enable serde retrieval qa with sources (#10132)

#3983 mentions serialization/deserialization issues with both
`RetrievalQA` & `RetrievalQAWithSourcesChain`.
`RetrievalQA` has already been fixed in #5818. 

Mimicing #5818, I added the logic for `RetrievalQAWithSourcesChain`.

---------

Co-authored-by: Markus Tretzmüller <markus.tretzmueller@cortecs.at>
Co-authored-by: Bagatur <baskaryan@gmail.com>

libs/langchain/langchain/chains/loading.py

@@ -20,6 +20,7 @@
 from langchain.chains.llm_math.base import LLMMathChain
 from langchain.chains.llm_requests import LLMRequestsChain
 from langchain.chains.qa_with_sources.base import QAWithSourcesChain
+from langchain.chains.qa_with_sources.retrieval import RetrievalQAWithSourcesChain
 from langchain.chains.qa_with_sources.vector_db import VectorDBQAWithSourcesChain
 from langchain.chains.retrieval_qa.base import RetrievalQA, VectorDBQA
 from langchain.llms.loading import load_llm, load_llm_from_config
@@ -424,6 +425,30 @@ def _load_retrieval_qa(config: dict, **kwargs: Any) -> RetrievalQA:
     )
 
 
+def _load_retrieval_qa_with_sources_chain(
+    config: dict, **kwargs: Any
+) -> RetrievalQAWithSourcesChain:
+    if "retriever" in kwargs:
+        retriever = kwargs.pop("retriever")
+    else:
+        raise ValueError("`retriever` must be present.")
+    if "combine_documents_chain" in config:
+        combine_documents_chain_config = config.pop("combine_documents_chain")
+        combine_documents_chain = load_chain_from_config(combine_documents_chain_config)
+    elif "combine_documents_chain_path" in config:
+        combine_documents_chain = load_chain(config.pop("combine_documents_chain_path"))
+    else:
+        raise ValueError(
+            "One of `combine_documents_chain` or "
+            "`combine_documents_chain_path` must be present."
+        )
+    return RetrievalQAWithSourcesChain(
+        combine_documents_chain=combine_documents_chain,
+        retriever=retriever,
+        **config,
+    )
+
+
 def _load_vector_db_qa(config: dict, **kwargs: Any) -> VectorDBQA:
     if "vectorstore" in kwargs:
         vectorstore = kwargs.pop("vectorstore")
@@ -537,6 +562,7 @@ def _load_llm_requests_chain(config: dict, **kwargs: Any) -> LLMRequestsChain:
     "vector_db_qa_with_sources_chain": _load_vector_db_qa_with_sources_chain,
     "vector_db_qa": _load_vector_db_qa,
     "retrieval_qa": _load_retrieval_qa,
+    "retrieval_qa_with_sources_chain": _load_retrieval_qa_with_sources_chain,
     "graph_cypher_chain": _load_graph_cypher_chain,
 }
 

libs/langchain/langchain/chains/qa_with_sources/retrieval.py

@@ -60,3 +60,8 @@ async def _aget_docs(
             question, callbacks=run_manager.get_child()
         )
         return self._reduce_tokens_below_limit(docs)
+
+    @property
+    def _chain_type(self) -> str:
+        """Return the chain type."""
+        return "retrieval_qa_with_sources_chain"

libs/langchain/tests/integration_tests/chains/test_retrieval_qa_with_sources.py

@@ -0,0 +1,28 @@
+"""Test RetrievalQA functionality."""
+from langchain.chains import RetrievalQAWithSourcesChain
+from langchain.chains.loading import load_chain
+from langchain.document_loaders import DirectoryLoader
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.llms import OpenAI
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.vectorstores import FAISS
+
+
+def test_retrieval_qa_with_sources_chain_saving_loading(tmp_path: str) -> None:
+    """Test saving and loading."""
+    loader = DirectoryLoader("docs/extras/modules/", glob="*.txt")
+    documents = loader.load()
+    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+    texts = text_splitter.split_documents(documents)
+    embeddings = OpenAIEmbeddings()
+    docsearch = FAISS.from_documents(texts, embeddings)
+    qa = RetrievalQAWithSourcesChain.from_llm(
+        llm=OpenAI(), retriever=docsearch.as_retriever()
+    )
+    qa("What did the president say about Ketanji Brown Jackson?")
+
+    file_path = tmp_path + "/RetrievalQAWithSourcesChain.yaml"
+    qa.save(file_path=file_path)
+    qa_loaded = load_chain(file_path, retriever=docsearch.as_retriever())
+
+    assert qa_loaded == qa