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core[patch]: Fix "argument of type 'NoneType' is not iterable" error in LangChainTracer (#26576)
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, ccurme, vbarda, hwchase17. --------- Co-authored-by: Erick Friis <erick@langchain.dev>
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@ -301,7 +301,7 @@ class OpenAIAssistantV2Runnable(OpenAIAssistantRunnable):
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inheritable_metadata=config.get("metadata"),
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)
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run_manager = callback_manager.on_chain_start(
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dumpd(self), input, name=config.get("run_name")
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dumpd(self), input, name=config.get("run_name") or self.get_name()
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)
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files = _convert_file_ids_into_attachments(kwargs.get("file_ids", []))
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@ -437,7 +437,7 @@ class OpenAIAssistantV2Runnable(OpenAIAssistantRunnable):
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inheritable_metadata=config.get("metadata"),
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)
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run_manager = callback_manager.on_chain_start(
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dumpd(self), input, name=config.get("run_name")
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dumpd(self), input, name=config.get("run_name") or self.get_name()
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)
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files = _convert_file_ids_into_attachments(kwargs.get("file_ids", []))
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@ -121,4 +121,4 @@ def test_callback_manager_configure_context_vars(
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assert cb.completion_tokens == 1
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assert cb.total_cost > 0
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wait_for_all_tracers()
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assert LangChainTracer._persist_run_single.call_count == 1 # type: ignore
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assert LangChainTracer._persist_run_single.call_count == 4 # type: ignore
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@ -135,11 +135,13 @@ class Run(BaseRunV2):
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@root_validator(pre=True)
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def assign_name(cls, values: dict) -> dict:
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"""Assign name to the run."""
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if values.get("name") is None:
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if values.get("name") is None and values["serialized"] is not None:
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if "name" in values["serialized"]:
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values["name"] = values["serialized"]["name"]
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elif "id" in values["serialized"]:
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values["name"] = values["serialized"]["id"][-1]
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if values.get("name") is None:
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values["name"] = "Unnamed"
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if values.get("events") is None:
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values["events"] = []
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return values
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@ -310,7 +310,7 @@ class OpenAIAssistantRunnable(RunnableSerializable[Dict, OutputType]):
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inheritable_metadata=config.get("metadata"),
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)
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run_manager = callback_manager.on_chain_start(
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dumpd(self), input, name=config.get("run_name")
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dumpd(self), input, name=config.get("run_name") or self.get_name()
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)
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try:
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# Being run within AgentExecutor and there are tool outputs to submit.
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@ -429,7 +429,7 @@ class OpenAIAssistantRunnable(RunnableSerializable[Dict, OutputType]):
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inheritable_metadata=config.get("metadata"),
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)
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run_manager = callback_manager.on_chain_start(
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dumpd(self), input, name=config.get("run_name")
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dumpd(self), input, name=config.get("run_name") or self.get_name()
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)
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try:
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# Being run within AgentExecutor and there are tool outputs to submit.
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@ -242,6 +242,7 @@ class LLMChain(Chain):
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run_manager = callback_manager.on_chain_start(
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None,
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{"input_list": input_list},
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name=self.get_name(),
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)
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try:
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response = self.generate(input_list, run_manager=run_manager)
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@ -262,6 +263,7 @@ class LLMChain(Chain):
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run_manager = await callback_manager.on_chain_start(
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None,
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{"input_list": input_list},
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name=self.get_name(),
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)
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try:
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response = await self.agenerate(input_list, run_manager=run_manager)
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