First demo with no history memory db and curent time

NLP_model/chatbot.py

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+import os
+import google.generativeai as genai
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain.chains import RetrievalQA, ConversationalRetrievalChain
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain.prompts import PromptTemplate
+from langchain_ollama import OllamaLLM
+from pinecone import Pinecone, ServerlessSpec
+from langchain_pinecone import PineconeVectorStore
+from dotenv import load_dotenv
+import threading
+from datetime import datetime
+from langchain.schema import HumanMessage, AIMessage
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+# Load environment variables
+load_dotenv()
+
+# Configure API keys from environment variables
+google_api_key = os.getenv("GOOGLE_API_KEY")
+pinecone_api_key = os.getenv("PINECONE_API_KEY")
+
+if not google_api_key or not pinecone_api_key:
+    raise ValueError("Missing required API keys in environment variables")
+
+os.environ["GOOGLE_API_KEY"] = google_api_key
+os.environ["PINECONE_API_KEY"] = pinecone_api_key
+
+genai.configure(api_key=google_api_key)
+
+#lấy model chatbot
+model = ChatGoogleGenerativeAI(model="gemini-1.5-flash-8b-latest",
+                             temperature=0.8)
+# model = OllamaLLM(model="llama2")
+# print("Llama2 đã được tải thành công!")
+
+#lấy model embedding
+embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+
+
+# Biến lưu history cho từng user (dạng chuỗi)
+user_histories = {}
+history_lock = threading.Lock()
+
+# Create a prompt template with conversation history
+prompt = PromptTemplate(
+    template = """Goal:
+You are a professional tour guide assistant that assists users in finding information about places in Da Nang, Vietnam.
+You can provide details on restaurants, cafes, hotels, attractions, and other local venues. You have to chat with users, who are Da Nang tourists. 
+
+
+Return Format:
+- Respond in clear, natural, and concise English.
+- If you do not have enough information to answer user's question, reply with "I don't know", and explain that you are not sure about the information.
+- When sufficient information is available in the Context, provide a specific and informative answer.
+- Let's support users like a real tour guide, not a bot. The information in context is your own knowledge.
+- You just care about time that user mention when they ask about Solana event.
+
+Warning:
+- Your knowledge is provided in the Context. All of information in Context is about Da Nang, Vietnam.
+- Do not fabricate or guess information.
+- Answer with "I don't know" if you don't have enough information.
+
+Context:
+{context}
+
+Conversation History:
+{chat_history}
+
+User question:
+{question}
+
+Your answer:
+""",
+    input_variables = ["context", "question", "chat_history"],
+)
+
+def get_history(user_id):
+    """Get conversation history for a specific user"""
+    with history_lock:
+        return user_histories.get(user_id, "")
+
+def update_history(user_id, new_entry):
+    """Update conversation history for a user.
+    new_entry should be a string containing the new conversation information, e.g.:
+      "User: {question}\nBot: {answer}\n"
+    """
+    with history_lock:
+        current_history = user_histories.get(user_id, "")
+        # Store only the last 30 interactions by keeping the 60 most recent lines
+        # (assuming 2 lines per interaction: 1 for user, 1 for bot)
+        history_lines = current_history.split('\n')
+        if len(history_lines) > 60:
+            history_lines = history_lines[-60:]
+            current_history = '\n'.join(history_lines)
+        
+        updated_history = current_history + new_entry + "\n"
+        user_histories[user_id] = updated_history
+
+def string_to_message_history(history_str):
+    """Convert string-based history to LangChain message history format"""
+    if not history_str.strip():
+        return []
+    
+    messages = []
+    lines = history_str.strip().split('\n')
+    i = 0
+    
+    while i < len(lines):
+        line = lines[i].strip()
+        if line.startswith("User:"):
+            user_message = line[5:].strip()  # Get the user message without "User:"
+            messages.append(HumanMessage(content=user_message))
+            
+            # Look for a Bot response (should be the next line)
+            if i + 1 < len(lines) and lines[i + 1].strip().startswith("Bot:"):
+                bot_response = lines[i + 1][4:].strip()  # Get bot response without "Bot:"
+                messages.append(AIMessage(content=bot_response))
+                i += 2  # Skip the bot line too
+            else:
+                i += 1
+        else:
+            i += 1  # Skip any unexpected format lines
+            
+    return messages
+
+def get_chain():
+    """Get the retrieval chain with Pinecone vector store"""
+    try:
+        pc = Pinecone(
+            api_key=os.environ["PINECONE_API_KEY"]
+        )
+        
+        # Get the vector store from the existing index
+        vectorstore = PineconeVectorStore.from_existing_index(
+            index_name="testbot768",
+            embedding=embeddings,
+            text_key="text"
+        )
+        
+        retrieve = vectorstore.as_retriever(search_kwargs={"k": 3})
+            
+        return retrieve
+    except Exception as e:
+        print(f"Error getting vector store: {e}")
+        return None
+
+def chat(request, user_id="default_user"):
+    """Process a chat request from a specific user"""
+    try:
+        # Get retrieval chain
+        retriever = get_chain()
+        if not retriever:
+            return "Error: Could not initialize retriever"
+        
+        # Get current conversation history as string
+        conversation_history_str = get_history(user_id)
+        
+        # Convert string history to LangChain message format
+        message_history = string_to_message_history(conversation_history_str)
+        
+        # Get current time
+        current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        
+        # Add timestamp to question
+        question_with_time = f"{request}\n(Current time: {current_time})"
+        # print("User question:", question_with_time)
+        
+        # Create a ConversationalRetrievalChain
+        # Get relevant documents from retriever
+        retrieved_docs = retriever.get_relevant_documents(question_with_time)
+        print("Retrieved documents page content:", [doc.page_content for doc in retrieved_docs])
+        
+        conversation_chain = ConversationalRetrievalChain.from_llm(
+            llm=model,
+            retriever=retriever,
+            combine_docs_chain_kwargs={"prompt": prompt}
+        )
+        
+        # Call the chain with question and converted message history
+        response = conversation_chain({"question": question_with_time, "chat_history": message_history})
+        answer = str(response['answer'])
+        
+        # Update conversation history string
+        new_entry = f"User: {question_with_time}\nBot: {answer}"
+        update_history(user_id, new_entry)
+        print(get_history(user_id))
+        
+        print(answer)
+        return answer
+    except Exception as e:
+        print(f"Error in chat: {e}")
+        return f"I encountered an error: {str(e)}"
+
+def clear_memory(user_id="default_user"):
+    """Clear the conversation history for a specific user"""
+    with history_lock:
+        if user_id in user_histories:
+            del user_histories[user_id]
+            return f"Conversation history cleared for user {user_id}"
+        return f"No conversation history found for user {user_id}"