LlamaIndex Llms Integration: Mistral

Installation

Install the required packages using the following commands:

%pip install llama-index-llms-mistralai
!pip install llama-index

Basic Usage

Initialize the MistralAI Model

To use the MistralAI model, create an instance and provide your API key:

from llama_index.llms.mistralai import MistralAI

llm = MistralAI(api_key="<replace-with-your-key>")

Generate Completions

To generate a text completion for a prompt, use the complete method:

resp = llm.complete("Paul Graham is ")
print(resp)

Chat with the Model

You can also chat with the model using a list of messages. Here’s an example:

from llama_index.core.llms import ChatMessage

messages = [
    ChatMessage(role="system", content="You are CEO of MistralAI."),
    ChatMessage(role="user", content="Tell me the story about La plateforme"),
]
resp = MistralAI().chat(messages)
print(resp)

Using Random Seed

To set a random seed for reproducibility, initialize the model with the random_seed parameter:

resp = MistralAI(random_seed=42).chat(messages)
print(resp)

Streaming Responses

Stream Completions

You can stream responses using the stream_complete method:

resp = llm.stream_complete("Paul Graham is ")
for r in resp:
    print(r.delta, end="")

Stream Chat Responses

To stream chat messages, use the following code:

messages = [
    ChatMessage(role="system", content="You are CEO of MistralAI."),
    ChatMessage(role="user", content="Tell me the story about La plateforme"),
]
resp = llm.stream_chat(messages)
for r in resp:
    print(r.delta, end="")

Configure Model

To use a specific model configuration, initialize the model with the desired model name:

llm = MistralAI(model="mistral-medium")
resp = llm.stream_complete("Paul Graham is ")
for r in resp:
    print(r.delta, end="")

Mistral Azure SDK Usage

To use the Mistral Azure SDK implementation, pass the Azure endpoint and API key. When these are provided, the client automatically uses the Mistral Azure SDK instead of the public Mistral endpoint.

from llama_index.llms.mistralai import MistralAI

llm = MistralAI(
    azure_endpoint="https://<your-resource-name>.openai.azure.com",
    azure_api_key="<replace-with-your-azure-key>",
    model="mistral-large-latest",
)

resp = llm.complete("Paul Graham is ")
print(resp)

Function Calling

You can call functions from the model by defining tools. Here’s an example:

from llama_index.llms.mistralai import MistralAI
from llama_index.core.tools import FunctionTool


def multiply(a: int, b: int) -> int:
    """Multiply two integers and return the result."""
    return a * b


def mystery(a: int, b: int) -> int:
    """Mystery function on two integers."""
    return a * b + a + b


mystery_tool = FunctionTool.from_defaults(fn=mystery)
multiply_tool = FunctionTool.from_defaults(fn=multiply)

llm = MistralAI(model="mistral-large-latest")
response = llm.predict_and_call(
    [mystery_tool, multiply_tool],
    user_msg="What happens if I run the mystery function on 5 and 7",
)
print(str(response))

LLM Implementation example

https://docs.llamaindex.ai/en/stable/examples/llm/mistralai/

Metadata

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