Here’s a markdown document to describe setting up a Python-based service that uses a GitHub repository containing prompts to leverage when calling the OpenAI Assistant API:

markdown Copy code

Setting Up a Python-Based Service for OpenAI API with GitHub-Hosted Prompts

This guide walks through setting up a Python-based service that retrieves prompts from a GitHub repository and uses them to make calls to the OpenAI Assistant API.

Prerequisites

  1. Python Environment: Ensure Python 3.8+ is installed.
  2. GitHub Repository: A repository containing your prompt files in structured text (e.g., JSON, YAML, or plain text).
  3. OpenAI API Key: Obtain an API key from OpenAI.
  4. Git Access: Set up access (HTTPS/SSH) to clone the GitHub repository.

Step 1: Create a GitHub Repository for Prompts

  1. Create a repository on GitHub to store your prompts.
  2. Organize prompts into a structured format:
    • Example: Store JSON files in a /prompts directory.
    {
    "name": "example_prompt",
    "description": "An example prompt",
    "content": "Write a detailed guide on {topic}."
}

Commit and push the changes: bash Copy code git add . git commit -m “Add initial prompts” git push origin main Step 2: Set Up the Python Service 2.1 Install Required Libraries Set up a Python virtual environment and install dependencies:

bash Copy code python3 -m venv venv source venv/bin/activate pip install openai gitpython pyyaml 2.2 Create the Python Service Script Save the following script as service.py:

python Copy code import os import git import openai import json

Configuration

GITHUB_REPO_URL = “https://github.com/your-username/your-repo" LOCAL_REPO_PATH = “./prompts_repo” OPENAI_API_KEY = “your-openai-api-key”

Clone or Pull Repository

if not os.path.exists(LOCAL_REPO_PATH): git.Repo.clone_from(GITHUB_REPO_URL, LOCAL_REPO_PATH) else: repo = git.Repo(LOCAL_REPO_PATH) repo.git.pull()

Load Prompts

def load_prompts(): prompts_dir = os.path.join(LOCAL_REPO_PATH, “prompts”) prompts = [] for file_name in os.listdir(prompts_dir): if file_name.endswith(".json”): with open(os.path.join(prompts_dir, file_name), “r”) as f: prompts.append(json.load(f)) return prompts

Call OpenAI API

def call_openai_api(prompt): openai.api_key = OPENAI_API_KEY response = openai.Completion.create( model=“text-davinci-003”, prompt=prompt, max_tokens=200 ) return response.choices[0].text.strip()

Main Function

if name == “main”: prompts = load_prompts() for prompt in prompts: print(f"Prompt: {prompt[‘content’]}") response = call_openai_api(prompt[‘content’]) print(f"Response: {response}") Step 3: Run the Service Clone Repository & Setup:

Replace your-username/your-repo with your GitHub repository path. Replace your-openai-api-key with your OpenAI API key. Run the Service:

bash Copy code python service.py Customizations Prompt Parsing: Modify the load_prompts function to support different formats like YAML. Error Handling: Add robust error handling for API calls and Git operations. Scheduling: Use a task scheduler like cron or apscheduler for periodic updates and executions. Deployment: Containerize the service with Docker for consistent deployment. Next Steps Enhance Prompt Structure: Add metadata like categories or tags for dynamic prompt selection. Integrate CI/CD: Automate deployment using tools like GitHub Actions. Logging: Integrate a logging framework to monitor API usage and errors. With this setup, your service can dynamically fetch and use prompts from GitHub, allowing for a streamlined and scalable workflow with OpenAI’s Assistant API.