​
1. Installation

Install the FutureAGI package to access the observability framework.

pip install futureagi

​
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"

​
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_instructor_1"
    )
]

​
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="instructor_app",
    project_version_name="v1",
    eval_tags=eval_tags
)

​
5. Configure Instructor Instrumentation

Initialize the Instructor instrumentor to enable automatic tracing.

from fi.integrations.otel import InstructorInstrumentor

InstructorInstrumentor().instrument(tracer_provider=trace_provider)

​
6. Install Required Dependencies

Install the necessary Instructor components required for your project.

pip install instructor

​
7. Create Instructor Components

Set up your Instructor components with built-in observability.

import instructor
from openai import OpenAI
from pydantic import BaseModel

# Define the output structure
class UserInfo(BaseModel):
    name: str
    age: int

# Patch the OpenAI client
client = instructor.patch(client=OpenAI())

​
8. Execute

Run your Instructor application.

user_info = client.chat.completions.create(
    model="gpt-3.5-turbo",
    response_model=UserInfo,
    messages=[
        {
            "role": "system",
            "content": "Extract the name and age from the text and return them in a structured format.",
        },
        {"role": "user", "content": "John Doe is nine years old."},
    ],
)

print(user_info, type(user_info))
HaystackLiteLLM