langchain.chains.retrieval.create_retrieval_chain¶
- langchain.chains.retrieval.create_retrieval_chain(retriever: Union[BaseRetriever, Runnable[dict, List[Document]]], combine_docs_chain: Runnable[Dict[str, Any], str]) Runnable[source]¶
Create retrieval chain that retrieves documents and then passes them on.
- Parameters
retriever (Union[BaseRetriever, Runnable[dict, List[Document]]]) – 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[Dict[str, Any], str]) – 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.
- Return type
Example
# 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": "..."})