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68 lines
2.5 KiB
Markdown
68 lines
2.5 KiB
Markdown
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# Using the OpenAI API with Python
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### Step 1: Setting Up the Environment
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1. **Install Python**: Make sure you have Python 3.x installed. You can download it from the [official website](https://www.python.org/).
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2. **Set Up a Virtual Environment** (optional but recommended):
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```bash
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python3 -m venv openai-lab-env
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source openai-lab-env/bin/activate # On Windows, use `openai-lab-env\Scripts\activate`
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```
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3. **Install Necessary Packages**:
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```bash
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pip3 install openai requests
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```
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### Step 2: Configuring API Credentials
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4. **Register on OpenAI**:
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- Go to the [OpenAI website](https://www.openai.com/) and register to obtain API credentials.
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5. **Configure API Credentials**:
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- Store your API credentials securely, possibly using environment variables. In your terminal, you can set it up using the following command (replace `your_api_key_here` with your actual API key):
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```bash
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export OPENAI_API_KEY=your_api_key_here
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```
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### Step 3: Making API Calls
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6. **Create a Python Script**:
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- Create a new Python script (let’s name it `openai_lab.py`) and open it in a text editor.
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7. **Import Necessary Libraries**:
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```python
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import openai
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openai.api_key = 'your_api_key_here' # Alternatively, use the environment variable to store the API key
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```
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8. **Make a Simple API Call**:
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```python
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# Generate the AI response using the GPT-3.5 model (16k)
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# https://beta.openai.com/docs/api-reference/create-completion
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response = openai.ChatCompletion.create(
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model="gpt-3.5-turbo-16k",
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messages=prompt,
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max_tokens=15000
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)
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# print the AI response
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print(response.choices[0].message.content)
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```
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### Step 4: Experimenting with the API
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9. **Experiment with Different Parameters**:
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- Modify the `max_tokens`, `temperature`, and `top_p` parameters and observe how the responses change.
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10. **Handle API Responses**:
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- Learn how to handle API responses and extract the required information.
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### Step 5: Building a Simple Application
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11. **Develop a Simple Application**:
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- Create a more complex script that could function as a Q&A system or a content generation tool. You can use [the "Article Generator" example](https://github.com/The-Art-of-Hacking/h4cker/blob/master/ai_research/ML_Fundamentals/ai_generated/article_generator.py) we discussed during class for reference.
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12. **Testing Your Application**:
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- Run various tests to ensure the functionality and robustness of your application.
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