vector-search
Search for similar items in a Meilisearch index using vector embeddings to find content based on semantic similarity rather than exact text matches.
Instructions
Perform a vector search in a Meilisearch index
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| indexUid | Yes | Unique identifier of the index | |
| vector | Yes | JSON array representing the vector to search for | |
| limit | No | Maximum number of results to return (default: 20) | |
| offset | No | Number of results to skip (default: 0) | |
| filter | No | Filter to apply (e.g., 'genre = horror AND year > 2020') | |
| embedder | No | Name of the embedder to use (if omitted, a 'vector' must be provided) | |
| attributes | No | Attributes to include in the vector search | |
| query | No | Text query to search for (if using 'embedder' instead of 'vector') | |
| hybrid | No | Whether to perform a hybrid search (combining vector and text search) | |
| hybridRatio | No | Ratio of vector vs text search in hybrid search (0-1, default: 0.5) |