Docrag FAQs

Question 1

Is Docrag easy to set up and use?

Accepted Answer

Yes, Docrag is designed for ease of use. It's a single pip-installable Python package with a straightforward CLI. You can initialize it, add documentation collections from local paths or scraped web content, and start the MCP server with simple commands.

Question 2

What kind of documentation can I use with Docrag?

Accepted Answer

Docrag supports ingesting documentation from local files and performing smart web scraping from websites. It organizes these into 'project-based collections' and uses intelligent chunking to prepare them for efficient retrieval, regardless of the original format.

Question 3

What is Docrag and its primary purpose?

Accepted Answer

Docrag is a lightweight, installable Python RAG (Retrieval Augmented Generation) system designed to empower large language models (LLMs), particularly Claude Code, to access and retrieve technical documentation on-demand through an MCP (Model Context Protocol) server. It acts as an external knowledge base for AI.

Question 4

How does Docrag integrate with Claude Code?

Accepted Answer

Docrag integrates seamlessly with Claude Code by providing an MCP server. Once configured in Claude Code's settings, it exposes tools like 'search_docs' and 'list_collections', allowing Claude to query and retrieve relevant documentation snippets directly during its reasoning process.

Question 5

What are the core technical components of Docrag?

Accepted Answer

Docrag utilizes LanceDB as an efficient local vector database for storing embeddings, generated using sentence-transformers (all-MiniLM-L6-v2). It features intelligent document indexing, a user-friendly CLI for management, and an MCP server for LLM interaction.

Docrag

Docrag

Key Features

Use Cases

Key Features

Use Cases