RAG и базы знаний
llm-wiki-agent
A personal knowledge base that builds itself — drop in sources, get a persistent interlinked wiki.
★ 3,6 тыс. звёздMIT
Смотреть руководство →OpenKB — проект с открытым исходным кодом из категории «RAG и базы знаний» от VectifyAI (VectifyAI/OpenKB на GitHub). У него 4 759 звёзд на GitHub и 495 форков, он распространяется по лицензии Apache-2.0 и последний раз обновлялся на GitHub 22 июля 2026.
OpenKB compiles your documents into a persistent, interlinked wiki knowledge base — no vector DB
Collect the papers, docs, or URLs you want compiled into one knowledge base.
Install OpenKB from PyPI, create a folder, and run the init command, choosing your LLM model.
Add files, a whole directory, or a URL; OpenKB compiles them into wiki pages with cross-links.
Ask questions with cited answers, chat interactively, or generate outputs like skills and slide decks.
Documents become summary, concept, and entity pages with cross-references that accumulate over time; contradictions get flagged.
PageIndex tree indexing handles long PDFs by reasoning over a hierarchical index — no vector database required.
PDF, Word, Markdown, PowerPoint, HTML, Excel, CSV, text, and URLs, with native handling of figures, tables, and images.
One-off cited answers or multi-turn chat over the wiki, with persisted sessions you can resume.
Distill redistributable agent skills from the wiki, visualize an interactive knowledge graph, or generate single-file HTML slide decks.
The wiki is plain Markdown with wikilinks that opens in Obsidian, and pages follow Google's Open Knowledge Format specification.
Clone for a source install, or simply install from PyPI in the next step.
git clone https://github.com/VectifyAI/OpenKB.git
cd OpenKBInstall the package (or `pip install -e .` from the clone for development).
pip install openkbCreate a knowledge base, add a document, and ask your first question (set your LLM API key in .env first).
openkb init
openkb add paper.pdf
openkb query "What are the main findings?"VectifyAI, the team behind PageIndex; OpenKB is powered by PageIndex's open-source vectorless retrieval.
Yes, it is Apache-2.0 licensed. You pay only for the LLM API usage of the provider you configure (OpenAI, Claude, Gemini, and others via LiteLLM); some subscription providers can authenticate via OAuth instead of a key.
It needs an LLM — usually your own API key set in .env. PageIndex runs locally by default; the optional PageIndex Cloud features need a separate key.
RAG rediscovers knowledge from chunks on every query and nothing accumulates. OpenKB compiles knowledge once into a persistent wiki with cross-references already built, then keeps it current as documents are added.
Run `pip install openkb`, then `openkb init` in a fresh folder, `openkb add <file>`, and `openkb query` or `openkb chat`.
A personal knowledge base that builds itself — drop in sources, get a persistent interlinked wiki.
Agent-fed knowledge base that distills learnings into cited wiki pages.
Open-source LLM knowledge platform: turn raw documents into a queryable RAG, an autonomous reasoning agent, and a self-maintaining Wiki.
Turn your GitHub stars into a searchable AI knowledge base.
A knowledge-base platform with visual AI workflows — build Q&A systems on your own documents
Graph RAG conversational knowledge base built on TiDB Serverless vector storage
Другие проекты из категории «RAG и базы знаний» с наибольшим числом звёзд в каталоге LibreAI — реалистичный список альтернатив, если OpenKB вам не подходит.
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
Соседние проекты из категории «RAG и базы знаний» в каталоге, ближайшие по числу звёзд к OpenKB — продолжайте изучать полку по обе стороны от него.
An agent that finds anything on your computer — federated search over your files, in place.
AI-native database for hybrid search across dense, sparse, and tensor vectors plus full text
RAG for your monorepo: query, understand and edit multi-language codebases with a knowledge graph.
RAGApp: deploy a RAG app over your documents with Docker — configure it like a custom GPT
Build Neo4j knowledge graphs from unstructured data using LLMs — then chat with your graph.
Ragent (容纳智能体): a production-grade Java Agentic RAG platform with hybrid retrieval and an admin console
Бесплатно и открыто — забирайте исходный код, ставьте звезду репозиторию и начинайте создавать.