# Use AI-powered search with user-provided embeddings
Source: https://www.meilisearch.com/docs/learn/ai_powered_search/search_with_user_provided_embeddings

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

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`:

```sh theme={null}
curl \
  -X PATCH 'MEILISEARCH_URL/indexes/movies/settings' \
  -H 'Content-Type: application/json' \
  --data-binary '{
    "embedders": {
      "image2text": {
        "source":  "userProvided",
        "dimensions": 3
      }
    }
  }'
```

<Warning>
  Embedders with `source: userProvided` are incompatible with  `documentTemplate` and `documentTemplateMaxBytes`.
</Warning>

## Add documents to Meilisearch

Next, use [the `/documents` endpoint](/reference/api/documents?utm_campaign=vector-search\&utm_source=docs\&utm_medium=vector-search-guide) to upload vectorized documents. Place vector data in your documents' `_vectors` field:

```sh theme={null}
curl -X POST -H 'content-type: application/json' \
'localhost:7700/indexes/products/documents' \
--data-binary '[
    { "id": 0, "_vectors": {"image2text": [0, 0.8, -0.2]}, "text": "frying pan" },
    { "id": 1, "_vectors": {"image2text": [1, -0.2, 0]}, "text": "baking dish" }
]'
```

## 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:

```sh theme={null}
curl -X POST -H 'content-type: application/json' \
  'localhost:7700/indexes/products/search' \
  --data-binary '{ 
    "vector": [0, 1, 2],
    "hybrid": {
      "embedder": "image2text"
    }
  }'
```

`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](/reference/api/search?utm_campaign=vector-search\&utm_source=docs\&utm_medium=vector-search-guide), including [`filter`](/reference/api/search#filter) and [`sort`](/reference/api/search#sort):

```sh theme={null}
curl -X POST -H 'content-type: application/json' \
  'localhost:7700/indexes/products/search' \
  --data-binary '{
    "vector": [0, 1, 2],
    "filter": "price < 10",
    "sort": ["price:asc"],
    "hybrid": {
      "embedder": "image2text"
    }
  }'
```