Openai Docs

Openai Docs is a community skill for working with OpenAI API documentation, providing quick access to API references, usage patterns, model capabilities, and integration examples for building applications with OpenAI services.

What Is This?

Overview

Openai Docs provides structured access to OpenAI API documentation covering the Chat Completions API, Assistants API, Embeddings, Fine-tuning, image generation, and audio processing endpoints. It surfaces key API patterns, parameter options, error handling approaches, and best practices that developers need when building with OpenAI models. The skill serves as a reference layer that reduces time spent navigating documentation and finding specific configuration details, particularly useful when working across multiple API surfaces simultaneously.

Who Should Use This

This skill serves developers integrating OpenAI APIs into applications, teams evaluating different OpenAI model capabilities for specific use cases, and engineers debugging API integration issues who need quick access to parameter specifications and error codes. It is equally valuable for developers new to the OpenAI ecosystem who need a faster path to working implementations.

Why Use It?

Problems It Solves

OpenAI API documentation spans multiple products and versions, making it time-consuming to find specific parameter details. Model capability differences between GPT-4, GPT-4o, and other variants are not always obvious without careful comparison. Error codes and rate limit behaviors require understanding that is scattered across different documentation sections. New API features like structured outputs and function calling have specific requirements that are easy to misconfigure, especially when combining multiple features within a single request.

Core Highlights

API endpoint references provide parameter specifications with type information and default values. Model comparison tables surface capability differences across available models. Error handling guides document common failure codes with resolution strategies. Code examples demonstrate integration patterns for each API endpoint using official client libraries.

How to Use It?

Basic Usage

from openai import OpenAI

client = OpenAI()

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "Extract structured data."},
        {"role": "user", "content": "Parse this invoice: Item A $10, Item B $20"}
    ],
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "invoice",
            "schema": {
                "type": "object",
                "properties": {
                    "items": {
                        "type": "array",
                        "items": {
                            "type": "object",
                            "properties": {
                                "name": {"type": "string"},
                                "price": {"type": "number"}
                            }
                        }
                    }
                }
            }
        }
    }
)
print(response.choices[0].message.content)

Real-World Examples

from openai import OpenAI

client = OpenAI()

def get_embeddings(texts: list[str], model: str = "text-embedding-3-small") -> list:
    response = client.embeddings.create(input=texts, model=model)
    return [item.embedding for item in response.data]

def safe_completion(messages: list[dict], retries: int = 3) -> str:
    for attempt in range(retries):
        try:
            resp = client.chat.completions.create(
                model="gpt-4o", messages=messages, timeout=30
            )
            return resp.choices[0].message.content
        except Exception as e:
            if attempt == retries - 1:
                raise
            if "rate_limit" in str(e).lower():
                import time
                time.sleep(2 ** attempt)
    return ""

Advanced Tips

Use the streaming API for chat completions to reduce perceived latency in user-facing applications. Specify response_format for structured outputs when you need reliable JSON parsing without post-processing. Track token usage from response metadata to monitor costs accurately across different model tiers. When building production pipelines, log both prompt and completion token counts separately, as they are priced differently and the distinction matters for cost forecasting.

When to Use It?

Use Cases

Look up API parameter specifications when configuring chat completion requests. Compare model capabilities for selecting the right model for a specific task, such as choosing between a reasoning model and a standard completion model based on latency and accuracy requirements. Debug API error responses using documented error codes and resolution steps.

Related Topics

OpenAI API client libraries, prompt engineering techniques, token counting and cost optimization, rate limit management, and LLM application architecture.

Important Notes

Requirements

An OpenAI API key with access to the target models, the openai Python package or equivalent HTTP client, and understanding of token-based pricing for cost management.

Usage Recommendations

Do: check the API changelog for recent changes before debugging unexpected behavior. Use the official client library which handles authentication, retries, and response parsing. Set explicit timeouts on API calls to prevent hanging requests in production.

Don't: hardcode model names without a configuration layer, as model availability changes over time. Ignore deprecation notices for older model versions that will be retired. Send requests without error handling for rate limits and service interruptions.

Limitations

Documentation may lag behind the latest API features by a brief period after launch. Model behavior can change between versions even when the API interface remains stable. Token limits and pricing vary by model, requiring careful selection based on both capability and cost requirements. Beta features may have different stability guarantees than general availability endpoints.