RAG Docs FAQs

Question 1

What is RAG Docs?

Accepted Answer

RAG Docs is an AI-powered Retrieval-Augmented Generation (RAG) system designed to provide intelligent question-answering over your documents and code repositories. It integrates directly with Google AI Studio for embeddings and LLM generation.

Question 2

How does 'Smart Query Routing' work in RAG Docs?

Accepted Answer

Smart Query Routing automatically classifies user questions and directs them to the most effective retrieval strategy. This includes combining documentation search, semantic code search, and multi-mode retrieval for highly accurate and contextual answers.

Question 3

What types of content can RAG Docs process?

Accepted Answer

It can process various text-based documents, including .txt and .md files. For Markdown, it uses hierarchical chunking to preserve document structure. It also offers semantic indexing and search for source code repositories.

Question 4

What are the integration options for RAG Docs?

Accepted Answer

RAG Docs provides dual API interfaces: a FastAPI-based REST API with automatic OpenAPI documentation and OpenAI-compatible endpoints, and a Model Context Protocol (MCP) server for seamless integration with MCP clients like Claude.

Question 5

How does RAG Docs leverage Google AI?

Accepted Answer

RAG Docs uses Google AI Studio for both creating document and query embeddings (text-embedding-004) and for generating comprehensive answers with its powerful LLM (gemini-1.5-flash), ensuring high-quality, relevant responses.

RAG Docs

RAG Docs

Características Principales

Casos de Uso

Características Principales

Casos de Uso