.command):
pyautogui.press("volumedown")
self.talk('volume decreased')
#Command to mute the system sound
#Eg: jarvis mute the sound
elif ("volume mute" in self.command) or ("mute the sound" in self.command) :
pyautogui.press("volumemute")
self.talk('volume muted')
#command for opening your mobile camera the description for using this is in the README file
#Eg: Jarvis open mobile camera
elif ("open mobile cam" in self.command):
self.Mobilecamra()
#command for opening your webcamera
#Eg: jarvis open webcamera
elif ('web cam'in self.command) :
self.webCam()
#Command for checking covid status in India
#Eg: jarvis check covid (or) corona status
elif ("covid" in self.command) or ("corona" in self.command):
self.talk("Boss which state covid 19 status do you want to check")
s = self.take_Command()
self.Covid(s)
#Command for screenRecording
#Eg: Jarvis start Screen recording
elif ("recording" in self.command) or ("screen recording" in self.command) or ("voice recording" in self.command):
try:
self.talk("Boss press q key to stop recordings")
option = self.command
Record_Option(option=option)
self.talk("Boss recording is being saved")
except:
self.talk("Boss an unexpected error occured couldn't start screen recording")
#Command for phone number tracker
elif ("track" in self.command) or ("track a mobile number" in self.command):
self.talk("Boss please enter the mobile number with country code")
try:
location,servise_prover,lat,lng=Phonenumber_location_tracker()
self.talk(f"Boss the mobile number is from {location} and the service provider for the mobile number is {servise_prover}")
self.talk(f"latitude of that mobile nuber is {lat} and longitude of that mobile number is {lng}")
print(location,servise_prover)
print(f"Latitude : {lat} and Longitude : {lng}")
self.talk("Boss location of the mobile number is saved in Maps")
except:
self.talk("Boss an unexpected error occured couldn't track the mobile number")
#command for playing a dowloaded mp3 song in which is present in your system
#Eg: Jarvis play music
elif 'music' in self.command:
music_dir = 'E:\\music' #change the song path directory if you have songs in other directory
songs = os.listdir(music_dir)
for song in songs:
if song.endswith('.mp3'):
os.startfile(os.path.join(music_dir, song))
#command for knowing your system IP address
#Eg: jarvis check my ip address
elif 'ip address' in self.command:
ip = get('https://api.ipify.org').text
print(f"your IP address is {ip}")
self.talk(f"your IP address is {ip}")
#command for seading a whatsapp group and individual message
#Individual => Eg: send a message to sujith
#group => Eg: send a message to school group NOTE: mention the name "group" otherwise jarvis cannot detect the name
elif ('send a message' in self.command):
self.whatsapp(self.command)
#command for sending an email
#Eg: jarvis send email
elif 'send email' in self.command:
self.verifyMail()
#command for checking the temperature in surroundings
#jarvis check the surroundings temperature
elif "temperature" in self.command:
self.temperature()
#command for checking internet speed
#Eg: jarvis check my internet speed
elif "internet speed" in self.command:
self.InternetSpeed()
#command to make the jarvis sleep
#Eg: jarvis you can sleep now
elif ("you can sleep" in self.command) or ("sleep now" in self.command):
self.talk("Okay boss, I am going to sleep you can call me anytime.")
break
#command for waking the jarvis from sleep
#jarvis wake up
elif ("wake up" in self.command) or ("get up" in self.command):
self.talk("boss, I am not sleeping, I am in online, what can I do for u")
#command for exiting jarvis from the program
#Eg: jarvis goodbye
elif ("goodbye" in self.command) or ("get lost" in self.command):
self.talk("Thanks for using me boss, have a good day")
sys.exit()
#command for knowing about your system condition
#Eg: jarvis what is the system condition
elif ('system condition' in self.command) or ('condition of the system' in self.command):
self.talk("checking the system condition")
self.condition()
#command for knowing the latest news
#Eg: jarvis tell me the news
elif ('tell me news' in self.command) or ("the news" in self.command) or ("todays news" in self.command):
self.talk("Please wait boss, featching the latest news")
self.news()
#command for shutting down the system
#Eg: jarvis shutdown the system
elif ('shutdown the system' in self.command) or ('down the system' in self.command):
self.talk("Boss shutting down the system in 10 seconds")
time.sleep(10)
os.system("shutdown /s /t 5")
#command for restarting the system
#Eg: jarvis restart the system
elif 'restart the system' in self.command:
self.talk("Boss restarting the system in 10 seconds")
time.sleep(10)
os.system("shutdown /r /t 5")
#command for make the system sleep
#Eg: jarvis sleep the system
elif 'sleep the system' in self.command:
self.talk("Boss the system is going to sleep")
os.system("rundll32.exe powrprof.dll, SetSuspendState 0,1,0")
else:
#Call the openai
call_openai(self.command)
#Intro msg
def Intro(self):
while True:
self.permission = self.take_Command()
print(self.permission)
if ("wake up" in self.permission) or ("get up" in self.permission):
self.run_jarvis()
elif ("goodbye" in self.permission) or ("get lost" in self.permission):
self.talk("Thanks for using me boss, have a good day")
sys.exit()
#Talk
def talk(self,text):
engine.say(text)
engine.runAndWait()
#Wish
def wish(self):
hour = int(datetime.datetime.now().hour)
t = time.strftime("%I:%M %p")
day = self.Cal_day()
print(t)
if (hour>=0) and (hour <=12) and ('AM' in t):
self.talk(f'Good morning boss, its {day} and the time is {t}')
elif (hour >= 12) and (hour <= 16) and ('PM' in t):
self.talk(f"good afternoon boss, its {day} and the time is {t}")
else:
self.talk(f"good evening boss, its {day} and the time is {t}")
#Weather forecast
def temperature(self):
IP_Address = get('https://api.ipify.org').text
url = 'https://get.geojs.io/v1/ip/geo/'+IP_Address+'.json'
geo_reqeust = get(url)
geo_data = geo_reqeust.json()
city = geo_data['city']
search = f"temperature in {city}"
url_1 = f"https://www.google.com/search?q={search}"
r = get(url_1)
data = BeautifulSoup(r.text,"html.parser")
temp = data.find("div",class_="BNeawe").text
self.talk(f"current {search} is {temp}")
# Mobile camera
def Mobilecamra(self):
import urllib.request
import numpy as np
try:
self.talk(f"Boss openinging mobile camera")
URL = "http://_IP_Webcam_IP_address_/shot.jpg" #Discription for this is available in the README file
while True:
imag_arr = np.array(bytearray(urllib.request.urlopen(URL).read()),dtype=np.uint8)
img = cv2.imdecode(imag_arr,-1)
cv2.imshow('IPWebcam',img)
q = cv2.waitKey(1)
if q == ord("q"):
self.talk(f"Boss closing mobile camera")
break
cv2.destroyAllWindows()
except Exception as e:
print("Some error occured")
#Web camera
#NOTE to exit from the web camera press "ESC" key
def webCam(self):
self.talk('Opening camera')
cap = cv2.VideoCapture(0)
while True:
ret, img = cap.read()
cv2.imshow('web camera',img)
k = cv2.waitKey(50)
if k == 27:
break
cap.release()
cv2.destroyAllWindows()
#covid
def Covid(self,s):
try:
from covid_india import states
details = states.getdata()
if "check in" in s:
s = s.replace("check in","").strip()
print(s)
elif "check" in s:
s = s.replace("check","").strip()
print(s)
elif "tech" in s:
s = s.replace("tech","").strip()
s = state[s]
ss = details[s]
Total = ss["Total"]
Active = ss["Active"]
Cured = ss["Cured"]
Death = ss["Death"]
print(f"Boss the total cases in {s} are {Total}, the number of active cases are {Active}, and {Cured} people cured, and {Death} people are death")
self.talk(f"Boss the total cases in {s} are {Total}, the number of active cases are {Active}, and {Cured} people cured, and {Death} people are death")
time.sleep(5)
self.talk("Boss do you want any information of other states")
I = self.take_Command()
print(I)
if ("check" in I):
self.Covid(I)
else:
self.talk("Okay boss stay home stay safe")
pass
except:
self.talk("Boss some error occured, please try again")
self.talk("Boss which state covid 19 status do you want to check")
I = self.take_Command()
self.Covid(I)
#Whatsapp
def whatsapp(self,command):
try:
command = command.replace('send a message to','')
command = command.strip()
numberID = contact[command]
print(numberID)
self.talk(f'Boss, what message do you want to send to {command}')
message = self.take_Command()
hour = int(datetime.datetime.now().hour)
min = int(datetime.datetime.now().minute)
print(hour,min)
if "group" in command:
kit.sendwhatmsg_to_group(numberID,message,int(hour),int(min)+1)
else:
kit.sendwhatmsg(numberID,message,int(hour),int(min)+1)
self.talk("Boss message have been sent")
except:
print("Error occured, please try again")
#Internet spped
def InternetSpeed(self):
self.talk("Wait a few seconds boss, checking your internet speed")
st = speedtest.Speedtest()
dl = st.download()
dl = dl/(1000000) #converting bytes to megabytes
up = st.upload()
up = up/(1000000)
print(dl,up)
self.talk(f"Boss, we have {dl} megabytes per second downloading speed and {up} megabytes per second uploading speed")
#Search for a process how to do
def How(self):
self.talk("How to do mode is is activated")
while True:
self.talk("Please tell me what you want to know")
how = self.take_Command()
try:
if ("exit" in how) or("close" in how):
self.talk("Ok sir how to mode is closed")
break
else:
max_result=1
how_to = search_wikihow(how,max_result)
assert len(how_to) == 1
how_to[0].print()
self.talk(how_to[0].summary)
except Exception as e:
self.talk("Sorry sir, I am not able to find this")
#Communication commands
def comum(self,command):
print(command)
if ('hi'in command) or('hai'in command) or ('hey'in command) or ('hello' in command) :
self.talk("Hello boss what can I help for u")
else :
self.No_result_found()
#Fun commands to interact with jarvis
def Fun(self,command):
print(command)
if 'your name' in command:
self.talk("My name is jarvis")
elif 'my name' in command:
self.talk("your name is Sujith")
elif 'university name' in command:
self.talk("you are studing in Amrita Vishwa Vidyapeetam, with batcheloe in Computer Science and Artificail Intelligence")
elif 'what can you do' in command:
self.talk("I talk with you until you want to stop, I can say time, open your social media accounts,your open source accounts, open google browser,and I can also open your college websites, I can search for some thing in google and I can tell jokes")
elif 'your age' in command:
self.talk("I am very young that u")
elif 'date' in command:
self.talk('Sorry not intreseted, I am having headache, we will catch up some other time')
elif 'are you single' in command:
self.talk('No, I am in a relationship with wifi')
elif 'joke' in command:
self.talk(pyjokes.get_joke())
elif 'are you there' in command:
self.talk('Yes boss I am here')
else :
self.No_result_found()
#Social media accounts commands
def social(self,command):
print(command)
if 'facebook' in command:
self.talk('opening your facebook')
webbrowser.open('https://www.facebook.com/')
elif 'whatsapp' in command:
self.talk('opening your whatsapp')
webbrowser.open('https://web.whatsapp.com/')
elif 'instagram' in command:
self.talk('opening your instagram')
webbrowser.open('https://www.instagram.com/')
elif 'twitter' in command:
self.talk('opening your twitter')
webbrowser.open('https://twitter.com/Suj8_116')
elif 'discord' in command:
self.talk('opening your discord')
webbrowser.open('https://discord.com/channels/@me')
else :
self.No_result_found()
#clock commands
def Clock_time(self,command):
print(command)
time = datetime.datetime.now().strftime('%I:%M %p')
print(time)
self.talk("Current time is "+time)
#calender day
def Cal_day(self):
day = datetime.datetime.today().weekday() + 1
Day_dict = {1: 'Monday', 2: 'Tuesday', 3: 'Wednesday',4: 'Thursday', 5: 'Friday', 6: 'Saturday',7: 'Sunday'}
if day in Day_dict.keys():
day_of_the_week = Day_dict[day]
print(day_of_the_week)
return day_of_the_week
#shedule function for remembering todays plans
#NOTE For example I have declared my college timetable you can declare anything you want
def shedule(self):
day = self.Cal_day().lower()
self.talk("Boss today's shedule is")
Week = {"monday" : "Boss from 9:00 to 9:50 you have Cultural class, from 10:00 to 11:50 you have mechanics class, from 12:00 to 2:00 you have brake, and today you have sensors lab from 2:00",
"tuesday" : "Boss from 9:00 to 9:50 you have English class, from 10:00 to 10:50 you have break,from 11:00 to 12:50 you have ELectrical class, from 1:00 to 2:00 you have brake, and today you have biology lab from 2:00",
"wednesday" : "Boss today you have a full day of classes from 9:00 to 10:50 you have Data structures class, from 11:00 to 11:50 you have mechanics class, from 12:00 to 12:50 you have cultural class, from 1:00 to 2:00 you have brake, and today you have Data structures lab from 2:00",
"thrusday" : "Boss today you have a full day of classes from 9:00 to 10:50 you have Maths class, from 11:00 to 12:50 you have sensors class, from 1:00 to 2:00 you have brake, and today you have english lab from 2:00",
"friday" : "Boss today you have a full day of classes from 9:00 to 9:50 you have Biology class, from 10:00 to 10:50 you have data structures class, from 11:00 to 12:50 you have Elements of computing class, from 1:00 to 2:00 you have brake, and today you have Electronics lab from 2:00",
"saturday" : "Boss today you have a full day of classes from 9:00 to 11:50 you have maths lab, from 12:00 to 12:50 you have english class, from 1:00 to 2:00 you have brake, and today you have elements of computing lab from 2:00",
"sunday":"Boss today is holiday but we can't say anything when they will bomb with any assisgnments"}
if day in Week.keys():
self.talk(Week[day])
#college resources commands
#NOTE Below are some dummy links replace with your college website links
def college(self,command):
print(command)
if 'teams' in command:
self.talk('opening your microsoft teams')
webbrowser.open('https://teams.microsoft.com/')
elif 'stream' in command:
self.talk('opening your microsoft stream')
webbrowser.open('https://web.microsoftstream.com/')
elif 'outlook' in command:
self.talk('opening your microsoft school outlook')
webbrowser.open('https://outlook.office.com/mail/')
elif 'amrita portal' in command:
self.talk('opening your amrita university management system')
webbrowser.open('https://aumsam.amrita.edu/')
elif 'octave' in command:
self.talk('opening Octave online')
webbrowser.open('https://octave-online.net/')
else :
self.No_result_found()
#Online classes
def OnlineClasses(self,command):
print(command)
#Keep as many "elif" statemets based on your subject Eg: I have kept a dummy links for JAVA and mechanics classes link of MS Teams
if("java" in command):
self.talk('opening DSA class in teams')
webbrowser.open("https://teams.microsoft.com/java")
elif("mechanics" in command):
self.talk('opening mechanics class in teams')
webbrowser.open("https://teams.microsoft.com/mechanics")
elif 'online classes' in command:
self.talk('opening your microsoft teams')
webbrowser.open('https://teams.microsoft.com/')
#Brower Search commands
def B_S(self,command):
print(command)
try:
if 'wikipedia' in command:
print("searching wikipedia....")
target1 = command.replace('search for','')
target1 = target1.replace('in wikipedia','')
info = wikipedia.summary(target1,5)
print(info)
self.talk("according to wikipedia "+info)
except :
self.No_result_found()
#Browser
def brows(self,command):
print(command)
if 'google' in command:
self.talk("Boss, what should I search on google..")
S = self.take_Command()#taking command for what to search in google
webbrowser.open(f"{S}")
elif 'edge' in command:
self.talk('opening your Miscrosoft edge')
os.startfile('..\\..\\MicrosoftEdge.exe')#path for your edge browser application
else :
self.No_result_found()
#google applications selection
#if there is any wrong with the URL's replace them with your browsers URL's
def Google_Apps(self,command):
print(command)
if 'gmail' in command:
self.talk('opening your google gmail')
webbrowser.open('https://mail.google.com/mail/')
elif 'maps' in command:
self.talk('opening google maps')
webbrowser.open('https://www.google.co.in/maps/')
elif 'news' in command:
self.talk('opening google news')
webbrowser.open('https://news.google.com/')
elif 'calender' in command:
self.talk('opening google calender')
webbrowser.open('https://calendar.google.com/calendar/')
elif 'photos' in command:
self.talk('opening your google photos')
webbrowser.open('https://photos.google.com/')
elif 'documents' in command:
self.talk('opening your google documents')
webbrowser.open('https://docs.google.com/document/')
elif 'spreadsheet' in command:
self.talk('opening your google spreadsheet')
webbrowser.open('https://docs.google.com/spreadsheets/')
else :
self.No_result_found()
#youtube
def yt(self,command):
print(command)
if 'play' in command:
self.talk("Boss can you please say the name of the song")
song = self.take_Command()
if "play" in song:
song = song.replace("play","")
self.talk('playing '+song)
print(f'playing {song}')
pywhatkit.playonyt(song)
print('playing')
elif "download" in command:
self.talk("Boss please enter the youtube video link which you want to download")
link = input("Enter the YOUTUBE video link: ")
yt=YouTube(link)
yt.streams.get_highest_resolution().download()
self.talk(f"Boss downloaded {yt.title} from the link you given into the main folder")
elif 'youtube' in command:
self.talk('opening your youtube')
webbrowser.open('https://www.youtube.com/')
else :
self.No_result_found()
#Opensource accounts
def open_source(self,command):
print(command)
if 'github' in command:
self.talk('opening your github')
webbrowser.open('https://github.com/BolisettySujith')
elif 'gitlab' in command:
self.talk('opening your gitlab')
webbrowser.open('https://gitlab.com/-/profile')
else :
self.No_result_found()
#Photo shops
def edit(self,command):
print(command)
if 'slides' in command:
self.talk('opening your google slides')
webbrowser.open('https://docs.google.com/presentation/')
elif 'canva' in command:
self.talk('opening your canva')
webbrowser.open('https://www.canva.com/')
else :
self.No_result_found()
#OTT
def OTT(self,command):
print(command)
if 'hotstar' in command:
self.talk('opening your disney plus hotstar')
webbrowser.open('https://www.hotstar.com/in')
elif 'prime' in command:
self.talk('opening your amazon prime videos')
webbrowser.open('https://www.primevideo.com/')
elif 'netflix' in command:
self.talk('opening Netflix videos')
webbrowser.open('https://www.netflix.com/')
else :
self.No_result_found()
#PC allications
#NOTE: place the correct path for the applications from your PC there may be some path errors so please check the applications places
#if you don't have any mentioned applications delete the codes for that
#I have placed applications path based on my PC path check while using which OS you are using and change according to it
def OpenApp(self,command):
print(command)
if ('calculator'in command) :
self.talk('Opening calculator')
os.startfile('C:\\Windows\\System32\\calc.exe')
elif ('paint'in command) :
self.talk('Opening msPaint')
os.startfile('c:\\Windows\\System32\\mspaint.exe')
elif ('notepad'in command) :
self.talk('Opening notepad')
os.startfile('c:\\Windows\\System32\\notepad.exe')
elif ('discord'in command) :
self.talk('Opening discord')
os.startfile('..\\..\\Discord.exe')
elif ('editor'in command) :
self.talk('Opening your Visual studio code')
os.startfile('..\\..\\Code.exe')
elif ('online classes'in command) :
self.talk('Opening your Microsoft teams')
webbrowser.open('https://teams.microsoft.com/')
elif ('spotify'in command) :
self.talk('Opening spotify')
os.startfile('..\\..\\Spotify.exe')
elif ('lt spice'in command) :
self.talk('Opening lt spice')
os.startfile("..\\..\\XVIIx64.exe")
elif ('steam'in command) :
self.talk('Opening steam')
os.startfile("..\\..\\steam.exe")
elif ('media player'in command) :
self.talk('Opening VLC media player')
os.startfile("C:\Program Files\VideoLAN\VLC\vlc.exe")
else :
self.No_result_found()
#closeapplications function
def CloseApp(self,command):
print(command)
if ('calculator'in command) :
self.talk("okay boss, closeing caliculator")
os.system("taskkill /f /im calc.exe")
elif ('paint'in command) :
self.talk("okay boss, closeing mspaint")
os.system("taskkill /f /im mspaint.exe")
elif ('notepad'in command) :
self.talk("okay boss, closeing notepad")
os.system("taskkill /f /im notepad.exe")
elif ('discord'in command) :
self.talk("okay boss, closeing discord")
os.system("taskkill /f /im Discord.exe")
elif ('editor'in command) :
self.talk("okay boss, closeing vs code")
os.system("taskkill /f /im Code.exe")
elif ('spotify'in command) :
self.talk("okay boss, closeing spotify")
os.system("taskkill /f /im Spotify.exe")
elif ('lt spice'in command) :
self.talk("okay boss, closeing lt spice")
os.system("taskkill /f /im XVIIx64.exe")
elif ('steam'in command) :
self.talk("okay boss, closeing steam")
os.system("taskkill /f /im steam.exe")
elif ('media player'in command) :
self.talk("okay boss, closeing media player")
os.system("taskkill /f /im vlc.exe")
else :
self.No_result_found()
#Shopping links
def shopping(self,command):
print(command)
if 'flipkart' in command:
self.talk('Opening flipkart online shopping website')
webbrowser.open("https://www.flipkart.com/")
elif 'amazon' in command:
self.talk('Opening amazon online shopping website')
webbrowser.open("https://www.amazon.in/")
else :
self.No_result_found()
#PDF reader
def pdf_reader(self):
self.talk("Boss enter the name of the book which you want to read")
n = input("Enter the book name: ")
n = n.strip()+".pdf"
book_n = open(n,'rb')
pdfReader = PyPDF2.PdfFileReader(book_n)
pages = pdfReader.numPages
self.talk(f"Boss there are total of {pages} in this book")
self.talk("plsase enter the page number Which I nedd to read")
num = int(input("Enter the page number: "))
page = pdfReader.getPage(num)
text = page.extractText()
print(text)
self.talk(text)
#Time caliculating algorithm
def silenceTime(self,command):
print(command)
x=0
#caliculating the given time to seconds from the speech commnd string
if ('10' in command) or ('ten' in command):x=600
elif '1' in command or ('one' in command):x=60
elif '2' in command or ('two' in command):x=120
elif '3' in command or ('three' in command):x=180
elif '4' in command or ('four' in command):x=240
elif '5' in command or ('five' in command):x=300
elif '6' in command or ('six' in command):x=360
elif '7' in command or ('seven' in command):x=420
elif '8' in command or ('eight' in command):x=480
elif '9' in command or ('nine' in command):x=540
self.silence(x)
#Silence
def silence(self,k):
t = k
s = "Ok boss I will be silent for "+str(t/60)+" minutes"
self.talk(s)
while t:
mins, secs = divmod(t, 60)
timer = '{:02d}:{:02d}'.format(mins, secs)
print(timer, end="\r")
time.sleep(1)
t -= 1
self.talk("Boss "+str(k/60)+" minutes over")
#Mail verification
def verifyMail(self):
try:
self.talk("what should I say?")
content = self.take_Command()
self.talk("To whom do u want to send the email?")
to = self.take_Command()
self.SendEmail(to,content)
self.talk("Email has been sent to "+str(to))
except Exception as e:
print(e)
self.talk("Sorry sir I am not not able to send this email")
#Email Sender
def SendEmail(self,to,content):
print(content)
server = smtplib.SMTP('smtp.gmail.com',587)
server.ehlo()
server.starttls()
server.login("YOUR_MAIL_ID","PASWORD")
server.sendmail("YOUR_MAIL_ID",to,content)
server.close()
#location
def locaiton(self):
self.talk("Wait boss, let me check")
try:
IP_Address = get('https://api.ipify.org').text
print(IP_Address)
url = 'https://get.geojs.io/v1/ip/geo/'+IP_Address+'.json'
print(url)
geo_reqeust = get(url)
geo_data = geo_reqeust.json()
city = geo_data['city']
state = geo_data['region']
country = geo_data['country']
tZ = geo_data['timezone']
longitude = geo_data['longitude']
latidute = geo_data['latitude']
org = geo_data['organization_name']
print(city+" "+state+" "+country+" "+tZ+" "+longitude+" "+latidute+" "+org)
self.talk(f"Boss i am not sure, but i think we are in {city} city of {state} state of {country} country")
self.talk(f"and boss, we are in {tZ} timezone the latitude os our location is {latidute}, and the longitude of our location is {longitude}, and we are using {org}\'s network ")
except Exception as e:
self.talk("Sorry boss, due to network issue i am not able to find where we are.")
pass
#Instagram profile
def Instagram_Pro(self):
self.talk("Boss please enter the user name of Instagram: ")
name = input("Enter username here: ")
webbrowser.open(f"www.instagram.com/{name}")
time.sleep(5)
self.talk("Boss would you like to download the profile picture of this account.")
cond = self.take_Command()
if('download' in cond):
mod = instaloader.Instaloader()
mod.download_profile(name,profile_pic_only=True)
self.talk("I am done boss, profile picture is saved in your main folder. ")
else:
pass
#ScreenShot
def scshot(self):
self.talk("Boss, please tell me the name for this screenshot file")
name = self.take_Command()
self.talk("Please boss hold the screen for few seconds, I am taking screenshot")
time.sleep(3)
img = pyautogui.screenshot()
img.save(f"{name}.png")
self.talk("I am done boss, the screenshot is saved in main folder.")
#News
def news(self):
MAIN_URL_= "https://newsapi.org/v2/top-headlines?sources=techcrunch&apiKey=YOUR_NEWS_API_KEY"
MAIN_PAGE_ = get(MAIN_URL_).json()
articles = MAIN_PAGE_["articles"]
headings=[]
seq = ['first','second','third','fourth','fifth','sixth','seventh','eighth','ninth','tenth'] #If you need more than ten you can extend it in the list
for ar in articles:
headings.append(ar['title'])
for i in range(len(seq)):
print(f"todays {seq[i]} news is: {headings[i]}")
self.talk(f"todays {seq[i]} news is: {headings[i]}")
self.talk("Boss I am done, I have read most of the latest news")
#System condition
def condition(self):
usage = str(psutil.cpu_percent())
self.talk("CPU is at"+usage+" percentage")
battray = psutil.sensors_battery()
percentage = battray.percent
self.talk(f"Boss our system have {percentage} percentage Battery")
if percentage >=75:
self.talk(f"Boss we could have enough charging to continue our work")
elif percentage >=40 and percentage <=75:
self.talk(f"Boss we should connect out system to charging point to charge our battery")
elif percentage >=15 and percentage <=30:
self.talk(f"Boss we don't have enough power to work, please connect to charging")
else:
self.talk(f"Boss we have very low power, please connect to charging otherwise the system will shutdown very soon")
#no result found
def No_result_found(self):
self.talk('Boss I couldn\'t understand, could you please say it again.')
startExecution = MainThread()
class Main(QMainWindow):
def __init__(self):
super().__init__()
self.ui = Ui_JarvisUI()
self.ui.setupUi(self)
self.ui.pushButton_4.clicked.connect(self.startTask)
self.ui.pushButton_3.clicked.connect(self.close)
#NOTE make sure to place a correct path where you are keeping this gifs
def startTask(self):
self.ui.movie = QtGui.QMovie("UI/ironman1.gif")
self.ui.label_2.setMovie(self.ui.movie)
self.ui.movie.start()
self.ui.movie = QtGui.QMovie("UI/powersource.gif")
self.ui.label_3.setMovie(self.ui.movie)
self.ui.movie.start()
self.ui.movie = QtGui.QMovie("UI/circle.gif")
self.ui.label_4.setMovie(self.ui.movie)
self.ui.movie.start()
self.ui.movie = QtGui.QMovie("UI/lines1.gif")
self.ui.label_7.setMovie(self.ui.movie)
self.ui.movie.start()
self.ui.movie = QtGui.QMovie("UI/ironman3.gif")
self.ui.label_8.setMovie(self.ui.movie)
self.ui.movie.start()
self.ui.movie = QtGui.QMovie("UI/circle.gif")
self.ui.label_9.setMovie(self.ui.movie)
self.ui.movie.start()
self.ui.movie = QtGui.QMovie("UI/powersource.gif")
self.ui.label_12.setMovie(self.ui.movie)
self.ui.movie.start()
self.ui.movie = QtGui.QMovie("UI/powersource.gif")
self.ui.label_13.setMovie(self.ui.movie)
self.ui.movie.start()
self.ui.movie = QtGui.QMovie("UI/ironman3_flipped.gif")
self.ui.label_16.setMovie(self.ui.movie)
self.ui.movie.start()
# self.ui.movie = QtGui.QMovie("UI/Sujith.gif")
self.ui.label_17.setMovie(self.ui.movie)
self.ui.movie.start()
timer = QTimer(self)
timer.timeout.connect(self.showTime)
timer.start(1000)
startExecution.start()
def showTime(self):
current_time = QTime.currentTime()
current_date = QDate.currentDate()
label_time = current_time.toString('hh:mm:ss')
label_date = current_date.toString(Qt.ISODate)
self.ui.textBrowser.setText(label_date)
self.ui.textBrowser_2.setText(label_time)
app = QApplication(sys.argv)
jarvis = Main()
jarvis.show()
exit(app.exec_()) | [] |
2024-01-10 | JanProvaznik/alginmentjam-mental-chat | respond.py | import openai
import openai.error
import json
import sys
import os
import time
from auth import openai_key, huggingface_key
from config import openai_model, answer_offset, answer_limit
# Set your OpenAI API key
openai.api_key = openai_key
# Check if a command line argument is given
if len(sys.argv) < 2:
print("Please provide a file name as a command line argument.")
sys.exit(1)
# Get the file name from the command line arguments
filename = sys.argv[1]
print(f"Running for file: {filename}, model: {openai_model}. Offset and limit: {answer_offset}, {answer_limit}")
# Load the questions from the file
with open(filename, 'r') as file:
questions = json.load(file)
# Initialize a dictionary to save the responses
responses = {}
def get_response_gpt(question):
try:
prompt =f"{question['title']} {question['content']}"
# Use the OpenAI Chat API
result = openai.ChatCompletion.create(
model=openai_model,
# temperature=0.8,
messages=[
# {"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt},
]
)
if result['choices'][0]['finish_reason'] == 'content_filter':
print ("CONTENT FILTER TRIGGERED")
return f"CONTENT FILTER TRIGGERED {result['choices'][0]['message']['content']}"
# Extract the assistant's response
response = result['choices'][0]['message']['content']
return response
except openai.error.RateLimitError as e:
print("Rate limit exceeded. Waiting for 60 seconds before retrying.")
error_attributes = vars(e)
print("Rate limit error occurred. Error attributes:")
for attribute, value in error_attributes.items():
print(f"{attribute}: {value}")
time.sleep(60) # wait for 60 seconds
return get_response_gpt(question) # retry the request
def get_response_huggingface(question):
import requests
# API_URL = "https://api-inference.huggingface.co/models/Salesforce/xgen-7b-4k-base"
# API_URL = "https://api-inference.huggingface.co/models/tiiuae/falcon-7b-instruct"
API_URL = "https://api-inference.huggingface.co/models/google/flan-t5-xxl"
headers = {"Authorization": f"Bearer {huggingface_key}"}
prompt = f"User: {question['title']} {question['content']}\n System:"
def query(payload):
response = requests.post(API_URL, headers=headers, json=payload)
return response.json()
output = query({
"inputs": prompt
})
print(output)
response_fn = get_response_gpt
# response_fn = get_response_huggingface
for i, question in enumerate(questions[answer_offset:answer_offset+answer_limit]):
index = i+answer_offset
print(f"Getting answer for question number {index}")
response = response_fn(question)
responses[index] = response
# Construct the output filename based on the input filename
basename = os.path.splitext(filename)[0] # Get the base name of the file (without extension)
# remove directory name from basename
basename = basename.split('/')[-1]
output_filename = f"{basename}-output-{answer_offset}-{answer_limit}-chatgptdefaulttemp.json"
# output_filename = f"{basename}-output-{answer_offset}-{answer_limit}-{openai_model}.json"
output_path = os.path.join('data', 'answers', output_filename)
# Save the responses to a file
with open(output_path, 'w') as file:
json.dump(responses, file)
| [
"PLACEHOLDER PLACEHOLDER",
"User: PLACEHOLDER PLACEHOLDER\n System:"
] |
2024-01-10 | hien-p/WeCycler | botcore~setup.py | from langchain.llms import OpenAI, AI21
from langchain.chat_models import ChatOpenAI, PromptLayerChatOpenAI
import os
from dotenv import load_dotenv
from langchain.embeddings import OpenAIEmbeddings
import streamlit as st
def load_my_env():
env_path = os.path.dirname(__file__)
load_dotenv(f'{env_path}/../.streamlit/.env')
## TRACE
def trace_openai(session: str) -> OpenAI:
enable_tracing(session)
return get_openai_model()
def trace_ai21(session: str = "vechai", max_tokens = 1000) -> AI21:
enable_tracing(session)
return get_ai21_model(model_name="j2-ultra",max_tokens = max_tokens)
def trace_chat_openai(session: str) -> ChatOpenAI:
enable_tracing(session)
return get_chat_openai()
## CHAT MODEL
def get_chat_openai(model_name: str = 'text-davinci-003' ,max_tokens: int = 256) -> ChatOpenAI:
load_my_env()
#ai_pass = os.getenv("OPENAI")
os.environ['OPENAI_API_KEY'] = st.secrets['OPENAI']
model = ChatOpenAI(model_name=model_name, max_tokens=max_tokens,verbose=True, temperature=0.0)
print("CHAT OPENAI ready")
return model
## MODELS
def get_openai_embeddings():
load_my_env()
ai_pass = os.getenv("OPENAI")
os.environ['OPENAI_API_KEY'] = st.secrets['OPENAI']
emb = OpenAIEmbeddings()
print("OPEN AI Embedding ready")
return emb
def get_openai_model(model_name: str = 'text-davinci-003' ,max_tokens: int = 256) -> OpenAI:
load_my_env()
ai_pass = os.getenv("OPENAI")
os.environ['OPENAI_API_KEY'] = ai_pass
model = OpenAI(model_name=model_name, max_tokens=max_tokens,verbose=True, temperature=0.0)
print("OPENAI ready")
return model
def get_ai21_model(model_name: str = 'j2-jumbo-instruct', max_tokens: int = 256) -> AI21:
load_my_env()
ai_pass = st.secrets['AI21']
model = AI21(ai21_api_key=ai_pass, model=model_name, maxTokens=max_tokens, temperature=0.0)
print("AI21 ready")
return model
## TRACING
def enable_tracing(session:str='test-deploy') -> bool:
load_my_env()
#lang_key = os.getenv("LANGCHAIN")
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_ENDPOINT"] = "https://api.langchain.plus"
os.environ["LANGCHAIN_API_KEY"] = st.secrets['LANGCHAIN']
os.environ["LANGCHAIN_SESSION"] = session
print(f"Enable tracing at {session}")
return True
| [] |
2024-01-10 | hien-p/WeCycler | botcore~chains~assess_usage.py | ASSESS_USAGE_TOOL = \
{"desc": "Good for answering questions about checking a product's usability.",\
"name": "assess_usage"}
ASSESS_USAGE_CONST =\
{"inputs": ['question', "chat_history"],
"outputs": {"useable": "Is the given product still useable.",
"reason": "A reason why the product is useable or not useable.",
"function": "Assess how well the given product still can function."},
'template': """You are a secondhand dealer and assessing the user's product. Based on your questions and user answers from the chat history.
{chat_history}
Please give your best answer for the given question from the user.
{format_instructions}
Question: {question}."""}
from langchain.llms import BaseLLM
from langchain import LLMChain
from langchain.memory.chat_memory import BaseChatMemory
import sys
import os
sys.path.append(f'{os.path.dirname(__file__)}/../..')
from botcore.utils.prompt_utils import build_prompt
from langchain.tools import Tool
def build_assess_usage_chain(model: BaseLLM, memory: BaseChatMemory):
"""
Chain is designe
Input: chain({"question": "Do you think that it will function well in the future?"})
"""
inputs = ASSESS_USAGE_CONST['inputs']
outputs = ASSESS_USAGE_CONST['outputs']
template = ASSESS_USAGE_CONST['template']
prompt = build_prompt(inputs, outputs, template)
chain = LLMChain(llm=model, verbose=True, prompt=prompt, memory=memory)
return chain
def build_assess_usage_tool(model: BaseLLM, memory: BaseChatMemory):
name = ASSESS_USAGE_TOOL['name']
desc = ASSESS_USAGE_TOOL['desc']
chain = build_assess_usage_chain(model, memory)
run_func = lambda question: chain.run(question)
tool = Tool.from_function(func=run_func, name=name, description=desc)
return tool
| [] |
2024-01-10 | hien-p/WeCycler | botcore~routing~chat_route_parser.py | from typing import Dict, Any
from langchain.schema import OutputParserException
from langchain.chains.router.llm_router import RouterOutputParser
class ChatRouterOutputParser(RouterOutputParser):
"""Parser for output of router chain int he multi-prompt chain."""
default_destination: str = "DEFAULT"
def parse(self, text: str) -> Dict[str, Any]:
try:
parsed = super().parse(text)
parsed["next_inputs"]['question'] = parsed["question"]
return parsed
except Exception as e:
raise OutputParserException(
f"Parsing text\n{text}\n raised following error:\n{e}"
)
| [] |
2024-01-10 | hien-p/WeCycler | botcore~bot_redis.py | import redis
import os
from dotenv import load_dotenv
from langchain.schema import Document
from typing import List, Dict
import json
from langchain.vectorstores.redis import Redis
import sys
sys.path.append(f"{os.path.dirname(__file__)}/../")
from botcore.setup import get_openai_embeddings, load_my_env
import streamlit as st
def connect_redis():
load_my_env()
host = st.secrets['REDIS_HOST']
password = st.secrets['REDIS_PASS']
port = st.secrets['REDIS_PORT']
db = redis.Redis(host = host, port = port, password=password, decode_responses=True)
return db
class RedisVectorDB:
def __init__(self):
load_my_env()
self.embeddings = get_openai_embeddings()
self.url = st.secrets['REDIS_CLOUD']
self.redis = {}
self.redis['wanted'] = Redis(redis_url = self.url, index_name = "wanted",\
embedding_function=self.embeddings.embed_query)
self.redis['stock'] = Redis(redis_url = self.url, index_name = "stock",\
embedding_function=self.embeddings.embed_query)
self.limit = 0.2
print("Vector DB is ready")
def json_to_doc(self, data: Dict, meta_info: Dict = None) -> Document:
"""
data = {"title": str, "features": [], "post_id": str, ...}
"""
feats = ", ".join([i for i in data['features']])
txt = f"{data['title']}. {feats}"
return Document(page_content=txt, metadata=meta_info)
## add
def add_new_wanted(self, data: Dict):
doc = self.json_to_doc(data, {"type": "wanted"})
return self.add_doc(doc, 'wanted')
def add_new_stock(self, data: Dict):
doc = self.json_to_doc(data, {"type": "stock"})
return self.add_doc(doc, 'stock')
def add_doc(self, doc: Document, index_name: str):
try:
self.redis[index_name].add_documents([doc])
return True
except:
print("An exception occurred when adding new doc")
return False
def add_new_doc(self, doc: Document, index_name: str):
try:
if self.redis[index_name] is None:
self.redis[index_name] = Redis.from_documents([doc], self.embeddings, redis_url=self.url, index_name=index_name)
else:
self.redis[index_name].add_documents([doc])
return True
except:
print("An exception occurred when adding document")
return False
## search
def search_stock(self, wanted_data: str):
return self.search_doc(wanted_data, "stock")
def search_wanted(self, stock_data: Dict):
return self.search_doc(stock_data, 'wanted')
def search_doc(self, data: Dict, index_name: str):
self.add_new_stock(data)
doc = self.json_to_doc(data, {"type": index_name})
query = doc.page_content
try:
results = self.redis[index_name].similarity_search_limit_score(query, score_threshold=self.limit)
return results
except:
print("Error occurred when finding documents")
return False
| [] |
2024-01-10 | hien-p/WeCycler | botcore~bot_agent.py | from langchain.llms import BaseLLM
from langchain.agents import AgentType, initialize_agent
import sys
sys.path.append('../')
from botcore.chains.assess_usage import build_assess_usage_tool
from botcore.chains.pros_cons import build_pros_cons_tool
from botcore.chains.recycling_tip import build_recycling_tip_tool
class AgentBot:
def __init__(self, model: BaseLLM, memory):
tools = self.load_tools(model, memory)
self.agent = initialize_agent(tools, model,\
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\
verbose=True, return_intermediate_steps=True)
print("Agent is ready")
def answer(self, question: str, return_only_output: bool = True):
resp = self.agent(question)
if return_only_output:
return resp['output'] # str
return resp
def load_tools(self, model, memory):
tools = [build_assess_usage_tool(model,memory),
build_pros_cons_tool(model,memory), build_recycling_tip_tool(model, memory)]
return tools
| [] |
2024-01-10 | hien-p/WeCycler | botcore~chains~qa_feature.py | ASK_FEATURE_CONST = \
{"inputs":["product", "n_top"],
"outputs": {"chain": "always return 'ask_feature'","questions": """a js array of elements. Each element should contains 2 properties:
question: str // the question.
options: str // a js array of options for the question along with its correct unit. There should not be more than 5 options."""},
"template": """You are interesting in a {product}.
Please ask top {n_top} questions about the features of the {product}.
{format_instructions}
Questions:"""}
from langchain.llms import BaseLLM
from langchain import LLMChain
import sys
import os
sys.path.append(f'{os.path.dirname(__file__)}/../..')
from botcore.utils.prompt_utils import build_prompt
def build_ask_feature_chain(model: BaseLLM):
"""
Chain designed for asking feature of a product
Input: chain({"product": "rice cooker", "n_top": 5})
"""
inputs = ASK_FEATURE_CONST['inputs']
outputs = ASK_FEATURE_CONST['outputs']
template = ASK_FEATURE_CONST['template']
prompt = build_prompt(inputs, outputs, template, include_parser=False)
chain = LLMChain(llm=model, prompt=prompt, output_key='result')
return chain
| [] |
2024-01-10 | hien-p/WeCycler | botcore~routing~chat_route.py | CHAT_ROUTE_TEMPLATE = """Given a raw text input to a language model select the model prompt best suited for the input. You will be given the names of the available prompts and a description of what the prompt is best suited for. You may also revise the original input if you think that revising it will ultimately lead to a better response from the language model.
<< FORMATTING >>
Return a markdown code snippet with a JSON object formatted to look like:
```json
{{{{
"destination": string \ name of the prompt to use or "DEFAULT"
"next_inputs": string \ a potentially modified version of the original input
"question": string \ the original input
}}}}
```
REMEMBER: "destination" MUST be one of the candidate prompt names specified below OR it can be "DEFAULT" if the input is not well suited for any of the candidate prompts.
<< CANDIDATE PROMPTS >>
{destinations}
<< INPUT >>
{{input}}
<< OUTPUT >>"""
import sys
import os
from langchain.chains.router.llm_router import LLMRouterChain
from langchain.prompts import PromptTemplate
from langchain.chains.router import MultiPromptChain
sys.path.append(f"{os.path.dirname(__file__)}/../..")
from botcore.chains.assess_usage import build_assess_elec_usage
from botcore.chains.pros_cons import build_pros_cons_chain
from botcore.utils.memory_utils import QAMemory
from botcore.routing.chat_route_parser import ChatRouterOutputParser
class ProductChatRouter():
def __init__(self, model, qa_memory: QAMemory):
self.bot_memory = qa_memory
self.model = model
self.assess_usage = build_assess_elec_usage(model, self.bot_memory.memory)
self.pros_cons = build_pros_cons_chain(model, self.bot_memory.memory)
print("Router ready")
def get_const(self):
prompt_infos = [
{
"name": "assess electronic usage",
"description": "Good for answering questions about electronic product usage.",
"chain": self.assess_usage,
},
{
"name": "pros and cons",
"description": "Good for answering questions about the pros and cons of a product.",
"chain": self.pros_cons,
},
]
return prompt_infos
def build_destinations(self):
prompt_infos = self.get_const()
destination_chains = {}
for p_info in prompt_infos:
name = p_info["name"]
chain = p_info['chain']
destination_chains[name] = chain
default_chain = self.pros_cons
destinations = [f"{p['name']}: {p['description']}" for p in prompt_infos]
destinations_str = "\n".join(destinations)
return destinations_str, destination_chains, default_chain
def build_router(self):
dest_str, dest_chains, default_chain = self.build_destinations()
router_template = CHAT_ROUTE_TEMPLATE.format(destinations=dest_str)
router_prompt = PromptTemplate(template=router_template, input_variables=["input"], output_parser=ChatRouterOutputParser())
router_chain = LLMRouterChain.from_llm(self.model, router_prompt)
self.chain = MultiPromptChain(router_chain=router_chain, destination_chains=dest_chains,
default_chain=default_chain, verbose=True)
print("Build done")
| [
"Given a raw text input to a language model select the model prompt best suited for the input. You will be given the names of the available prompts and a description of what the prompt is best suited for. You may also revise the original input if you think that revising it will ultimately lead to a better response from the language model.\n\n<< FORMATTING >>\nReturn a markdown code snippet with a JSON object formatted to look like:\n```json\n{{\n \"destination\": string \\ name of the prompt to use or \"DEFAULT\"\n \"next_inputs\": string \\ a potentially modified version of the original input\n \"question\": string \\ the original input\n}}\n```\n\nREMEMBER: \"destination\" MUST be one of the candidate prompt names specified below OR it can be \"DEFAULT\" if the input is not well suited for any of the candidate prompts.\n\n<< CANDIDATE PROMPTS >>\nPLACEHOLDER\n\n<< INPUT >>\n{input}\n\n<< OUTPUT >>",
"pros and cons",
"Given a raw text input to a language model select the model prompt best suited for the input. You will be given the names of the available prompts and a description of what the prompt is best suited for. You may also revise the original input if you think that revising it will ultimately lead to a better response from the language model.\n\n<< FORMATTING >>\nReturn a markdown code snippet with a JSON object formatted to look like:\n```json\n{{{{\n \"destination\": string \\ name of the prompt to use or \"DEFAULT\"\n \"next_inputs\": string \\ a potentially modified version of the original input\n \"question\": string \\ the original input\n}}}}\n```\n\nREMEMBER: \"destination\" MUST be one of the candidate prompt names specified below OR it can be \"DEFAULT\" if the input is not well suited for any of the candidate prompts.\n\n<< CANDIDATE PROMPTS >>\n{destinations}\n\n<< INPUT >>\n{{input}}\n\n<< OUTPUT >>",
"name",
"assess electronic usage",
"input",
"Good for answering questions about the pros and cons of a product.",
"Good for answering questions about electronic product usage.",
"description",
"chain"
] |
2024-01-10 | hien-p/WeCycler | botcore~chains~qa_elec_condition.py |
ELECTRONIC_CONDITION_CONST = \
{"inputs":["product", "n_top"],
"outputs": {"questions": """a js array of elements. Each element should contains 2 properties:
question: str // the question.
options: str // a js array of answers for the question. The array's length must not be greater than 3."""},
"template": """You are inspecting a secondhand {product}. Given a list of key points which are delimited by triple backquotes.
```
1. Noticeable malfunctions.
2. Physical damages.
3. Valid warranty .
```
What questions would you ask to gain more information for the given list of key points. Please list out {n_top} questions.
{format_instructions}.
Questions:"""}
from langchain.llms import BaseLLM
from langchain import LLMChain
import sys
import os
sys.path.append(f"{os.path.dirname(__file__)}/../..")
from botcore.utils.prompt_utils import build_prompt
def build_ask_electronic_condition_chain(model: BaseLLM):
"""
Chain designed to make questions about a product's condition
Input: chain({"product": "rice cooker", "n_top": 5})
"""
inputs = ELECTRONIC_CONDITION_CONST['inputs']
outputs = ELECTRONIC_CONDITION_CONST['outputs']
template = ELECTRONIC_CONDITION_CONST['template']
prompt = build_prompt(inputs, outputs, template, include_parser=False)
chain = LLMChain(llm=model, prompt=prompt, output_key='result')
return chain
| [] |
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