Building a Local RAG System with Olama and Python

Introduction

The lecture demonstrates how to build a local RAG (Retrieval-Augmented Generation) system using Olama and Python.
Benefits of a local system:
- Security for sensitive documents (e.g., medical and financial).
- Avoids sharing personal data with online systems.

Goal: Interact with local documents (e.g., PDFs) using a local model.
Use Cases:
- Chat over private documents that the model hasn’t seen during its training.
- Example documents: resumes, lecture notes, books.

Longchain:
- An AI Python framework used to build AI applications.
- Abstracts the loading and processing of files for LLMs (Large Language Models).
Unstructured PDF Loader:
- Used to load PDF files for processing.
- Unstructured.io provides tools to load various file types.

Loading PDFs:
- Use Unstructured PDF Loader to extract content.
Chunking:
- Split content into chunks for processing.
- Set chunk size (e.g., 7,500 characters) and overlap to maintain context.
Embedding:
- Convert text into vector embeddings using Nomic Embed Text.
- Load embeddings into Chroma DB (or other vector databases).
Querying:
- Multiquery retriever to optimize user queries by generating variations.
- Retrieve and summarize relevant documents.

Ingesting PDFs:
- Install and import necessary libraries (Unstructured, Longchain).
- Load PDF files from local or online sources.
Vector Embeddings:
- Install and set up Nomic Embed Text and Chroma DB.
- Chunk the text while maintaining coherence to retrieve accurate answers.
Retrieval:
- Define a prompt template for generating varied questions based on user input.
- Use a retriever to query the vector database and pass context to the LLM.

The demonstrated system operates offline, ensuring data privacy.
A potential future project includes creating a user-friendly Streamlit app for easier interaction with the RAG system.