Add guide on Semantic search with gemini (#3405)

Author: CaroFG

docs.json

@@ -410,6 +410,7 @@
                   "guides/embedders/cohere",
                   "guides/embedders/mistral",
                   "guides/embedders/voyage",
+                  "guides/embedders/gemini",
                   "guides/computing_hugging_face_embeddings_gpu"
                 ]
               },

guides/embedders/gemini.mdx

@@ -0,0 +1,99 @@
+---
+title: Semantic Search with Gemini Embeddings
+description: This guide will walk you through the process of setting up Meilisearch with Gemini embeddings to enable semantic search capabilities.
+---
+
+## Requirements
+
+To follow this guide, you'll need:
+
+- A [Meilisearch Cloud](https://www.meilisearch.com/cloud) project running version >=1.13
+- A Google account with an API key for embedding generation. You can sign up for a Google account at [Google](https://google.com/)
+
+## Setting up Meilisearch
+
+To set up an embedder in Meilisearch, you need to configure it to your settings. You can refer to the [Meilisearch documentation](/reference/api/settings) for more details on updating the embedder settings.
+
+While using Gemini to generate embeddings, you'll need to use the model `gemini-embedding-001`. Unlike some other services, Gemini currently offers only one embedding model.
+
+Here's an example of embedder settings for Gemini:
+
+```json
+{
+  "gemini": {
+    "source": "rest",
+    "dimensions": 3072,
+    "documentTemplate": "<Custom template (Optional, but recommended)>",
+    "headers": {
+    "Content-Type": "application/json",
+    "x-goog-api-key": "<Google API Key>"
+    },
+    "url": "https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-001:batchEmbedContents",
+    "request": {
+    "requests": [
+        {
+        "model": "models/gemini-embedding-001",
+        "content": {
+            "parts": [
+            { "text": "{{text}}" }
+            ]
+        }
+        },
+        "{{..}}"
+    ]
+    },
+    "response": {
+    "embeddings": [
+        { "values": "{{embedding}}" },
+        "{{..}}"
+    ]
+    }
+  }
+}
+  ```
+
+In this configuration:
+
+- `source`: Specifies the source of the embedder, which is set to "rest" for using a REST API.
+- `headers`: Replace `<Google API Key>` with your actual Google API key.
+- `dimensions`: Specifies the dimensions of the embeddings, set to 3072 for the `gemini-embedding-001` model.
+- `documentTemplate`: Optionally, you can provide a [custom template](/learn/ai_powered_search/getting_started_with_ai_search) for generating embeddings from your documents.
+- `url`: Specifies the URL of the Gemini API endpoint.
+- `request`: Defines the request structure for the Gemini API, including the model name and input parameters.
+- `response`: Defines the expected response structure from the Gemini API, including the embedding data.
+
+Once you've configured the embedder settings, Meilisearch will automatically generate embeddings for your documents and store them in the vector store.
+
+Please note that most third-party tools have rate limiting, which is managed by Meilisearch. If you have a free account, the indexation process may take some time, but Meilisearch will handle it with a retry strategy.
+
+It's recommended to monitor the tasks queue to ensure everything is running smoothly. You can access the tasks queue using the Cloud UI or the [Meilisearch API](https://www.meilisearch.com/docs/reference/api/tasks).
+
+## Testing semantic search
+
+With the embedder set up, you can now perform semantic searches using Meilisearch. When you send a search query, Meilisearch will generate an embedding for the query using the configured embedder and then use it to find the most semantically similar documents in the vector store.
+To perform a semantic search, you simply need to make a normal search request but include the hybrid parameter:
+
+```json
+{
+  "q": "<Query made by the user>",
+  "hybrid": {
+    "semanticRatio": 1,
+    "embedder": "gemini"
+  }
+}
+```
+
+In this request:
+
+- `q`: Represents the user's search query.
+- `hybrid`: Specifies the configuration for the hybrid search.
+  - `semanticRatio`: Allows you to control the balance between semantic search and traditional search. A value of 1 indicates pure semantic search, while a value of 0 represents full-text search. You can adjust this parameter to achieve a hybrid search experience.
+  - `embedder`: The name of the embedder used for generating embeddings. Make sure to use the same name as specified in the embedder configuration, which in this case is "gemini".
+
+You can use the Meilisearch API or client libraries to perform searches and retrieve the relevant documents based on semantic similarity.
+
+## Conclusion
+
+By following this guide, you should now have Meilisearch set up with Gemini embedding, enabling you to leverage semantic search capabilities in your application. Meilisearch's auto-batching and efficient handling of embeddings make it a powerful choice for integrating semantic search into your project.
+
+To explore further configuration options for embedders, consult the [detailed documentation about the embedder setting possibilities](/reference/api/settings).