To effectively implement a Retrieval-Augmented Generation (RAG) model using Azure OpenAI and Azure AI Search with the Python SDK, integrate Azure AI Search for document retrieval and Azure OpenAI's GPT models for response generation, ensuring seamless interaction between these services.
Here is the code snippet you can refer to:
In the above code we are using the following key points:
- Azure AI Search Integration: Utilizes the SearchClient from the Azure SDK to perform semantic searches, retrieving relevant documents based on the user's query.
- Azure OpenAI Integration: Employs the OpenAI Python SDK configured for Azure to generate responses using GPT models.
- RAG Pipeline Function (rag_pipeline): Combines document retrieval and response generation by constructing a prompt that includes both the user's query and the retrieved documents, ensuring that the AI's response is grounded in relevant information.
Hence, by integrating Azure AI Search for information retrieval and Azure OpenAI's GPT models for response generation, you can effectively implement a Retrieval-Augmented Generation (RAG) model using the Python SDK, enhancing your application's ability to provide contextually accurate and informative responses.
