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).
77 lines
3.3 KiB
Python
77 lines
3.3 KiB
Python
"""Configuration for conversation summarization."""
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from typing import Literal
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from pydantic import BaseModel, ConfigDict, Field
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ContextSizeType = Literal["fraction", "tokens", "messages"]
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class ContextSize(BaseModel):
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"""Context size specification for trigger or keep parameters."""
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model_config = ConfigDict(frozen=True)
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type: ContextSizeType = Field(description="Type of context size specification")
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value: int | float = Field(description="Value for the context size specification")
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def to_tuple(self) -> tuple[ContextSizeType, int | float]:
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"""Convert to tuple format expected by SummarizationMiddleware."""
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return (self.type, self.value)
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class SummarizationConfig(BaseModel):
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"""Configuration for automatic conversation summarization."""
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model_config = ConfigDict(frozen=True)
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enabled: bool = Field(
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default=False,
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description="Whether to enable automatic conversation summarization",
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)
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model_name: str | None = Field(
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default=None,
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description="Model name to use for summarization (None = use a lightweight model)",
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)
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trigger: ContextSize | list[ContextSize] | None = Field(
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default=None,
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description="One or more thresholds that trigger summarization. When any threshold is met, summarization runs. "
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"Examples: {'type': 'messages', 'value': 50} triggers at 50 messages, "
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"{'type': 'tokens', 'value': 4000} triggers at 4000 tokens, "
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"{'type': 'fraction', 'value': 0.8} triggers at 80% of model's max input tokens",
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)
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keep: ContextSize = Field(
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default_factory=lambda: ContextSize(type="messages", value=20),
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description="Context retention policy after summarization. Specifies how much history to preserve. "
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"Examples: {'type': 'messages', 'value': 20} keeps 20 messages, "
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"{'type': 'tokens', 'value': 3000} keeps 3000 tokens, "
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"{'type': 'fraction', 'value': 0.3} keeps 30% of model's max input tokens",
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)
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trim_tokens_to_summarize: int | None = Field(
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default=4000,
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description="Maximum tokens to keep when preparing messages for summarization. Pass null to skip trimming.",
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)
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summary_prompt: str | None = Field(
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default=None,
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description="Custom prompt template for generating summaries. If not provided, uses the default LangChain prompt.",
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)
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preserve_recent_skill_count: int = Field(
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default=5,
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ge=0,
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description="Number of most-recently-loaded skill files to exclude from summarization. Set to 0 to disable skill preservation.",
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)
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preserve_recent_skill_tokens: int = Field(
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default=25000,
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ge=0,
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description="Total token budget reserved for recently-loaded skill files that must be preserved across summarization.",
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)
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preserve_recent_skill_tokens_per_skill: int = Field(
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default=5000,
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ge=0,
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description="Per-skill token cap when preserving skill files across summarization. Skill reads above this size are not rescued.",
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)
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skill_file_read_tool_names: list[str] = Field(
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default_factory=lambda: ["read_file", "read", "view", "cat"],
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description="Tool names treated as skill file reads when preserving recently-loaded skills across summarization.",
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)
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