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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"

​
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_llamaindex_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="llama_index_app",
    project_version_name="v1",
    eval_tags=eval_tags
)

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5. Configure Llama Index Instrumentation

Initialize the Llama Index instrumentor to enable automatic tracing.

from fi.integrations.otel import LlamaIndexInstrumentor

LlamaIndexInstrumentor().instrument(tracer_provider=trace_provider)

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6. Install Required Dependencies

Install the necessary Llama Index components required for your project.

pip install llama-index

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7. Create Llama Index Components

Set up your Llama Index components with built-in observability.

from llama_index.agent.openai import OpenAIAgent
from llama_index.core import Settings
from llama_index.core.tools import FunctionTool
from llama_index.llms.openai import OpenAI

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

def add(a: int, b: int) -> int:
    """Add two integers and return the result."""
    return a + b

multiply_tool = FunctionTool.from_defaults(fn=multiply)
add_tool = FunctionTool.from_defaults(fn=add)
agent = OpenAIAgent.from_tools([multiply_tool, add_tool])
Settings.llm = OpenAI(model="gpt-3.5-turbo")

​
8. Execute

Run your Llama Index application.

if __name__ == "__main__":
    response = agent.query("What is (121 * 3) + 42?")
    print(response)
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