vector-searchlisted

# MoFlo Vector Search (RAG) Semantic search over your own documents, backed by moflo's HNSW index in `.moflo/moflo.db` (node:sqlite, Node 22+ built-in). Small enough to ship in a devDependency; fast enough for interactive retrieval at 100k–1M vectors. ## When to Use This vs `memory-patterns` - **`memory-patterns`** — structured, namespaced memory you own (sessions, learnings, patterns). Keys matter. Entries are conceptual units. - **This skill (`vector-search`)** — search over documents you've ingested for retrieval. Entries are content chunks. Keys are just stable IDs for dedupe. Both use the same index; the difference is how you chunk and what you put in the `value` field. ## Ingest Two paths. Pick the one that matches your source of truth: ### A. Ad-hoc ingest from Claude Code ```typescript for (const [id, text] of chunks) { await mcp.memory_store({ namespace: 'docs', // your RAG corpus key: id, // stable ID for this chunk (file path + offset, etc.) value: text, // the chunk content — what gets embedded tags: ['doc', docType], upsert: true, }); } ``` ### B. Repeatable ingest from the filesystem Use moflo's shipped indexers (cross-platform, skip unchanged chunks via a hash file): ```bash # Guidance docs → namespace 'guidance' node .claude/scripts/index-guidance.mjs # Code structure → namespace 'code-map' node .claude/scripts/generate-code-map.mjs # Your own corpus — write a small indexer that loops ov