vector-db-searchlisted

## Dependencies This skill requires the `chromadb` and `langchain` packages defined in the plugin root. --- # Vector DB Search Semantic (meaning-based) search against the ChromaDB vector store using a high-precision Parent-Child architecture. Use for Phase 2 of the 3-phase search protocol (RLM -> Vector -> Grep). ## Scripts | Script | Role | |:-------|:-----| | `scripts/query.py` | Semantic search CLI -- recovers context-rich parent chunks. | | `scripts/operations.py` | Core domain logic for retrieval. | | `scripts/vector_config.py` | Unified profile-based configuration loader. | ## Execution Mode This skill defaults to **In-Process mode** for zero-latency direct disk access. No background server is required. This ensures maximum stability in isolated project environments. ## When to Use - Phase 1 (RLM Summary Ledger) returned no match or insufficient detail. - User asks "how does X work?" / "find code that does Y". - You need specific high-context snippets (Parent chunks) for reasoning. ## Execution Protocol ### 1. Identify Search Profile Verify available profiles in `.agent/learning/vector_profiles.json`. The default profile is usually `wiki`. ### 2. Run Query Note: The `--profile` flag is mandatory to ensure the correct model and collection are loaded. ```bash python ./scripts/query.py "your natural language question" --profile wiki --limit 5 ``` Results include ranked parent chunks (2,000 chars) that provide broad context to the LLM for reasoning. ## Rules