lemon07r

lemon07r / Vera

Public

Local code search combining BM25, vector similarity, and cross-encoder reranking. Parses 60+ languages with tree-sitter, runs entirely offline, and returns structured results with file paths, line ranges, and symbol metadata. Built in Rust.

bm25 cli code-search code-search-engine cross-encoder
15
0
100% credibility
Found Mar 28, 2026 at 15 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Rust
AI Summary

Vera is a Rust-based code search tool that indexes 60+ languages using tree-sitter parsing and performs hybrid retrieval with BM25, vector search, and optional reranking, running locally or via API.

How It Works

1
🔍 Discover Vera

You hear about Vera, a smart helper that finds exactly the right code in any project instantly.

2
📥 Get Vera

Download and install Vera with one simple command, no complicated setup needed.

3
Pick your AI brain
💻
Local

Everything stays on your machine, fast and private.

☁️
Cloud

Tap into powerful online AI for the best results.

4
📚 Feed it your code

Point Vera at your project folder and let it learn every file, function, and detail.

5
🔥 Ask and find magic

Type a question like 'show login code' and Vera highlights the perfect matches with context.

6
🔄 Keep it fresh

Vera automatically updates as you change code, always ready.

✅ Code like a pro

You search code effortlessly, build faster, and let AI agents use Vera to help you shine.

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Star Growth

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AI-Generated Review

What is Vera?

Vera is a Rust-powered local code search tool for developers digging through codebases without cloud dependencies. It parses 60+ languages offline via tree-sitter, blending BM25 keywords, vector similarity, and cross-encoder reranking to return structured JSON with file paths, line ranges, and symbol metadata. Install the single binary with `bunx @vera-ai/cli install`, setup local ONNX models via `vera setup`, index repos with `vera index .`, and query like `vera search "authentication logic"`.

Why is it gaining traction?

It crushes benchmarks—0.91 MRR@10 and 0.84 nDCG@10 on real tasks across ripgrep, Flask, and more—while spitting out token-efficient chunks ideal for local code AI agents or local GitHub Copilot alternatives. Zero deps, GPU acceleration, and MCP JSON-RPC server make it a drop-in local code assistant that works offline, unlike Python-heavy tools needing LSPs or APIs. Ships agent skills for effective LLM queries, plus `vera update` for fast incremental indexing.

Who should use this?

Backend engineers scanning large monorepos for symbols or configs, local code LLM builders chaining it as a code interpreter local retrieval step, or AI agent devs needing a local code repository search over full-file dumps. Perfect for local GitHub actions runners or offline code review tools where privacy and speed matter.

Verdict

Grab it if you need a mature local code completion LLM backend—benchmarks and docs are solid despite 15 stars and 1.0% credibility score signaling early days. Test on your repo; incremental updates and agent integration make it production-ready for solo use, but watch for community growth.

(198 words)

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