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1. Installation

First 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 both OpenAI and FutureAGI services. These credentials enable:

  • Secure access to OpenAI’s language models
  • Authentication with FutureAGI’s observability platform
  • Encrypted telemetry data transmission
import os
os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
os.environ["FI_API_KEY"] = "your-futureagi-api-key"
os.environ["FI_SECRET_KEY"] = "your-futureagi-secret-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_langchain_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(
    endpoint = "https://api.futureagi.com/tracer/observation-span/create_otel_span/",
    project_type=ProjectType.EXPERIMENT,
    project_name="langchain_app",
    project_version_name="v1",
    eval_tags=eval_tags
)

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5. Configure LangChain Instrumentation

Initialize the LangChain instrumentor to enable automatic tracing.

from fi.integrations.otel import LangChainInstrumentor

LangChainInstrumentor().instrument(tracer_provider=trace_provider)

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6. Install Required Dependencies

Install the necessary LangChain components required for your project.

pip install langchain_openai

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7. Create LangChain Components

Set up your LangChain pipeline with built-in observability.

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_template("{x} {y} {z}?").partial(x="why is", z="blue")
chain = prompt | ChatOpenAI(model_name="gpt-3.5-turbo")

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8. Execute

Run your LangChain application.

def run_chain():
    try:
        result = chain.invoke({"y": "sky"})
        print(f"Response: {result}")
    except Exception as e:
        print(f"Error executing chain: {e}")

if __name__ == "__main__":
    run_chain()