Pdf-Rag FAQs

Question 1

What are the prerequisites to run Pdf-Rag?

Accepted Answer

You need Python 3.8 or later, uv for package management, Git, and optionally Cursor for MCP integration. See the README for detailed installation instructions.

Question 2

What are the main features of Pdf-Rag?

Accepted Answer

Key features include PDF document upload and processing, real-time processing status updates, semantic search across documents, MCP protocol support for AI integration, and a modern web interface.

Question 3

What is MCP and how does Pdf-Rag use it?

Accepted Answer

MCP (Model Context Protocol) allows Pdf-Rag to seamlessly integrate with AI tools. You can configure Cursor to use Pdf-Rag as a knowledge base, enabling context-aware suggestions and information retrieval.

Question 4

How does Pdf-Rag work?

Accepted Answer

It extracts text from PDFs, chunks the content, converts the chunks into embeddings, and stores them in a vector database. You can then search using semantic queries, either through the web interface or via MCP integration.

Question 5

What is Pdf-Rag?

Accepted Answer

Pdf-Rag is a system that allows you to upload, process, and query PDF documents semantically. It uses vector storage and the Model Context Protocol (MCP) for integration with AI tools like Cursor.

Pdf-Rag

Pdf-Rag

Key Features

Use Cases

Key Features

Use Cases