ictnlp/LevelRAG
The official implementation of "LevelRAG: Enhancing Retrieval-Augmented Generation with Multi-hop Logic Planning over Rewriting Augmented Searchers"
This helps with answering complex, knowledge-intensive questions by finding and combining information from various sources. You input a multi-part question, and it breaks it down, searches different databases (like Wikipedia or the web), and then synthesizes a comprehensive answer. It's designed for researchers, analysts, or anyone who needs detailed, accurate answers from vast amounts of text.
No commits in the last 6 months.
Use this if you frequently need to answer complex questions that require stitching together facts from multiple documents or the internet, where a simple keyword search isn't enough.
Not ideal if your questions are simple, single-fact lookups or if you only need answers from a very limited, static set of documents.
Stars
51
Forks
6
Language
Python
License
MIT
Category
Last pushed
Apr 12, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/rag/ictnlp/LevelRAG"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
denser-org/denser-retriever
An enterprise-grade AI retriever designed to streamline AI integration into your applications,...
rayliuca/T-Ragx
Enhancing Translation with RAG-Powered Large Language Models
neuml/rag
🚀 Retrieval Augmented Generation (RAG) with txtai. Combine search and LLMs to find insights with...
NovaSearch-Team/RAG-Retrieval
Unify Efficient Fine-tuning of RAG Retrieval, including Embedding, ColBERT, ReRanker.
RulinShao/retrieval-scaling
Official repository for "Scaling Retrieval-Based Langauge Models with a Trillion-Token Datastore".