LlamaIndex
ActiveDescription
LlamaIndex is a data framework that provides the data connection layer for LLM applications, with strong RAG capabilities across diverse data sources and vector databases.
LlamaIndex is a data framework that provides the data connection layer for LLM applications, with strong RAG capabilities across diverse data sources and vector databases.
A MemAgent framework that can extrapolate to 3.5M context tokens, along with a training framework for RL training of any agent workflow.
LightRAG is a simple and fast Retrieval-Augmented Generation framework using graph-enhanced retrieval, published at EMNLP 2025.
Opinionated RAG framework for integrating GenAI into your apps. Works with any LLM, any vectorstore, any files — so you can focus on your product instead of building RAG pipelines.
100+ AI Agent and RAG apps you can actually run — clone, customize, and ship. A great reference for quickly building LLM-powered applications.
Production-focused best practices for index design, filtering, reranking, and evaluation when building RAG retrieval layers with Qdrant.
Read more →Most RAG pipelines fail at retrieval, not generation. This article covers five chunking strategies, hybrid search, reranking pipelines, and a production-ready decision framework.
Read more →An in-depth explanation of Retrieval-Augmented Generation and how to build private knowledge bases for AI agents to improve accuracy and reliability.
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