vector-db-search

Original：🇺🇸 English
Translated
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.

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Sourcerichfrem/agent-plugins-skills
Added on

NPX Install

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

Tags

Translated version includes tags in frontmatter
semantic-searchchromadb-vector-storerag-retrievalai-agentcode-documentation-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

ScriptRole
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]