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https://gitee.com/wanwujie/deer-flow
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* refactor: extract shared utils to break harness→app cross-layer imports Move _validate_skill_frontmatter to src/skills/validation.py and CONVERTIBLE_EXTENSIONS + convert_file_to_markdown to src/utils/file_conversion.py. This eliminates the two reverse dependencies from client.py (harness layer) into gateway/routers/ (app layer), preparing for the harness/app package split. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * refactor: split backend/src into harness (deerflow.*) and app (app.*) Physically split the monolithic backend/src/ package into two layers: - **Harness** (`packages/harness/deerflow/`): publishable agent framework package with import prefix `deerflow.*`. Contains agents, sandbox, tools, models, MCP, skills, config, and all core infrastructure. - **App** (`app/`): unpublished application code with import prefix `app.*`. Contains gateway (FastAPI REST API) and channels (IM integrations). Key changes: - Move 13 harness modules to packages/harness/deerflow/ via git mv - Move gateway + channels to app/ via git mv - Rename all imports: src.* → deerflow.* (harness) / app.* (app layer) - Set up uv workspace with deerflow-harness as workspace member - Update langgraph.json, config.example.yaml, all scripts, Docker files - Add build-system (hatchling) to harness pyproject.toml - Add PYTHONPATH=. to gateway startup commands for app.* resolution - Update ruff.toml with known-first-party for import sorting - Update all documentation to reflect new directory structure Boundary rule enforced: harness code never imports from app. All 429 tests pass. Lint clean. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * chore: add harness→app boundary check test and update docs Add test_harness_boundary.py that scans all Python files in packages/harness/deerflow/ and fails if any `from app.*` or `import app.*` statement is found. This enforces the architectural rule that the harness layer never depends on the app layer. Update CLAUDE.md to document the harness/app split architecture, import conventions, and the boundary enforcement test. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * feat: add config versioning with auto-upgrade on startup When config.example.yaml schema changes, developers' local config.yaml files can silently become outdated. This adds a config_version field and auto-upgrade mechanism so breaking changes (like src.* → deerflow.* renames) are applied automatically before services start. - Add config_version: 1 to config.example.yaml - Add startup version check warning in AppConfig.from_file() - Add scripts/config-upgrade.sh with migration registry for value replacements - Add `make config-upgrade` target - Auto-run config-upgrade in serve.sh and start-daemon.sh before starting services - Add config error hints in service failure messages Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix comments * fix: update src.* import in test_sandbox_tools_security to deerflow.* Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: handle empty config and search parent dirs for config.example.yaml Address Copilot review comments on PR #1131: - Guard against yaml.safe_load() returning None for empty config files - Search parent directories for config.example.yaml instead of only looking next to config.yaml, fixing detection in common setups Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: correct skills root path depth and config_version type coercion - loader.py: fix get_skills_root_path() to use 5 parent levels (was 3) after harness split, file lives at packages/harness/deerflow/skills/ so parent×3 resolved to backend/packages/harness/ instead of backend/ - app_config.py: coerce config_version to int() before comparison in _check_config_version() to prevent TypeError when YAML stores value as string (e.g. config_version: "1") - tests: add regression tests for both fixes Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix: update test imports from src.* to deerflow.*/app.* after harness refactor Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
79 lines
2.3 KiB
Python
79 lines
2.3 KiB
Python
"""Configuration for memory mechanism."""
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from pydantic import BaseModel, Field
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class MemoryConfig(BaseModel):
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"""Configuration for global memory mechanism."""
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enabled: bool = Field(
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default=True,
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description="Whether to enable memory mechanism",
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)
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storage_path: str = Field(
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default="",
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description=(
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"Path to store memory data. "
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"If empty, defaults to `{base_dir}/memory.json` (see Paths.memory_file). "
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"Absolute paths are used as-is. "
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"Relative paths are resolved against `Paths.base_dir` "
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"(not the backend working directory). "
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"Note: if you previously set this to `.deer-flow/memory.json`, "
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"the file will now be resolved as `{base_dir}/.deer-flow/memory.json`; "
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"migrate existing data or use an absolute path to preserve the old location."
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),
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)
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debounce_seconds: int = Field(
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default=30,
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ge=1,
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le=300,
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description="Seconds to wait before processing queued updates (debounce)",
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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 memory updates (None = use default model)",
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)
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max_facts: int = Field(
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default=100,
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ge=10,
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le=500,
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description="Maximum number of facts to store",
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)
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fact_confidence_threshold: float = Field(
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default=0.7,
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ge=0.0,
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le=1.0,
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description="Minimum confidence threshold for storing facts",
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)
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injection_enabled: bool = Field(
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default=True,
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description="Whether to inject memory into system prompt",
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)
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max_injection_tokens: int = Field(
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default=2000,
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ge=100,
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le=8000,
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description="Maximum tokens to use for memory injection",
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)
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# Global configuration instance
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_memory_config: MemoryConfig = MemoryConfig()
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def get_memory_config() -> MemoryConfig:
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"""Get the current memory configuration."""
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return _memory_config
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def set_memory_config(config: MemoryConfig) -> None:
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"""Set the memory configuration."""
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global _memory_config
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_memory_config = config
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def load_memory_config_from_dict(config_dict: dict) -> None:
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"""Load memory configuration from a dictionary."""
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global _memory_config
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_memory_config = MemoryConfig(**config_dict)
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