propagate callbacks to ConversationalRetrievalChain (#5572)

# Allow callbacks to monitor ConversationalRetrievalChain

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I ran into an issue where load_qa_chain was not passing the callbacks
down to the child LLM chains, and so made sure that callbacks are
propagated. There are probably more improvements to do here but this
seemed like a good place to stop.

Note that I saw a lot of references to callbacks_manager, which seems to
be deprecated. I left that code alone for now.



## Before submitting

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## Who can review?

Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
@agola11
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  @hwchase17 - project lead

  Tracing / Callbacks
  - @agola11

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langchain/chains/conversational_retrieval/base.py

@@ -12,6 +12,7 @@ from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForChainRun,
     CallbackManagerForChainRun,
+    Callbacks,
 )
 from langchain.chains.base import Chain
 from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
@@ -204,6 +205,7 @@ class ConversationalRetrievalChain(BaseConversationalRetrievalChain):
         verbose: bool = False,
         condense_question_llm: Optional[BaseLanguageModel] = None,
         combine_docs_chain_kwargs: Optional[Dict] = None,
+        callbacks: Callbacks = None,
         **kwargs: Any,
     ) -> BaseConversationalRetrievalChain:
         """Load chain from LLM."""
@@ -212,17 +214,22 @@ class ConversationalRetrievalChain(BaseConversationalRetrievalChain):
             llm,
             chain_type=chain_type,
             verbose=verbose,
+            callbacks=callbacks,
             **combine_docs_chain_kwargs,
         )
 
         _llm = condense_question_llm or llm
         condense_question_chain = LLMChain(
-            llm=_llm, prompt=condense_question_prompt, verbose=verbose
+            llm=_llm,
+            prompt=condense_question_prompt,
+            verbose=verbose,
+            callbacks=callbacks,
         )
         return cls(
             retriever=retriever,
             combine_docs_chain=doc_chain,
             question_generator=condense_question_chain,
+            callbacks=callbacks,
             **kwargs,
         )
 
@@ -264,6 +271,7 @@ class ChatVectorDBChain(BaseConversationalRetrievalChain):
         condense_question_prompt: BasePromptTemplate = CONDENSE_QUESTION_PROMPT,
         chain_type: str = "stuff",
         combine_docs_chain_kwargs: Optional[Dict] = None,
+        callbacks: Callbacks = None,
         **kwargs: Any,
     ) -> BaseConversationalRetrievalChain:
         """Load chain from LLM."""
@@ -271,12 +279,16 @@ class ChatVectorDBChain(BaseConversationalRetrievalChain):
         doc_chain = load_qa_chain(
             llm,
             chain_type=chain_type,
+            callbacks=callbacks,
             **combine_docs_chain_kwargs,
         )
-        condense_question_chain = LLMChain(llm=llm, prompt=condense_question_prompt)
+        condense_question_chain = LLMChain(
+            llm=llm, prompt=condense_question_prompt, callbacks=callbacks
+        )
         return cls(
             vectorstore=vectorstore,
             combine_docs_chain=doc_chain,
             question_generator=condense_question_chain,
+            callbacks=callbacks,
             **kwargs,
         )

langchain/chains/question_answering/__init__.py

@@ -3,6 +3,7 @@ from typing import Any, Mapping, Optional, Protocol
 
 from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.base import BaseCallbackManager
+from langchain.callbacks.manager import Callbacks
 from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
 from langchain.chains.combine_documents.map_reduce import MapReduceDocumentsChain
 from langchain.chains.combine_documents.map_rerank import MapRerankDocumentsChain
@@ -35,10 +36,15 @@ def _load_map_rerank_chain(
     rank_key: str = "score",
     answer_key: str = "answer",
     callback_manager: Optional[BaseCallbackManager] = None,
+    callbacks: Callbacks = None,
     **kwargs: Any,
 ) -> MapRerankDocumentsChain:
     llm_chain = LLMChain(
-        llm=llm, prompt=prompt, verbose=verbose, callback_manager=callback_manager
+        llm=llm,
+        prompt=prompt,
+        verbose=verbose,
+        callback_manager=callback_manager,
+        callbacks=callbacks,
     )
     return MapRerankDocumentsChain(
         llm_chain=llm_chain,
@@ -57,11 +63,16 @@ def _load_stuff_chain(
     document_variable_name: str = "context",
     verbose: Optional[bool] = None,
     callback_manager: Optional[BaseCallbackManager] = None,
+    callbacks: Callbacks = None,
     **kwargs: Any,
 ) -> StuffDocumentsChain:
     _prompt = prompt or stuff_prompt.PROMPT_SELECTOR.get_prompt(llm)
     llm_chain = LLMChain(
-        llm=llm, prompt=_prompt, verbose=verbose, callback_manager=callback_manager
+        llm=llm,
+        prompt=_prompt,
+        verbose=verbose,
+        callback_manager=callback_manager,
+        callbacks=callbacks,
     )
     # TODO: document prompt
     return StuffDocumentsChain(
@@ -84,6 +95,7 @@ def _load_map_reduce_chain(
     collapse_llm: Optional[BaseLanguageModel] = None,
     verbose: Optional[bool] = None,
     callback_manager: Optional[BaseCallbackManager] = None,
+    callbacks: Callbacks = None,
     **kwargs: Any,
 ) -> MapReduceDocumentsChain:
     _question_prompt = (
@@ -97,6 +109,7 @@ def _load_map_reduce_chain(
         prompt=_question_prompt,
         verbose=verbose,
         callback_manager=callback_manager,
+        callbacks=callbacks,
     )
     _reduce_llm = reduce_llm or llm
     reduce_chain = LLMChain(
@@ -104,6 +117,7 @@ def _load_map_reduce_chain(
         prompt=_combine_prompt,
         verbose=verbose,
         callback_manager=callback_manager,
+        callbacks=callbacks,
     )
     # TODO: document prompt
     combine_document_chain = StuffDocumentsChain(
@@ -111,6 +125,7 @@ def _load_map_reduce_chain(
         document_variable_name=combine_document_variable_name,
         verbose=verbose,
         callback_manager=callback_manager,
+        callbacks=callbacks,
     )
     if collapse_prompt is None:
         collapse_chain = None
@@ -127,6 +142,7 @@ def _load_map_reduce_chain(
                 prompt=collapse_prompt,
                 verbose=verbose,
                 callback_manager=callback_manager,
+                callbacks=callbacks,
             ),
             document_variable_name=combine_document_variable_name,
             verbose=verbose,
@@ -139,6 +155,7 @@ def _load_map_reduce_chain(
         collapse_document_chain=collapse_chain,
         verbose=verbose,
         callback_manager=callback_manager,
+        callbacks=callbacks,
         **kwargs,
     )
 
@@ -152,6 +169,7 @@ def _load_refine_chain(
     refine_llm: Optional[BaseLanguageModel] = None,
     verbose: Optional[bool] = None,
     callback_manager: Optional[BaseCallbackManager] = None,
+    callbacks: Callbacks = None,
     **kwargs: Any,
 ) -> RefineDocumentsChain:
     _question_prompt = (
@@ -165,6 +183,7 @@ def _load_refine_chain(
         prompt=_question_prompt,
         verbose=verbose,
         callback_manager=callback_manager,
+        callbacks=callbacks,
     )
     _refine_llm = refine_llm or llm
     refine_chain = LLMChain(
@@ -172,6 +191,7 @@ def _load_refine_chain(
         prompt=_refine_prompt,
         verbose=verbose,
         callback_manager=callback_manager,
+        callbacks=callbacks,
     )
     return RefineDocumentsChain(
         initial_llm_chain=initial_chain,