vector-db-search

Semantic (meaning-based) search against the ChromaDB vector store. Use for Phase 2 of the 3-phase search protocol -- after the RLM Summary Ledger (Phase 1) returns insufficient results.

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Install skill "vector-db-search" with this command: npx skills add richfrem/agent-plugins-skills/richfrem-agent-plugins-skills-vector-db-search

Vector DB Search

Semantic (meaning-based) search against the ChromaDB vector store. Use for Phase 2 of the 3-phase search protocol -- after the RLM Summary Ledger (Phase 1) returns insufficient results.

Scripts

Script Role

scripts/query.py

Semantic search -- CLI entry point

scripts/operations.py

Core Parent-Child retrieval library

scripts/vector_config.py

Profile config helper (vector_profiles.json )

scripts/vector_consistency_check.py

Integrity validation

Write operations (ingest, cleanup) are handled by dedicated agents: vdb-ingest , vdb-cleanup .

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 snippets, not just file-level summaries

Execution Protocol

  1. Verify ChromaDB is running

curl -sf http://127.0.0.1:8110/api/v1/heartbeat

If connection refused: run vector-db-launch skill (plugins/vector-db/skills/vector-db-launch/SKILL.md ). For first-time setup: run vector-db-init skill (plugins/vector-db/skills/vector-db-init/scripts/init.py ).

  1. Select Profile and Search

Profiles are project-defined in vector_profiles.json (see vector-db-init skill). Any number can exist. Discover what's available:

cat .agent/learning/vector_profiles.json

Common default is knowledge -- your project may define more (e.g. separate profiles for code vs docs). When topic is ambiguous, search all profiles.

python3 ./scripts/query.py
"your natural language question" --profile knowledge --limit 5

Results include ranked parent chunks with RLM Super-RAG context pre-injected.

Architectural Constraints (Electric Fence)

NEVER -- direct database reads

Do not cat , strings , or sqlite3 the .vector_data/ directory. Binary blobs will corrupt your context window and the retrieval pipeline.

ALWAYS -- use the API

All access goes through query.py . No exceptions.

Source Transparency Declaration (L5 Pattern)

When search returns empty results, explicitly state:

Not Found in Vector Store Searched profile: [profile_name] for "[query]" Profile covers: [scope] Not searched: [out-of-scope areas]

Source Transparency

This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.

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