From f4568fe0c609f8a4a832571fc927349c158f04f0 Mon Sep 17 00:00:00 2001 From: Nuno Campos Date: Tue, 2 Apr 2024 16:40:27 -0700 Subject: [PATCH] core: BaseChatModel modify chat message before passing to run_manager (#19939) Thank you for contributing to LangChain! - [ ] **PR title**: "package: description" - Where "package" is whichever of langchain, community, core, experimental, etc. is being modified. Use "docs: ..." for purely docs changes, "templates: ..." for template changes, "infra: ..." for CI changes. - Example: "community: add foobar LLM" - [ ] **PR message**: ***Delete this entire checklist*** and replace with - **Description:** a description of the change - **Issue:** the issue # it fixes, if applicable - **Dependencies:** any dependencies required for this change - **Twitter handle:** if your PR gets announced, and you'd like a mention, we'll gladly shout you out! - [ ] **Add tests and docs**: If you're adding a new integration, please include 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/docs/integrations` directory. - [ ] **Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. See contribution guidelines for more: https://python.langchain.com/docs/contributing/ Additional guidelines: - Make sure optional dependencies are imported within a function. - Please do not add dependencies to pyproject.toml files (even optional ones) unless they are required for unit tests. - Most PRs should not touch more than one package. - Changes should be backwards compatible. - If you are adding something to community, do not re-import it in langchain. If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, hwchase17. --- .../language_models/chat_models.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/libs/core/langchain_core/language_models/chat_models.py b/libs/core/langchain_core/language_models/chat_models.py index daf6aeb3a736b..7040900aeef78 100644 --- a/libs/core/langchain_core/language_models/chat_models.py +++ b/libs/core/langchain_core/language_models/chat_models.py @@ -221,12 +221,12 @@ def stream( generation: Optional[ChatGenerationChunk] = None try: for chunk in self._stream(messages, stop=stop, **kwargs): - run_manager.on_llm_new_token( - cast(str, chunk.message.content), chunk=chunk - ) if chunk.message.id is None: chunk.message.id = f"run-{run_manager.run_id}" chunk.message.response_metadata = _gen_info_and_msg_metadata(chunk) + run_manager.on_llm_new_token( + cast(str, chunk.message.content), chunk=chunk + ) yield chunk.message if generation is None: generation = chunk @@ -293,12 +293,12 @@ async def astream( stop=stop, **kwargs, ): - await run_manager.on_llm_new_token( - cast(str, chunk.message.content), chunk=chunk - ) if chunk.message.id is None: chunk.message.id = f"run-{run_manager.run_id}" chunk.message.response_metadata = _gen_info_and_msg_metadata(chunk) + await run_manager.on_llm_new_token( + cast(str, chunk.message.content), chunk=chunk + ) yield chunk.message if generation is None: generation = chunk @@ -610,13 +610,13 @@ def _generate_with_cache( ): chunks: List[ChatGenerationChunk] = [] for chunk in self._stream(messages, stop=stop, **kwargs): + chunk.message.response_metadata = _gen_info_and_msg_metadata(chunk) if run_manager: if chunk.message.id is None: chunk.message.id = f"run-{run_manager.run_id}" run_manager.on_llm_new_token( cast(str, chunk.message.content), chunk=chunk ) - chunk.message.response_metadata = _gen_info_and_msg_metadata(chunk) chunks.append(chunk) result = generate_from_stream(iter(chunks)) else: @@ -691,13 +691,13 @@ async def _agenerate_with_cache( ): chunks: List[ChatGenerationChunk] = [] async for chunk in self._astream(messages, stop=stop, **kwargs): + chunk.message.response_metadata = _gen_info_and_msg_metadata(chunk) if run_manager: if chunk.message.id is None: chunk.message.id = f"run-{run_manager.run_id}" await run_manager.on_llm_new_token( cast(str, chunk.message.content), chunk=chunk ) - chunk.message.response_metadata = _gen_info_and_msg_metadata(chunk) chunks.append(chunk) result = generate_from_stream(iter(chunks)) else: