Show HN: txtai: open-source, production-focused vector search and RAG
The txtai tool is a versatile embeddings database for semantic search, LLM orchestration, and language model workflows. It supports vector search with SQL, RAG, topic modeling, and more. Users can create embeddings for various data types and utilize language models for diverse tasks. Txtai is open-source and supports multiple programming languages.
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The txtai tool is an embeddings database designed for semantic search, LLM orchestration, and language model workflows. It combines vector indexes, graph networks, and relational databases to enable vector search with SQL, topic modeling, retrieval augmented generation (RAG), and more. This tool can function independently or as a valuable knowledge source for large language model prompts. Key features include vector search with SQL, object storage, topic modeling, graph analysis, and multimodal indexing. Users can create embeddings for text, documents, audio, images, and video, and utilize pipelines powered by language models for tasks like question-answering, labeling, transcription, translation, and summarization. Workflows can be created to join pipelines and aggregate business logic, with the flexibility to build using Python or YAML. txtai supports API bindings for JavaScript, Java, Rust, and Go, and can be run locally or scaled out with container orchestration. Built with Python 3.8+, Hugging Face Transformers, Sentence Transformers, and FastAPI, txtai is open-source under an Apache 2.0 license. Interested users can explore the txtai.cloud preview for easy and secure hosted applications.
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1 commentsThe 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 doing this earlier in 2024.
txtai has a commitment to quality and performance, especially with local models. For example, it's vector embeddings component automatically 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 ground up to work efficiently in Python leading to 6x better memory utilization and faster search performance than the BM25 Python library most commonly used.
txtai is Apache 2.0 licensed and all code is available at https://github.com/neuml/txtai
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