create_history_aware_retriever#

langchain.chains.history_aware_retriever.create_history_aware_retriever(llm: Runnable[PromptValue | str | Sequence[BaseMessage | list[str] | tuple[str, str] | str | dict[str, Any]], BaseMessage | str], retriever: Runnable[str, list[Document]], prompt: BasePromptTemplate) → Runnable[Any, list[Document]][source]#

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.

Parameters:

  • llm (Runnable[PromptValue | str | Sequence[BaseMessage | list[str] | tuple[str, str] | str | dict[str, Any]], BaseMessage | str]) – Language model to use for generating a search term given chat history

  • retriever (Runnable[str, list[Document]]) – RetrieverLike object that takes a string as input and outputs a list of Documents.

  • prompt (BasePromptTemplate) – The prompt used to generate the search query for the retriever.

Returns:

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

Return type:

Runnable[Any, list[Document]]

Example

# pip install -U langchain langchain-community

from langchain_community.chat_models import ChatOpenAI
from langchain.chains import create_history_aware_retriever
from langchain import hub

rephrase_prompt = hub.pull("langchain-ai/chat-langchain-rephrase")
llm = ChatOpenAI()
retriever = ...
chat_retriever_chain = create_history_aware_retriever(
    llm, retriever, rephrase_prompt
)

chain.invoke({"input": "...", "chat_history": })

Examples using create_history_aware_retriever