zvec

name: zvec description: "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." metadata: version: "0.5.0" release_date: "2026-06-12"

Zvec is a lightweight, in-process vector database meant to be embedded into applications ("SQLite for vectors").

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.

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.

Full-text search (FTS): attach an FTS index to any string field via create_index() / drop_index() and query it with natural-language or structured expressions, alongside vector indexes.
Hybrid retrieval: the MultiQuery API combines dense vectors, sparse vectors, scalar filters, and text in one query with consistent reranking across Python, Go, Rust, and C++.
DiskANN index: keeps the bulk of the index on disk instead of RAM, cutting memory use for billion-scale datasets on memory-constrained hosts.
Output field selection: fetch() accepts an output_fields parameter to control which fields are returned.
New SDKs and tooling: official Go SDK (cgo, prebuilt Linux/macOS/Windows libs), Rust SDK (RAII, builder APIs), and Zvec Studio (pip install zvec-studio) for visual data browsing and query testing.

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.