mladenpop-oss

mladenpop-oss / vibe-index

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Roaring Bitmap positional phrase matching for low-latency LLM context retrieval.

12
0
100% credibility
Found Apr 27, 2026 at 12 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Rust
AI Summary

A tool for quickly finding exact phrases and nearby context in large collections of text or code, designed to provide precise snippets for AI assistants.

How It Works

1
🔍 Discover the tool

You hear about a handy tool that helps AI assistants find exact spots in your documents lightning-fast, saving time and making answers precise.

2
📦 Add it to your project

With one easy step, you bring this tool into your work, like adding a new helper to your toolbox.

3
📄 Feed in your documents

You share your files or notes with the tool, and it quietly builds a super-quick map of everything inside.

4
💡 Ask in plain English

You type a question like 'where's the login part?' and in a blink, it shows exact matches with surrounding details.

5
🤖 Share just the right bits

You grab those perfect snippets and give them to your AI chat, so it focuses only on what matters.

🎉 Get spot-on answers

Your AI responds with accurate, helpful replies using way less info, feeling smarter and faster every time.

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Star Growth

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AI-Generated Review

What is vibe-index?

Vibe-index is a Rust library using roaring bitmaps for sub-microsecond exact phrase matching at precise positions in tokenized text, tailored for low-latency LLM context retrieval in RAG pipelines. It indexes code or docs into compact bitmaps per token, letting you search "fn authenticate" and pull just the surrounding 50 tokens instead of stuffing 8K irrelevant ones into prompts. No embeddings, no GPU—just blazing positional lookups that slash token waste by 95% and inference time.

Why is it gaining traction?

Unlike embedding search (5-20ms, 20MB+) or Tantivy/BM25 (document-level, µs but fuzzy), vibe-index delivers ns-µs exact positions with 0.5MB memory on 50K tokens, plus fuzzy tolerance and hybrid BM25 integration. Devs dig the Rust roaring bitmaps efficiency—faster than Golang/Java/C# ports—paired with llama.cpp/vLLM hooks for instant RAG prototypes. Benchmarks crush alternatives, making it a drop-in for token-budget crunches.

Who should use this?

RAG engineers building code-aware AI agents who need pinpoint function/line retrieval without vector DB overhead. Local LLM tinkerers indexing repos for "where's authenticate?" queries. Hybrid search fans combining it post-BM25 for precise context injection in production pipelines.

Verdict

Grab it for RAG prototypes if low-latency exact search fits—docs shine, benchmarks convince, Rust roaring bitmaps implementation is solid. At 12 stars and 1.0% credibility, it's early (basic tests, no SIMD), so pair with mature tools until it matures.

(198 words)

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