Willem Jiang
a55de566b9
Extract duplicated thread_id fallback logic from 11 files into a single deerflow.utils.runtime.get_thread_id() function with a documented 3-level cascade (runtime.context → runtime.config → get_config()). The module docstring also clarifies the __pregel_runtime injection pattern used in gateway mode.
89 lines
3.3 KiB
Python
89 lines
3.3 KiB
Python
"""Runtime utilities for thread_id resolution and context access.
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Thread ID Resolution Strategy
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=============================
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DeerFlow resolves the current ``thread_id`` from a three-level cascade:
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1. **runtime.context["thread_id"]** -- Set by ``worker.py`` (gateway mode)
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or by LangGraph Server (standard mode) when constructing the Runtime.
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2. **runtime.config["configurable"]["thread_id"]** -- Available on
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``ToolRuntime`` instances passed to tools via the ``@tool`` decorator.
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Not available on ``Runtime`` instances received by middlewares.
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3. **get_config()["configurable"]["thread_id"]** -- LangGraph's thread-local
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config, available when executing inside a graph's runnable context.
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About ``__pregel_runtime``
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===========================
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In gateway mode (``run_agent()`` in ``worker.py``), the agent graph does not
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run inside the LangGraph Server. The server normally injects a ``Runtime``
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object automatically. Since we run the graph ourselves, we must inject the
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Runtime manually via ``config["configurable"]["__pregel_runtime"]``. This is
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the standard mechanism provided by LangGraph's Pregel engine for injecting
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runtime context into graph nodes. It is not a private/internal hack -- it is
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the documented way to pass Runtime when running a graph outside the server.
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Duck Typing
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===========
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Both ``langgraph.runtime.Runtime`` (middlewares) and
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``langchain.tools.ToolRuntime`` (tools) expose a ``.context`` attribute (a
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dict or None). ``ToolRuntime`` additionally exposes ``.config``. The
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function below uses ``getattr`` with safe defaults so it works with either
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type, with ``SimpleNamespace`` in tests, or with ``None``.
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"""
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from __future__ import annotations
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from typing import Any
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def get_thread_id(runtime: Any | None) -> str | None:
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"""Resolve the current thread_id from a runtime object.
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Follows a three-level fallback chain:
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1. ``runtime.context.get("thread_id")`` -- if context is a non-empty dict.
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2. ``runtime.config.get("configurable", {}).get("thread_id")`` -- if
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the runtime has a config dict (ToolRuntime).
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3. ``get_config().get("configurable", {}).get("thread_id")`` -- LangGraph's
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thread-local config. Wrapped in ``try/except RuntimeError`` because it
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raises outside a runnable context (e.g., unit tests).
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Args:
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runtime: A Runtime, ToolRuntime, SimpleNamespace, or None.
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Returns:
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The thread_id string, or None if it cannot be resolved.
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"""
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if runtime is None:
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return None
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# Level 1: runtime.context["thread_id"]
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context = getattr(runtime, "context", None)
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if context and isinstance(context, dict):
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thread_id = context.get("thread_id")
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if thread_id:
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return thread_id
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# Level 2: runtime.config["configurable"]["thread_id"]
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config = getattr(runtime, "config", None)
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if config and isinstance(config, dict):
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thread_id = config.get("configurable", {}).get("thread_id")
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if thread_id:
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return thread_id
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# Level 3: langgraph.config.get_config() -- only works inside runnable context
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try:
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from langgraph.config import get_config
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config_data = get_config()
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thread_id = config_data.get("configurable", {}).get("thread_id")
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if thread_id:
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return thread_id
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except RuntimeError:
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pass
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return None
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