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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.
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/richfrem/Project_Sanctuary --skill vector-db-search명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
SOC 직업 분류 기준
SKILL.md 표시 중
| name | vector-db-search |
| 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 | ["pip:chromadb","pip:frontmatter","pip:langchain","pip:langchain_chroma","pip:langchain_classic","pip:langchain_community","pip:langchain_core","pip:langchain_huggingface","pip:langchain_text_splitters","skill:vector-db-init"] |
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.
| 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.
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).
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.
Do not cat, strings, or sqlite3 the .vector_data/ directory.
Binary blobs will corrupt your context window and the retrieval pipeline.
All access goes through query.py. No exceptions.
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]