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workshop-local-rag

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Workshop on development of a Local Retrieval Augmented Generation (RAG) system

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[![logo.png](https://raw.githubusercontent.com/aihpi/workshop-local-rag/main/images/logo.png)](https://hpi.de/en/research/hpi-data-center/ai-service-center/)

---

# 02-07-2025 UPDATE!!! This repository has been merged into [RAGSST](https://github.com/aihpi/ragsst) and will no longer be supported.

---

#

# Ask your documents!

## Implement a Retrieval Augmented Generation (RAG) system to query and retrieve information from your local documents efficiently.

## Hands-on Workshop.

![Local RAG](https://raw.githubusercontent.com/aihpi/workshop-local-rag/main/images/ragshow.gif)

## Gain practical experience with [embeddings](https://github.com/aihpi/kisz-nlp-embeddings), vector databases, and local Large Language Models (LLMs)

**Repository:** [https://github.com/aihpi/workshop-local-rag](https://github.com/aihpi/workshop-local-rag)

![Flowchart](https://raw.githubusercontent.com/aihpi/workshop-local-rag/main/images/ragflowchart.png)

---

## Getting Started

### Download or clone the repository

### Installation

Install it step by step
(or check Auto Installation for a single command)

#### Create and activate a virtual environment

```shell
$ python3 -m venv myvenv
$ source myvenv/bin/activate
```

#### Install dependencies

```shell
$ pip3 install -r requirements.txt
```

#### Ollama

Install to run large language models locally.

```shell
$ curl -fsSL https://ollama.ai/install.sh | sh
```

Or follow the installation instructions for your operating system: [Install Ollama](https://ollama.com/download)

Choose and download an LLM model. For example:

```shell
$ ollama pull llama3.2
```
---

### Auto Installation

Alternatively, on bash, run the following installation script:

```shell
$ bin/install.sh
```

---

## Usage

```shell
(myvenv)$ python3 local-rag-gui.py
```

And open the exposed link with your browser for the Graphical User Interface version.

Or, run the following for the command line input version

```shell
(myvenv)$ python3 local-rag-cli.py
```

In case the LLM server is not running start it in a different terminal with:

```shell
$ ollama serve
```

#### Additional Input parameters on the Frontend

- Top k: Ranks the output tokens in descending order of probability, selects the first k tokens to create a new distribution, and it samples the output from it. Higher values result in more diverse answers, and lower values will produce more conservative answers. ([0, 10]. Default: 5)

- Top p: Works together with Top k, but instead of selecting a fixed number of tokens, it selects enough tokens to cover the given cumulative probability. A higher value will produce more varied text, and a lower value will lead to more focused and conservative answers. ([0.1, 1] Default: 0.9)

- Temp: This affects the “randomness” of the answers  by scaling the probability distribution of the output elements. Increasing the temperature will make the model answer more creatively. ([0.1, 1]. Default: 0.5)

---

## Development

Before commiting, format the code by using black as following on the project folder:

```shell
$ black -t py311 -S -l 99 .
```

You can Install Black with:

```shell
$ python3 -m pip install black
```

---

## License

[GPLv3](./LICENSE)

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