AI PDF RAG Chatbot
This project transforms static PDF documents into an interactive AI knowledge base. Users can upload documents and ask questions using natural language. The system processes the uploaded files, extracts and splits their content into manageable chunks, generates vector embeddings, and stores the document knowledge for semantic retrieval. When a user asks a question, the system retrieves the most relevant information and provides an AI-generated answer grounded in the uploaded documents.
Problem
Important information is often buried inside long PDFs, manuals, reports, and business documents. Finding specific information manually takes time and makes it difficult for users to quickly extract useful answers from their existing documentation.
Solution
A RAG-powered AI chatbot that converts uploaded PDF documents into a searchable knowledge base. The system processes and chunks documents, creates embeddings, retrieves relevant content based on the user's question, and uses AI to generate answers grounded in the document knowledge.
Key Features
Workflow
User uploads a PDF document
n8n receives and processes the uploaded file
PDF content is extracted using a document loader
Document text is split into smaller searchable chunks
Chunks are converted into vector embeddings
Embeddings are stored in the vector database
User asks a question about the uploaded document
Relevant document chunks are retrieved using semantic search
AI generates a context-aware answer using the retrieved information
The response is returned to the user
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