Fix chat completions feature naming consistency

Author: Kerollmops

guides/ai/getting_started_with_chat.mdx

@@ -6,10 +6,10 @@ description: Learn how to implement AI-powered conversational search in your app
 
 import { Warning, Note } from '/snippets/notice_tag.mdx'
 
-This guide walks you through implementing Meilisearch's chatCompletions feature to create conversational search experiences in your application.
+This guide walks you through implementing Meilisearch's chat completions feature to create conversational search experiences in your application.
 
 <Warning>
-The chatCompletions feature is experimental and must be enabled before use. See [experimental features](/reference/api/experimental_features) for activation instructions.
+The chat completions feature is experimental and must be enabled before use. See [experimental features](/reference/api/experimental_features) for activation instructions.
 </Warning>
 
 ## Prerequisites
@@ -18,13 +18,13 @@ Before starting, ensure you have:
 - Meilisearch instance running (v1.15.1 or later)
 - An API key from an LLM provider (OpenAI, Azure OpenAI, Mistral, Gemini, or access to a vLLM server)
 - At least one index with searchable content
-- The chatCompletions experimental feature enabled
+- The chat completions experimental feature enabled
 
 ## Quick start
 
-### 1. Enable the chatCompletions feature
+### 1. Enable the chat completions feature
 
-First, enable the chatCompletions experimental feature:
+First, enable the chat completions experimental feature:
 
 ```bash
 curl \
@@ -36,7 +36,7 @@ curl \
   }'
 ```
 
-### 2. Configure a chatCompletions workspace
+### 2. Configure a chat completions workspace
 
 Create a workspace with your LLM provider settings. Here are examples for different providers:
 
@@ -115,7 +115,7 @@ curl \
 
 </CodeGroup>
 
-### 3. Send your first chatCompletions request
+### 3. Send your first chat completions request
 
 Now you can start a conversation:
 
@@ -219,4 +219,4 @@ for await (const chunk of stream) {
 
 - Explore [advanced chat API features](/reference/api/chats)
 - Learn about [conversational search concepts](/learn/ai_powered_search/conversational_search_with_chat)
-- Review [security best practices](/learn/security/basic_security)
+- Review [security best practices](/learn/security/basic_security)

learn/ai_powered_search/conversational_search_with_chat.mdx

@@ -6,10 +6,10 @@ description: Learn how to implement AI-powered conversational search using Meili
 
 import { Warning } from '/snippets/notice_tag.mdx'
 
-Meilisearch's chatCompletions feature enables AI-powered conversational search, allowing users to ask questions in natural language and receive direct answers based on your indexed content. This feature transforms the traditional search experience into an interactive dialogue.
+Meilisearch's chat completions feature enables AI-powered conversational search, allowing users to ask questions in natural language and receive direct answers based on your indexed content. This feature transforms the traditional search experience into an interactive dialogue.
 
 <Warning>
-The chatCompletions feature is experimental and must be enabled through [experimental features](/reference/api/experimental_features). API specifications may change in future releases.
+The chat completions feature is experimental and must be enabled through [experimental features](/reference/api/experimental_features). API specifications may change in future releases.
 </Warning>
 
 ## What is conversational search?
@@ -22,7 +22,7 @@ Conversational search allows users to:
 
 This approach bridges the gap between traditional search and modern AI experiences, making information more accessible and intuitive to find.
 
-## How chatCompletions differs from traditional search
+## How chat completions differs from traditional search
 
 ### Traditional search workflow
 1. User enters keywords
@@ -37,21 +37,21 @@ This approach bridges the gap between traditional search and modern AI experienc
 
 ## RAG implementation simplified
 
-The chatCompletions feature implements a complete Retrieval Augmented Generation (RAG) pipeline in a single API endpoint. Traditional RAG implementations require:
+The chat completions feature implements a complete Retrieval Augmented Generation (RAG) pipeline in a single API endpoint. Traditional RAG implementations require:
 
 - Multiple LLM calls for query optimization
 - Separate vector database for semantic search
 - Custom reranking solutions
 - Complex pipeline management
 
-Meilisearch's chatCompletions consolidates these into one streamlined process:
+Meilisearch's chat completions consolidates these into one streamlined process:
 
 1. **Query understanding**: Automatically transforms questions into optimal search parameters
 2. **Hybrid retrieval**: Combines keyword and semantic search for superior relevance
 3. **Answer generation**: Uses your chosen LLM to generate responses
 4. **Context management**: Maintains conversation history automatically
 
-## When to use chatCompletions vs traditional search
+## When to use chat completions vs traditional search
 
 ### Use conversational search when:
 - Users need direct answers to specific questions
@@ -67,7 +67,7 @@ Meilisearch's chatCompletions consolidates these into one streamlined process:
 
 ## Architecture overview
 
-The chatCompletions feature operates through workspaces, which are isolated configurations for different use cases or tenants. Each workspace can:
+The chat completions feature operates through workspaces, which are isolated configurations for different use cases or tenants. Each workspace can:
 
 - Use different LLM sources (openAi, azureOpenAi, mistral, gemini, vLlm)
 - Apply custom prompts
@@ -93,7 +93,7 @@ The chatCompletions feature operates through workspaces, which are isolated conf
 
 ## Security considerations
 
-The chatCompletions feature integrates with Meilisearch's existing security model:
+The chat completions feature integrates with Meilisearch's existing security model:
 
 - **API key permissions**: Chat only accesses indexes visible to the provided API key
 - **Tenant tokens**: Support for multi-tenant applications
@@ -102,6 +102,6 @@ The chatCompletions feature integrates with Meilisearch's existing security mode
 
 ## Next steps
 
-- [Get started with chatCompletions implementation](/guides/ai/getting_started_with_chat)
-- [Explore the chatCompletions API reference](/reference/api/chats)
-- [Learn about experimental features](/reference/api/experimental_features)
+- [Get started with chat completions implementation](/guides/ai/getting_started_with_chat)
+- [Explore the chat completions API reference](/reference/api/chats)
+- [Learn about experimental features](/reference/api/experimental_features)

reference/api/chats.mdx

@@ -10,10 +10,10 @@ import { Warning } from '/snippets/notice_tag.mdx'
 The `/chats` route enables AI-powered conversational search by integrating Large Language Models (LLMs) with your Meilisearch data. This feature allows users to ask questions in natural language and receive contextual answers based on your indexed content.
 
 <Warning>
-The chatCompletions feature is experimental and must be enabled through [experimental features](/reference/api/experimental_features). To enable it, set `"chatCompletions": true` in your experimental features configuration.
+The chat completions feature is experimental and must be enabled through [experimental features](/reference/api/experimental_features). To enable it, set `"chatCompletions": true` in your experimental features configuration.
 </Warning>
 
-## ChatCompletions workspace object
+## Chat completions workspace object
 
 ```json
 {
@@ -23,9 +23,9 @@ The chatCompletions feature is experimental and must be enabled through [experim
 
 | Name        | Type   | Description                                         |
 | :---------- | :----- | :-------------------------------------------------- |
-| **`uid`**   | String | Unique identifier for the chatCompletions workspace |
+| **`uid`**   | String | Unique identifier for the chat completions workspace |
 
-## ChatCompletions settings object
+## Chat completions settings object
 
 ```json
 {
@@ -385,4 +385,4 @@ These tools are handled internally and are not directly accessible through the A
 - Only streaming responses are currently supported
 - Conversation history must be managed client-side
 - Token limits depend on the chosen LLM provider
-- No built-in conversation persistence
+- No built-in conversation persistence