RAG・知識ベース
rag.computer
Rag.computer — self-hostable rag platform - document ingestion, embedd, open-source.
★ 100 スターMIT
ガイドを見る →trieveは、devflowinc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: devflowinc/trieve）。GitHubスターは2,719、フォークは254で、MIT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1月25日です。
All-in-one API platform for search, recommendations, RAG and analytics — self-hostable
Decide between the hosted API for speed or self-hosting for control, and what you'll index — docs, products or content chunks.
Sign up for the hosted API, or clone the repo for self-hosting/local development.
For local dev, install system deps, Rust and Node/Yarn, copy the .env files, set your OPENAI_API_KEY/LLM_API_KEY, and start the Docker services and embedding servers.
Create a dataset, upload chunks, and run a semantic or hybrid search from the dashboard or SDK.
Dense vector search using OpenAI or Jina embeddings stored in Qdrant.
Every chunk is also indexed with a SPLADE sparse-vector model for quality full-text/neural search.
Combine dense and sparse with BAAI/bge-reranker-large cross-encoder reranking; bring your own embedding, SPLADE, reranker or LLM models.
Find similar chunks or grouped files — useful where users favorite, bookmark or upvote content.
RAG endpoints via OpenRouter with topic-based memory management, or bring your own context.
Recency biasing, merchandising signals (clicks, add-to-carts, citations), rich filters, grouping, and sub-sentence highlighting.
Clone Trieve for self-hosting or local development; hosted users can skip to the dashboard.
git clone https://github.com/devflowinc/trieve.git
cd trieveCopy the example env files as the local-development section shows, and set your keys in server/.env.
cp .env.example ./server/.env
# set OPENAI_API_KEY and LLM_API_KEY in ./server/.envBring up the Docker services and the CPU embedding stack, then run the server and frontends per the README.
./convenience.sh -l
docker compose -f docker-compose-cpu-embeddings.yml up -ddevflowinc (the Trieve company) maintains it, with documentation at docs.trieve.ai.
The code is MIT-licensed and self-hostable for free. The hosted service is commercial — the README links a sign-up with 1k chunks free.
RAG and embedding flows use model providers: the local-dev guide sets OPENAI_API_KEY/LLM_API_KEY, and you can bring your own models instead.
SWIRL federates live queries across your existing sources without copying data. Trieve is an indexed search/RAG platform you push chunks into, with recommendations and analytics built in.
Sign up at dashboard.trieve.ai for the hosted API, or follow the README's local-development and self-hosting guides.
Rag.computer — self-hostable rag platform - document ingestion, embedd, open-source.
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