This guide walks you through implementing Meilisearch’s chat completions feature to create conversational search experiences in your application.
The chat completions feature is experimental and must be enabled before use. See experimental features for activation instructions.

​
Prerequisites

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 chat completions experimental feature enabled

​
Quick start

​
Enable the chat completions feature

First, enable the chat completions experimental feature: 
curl \
  -X PATCH 'http://localhost:7700/experimental-features' \
  -H 'Authorization: Bearer MASTER_KEY' \
  -H 'Content-Type: application/json' \
  --data-binary '{
    "chatCompletions": true
  }'

​
Configure a chat completions workspace

Create a workspace with your LLM provider settings. Here are examples for different providers: 
curl \
  -X PATCH 'http://localhost:7700/chats/my-assistant/settings' \
  -H 'Authorization: Bearer MASTER_KEY' \
  -H 'Content-Type: application/json' \
  --data-binary '{
    "source": "openAi",
    "apiKey": "sk-abc...",
    "prompts": {
      "system": "You are a helpful assistant. Answer questions based only on the provided context."
    }
  }'

​
Send your first chat completions request

Now you can start a conversation: 
curl \
  -X POST 'http://localhost:7700/chats/my-assistant/chat/completions' \
  -H 'Authorization: Bearer DEFAULT_CHAT_KEY' \
  -H 'Content-Type: application/json' \
  --data-binary '{
    "model": "gpt-3.5-turbo",
    "messages": [
      {
        "role": "user",
        "content": "What is Meilisearch?"
      }
    ],
    "stream": true
  }'

​
Understanding workspaces

Workspaces allow you to create isolated chat configurations for different use cases:
  • Customer support: Configure with support-focused prompts
  • Product search: Optimize for e-commerce queries
  • Documentation: Tune for technical Q&A
Each workspace maintains its own:
  • LLM provider configuration
  • System prompt

​
Building a chat interface with OpenAI SDK

Since Meilisearch’s chat endpoint is OpenAI-compatible, you can use the official OpenAI SDK: 
import OpenAI from 'openai';

const client = new OpenAI({
  baseURL: 'http://localhost:7700/chats/my-assistant',
  apiKey: 'YOUR_MEILISEARCH_API_KEY',
});

const completion = await client.chat.completions.create({
  model: 'gpt-3.5-turbo',
  messages: [{ role: 'user', content: 'What is Meilisearch?' }],
  stream: true,
});

for await (const chunk of completion) {
  console.log(chunk.choices[0]?.delta?.content || '');
}

​
Error handling

When using the OpenAI SDK with Meilisearch’s chat completions endpoint, errors from the streamed responses are natively handled by the official OpenAI SDK. This means you can use the SDK’s built-in error handling mechanisms without additional configuration: 
import OpenAI from 'openai';

const client = new OpenAI({
  baseURL: 'http://localhost:7700/chats/my-assistant',
  apiKey: 'YOUR_MEILISEARCH_API_KEY',
});

try {
  const stream = await client.chat.completions.create({
    model: 'gpt-3.5-turbo',
    messages: [{ role: 'user', content: 'What is Meilisearch?' }],
    stream: true,
  });

  for await (const chunk of stream) {
    console.log(chunk.choices[0]?.delta?.content || '');
  }
} catch (error) {
  // OpenAI SDK automatically handles streaming errors
  console.error('Chat completion error:', error);
}

​
Next steps