https://github.com/typek22/doggo-rag-chatbot
A Streamlit GenAI chatbot that uses retrieval-augmented generation (RAG) to teach a public LLM respond questions about my dog. Powered by OpenAIs gpt-4o and embeddings model and embedding-based search (ChromaDB), this app offers real-time interactions with personalized answers.
https://github.com/typek22/doggo-rag-chatbot
ai chromadb embeddings genai genai-chatbot llm openai python rag streamlit
Last synced: 7 months ago
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A Streamlit GenAI chatbot that uses retrieval-augmented generation (RAG) to teach a public LLM respond questions about my dog. Powered by OpenAIs gpt-4o and embeddings model and embedding-based search (ChromaDB), this app offers real-time interactions with personalized answers.
- Host: GitHub
- URL: https://github.com/typek22/doggo-rag-chatbot
- Owner: typek22
- License: mit
- Created: 2025-01-30T17:55:38.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-01-30T18:11:03.000Z (9 months ago)
- Last Synced: 2025-01-30T18:36:38.242Z (9 months ago)
- Topics: ai, chromadb, embeddings, genai, genai-chatbot, llm, openai, python, rag, streamlit
- Language: Python
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Doggo RAG Chatbot 🐶
A simple streamlit chatbot to showcase functioning of Retrieval Augmented Generation (RAG) and how to integrate it with
a public LLM to create custom chatbot with a specific knowledge.
I write about this code in my LinkedIn article https://www.linkedin.com/pulse/creating-custom-chatbot-based-rag-llm-ivan-magdolen-splfe
- 1st article
- branch "part_1"
- 2nd article
- branch "part_2"
## Features
- You can chat with custom chatbot who knows data about Zaira (my dog)
## Requirements
- Python 3.8 or higher
- see requirements.txt
## Usage
### Clone and run virtual environment
```bash
$ git clone https://github.com/doggo-rag-chatbot.git
$ cd doggo-rag-chatbot
$ python -m venv .
$ ./Scripts/activate
$ pip install -r requirements.txt
```
### Set you API key in environment variables
```python
OPEN_AI_KEY='mykey'
```
### Create embeddings database
```shell
$ python embeddings_init.py
```
### Run the chatbot
```shell
$ streamlit run streamlit-app.py
```
### And go to
```shell
http://localhost:8501
```