All-in-one embeddings database

2023-08-10

txtai is an all-in-one embeddings database for semantic search, LLM orchestration and language model workflows. Embeddings databases are a union of vector indexes (sparse and dense), graph networks and relational databases. This enables vector search with SQL, topic modeling, retrieval augmented generation and more. Embeddings databases can stand on their own and/or serve as a powerful knowledge source for large language model (LLM) prompts. txtai features include vector search with SQL, object storage, topic modeling, graph analysis and multimodal indexing, creating embeddings for text, documents, audio, images and video, pipelines powered by language models that run LLM prompts, question-answering, labeling, transcription, translation, summarizations and more, workflows to join pipelines together and aggregate business logic, txtai processes can be simple microservices or multi-model workflows, building with Python or YAML, API bindings available for JavaScript, Java, Rust and Go, cloud-native architecture that scales out with container orchestration systems (e.g. Kubernetes), txtai is built with Python 3.8+, Hugging Face Transformers, Sentence Transformers and FastAPI, and it is open-source under an Apache 2.0 license. txtai provides an easy and efficient way to build vector search applications, with features such as embedding generation, indexing, and searching. It can be used with different types of data such as text, documents, audio, images, and video, and it supports various functionalities like topic modeling, graph analysis, multimodal indexing, and more. txtai also offers powerful language model pipelines that can run LLM prompts, question-answering, labeling, transcription, translation, summarizations, and other tasks. The workflows in txtai can be customized to join different pipelines and aggregate business logic, making it a flexible tool for various use cases. Additionally, txtai is designed to be cloud-native and scalable, allowing it to be deployed in container orchestration systems like Kubernetes.

Link [ https://github.com/neuml/txtai ]

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All-in-one embeddings database

2023-08-10

txtai is an all-in-one embeddings database for semantic search, LLM orchestration and language model workflows. Embeddings databases are a union of vector indexes (sparse and dense), graph networks and relational databases. This enables vector search with SQL, topic modeling, retrieval augmented generation and more. Embeddings databases can stand on their own and/or serve as a powerful knowledge source for large language model (LLM) prompts. txtai features include vector search with SQL, object storage, topic modeling, graph analysis and multimodal indexing, creating embeddings for text, documents, audio, images and video, pipelines powered by language models that run LLM prompts, question-answering, labeling, transcription, translation, summarizations and more, workflows to join pipelines together and aggregate business logic, txtai processes can be simple microservices or multi-model workflows, building with Python or YAML, API bindings available for JavaScript, Java, Rust and Go, cloud-native architecture that scales out with container orchestration systems (e.g. Kubernetes), txtai is built with Python 3.8+, Hugging Face Transformers, Sentence Transformers and FastAPI, and it is open-source under an Apache 2.0 license. txtai provides an easy and efficient way to build vector search applications, with features such as embedding generation, indexing, and searching. It can be used with different types of data such as text, documents, audio, images, and video, and it supports various functionalities like topic modeling, graph analysis, multimodal indexing, and more. txtai also offers powerful language model pipelines that can run LLM prompts, question-answering, labeling, transcription, translation, summarizations, and other tasks. The workflows in txtai can be customized to join different pipelines and aggregate business logic, making it a flexible tool for various use cases. Additionally, txtai is designed to be cloud-native and scalable, allowing it to be deployed in container orchestration systems like Kubernetes.

Link [ https://github.com/neuml/txtai ]

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