Update main.py

main.py

@@ -10,9 +10,9 @@ from langchain_huggingface import HuggingFaceEmbeddings
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_community.vectorstores import FAISS
 from langchain.prompts import PromptTemplate
-from langchain_together import Together
 from langchain.memory import ConversationBufferMemory
 from langchain.chains import ConversationalRetrievalChain
+from langchain.schema import Document
 
 # ==========================
 # Logging Configuration
@@ -30,17 +30,12 @@ warnings.filterwarnings("ignore", message="You are using `torch.load` with `weig
 # Load Environment Variables
 # ==========================
 load_dotenv()
-TOGETHER_AI_API = os.getenv("TOGETHER_AI")
 HF_HOME = os.getenv("HF_HOME", "./cache")
 os.environ["HF_HOME"] = HF_HOME
 
 # Ensure the HF_HOME directory exists
 os.makedirs(HF_HOME, exist_ok=True)
 
-# Validate required environment variables
-if not TOGETHER_AI_API:
-    raise ValueError("The TOGETHER_AI_API environment variable is missing. Please set it in your .env file.")
-
 # ==========================
 # Initialize Embeddings
 # ==========================
@@ -76,34 +71,20 @@ QUESTION: {question}
 ANSWER:
 </s>[INST]"""
 
-
 prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question", "chat_history"])
 
-# ==========================
-# Initialize Together API
-# ==========================
-try:
-    llm = Together(
-        model="mistralai/Mistral-7B-Instruct-v0.2",
-        temperature=0.5,
-        max_tokens=1024,
-        together_api_key=TOGETHER_AI_API,
-    )
-    logger.info("Together API successfully initialized.")
-except Exception as e:
-    logger.error(f"Error initializing Together API: {e}")
-    raise RuntimeError("Something went wrong with the Together API setup. Please verify your API key and configuration.")
-
 # ==========================
 # Conversational Retrieval Chain
 # ==========================
 memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+
 qa = ConversationalRetrievalChain.from_llm(
-    llm=llm,
+    llm="nomic-ai/nomic-embed-text-v1",
     memory=memory,
     retriever=db_retriever,
     combine_docs_chain_kwargs={"prompt": prompt},
 )
+
 logger.info("Conversational Retrieval Chain initialized.")
 
 # ==========================
@@ -125,21 +106,31 @@ async def root():
 async def chat(request: ChatRequest):
     try:
         logger.debug(f"Received user question: {request.question}")
+        
+        # Retrieve relevant documents
+        docs = db_retriever.get_relevant_documents(request.question)
+        if not docs:
+            logger.warning("No relevant documents found.")
+            return ChatResponse(answer="I'm sorry, I couldn't find relevant information for your query.")
+
+        # Log retrieved documents for debugging
+        for i, doc in enumerate(docs, start=1):
+            logger.debug(f"Retrieved Document {i}: {doc.page_content[:500]}...")
+
+        # Invoke the conversational retrieval chain
         result = qa.invoke(input=request.question)
-        answer = result.get("answer")
-        if not answer or "The information is not available in the provided context" in answer:
-            answer = "I'm sorry, I couldn't find relevant information for your query. Please try rephrasing or providing more details."
+        answer = result.get("answer", "I'm sorry, I couldn't find relevant information for your query.")
+        
+        # Log the final answer
+        logger.debug(f"Final Answer: {answer}")
         return ChatResponse(answer=answer)
+
     except Exception as e:
         logger.error(f"Error during chat invocation: {e}")
         raise HTTPException(status_code=500, detail="Oops! Something went wrong on our end. Please try again later.")
 
-
 # ==========================
 # Run Uvicorn Server
 # ==========================
 if __name__ == "__main__":
     uvicorn.run("main:app", host="0.0.0.0", port=7860)
-
-    
-