Update app.py

app.py

@@ -42,51 +42,51 @@ 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):
-    # Initialize the retriever
-    retriever = vector_store.as_retriever()
-    
-    # Define the RAG template and RAG prompt template
-    prompt = hub.pull("rlm/rag-prompt")
-    
-    # Initialize the Hugging Face language model (LLM)
-    llm = HuggingFaceHub(repo_id="mistralai/Mistral-7B-Instruct-v0.2", model_kwargs={"temperature":0.6, "max_length":512})
-    
-    # Construct the RAG pipeline
-    rag_chain = (
-        {"context": retriever, "question": RunnablePassthrough()}
-        | prompt
-        | llm
-        | StrOutputParser()
-    )
-    return rag_chain.invoke(str(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)
+#     # Define the RAG template and RAG prompt template
+#     prompt = hub.pull("rlm/rag-prompt")
     
 #     # Initialize the Hugging Face language model (LLM)
 #     llm = HuggingFaceHub(repo_id="mistralai/Mistral-7B-Instruct-v0.2", model_kwargs={"temperature":0.6, "max_length":512})
     
 #     # Construct the RAG pipeline
-#     after_rag_chain = (
+#     rag_chain = (
 #         {"context": retriever, "question": RunnablePassthrough()}
-#         | after_rag_prompt
+#         | prompt
 #         | llm
 #         | StrOutputParser()
 #     )
+#     return rag_chain.invoke(str(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", model_kwargs={"temperature":0.6, "max_length":512})
+    
+    # Construct the RAG pipeline
+    after_rag_chain = (
+        {"context": retriever, "question": RunnablePassthrough()}
+        | after_rag_prompt
+        | llm
+        | StrOutputParser()
+    )
     
-#     return after_rag_chain.invoke(question)
+    return after_rag_chain.invoke(question)
 
 def main():
     st.set_page_config(page_title="Chat with websites", page_icon="🤖")
@@ -116,6 +116,8 @@ def main():
                 # Generate response using the RAG pipeline
                 answer = get_context_retriever_chain(vector_store,question)
                 # Display the generated answer
+                st.write(type(answer))
+                st.write(answer)
                 st.text_area("Answer", value=answer, height=300, disabled=True)
 
 if __name__ == '__main__':