AdalFlow: The library to build & auto-optimize LLM applications.
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Updated
Nov 10, 2024 - Python
AdalFlow: The library to build & auto-optimize LLM applications.
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.
🥤 RAGLite is a Python toolkit for Retrieval-Augmented Generation (RAG) with PostgreSQL or SQLite
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
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
AICUP 2024 Esan LLM RAG QA
Discriminative Reranker in Java
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