| from __future__ import annotations |
|
|
| from typing import Any, Dict, Union |
|
|
| from langchain_core.retrievers import ( |
| BaseRetriever, |
| RetrieverOutput, |
| ) |
| from langchain_core.runnables import Runnable, RunnablePassthrough |
|
|
|
|
| def create_retrieval_chain( |
| retriever: Union[BaseRetriever, Runnable[dict, RetrieverOutput]], |
| combine_docs_chain: Runnable[Dict[str, Any], str], |
| ) -> Runnable: |
| """Create retrieval chain that retrieves documents and then passes them on. |
| |
| Args: |
| retriever: Retriever-like object that returns list of documents. Should |
| either be a subclass of BaseRetriever or a Runnable that returns |
| a list of documents. If a subclass of BaseRetriever, then it |
| is expected that an `input` key be passed in - this is what |
| is will be used to pass into the retriever. If this is NOT a |
| subclass of BaseRetriever, then all the inputs will be passed |
| into this runnable, meaning that runnable should take a dictionary |
| as input. |
| combine_docs_chain: Runnable that takes inputs and produces a string output. |
| The inputs to this will be any original inputs to this chain, a new |
| context key with the retrieved documents, and chat_history (if not present |
| in the inputs) with a value of `[]` (to easily enable conversational |
| retrieval. |
| |
| Returns: |
| An LCEL Runnable. The Runnable return is a dictionary containing at the very |
| least a `context` and `answer` key. |
| |
| Example: |
| .. code-block:: python |
| |
| # pip install -U langchain langchain-community |
| |
| from langchain_community.chat_models import ChatOpenAI |
| from langchain.chains.combine_documents import create_stuff_documents_chain |
| from langchain.chains import create_retrieval_chain |
| from langchain import hub |
| |
| retrieval_qa_chat_prompt = hub.pull("langchain-ai/retrieval-qa-chat") |
| llm = ChatOpenAI() |
| retriever = ... |
| combine_docs_chain = create_stuff_documents_chain( |
| llm, retrieval_qa_chat_prompt |
| ) |
| retrieval_chain = create_retrieval_chain(retriever, combine_docs_chain) |
| |
| chain.invoke({"input": "..."}) |
| |
| """ |
| if not isinstance(retriever, BaseRetriever): |
| retrieval_docs: Runnable[dict, RetrieverOutput] = retriever |
| else: |
| retrieval_docs = (lambda x: x["input"]) | retriever |
|
|
| retrieval_chain = ( |
| RunnablePassthrough.assign( |
| context=retrieval_docs.with_config(run_name="retrieve_documents"), |
| ).assign(answer=combine_docs_chain) |
| ).with_config(run_name="retrieval_chain") |
|
|
| return retrieval_chain |
|
|
