Have you ever received a long contract or technical document and wished you could just ask it a question?

I’ll walk you through how to build a simple AI-powered PDF Chatbot using Python, Streamlit, LangChain, and OpenAI. We’ll use a hardware manufacturing rate contract as a real-world example to showcase how the chatbot can answer natural language questions from a dense business document.

What This Chatbot Does:

1. Upload any PDF file (e.g., contract or report)

2. It extracts and splits the text intelligently

3. You can ask natural language questions like: “What’s the unit price of an industrial router?”, “What’s the penalty for late delivery?”

The chatbot answers using GPT, but the answers are grounded strictly in the content of the uploaded PDF - thanks to retrieval-augmented generation (RAG).

Tech Stack:

• Python

• Streamlit: Provided interface for interacting with the app.

• PyPDF2: Handled PDF parsing to extract and prepare textual content.
  When a user uploads a contract PDF, PyPDF2 pulls out the text so it can be indexed and queried.

• LangChain: Managed prompt templates and retrieval logic for streamlined LLM interactions.

• OpenAI API: Interprets user questions and generates natural, context-aware responses.

• FAISS: Finds the most relevant parts of the document by meaning, not just keywords.

    If user asks, “When can this contract be canceled?”, FAISS retrieves semantically similar chunks, even if the word “cancel” isn't used in the doc.

I tried it with a 173-page Rate Contract. Let’s imagine we’re working with a Rate Contract PDF for a hardware manufacturing company. It includes pricing, payment terms, warranty conditions, and delivery timelines.

Libraries required: Build the frontend (Streamlit), Read PDFs (Example: PyPDF2), Embed and retrieve text (LangChain + FAISS), Chat using GPT (OpenAI) 

Load OpenAI API Key:

Set Up the Streamlit App:
We display a simple title and use centered layout.

Upload the PDF:
Let’s say we upload Rate_Contract_HardwareCorp.pdf.

Extract Text from PDF:
This function goes through each page and combines all the text.

Chunk the Text:
Chunking makes the text manageable for GPT, while overlap preserves context across sections.

Process the PDF:
Once the PDF is uploaded, we extract and chunk the text.

Embed and Store Chunks:
Each chunk is turned into a vector (semantic representation) using OpenAI, then stored in a FAISS index.

Create a Retrieval-Based QA Chain:
This connects vector DB with GPT to create a RAG pipeline - answers come only from the PDF content.