UPDATE: chat history retention

functions.py

@@ -180,6 +180,7 @@ def answerQuery(query: str, vectorstore: str, llmModel: str = "llama3-70b-8192")
     global prompt 
     global client
     global embeddings
+    vectorStoreName = vectorstore
     vectorstore = QdrantVectorStore.from_existing_collection(
         embedding = embeddings,
         collection_name=vectorstore,
@@ -197,7 +198,7 @@ def answerQuery(query: str, vectorstore: str, llmModel: str = "llama3-70b-8192")
         base_compressor=compressor, base_retriever=retriever
     )
     baseChain = (
-        {"context": retriever | RunnableLambda(format_docs), "question": RunnablePassthrough(), "chatHistory": RunnablePassthrough()}
+        {"context": RunnableLambda(lambda x: x["question"]) | retriever | RunnableLambda(format_docs), "question": RunnablePassthrough(), "chatHistory": RunnablePassthrough()}
         | prompt
         | ChatGroq(model = llmModel, temperature = 0.75, max_tokens = 512)
         | StrOutputParser()
@@ -212,7 +213,7 @@ def answerQuery(query: str, vectorstore: str, llmModel: str = "llama3-70b-8192")
     return {
         "output": chain.invoke(
             {"question": query},
-            {"configurable": {"session_id": vectorstore}}
+            {"configurable": {"session_id": vectorStoreName}}
         )
     }