https://github.com/amiorhmn/rag-chatbot-from-scratch
A command-line RAG Chatbot application built from scratch
https://github.com/amiorhmn/rag-chatbot-from-scratch
chatbot ollama rag rag-application rag-chatbot rag-implementation scratch-implementation
Last synced: 2 months ago
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A command-line RAG Chatbot application built from scratch
- Host: GitHub
- URL: https://github.com/amiorhmn/rag-chatbot-from-scratch
- Owner: amiorhmn
- Created: 2025-03-16T14:41:42.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2025-03-16T18:08:14.000Z (2 months ago)
- Last Synced: 2025-03-16T18:55:47.495Z (2 months ago)
- Topics: chatbot, ollama, rag, rag-application, rag-chatbot, rag-implementation, scratch-implementation
- Language: Python
- Homepage:
- Size: 9.77 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# RAG-Chatbot-from-Scratch
A simple command-line RAG (Retieval Augmented Generation) chatbot application built from scratch. No external framework or library has been used except *requests* and *scikit-learn* modules. It uses a locally hosted LLM via *Ollama* to generate embedding and response to user's query based on relevant information from a text file.
## Features
- Implementation of RAG from scratch without any third party framework
- Uses locally hosted open-source LLM and embedding model
- Advanced prompting for LLM input
- Command-line interface
- Handling of error during document loading
- Exit program via `KeyboardInterrupt` or `exit` and `quit` keywords
## Conversation Example
```
(.venv) PS C:\Users\Amio\Desktop\RAG-Chatbot-from-Scratch> python main.py
Please enter the path of your document: cat-facts.txt
Document loaded with 150 entries.
Ask a question: What do cats eat?
Chatbot: According to the context, cats eat about five mice per meal.
Ask a question: How long does a cat live?
Chatbot: According to the provided context, the average life span of a domestic cat is 14 years.
Ask a question: quit
(.venv) PS C:\Users\Amio\Desktop\RAG-Chatbot-from-Scratch>
```
## Requirements
- *Ollama* for hosting open-source embedding and large language models
- *Requests* library for communicating with the models
- *Scikit-learn* library for calculating `cosine-similarity` during retrieval step
This specific example uses *Gemma3* model for response generation and *all-minilm* for embedding generation. The models can be changed from the `utils.py` file.
## Structure of the code
The application contains two files:
- main.py
- utils.py
- `get_embeddings`
- `retrieve`
- `generate_response`
The `utils.py` contains three functions. Each of them does a specific task in the RAG pipeline. The app is run from the `main.py` file.
## How to run
1. Install *Ollama* from [ollama.com](https://ollama.com)
2. Download an embedding model and a text model (e.g. *all-minilm* and *Gemma3*)
```
ollama pull all-minilm
ollama pull gemma3
```
3. Clone the repository
```
git clone https://github.com/amiorhmn/RAG-Chatbot-from-Scratch.git
cd RAG-Chatbot-from-Scratch
```
4. Create and activate a virtual environment
```
python -m venv .venv
source .venv/bin/activate # On Windows, use `.venv\Scripts\activate`
```
5. Install the requirements
```
pip install -r requirements.txt
```
6. Run the app
```
python main.py
```