https://github.com/neondatabase/sql-query-assistant
Building an Intelligent SQL Query Assistant with Neon, .NET, Azure Functions, and Azure OpenAI service
https://github.com/neondatabase/sql-query-assistant
azure azure-functions neondb openai sql
Last synced: about 2 months ago
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Building an Intelligent SQL Query Assistant with Neon, .NET, Azure Functions, and Azure OpenAI service
- Host: GitHub
- URL: https://github.com/neondatabase/sql-query-assistant
- Owner: neondatabase
- Created: 2025-01-08T13:28:15.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-15T16:19:43.000Z (about 1 year ago)
- Last Synced: 2026-01-29T22:53:53.891Z (2 months ago)
- Topics: azure, azure-functions, neondb, openai, sql
- Language: C#
- Homepage: https://neon.tech/blog/building-sql-query-assistant-with-dotnet-azure-functions-openai
- Size: 172 KB
- Stars: 3
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Intelligent SQL Query Assistant
This project demonstrates how to build an **Intelligent SQL Query Assistant** that enables users to interact with their **Neon** database using natural language. The assistant uses **Azure OpenAI**, **pgvector** extension in Neon, and **Retrieval-Augmented Generation (RAG)** approach to dynamically generate and execute SQL queries. Read more on [how to guide blog post](https://neon.tech/blog/building-sql-query-assistant-with-dotnet-azure-functions-openai).
## Features
- **Natural Language Querying**: Ask questions in plain English, and the assistant translates them into SQL queries.
- **Vector-Based Schema Matching**: Uses embeddings and Neon's `pgvector` extension to find the most relevant database schema.
- **Customizable APIs**:
- **SchemaTraining API**: Extracts the database schema, generates embeddings, and stores them in Neon.
- **QueryAssistant API**: Processes user queries, matches the schema, generates SQL, and executes it.
- **Azure OpenAI Integration**: Leverages GPT models like `gpt-4` and `text-embedding-ada-002`.
---
## Technologies Used
- **.NET Core**: Backend logic and API development.
- **Azure Functions**: Serverless platform for API hosting.
- **Neon**: Vector storage using the [pgvector](https://neon.tech/docs/extensions/pgvector) extension.
- **Azure OpenAI**: For generating embeddings and SQL queries.
- **Azure AI Foundry**: For deploying and managing AI models.
---
## Getting Started
### Prerequisites
Before we begin, make sure you have the following:
1. **Install Tools**
- .[NET Core SDK](https://dotnet.microsoft.com/en-us/download/dotnet)
- [Azure Functions Core Tools](https://learn.microsoft.com/en-us/azure/azure-functions/functions-run-local?tabs=macos%2Cisolated-process%2Cnode-v4%2Cpython-v2%2Chttp-trigger%2Ccontainer-apps&pivots=programming-language-csharp) installed
- A free [Neon account](https://console.neon.tech/signup)
- An [Azure account](https://azure.microsoft.com/free/) with an active subscription
2. **Create Neon Project**
- Sign up for [Neon](https://neon.tech/).
- Create the database tables
3. **Azure OpenAI Resource**
- Create an Azure OpenAI resource.
- Deploy models: `gpt-4` and `text-embedding-ada-002`.
---
### Project Structure
```
SqlQueryAssistant
│ SqlQueryAssistant.sln
|
├───SqlQueryAssistant.Common
│ │ ChatCompletionService.cs
│ │ EmbeddingService.cs
│ │ SchemaService.cs
│ │ SchemaConverter.cs
│ │ SqlExecutorService.cs
│ │ SchemaRetrievalService.cs
│ │ VectorStorageService.cs
│ └───SqlQueryAssistant.Common.csproj
│
├───SqlQueryAssistant.Data
| | customers.sql
| | schema.sql
└───SqlQueryAssistant.Functions
│ host.json
│ local.settings.json
│ QueryAssistantFunction.cs
└───SqlQueryAssistant.Functions.csproj
```
## Installation
### Clone the Repository
```bash
git clone https://github.com/neondatabase-labs/sql-query-assistant.git
cd sql-query-assistant
```
### Set Up Configuration
Create a `local.settings.json` file in the `SqlQueryAssistant.Functions` project:
```json
{
"IsEncrypted": false,
"Values": {
"AzureWebJobsStorage": "UseDevelopmentStorage=true",
"FUNCTIONS_WORKER_RUNTIME": "dotnet-isolated",
"AzureOpenAI__Endpoint": "https://your-azure-openai-endpoint/",
"AzureOpenAI__ApiKey": "your-api-key",
"AzureOpenAI__EmbeddingDeploymentName": "text-embedding-ada-002",
"NeonDatabaseConnectionString": "Host=your-neon-host;Database=your-database;Username=your-username;Password=your-password;SSL Mode=Require;Trust Server Certificate=true"
}
}
```
### Install Dependencies
```bash
dotnet restore build
```
### Run the Project Locally
```bash
func start
```
## Usage
### **SchemaTraining API**
- **Endpoint**: `http://localhost:7071/api/schema-training`
- **Method**: `POST`
- **Description**: Trains the assistant by extracting the schema from Neon, generating embeddings, and storing them in the database.
### Test with cURL:
```bash
curl -X POST http://localhost:7071/api/schema-training
```
---
### **QueryAssistant API**
- **Endpoint**: `http://localhost:7071/api/query-assistant`
- **Method**: `POST`
- **Description**: Processes user queries, retrieves the relevant schema, generates SQL, and returns the query result.
### Test with cURL:
```bash
curl -X POST http://localhost:7071/api/query-assistant \
-H "Content-Type: application/json" \
-d '{"Query": "List all customers who signed up after 2022-01-01."}'
```