Azure SQL DB, Langchain and Chainlit

Sample RAG pattern using Azure SQL DB, Langchain and Chainlit as demonstrated in the #RAGHack conference. Full details and video recording available here: RAG on Azure SQL Server.

Architecture

Solution

The solution works locally and in Azure. The solution is composed of three main Azure components:

• Azure SQL Database: The database that stores the data.
• Azure Open AI: The language model that generates the text and the embeddings.
• Azure Functions: The serverless function to automate the process of generating the embeddings (this is optional for this sample)

Azure Open AI

Make sure to have two models deployed, one for generating embeddings (text-embedding-3-small model recommended) and one for handling the chat (gpt-4 turbo recommended). You can use the Azure OpenAI service to deploy the models. Make sure to have the endpoint and the API key ready. The two models are assumed to be deployed with the following names:

• Embedding model: text-embedding-3-small
• Chat model: gpt-4

Database

Note

Vector Functions are in Public Preview. Learn the details about vectors in Azure SQL here: https://aka.ms/azure-sql-vector-public-preview

To deploy the database, you can either the provided .NET 8 Core console application or do it manually.

To use .NET 8 Core console application move into the /database and then make sure to create a .env file in the /database folder starting from the .env.example file:

• MSSQL: the connection string to the Azure SQL database where you want to deploy the database objects and sample data
• OPENAI_URL: specify the URL of your Azure OpenAI endpoint, eg: 'https://my-open-ai.openai.azure.com/'
• OPENAI_KEY: specify the API key of your Azure OpenAI endpoint
• OPENAI_MODEL: specify the deployment name of your Azure OpenAI embedding endpoint, eg: 'text-embedding-3-small'

If you want to deploy the database manually, make sure to execute the script in the /database/sql folder in the order specifed by the number in the file name. Some files (020-security.sql and 060-get_embedding.sql) with have placeholders that you have to replace with your own values:

• $OPENAI_URL$: replace with the URL of your Azure OpenAI endpoint, eg: 'https://my-open-ai.openai.azure.com/'
• $OPENAI_KEY$: replace with the API key of your Azure OpenAI endpoint
• $OPENAI_MODEL$: replace with the deployment name of your Azure OpenAI embedding endpoint, eg: 'text-embedding-3-small'

Chainlit

Chainlit solution is in chainlit folder. Move into the folder, create a virtual environment and install the requirements:

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

or, on Windows:

python -m venv .venv
.venv/Script/activate
pip install -r requirements.txt

Then make sure to create a .env file in the /chainlit folder starting from the .env.example file and it with your own values, then run the chainlit solution:

chainlit run app.py

Once the application is running, you'll be able to ask question about your data and get the answer from the Azure OpenAI model. For example you can ask question on the data you have in the database:

Is there any session on Retrieval Augmented Generation?

You'll see that Langchain will call the function get_similar_sessions that behind the scenes connects to the database and excute the stored procedure web.find_sessions which perform vector search on database data.

The RAG process is defined using Langchain's LCEL Langchain Expression Language that can be easily extended to include more complex logic, even including complex agent actions with the aid of LangGraph, where the function calling the stored procedure will be a tool available to the agent.

Azure Functions (optional)

In order to automate the process of generating the embeddings, you can use the Azure Functions. Thanks to Azure SQL Trigger Binding, it is possible to have tables monitored for changes and then react to those changes by executing some code in the Azure Function itself. As a result it is possible to automate the process of generating the embeddings and storing them in the database.

In a perfect microservices architecture, the Azure Functions are written in C#, but you can easily create the same solutoin using Python, Node.js or any other supported language.

The Azure Functions solution is in the azure-functions folder. Move into the folder, then create a local.settings.json starting from the provided local.settings.json.example file and fill it with your own values. Then run the Azure Functions locally (make sure to have the Azure Function core tools installed):

func start

the Azure Function will monitor the configured tables for changes and automatically call the Azure OpenAI endpoint to generate the embeddings for the new or updated data.