AlphaCorp-AI

AlphaCorp-AI / AlphaRustyRAG

Public

⚡ Sub-second RAG API built in Rust — document ingestion, Milvus vector search, and LLM streaming in a single async binary. Powered by Groq + Cohere.

actix-web cohere groq llm milvus
59
8
100% credibility
Found Feb 17, 2026 at 50 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Rust
AI Summary

AlphaRustyRAG is a high-performance web service that lets users upload documents for fast semantic search and AI-generated answers using retrieval-augmented generation.

How It Works

1
🔍 Discover the tool

You hear about AlphaRustyRAG, a super-fast way to upload your documents and get instant smart answers from an AI chat.

2
💻 Get it on your computer

Download the files to your computer and prepare a simple settings note with links to helpful AI services.

3
🚀 Start helper services

With one easy command, turn on the behind-the-scenes storage for your documents and user info.

4
🔗 Connect AI helpers

Add your free accounts from two speedy AI services so the tool can understand words and think up answers.

5
⚡ Launch the service

Run the main program and watch it come alive on your web browser.

6
📤 Upload documents

Send in your PDFs, text files, or zip folders full of files, and it automatically learns from them.

7
💬 Chat with your data

Open the simple chat page, ask a question about your files, and see sources plus answers stream in under a second.

🎉 Instant smart insights

Enjoy lightning-fast, accurate answers grounded in your own documents, perfect for quick research or questions.

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Star Growth

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AI-Generated Review

What is AlphaRustyRAG?

AlphaRustyRAG is a Rust-built RAG API that handles full document ingestion, chunking, Cohere embeddings, Milvus vector search, and Groq-powered LLM streaming in a single async binary. Upload PDFs, TXT, or ZIPs via POST /documents/upload, query collections with /chat-rag/stream for sub-second responses grounded in your data, and get SSE token streaming with sources. It solves the latency bloat of Python RAG stacks by delivering end-to-end retrieval and generation under 1s, even on 1,000-document benchmarks.

Why is it gaining traction?

Devs love collapsing ingestion, search, and LLM into one deployable binary—no Kubernetes glue needed. Benchmarks hit <160ms TTFT locally with Groq's gpt-oss-20b and Cohere's embed-english-light-v3.0, plus Docker Compose spins up Postgres and Milvus instantly. Built-in Swagger UI, chat frontend at /static/chat.html, and JWT auth make prototyping dead simple.

Who should use this?

Backend engineers building internal doc search tools or AI assistants over proprietary PDFs. Startups needing a fast RAG backend for customer support bots without vendor lock-in. Rust enthusiasts ditching slow async Python services for production APIs.

Verdict

Grab it if you want a lean RAG API in Rust—quickstart and benchmarks impress. With 47 stars and 1.0% credibility, it's early-stage but well-documented under MIT; run docker-compose up and test your docs before committing.

(198 words)

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