DevDocs RAG FAQs

Question 1

How does DevDocs RAG ensure privacy and support offline use?

Accepted Answer

DevDocs RAG operates entirely locally. Your documentation is indexed and searched on your machine using ChromaDB and a local embedding model (Nomic Embed Text V2), ensuring no data leaves your environment. This design enables complete offline functionality and data privacy.

Question 2

Can I add custom documentation sets beyond the default?

Accepted Answer

Absolutely. DevDocs RAG allows you to easily create and manage independent named collections for any documentation set. Simply get your docs as local HTML, PDF, or Markdown files, then use the provided ingestion scripts to add them to your local vector database.

Question 3

What is DevDocs RAG and how does it work with Claude Code?

Accepted Answer

DevDocs RAG integrates a local, RAG-powered documentation search engine directly into your Claude Code sessions. It indexes your documents locally using ChromaDB and a local embedding model, allowing Claude to perform semantic searches over your private knowledge base via MCP tools.

Question 4

What types of documentation can I ingest and search?

Accepted Answer

You can ingest diverse file types including HTML, PDF, and Markdown. DevDocs RAG supports creating multiple, isolated, and customizable documentation collections, allowing you to organize and search specific sets of guides, API references, or specs.

Question 5

Do I need external APIs or network access for DevDocs RAG?

Accepted Answer

No. DevDocs RAG is designed to be fully self-contained. All processes, from document ingestion and embedding to semantic search, run locally on your machine, eliminating the need for external APIs or network exposure during operation.

DevDocs RAG

DevDocs RAG

Key Features

Use Cases

Key Features

Use Cases