hello guys how can i impolement chat memory and chat history for llama index

hello guys i m using Mongodb to store documents as chunks and faiss to do the similarity search when i query the response is on the source but the query says there is no answer for this question

hello guys i have a documents with legal articles but when i ask my chatbot about a certain article it give me a random answer

hello sorry for bothering you can u help me with this error traceback for the query engine

hello guys i have this code service_context = ServiceContext(
llm=llm,
embed_model=Settings.embed_model,
llm_predictor=None,
prompt_helper=prompt_helper,
llama_logger=None,
callback_manager=None,
transformations=None,
)

Initialize a VectorStoreIndex for document management

vector_store = FaissVectorStore(faiss_index=faiss_index)

Create a QueryEngine for handling queries

query_engine = RetrieverQueryEngine(service_context, vector_store)
and when i excute him i have this error llama_index\core\query_engine\retriever_query_engine.py", line 58, in init
callback_manager or self._response_synthesizer.callback_manager
AttributeError: 'FaissVectorStore' object has no attribute 'callback_manager' can any one tell me what is the problem ?

hello guys i have a problem in my code im using "sentence-transformers/all-mpnet-base-v2" as embedding model but when i run my code it said ValueError:
**
Could not load OpenAI embedding model. If you intended to use OpenAI, please check your OPENAI_API_KEY.
Original error:
No API key found for OpenAI.
Please set either the OPENAI_API_KEY environment variable or openai.api_key prior to initialization.
API keys can be found or created at https://platform.openai.com/account/api-keys

Consider using embed_model='local'.
Visit our documentation for more embedding options: https://docs.llamaindex.ai/en/stable/module_guides/models/embeddings.html#modules
** what sould i do ?

Hello guys I want to know how the similarity search with faiss is working using llama-index

does ollame have a local embedding for law documents

hello guys how can i fetch documents metadata stocked in faiss index using llama index

hello guys i want to know how the chat memory system can work with llama index and if there is a documentation about this

hello guys i want to ask about two things firstly , how can i for example know the pdf page where the answer is coming and secondly i want to know how the chat memory system can work with llama index