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1. Installation

First install the traceAI package to access the observability framework

pip install traceAI-openai

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2. Set Environment Variables

Set up your environment variables to authenticate with both FutureAGI and OpenAI services.

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. Initialize Trace Provider

Set up the trace provider to create a new project in FutureAGI, establish telemetry data pipelines .

from fi_instrumentation import register
from fi_instrumentation.fi_types import ProjectType

trace_provider = register(
    project_type=ProjectType.OBSERVE,
    project_name="openai_project",
)

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4. Instrument your Project

Instrument your Project with OpenAI Instrumentor. This step ensures that all interactions with the OpenAI are tracked and monitored.

from traceai_openai import OpenAIInstrumentor

OpenAIInstrumentor().instrument(tracer_provider=trace_provider)

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5. Interact with OpenAI

Interact with the OpenAI as you normally would. Our Instrumentor will automatically trace and send the telemetry data to our platform.

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Chat Completion

import httpx
import base64
from openai import OpenAI

client = OpenAI()

image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
image_media_type = "image/jpeg"
image_data = base64.standard_b64encode(httpx.get(image_url).content).decode("utf-8")

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What is in this image?"},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg",
                    },
                }
            ],
        },
    ],
)

print(response.choices[0].message.content)

​
Audio and speech

import requests
import base64
from openai import OpenAI

client = OpenAI()

# Fetch the audio file and convert it to a base64 encoded string
url = "https://cdn.openai.com/API/docs/audio/alloy.wav"
response = requests.get(url)
response.raise_for_status()
wav_data = response.content
encoded_string = base64.b64encode(wav_data).decode("utf-8")

completion = client.chat.completions.create(
    model="gpt-4o-audio-preview",
    modalities=["text", "audio"],
    audio={"voice": "alloy", "format": "wav"},
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What is in this recording?"},
                {
                    "type": "input_audio",
                    "input_audio": {"data": encoded_string, "format": "wav"},
                },
            ],
        },
    ],
)

​
Image Generation

from openai import OpenAI

client = OpenAI()

response = client.images.generate(
    model="dall-e-3",
    prompt="a horse running through a field of flowers",
    size="1024x1024",
    n=1,
)

print(response.data[0].url)

​
Chat Streaming

from openai import OpenAI

client = OpenAI()

completion = client.chat.completions.create(
    model="gpt-4o",
    stream=True,
    messages=[
        {
            "role": "user",
            "content": "What is OpenAI?",
        },
    ],
)

for chunk in completion:
    print(chunk.choices[0].delta.content, end="")