Create basic_openai_api.md

This commit is contained in:
Omar Santos 2023-09-05 21:16:34 -04:00 committed by GitHub
parent 18ab8550b4
commit a92d0d5ec8
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23

View File

@ -0,0 +1,67 @@
# Using the OpenAI API with Python
### Step 1: Setting Up the Environment
1. **Install Python**: Make sure you have Python 3.x installed. You can download it from the [official website](https://www.python.org/).
2. **Set Up a Virtual Environment** (optional but recommended):
```bash
python3 -m venv openai-lab-env
source openai-lab-env/bin/activate # On Windows, use `openai-lab-env\Scripts\activate`
```
3. **Install Necessary Packages**:
```bash
pip3 install openai requests
```
### Step 2: Configuring API Credentials
4. **Register on OpenAI**:
- Go to the [OpenAI website](https://www.openai.com/) and register to obtain API credentials.
5. **Configure API Credentials**:
- 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):
```bash
export OPENAI_API_KEY=your_api_key_here
```
### Step 3: Making API Calls
6. **Create a Python Script**:
- Create a new Python script (lets name it `openai_lab.py`) and open it in a text editor.
7. **Import Necessary Libraries**:
```python
import openai
openai.api_key = 'your_api_key_here' # Alternatively, use the environment variable to store the API key
```
8. **Make a Simple API Call**:
```python
# Generate the AI response using the GPT-3.5 model (16k)
# https://beta.openai.com/docs/api-reference/create-completion
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo-16k",
messages=prompt,
max_tokens=15000
)
# print the AI response
print(response.choices[0].message.content)
```
### Step 4: Experimenting with the API
9. **Experiment with Different Parameters**:
- Modify the `max_tokens`, `temperature`, and `top_p` parameters and observe how the responses change.
10. **Handle API Responses**:
- Learn how to handle API responses and extract the required information.
### Step 5: Building a Simple Application
11. **Develop a Simple Application**:
- 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.
12. **Testing Your Application**:
- Run various tests to ensure the functionality and robustness of your application.