Agenti e multi-agente

Zvec: An Embedded, In-Process Vector Database

alibaba/zvec

Zvec is an Apache-2.0-licensed vector database designed to run directly inside applications. It supports dense and sparse vectors, full-text and hybrid search, structured filtering, local durable storage, and SDKs for several programming languages.

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Apache-2.0Licenza
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Panoramica

Developed under the Alibaba GitHub organization, Zvec targets low-latency similarity search without a separate database server. Its primary implementation language is C++. The repository describes support for memory-to-disk vector indexes, multi-vector queries, full-text search, hybrid retrieval, write-ahead logging, and concurrent readers. The supplied metadata reports 15,360 stars, 973 forks, 76 open issues, and an active, non-archived main branch.

Funzionalità principali

  • In-process operation without a separate database server
  • Dense and sparse vector support
  • Multi-vector queries and multiple vector index types
  • Native full-text search
  • Hybrid vector, full-text, and structured-filter queries
  • Write-ahead logging for durable local storage
  • Multiple concurrent readers with single-process-exclusive writes
  • Group-by top-K search across Flat, HNSW, HNSW-RaBitQ, and sparse indexes
  • INT8 and INT4 quantization with optional random rotation
  • Unicode-aware tokenization, character folding, and Snowball stemming for 34+ languages
  • Officially listed SDKs or bindings for Python, Node.js, Go, Rust, and Dart/Flutter

Requisiti, installazione e avvio rapido

Python: `pip install zvec`. Node.js: `npm install @zvec/zvec`. Rust: `cargo add zvec-rust`. Dart/Flutter: `flutter pub add zvec`. Go bindings are available at https://github.com/zvec-ai/zvec-go, but an installation command is not stated. Source-build instructions are available at https://zvec.org/en/docs/db/build/.

Utilizzo

Python quick start: import `zvec`; create a `CollectionSchema` with a `VectorSchema`; call `zvec.create_and_open` with a local path; insert `zvec.Doc` records; then call `collection.query` with a `zvec.Query`, a query vector, and `topk`. Results are described as relevance-sorted records containing an ID and score. See https://zvec.org/en/docs/db/quickstart/ for the documented walkthrough.

Compatibilità del modello e casi d'uso

Zvec stores and searches embeddings but the supplied repository material does not name compatible embedding models, LLM providers, or MCP integrations. Its RAG, agent-memory, and LLM-memory relevance comes from local dense, sparse, full-text, and hybrid retrieval rather than a stated dependency on a particular model.

Note su licenza e rischi

The repository metadata identifies the license as Apache-2.0. License reference: https://api.github.com/licenses/apache-2.0. Users should review the repository license and dependency terms before redistribution.

Rilascio e manutenzione

The README highlights v0.6.0, dated July 20, 2026. It adds group-by search, optional random-rotation quantization for INT8/INT4, an enhanced full-text pipeline, block-max skipping for conjunction queries, a DiskANN C API, and stability fixes. Release details: https://github.com/alibaba/zvec/releases/tag/v0.6.0. The supplied repository metadata records the latest push as August 3, 2026.

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