AlphaCorp-AI/RustyRAG
⚡ Sub-200ms RAG API built in Rust — document ingestion, Milvus vector search, Jina AI local embeddings, and LLM streaming in a single async binary. Powered by Cerebras, Groq, and more.
This tool helps professionals quickly find precise answers within large collections of documents like legal contracts, research papers, or internal knowledge bases. You input your documents in various formats (PDFs, Word docs, spreadsheets, etc.), ask a question, and get fast, accurate answers streamed back with source citations. It's designed for anyone needing instant, reliable information from their documents, such as legal professionals, researchers, or support agents.
Use this if you need extremely fast and accurate question-answering across your documents, especially for real-time applications like voice assistants or AI agents where latency is critical.
Not ideal if your primary need is general conversational AI without specific document grounding, or if you only work with very small, easily searchable text files.
Stars
66
Forks
8
Language
Rust
License
MIT
Category
Last pushed
Mar 12, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/rag/AlphaCorp-AI/RustyRAG"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
raphaelmansuy/edgequake
High-performance GraphRAG inspired from LightRag written in Rust
bosun-ai/swiftide
Fast, streaming indexing, query, and agentic LLM applications in Rust
cool-japan/oxirag
A four-layer Retrieval-Augmented Generation (RAG) engine in Rust with SMT-based logic...
kkollsga/kglite
Lightweight in-memory knowledge graph with Cypher query support
pixlie/PixlieAI
Please check our new project with similar targets: https://github.com/pixlie/Pixlie