This notebook demonstrates a prototype that:
- Parses uploaded PDFs
- Embeds the content using OpenAI
- Stores it in Pinecone
- Answers questions via LangChain
The purpose of this project is to create an AI-powered system that can answer natural language questions about the contents of any uploaded PDF document. Here's what each component does and why it matters:
📌 Project Purpose Summary Goal: Let users upload a PDF → the system reads it → stores the content in a searchable way → users can then ask questions in natural language and get intelligent answers.
🔍 Detailed Component Breakdown 📄 Parse uploaded PDFs
Why: To extract readable text from PDF files (e.g., reports, research papers, documentation).
How: Use libraries like PyPDF2 or pdfplumber.
🧠 Embed the content using OpenAI
Why: To convert text into numerical vectors that capture meaning/context, enabling semantic search (not just keyword match).
How: Use OpenAI's embedding models like text-embedding-ada-002.
🗂️ Store in Pinecone
Why: To efficiently store and search large volumes of embedded data using vector search.
How: Pinecone acts as a high-performance vector database.
💬 Answer questions via LangChain
Why: LangChain connects everything and uses the OpenAI language model to generate answers based on relevant chunks from the PDF.
How: Combines retrieved chunks with the user’s question to generate a meaningful answer.
🧠 Real-World Use Cases: Legal teams searching case files
Students asking questions about lecture PDFs
Researchers querying large academic papers
Enterprises building knowledge assistants over documentation