use user-powered embedder guide as base

Author: guimachiavelli

learn/ai_powered_search/multimodal.mdx

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+---
+title: Implement image search — Meilisearch documentation
+description: 
+---
+
+# Use AI-powered 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.
+
+## Requirements
+
+- A Meilisearch project with AI-powered search activated
+
+## Configure a custom embedder
+
+Configure the `embedder` index setting, settings its source to `userProvided`:
+
+```sh
+curl \
+  -X PATCH 'MEILISEARCH_URL/indexes/movies/settings' \
+  -H 'Content-Type: application/json' \
+  --data-binary '{
+    "embedders": {
+      "image2text": {
+        "source":  "userProvided",
+        "dimensions": 3
+      }
+    }
+  }'
+```
+
+## 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
+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
+curl -X POST -H 'content-type: application/json' \
+  'localhost:7700/indexes/products/search' \
+  --data-binary '{ "vector": [0, 1, 2] }'
+```
+
+`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
+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"]
+  }'
+```