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Mistral AI API

https://docs.mistral.ai/api/

API Key

# env variable
os.environ['MISTRAL_API_KEY']

Sample Usage

from litellm import completion
import os

os.environ['MISTRAL_API_KEY'] = ""
response = completion(
model="mistral/mistral-tiny",
messages=[
{"role": "user", "content": "hello from litellm"}
],
)
print(response)

Sample Usage - Streaming

from litellm import completion
import os

os.environ['MISTRAL_API_KEY'] = ""
response = completion(
model="mistral/mistral-tiny",
messages=[
{"role": "user", "content": "hello from litellm"}
],
stream=True
)

for chunk in response:
print(chunk)

Supported Models

All models listed here https://docs.mistral.ai/platform/endpoints are supported. We actively maintain the list of models, pricing, token window, etc. here.

Model NameFunction Call
Mistral Smallcompletion(model="mistral/mistral-small-latest", messages)
Mistral Mediumcompletion(model="mistral/mistral-medium-latest", messages)
Mistral Largecompletion(model="mistral/mistral-large-latest", messages)
Mistral 7Bcompletion(model="mistral/open-mistral-7b", messages)
Mixtral 8x7Bcompletion(model="mistral/open-mixtral-8x7b", messages)
Mixtral 8x22Bcompletion(model="mistral/open-mixtral-8x22b", messages)

Function Calling

from litellm import completion

# set env
os.environ["MISTRAL_API_KEY"] = "your-api-key"

tools = [
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA",
},
"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
},
"required": ["location"],
},
},
}
]
messages = [{"role": "user", "content": "What's the weather like in Boston today?"}]

response = completion(
model="mistral/mistral-large-latest",
messages=messages,
tools=tools,
tool_choice="auto",
)
# Add any assertions, here to check response args
print(response)
assert isinstance(response.choices[0].message.tool_calls[0].function.name, str)
assert isinstance(
response.choices[0].message.tool_calls[0].function.arguments, str
)

Sample Usage - Embedding

from litellm import embedding
import os

os.environ['MISTRAL_API_KEY'] = ""
response = embedding(
model="mistral/mistral-embed",
input=["good morning from litellm"],
)
print(response)

Supported Models

All models listed here https://docs.mistral.ai/platform/endpoints are supported

Model NameFunction Call
Mistral Embeddingsembedding(model="mistral/mistral-embed", input)