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https://github.com/anthonypuppo/sk-nl2ef-plugin
A ChatGPT plugin built with Semantic Kernel that queries a database via natural language. Winner of Microsoft's first Semantic Kernel hackathon in the "Most Useful for the Enterprise" category.
https://github.com/anthonypuppo/sk-nl2ef-plugin
ai artificial-intelligence chatgpt entity-framework llm netcore openai openai-plugin semantic-kernel
Last synced: 27 days ago
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A ChatGPT plugin built with Semantic Kernel that queries a database via natural language. Winner of Microsoft's first Semantic Kernel hackathon in the "Most Useful for the Enterprise" category.
- Host: GitHub
- URL: https://github.com/anthonypuppo/sk-nl2ef-plugin
- Owner: anthonypuppo
- License: mit
- Created: 2023-07-25T05:17:39.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2023-09-13T02:05:13.000Z (10 months ago)
- Last Synced: 2024-04-16T02:17:13.115Z (3 months ago)
- Topics: ai, artificial-intelligence, chatgpt, entity-framework, llm, netcore, openai, openai-plugin, semantic-kernel
- Language: C#
- Homepage:
- Size: 1.22 MB
- Stars: 39
- Watchers: 3
- Forks: 7
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome-semantickernel - NL2EF - in service that can expose an existing database to be queried via natural language (Plugins)
- awesome-ChatGPT-repositories - sk-nl2ef-plugin - A ChatGPT plugin built with Semantic Kernel that queries a database via natural language. Winner of Microsoft's first Semantic Kernel hackathon in the "Most Useful for the Enterprise" category. (Browser-extensions)
README
# NL2EF
#### Plugin which translates a question into SQL, fetches relevant data from the database, and formulates a response based on the retrieved data
#### ⭐ Winner of Microsoft's first Semantic Kernel hackathon in the "Most Useful for the Enterprise" category.
## Semantic Kernel Plugin Hackathon Entry
This project is my entry to the Semantic Kernel Plugin Hackathon. It's designed to be a drop-in plugin service that can expose an existing database to be queried via natural language. It accomplishes this by leveraging the power of Entity Framework Core and OpenAI's embedding/GPT models to generate and construct SQL queries that retrieve relevant information for RAG based responses.
The demo connects to a modified version of the SQLite movies database available [here](https://www.kaggle.com/datasets/luizpaulodeoliveira/imdb-project-sql).
Via Chat Copilot:
Via Swagger:
## Built With
- [Semantic Kernel](https://github.com/microsoft/semantic-kernel)
- .NET 7 Minimal Web APIs
- Visual Studio Code (C# and C# Dev Kit Extensions)
## How It Works
1. **Database Creation Script Generation**: Use Entity Framework Core to generate the database creation script.
2. **Embedding Creation**: Create embeddings for each part of the database creation script.
3. **User Input Processing**: Take a user's input and get the most relevant parts of the database creation script that will help build a SQL query.
4. **SQL Query Construction**: Build the SQL query using a GPT model.
5. **Query Execution**: Run the query and attempt to retry and have the model fix its query if it fails.
6. **Response Formatting**: Format the response data as a CSV which the model can easily parse.
7. **Answer Generation**: Answer the user's question using the retrieved data for grounding (RAG).
## Future Enhancements
- Code cleanup
- Modifying the prompts to produce better results
- Moving hardcoded options to be environment configurable
## Pitfalls Before Moving to Production
- Ensure the user connecting to the database has the appropriate permissions (or lack thereof) to prevent SQL injection or users viewing data they shouldn't.
- Seed the kernel database schema memories as part of a preprocessing pipeline instead of every run.
- Be aware of responses overloading the model token window.
## Getting Started
To get a local copy up and running follow the below steps.
### Prerequisites
- .NET 7
- Visual Studio Code
- C# and C# Dev Kit Extensions
### Installation
1. Clone the repo
```sh
git clone https://github.com/anthonypuppo/sk-nl2ef-plugin.git
```
2. Install .NET packages
```sh
dotnet restore
```
3. Open `appsettings.json`
- Update the `SemanticKernel:AIService` configuration section:
- Update `Type` to the AI service you will be using (i.e., `AzureOpenAI` or `OpenAI`).
- If your are using Azure OpenAI, add/update `Endpoint` to your Azure OpenAI resource Endpoint address.
> If you are using OpenAI, this property will be ignored.
- Set your Azure OpenAI or OpenAI key by opening a terminal in the project directory and using `dotnet user-secrets`
```bash
dotnet user-secrets set "SemanticKernel:AIService:Key" "MY_AZUREOPENAI_OR_OPENAI_KEY"
```
4. Run the project
```sh
dotnet run
```
## Usage
Configure the DB context to expose the relevant parts of the database to the model. The service will automatically seed the database creation script embeddings at startup.
## Plugin Manifest
When running locally the plugin will be exposed at https://localhost:7012/.well-known/ai-plugin.json. CORS defaults to allowing ChatGPT as well as https://localhost:7012.