micsapp Mistral AI Hello World
Overview
Minimal working example demonstrating core Mistral AI chat completion functionality.
Prerequisites
- Completed
mistral-install-authsetup - Valid API credentials configured
- Development environment ready
Instructions
Step 1: Create Entry File
TypeScript (hello-mistral.ts)
import Mistral from '@mistralai/mistralai';
const client = new Mistral({
apiKey: process.env.MISTRAL_API_KEY,
});
async function main() {
const response = await client.chat.complete({
model: 'mistral-small-latest',
messages: [
{ role: 'user', content: 'Say "Hello, World!" in a creative way.' }
],
});
console.log(response.choices?.[0]?.message?.content);
}
main().catch(console.error);
Python (hello_mistral.py)
import os
from mistralai import Mistral
client = Mistral(api_key=os.environ.get("MISTRAL_API_KEY"))
def main():
response = client.chat.complete(
model="mistral-small-latest",
messages=[
{"role": "user", "content": "Say 'Hello, World!' in a creative way."}
],
)
print(response.choices[0].message.content)
if __name__ == "__main__":
main()
Step 2: Run the Example
# TypeScript
npx tsx hello-mistral.ts
# Python
python hello_mistral.py
Step 3: Streaming Response (Advanced)
TypeScript Streaming
import Mistral from '@mistralai/mistralai';
const client = new Mistral({
apiKey: process.env.MISTRAL_API_KEY,
});
async function streamChat() {
const stream = await client.chat.stream({
model: 'mistral-small-latest',
messages: [
{ role: 'user', content: 'Tell me a short story about AI.' }
],
});
for await (const event of stream) {
const content = event.data?.choices?.[0]?.delta?.content;
if (content) {
process.stdout.write(content);
}
}
console.log(); // newline
}
streamChat().catch(console.error);
Python Streaming
import os
from mistralai import Mistral
client = Mistral(api_key=os.environ.get("MISTRAL_API_KEY"))
def stream_chat():
stream = client.chat.stream(
model="mistral-small-latest",
messages=[
{"role": "user", "content": "Tell me a short story about AI."}
],
)
for event in stream:
content = event.data.choices[0].delta.content
if content:
print(content, end="", flush=True)
print() # newline
if __name__ == "__main__":
stream_chat()
Output
- Working code file with Mistral client initialization
- Successful API response with generated text
- Console output showing:
Hello, World!
(But spoken by a million synchronized starlings,
spelling it across the twilight sky...)
Error Handling
| Error | Cause | Solution |
|---|---|---|
| Import Error | SDK not installed | Verify with npm list @mistralai/mistralai |
| Auth Error | Invalid credentials | Check MISTRAL_API_KEY is set |
| Timeout | Network issues | Increase timeout or check connectivity |
| Rate Limit | Too many requests | Wait and retry with exponential backoff |
Examples
Multi-turn Conversation
const messages = [
{ role: 'system', content: 'You are a helpful assistant.' },
{ role: 'user', content: 'What is the capital of France?' },
];
const response1 = await client.chat.complete({
model: 'mistral-small-latest',
messages,
});
// Add assistant response to conversation
messages.push({
role: 'assistant',
content: response1.choices?.[0]?.message?.content || '',
});
// Continue conversation
messages.push({ role: 'user', content: 'What about Germany?' });
const response2 = await client.chat.complete({
model: 'mistral-small-latest',
messages,
});
console.log(response2.choices?.[0]?.message?.content);
With Temperature Control
const response = await client.chat.complete({
model: 'mistral-small-latest',
messages: [{ role: 'user', content: 'Write a haiku about coding.' }],
temperature: 0.7, // 0-1, higher = more creative
maxTokens: 100,
});
Resources
Next Steps
Proceed to mistral-local-dev-loop for development workflow setup.