443500dc45
* feat: add FTS support for Collection::CreateIndex/DropIndex
Enable dynamic creation and removal of FTS indexes on existing STRING
columns through the standard CreateIndex/DropIndex API, matching the
lifecycle model already used by vector and scalar (invert) indexes.
Key changes:
- New FtsIndexer class (fts_indexer.h/cc) encapsulating per-segment FTS
RocksDB management: multi-field lifecycle, snapshot, insert, seal
- New BlockType::FTS_INDEX with block_id-based directory naming
(fts.<block_id>.rocksdb) for crash-safe snapshot-and-swap
- Segment::create_fts_index builds FTS index on a snapshot copy by
scanning forward store, then outputs new SegmentMeta + FtsIndexer
for atomic reload (same pattern as create_scalar_index)
- Segment::drop_fts_index snapshots, removes field CFs, outputs updated
meta (or nullptr when last FTS field is removed)
- Collection layer wires FTS into the existing task dispatch, version
update, and reload loops alongside vector/invert paths
- CreateIndex/DropIndex reject unsupported index types explicitly
instead of falling through to the wrong branch
* fixup! feat: add FTS support for Collection::CreateIndex/DropIndex
fix: address review comments for FTS CreateIndex/DropIndex
- Reject CreateIndex when column already has a different index type
(e.g. FTS on an INVERT-indexed column) at both Collection and
Segment layers
- Allow same-type different-params CreateIndex to rebuild the index
(remove old + create new + replay data), aligned with INVERT behavior
- Return OK when CreateIndex is called with identical params
- Rename operator[] to get() in both FtsIndexer and InvertedIndexer
- Add test cases: create→drop→create→drop cycle, and params-change
rebuild with case-sensitivity verification
* android skip Feature_CreateOrDropFtsIndex
* fixup! feat: add FTS support for Collection::CreateIndex/DropIndex
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| tests | ||
| thirdparty | ||
| tools | ||
| .clang-format | ||
| .clang-tidy | ||
| .gitattributes | ||
| .gitignore | ||
| .gitmodules | ||
| .pre-commit-config.yaml | ||
| CMakeLists.txt | ||
| CODE_OF_CONDUCT.md | ||
| CONTRIBUTING.md | ||
| LICENSE | ||
| README.md | ||
| README_CN.md | ||
| pyproject.toml | ||
README.md
English | 中文
🚀 Quickstart | 🏠 Home | 📚 Docs | 📊 Benchmarks | 🔎 DeepWiki | 🎮 Discord | 🐦 X (Twitter)
Zvec is an open-source, in-process vector database — lightweight, lightning-fast, and designed to embed directly into applications. Battle-tested within Alibaba Group, it delivers production-grade, low-latency and scalable similarity search with minimal setup.
[!Important] 🚀 v0.4.0 (May 9, 2026)
- Dart/Flutter SDK: Published the official zvec Flutter package with FFI bindings. Supports Android (arm64-v8a) and iOS (arm64) — no manual native compilation required.
- iOS Build Support: Added support for building on iOS platforms, expanding cross-platform coverage.
- Enlarged topK Limit: Relaxed the upper bound on topK to support larger-scale recall scenarios.
- Bug Fixes: SQ8 quantizer recall drop; Windows path handling; sparse vector index ordering.
💫 Features
- Blazing Fast: Searches billions of vectors in milliseconds.
- Simple, Just Works: Install and start searching in seconds. Pure local, no servers, no config, no fuss.
- Dense + Sparse Vectors: Work with both dense and sparse embeddings, with native support for multi-vector queries in a single call.
- Hybrid Search: Combine semantic similarity with structured filters for precise results.
- Durable Storage: Write-ahead logging (WAL) guarantees persistence — data is never lost, even on process crash or power failure.
- Concurrent Access: Multiple processes can read the same collection simultaneously; writes are single-process exclusive.
- Runs Anywhere: As an in-process library, Zvec runs wherever your code runs — notebooks, servers, CLI tools, or even edge devices.
📦 Installation
Python
Requirements: Python 3.10 - 3.14
pip install zvec
Node.js
npm install @zvec/zvec
✅ Supported Platforms
- Linux (x86_64, ARM64)
- macOS (ARM64)
- Windows (x86_64)
🛠️ Building from Source
If you prefer to build Zvec from source, please check the Building from Source guide.
⚡ One-Minute Example
import zvec
# Define collection schema
schema = zvec.CollectionSchema(
name="example",
vectors=zvec.VectorSchema("embedding", zvec.DataType.VECTOR_FP32, 4),
)
# Create collection
collection = zvec.create_and_open(path="./zvec_example", schema=schema)
# Insert documents
collection.insert([
zvec.Doc(id="doc_1", vectors={"embedding": [0.1, 0.2, 0.3, 0.4]}),
zvec.Doc(id="doc_2", vectors={"embedding": [0.2, 0.3, 0.4, 0.1]}),
])
# Search by vector similarity
results = collection.query(
zvec.VectorQuery("embedding", vector=[0.4, 0.3, 0.3, 0.1]),
topk=10
)
# Results: list of {'id': str, 'score': float, ...}, sorted by relevance
print(results)
📈 Performance at Scale
Zvec delivers exceptional speed and efficiency, making it ideal for demanding production workloads.
For detailed benchmark methodology, configurations, and complete results, please see our Benchmarks documentation.
🤝 Join Our Community
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❤️ Contributing
We welcome and appreciate contributions from the community! Whether you're fixing a bug, adding a feature, or improving documentation, your help makes Zvec better for everyone.
Check out our Contributing Guide to get started!