openai-api-development

SKILL.md

OpenAI API Development

You are an expert in OpenAI API development, including GPT models, Assistants API, function calling, embeddings, and building production-ready AI applications.

Key Principles

Write concise, technical responses with accurate Python examples

Use type hints for all function signatures

Implement proper error handling and retry logic

Never hardcode API keys; use environment variables

Follow OpenAI's usage policies and rate limit guidelines

Setup and Configuration

Environment Setup

import os
from openai import OpenAI

Always use environment variables for API keys

client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))

### Best Practices

- Store API keys in `.env` files, never commit them
- Use `python-dotenv` for local development
- Implement proper key rotation strategies
- Set up separate keys for development and production

## Chat Completions API

### Basic Usage

```python
from openai import OpenAI

client = OpenAI()

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello!"}
    ],
    temperature=0.7,
    max_tokens=1000
)

message = response.choices[0].message.content

Streaming Responses

stream = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Tell me a story"}],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end="")

Model Selection

Use gpt-4o for complex reasoning and multimodal tasks

Use gpt-4o-mini for faster, cost-effective responses

Use o1 models for advanced reasoning tasks

Consider gpt-3.5-turbo for simple tasks requiring speed

Function Calling

Defining Functions

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather for a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "City and state, e.g., San Francisco, CA"
                    },
                    "unit": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"],
                        "description": "Temperature unit"
                    }
                },
                "required": ["location"]
            }
        }
    }
]

response = client.chat.completions.create(
    model="gpt-4o",
    messages=messages,
    tools=tools,
    tool_choice="auto"
)

Handling Tool Calls

import json

def process_tool_calls(response, messages):
    tool_calls = response.choices[0].message.tool_calls

    if tool_calls:
        messages.append(response.choices[0].message)

        for tool_call in tool_calls:
            function_name = tool_call.function.name
            function_args = json.loads(tool_call.function.arguments)

            # Execute the function
            result = execute_function(function_name, function_args)

            messages.append({
                "role": "tool",
                "tool_call_id": tool_call.id,
                "content": json.dumps(result)
            })

        # Get final response
        return client.chat.completions.create(
            model="gpt-4o",
            messages=messages,
            tools=tools
        )

    return response

Assistants API

Creating an Assistant

assistant = client.beta.assistants.create(
    name="Data Analyst",
    instructions="You are a data analyst. Analyze data and provide insights.",
    tools=[
        {"type": "code_interpreter"},
        {"type": "file_search"}
    ],
    model="gpt-4o"
)

Managing Threads

# Create a thread
thread = client.beta.threads.create()

# Add a message
message = client.beta.threads.messages.create(
    thread_id=thread.id,
    role="user",
    content="Analyze this data..."
)

# Run the assistant
run = client.beta.threads.runs.create_and_poll(
    thread_id=thread.id,
    assistant_id=assistant.id
)

# Get messages
if run.status == "completed":
    messages = client.beta.threads.messages.list(thread_id=thread.id)

Embeddings

Generating Embeddings

response = client.embeddings.create(
    model="text-embedding-3-small",
    input="Your text to embed",
    encoding_format="float"
)

embedding = response.data[0].embedding

Best Practices for Embeddings

Use text-embedding-3-small for cost-effective solutions

Use text-embedding-3-large for maximum accuracy

Batch requests for efficiency (up to 2048 inputs)

Cache embeddings to avoid redundant API calls

Use appropriate dimensions parameter for storage optimization

Vision and Multimodal

Image Analysis

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What's in this image?"},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://example.com/image.jpg",
                        "detail": "high"
                    }
                }
            ]
        }
    ]
)

Error Handling

Retry Logic

from openai import RateLimitError, APIError
import time

def call_with_retry(func, max_retries=3, base_delay=1):
    for attempt in range(max_retries):
        try:
            return func()
        except RateLimitError:
            delay = base_delay * (2 ** attempt)
            time.sleep(delay)
        except APIError as e:
            if attempt == max_retries - 1:
                raise
            time.sleep(base_delay)
    raise Exception("Max retries exceeded")

Common Error Types

RateLimitError: Implement exponential backoff

APIError: Check API status, retry with backoff

AuthenticationError: Verify API key

InvalidRequestError: Validate input parameters

Cost Optimization

Use appropriate models for task complexity

Implement token counting before requests

Use streaming for long responses

Cache responses when appropriate

Set reasonable max_tokens limits

Use batch API for non-time-sensitive requests

Security Best Practices

Never expose API keys in client-side code

Implement rate limiting on your endpoints

Validate and sanitize user inputs

Use content moderation for user-generated content

Log API usage for monitoring and auditing

Dependencies

openai

python-dotenv

tiktoken (for token counting)

pydantic (for input validation)

tenacity (for retry logic)