Issue: <Please write a comprehensive title after the 'Issue: ' prefix>

[Locke-bot]

Issue you'd like to raise.

full_chain = {
    "source1": {"question": lambda x: x["question"]} | query_chain | db.run,
    "source2": (lambda x: x['question']) | vectorstore.as_retriever(),
    "question": lambda x: x['question'],

} | qa_prompt | ChatOpenAI(streaming=True, callbacks=[QueueCallback(q)], temperature=0, model_name='gpt-4')

how can I add chat history to this?

Suggestion:

No response

[dosubot]

🤖

Hello,

To incorporate chat history into your full_chain code snippet, you can use the ConversationalRetrievalChain as suggested in a similar issue #1246. This chain allows for passing in a chat history which can be used to allow for follow-up questions.

However, if you want to modify the _generate method in the ChatLiteLLM class, you can do so by passing the chat history as a list of BaseMessage instances to this method. Here's an example of how you can modify this method:

def _generate(
    self,
    messages: List[BaseMessage],
    stop: Optional[List[str]] = None,
    run_manager: Optional[CallbackManagerForLLMRun] = None,
    stream: Optional[bool] = None,
    **kwargs: Any,
) -> ChatResult:
    should_stream = stream if stream is not None else self.streaming
    if should_stream:
        stream_iter = self._stream(
            messages, stop=stop, run_manager=run_manager, **kwargs
        )
        return _generate_from_stream(stream_iter)

    message_dicts, params = self._create_message_dicts(messages, stop)
    params = {**params, **kwargs}
    response = self.completion_with_retry(
        messages=message_dicts, run_manager=run_manager, **params
    )
    return self._create_chat_result(response)

In this code:

• The messages argument is a list of BaseMessage instances, which should include the chat history.
• The _create_message_dicts method is used to convert the chat history into a format that can be passed to the model.
• The completion_with_retry method is used to generate a response from the model based on the chat history.

You need to create BaseMessage instances for each message in the chat history and pass them to the _generate method. You can use the HumanMessage, AIMessage, and SystemMessage classes to create these instances.

You can find more details in the following files:

• libs/langchain/langchain/memory/chat_message_histories/zep.py
• libs/langchain/langchain/chat_models/ollama.py
• libs/langchain/langchain/chat_models/litellm.py

I hope this helps! If you have any other questions, feel free to ask.

Best,
Dosu

Sources

• Is there no chain for question answer with sources and memory?
• libs/langchain/langchain/memory/chat_message_histories/zep.py
• libs/langchain/langchain/chat_models/ollama.py
• libs/langchain/langchain/chat_models/litellm.py
• libs/langchain/langchain/chains/api/openapi/chain.py
• libs/langchain/langchain/chains/openai_functions/qa_with_structure.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

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[Locke-bot]

how can I use ConversationalRetrievalChain with multiple sources, with some as databases and some as vectorstores

[dosubot]

Hi, @Locke-bot,

I'm helping the LangChain team manage their backlog and am marking this issue as stale. From what I understand, the issue was opened to add chat history to a code snippet involving a chain of functions for processing questions and generating responses. Dosubot provided a detailed response, suggesting the use of the ConversationalRetrievalChain and providing a code snippet for modifying the _generate method in the ChatLiteLLM class to incorporate chat history. You followed up with a question about using ConversationalRetrievalChain with multiple sources.

Could you please confirm if this issue is still relevant to the latest version of the LangChain repository? If it is, please let the LangChain team know by commenting on the issue. Otherwise, feel free to close the issue yourself, or it will be automatically closed in 7 days. Thank you!