Framewrok for RAG (FRAG)

⚠️ Important Note

This project is still under active development and should be considered in a pre-release state. It has not been thoroughly tested yet, and some features may be incomplete or unstable. Use it at your own discretion and report any issues or bugs.

Contributions, feedback, and suggestions are welcome as we work toward a stable release!

RAG Framework

This repository contains a Retrieval-Augmented Generation (RAG) framework for efficient information retrieval and natural language generation.

Repository Structure

.
├── LICENSE
├── cl-tools
│   ├── __init__.py
│   ├── chat.py
│   └── search.py
├── config
│   ├── __init__.py
│   ├── embedding_config.py
│   └── llm_config.py
├── django-server
│   ├── manage.py
│   ├── rag_app
│   │   ├── admin.py
│   │   ├── apps.py
│   │   ├── migrations
│   │   │   └── __init__.py
│   │   ├── models.py
│   │   ├── static
│   │   │   └── rag_app
│   │   │       ├── css
│   │   │       │   └── styles.css
│   │   │       └── images
│   │   │           └── frag.jpg
│   │   ├── templates
│   │   │   └── rag_app
│   │   │       ├── base.html
│   │   │       ├── chat.html
│   │   │       ├── home.html
│   │   │       └── search.html
│   │   ├── tests.py
│   │   ├── urls.py
│   │   └── views.py
│   └── rag_server
│       ├── asgi.py
│       ├── settings.py
│       ├── urls.py
│       └── wsgi.py
├── docs
│   └── diagram
│       └── RAG_Framework.svg
├── embedding
│   ├── __init__.py
│   ├── utils.py
│   └── vector_db_setup.py
├── llm
│   └── main.py
├── readme.md
├── requirement.txt
├── retrieval
│   ├── main.py
│   └── simple_query.py
└── tests

How to Get Started

Prerequisites:

• Ollama server. Install from https://ollama.com/

Steps:

1. Create virtual environment:

   python -m venv venv

2. Activate virtual environment:

   source venv/bin/activate # For Linux/macOS
   venv\Scripts\activate # For Windows

3. Install packages:

   pip install -r requirement.txt

4. Add a vector database:

   • Edit the file config/embedding_config.py where you can add the path for the data that needs to be used for the embedding vector database.

   Here's a breakdown of the editable parameters in the file:

   • model_name: This specifies the pre-trained model used for creating the embedding vectors. The example shows "Lajavaness/bilingual-embedding-large", but you can choose a different model name depending on your needs.
   • vector_db: This defines the type of vector database to use. Currently, only 'chromaDB' is supported.
   • collection_name: This specifies the name of the collection within the vector database where the embeddings will be stored. You can choose a name that suits your project.
   • raw_db: This is the root directory where your raw documents are stored. Edit this path to point to your actual data location. For example: raw_db = "/path/to/my/data"
   • data_language: This specifies the language of your data. The file provides a list of supported languages. Choose the one that matches your data.
   • db_directory: This defines the location where the vector database will be stored. By default, it's set to the user's home directory under a .db folder. You can change this path to a different location if needed.
   • chunk_size: This determines the number of sentences processed together when creating the vector database. You can adjust this value based on your data size and hardware capabilities.

   Example embedding_config.py (Remember to adapt these values to your specific setup):

   import os
   
   model_name = "Lajavaness/bilingual-embedding-large"  
   
   vector_db = "chromaDB"
   
   collection_name = "my_rag_collection"
   
   raw_db = "/path/to/my/data"  # Replace with the actual path
   
   data_language = "english"
   
   db_directory = os.path.join(os.path.expanduser('~'), '.my_rag_db')
   
   chunk_size = 20

5. Create vector database:

   • After setting the data paths in embedding_config.py, run the following command to create the vector database:

   python embedding/vector_db_setup.py

   • This will create a Chroma vector database using the configurations you provided.

6. Additional configurations (optional):

   • The file config/llm_config.py allows you to configure the large language model (LLM) used for text generation. You can specify the LLM and potentially edit the prompts used for generating text.

7. Run the system:

   • Once the vector database is created, you can run the chat and search functionalities using either the Django web app or the command-line tools.

   • To run the Django app:

   python django-server/manage.py runserver

   • This will start the Django development server, allowing you to access the web interface for chat and search (usually at http://127.0.0.1:8000/ in your web browser).

   • To use the command-line tools:

   • The functionalities are likely defined in the cl-tools directory (chat.py and search.py). You can refer to those files to understand how to use the command-line interface for chat and search.