RAG・知識ベース
helix-db
An OLTP graph database with native vector and full-text search, built in Rust on object storage.
★ 6.1k スターApache-2.0
ガイドを見る →seekdbは、oceanbaseが公開しているオープンソースのRAG・知識ベースプロジェクトです（GitHub: oceanbase/seekdb）。GitHubスターは3,107、フォークは370で、Apache-2.0ライセンスの下で公開されています。GitHubでの最終更新は2026年10月8日です。
seekdb — the AI-native search database unifying vector, text and structured data for agents.
Decide the first concrete outcome you want from it, so you can pick the right example, template or mode instead of exploring everything at once.
Clone the repository (or install the published package) and skim its README quick start for the requirements for your platform.
Run the documented install step and set any required configuration — such as provider keys, data sources or local services — before the first run.
Execute the smallest documented example end to end, confirm the output is what you expected, then adapt it to your real data or workflow.
Vector + full-text + relational filters together, served by incremental HNSW right after writes.
FORK DATABASE lets agents experiment safely; MERGE accepts results and DROP rolls back.
Write a memory and query it in the next millisecond without P99 spikes from background rebuilds.
Works with the MySQL ecosystem and with LangChain, LlamaIndex and Dify out of the box.
Embedded library, micro-server for edge devices, single node, or the OceanBase cluster.
Positioned as the state store for agents: observations persist and are retrievable immediately.
Get the source so you can inspect, configure and run it locally.
git clone https://github.com/oceanbase/seekdb.git
cd seekdbInstall pyseekdb to use seekdb as an embedded agent store from Python.
pip install -U pyseekdbUse the one-line Linux install, Homebrew on macOS, Docker or the zero-install cloud as the README's Installation section describes.
# see README Quick Start / Installation for your platform's exact commandIt is maintained by oceanbase on GitHub (oceanbase/seekdb), where the source, issues and releases live.
Yes — it is free and open source under Apache-2.0, which permits use, modification and commercial redistribution, with licence/notice retention.
No LLM key is needed by the database; your application supplies embeddings from whichever model you choose.
Compared with a standalone vector database such as Qdrant or a plain MySQL instance, seekdb's distinguishing focus is: seekdb — the AI-native search database unifying vector, text and structured data for agents. Pick it when that focus matches your need; the alternative may suit you better if its broader or different scope is the priority.
Clone https://github.com/oceanbase/seekdb (or install its published package), follow the README quick start to install and configure it, then run the smallest documented example — the Run section on this page mirrors those steps.
An OLTP graph database with native vector and full-text search, built in Rust on object storage.
AI-native database for hybrid search across dense, sparse, and tensor vectors plus full text
Polyglot document intelligence in Rust — extract text, tables and structured data from 106 formats.
Vector similarity search inside Postgres - store embeddings next to your data
Knowhere turns documents into structured, hierarchy-native chunks ready for agents and RAG.
AI-native data platform: 60+ connectors, ETL, scheduling, and RAG search.
LibreAIカタログに掲載されたRAG・知識ベースプロジェクトの中で最もスターの多いもの — seekdbが合わなかった場合に検討したい、現実的な候補リストです。
Build Agentic workflows, RAG pipelines, with rich AI model and tool support on one collaborative workspace. Deploy on cloud, VPC, or…
RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context…
Lightweight OCR toolkit turning PDFs and images into structured, LLM-ready data in 100+ languages
MinerU — turns PDFs and Office documents into LLM-ready Markdown and JSON for agents and RAG pipelines, Apache-2.0 plus terms
Stop renting your intelligence. Own it with AnythingLLM. Everything you need for a powerful local-first agent experience
カタログの中でseekdbとスター数が近いRAG・知識ベースプロジェクト — 前後の棚もそのまま見て回れます。
Fast, lightweight Python embeddings from Qdrant — dense, sparse, ColBERT, image and rerankers.
Federated AI search and RAG across 100+ apps — answers with sources, without moving your data
VideoRAG — chat with your videos, even hundreds of hours of them, with graph-driven retrieval.
Graph RAG conversational knowledge base built on TiDB Serverless vector storage
A personal knowledge base that builds itself — drop in sources, get a persistent interlinked wiki.
The semantic intelligence layer for ontologies — governed, traceable knowledge for AI agents