Source code for langchain.chains.history_aware_retriever

from __future__ import annotations

from langchain_core.language_models import LanguageModelLike
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import BasePromptTemplate
from langchain_core.retrievers import RetrieverLike, RetrieverOutputLike
from langchain_core.runnables import RunnableBranch

[docs]def create_history_aware_retriever( llm: LanguageModelLike, retriever: RetrieverLike, prompt: BasePromptTemplate, ) -> RetrieverOutputLike: """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. Args: llm: Language model to use for generating a search term given chat history retriever: RetrieverLike object that takes a string as input and outputs a list of Documents. prompt: 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 Example: .. code-block:: python # 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": }) """ if "input" not in prompt.input_variables: raise ValueError( "Expected `input` to be a prompt variable, " f"but got {prompt.input_variables}" ) retrieve_documents: RetrieverOutputLike = RunnableBranch( ( # Both empty string and empty list evaluate to False lambda x: not x.get("chat_history", False), # If no chat history, then we just pass input to retriever (lambda x: x["input"]) | retriever, ), # If chat history, then we pass inputs to LLM chain, then to retriever prompt | llm | StrOutputParser() | retriever, ).with_config(run_name="chat_retriever_chain") return retrieve_documents