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