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observed_at2026-10-08T06:04:28.986Z
origin_date2026-10-07T16:49:29.000Z
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final URLhttps://docs.mistral.ai/llms.txt
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last-modifiedWed, 07 Oct 2026 16:49:29 GMT
dateThu, 08 Oct 2026 06:04:28 GMT
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@@@ -171,7 +171,7 @@ Every link points to the markdown version of a page (same URL with a `.md` exten
- [Studio](https://docs.mistral.ai/studio.md): Studio is Mistral's developer platform for AI applications.
- [Moderation & Guardrailing](https://docs.mistral.ai/studio/safety-moderation.md): When deploying LLMs in production, different verticals may require different levels of guardrailing.
- [Agentic Search](https://docs.mistral.ai/studio/search/agentic-search.md): Agentic Search helps agents answer questions when the answer is not in the first chunk returned by retrieval.
-- [Libraries](https://docs.mistral.ai/studio/search/libraries.md): This page covers how to create Libraries, upload documents, check processing status, and control access — all through the API.
+- [Libraries](https://docs.mistral.ai/studio/search/libraries.md): This page covers how to create Libraries, upload documents, index web pages, check processing status, and control access.
- [Document model](https://docs.mistral.ai/studio/search/search-toolkit/concepts/document-model.md): How Search Toolkit represents documents and chunks, and the deterministic identity that ties them together.
- [Embedding model](https://docs.mistral.ai/studio/search/search-toolkit/concepts/embedding-model.md): How Search Toolkit describes embedding vector dimensions, data type, and distance metric separately from the embedder that produces them.
- [Embedders](https://docs.mistral.ai/studio/search/search-toolkit/ingestion/embedders.md): Convert text into vector embeddings for semantic search.