https://github.com/tomtolleson/rag-system
A Retrieval-Augmented Generation (RAG) system using Milvus and LangChain
https://github.com/tomtolleson/rag-system
langchain milvus rag retrieval-augmented-generation
Last synced: 4 months ago
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A Retrieval-Augmented Generation (RAG) system using Milvus and LangChain
- Host: GitHub
- URL: https://github.com/tomtolleson/rag-system
- Owner: TomTolleson
- License: mit
- Created: 2024-12-14T15:19:56.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-12-16T13:56:58.000Z (10 months ago)
- Last Synced: 2025-04-03T05:45:03.116Z (6 months ago)
- Topics: langchain, milvus, rag, retrieval-augmented-generation
- Language: Python
- Homepage:
- Size: 72.3 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# RAG System with Milvus and LangChain
[](https://github.com/TomTolleson/RAG-System/actions/workflows/ci.yml)
[](https://codecov.io/gh/TomTolleson/RAG-System)
[](https://www.python.org/downloads/release/python-3120/)
[](https://opensource.org/licenses/MIT)
[](https://github.com/psf/black)
[](https://github.com/PyCQA/bandit)
[](https://github.com/astral-sh/ruff)
[](/LICENSE)
A Retrieval-Augmented Generation (RAG) system that combines Milvus vector database with LangChain and OpenAI for intelligent document querying and response generation.
## System Overview
This system implements RAG architecture to provide accurate, context-aware responses to questions by:
1. Storing document embeddings in Milvus vector database
2. Retrieving relevant context when queried
3. Generating human-like responses using OpenAI's language models
### Components
#### Vector Store (src/vector_store/milvus_store.py)
- Uses Milvus for efficient vector similarity search
- Stores document embeddings using OpenAI's embedding model
- Handles document addition and retrieval operations
- Configurable connection settings for Milvus database
#### RAG Chain (src/rag/rag_chain.py)
- Implements the core RAG logic
- Integrates OpenAI's ChatGPT for response generation
- Manages the retrieval-generation pipeline
- Configurable temperature and other LLM parameters
#### Document Processing (main.py)
- Handles document loading and chunking
- Configurable chunk size and overlap
- Supports text documents (expandable to other formats)
## Prerequisites
- Python 3.12+
- Docker and Docker Compose
- OpenAI API key
## Installation
1. Clone the repository:
```bash
git clone [repository-url]
cd RAG-System
```
2. Create 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
# Create .env file with your OpenAI API key
echo "OPENAI_API_KEY=your_key_here" > .env
```
5. Start Milvus services:
```bash
docker-compose up -d
```
## Usage
1. Add your documents to the data directory:
```bash
mkdir data
echo "Your document content here" > data/test_document.txt
```
2. Run the system:
```bash
python main.py
```
## Configuration
### Milvus Settings
- Host: localhost (default)
- Port: 19530 (default)
- Configurable in src/config/settings.py
### Document Processing
- Chunk size: 1000 (default)
- Chunk overlap: 0 (default)
- Configurable in src/config/settings.py
### LLM Settings
- Model: OpenAI ChatGPT
- Temperature: 0.2 (default)
- Configurable in src/config/settings.py
## Use Cases
This RAG system is ideal for:
- Document question-answering
- Knowledge base augmentation
- Contextual information retrieval
- Research assistance
- Technical documentation queries
## Docker Services
The system uses several Docker containers:
- Milvus standalone server
- Etcd for metadata storage
- MinIO for object storage
Access MinIO console:
- URL: http://localhost:9015
- Credentials: minioadmin/minioadmin
## Extending the System
The system can be extended with:
- Additional document types (PDF, HTML, etc.)
- Custom embedding models
- Alternative vector databases
- Web interface
- Batch processing
- Caching mechanisms
## Contributing
Contributions are welcome! Please feel free to submit pull requests.
## License
[Your license information here]
## References
- [Milvus Documentation](https://milvus.io/docs)
- [LangChain Documentation](https://python.langchain.com/docs/get_started/introduction)
- [OpenAI API Documentation](https://platform.openai.com/docs/introduction)