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Hybrid RAG
Zero-cloud SQLite FTS5 RAG engine & GitHub Action
Context-grounded repository AI assistant using Hybrid RAG (Okapi BM25 + Dense Semantic Vector Search + Reciprocal Rank Fusion k=60). Features markdown AST-aware chunking for deterministic line-span citations ([file#L-L]), zero-dependency @vercel/ncc bundling, and zero-cloud SQLite FTS5 execution.
Hey Product Hunt! 👋
I'm Çağrı Giray Keşan, a Full-Stack & AI Systems Engineer and author of Hybrid RAG Issue & PR Assistant.
While developing code intelligence and repository tools, I noticed a critical challenge in codebase RAG: dense embeddings alone often struggle with exact token matches like variable names, error codes, and configuration flags because identifiers get blurred in high-dimensional latent space.
To address this, I built **Hybrid RAG**:
- ⚡ **Lexical Search:** Embedded SQLite FTS5 (Okapi BM25) for sub-millisecond exact token precision with zero cloud database dependency.
- 🧠 **Semantic Search:** Dense vector embeddings for contextual semantic intent.
- 🔀 **Rank Fusion:** Reciprocal Rank Fusion (k=60) to blend lexical and dense scores deterministically.
- 🤖 **AST Code Triage:** Automatic repository issue and PR triage with markdown AST-aware chunking and verified line-span citations ([file#L-L]).
The core recipe is merged in microsoft/PhiCookBook (PR #571) and is packaged as a zero-dependency GitHub Marketplace Action via @vercel/ncc.
- 📦 GitHub Marketplace: https://github.com/marketplace/a...
- 📖 Engineering Deep-Dive: https://dev.to/cagrik34/i-ditche...
Looking forward to your thoughts and feedback! 🚀
Hybrid RAG was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #83 on the daily leaderboard. Context-grounded repository AI assistant using Hybrid RAG (Okapi BM25 + Dense Semantic Vector Search + Reciprocal Rank Fusion k=60). Features markdown AST-aware chunking for deterministic line-span citations ([file#L-L]), zero-dependency @vercel/ncc bundling, and zero-cloud SQLite FTS5 execution.
On the analytics side, Hybrid RAG competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how Hybrid RAG performed against the three products that launched closest to it on the same day.
Who hunted Hybrid RAG?
Hybrid RAG was hunted by Çağrı Giray Keşan. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Hybrid RAG including community comment highlights and product details, visit the product overview.
Çağrı Giray Keşan