index = VectorStoreIndex.from_vector_store(vector_store=qdrant_vector_storage) retriever = VectorIndexRetriever( index=index, similarity_top_k=150 ) retrived_nodes = retriever.retrieve("your_query") chat_engine = ContextChatEngine( retriever=retriever, chat_history=[], system_prompt=self.PROMPT_TEMPLATE ) response = chat_engine.chat("your_query")
query_engine
inchat_engine
as well.chat_engine= index.as_chat_engine(...) response= chat_engine.chat("query") #print source nodes print(response.source_nodes)