Product Introduction

1. Cloudflare AutoRAG is a fully managed Retrieval-Augmented Generation (RAG) pipeline service that automates the integration of context-aware AI into applications by connecting to data stored in Cloudflare R2.
2. The core value of AutoRAG lies in its ability to eliminate the complexity of building and maintaining RAG systems, handling indexing, vector storage, retrieval, and response generation through Cloudflare’s infrastructure.

Main Features

1. AutoRAG automatically processes data from R2 buckets, converting files (PDFs, HTML, images, etc.) into structured Markdown, chunking text, generating embeddings via Workers AI, and storing vectors in Vectorize for semantic search.
2. Continuous background synchronization ensures data freshness by reprocessing updated or new files in cyclical indexing jobs, maintaining relevance without manual intervention.
3. Integrated query workflows combine query rewriting, vector search, and LLM-powered response generation using Workers AI models, enabling real-time AI answers grounded in private data.

Problems Solved

1. Developers no longer need to manually stitch together fragmented tools (vector databases, embedding models, LLMs) or maintain complex pipelines, reducing development time and operational overhead.
2. The product targets developers and enterprises building AI-driven applications like support bots, internal knowledge bases, or semantic search tools that require accurate, up-to-date responses from proprietary data.
3. Typical use cases include customer support automation using dynamic documentation, real-time Q&A systems for internal teams, and AI-enhanced search interfaces for technical or domain-specific content.

Unique Advantages

1. Unlike self-built RAG solutions, AutoRAG provides a zero-code pipeline managed entirely by Cloudflare, including automatic data synchronization, embedding updates, and infrastructure scaling.
2. Unique innovations include built-in Markdown conversion for diverse file types, Browser Rendering API integration for webpage ingestion, and serverless execution via Workers AI for cost-efficient processing.
3. Competitive differentiation stems from tight integration with Cloudflare’s global network, enabling low-latency retrieval and generation, and consolidated billing for R2, Vectorize, and Workers AI usage.

Frequently Asked Questions (FAQ)

1. How do I connect existing data to AutoRAG? AutoRAG integrates directly with Cloudflare R2 buckets, allowing users to upload files (e.g., PDFs, HTML) into a designated bucket, which triggers automatic indexing and embedding generation.
2. What file formats does AutoRAG support? The service processes PDFs, text files, HTML, CSV, and images (via Workers AI’s vision-to-language conversion), with Markdown standardization ensuring consistent data formatting.
3. How does AutoRAG handle data updates? The system continuously monitors connected R2 buckets, reprocessing new or modified files in background cycles to update embeddings and maintain search relevance without manual reindexing.
4. Can I customize the LLM used for response generation? AutoRAG uses Workers AI’s default LLM for generation but allows users to select alternative models during setup, with future updates planned for fine-tuning and model swapping.
5. Is there a cost during the beta period? While AutoRAG itself is free during the open beta, usage of underlying Cloudflare services (R2 storage, Vectorize queries, Workers AI inference) follows standard billing based on resource consumption.