jacobmarks/fiftyone-multimodal-rag-plugin
Testbed for multimodal retrieval augmented generation techniques with FiftyOne, LlamaIndex, and Milvus
This project helps AI developers and researchers experiment with and refine multimodal Retrieval Augmented Generation (RAG) techniques. It takes directories containing various multimodal data (images, text, PDFs) as input and allows users to index this data using different strategies. The output is a highly configurable system for querying your data and generating improved responses from advanced AI models, offering insights into what works best for specific use cases.
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Use this if you are an AI developer or researcher working with multimodal data and need a flexible testbed to explore and optimize RAG workflows for large language models.
Not ideal if you are looking for a plug-and-play solution for general content creation or do not have experience working with AI models and data pipelines.
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Python
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Last pushed
Aug 09, 2024
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curl "https://pt-edge.onrender.com/api/v1/quality/vector-db/jacobmarks/fiftyone-multimodal-rag-plugin"
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