OpenAI Responses API: EASY Beginners Tutorial in 14 Mins!

Описание: 🔥 *Complete Guide to OpenAI's Responses API - A Beginner's Tutorial* 🔥

In this comprehensive tutorial, I'll walk you through OpenAI's powerful new Responses API, which is replacing the Chat Completions API and powering millions of AI applications. Learn why this update is crucial for developers and how to implement it in your projects!

NVIDIA GTC Conference: https://nvda.ws/3ClSmMF
Code: https://mer.vin/2025/03/openai-respon...

What You'll Learn:
Why Responses API is a game-changer (supports file search, computer use, and soon Code Interpreter)
Step-by-step implementation with complete working code
How to create basic chatbots with Python
Adding image analysis capabilities
Using pre-built tools like web search
Creating custom tools for your AI applications
Implementing streaming responses for better user engagement
How to track interactions using the OpenAI platform

📋 Complete Code in Repository
All code examples from this tutorial are available in the description below for easy implementation.

🔧 Required Packages:
```python
pip install openai geopy gradio
```

📢 Key Advantages of Responses API:
File search for RAG (Retrieval Augmented Generation)
Computer use for automating repetitive tasks
Future Code Interpreter support for creating and running code
Simplified response format compared to Chat Completions

🔗 Important Links:
OpenAI Platform: https://platform.openai.com
API Documentation: https://platform.openai.com/docs/api-...
LM Studio: https://lmstudio.ai

OpenAI Responses API Overview

The tutorial explains how OpenAI's Responses API is replacing the older Chat Completions API, providing enhanced capabilities for AI applications. Here are the main features and benefits highlighted:

1. **Enhanced Capabilities**: Unlike the Chat Completions API, the Responses API supports:
File search (for RAG implementations)
Computer use (for controlling your computer and automating tasks)
Code Interpreter (forthcoming at the time of the tutorial)

2. **Simple Migration**: Switching from Chat Completions to Responses API is straightforward - just replace "chat.completions" with "responses" in your code.

3. **Input vs Messages**: A key difference is that the older API used "messages" parameter while Responses API uses "input" parameter.

Tutorial Components

The tutorial walks through several implementations:

1. **Basic Chatbot**: Creating a simple terminal-based chatbot
2. **UI Integration**: Adding a Gradio interface for a user-friendly experience
3. **Image Analysis**: Using the API to analyze images
4. **Built-in Tools**:
Web search tool for retrieving internet information
File search tool for searching within uploaded documents
Computer use tool (covered in a separate video)
5. **Custom Tools**: Creating and integrating custom tools like a weather API
6. **Response Streaming**: Implementing stream=true for progressive responses

Code Structure

The tutorial demonstrates several Python scripts:
Basic implementation (app.py)
UI implementation (ui.py)
Image analysis (image.py)
Built-in tools usage (tools.py)
RAG implementation (rag.py)
Custom tools implementation
Streaming implementation (stream.py)

Additional Mention

The tutorial also mentions the possibility of running these capabilities locally using LM Studio, allowing for private execution without API keys by utilizing local models like Gemma3.

Would you like me to elaborate on any specific aspect of this tutorial or explain any particular part in more detail?

Timestamps:
0:00 - Introduction to OpenAI Responses API
0:25 - Why Responses API is important
0:53 - Overview of tutorial structure
1:26 - Mention of Nvidia GTC conference
1:58 - Difference between Chat Completions and Responses API
2:25 - Tutorial roadmap
2:42 - Basic chatbot setup
3:32 - Creating a simple chatbot with code
4:53 - Setting up user interface with Gradio
6:12 - Image analysis capabilities
7:04 - Using built-in tools (web search, file search)
7:56 - File search and vector database setup
9:23 - Creating custom tools
9:50 - Building a weather tool example
12:30 - Streaming responses for faster feedback
13:12 - Using LM Studio locally