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import yaml
import fitz
import torch
import gradio as gr
from PIL import Image
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import ConversationalRetrievalChain
from langchain.document_loaders import PyPDFLoader
from langchain.prompts import PromptTemplate
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import spaces
from langchain_text_splitters import RecursiveCharacterTextSplitter
from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType
from datasets import Dataset, load_from_disk
import faiss
import numpy as np
from pastebin_api import get_protected_content
class RAGbot:
def __init__(self, config_path="config.yaml"):
self.processed = False
self.page = 0
self.chat_history = []
self.prompt = None
self.documents = None
self.embeddings = None
self.zilliz_vectordb = None
self.hf_vectordb = None
self.tokenizer = None
self.model = None
self.pipeline = None
self.chain = None
self.chunk_size = 512
self.overlap_percentage = 50
self.max_chunks_in_context = 2
self.current_context = None
self.model_temperatue = 0.5
self.format_seperator = "\n\n--\n\n"
self.pipe = None
with open(config_path, "r") as file:
config = yaml.safe_load(file)
self.model_embeddings = config["modelEmbeddings"]
self.auto_tokenizer = config["autoTokenizer"]
self.auto_model_for_causal_lm = config["autoModelForCausalLM"]
self.zilliz_config = config["zilliz"]
self.persona_paste_key = config["personaPasteKey"]
def connect_to_zilliz(self):
connections.connect(
host=self.zilliz_config["host"],
port=self.zilliz_config["port"],
user=self.zilliz_config["user"],
password=self.zilliz_config["password"],
secure=True
)
self.zilliz_vectordb = Collection(self.zilliz_config["collection"])
def load_embeddings(self):
self.embeddings = HuggingFaceEmbeddings(model_name=self.model_embeddings)
def load_hf_vectordb(self, dataset_path, index_path):
dataset = load_from_disk(dataset_path)
index = faiss.read_index(index_path)
self.hf_vectordb = (dataset, index)
@spaces.GPU
def load_tokenizer(self):
self.tokenizer = AutoTokenizer.from_pretrained(self.auto_tokenizer)
@spaces.GPU
def create_organic_pipeline(self):
self.pipe = pipeline(
"text-generation",
model=self.auto_model_for_causal_lm,
model_kwargs={"torch_dtype": torch.bfloat16},
device="cuda",
)
def get_organic_context(self, query, use_hf=False):
if use_hf:
dataset, index = self.hf_vectordb
D, I = index.search(np.array([self.embeddings.embed_query(query)]), self.max_chunks_in_context)
context = self.format_seperator.join([dataset[i] for i in I[0]])
else:
result = self.zilliz_vectordb.search(
data=[self.embeddings.embed_query(query)],
anns_field="embeddings",
param={"metric_type": "IP", "params": {"nprobe": 10}},
limit=self.max_chunks_in_context,
expr=None,
)
context = self.format_seperator.join([hit.entity.get('text') for hit in result[0]])
self.current_context = context
def load_persona_data(self):
persona_content = get_protected_content(self.persona_paste_key)
persona_data = yaml.safe_load(persona_content)
self.persona_text = persona_data["persona_text"]
@spaces.GPU
def create_organic_response(self, history, query, use_hf=False):
self.get_organic_context(query, use_hf=use_hf)
messages = [
{"role": "system", "content": f"Based on the given context, answer the user's question while maintaining the persona:\n{self.persona_text}\n\nContext:\n{self.current_context}"},
{"role": "user", "content": query},
]
prompt = self.pipe.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
temp = 0.1
outputs = self.pipe(
prompt,
max_new_tokens=1024,
do_sample=True,
temperature=temp,
top_p=0.9,
)
return outputs[0]["generated_text"][len(prompt):]
def process_file(self, file):
self.documents = PyPDFLoader(file.name).load()
self.load_embeddings()
self.connect_to_zilliz()
@spaces.GPU
def generate_response(self, history, query, file, chunk_size, chunk_overlap_percentage, model_temperature, max_chunks_in_context, use_hf_index=False, hf_dataset_path=None, hf_index_path=None):
self.chunk_size = chunk_size
self.overlap_percentage = chunk_overlap_percentage
self.model_temperatue = model_temperature
self.max_chunks_in_context = max_chunks_in_context
if not query:
raise gr.Error(message='Submit a question')
if use_hf_index:
if not hf_dataset_path or not hf_index_path:
raise gr.Error(message='Provide HuggingFace dataset and index paths')
self.load_hf_vectordb(hf_dataset_path, hf_index_path)
result = self.create_organic_response(history="", query=query, use_hf=True)
else:
if not file:
raise gr.Error(message='Upload a PDF')
if not self.processed:
self.process_file(file)
self.processed = True
result = self.create_organic_response(history="", query=query)
self.load_persona_data()
result = f"{self.persona_text}\n\n{result}"
for char in result:
history[-1][-1] += char
return history, ""
def render_file(self, file, chunk_size, chunk_overlap_percentage, model_temperature, max_chunks_in_context):
doc = fitz.open(file.name)
page = doc[self.page]
self.chunk_size = chunk_size
self.overlap_percentage = chunk_overlap_percentage
self.model_temperatue = model_temperature
self.max_chunks_in_context = max_chunks_in_context
pix = page.get_pixmap(matrix=fitz.Matrix(300 / 72, 300 / 72))
image = Image.frombytes('RGB', [pix.width, pix.height], pix.samples)
return image
def add_text(self, history, text):
if not text:
raise gr.Error('Enter text')
history.append((text, ''))
return history |