vector-db-search

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7 scripts

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.

1installs

Sourcerichfrem/agent-plugins-skills

Added on2026-03-07

NPX Install

npx skill4agent add richfrem/agent-plugins-skills vector-db-search

SKILL.md Content

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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

bash

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

).

2. 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:

bash

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.

bash

python3 plugins/vector-db/skills/vector-db-agent/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]

vector-db-search

NPX Install

Tags

SKILL.md Content

Vector DB Search

Scripts

When to Use

Execution Protocol

1. Verify ChromaDB is running

2. Select Profile and Search

Architectural Constraints (Electric Fence)

NEVER -- direct database reads

ALWAYS -- use the API

Source Transparency Declaration (L5 Pattern)