https://github.com/kts-o7/graph-rca
Minor Project Repo
https://github.com/kts-o7/graph-rca
Last synced: about 2 months ago
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Minor Project Repo
- Host: GitHub
- URL: https://github.com/kts-o7/graph-rca
- Owner: KTS-o7
- Created: 2025-01-09T16:37:25.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-02-21T14:56:44.000Z (2 months ago)
- Last Synced: 2025-02-21T15:26:22.902Z (2 months ago)
- Language: Python
- Size: 161 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Log Analysis & Incident Resolution System
This application provides automated log analysis and incident resolution using AI. It processes log files, generates insights, and provides solutions based on your documentation.
## Features
- 📊 Automated log file analysis
- 🔍 Root cause identification
- 📝 Documentation integration for context-aware solutions
- 🤖 AI-powered incident resolution
- 📈 Causal chain visualization
- 🗄️ Persistent storage of analysis results
## Prerequisites
- Docker and Docker Compose
- NVIDIA GPU (optional, for better performance)
- Python 3.8+
## Installation
1. Clone the repository:
```bash
git clone https://github.com/KTS-o7/graph-rca.git
cd graph-rca
```
2. Create a Python virtual environment and activate it:
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
## Running the Application
1. Start the required services using Docker Compose:
```bash
docker-compose up -d
```
This will start:
- ChromaDB (Vector Database) on port 8000
- MongoDB on port 27017
- Ollama (Local LLM) on port 11435
2. Check available Ollama models:
```bash
docker exec -it $(docker ps -qf "name=ollama") ollama list
```
3. Install the ollama model:
```bash
docker exec -it $(docker ps -qf "name=ollama") ollama pull llama3.2:3b
```
4. Run the Streamlit application:
```bash
streamlit run frontend/app.py
```
The application will be available at `http://localhost:8501`
## Usage
1. **Upload Log File**
- Click on "Upload Log File" expander
- Select your log file (.log or .txt)
- The system will automatically analyze the log and display a summary
2. **Add Documentation**
- Click on "Add Documentation" expander
- Upload relevant documentation files (.txt or .md)
- These documents will be used as context for generating solutions
3. **Get Automated Solutions**
- Once both log and documentation are processed
- The system will automatically generate solutions based on the analysis
- View root cause analysis and recommended solutions
## Environment Variables
The following environment variables can be configured in the `docker-compose.yaml`:
```yaml
MONGO_INITDB_ROOT_USERNAME=admin
MONGO_INITDB_ROOT_PASSWORD=password
```
## Troubleshooting
- If the GPU is not detected, ensure NVIDIA drivers and CUDA are properly installed
- For database connection issues, verify that all Docker containers are running:
```bash
docker-compose ps
```
- Check container logs for specific issues:
```bash
docker-compose logs
```
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.