For AI agents: a documentation index is available at the root level at /llms.txt and /llms-full.txt. Append /llms.txt to any URL for a page-level index, or .md for the markdown version of any page.
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Guides and conceptsAPI ReferenceRelease NotesLLMUCookbooks
  • Get Started
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    • An Overview of Cohere's Models
    • Aya
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    • Introduction to Embeddings at Cohere
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      • Elasticsearch and Cohere
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      • Pinecone and Cohere
      • Weaviate and Cohere
      • Open Search and Cohere
      • Vespa and Cohere
      • Qdrant and Cohere
      • Milvus and Cohere
      • Zilliz and Cohere
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IntegrationsIntegrating Embedding Models with Other Tools

Qdrant and Cohere (Integration Guide)

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Milvus and Cohere (Integration Guide)

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Qdrant is an open-source vector similarity search engine and vector database. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. Qdrant is tailored to extended filtering support. It makes it useful for all sorts of neural-network or semantic-based matching, faceted search, and other applications.

Qdrant is written in Rust, which makes it fast and reliable even under high load.

To learn more about how to work with Cohere’s embeddings on Qdrant, read this guide