Chat With Your Own Data
Azure chat completion models with your own data (preview)
This example shows how to use Azure OpenAI service models with your own data. The feature is currently in preview.
Azure OpenAI on your data enables you to run supported chat models such as GPT-3.5-Turbo and GPT-4 on your data without needing to train or fine-tune models. Running models on your data enables you to chat on top of, and analyze your data with greater accuracy and speed. One of the key benefits of Azure OpenAI on your data is its ability to tailor the content of conversational AI. Because the model has access to, and can reference specific sources to support its responses, answers are not only based on its pretrained knowledge but also on the latest information available in the designated data source. This grounding data also helps the model avoid generating responses based on outdated or incorrect information.
Azure OpenAI on your own data with Azure Cognitive Search provides a customizable, pre-built solution for knowledge retrieval, from which a conversational AI application can be built. To see alternative methods for knowledge retrieval and semantic search, check out the cookbook examples for vector databases.
How it works
Azure OpenAI on your own data connects the model with your data, giving it the ability to retrieve and utilize data in a way that enhances the model's output. Together with Azure Cognitive Search, data is retrieved from designated data sources based on the user input and provided conversation history. The data is then augmented and resubmitted as a prompt to the model, giving the model contextual information it can use to generate a response.
See the Data, privacy, and security for Azure OpenAI Service for more information.
Chat completion model with your own data
Setting the context
In this example, we want our model to base its responses on Azure AI services documentation data. Following the Quickstart shared previously, we have added the markdown file for the Azure AI services and machine learning documentation page to our search index. The model is now ready to answer questions about Azure AI services and machine learning.