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venv/Lib/site-packages/langchain_classic/chains/retrieval.py

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+from __future__ import annotations
+
+from typing import Any
+
+from langchain_core.retrievers import (
+    BaseRetriever,
+    RetrieverOutput,
+)
+from langchain_core.runnables import Runnable, RunnablePassthrough
+
+
+def create_retrieval_chain(
+    retriever: 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:
+        ```python
+        # pip install -U langchain langchain-openai
+
+        from langchain_openai import ChatOpenAI
+        from langchain_classic.chains.combine_documents import (
+            create_stuff_documents_chain,
+        )
+        from langchain_classic.chains import create_retrieval_chain
+        from langchain_classic import hub
+
+        retrieval_qa_chat_prompt = hub.pull("langchain-ai/retrieval-qa-chat")
+        model = ChatOpenAI()
+        retriever = ...
+        combine_docs_chain = create_stuff_documents_chain(
+            model, retrieval_qa_chat_prompt
+        )
+        retrieval_chain = create_retrieval_chain(retriever, combine_docs_chain)
+
+        retrieval_chain.invoke({"input": "..."})
+        ```
+    """
+    if not isinstance(retriever, BaseRetriever):
+        retrieval_docs: Runnable[dict, RetrieverOutput] = retriever
+    else:
+        retrieval_docs = (lambda x: x["input"]) | retriever
+
+    return (
+        RunnablePassthrough.assign(
+            context=retrieval_docs.with_config(run_name="retrieve_documents"),
+        ).assign(answer=combine_docs_chain)
+    ).with_config(run_name="retrieval_chain")