Update app.py

app.py

@@ -37,7 +37,7 @@ def method_get_vectorstore(document_chunks):
     vector_store = Chroma.from_documents(document_chunks, embeddings)
     return vector_store
 
-def get_context_retriever_chain(vector_store,question):
+def get_context_retriever_chain(vector_store, question):
     # Initialize the retriever
     retriever = vector_store.as_retriever()
     
@@ -55,13 +55,40 @@ def get_context_retriever_chain(vector_store,question):
     
     # Construct the RAG pipeline
     after_rag_chain = (
-        {"context": retriever, "question": RunnablePassthrough()}
+        {"context": retriever, "question": question}
         | after_rag_prompt
         | llm
         | StrOutputParser()
     )
     
+    # Invoke the RAG pipeline and return the generated answer
     return after_rag_chain.invoke(question)
+    
+# def get_context_retriever_chain(vector_store,question):
+#     # Initialize the retriever
+#     retriever = vector_store.as_retriever()
+    
+#     # Define the RAG template
+#     after_rag_template = """Answer the question based only on the following context:
+#     {context}
+#     Question: {question}
+#     """
+    
+#     # Create the RAG prompt template
+#     after_rag_prompt = ChatPromptTemplate.from_template(after_rag_template)
+    
+#     # Initialize the Hugging Face language model (LLM)
+#     llm = HuggingFaceHub(repo_id="mistralai/Mistral-7B-Instruct-v0.2")
+    
+#     # Construct the RAG pipeline
+#     after_rag_chain = (
+#         {"context": retriever, "question": RunnablePassthrough()}
+#         | after_rag_prompt
+#         | llm
+#         | StrOutputParser()
+#     )
+    
+#     return after_rag_chain.invoke(question)
 
 def main():
     st.set_page_config(page_title="Chat with websites", page_icon="🤖")