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1. Installation

Install the FutureAGI package to access the observability framework.

pip install futureagi

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2. Environment Configuration

Set up your environment variables to authenticate with FutureAGI services. These credentials enable:

  • Authentication with FutureAGI’s observability platform
  • Encrypted telemetry data transmission
import os
os.environ["FI_API_KEY"] = "your-futureagi-api-key"
os.environ["FI_SECRET_KEY"] = "your-futureagi-secret-key"
os.environ["MISTRAL_API_KEY"] = "your-mistral-api-key"

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3. Configure Evaluation Tags

Define evaluation criteria for monitoring LLM responses. Evaluation tags allow you to:

  • Define custom evaluation criteria
  • Set up automated response quality checks
  • Track model performance metrics

from fi.integrations.otel.types import EvalName, EvalSpanKind, EvalTag, EvalTagType

eval_tags = [
    EvalTag(
        eval_name=EvalName.DETERMINISTIC_EVALS,
        value=EvalSpanKind.TOOL,
        type=EvalTagType.OBSERVATION_SPAN,
        config={
            "multi_choice": False,
            "choices": ["Yes", "No"],
            "rule_prompt": "Evaluate if the response is correct",
        },
        custom_eval_name="det_eval_mistralai_1"
    )
]

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4. Initialize Trace Provider

Set up the trace provider to establish the observability pipeline. The trace provider:

  • Creates a new project in FutureAGI
  • Establishes telemetry data pipelines
  • Configures version tracking
  • Sets up evaluation frameworks
from fi.integrations.otel import register
from fi.integrations.otel.types import ProjectType

trace_provider = register(
    project_type=ProjectType.EXPERIMENT,
    project_name="mistral_ai_app",
    project_version_name="v1",
    eval_tags=eval_tags
)

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5. Configure Mistral AI Instrumentation

Initialize the Mistral AI instrumentor to enable automatic tracing.

from fi.integrations.otel import MistralAIInstrumentor

MistralAIInstrumentor().instrument(tracer_provider=trace_provider)

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6. Install Required Dependencies

Install the necessary Mistral AI components required for your project.

pip install mistralai

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7. Create Mistral AI Components

Set up your Mistral AI components with built-in observability.

from mistralai import Mistral

client = Mistral(api_key=os.environ["MISTRAL_API_KEY"])

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8. Execute

Run your Mistral AI application.

if __name__ == "__main__":
    response = client.agents.complete(
        agent_id="agent_id",
        messages=[
            {"role": "user", "content": "plan a vacation for me in Tbilisi"},
        ],
    )
    print(response)
Llama IndexVertex AI