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https://github.com/dongdongunique/LLM_RAG
This repository implements a Retrieval-Augmented Generation (RAG) system using FAISS for vector-based retrieval and GPT for generative response. It is designed to process large datasets, index them with FAISS, and use GPT to answer queries with retrieved context.
https://github.com/dongdongunique/LLM_RAG
Last synced: 3 days ago
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This repository implements a Retrieval-Augmented Generation (RAG) system using FAISS for vector-based retrieval and GPT for generative response. It is designed to process large datasets, index them with FAISS, and use GPT to answer queries with retrieved context.
- Host: GitHub
- URL: https://github.com/dongdongunique/LLM_RAG
- Owner: dongdongunique
- Created: 2024-11-24T10:03:11.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2025-01-01T08:47:41.000Z (7 days ago)
- Last Synced: 2025-01-01T09:25:30.423Z (7 days ago)
- Language: Python
- Homepage:
- Size: 12.6 MB
- Stars: 5
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: Readme.md
Awesome Lists containing this project
- awesome_ai_agents - Llm_Rag - This repository implements a Retrieval-Augmented Generation (RAG) system using FAISS for vector-based retrieval and GPT for generative re… (Building / Datasets)
- awesome_ai_agents - Llm_Rag - This repository implements a Retrieval-Augmented Generation (RAG) system using FAISS for vector-based retrieval and GPT for generative re… (Building / Datasets)
README
# **RAG System with FAISS and GPT** (Homework for Advanced Database)
This repository implements a **Retrieval-Augmented Generation (RAG)** system using **FAISS** for vector-based retrieval and **GPT** for generative response. It is designed to process large datasets, index them with FAISS, and use GPT to answer queries with context retrieved from the documents.
## **Features**
- **Document Loading**: Load and preprocess datasets (e.g., CSV, plain text).
- **Embedding Generation**: Convert documents and queries into vector embeddings.
- **Efficient Retrieval**: Use FAISS for similarity search over large corpora.
- **GPT Integration**: Generate answers using GPT with context from retrieved documents.
- **Modular Design**: Easily extend the system with new vector stores, LLMs, or document loaders.
- **Interactive User Interface**: A Gradio-powered UI for easy interaction with the system. Supports:
- Uploading and viewing CSV files.
- Searching the indexed documents.
- Managing document chunks.
- Interacting with the system through intuitive input fields.
- **CRUD Operations**: Add, delete, update, and query document chunks in real-time.
---
## **Installation**
### **Requirements**
- Python 3.8 or higher
- FAISS
- OpenAI API Key
- Gradio (for the UI)
### **Dependencies**
Install the required packages using `pip`:
```bash
pip install -U -r requirements.txt
```
**`requirements.txt`:**
```plaintext
langchain
openai
faiss-cpu
python-dotenv
pandas
langchain-community
tiktoken
gradio
```
---
## **Usage**
### **1. Set Up Environment Variables**
Create a `.env` file in the project root directory and add your OpenAI API key:
```env
OPENAI_API_KEY=your_openai_api_key
```
### **2. Prepare Your Dataset**
- Use the provided example dataset (`amazon_products.csv`) or upload your own CSV dataset.
- Ensure the dataset contains a column with text-based content (e.g., `description`) to generate embeddings.
### **3. Run the System**
You can run the system in two modes:
#### **Command-Line Interface (CLI)**
Run the system interactively via CLI:
```bash
python main.py
```
This mode allows you to interact with the RAG system by asking questions and retrieving answers.
#### **Gradio User Interface**
Run the system with the Gradio-powered UI:
```bash
python app.py
```
This launches a web-based interface for uploading datasets, managing document chunks, and querying the RAG system.
---
## **Project Structure**
```plaintext
.
├── amazon_products.csv # Example dataset for testing
├── app.py # Gradio-based user interface
├── app.log # Log file for application events
├── dataset_cache.ipynb # Notebook for dataset caching or analysis
├── main.py # CLI entry point for the RAG system
├── rag_system/ # Main source code for the RAG system
│ ├── __init__.py
│ ├── config.py # Configuration settings (API keys, paths, etc.)
│ ├── core.py # Core logic for RAG system initialization
│ ├── loaders.py # Document loading and preprocessing
│ ├── llms.py # Integration with GPT or other LLMs
│ ├── utils.py # Utility functions (e.g., splitting documents)
│ ├── vector_stores.py # FAISS and other vector store implementations
├── vector_store_index/ # Directory for storing FAISS index files
├── requirements.txt # Python dependencies
├── SearchQ.md # Markdown for documenting queries or use cases
├── README.md # Project documentation (this file)
├── .env # Environment variables for API keys
```
---
## **Gradio User Interface**
The Gradio UI provides an intuitive interface for interacting with the RAG system. It supports:
1. **Upload CSV Files**:
- Upload datasets containing documents for indexing.
- Automatically preprocesses and splits documents into chunks for embedding generation.
2. **Search Documents**:
- Enter natural language queries in the search box.
- The system retrieves the most relevant documents and generates a response using GPT.
3. **CRUD Operations**:
- Add new document chunks to the indexed dataset.
- Delete or update existing chunks based on specific criteria.
### **Starting the Gradio Interface**
Run the Gradio UI with:
```bash
python app.py
```
After launching, open the provided URL in a web browser to interact with the system.
---
## **Customization**
### **1. Vector Store**
The system uses **FAISS** for vector-based retrieval by default. To integrate another vector database (e.g., Pinecone, Weaviate, Milvus):
1. Create a new class in `rag_system/vector_stores.py` inheriting from `BaseVectorStore`.
2. Update the `Config.VECTOR_STORE_TYPE` in `config.py`.
### **2. LLM**
The system integrates with **OpenAI GPT**. To switch to another LLM (e.g., HuggingFace models):
1. Add a new class in `rag_system/llms.py` inheriting from `BaseLLM`.
2. Update `Config.LLM_TYPE` in `config.py`.
### **3. Document Loaders**
To support additional formats (e.g., PDFs, JSON):
1. Add a new class in `rag_system/loaders.py` inheriting from `BaseDocumentLoader`.
2. Update the `load_documents` function to detect and handle the new format.
### **4. Gradio UI**
The UI is modular and can be extended. To add new components:
1. Modify `app.py` to include new Gradio widgets.
2. Update the callback functions to handle the added functionality.
---
## **Logging**
The system logs important events (e.g., errors, indexing operations) in `app.log`. Check this file for debugging or monitoring purposes.
---
## **Future Improvements**
- **Distributed Vector Stores**: Add support for scalable vector stores like Pinecone or Weaviate.
- **Advanced Query Features**: Implement query expansion, semantic search, and ranking.
- **Custom Embeddings**: Allow users to upload precomputed embeddings.
- **User Authentication**: Add authentication and access control for the Gradio interface.
- **Visualization**: Display results with data visualizations (e.g., charts for document relevance scores).
- **Batch Processing**: Optimize retrieval and generation for bulk queries.
---
## **Contributing**
Contributions are welcome! To contribute:
1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Submit a pull request with detailed explanations of your changes.
---
## **License**
This project is licensed under the [MIT License](https://opensource.org/licenses/MIT).
---
## **Acknowledgments**
- [FAISS](https://github.com/facebookresearch/faiss): Efficient similarity search.
- [OpenAI](https://openai.com/): Embedding and generative APIs.
- [LangChain](https://langchain.readthedocs.io/): Framework for building applications with LLMs.
- [Gradio](https://gradio.app/): User-friendly interface for ML applications.