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An architectural diagram of Ahnlich

/ˈɛːnlɪç/

Smarter search with
a vector engine that gets out of your way

Index and search items

Quickly index data and retrieve the most similar items using vector-based semantic search.

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Store item properties

Attach custom metadata to each item so searches can use both vectors and structured properties.

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Utilize custom AI models

Plug in your own embedding or AI models to control how items are encoded and stored.

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Configure similarity score

Customize the similarity metric (e.g., cosine, Euclidean) to match your retrieval behavior.

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Query by properties

Filter search results using metadata constraints for more targeted and precise retrieval.

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