Retrieval-augmented generation (RAG) integrates retrieval mechanisms with large language models (LLMs) to generate contextually relevant text. RAGs divide documents into chunks before retrieving relevant chunk-based queries and augment input prompts with chunks as context to answer queries.
LlamaIndex is a data framework that enhances the capabilities of LLMs through context augmentation. Tools are provided for ingesting, processing, and implementing complex query workflows combining data access with LLM prompting for various AI applications.
LlamaIndex, while popular and easy to set up, faces drawbacks in performance and scalability due to resource demands, and integration challenges with existing systems. However, it remains a prominent choice with solid community support for overcoming these obstacles.
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