Update app.py

app.py

@@ -906,7 +906,7 @@ pc = Pinecone(api_key=os.environ['PINECONE_API_KEY'])
 
 index_name = "radardata07242024"
 vectorstore = PineconeVectorStore(index_name=index_name, embedding=embeddings)
-retriever = vectorstore.as_retriever(search_kwargs={'k': 5})
+retriever = vectorstore.as_retriever(search_kwargs={'k': 5, 'filter': {}})
 
 chat_model = ChatOpenAI(api_key=os.environ['OPENAI_API_KEY'], temperature=0, model='gpt-4o')
 
@@ -1034,37 +1034,6 @@ def retriever_neo4j(question: str):
     logging.debug(f"Structured data: {structured_data}")
     return structured_data
 
-_template = """Given the following conversation and a follow-up question, rephrase the follow-up question to be a standalone question,
-in its original language.
-Chat History:
-{chat_history}
-Follow Up Input: {question}
-Standalone question:"""
-
-CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
-
-def _format_chat_history(chat_history: list[tuple[str, str]]) -> list:
-    buffer = []
-    for human, ai in chat_history:
-        buffer.append(HumanMessage(content=human))
-        buffer.append(AIMessage(content=ai))
-    return buffer
-
-_search_query = RunnableBranch(
-    (
-        RunnableLambda(lambda x: bool(x.get("chat_history"))).with_config(
-            run_name="HasChatHistoryCheck"
-        ),
-        RunnablePassthrough.assign(
-            chat_history=lambda x: _format_chat_history(x["chat_history"])
-        )
-        | CONDENSE_QUESTION_PROMPT
-        | ChatOpenAI(temperature=0, api_key=os.environ['OPENAI_API_KEY'])
-        | StrOutputParser(),
-    ),
-    RunnableLambda(lambda x : x["question"]),
-)
-
 template = """Answer the question based only on the following context:
 {context}
 Question: {question}
@@ -1076,7 +1045,7 @@ qa_prompt = ChatPromptTemplate.from_template(template)
 chain_neo4j = (
     RunnableParallel(
         {
-            "context": _search_query | retriever_neo4j,
+            "context": RunnablePassthrough() | retriever_neo4j,
             "question": RunnablePassthrough(),
         }
     )
@@ -1104,8 +1073,7 @@ def generate_answer(message, choice, retrieval_mode):
     elif retrieval_mode == "Knowledge-Graph":
         response = chain_neo4j.invoke({"question": message})
         logging.debug(f"Knowledge-Graph response: {response}")
-        formatted_response = prompt_template.format(context=response, question=message)
-        return formatted_response, extract_addresses(formatted_response)
+        return response, extract_addresses(response)
     else:
         return "Invalid retrieval mode selected.", []
 
@@ -1686,3 +1654,4 @@ with gr.Blocks(theme='Pijush2023/scikit-learn-pijush') as demo:
 
 demo.queue()
 demo.launch(share=True)
+