Mistral SDK Patterns

Overview

Production-ready patterns for the Mistral AI SDK (mistralai Python package). Covers client initialization, chat completions, streaming, function calling, and embeddings with idiomatic error handling.

Prerequisites

• pip install mistralai (v1.0+)

• MISTRAL_API_KEY environment variable set

• Familiarity with async Python patterns

Instructions

Step 1: Client Initialization with Configuration

from mistralai import Mistral import os

Singleton client with configuration

_client = None

def get_mistral_client() -> Mistral: global _client if _client is None: _client = Mistral( api_key=os.environ["MISTRAL_API_KEY"], timeout_ms=30000, # 30000: 30 seconds in ms max_retries=3 ) return _client

Step 2: Chat Completions with Structured Output

from mistralai import Mistral import json

client = get_mistral_client()

Basic chat

response = client.chat.complete( model="mistral-small-latest", messages=[ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Explain quantum computing briefly."} ], temperature=0.7, max_tokens=500 # HTTP 500 Internal Server Error ) print(response.choices[0].message.content)

JSON mode for structured output

response = client.chat.complete( model="mistral-small-latest", messages=[{"role": "user", "content": "List 3 programming languages as JSON array"}], response_format={"type": "json_object"} ) data = json.loads(response.choices[0].message.content)

Step 3: Streaming Responses

def stream_response(prompt: str): stream = client.chat.stream( model="mistral-small-latest", messages=[{"role": "user", "content": prompt}] ) full_response = "" for event in stream: chunk = event.data.choices[0].delta.content or "" full_response += chunk yield chunk return full_response

Step 4: Function Calling

import json

tools = [{ "type": "function", "function": { "name": "get_weather", "description": "Get current weather for a city", "parameters": { "type": "object", "properties": { "city": {"type": "string", "description": "City name"}, "units": {"type": "string", "enum": ["celsius", "fahrenheit"]} }, "required": ["city"] } } }]

response = client.chat.complete( model="mistral-small-latest", messages=[{"role": "user", "content": "What's the weather in Paris?"}], tools=tools, tool_choice="auto" )

Handle tool calls

if response.choices[0].message.tool_calls: for call in response.choices[0].message.tool_calls: args = json.loads(call.function.arguments) result = get_weather(**args) # your implementation # Send result back messages.append(response.choices[0].message) messages.append({"role": "tool", "name": call.function.name, "content": json.dumps(result), "tool_call_id": call.id})

Step 5: Embeddings

def embed_texts(texts: list[str]) -> list[list[float]]: response = client.embeddings.create( model="mistral-embed", inputs=texts ) return [d.embedding for d in response.data]

Batch large sets

def embed_batch(texts: list[str], batch_size: int = 64) -> list[list[float]]: embeddings = [] for i in range(0, len(texts), batch_size): batch = texts[i:i+batch_size] embeddings.extend(embed_texts(batch)) return embeddings

Error Handling

Error Cause Solution

401 Unauthorized

Invalid API key Check MISTRAL_API_KEY

429 Too Many Requests

Rate limit hit Use built-in retry or add backoff

400 Bad Request

Invalid model or params Check model name and parameter ranges

Timeout Large prompt or slow network Increase timeout_ms

Examples

Basic usage: Apply mistral sdk patterns to a standard project setup with default configuration options.

Advanced scenario: Customize mistral sdk patterns for production environments with multiple constraints and team-specific requirements.

Resources

• Mistral AI SDK

• API Reference

Output

• Configuration files or code changes applied to the project

• Validation report confirming correct implementation

• Summary of changes made and their rationale