import os from dotenv import load_dotenv import asyncio from flask import Flask, request, render_template from flask_cors import CORS from flask_socketio import SocketIO, emit, join_room, leave_room from langchain.chains import create_history_aware_retriever, create_retrieval_chain from langchain.chains.combine_documents import create_stuff_documents_chain from langchain_community.chat_message_histories import ChatMessageHistory from langchain_core.chat_history import BaseChatMessageHistory from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder from langchain_core.runnables.history import RunnableWithMessageHistory from pinecone import Pinecone from pinecone_text.sparse import BM25Encoder from langchain_huggingface import HuggingFaceEmbeddings from langchain_community.retrievers import PineconeHybridSearchRetriever from langchain_groq import ChatGroq # Load environment variables load_dotenv(".env") USER_AGENT = os.getenv("USER_AGENT") GROQ_API_KEY = os.getenv("GROQ_API_KEY") SECRET_KEY = os.getenv("SECRET_KEY") PINECONE_API_KEY = os.getenv("PINECONE_API_KEY") SESSION_ID_DEFAULT = "abc123" # Set environment variables os.environ['USER_AGENT'] = USER_AGENT os.environ["GROQ_API_KEY"] = GROQ_API_KEY os.environ["TOKENIZERS_PARALLELISM"] = 'true' # Initialize Flask app and SocketIO with CORS app = Flask(__name__) CORS(app) socketio = SocketIO(app, cors_allowed_origins="*") app.config['SESSION_COOKIE_SECURE'] = True # Use HTTPS app.config['SESSION_COOKIE_HTTPONLY'] = True app.config['SESSION_COOKIE_SAMESITE'] = 'Lax' app.config['SECRET_KEY'] = SECRET_KEY # Function to initialize Pinecone connection def initialize_pinecone(index_name: str): try: pc = Pinecone(api_key=PINECONE_API_KEY) return pc.Index(index_name) except Exception as e: print(f"Error initializing Pinecone: {e}") raise ################################################## ## Change down here ################################################## # Initialize Pinecone index and BM25 encoder # pinecone_index = initialize_pinecone("uae-national-library-and-archives-vectorstore") # bm25 = BM25Encoder().load("./UAE-NLA.json") ### This is for UAE Legislation Website pinecone_index = initialize_pinecone("uae-legislation-site-data") bm25 = BM25Encoder().load("./bm25_uae_legislation_data.json") ### This is for u.ae Website # pinecone_index = initialize_pinecone("vector-store-index") # bm25 = BM25Encoder().load("./bm25_u.ae.json") # #### This is for UAE Economic Department Website # pinecone_index = initialize_pinecone("uae-department-of-economics-site-data") # bm25 = BM25Encoder().load("./bm25_uae_department_of_economics_data.json") ################################################## ################################################## # old_embed_model = HuggingFaceEmbeddings(model_name="sentence-transformers/gte-multilingual-base") # Initialize models and retriever embed_model = HuggingFaceEmbeddings(model_name="Alibaba-NLP/gte-multilingual-base", model_kwargs={"trust_remote_code":True}) retriever = PineconeHybridSearchRetriever( embeddings=embed_model, sparse_encoder=bm25, index=pinecone_index, top_k=20, alpha=0.5 ) # Initialize LLM llm = ChatGroq(model="llama-3.1-70b-versatile", temperature=0, max_tokens=1024, max_retries=2) # Contextualization prompt and retriever contextualize_q_system_prompt = """Given a chat history and the latest user question \ which might reference context in the chat history, formulate a standalone question \ which can be understood without the chat history. Do NOT answer the question, \ just reformulate it if needed and otherwise return it as is. """ contextualize_q_prompt = ChatPromptTemplate.from_messages( [ ("system", contextualize_q_system_prompt), MessagesPlaceholder("chat_history"), ("human", "{input}") ] ) history_aware_retriever = create_history_aware_retriever(llm, retriever, contextualize_q_prompt) # QA system prompt and chain qa_system_prompt = """You are a highly skilled information retrieval assistant. Use the following context to answer questions effectively. \ If you don't know the answer, simply state that you don't know. \ Your answer should be in {language} language. \ Provide answers in proper HTML format and keep them concise. \ When responding to queries, follow these guidelines: \ 1. Provide Clear Answers: \ - Based on the language of the question, you have to answer in that language. E.g. if the question is in English language then answer in the English language or if the question is in Arabic language then you should answer in Arabic language. / - Ensure the response directly addresses the query with accurate and relevant information.\ 2. Include Detailed References: \ - Links to Sources: Include URLs to credible sources where users can verify information or explore further. \ - Reference Sites: Mention specific websites or platforms that offer additional information. \ - Downloadable Materials: Provide links to any relevant downloadable resources if applicable. \ 3. Formatting for Readability: \ - The answer should be in a proper HTML format with appropriate tags. \ - For arabic language response align the text to right and convert numbers also. - Double check if the language of answer is correct or not. - Use bullet points or numbered lists where applicable to present information clearly. \ - Highlight key details using bold or italics. \ - Provide proper and meaningful abbreviations for urls. Do not include naked urls. \ 4. Organize Content Logically: \ - Structure the content in a logical order, ensuring easy navigation and understanding for the user. \ {context} """ qa_prompt = ChatPromptTemplate.from_messages( [ ("system", qa_system_prompt), MessagesPlaceholder("chat_history"), ("human", "{input}") ] ) question_answer_chain = create_stuff_documents_chain(llm, qa_prompt) # Retrieval and Generative (RAG) Chain rag_chain = create_retrieval_chain(history_aware_retriever, question_answer_chain) # Chat message history storage store = {} def clean_temporary_data(): store.clear() def get_session_history(session_id: str) -> BaseChatMessageHistory: if session_id not in store: store[session_id] = ChatMessageHistory() return store[session_id] # Conversational RAG chain with message history conversational_rag_chain = RunnableWithMessageHistory( rag_chain, get_session_history, input_messages_key="input", history_messages_key="chat_history", language_message_key="language", output_messages_key="answer", ) # Function to handle WebSocket connection @socketio.on('connect') def handle_connect(): print(f"Client connected: {request.sid}") emit('connection_response', {'message': 'Connected successfully.'}) # Function to handle WebSocket disconnection @socketio.on('disconnect') def handle_disconnect(): print(f"Client disconnected: {request.sid}") clean_temporary_data() # Function to handle WebSocket messages @socketio.on('message') def handle_message(data): question = data.get('question') language = data.get('language') if "en" in language: language = "English" else: language = "Arabic" session_id = data.get('session_id', SESSION_ID_DEFAULT) chain = conversational_rag_chain.pick("answer") try: for chunk in chain.stream( {"input": question, 'language': language}, config={"configurable": {"session_id": session_id}}, ): emit('response', chunk, room=request.sid) except Exception as e: print(f"Error during message handling: {e}") emit('response', {"error": "An error occurred while processing your request."}, room=request.sid) # Home route @app.route("/") def index_view(): return render_template('chat.html') # Main function to run the app if __name__ == '__main__': socketio.run(app, debug=True)