Fix issue where response_if_no_docs_found is not implemented on async… (#13297)

Response_if_no_docs_found is not implemented in
ConversationalRetrievalChain for async code paths. Implemented it and
added test cases

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>

libs/langchain/langchain/chains/conversational_retrieval/base.py

@@ -204,14 +204,19 @@ class BaseConversationalRetrievalChain(Chain):
         else:
             docs = await self._aget_docs(new_question, inputs)  # type: ignore[call-arg]
 
-        new_inputs = inputs.copy()
-        if self.rephrase_question:
-            new_inputs["question"] = new_question
-        new_inputs["chat_history"] = chat_history_str
-        answer = await self.combine_docs_chain.arun(
-            input_documents=docs, callbacks=_run_manager.get_child(), **new_inputs
-        )
-        output: Dict[str, Any] = {self.output_key: answer}
+        output: Dict[str, Any] = {}
+        if self.response_if_no_docs_found is not None and len(docs) == 0:
+            output[self.output_key] = self.response_if_no_docs_found
+        else:
+            new_inputs = inputs.copy()
+            if self.rephrase_question:
+                new_inputs["question"] = new_question
+            new_inputs["chat_history"] = chat_history_str
+            answer = await self.combine_docs_chain.arun(
+                input_documents=docs, callbacks=_run_manager.get_child(), **new_inputs
+            )
+            output[self.output_key] = answer
+
         if self.return_source_documents:
             output["source_documents"] = docs
         if self.return_generated_question:

libs/langchain/tests/unit_tests/chains/test_conversation_retrieval.py

@@ -1,4 +1,5 @@
 """Test conversation chain and memory."""
+import pytest
 from langchain_core.documents import Document
 
 from langchain.chains.conversational_retrieval.base import ConversationalRetrievalChain
@@ -7,6 +8,54 @@ from langchain.memory.buffer import ConversationBufferMemory
 from tests.unit_tests.retrievers.sequential_retriever import SequentialRetriever
 
 
+@pytest.mark.asyncio
+async def atest_simple() -> None:
+    fixed_resp = "I don't know"
+    answer = "I know the answer!"
+    llm = FakeListLLM(responses=[answer])
+    retriever = SequentialRetriever(sequential_responses=[[]])
+    memory = ConversationBufferMemory(
+        k=1, output_key="answer", memory_key="chat_history", return_messages=True
+    )
+    qa_chain = ConversationalRetrievalChain.from_llm(
+        llm=llm,
+        memory=memory,
+        retriever=retriever,
+        return_source_documents=True,
+        rephrase_question=False,
+        response_if_no_docs_found=fixed_resp,
+        verbose=True,
+    )
+    got = await qa_chain.acall("What is the answer?")
+    assert got["chat_history"][1].content == fixed_resp
+    assert got["answer"] == fixed_resp
+
+
+@pytest.mark.asyncio
+async def atest_fixed_message_response_when_docs_found() -> None:
+    fixed_resp = "I don't know"
+    answer = "I know the answer!"
+    llm = FakeListLLM(responses=[answer])
+    retriever = SequentialRetriever(
+        sequential_responses=[[Document(page_content=answer)]]
+    )
+    memory = ConversationBufferMemory(
+        k=1, output_key="answer", memory_key="chat_history", return_messages=True
+    )
+    qa_chain = ConversationalRetrievalChain.from_llm(
+        llm=llm,
+        memory=memory,
+        retriever=retriever,
+        return_source_documents=True,
+        rephrase_question=False,
+        response_if_no_docs_found=fixed_resp,
+        verbose=True,
+    )
+    got = await qa_chain.acall("What is the answer?")
+    assert got["chat_history"][1].content == answer
+    assert got["answer"] == answer
+
+
 def test_fixed_message_response_when_no_docs_found() -> None:
     fixed_resp = "I don't know"
     answer = "I know the answer!"