AsterVec

Memory-friendly vector engine for on-device AI memory.

Quick Start · Why · How it works · Docs

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AsterVec is a vector search engine that keeps its index on disk and puts as little as possible in memory. RAM holds only a small navigation structure and caches, so an application can search millions of embeddings within a few hundred MB of memory. It is built for local AI agents and desktop RAG.

At the heart of AsterVec is one architectural decision: on disk, the graph and the vectors are stored separately, because the two parts are updated and accessed in essentially different patterns.

  • Graph — edges churn in small, scattered writes, so they live in Aster, a graph-oriented LSM-tree that absorbs them out of place.
  • Vectors — they rarely change once written, so they live in packed pages built for bulk reads.

Keeping the two apart is what makes updates cheap: a graph change writes only edges and never touches a vector page.

Quick start

pip install aster-vec

import astervec

opts = astervec.AsterVecDBOptions()
opts.dim = 128
opts.vector_file_path = "./db/vectors.bin"
db = astervec.AsterVecDB.open("./db", opts)

db.insert(1, [0.1] * 128, metadata={"source": "notes"})

hits = db.search([0.1] * 128, k=10, filter={"source": "notes"})
for h in hits:
    print(h["id"], h["distance"])

db.close()

Your data stays live — no index rebuilds:

db.update(7, new_vector)          # document re-embedded? just update it
db.delete(3)                      # real deletes — space is reclaimed
db.bulk_build(embeddings)         # fast initial load (NumPy accepted)

Why AsterVec

  • 🪶 Small memory, by design — RAM holds only the navigation structure and caches. You choose the memory budget, and it stays flat as your data grows.
  • 🔁 Live data — insert, update, and delete without ever rebuilding.
  • 🏷️ Filtered search — attach JSON metadata, query it Mongo-style.
  • 🔌 Runs inside your app — a thread-safe Python/C++ library that needs no separate service. An optional HTTP server is included for when you want one.
Search speed Memory needed
In-memory graph index (HNSW libraries) fast grows with your data
On-disk list index (IVF) slower small
AsterVec — graph index, on disk graph-fast small

How it works

Your app's process
  └─ AsterVec
       ├─ Memory — upper navigation layers + caches   (small, bounded)
       └─ Disk — two separate stores, updated independently:
            ├─ Graph store   — base-layer edges in Aster, a graph-oriented LSM-tree, built for small, frequent link updates
            └─ Vector store  — related vectors stored in nearby pages, compressed

For example, when graph and vectors share one disk record, recording a single new edge can move about 8 KB of vector data that did not change. In AsterVec, that edge lands in the graph store alone. On the vector side, related vectors are packed onto the same pages, so one read serves many candidate evaluations.

Documentation

Guide
Python & C++ API reference docs/API_REFERENCE.md
Python SDK guide docs/python_sdk_guide.md
Configuration options docs/API_REFERENCE.md §7
HTTP server (optional) docs/HTTP_API.md
Troubleshooting docs/TROUBLESHOOTING.md
Build from source CONTRIBUTING.md

Prefer a REST boundary? astervec_http serves the same engine over HTTP — see docs/HTTP_API.md.

Contributing

Contributions are welcome — CONTRIBUTING.md has build steps, tests, and PR expectations.

License

Apache-2.0 — see LICENSE.

Project links

Data verified by PyPI on Aug 26, 2026
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Credits

Author: NTU-Siqiang-Group

GitHub Statistics

Data verified by PyPI on Aug 26, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

Apache Software License (Apache-2.0)

Requires

Python >=3.9

Tags

vector-database ann hnsw rocksdb similarity-search

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The following attestation bundles were made for aster_vec-0.2.1-cp39-cp39-macosx_13_0_x86_64.whl:

Publisher: publish.yml on NTU-Siqiang-Group/AsterVec

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This release

0.2.1 This release

20 files

0.2.0

10 files

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