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This guide shows how to perform AI-powered searches with user-generated embeddings instead of relying on a third-party tool.

Requirements

  • A Meilisearch project

Configure a custom embedder

Configure the embedder index setting, settings its source to userProvided:
Embedders with source: userProvided are incompatible with documentTemplate and documentTemplateMaxBytes.

Add documents to Meilisearch

Next, use the /documents endpoint to upload vectorized documents. Place vector data in your documents’ _vectors field:

Vector search with user-provided embeddings

When using a custom embedder, you must vectorize both your documents and user queries. Once you have the query’s vector, pass it to the vector search parameter to perform an AI-powered search:
vector must be an array of numbers indicating the search vector. You must generate these yourself when using vector search with user-provided embeddings. vector can be used together with other search parameters, including filter and sort: