Update app.py

app.py

@@ -4,7 +4,6 @@ import os
 from langchain_chroma import Chroma
 from langchain_community.document_loaders import WebBaseLoader
 from langchain_core.output_parsers import StrOutputParser
-from langchain_core.runnables import RunnablePassthrough
 from langchain_text_splitters import RecursiveCharacterTextSplitter
 from sentence_transformers import SentenceTransformer
 import bs4
@@ -161,15 +160,11 @@ if st.button("Submit Query"):
         # Initialize RAG chain using the prompt
         prompt = RAGPrompt()
 
-        rag_chain = (
-            {"context": context, "question": query}  # Start the chain with context and question
-            | prompt                                 # Use the custom prompt
-            | (lambda data: custom_llm.generate(data["question"], data["context"]))  # Pass question and context to LLM
-            | StrOutputParser()  # Parse the output
-        )
+        # Apply the prompt directly to the data (no chaining using `|`)
+        prompt_data = prompt({"question": query, "context": context})
 
-        # Generate the answer using the user's query
-        result = rag_chain.invoke({"question": query, "context": context})
+        # Generate the response using the language model
+        result = custom_llm.generate(prompt_data["question"], prompt_data["context"])
 
         # Store query and response in session for chat history
         st.session_state['chat_history'].append((query, result))