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 OpenAIAlways 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")