https://github.com/djpapzin/rag-based-support-agent
A RAG-based support agent that automatically learns from resolved Zendesk tickets to provide intelligent responses to new support queries. Built with LangChain and HuggingFace embeddings.
https://github.com/djpapzin/rag-based-support-agent
huggingface langchain machine-learning python rag support-agent zendesk
Last synced: 9 months ago
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A RAG-based support agent that automatically learns from resolved Zendesk tickets to provide intelligent responses to new support queries. Built with LangChain and HuggingFace embeddings.
- Host: GitHub
- URL: https://github.com/djpapzin/rag-based-support-agent
- Owner: djpapzin
- Created: 2025-04-03T19:07:45.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-04-03T19:38:00.000Z (9 months ago)
- Last Synced: 2025-04-09T16:16:28.478Z (9 months ago)
- Topics: huggingface, langchain, machine-learning, python, rag, support-agent, zendesk
- 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-based Support Agent
A RAG (Retrieval-Augmented Generation) based support agent that learns from resolved Zendesk tickets to provide accurate responses to new support queries.
## Features
- Fetches resolved tickets from Zendesk
- Builds a knowledge base using RAG
- Processes open tickets and suggests responses
- Interactive mode for testing responses
- Environment variable configuration for sensitive data
## Setup
1. Clone the repository:
```bash
git clone https://github.com/djpapzin/rag-based-support-agent.git
cd rag-based-support-agent
```
2. Create and activate a virtual environment:
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
4. Set up environment variables:
```bash
cp .env.example .env
```
Edit `.env` with your configuration values.
## Usage
1. Process resolved tickets to build knowledge base:
```bash
python -m src.main
```
2. Enter interactive mode to test responses:
```bash
python -m src.main --interactive
```
## Configuration
The following environment variables can be configured in `.env`:
- `ZENDESK_API_URL`: Your Zendesk API URL
- `ZENDESK_API_KEY`: Your Zendesk API key
- `OPENAI_API_KEY`: Your OpenAI API key
- `HUGGINGFACE_API_KEY`: Your Hugging Face API key
- `VECTOR_STORE_PATH`: Path to store vector embeddings (default: "data/vector_store")
## License
MIT License