🚀 OpenAI Platform Overview
OpenAI provides a comprehensive platform for building AI applications with state-of-the-art language models, vision capabilities, and specialized APIs for complex workflows. From simple completions to sophisticated assistants, OpenAI's APIs enable developers to integrate advanced AI into their applications.
GPT Models
GPT-4, GPT-3.5 for text generation
Multimodal
Vision, DALL-E, Whisper APIs
Developer Tools
Assistants, fine-tuning, embeddings
Key Capabilities
Assistants API
Build stateful AI assistants with persistent conversation threads
Function Calling
Connect models to external tools and APIs with structured outputs
Vision Capabilities
Analyze images, extract text, and understand visual content
Embeddings
Create vector representations for semantic search and similarity
Fine-tuning
Customize models with your own training data
Moderation
Content filtering and safety checks for responsible AI
Implementation Examples
Assistants API
Build AI assistants with persistent threads and tools
from openai import OpenAI
import time
client = OpenAI()
# Create an assistant with code interpreter
assistant = client.beta.assistants.create(
name="Data Analyst",
instructions="""You are a data analyst assistant.
Use code interpreter to analyze data and create visualizations.""",
model="gpt-4-turbo",
tools=[{"type": "code_interpreter"}]
)
# Upload a file for analysis
file = client.files.create(
file=open("sales_data.csv", "rb"),
purpose="assistants"
)
# Create a thread
thread = client.beta.threads.create()
# Add a message to the thread
message = client.beta.threads.messages.create(
thread_id=thread.id,
role="user",
content="Analyze the sales data and create a visualization of monthly trends",
file_ids=[file.id]
)
# Run the assistant
run = client.beta.threads.runs.create(
thread_id=thread.id,
assistant_id=assistant.id
)
# Poll for completion
while run.status in ['queued', 'in_progress']:
time.sleep(1)
run = client.beta.threads.runs.retrieve(
thread_id=thread.id,
run_id=run.id
)
# Get the response
messages = client.beta.threads.messages.list(thread_id=thread.id)
for message in messages.data:
print(f"{message.role}: {message.content[0].text.value}")
# Assistant will analyze data and create visualizations using code interpreterAPI Features & Patterns
Streaming Responses
Stream tokens as they're generated for real-time user experiences
- • Reduces perceived latency
- • Enables progressive rendering
- • Better user experience for long responses
Structured Outputs
Force models to return JSON with specific schemas
- • Guaranteed valid JSON responses
- • Type-safe integration with applications
- • Reliable data extraction
Rate Limiting & Quotas
Manage API usage with built-in limits and monitoring
- • Token-based and request-based limits
- • Usage tracking and billing alerts
- • Automatic retry with exponential backoff
Best Practices
✅ Do's
- • Use appropriate models for each task
- • Implement proper error handling and retries
- • Cache responses when possible
- • Use streaming for better UX
- • Validate and sanitize outputs
- • Monitor token usage and costs
- • Use function calling for structured data
- • Implement rate limiting on your side
❌ Don'ts
- • Don't expose API keys in client code
- • Don't skip input validation
- • Don't ignore rate limits
- • Don't use GPT-4 for simple tasks
- • Don't trust outputs without verification
- • Don't forget error handling
- • Don't ignore safety best practices
- • Don't store sensitive data in prompts