• Create a chain that takes conversation history and returns documents. If there is no chat_history, then the input is just passed directly to the retriever. If there is chat_history, then the prompt and LLM will be used to generate a search query. That search query is then passed to the retriever.

    Returns Promise<Toolkit<{
        chat_history: string | Toolkit[];
        input: string;
    }, Toolkit[]>>

    An LCEL Runnable. The runnable input must take in input, and if there is chat history should take it in the form of chat_history. The Runnable output is a list of Documents


    // yarn add langchain @langchain/openai

    import { ChatOpenAI } from "@langchain/openai";
    import { pull } from "langchain/hub";
    import { createRetrievalChain } from "langchain/chains/retrieval";
    import { createStuffDocumentsChain } from "langchain/chains/combine_documents";

    const rephrasePrompt = await pull("langchain-ai/chat-langchain-rephrase");
    const llm = new ChatOpenAI({});
    const retriever = ...
    const chain = await createHistoryAwareRetriever({
    const result = await chain.invoke({"input": "...", "chat_history": [] })

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