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https://github.com/philippmos/taiste

This project is a playground how to use Semantic Kernel with LLMs
https://github.com/philippmos/taiste

dotnet llm mongodb semantickernel

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This project is a playground how to use Semantic Kernel with LLMs

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README

          

# tAIste - Your Personalized AI Cooking Companion

*tAIste* is an AI-powered recipe assistant designed to help users create personalized recipes, automatically generate shopping lists, and follow step-by-step cooking instructions – all tailored to individual preferences using the power of generative AI.

Built with Microsoft's Semantic Kernel and OpenAI technologies, tAIste enables a fully dynamic and intelligent cooking experience.

## Local LLM Setup with LM Studio

This project demonstrates how to use Semantic Kernel with local Large Language Models (LLMs) using LM Studio.

### Setting Up LM Studio

[LM Studio](https://lmstudio.ai) provides a desktop application for running various local LLMs.

1. **Install LM Studio**
- Download and install from [lmstudio.ai](https://lmstudio.ai)

2. **Configure LM Studio**
- Open LM Studio
- Browse and download a model (fe. `qwen2.5-7b-instruct-1m`)
- Select the model
- Go to the "Developer" tab
- Click "Start Server" to launch the OpenAI-compatible API on port 1234
- The server now runs on `http://localhost:1234/v1`

## Start the local docker setup

```bash
docker-compose up -d
```

The docker-compose setup will handle all necessary dependencies and configurations automatically. Make sure [Docker](https://docs.docker.com/get-docker/) and [Docker Compose](https://docs.docker.com/compose/install/) are installed on your system before running these commands.

After all services started successfully, Open-WebUI can be accessed via `http://localhost:8181`.
The model with name `dotnetapi` should already be automatically selected.