LynseDB

LynseDB is a high-performance, low-cost vector database with a Python-first API and a Rust storage/search backend. It can run embedded inside a single Python process or as an HTTP server for multi-process and production deployments.

The API is intentionally small: create a database, create a collection, write vectors with metadata, build an index when you need faster search, and query the same collection locally or remotely with the same high-level Python calls.

Use it when you want vector search that starts as an embedded local library, can move to a service when your app grows, and does not require a heavy database cluster for common semantic search, RAG, agent memory, and multimodal retrieval workloads.

What LynseDB is good at

• Local development, notebooks, scripts, tests, and small services that want an embedded vector store.
• Remote deployments where several workers or services need to share one database process.
• Low-operational-cost deployments where predictable resource use matters.
• Standard SQL-style metadata filtering with where expressions.
• Multiple vector representations per record through named vector fields.
• Dense, sparse, text, and hybrid retrieval.
• Recall-first search with explicit index and search knobs.

Execution modes

Mode	How to start	Best for	Notes
Local	lynse.VectorDBClient() or lynse.VectorDBClient(uri="./data")	notebooks, scripts, single-process apps	No server and no network hop. Do not share the same local path between independent processes.
Remote	lynse serve ... plus lynse.VectorDBClient("http://host:7637")	web services, workers, production	Use HTTP mode when more than one process writes or searches the same data.

Install

pip install LynseDB

Python 3.9 or newer is required. Wheels are published for common macOS, Linux, and Linux environments. Native Windows environments are not supported; on Windows, use WSL 2 (Windows Subsystem for Linux) or Docker.

First search

import numpy as np
import lynse

client = lynse.VectorDBClient(uri="./lynsedb-data")
db = client.create_database("demo", drop_if_exists=True)
collection = db.require_collection("documents", dim=4, drop_if_exists=True)

items = [
    ([0.10, 0.20, 0.30, 0.40], 1, {"title": "intro", "lang": "en"}),
    ([0.11, 0.19, 0.29, 0.39], 2, {"title": "guide", "lang": "en"}),
    ([0.80, 0.10, 0.20, 0.10], 3, {"title": "notes", "lang": "fr"}),
]

with collection.insert_session() as session:
    session.bulk_add_items(items, enable_progress_bar=False)

collection.build_index("FLAT-L2")

result = collection.search(
    np.array([0.10, 0.20, 0.30, 0.40], dtype=np.float32),
    k=2,
    where="lang = 'en'",
    return_fields=True,
)

print(result.to_list())

Learn next

• Start with the Learning path if you want a step-by-step curriculum from zero to production.
• Use the Quickstart for the shortest complete write, index, search, query, and delete lifecycle.
• Read Core concepts to understand clients, databases, collections, IDs, fields, indexes, and result objects.
• Use Connect and deploy to choose local or remote mode and configure the server.
• Use Search and filter and the Metadata filter cookbook for vector search, standard SQL-style filters, text search, hybrid search, and reranking.
• Use Indexing guide and Performance tuning to choose between flat, HNSW, IVF, DiskANN, and quantized indexes.
• Use Build a RAG workflow for an end-to-end retrieval example.

Stability notes

LynseDB is still evolving. Pin versions for deployments and test migrations before upgrading. For concurrent production access, prefer the HTTP server over sharing one local data directory across independent Python processes.