In the previous lab, a LangChain agent was created armed with tools to do vector lookups and concrete document id lookups via function calling. In this lab, the agent functionality needs to be extracted into a backend api for the frontend application that will allow users to interact with the agent.
This lab implements a backend API using FastAPI that exposes the LangChain agent functionality. The provided code leverages Docker containers and includes full step-by-step instructions to run and test the API locally as well as deployed to Azure Container Apps (leveraging the Azure Container Registry).
This lab also requires the data provided in the previous lab titled Load data into Azure Cosmos DB API for MongoDB collections as well as the populated vector index created in the lab titled Vector Search using vCore-based Azure Cosmos DB for MongoDB. Run all cells in both notebooks to prepare the data for use in this lab.
Note: It is highly recommended to use a virtual environment for all labs.
Please visit the lab repository to complete this lab.