fix ai-powered-search to ai_powered_search

Author: guimachiavelli

config/redirects.json

@@ -548,5 +548,25 @@
     "source": "/docs/guides/database/meilisync_postgresql",
     "destination": "https://github.com/long2ice/meilisync",
     "permanent": true
+  },
+  {
+    "source": "/docs/learn/ai-powered-search/search_with_user_provided_embeddings",
+    "destination": "/docs/learn/ai_powered_search/search_with_user_provided_embeddings",
+    "permanent": true
+  },
+  {
+    "source": "/docs/learn/ai-powered-search/getting_started_with_ai_search",
+    "destination": "/docs/learn/ai_powered_search/getting_started_with_ai_search",
+    "permanent": true
+  },
+  {
+    "source": "/docs/learn/ai-powered-search/deactivate_ai_powered_search",
+    "destination": "/docs/learn/ai_powered_search/deactivate_ai_powered_search",
+    "permanent": true
+  },
+  {
+    "source": "/docs/learn/ai-powered-search/difference_full_text_ai_search",
+    "destination": "/docs/learn/ai_powered_search/difference_full_text_ai_search",
+    "permanent": true
   }
 ]

config/sidebar-learn.json

@@ -37,7 +37,7 @@
   },
   {
     "title": "AI-powered search",
-    "slug": "ai-powered-search",
+    "slug": "ai_powered_search",
     "routes": [
       {
         "source": "learn/ai_powered_search/getting_started_with_ai_search.mdx",

guides/embedders/cloudflare.mdx

@@ -55,7 +55,7 @@ In this configuration:
 - `source`: Specifies the source of the embedder, which is set to "rest" for using a REST API.
 - `apiKey`: Replace `<API Key>` with your actual Cloudflare API key.
 - `dimensions`: Specifies the dimensions of the embeddings. Set to 384 for `baai/bge-small-en-v1.5`, 768 for `baai/bge-base-en-v1.5`, or 1024 for `baai/bge-large-en-v1.5`.
-- `documentTemplate`: Optionally, you can provide a [custom template](/learn/ai-powered-search/getting_started_with_ai_search?utm_campaign=vector-search&utm_source=docs&utm_medium=cloudflare-embeddings-guide#documenttemplate) for generating embeddings from your documents.
+- `documentTemplate`: Optionally, you can provide a [custom template](/learn/ai_powered_search/getting_started_with_ai_search?utm_campaign=vector-search&utm_source=docs&utm_medium=cloudflare-embeddings-guide#documenttemplate) for generating embeddings from your documents.
 - `url`: Specifies the URL of the Cloudflare Worker AI API endpoint.
 - `request`: Defines the request structure for the Cloudflare Worker AI API, including the input parameters.
 - `response`: Defines the expected response structure from the Cloudflare Worker AI API, including the embedding data.

guides/embedders/cohere.mdx

@@ -58,7 +58,7 @@ In this configuration:
 - `source`: Specifies the source of the embedder, which is set to "rest" for using a REST API.
 - `apiKey`: Replace `<Cohere API Key>` with your actual Cohere API key.
 - `dimensions`: Specifies the dimensions of the embeddings, set to 1024 for the `embed-english-v3.0` model.
-- `documentTemplate`: Optionally, you can provide a [custom template](/learn/ai-powered-search/getting_started_with_ai_search?utm_campaign=vector-search&utm_source=docs&utm_medium=cohere-embeddings-guide#documenttemplate) for generating embeddings from your documents.
+- `documentTemplate`: Optionally, you can provide a [custom template](/learn/ai_powered_search/getting_started_with_ai_search?utm_campaign=vector-search&utm_source=docs&utm_medium=cohere-embeddings-guide#documenttemplate) for generating embeddings from your documents.
 - `url`: Specifies the URL of the Cohere API endpoint.
 - `request`: Defines the request structure for the Cohere API, including the model name and input parameters.
 - `response`: Defines the expected response structure from the Cohere API, including the embedding data.

guides/embedders/mistral.mdx

@@ -54,7 +54,7 @@ In this configuration:
 - `source`: Specifies the source of the embedder, which is set to "rest" for using a REST API.
 - `apiKey`: Replace `<Mistral API Key>` with your actual Mistral API key.
 - `dimensions`: Specifies the dimensions of the embeddings, set to 1024 for the `mistral-embed` model.
-- `documentTemplate`: Optionally, you can provide a [custom template](/learn/ai-powered-search/getting_started_with_ai_search?utm_campaign=vector-search&utm_source=docs&utm_medium=mistral-embeddings-guide#documenttemplate) for generating embeddings from your documents.
+- `documentTemplate`: Optionally, you can provide a [custom template](/learn/ai_powered_search/getting_started_with_ai_search?utm_campaign=vector-search&utm_source=docs&utm_medium=mistral-embeddings-guide#documenttemplate) for generating embeddings from your documents.
 - `url`: Specifies the URL of the Mistral API endpoint.
 - `request`: Defines the request structure for the Mistral API, including the model name and input parameters.
 - `response`: Defines the expected response structure from the Mistral API, including the embedding data.

guides/embedders/openai.mdx

@@ -46,7 +46,7 @@ In this configuration:
 - `source`: Specifies the source of the embedder, which is set to "openAi" for using OpenAI's API.
 - `apiKey`: Replace `<OpenAI API Key>` with your actual OpenAI API key.
 - `dimensions`: Specifies the dimensions of the embeddings. Set to 1536 for `text-embedding-3-small` and `text-embedding-ada-002`, or 3072 for `text-embedding-3-large`.
-- `documentTemplate`: Optionally, you can provide a [custom template](/learn/ai-powered-search/getting_started_with_ai_search?utm_campaign=vector-search&utm_source=docs&utm_medium=openai-embeddings-guide#documenttemplate) for generating embeddings from your documents.
+- `documentTemplate`: Optionally, you can provide a [custom template](/learn/ai_powered_search/getting_started_with_ai_search?utm_campaign=vector-search&utm_source=docs&utm_medium=openai-embeddings-guide#documenttemplate) for generating embeddings from your documents.
 - `model`: Specifies the OpenAI model to use for generating embeddings. Choose from `text-embedding-3-large`, `text-embedding-3-small`, or `text-embedding-ada-002`.
 
 Once you've configured the embedder settings, Meilisearch will automatically generate embeddings for your documents and store them in the vector store.

guides/embedders/voyage.mdx

@@ -59,7 +59,7 @@ In this configuration:
 - `source`: Specifies the source of the embedder, which is set to "rest" for using a REST API.
 - `apiKey`: Replace `<Voyage AI API Key>` with your actual Voyage AI API key.
 - `dimensions`: Specifies the dimensions of the embeddings. Set to 1024 for `voyage-2`, `voyage-large-2-instruct`, and `voyage-multilingual-2`, or 1536 for `voyage-large-2`.
-- `documentTemplate`: Optionally, you can provide a [custom template](/learn/ai-powered-search/getting_started_with_ai_search?utm_campaign=vector-search&utm_source=docs&utm_medium=voyage-embeddings-guide#documenttemplate) for generating embeddings from your documents.
+- `documentTemplate`: Optionally, you can provide a [custom template](/learn/ai_powered_search/getting_started_with_ai_search?utm_campaign=vector-search&utm_source=docs&utm_medium=voyage-embeddings-guide#documenttemplate) for generating embeddings from your documents.
 - `url`: Specifies the URL of the Voyage AI API endpoint.
 - `request`: Defines the request structure for the Voyage AI API, including the model name and input parameters.
 - `response`: Defines the expected response structure from the Voyage AI API, including the embedding data.

guides/langchain.mdx

@@ -167,4 +167,4 @@ For additional information, consult:
 
 [Meilisearch Python SDK docs](https://python-sdk.meilisearch.com/)
 
-Finally, should you want to use Meilisearch's vector search capabilities without LangChain or its hybrid search feature, refer to the [dedicated tutorial](/learn/ai-powered-search/getting_started_with_ai_search).
+Finally, should you want to use Meilisearch's vector search capabilities without LangChain or its hybrid search feature, refer to the [dedicated tutorial](/learn/ai_powered_search/getting_started_with_ai_search).