| name | vector-db-search |
| plugin | vector-db |
| description | Semantic search skill for retrieving code and documentation from the ChromaDB vector store. Use when you need concept-based search across the repository (Phase 2 of the 3-phase search protocol). V2 includes L4/L5 retrieval constraints. |
| allowed-tools | Bash, Read |
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.
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
- Profile Sovereignty: Always pass
--profile to ensure the correct semantic space is searched.
- API Integrity: NEVER attempt to read the database SQLite or parquet files directly. Always use
query.py.
- Transparency: When search returns empty results, state which profile and scope were searched.
