OpenAI - Future AGI Documentation

1. Installation

First install the traceAI package to access the observability framework

pip install traceAI-openai

npm install @traceai/openai

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"

process.env.OPENAI_API_KEY = OPENAI_API_KEY;
process.env.FI_API_KEY = FI_API_KEY;
process.env.FI_SECRET_KEY = FI_SECRET_KEY;

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",
)

import { register, ProjectType } from "@traceai/fi-core";

const tracerProvider = register({
    project_type: ProjectType.OBSERVE,
    project_name: "openai_project",
});

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)

import { OpenAIInstrumentation } from "@traceai/openai";
import { registerInstrumentations } from "@opentelemetry/instrumentation";

const openaiInstrumentation = new OpenAIInstrumentation({});

  registerInstrumentations({
    instrumentations: [openaiInstrumentation],
    tracerProvider: tracerProvider,
  });

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.

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)

import { OpenAI } from "openai";

const client = new OpenAI();

const response = await client.chat.completions.create({
    model: "gpt-4o",
    messages: [{ role: "user", content: "What is the capital of South Africa?" }],
});

console.log(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="")