zvec
Zvec in-process vector database. Covers collections, indexing, embeddings, reranking, and persistence. Use when embedding Zvec into applications or tuning retrieval/storage behavior. Keywords: Zvec, HNSW-RaBitQ, vector database, ANN.
What this skill does
# Zvec
Zvec is a lightweight, in-process vector database meant to be embedded into applications ("SQLite for vectors").
## Quick navigation
- Overview: `references/overview.md`
- Concepts: `references/concepts.md`
- Quickstart (first operations): `references/quickstart.md`
- Installation (only if needed): `references/installation.md`
- Index types & quantization: `references/indexing.md`
- Embedding pipelines: `references/embedding.md`
- Reranking pipelines: `references/reranker.md`
- Data modeling & collections: `references/collections.md`
- CRUD / search operations: `references/data-operations.md`
- Configuration & persistence: `references/configuration.md`
## Operator recipes (high signal)
- Minimal “embed Zvec” checklist
- (Optional) Configure globals once at startup via `zvec.init(...)` (logging, `query_threads`).
- Create a collection on disk with `create_and_open(path=..., schema=..., option=...)`.
- Ingest documents as `Doc(id=..., fields=..., vectors=...)` via `insert()` or `upsert()`.
- Query via `collection.query(vectors=VectorQuery(...), topk=...)`.
- Call `collection.optimize()` periodically after heavy ingestion.
- Bulk ingest + keep query latency stable
- Prefer batched `insert()` / `upsert()`.
- Monitor `collection.stats` and run `optimize()` when flat buffers grow.
- Hybrid retrieval patterns
- Filter-only: `collection.query(filter=..., topk=...)`.
- Vector + filter: pass both `vectors=...` and `filter=...`.
- Multi-vector fusion: pass multiple `VectorQuery` items and rerank using `WeightedReRanker` or RRF.
- Memory-sensitive ANN on x86_64
- Prefer `HNSW-RaBitQ` when HNSW-quality recall matters but memory is the limiting factor.
- Start with the documented defaults (`total_bits=7`, `num_clusters=16`) and tune query-time `ef` before changing quantization bits.
- Safe evolution of live collections
- Add/drop/alter scalar columns via `add_column()`, `drop_column()`, `alter_column()`.
- Manage indexes via `create_index()` / `drop_index()` (scalar). Vector indexes cannot be dropped.
## Critical prohibitions
- Do not mirror vendor docs verbatim; summarize in your own words.
- Do not assume a client/server deployment model: Zvec is in-process.
- Do not add project-specific paths, secrets, or environment assumptions.
- Do not choose `HNSW-RaBitQ` on unsupported hardware; current docs limit it to `x86_64` with `AVX2` or better.
## Release Highlights (0.3.0 -> 0.4.0)
- **Windows** support and official Windows packages for Python and Node.js
- **HNSW-RaBitQ** quantized vector indexing for lower-memory ANN on supported x86_64 hosts
- **Stable C API** for building or maintaining additional language bindings
- **MCP server / agent skills** ecosystem for AI-driven collection management and retrieval workflows
- **0.3.1 hotfixes** for relaxed collection path restrictions and better Windows cross-drive/path handling
- **0.4.0** adds official Dart/Flutter bindings, iOS build support, a larger `topK` ceiling, stricter `query_params` validation, and fixes an SQ8 quantizer recall regression.
## Links
- Documentation: https://zvec.org/en/docs/
- GitHub: https://github.com/alibaba/zvec
- Releases: https://github.com/alibaba/zvec/releases
- Issues: https://github.com/alibaba/zvec/issues
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