greatmengqi
3e6a34297d
Squashes 25 PR commits onto current main. AppConfig becomes a pure value object with no ambient lookup. Every consumer receives the resolved config as an explicit parameter — Depends(get_config) in Gateway, self._app_config in DeerFlowClient, runtime.context.app_config in agent runs, AppConfig.from_file() at the LangGraph Server registration boundary. Phase 1 — frozen data + typed context - All config models (AppConfig, MemoryConfig, DatabaseConfig, …) become frozen=True; no sub-module globals. - AppConfig.from_file() is pure (no side-effect singleton loaders). - Introduce DeerFlowContext(app_config, thread_id, run_id, agent_name) — frozen dataclass injected via LangGraph Runtime. - Introduce resolve_context(runtime) as the single entry point middleware / tools use to read DeerFlowContext. Phase 2 — pure explicit parameter passing - Gateway: app.state.config + Depends(get_config); 7 routers migrated (mcp, memory, models, skills, suggestions, uploads, agents). - DeerFlowClient: __init__(config=...) captures config locally. - make_lead_agent / _build_middlewares / _resolve_model_name accept app_config explicitly. - RunContext.app_config field; Worker builds DeerFlowContext from it, threading run_id into the context for downstream stamping. - Memory queue/storage/updater closure-capture MemoryConfig and propagate user_id end-to-end (per-user isolation). - Sandbox/skills/community/factories/tools thread app_config. - resolve_context() rejects non-typed runtime.context. - Test suite migrated off AppConfig.current() monkey-patches. - AppConfig.current() classmethod deleted. Merging main brought new architecture decisions resolved in PR's favor: - circuit_breaker: kept main's frozen-compatible config field; AppConfig remains frozen=True (verified circuit_breaker has no mutation paths). - agents_api: kept main's AgentsApiConfig type but removed the singleton globals (load_agents_api_config_from_dict / get_agents_api_config / set_agents_api_config). 8 routes in agents.py now read via Depends(get_config). - subagents: kept main's get_skills_for / custom_agents feature on SubagentsAppConfig; removed singleton getter. registry.py now reads app_config.subagents directly. - summarization: kept main's preserve_recent_skill_* fields; removed singleton. - llm_error_handling_middleware + memory/summarization_hook: replaced singleton lookups with AppConfig.from_file() at construction (these hot-paths have no ergonomic way to thread app_config through; AppConfig.from_file is a pure load). - worker.py + thread_data_middleware.py: DeerFlowContext.run_id field bridges main's HumanMessage stamping logic to PR's typed context. Trade-offs (follow-up work): - main's #2138 (async memory updater) reverted to PR's sync implementation. The async path is wired but bypassed because propagating user_id through aupdate_memory required cascading edits outside this merge's scope. - tests/test_subagent_skills_config.py removed: it relied heavily on the deleted singleton (get_subagents_app_config/load_subagents_config_from_dict). The custom_agents/skills_for functionality is exercised through integration tests; a dedicated test rewrite belongs in a follow-up. Verification: backend test suite — 2560 passed, 4 skipped, 84 failures. The 84 failures are concentrated in fixture monkeypatch paths still pointing at removed singleton symbols; mechanical follow-up (next commit).
52 lines
1.5 KiB
Python
52 lines
1.5 KiB
Python
"""Async stream bridge factory.
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Provides an **async context manager** aligned with
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:func:`deerflow.runtime.checkpointer.async_provider.make_checkpointer`.
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Usage (e.g. FastAPI lifespan)::
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from deerflow.agents.stream_bridge import make_stream_bridge
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async with make_stream_bridge() as bridge:
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app.state.stream_bridge = bridge
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"""
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from __future__ import annotations
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import contextlib
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import logging
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from collections.abc import AsyncIterator
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from deerflow.config.app_config import AppConfig
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from .base import StreamBridge
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logger = logging.getLogger(__name__)
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@contextlib.asynccontextmanager
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async def make_stream_bridge(app_config: AppConfig) -> AsyncIterator[StreamBridge]:
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"""Async context manager that yields a :class:`StreamBridge`.
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Falls back to :class:`MemoryStreamBridge` when no ``stream_bridge``
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section is configured.
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"""
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config = app_config.stream_bridge
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if config is None or config.type == "memory":
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from deerflow.runtime.stream_bridge.memory import MemoryStreamBridge
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maxsize = config.queue_maxsize if config is not None else 256
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bridge = MemoryStreamBridge(queue_maxsize=maxsize)
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logger.info("Stream bridge initialised: memory (queue_maxsize=%d)", maxsize)
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try:
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yield bridge
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finally:
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await bridge.close()
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return
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if config.type == "redis":
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raise NotImplementedError("Redis stream bridge planned for Phase 2")
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raise ValueError(f"Unknown stream bridge type: {config.type!r}")
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