LocalRAG

🧠 AI-Powered Document Chunking and Querying Project

This project is designed to update a database with new documents, split them into smaller chunks, and query the database using a given query text to generate a response based on the most relevant chunks retrieved. The core features of this project include document chunking, database updating, and querying using a vector database and pre-trained language models. The project aims to provide an efficient and effective way to manage and query large documents.

🚀 Features

• Document chunking: splits documents into smaller chunks based on a specified size
• Database updating: updates the database with new document chunks
• Querying: queries the database using a given query text to generate a response
• Vector database: uses a Chroma vector database to store and query document chunks
• Pre-trained language models: uses pre-trained language models for generating responses
• Embeddings generation: generates embeddings for document chunks and query texts

🛠️ Tech Stack

• langchain.vectorstores for Chroma vector database
• langchain.text_splitter for RecursiveCharacterTextSplitter
• langchain.document_loaders.pdf for PyPDFDirectoryLoader
• Embeddings for embeddings generation
• transformers for loading pre-trained language models
• torch for GPU acceleration and tensor operations
• sentence_transformers for natural language processing tasks
• huggingface_hub for downloading models from the Hugging Face Hub
• pathlib for handling file paths
• os for interacting with the operating system
• dotenv for loading environment variables

📦 Installation

To install the project, follow these steps:

1. Clone the repository using git clone
2. Install the required dependencies using pip install -r requirements.txt Additionally, you need to download the required models using the following scripts:

• installations/mistral_install.py or
• installations/phi2_download.py (whichever compatible)
• installations/embeddings_install.py

💻 Usage

To use the project, follow these steps:

1. Update the database with new documents using update_db.py
2. Query the database using a given query text using query_rag.py
3. Evaluate the response generated by query_rag.py using eval_resp.py

📂 Project Structure

.
├── update_db.py
├── query_rag.py
├── eval_resp.py
├── Embeddings.py
├── installations
│   ├── mistral_install.py
│   ├── phi2_download.py
│   ├── embeddings_install.py
├── models
│   ├── mistral
│   ├── phi-2
│   ├── all-MiniLM-L12-v2