Python SDK Complete Integration Guide
Use Python to call the LLM API, integrate LangChain, LlamaIndex, AutoGPT, and more to build powerful AI applications.
Getting Started
1. Install SDK
pip install openai2. Configure API
openai.api_key = "..."3. Make a call
response = openai...Code Examples
OpenAI official library
Use the OpenAI Python SDK to call the API
# Install OpenAI Python library
# pip install openai
import openai
# Configure API
openai.api_key = "YOUR_API_KEY"
openai.api_base = "https://api.example.com/v1" # Replace with your actual API base
# Basic chat
response = openai.ChatCompletion.create(
model="gpt-4o",
messages=[
{"role": "system", "content": "You are a helpful assistant"},
{"role": "user", "content": "What is a decorator in Python?"}
],
temperature=0.7,
max_tokens=1000
)
print(response.choices[0].message.content)
# Streaming output
stream = openai.ChatCompletion.create(
model="gpt-4o",
messages=[{"role": "user", "content": "Write a poem about programming"}],
stream=True
)
for chunk in stream:
if chunk.choices[0].delta.get("content"):
print(chunk.choices[0].delta.content, end="", flush=True)Other language SDKs
Python
The most popular AI development language
Main libraries:
openailangchainllama-indextransformersPopular framework integrations
Langchain
Framework for building LLM applications
Use cases:
LlamaIndex
Framework for data connection and indexing
Use cases:
AutoGPT
Autonomous AI agent
Use cases:
Semantic Kernel
Microsoft AI orchestration framework
Use cases:
Best Practices
Error handling
- • Implement retries with exponential backoff
- • Catch and handle API exceptions
- • Set reasonable timeouts
- • Log errors for easier debugging
Performance optimization
- • Use async calls to improve concurrency
- • Implement request batching to reduce latency
- • Cache frequent results to save cost
- • Choose appropriate models to balance cost and performance
Security recommendations
- • Store API keys in environment variables
- • Do not hardcode secrets in code
- • Implement access control and usage limits
- • Validate and sanitize user inputs
Debugging tips
- • Use detailed logs for API calls
- • Monitor token usage
- • Test edge cases and error scenarios
- • Use the Playground for quick validation
FAQ
Q: How to handle "Rate limit exceeded" errors?
A: Implement retries with exponential backoff. You can use libraries like tenacity or backoff to handle retries automatically.
Q: How to reduce API call costs?
A: Use smaller models (like GPT-3.5-turbo), cache frequent results, optimize prompt length, and batch requests.
Q: How to handle long text exceeding token limits?
A: Use text splitting strategies, implement sliding window processing, or use models that support longer contexts like Claude.
Q: How to improve the quality of generated content?
A: Optimize prompts, use few-shot examples, adjust the temperature parameter, and implement output validation and post-processing.
Need more help? See API Documentation or contact technical support