Jev RAG v0.2.0 — first public alpha

[aifabrice]

Jev RAG v0.2.0 is the first public alpha release of a vector-free local knowledge retrieval stack. It combines SQLite FTS5/BM25 retrieval, Jev evidence reranking, and optional grounded streaming answers through OpenRouter.

Highlights

• Index a local folder without embeddings, a vector database, or a GPU.
• Retrieve up to 30 BM25 candidates and rerank them with batched Jev requests.
• Stream cited MiniMax answers while measuring retrieval, reranking, first-token, generation, and total latency.
• Choose automatic, paragraph, or no-chunking modes.
• Load Markdown, text, HTML, JSON, CSV, YAML, DOCX, and PDF documents.
• Run a reproducible BM25/Jev retrieval benchmark with your own JSONL cases.
• Use the bilingual English and Simplified Chinese documentation.

Try it

python3 -m venv .venv
source .venv/bin/activate
python -m pip install -e ".[documents]"
cp .env.example .env
python local_kb.py index
python local_kb.py serve

Then open http://127.0.0.1:8765.

Important limitations

This is alpha software. Lexical retrieval may miss synonyms and paraphrases. Jev and the answer model are remote services, so queries and selected passages leave the machine when those features are enabled. The local web server has no authentication and should not be exposed directly to the public internet.

See the README, evaluation guide, and security policy before use.

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This discussion was created from the release Jev RAG v0.2.0 — first public alpha.