When you want to create an agent that uses your own data to generate accurate answers, you need to be able to search your data efficiently. When you build an agent with the Azure AI Foundry, you can use the integration with Azure AI Search to retrieve the relevant context in your chat flow.
Azure AI Search is a retriever that you can include when building a language model application with prompt flow. Azure AI Search allows you to bring your own data, index your data, and query the index to retrieve any information you need.
Using a vector index
While a text-based index will improve search efficiency, you can usually achieve a better data retrieval solution by using a vector-based index that contains embeddings that represent the text tokens in your data source.
An embedding is a special format of data representation that a search engine can use to easily find the relevant information. More specifically, an embedding is a vector of floating-point numbers.
For example, imagine you have two documents with the following contents:
- “The children played joyfully in the park.”
- “Kids happily ran around the playground.”
These two documents contain texts that are semantically related, even though different words are used. By creating vector embeddings for the text in the documents, the relation between the words in the text can be mathematically calculated.
Leave a Reply