xiami2019/UAR
[Findings of EMNLP'2024] Unified Active Retrieval for Retrieval Augmented Generation
This project helps developers build more efficient and accurate Retrieval-Augmented Generation (RAG) systems. It takes in user instructions and existing knowledge bases, then intelligently decides whether to retrieve external information for each query. The output is a more refined RAG workflow that avoids unnecessary retrievals, improving response quality and reducing computational cost. It is designed for engineers and researchers who are building and optimizing conversational AI and question-answering systems.
No commits in the last 6 months.
Use this if you are developing RAG applications and need to improve the precision and efficiency of information retrieval, ensuring external knowledge is only sought when truly beneficial for generating responses.
Not ideal if you are looking for a pre-built RAG application or a solution that doesn't require deep technical understanding of model training and deployment.
Stars
23
Forks
—
Language
Python
License
—
Category
Last pushed
Sep 30, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/rag/xiami2019/UAR"
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".