mirror of
https://github.com/catlog22/Claude-Code-Workflow.git
synced 2026-02-15 02:42:45 +08:00
feat: unified task.json schema migration and multi-module updates
- Create task-schema.json (JSON Schema draft-07) with 10 field blocks fusing Unified JSONL, 6-field Task JSON, and Solution Schema advantages - Migrate unified-execute-with-file from JSONL to .task/*.json directory scanning - Migrate 3 producers (lite-plan, plan-converter, collaborative-plan) to .task/*.json multi-file output - Add review-cycle Phase 7.5 export-to-tasks (FIX-*.json) and issue-resolve --export-tasks option - Add schema compatibility annotations to action-planning-agent, workflow-plan, and tdd-plan - Add spec-generator skill phases and templates - Add memory v2 pipeline (consolidation, extraction, job scheduler, embedder) - Add secret-redactor utility and core-memory enhancements - Add codex-lens accuracy benchmarks and staged env config overrides
This commit is contained in:
catlog22
@@ -315,119 +315,117 @@ class Config:
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def load_settings(self) -> None:
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"""Load settings from file if exists."""
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if not self.settings_path.exists():
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return
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if self.settings_path.exists():
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try:
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with open(self.settings_path, "r", encoding="utf-8") as f:
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settings = json.load(f)
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try:
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with open(self.settings_path, "r", encoding="utf-8") as f:
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settings = json.load(f)
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# Load embedding settings
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embedding = settings.get("embedding", {})
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if "backend" in embedding:
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backend = embedding["backend"]
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# Support 'api' as alias for 'litellm'
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if backend == "api":
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backend = "litellm"
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if backend in {"fastembed", "litellm"}:
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self.embedding_backend = backend
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else:
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log.warning(
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"Invalid embedding backend in %s: %r (expected 'fastembed' or 'litellm')",
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self.settings_path,
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embedding["backend"],
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)
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if "model" in embedding:
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self.embedding_model = embedding["model"]
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if "use_gpu" in embedding:
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self.embedding_use_gpu = embedding["use_gpu"]
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# Load embedding settings
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embedding = settings.get("embedding", {})
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if "backend" in embedding:
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backend = embedding["backend"]
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# Support 'api' as alias for 'litellm'
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if backend == "api":
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backend = "litellm"
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if backend in {"fastembed", "litellm"}:
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self.embedding_backend = backend
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else:
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log.warning(
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"Invalid embedding backend in %s: %r (expected 'fastembed' or 'litellm')",
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self.settings_path,
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embedding["backend"],
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)
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if "model" in embedding:
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self.embedding_model = embedding["model"]
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if "use_gpu" in embedding:
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self.embedding_use_gpu = embedding["use_gpu"]
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# Load multi-endpoint configuration
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if "endpoints" in embedding:
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self.embedding_endpoints = embedding["endpoints"]
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if "pool_enabled" in embedding:
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self.embedding_pool_enabled = embedding["pool_enabled"]
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if "strategy" in embedding:
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self.embedding_strategy = embedding["strategy"]
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if "cooldown" in embedding:
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self.embedding_cooldown = embedding["cooldown"]
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# Load multi-endpoint configuration
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if "endpoints" in embedding:
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self.embedding_endpoints = embedding["endpoints"]
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if "pool_enabled" in embedding:
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self.embedding_pool_enabled = embedding["pool_enabled"]
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if "strategy" in embedding:
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self.embedding_strategy = embedding["strategy"]
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if "cooldown" in embedding:
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self.embedding_cooldown = embedding["cooldown"]
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# Load LLM settings
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llm = settings.get("llm", {})
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if "enabled" in llm:
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self.llm_enabled = llm["enabled"]
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if "tool" in llm:
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self.llm_tool = llm["tool"]
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if "timeout_ms" in llm:
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self.llm_timeout_ms = llm["timeout_ms"]
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if "batch_size" in llm:
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self.llm_batch_size = llm["batch_size"]
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# Load LLM settings
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llm = settings.get("llm", {})
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if "enabled" in llm:
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self.llm_enabled = llm["enabled"]
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if "tool" in llm:
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self.llm_tool = llm["tool"]
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if "timeout_ms" in llm:
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self.llm_timeout_ms = llm["timeout_ms"]
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if "batch_size" in llm:
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self.llm_batch_size = llm["batch_size"]
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# Load reranker settings
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reranker = settings.get("reranker", {})
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if "enabled" in reranker:
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self.enable_cross_encoder_rerank = reranker["enabled"]
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if "backend" in reranker:
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backend = reranker["backend"]
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if backend in {"fastembed", "onnx", "api", "litellm", "legacy"}:
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self.reranker_backend = backend
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else:
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log.warning(
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"Invalid reranker backend in %s: %r (expected 'fastembed', 'onnx', 'api', 'litellm', or 'legacy')",
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self.settings_path,
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backend,
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)
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if "model" in reranker:
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self.reranker_model = reranker["model"]
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if "top_k" in reranker:
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self.reranker_top_k = reranker["top_k"]
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if "max_input_tokens" in reranker:
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self.reranker_max_input_tokens = reranker["max_input_tokens"]
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if "pool_enabled" in reranker:
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self.reranker_pool_enabled = reranker["pool_enabled"]
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if "strategy" in reranker:
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self.reranker_strategy = reranker["strategy"]
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if "cooldown" in reranker:
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self.reranker_cooldown = reranker["cooldown"]
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# Load reranker settings
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reranker = settings.get("reranker", {})
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if "enabled" in reranker:
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self.enable_cross_encoder_rerank = reranker["enabled"]
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if "backend" in reranker:
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backend = reranker["backend"]
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if backend in {"fastembed", "onnx", "api", "litellm", "legacy"}:
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self.reranker_backend = backend
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else:
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log.warning(
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"Invalid reranker backend in %s: %r (expected 'fastembed', 'onnx', 'api', 'litellm', or 'legacy')",
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self.settings_path,
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backend,
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)
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if "model" in reranker:
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self.reranker_model = reranker["model"]
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if "top_k" in reranker:
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self.reranker_top_k = reranker["top_k"]
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if "max_input_tokens" in reranker:
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self.reranker_max_input_tokens = reranker["max_input_tokens"]
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if "pool_enabled" in reranker:
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self.reranker_pool_enabled = reranker["pool_enabled"]
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if "strategy" in reranker:
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self.reranker_strategy = reranker["strategy"]
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if "cooldown" in reranker:
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self.reranker_cooldown = reranker["cooldown"]
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# Load cascade settings
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cascade = settings.get("cascade", {})
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if "strategy" in cascade:
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strategy = cascade["strategy"]
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if strategy in {"binary", "binary_rerank", "dense_rerank", "staged"}:
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self.cascade_strategy = strategy
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else:
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log.warning(
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"Invalid cascade strategy in %s: %r (expected 'binary', 'binary_rerank', 'dense_rerank', or 'staged')",
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self.settings_path,
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strategy,
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)
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if "coarse_k" in cascade:
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self.cascade_coarse_k = cascade["coarse_k"]
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if "fine_k" in cascade:
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self.cascade_fine_k = cascade["fine_k"]
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# Load cascade settings
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cascade = settings.get("cascade", {})
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if "strategy" in cascade:
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strategy = cascade["strategy"]
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if strategy in {"binary", "binary_rerank", "dense_rerank", "staged"}:
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self.cascade_strategy = strategy
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else:
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log.warning(
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"Invalid cascade strategy in %s: %r (expected 'binary', 'binary_rerank', 'dense_rerank', or 'staged')",
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self.settings_path,
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strategy,
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)
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if "coarse_k" in cascade:
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self.cascade_coarse_k = cascade["coarse_k"]
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if "fine_k" in cascade:
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self.cascade_fine_k = cascade["fine_k"]
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# Load API settings
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api = settings.get("api", {})
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if "max_workers" in api:
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self.api_max_workers = api["max_workers"]
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if "batch_size" in api:
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self.api_batch_size = api["batch_size"]
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if "batch_size_dynamic" in api:
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self.api_batch_size_dynamic = api["batch_size_dynamic"]
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if "batch_size_utilization_factor" in api:
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self.api_batch_size_utilization_factor = api["batch_size_utilization_factor"]
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if "batch_size_max" in api:
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self.api_batch_size_max = api["batch_size_max"]
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if "chars_per_token_estimate" in api:
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self.chars_per_token_estimate = api["chars_per_token_estimate"]
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except Exception as exc:
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log.warning(
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"Failed to load settings from %s (%s): %s",
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self.settings_path,
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type(exc).__name__,
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exc,
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)
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# Load API settings
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api = settings.get("api", {})
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if "max_workers" in api:
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self.api_max_workers = api["max_workers"]
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if "batch_size" in api:
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self.api_batch_size = api["batch_size"]
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if "batch_size_dynamic" in api:
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self.api_batch_size_dynamic = api["batch_size_dynamic"]
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if "batch_size_utilization_factor" in api:
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self.api_batch_size_utilization_factor = api["batch_size_utilization_factor"]
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if "batch_size_max" in api:
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self.api_batch_size_max = api["batch_size_max"]
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if "chars_per_token_estimate" in api:
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self.chars_per_token_estimate = api["chars_per_token_estimate"]
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except Exception as exc:
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log.warning(
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"Failed to load settings from %s (%s): %s",
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self.settings_path,
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type(exc).__name__,
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exc,
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)
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# Apply .env overrides (highest priority)
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self._apply_env_overrides()
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@@ -450,9 +448,9 @@ class Config:
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RERANKER_STRATEGY: Load balance strategy for reranker
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RERANKER_COOLDOWN: Rate limit cooldown for reranker
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"""
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from .env_config import load_global_env
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from .env_config import load_env_file
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env_vars = load_global_env()
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env_vars = load_env_file(self.data_dir / ".env")
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if not env_vars:
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return
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@@ -461,6 +459,43 @@ class Config:
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# Check prefixed version first (Dashboard format), then unprefixed
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return env_vars.get(f"CODEXLENS_{key}") or env_vars.get(key)
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def _parse_bool(value: str) -> bool:
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return value.strip().lower() in {"true", "1", "yes", "on"}
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# Cascade overrides
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cascade_enabled = get_env("ENABLE_CASCADE_SEARCH")
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if cascade_enabled:
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self.enable_cascade_search = _parse_bool(cascade_enabled)
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log.debug(
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"Overriding enable_cascade_search from .env: %s",
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self.enable_cascade_search,
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)
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cascade_strategy = get_env("CASCADE_STRATEGY")
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if cascade_strategy:
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strategy = cascade_strategy.strip().lower()
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if strategy in {"binary", "binary_rerank", "dense_rerank", "staged"}:
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self.cascade_strategy = strategy
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log.debug("Overriding cascade_strategy from .env: %s", self.cascade_strategy)
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else:
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log.warning("Invalid CASCADE_STRATEGY in .env: %r", cascade_strategy)
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cascade_coarse_k = get_env("CASCADE_COARSE_K")
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if cascade_coarse_k:
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try:
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self.cascade_coarse_k = int(cascade_coarse_k)
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log.debug("Overriding cascade_coarse_k from .env: %s", self.cascade_coarse_k)
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except ValueError:
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log.warning("Invalid CASCADE_COARSE_K in .env: %r", cascade_coarse_k)
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cascade_fine_k = get_env("CASCADE_FINE_K")
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if cascade_fine_k:
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try:
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self.cascade_fine_k = int(cascade_fine_k)
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log.debug("Overriding cascade_fine_k from .env: %s", self.cascade_fine_k)
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except ValueError:
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log.warning("Invalid CASCADE_FINE_K in .env: %r", cascade_fine_k)
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# Embedding overrides
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embedding_model = get_env("EMBEDDING_MODEL")
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if embedding_model:
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@@ -583,6 +618,136 @@ class Config:
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self.chunk_strip_docstrings = strip_docstrings.lower() in ("true", "1", "yes")
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log.debug("Overriding chunk_strip_docstrings from .env: %s", self.chunk_strip_docstrings)
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# Staged cascade overrides
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staged_stage2_mode = get_env("STAGED_STAGE2_MODE")
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if staged_stage2_mode:
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mode = staged_stage2_mode.strip().lower()
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if mode in {"precomputed", "realtime"}:
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self.staged_stage2_mode = mode
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log.debug("Overriding staged_stage2_mode from .env: %s", self.staged_stage2_mode)
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elif mode in {"live"}:
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self.staged_stage2_mode = "realtime"
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log.debug("Overriding staged_stage2_mode from .env: %s", self.staged_stage2_mode)
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else:
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log.warning("Invalid STAGED_STAGE2_MODE in .env: %r", staged_stage2_mode)
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staged_clustering_strategy = get_env("STAGED_CLUSTERING_STRATEGY")
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if staged_clustering_strategy:
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strategy = staged_clustering_strategy.strip().lower()
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if strategy in {"auto", "hdbscan", "dbscan", "frequency", "noop", "score", "dir_rr", "path"}:
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self.staged_clustering_strategy = strategy
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log.debug(
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"Overriding staged_clustering_strategy from .env: %s",
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self.staged_clustering_strategy,
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)
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elif strategy in {"none", "off"}:
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self.staged_clustering_strategy = "noop"
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log.debug(
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"Overriding staged_clustering_strategy from .env: %s",
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self.staged_clustering_strategy,
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)
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else:
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log.warning(
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"Invalid STAGED_CLUSTERING_STRATEGY in .env: %r",
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staged_clustering_strategy,
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)
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staged_clustering_min_size = get_env("STAGED_CLUSTERING_MIN_SIZE")
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if staged_clustering_min_size:
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try:
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self.staged_clustering_min_size = int(staged_clustering_min_size)
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log.debug(
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"Overriding staged_clustering_min_size from .env: %s",
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self.staged_clustering_min_size,
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)
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except ValueError:
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log.warning(
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"Invalid STAGED_CLUSTERING_MIN_SIZE in .env: %r",
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staged_clustering_min_size,
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)
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enable_staged_rerank = get_env("ENABLE_STAGED_RERANK")
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if enable_staged_rerank:
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self.enable_staged_rerank = _parse_bool(enable_staged_rerank)
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log.debug("Overriding enable_staged_rerank from .env: %s", self.enable_staged_rerank)
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rt_timeout = get_env("STAGED_REALTIME_LSP_TIMEOUT_S")
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if rt_timeout:
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try:
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self.staged_realtime_lsp_timeout_s = float(rt_timeout)
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log.debug(
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"Overriding staged_realtime_lsp_timeout_s from .env: %s",
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self.staged_realtime_lsp_timeout_s,
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)
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except ValueError:
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log.warning("Invalid STAGED_REALTIME_LSP_TIMEOUT_S in .env: %r", rt_timeout)
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rt_depth = get_env("STAGED_REALTIME_LSP_DEPTH")
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if rt_depth:
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try:
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self.staged_realtime_lsp_depth = int(rt_depth)
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log.debug(
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"Overriding staged_realtime_lsp_depth from .env: %s",
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self.staged_realtime_lsp_depth,
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)
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except ValueError:
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log.warning("Invalid STAGED_REALTIME_LSP_DEPTH in .env: %r", rt_depth)
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rt_max_nodes = get_env("STAGED_REALTIME_LSP_MAX_NODES")
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if rt_max_nodes:
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try:
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self.staged_realtime_lsp_max_nodes = int(rt_max_nodes)
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log.debug(
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"Overriding staged_realtime_lsp_max_nodes from .env: %s",
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self.staged_realtime_lsp_max_nodes,
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)
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except ValueError:
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log.warning("Invalid STAGED_REALTIME_LSP_MAX_NODES in .env: %r", rt_max_nodes)
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rt_max_seeds = get_env("STAGED_REALTIME_LSP_MAX_SEEDS")
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if rt_max_seeds:
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try:
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self.staged_realtime_lsp_max_seeds = int(rt_max_seeds)
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log.debug(
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"Overriding staged_realtime_lsp_max_seeds from .env: %s",
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self.staged_realtime_lsp_max_seeds,
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)
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except ValueError:
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log.warning("Invalid STAGED_REALTIME_LSP_MAX_SEEDS in .env: %r", rt_max_seeds)
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rt_max_concurrent = get_env("STAGED_REALTIME_LSP_MAX_CONCURRENT")
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if rt_max_concurrent:
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try:
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self.staged_realtime_lsp_max_concurrent = int(rt_max_concurrent)
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log.debug(
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"Overriding staged_realtime_lsp_max_concurrent from .env: %s",
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self.staged_realtime_lsp_max_concurrent,
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)
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except ValueError:
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log.warning(
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"Invalid STAGED_REALTIME_LSP_MAX_CONCURRENT in .env: %r",
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rt_max_concurrent,
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)
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rt_warmup = get_env("STAGED_REALTIME_LSP_WARMUP_S")
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if rt_warmup:
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try:
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self.staged_realtime_lsp_warmup_s = float(rt_warmup)
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log.debug(
|
||||
"Overriding staged_realtime_lsp_warmup_s from .env: %s",
|
||||
self.staged_realtime_lsp_warmup_s,
|
||||
)
|
||||
except ValueError:
|
||||
log.warning("Invalid STAGED_REALTIME_LSP_WARMUP_S in .env: %r", rt_warmup)
|
||||
|
||||
rt_resolve = get_env("STAGED_REALTIME_LSP_RESOLVE_SYMBOLS")
|
||||
if rt_resolve:
|
||||
self.staged_realtime_lsp_resolve_symbols = _parse_bool(rt_resolve)
|
||||
log.debug(
|
||||
"Overriding staged_realtime_lsp_resolve_symbols from .env: %s",
|
||||
self.staged_realtime_lsp_resolve_symbols,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def load(cls) -> "Config":
|
||||
"""Load config with settings from file."""
|
||||
|
||||
@@ -45,6 +45,22 @@ ENV_VARS = {
|
||||
# General configuration
|
||||
"CODEXLENS_DATA_DIR": "Custom data directory path",
|
||||
"CODEXLENS_DEBUG": "Enable debug mode (true/false)",
|
||||
# Cascade / staged pipeline configuration
|
||||
"ENABLE_CASCADE_SEARCH": "Enable cascade search (true/false)",
|
||||
"CASCADE_STRATEGY": "Cascade strategy: binary, binary_rerank, dense_rerank, staged",
|
||||
"CASCADE_COARSE_K": "Cascade coarse_k candidate count (int)",
|
||||
"CASCADE_FINE_K": "Cascade fine_k result count (int)",
|
||||
"STAGED_STAGE2_MODE": "Staged Stage 2 mode: precomputed, realtime",
|
||||
"STAGED_CLUSTERING_STRATEGY": "Staged clustering strategy: auto, score, path, dir_rr, noop, ...",
|
||||
"STAGED_CLUSTERING_MIN_SIZE": "Staged clustering min cluster size (int)",
|
||||
"ENABLE_STAGED_RERANK": "Enable staged reranking in Stage 4 (true/false)",
|
||||
"STAGED_REALTIME_LSP_TIMEOUT_S": "Realtime LSP expansion timeout budget (float seconds)",
|
||||
"STAGED_REALTIME_LSP_DEPTH": "Realtime LSP BFS depth (int)",
|
||||
"STAGED_REALTIME_LSP_MAX_NODES": "Realtime LSP max nodes (int)",
|
||||
"STAGED_REALTIME_LSP_MAX_SEEDS": "Realtime LSP max seeds (int)",
|
||||
"STAGED_REALTIME_LSP_MAX_CONCURRENT": "Realtime LSP max concurrent requests (int)",
|
||||
"STAGED_REALTIME_LSP_WARMUP_S": "Realtime LSP warmup wait after didOpen (float seconds)",
|
||||
"STAGED_REALTIME_LSP_RESOLVE_SYMBOLS": "Resolve symbols via documentSymbol in realtime expansion (true/false)",
|
||||
# Chunking configuration
|
||||
"CHUNK_STRIP_COMMENTS": "Strip comments from code chunks for embedding: true/false (default: true)",
|
||||
"CHUNK_STRIP_DOCSTRINGS": "Strip docstrings from code chunks for embedding: true/false (default: true)",
|
||||
|
||||
Reference in New Issue
Block a user