| 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
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 (0.5.0)
- 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.
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
