Hello, author of txtai here. txtai was created back in 2020 starting with semantic search of medical literature. It has since grown into a framework for vector

Hello, author of txtai here. txtai was created back in 2020 starting with semantic search of medical literature. It has since grown into a framework for vector search, retrieval augmented generation (RAG) and large language model (LLM) orchestration/workflows. The goal of txtai is to be simple, performant, innovative and easy-to-use. It had vector search before many current projects existed. Semantic Graphs were added in 2022 before the Generative AI wave of 2023/2024. GraphRAG is a hot topic but txtai had examples of using graphs to build search contexts back in 2022/2023. There is a commitment to quality and performance, especially with local models. For example, it's vector embeddings component streams vectors to disk during indexing and uses mmaped arrays to enable indexing large datasets locally on a single node. txtai's BM25 component is built from the scratch to work efficiently in Python leading to 6x better memory utilization and faster search performance than the BM25 Python library most commonly used. I often see others complain about AI/LLM/RAG frameworks, so I wanted to share this project as many don't know it exists. Link to source (Apache 2.0): https://github.com/neuml/txtai https://github.com/neuml/txtai