Search your documents with AI (RAG)
RAG pipelines, corpora and embedding models so an AI answers from your documents.
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nomic-embed-text embeddings locally with Ollama
nomic-embed-text for a 100% local English RAG with Ollama: 768 dimensions, long…
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BGE-M3 embeddings for RAG over long documents
BGE-M3 for RAG over long documents: 1024 dimensions, up to 8,192 tokens, 100+…
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Corpus: AI resource definitions (English)
Definitions of every AI resource type on Contexte Tech, one chunk each with the…
What does searching documents with AI mean?
AI can answer from your own documents through RAG (retrieval-augmented generation): documents are split, turned into vectors, and the passages closest to the question are given to the model, which answers and cites its sources.