Showing 1-7 of 7 projects
State-of-the-art text embedding library for building advanced natural language processing applications.
A low-code MCP framework for building complex and innovative RAG pipelines with AI tools.
MTEB is a benchmark for evaluating and comparing text embedding models across multiple tasks and languages.
Top2Vec learns jointly embedded topic, document and word vectors for semantic search and topic modeling.
A high-performance library for generating state-of-the-art static embeddings for natural language processing tasks.
Efficient retrieval augmentation and generation framework for multi-modal information retrieval and question-answering
On-premises conversational RAG with configurable containers for building AI-powered apps
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