Introduction
In the rapidly evolving landscape of artificial intelligence, setting up an efficient and scalable AI system is crucial for businesses looking to leverage the power of AI. This blog post will guide you through the process of setting up AI Foundry using the ChatGPT model and implementing a Retrieval-Augmented Generation (RAG) based chat approach.
What is AI Foundry?
AI Foundry is a comprehensive platform provided by Azure that allows you to design, customize, and manage AI applications at scale. It offers a unified SDK, access to over 200 Azure services, and more than 1,800 models, making it a powerful tool for building AI-driven applications.
Understanding ChatGPT
ChatGPT, developed by OpenAI, is a conversational AI model that interacts in a dialogue format. It can answer follow-up questions, admit mistakes, and reject inappropriate requests. This model is trained using Reinforcement Learning from Human Feedback (RLHF), making it highly effective for generating coherent and contextually relevant responses.
What is RAG-Based Chat?
Retrieval-Augmented Generation (RAG) is an architecture that enhances the capabilities of a Large Language Model (LLM) like ChatGPT by integrating an information retrieval system. This system provides grounding data, ensuring that the AI's responses are accurate and relevant. RAG is particularly useful for enterprise solutions, as it allows the AI to access and utilize proprietary content.
Step-by-Step Guide to Setting Up AI Foundry with ChatGPT and RAG
Prerequisites
- Azure account with access to AI Foundry.
- OpenAI API key for ChatGPT.
- Basic understanding of Python and Azure services.
Setting Up AI Foundry
- Sign In: Log into your Azure account and navigate to AI Foundry.
- Create a New Project: Start a new project and select the necessary services and models.
- Configure SDK: Install the AI Foundry SDK and set up your development environment.
pip install azure-ai-foundry
Integrating ChatGPT
- API Access: Obtain your OpenAI API key and integrate it into your project.
- Model Configuration: Configure the ChatGPT model within AI Foundry.
import openai
openai.api_key = 'your-api-key'
response = openai.ChatCompletion.create(
model="gpt-4",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "How can I set up AI Foundry?"}
]
)
print(response.choices[0].message["content"])Implementing RAG-Based Chat
- Data Retrieval System: Set up Azure AI Search to index and retrieve relevant data.
- Integration with ChatGPT: Combine the retrieval system with ChatGPT to enhance response accuracy.from azure.ai.search import SearchClientfrom azure.core.credentials import AzureKeyCredentialsearch_client = SearchClient(endpoint="your-search-endpoint", credential=AzureKeyCredential("your-key"))def retrieve_data(query):results = search_client.search(query)return resultsdef generate_response(query):data = retrieve_data(query)response = openai.ChatCompletion.create(model="gpt-4",messages=[{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": query},{"role": "assistant", "content": data}])return response.choices[0].message["content"]print(generate_response("Tell me about AI Foundry"))
Testing and Deployment
- Evaluation: Test the system using ground truth data to ensure coherence and relevance.
- Deployment: Deploy your AI application using Azure's scalable infrastructure.
Conclusion
Setting up AI Foundry with ChatGPT and implementing a RAG-based chat approach can significantly enhance the capabilities of your AI applications. By following this guide, you can create a robust and scalable AI system that leverages the latest advancements in AI technology.