AdalFlow: The library to build & auto-optimize LLM applications.
🥤 RAGLite is a Python toolkit for Retrieval-Augmented Generation (RAG) with PostgreSQL or SQLite
Rust library for generating vector embeddings, reranking locally
An easy-to-use python toolkit for flexibly adapting various neural ranking models to any target domain.
Testing speed and accuracy of RAG with, and without Cross Encoder Reranker.
A small reranker service using mixedbread.ai reranker model
This is RAG Modules Repo. This includes various modules in the RAG ecosystem.
The method of re-ranking involves a two-stage retrieval system, with re-rankers playing a crucial role in evaluating the relevance of each document to the query. RAG systems can be optimized to mitigate hallucinations and ensure dependable search outcomes by selecting the optimal reranking model.
Multi-Objective Recommender System
A comprehensive RAG FastAPI service that handles document uploads and retrievals, built with Python. Uses PyMuPDF for document processing, turbopuffer for vector storage, OpenAI for models, and cohere for reranking.
BETA - SearchAugmentedLLM empowers LLMs with relevant web information. Given a query, it searches Google, processes top results, chunks the content, ranks by relevance, and returns the most pertinent text to provide context to the LLM. Ideal for RAG (Retrieval Augmented Generation) applications.
A chatbot built on Ktor using GPT + Embeddings to answer questions
Discriminative Reranker in Java
Given user submitted bio, returns top 500 stories from Hacker News front page , ranked in the order of relevancy to user's interests.
Add a description, image, and links to the reranker topic page so that developers can more easily learn about it.
To associate your repository with the reranker topic, visit your repo's landing page and select "manage topics."