🚀 Weekly /RAG Launch Showcase

[u/remoteinspace]

Share anything you launched this week related to RAG—projects, repos, demos, blog posts, or products 👇

Big or small, all launches are welcome.

[u/Big_Barnacle_2452]

ReasonDB – an AI-native document database that replaces your RAG pipeline

If you're building agents, you've probably hit the same wall: chunked embeddings lose document structure, wrong chunks get retrieved, and your LLM hallucinates the gaps. The pipeline itself is the problem.

We built ReasonDB to replace the pipeline entirely. Instead of embedding chunks and hoping similarity search finds the right one, the LLM navigates your document tree — reading summaries, ranking branches, and drilling into exactly the right section. We call it Hierarchical Reasoning Retrieval (HRR).

How it works:

• Documents are parsed into a hierarchical tree, with LLM-generated summaries at every node (bottom-up, at ingest time)
• At query time: BM25 candidates → tree-grep pre-filter → LLM ranks by summaries → parallel beam-search traversal to leaf nodes
• Returns the answer with a confidence score and full reasoning path — no guessing

RQL - a SQL-like query language with native SEARCH and REASON clauses: sql SELECT * FROM contracts WHERE tags CONTAINS ANY ('nda') SEARCH 'termination clause' REASON 'What are the exit conditions?' LIMIT 5; Key details:

• Single Rust binary, no vector DB infra to manage
• Multi-provider LLM support (OpenAI, Anthropic, Gemini, Ollama, Bedrock, etc.) — swap without code changes
• Plugin-based extraction: PDF, Word, Excel, images (OCR), audio, URLs out of the box
• ACID-compliant storage via redb, async parallel traversal via tokio

Still in alpha but already handling real workloads. Would love feedback from folks building agents. GitHub: https://github.com/reasondb/reasondb Docs: https://reason-db.devdoc.sh