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How to Build a Custom AI Assistant Using OpenAI’s API: A Step-by-Step Tutorial
1. Prerequisites and Environment Setup
- Install Python 3.8+ and set up a virtual environment to manage dependencies.
- Obtain an OpenAI API key and configure environment variables for secure access.
- Install the `openai` Python library along with `dotenv` and `flask` for a quick web interface.
2. Understanding the API and Authentication
- Review the Chat Completions endpoint structure: model selection, messages array, and parameters like temperature and max_tokens.
- Write a simple authentication test script that sends a “Hello” message and prints the response.
- Handle common errors (rate limits, invalid keys) with try/except blocks and exponential backoff.
3. Designing the Conversation Flow
- Define the system message to set the assistant’s personality and constraints (e.g., “You are a helpful coding tutor”).
- Create a message history structure that alternates between user and assistant roles.
- Plan for user input sanitization and context window limits (e.g., trim oldest messages when nearing token limit).
4. Implementing the Core Logic
- Build a `chat_with_assistant(user_input)` function that appends the new user message, calls the API, and returns the assistant’s reply.
- Add a simple command-line loop that continuously accepts input and prints responses.
- Incorporate streaming responses using `stream=True` for a more interactive user experience.
5. Adding Memory and Context
- Implement a sliding window that keeps the last N exchanges (e.g., last 10 messages) to maintain coherent conversation.
- Optionally store conversation history in a local JSON file or a lightweight SQLite database for persistence.
- Use a summar
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