openai-api-development

•

6155 months ago

Expert guidance for OpenAI API development including GPT models, Assistants API, function calling, embeddings, and best practices for production applications.

Quick Install

npxskills add mindrally/skills--skill openai-api-development

Instructions

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

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)

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