Spaces:
Paused
Paused
File size: 10,394 Bytes
ae4e1e8 963efd5 ae4e1e8 561db8a ae4e1e8 561db8a ae4e1e8 561db8a ae4e1e8 561db8a ae4e1e8 561db8a ae4e1e8 5ed94e2 ae4e1e8 561db8a ae4e1e8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 |
import os
import json
import requests
import subprocess
import torch
import transformers
from urllib.parse import quote
from typing import Mapping, Optional, List, Any
from huggingface_hub import snapshot_download
from transformers import AutoTokenizer, AutoModelForCausalLM
from langchain import PromptTemplate, HuggingFaceHub, LLMChain
from langchain.llms import HuggingFacePipeline
from langchain.llms.base import LLM
from langchain.embeddings import HuggingFaceEmbeddings, HuggingFaceHubEmbeddings, HuggingFaceInstructEmbeddings
from langchain.vectorstores import FAISS
from .mocks import MockLocalBinaryModel
from api.logger import logger
from api.question_answering.response import Response
class LocalBinaryModel(LLM):
model_id: str = None
llm: None = None
def __init__(self, model_id: str = None):
super().__init__()
from llama_cpp import Llama
model_path = f'api/question_answering/{model_id}'
if not os.path.exists(model_path):
raise ValueError(f'{model_path} does not exist')
self.model_id = model_id
self.llm = Llama(model_path=model_path, n_ctx=4096)
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
prompt = f'Q: {prompt} A: '
output = self.llm(
prompt,
max_tokens=1024,
stop=['Q:'],
echo=False
)
output_text = output['choices'][0]['text']
return output_text
@property
def _identifying_params(self) -> Mapping[str, Any]:
return {"name_of_model": self.model_id}
@property
def _llm_type(self) -> str:
return self.model_id
class TransformersPipelineModel(LLM):
model_id: str = None
pipeline: str = None
def __init__(self, model_id: str = None):
super().__init__()
self.model_id = model_id
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
load_in_8bit=False,
device_map="auto",
resume_download=True,
)
self.pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=2048,
)
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
output_text = self.pipeline(prompt)[0]['generated_text']
output_text = output_text.replace(prompt+'\n', '')
return output_text
@property
def _identifying_params(self) -> Mapping[str, Any]:
return {"name_of_model": self.model_id}
@property
def _llm_type(self) -> str:
return self.model_id
class APIServedModel(LLM):
model_url: str = None
debug: bool = None
def __init__(self, model_url: str = None, debug: bool = None):
super().__init__()
if model_url[-1] == '/':
raise ValueError('URL should not end with a slash - "/"')
self.model_url = model_url
self.debug = debug
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
prompt_encoded = quote(prompt, safe='')
url = f'{self.model_url}/?prompt={prompt_encoded}'
if self.debug:
logger.info(f'URL: {url}')
try:
response = requests.get(url, timeout=1200, verify=False)
response.raise_for_status()
output_text = json.loads(response.content)['output_text']
return output_text
except Exception as err:
logger.error(f'Error: {err}')
return f'Error: {err}'
@property
def _identifying_params(self) -> Mapping[str, Any]:
return {"name_of_model": f'model url: {self.model_url}'}
@property
def _llm_type(self) -> str:
return 'api_model'
class QAModel():
"""
QAModel class, used for generating answers to questions.
Args:
llm_model_id (str): The ID of the LLM model to be used.
embedding_model_id (str): The ID of the embedding model to be used.
index_repo_id (str): The ID of the index repository to be used.
run_locally (bool, optional): Whether to run the models locally or on the Hugging Face hub. Defaults to True.
use_docs_for_context (bool, optional): Whether to use relevant documents as context for generating answers.
Defaults to True.
use_messages_for_context (bool, optional): Whether to use previous messages as context for generating answers.
Defaults to True.
debug (bool, optional): Whether to log debug information. Defaults to False.
Attributes:
use_docs_for_context (bool): Whether to use relevant documents as context for generating answers.
use_messages_for_context (bool): Whether to use previous messages as context for generating answers.
debug (bool): Whether to log debug information.
llm_model (Union[LocalBinaryModel, HuggingFacePipeline, HuggingFaceHub]): The LLM model to be used.
embedding_model (Union[HuggingFaceInstructEmbeddings, HuggingFaceHubEmbeddings]): The embedding model to be used.
prompt_template (PromptTemplate): The prompt template to be used.
llm_chain (LLMChain): The LLM chain to be used.
knowledge_index (FAISS): The FAISS index to be used.
"""
def __init__(
self,
llm_model_id: str,
embedding_model_id: str,
index_repo_id: str,
use_docs_for_context: bool = True,
add_sources_to_response: bool = True,
use_messages_for_context: bool = True,
num_relevant_docs: int = 3,
debug: bool = False
):
super().__init__()
self.use_docs_for_context = use_docs_for_context
self.add_sources_to_response = add_sources_to_response
self.use_messages_for_context = use_messages_for_context
self.num_relevant_docs = num_relevant_docs
self.debug = debug
if 'local_models/' in llm_model_id:
logger.info('using local binary model')
self.llm_model = LocalBinaryModel(
model_id=llm_model_id
)
elif 'api_models/' in llm_model_id:
logger.info('using api served model')
self.llm_model = APIServedModel(
model_url=llm_model_id.replace('api_models/', ''),
debug=self.debug
)
else:
logger.info('using transformers pipeline model')
self.llm_model = TransformersPipelineModel(
model_id=llm_model_id
)
prompt_template = \
"### Instruction:\n" \
"Give an answer that contains all the necessary information for the question.\n" \
"If the context contains necessary information to answer question, use it to generate an appropriate response.\n" \
"{context}\n### Input:\n{question}\n### Response:"
prompt = PromptTemplate(
template=prompt_template,
input_variables=['question', 'context']
)
self.llm_chain = LLMChain(prompt=prompt, llm=self.llm_model)
if self.use_docs_for_context:
logger.info(f'Downloading {index_repo_id}')
snapshot_download(
repo_id=index_repo_id,
allow_patterns=['*.faiss', '*.pkl'],
repo_type='dataset',
local_dir='indexes/run/'
)
logger.info('Loading embedding model')
embed_instruction = "Represent the Hugging Face library documentation"
query_instruction = "Query the most relevant piece of information from the Hugging Face documentation"
embedding_model = HuggingFaceInstructEmbeddings(
model_name=embedding_model_id,
embed_instruction=embed_instruction,
query_instruction=query_instruction
)
logger.info('Loading index')
self.knowledge_index = FAISS.load_local(f"./indexes/run/", embedding_model)
def get_response(self, question: str, messages_context: str = '') -> Response:
"""
Generate an answer to the specified question.
Args:
question (str): The question to be answered.
messages_context (str, optional): The context to be used for generating the answer. Defaults to ''.
Returns:
response (Response): The Response object containing the generated answer and the sources of information
used to generate the response.
"""
response = Response()
context = 'Give an answer that contains all the necessary information for the question.\n'
relevant_docs = ''
if self.use_messages_for_context and messages_context:
messages_context = f'\nPrevious questions and answers:\n{messages_context}'
context += messages_context
if self.use_docs_for_context:
logger.info(f'Retriving documents')
relevant_docs = self.knowledge_index.similarity_search(
query=messages_context+question,
k=self.num_relevant_docs
)
context += '\nExtracted documents:\n'
context += "".join([doc.page_content for doc in relevant_docs])
metadata = [doc.metadata for doc in relevant_docs]
response.set_sources(sources=[str(m['source']) for m in metadata])
logger.info(f'Running LLM chain')
answer = self.llm_chain.run(question=question, context=context)
response.set_answer(answer)
logger.info(f'Received answer')
if self.debug:
sep = '\n' + '-' * 100
logger.info(sep)
logger.info(f'messages_contex: {messages_context} {sep}')
logger.info(f'relevant_docs: {relevant_docs} {sep}')
sources_str = '\n'.join(response.get_sources())
logger.info(f"sources:\n{sources_str} {sep}")
logger.info(f'context len: {len(context)} {sep}')
logger.info(f'context: {context} {sep}')
logger.info(f'question len: {len(question)}')
logger.info(f'question: {question} {sep}')
logger.info(f'response: {response.get_answer()} {sep}')
return response
|