Understanding RAG by Building a ChatPDF App with NumPy (Part 1)

Summary and Key Takeaways

This article provides a detailed walkthrough of how to build a simple PDF chatbot using Python, NumPy, and local Large Language Models (LLMs) like Ollama. The goal is to understand the core principles behind Retrieval-Augmented Generation (RAG) systems, which combine semantic search with language generation to answer questions based on document content.

Key Components of the System

1. PDF Parsing: Extract text from PDF documents.
2. Embedding Text: Convert text into numerical vectors using an embedding model.
3. Vector Search: Use NumPy for simple vector similarity search.
4. Contextual Answer Generation: Feed relevant context to a language model to generate answers.

Steps Involved

1. PDF Parsing:

   • Utilize pdfplumber or similar libraries to extract text from PDF files.

2. Embedding Text:

   • Convert the extracted text into numerical vectors using an embedding model like Sentence Transformers (all-MiniLM-L6-v2).

3. Vector Search with NumPy:

   • Implement a simple similarity search using cosine similarity or dot product to find relevant chunks of text.

4. **Contextual Answer

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