easyrag.com

What It Is

EasyRAG is RAG as a Service, designed to let developers add “upload documents — semantic search — question answering based on documents” capabilities to their applications without having to build their own vector database, document parsing, embedding, retrieval, or LLM orchestration stack. Its workflow includes document chunking, vectorization, storage in Qdrant, retrieval of relevant snippets at query time, and optional GPT-4-powered answer generation.

Core Capabilities and Integration

It supports PDF, Word, Excel, PowerPoint, CSV, as well as audio and video files such as mp3, wav, and mp4, with transcription capabilities included. The API covers file upload, file management, search, Query Chat, token creation, and more. It also provides JavaScript/TypeScript SDKs and React components, including FileUpload, SearchBox, ChatBox, and FileViewer. Frontend tokens support short-lived, dataset-scoped access, making them suitable for secure integration in browsers or mobile apps. Typical use cases include customer support bots, enterprise knowledge bases, document management systems, and educational Q&A over learning materials.

Pricing and Trial

Pricing is credit-based pay-as-you-go, with no subscription or per-seat fees. New accounts receive 20 credits. Uploading a file costs 1 credit per file, search or Q&A costs 0.1 credit per request, and transcription costs 0.1 credit per minute. Paid packages include 100 credits for $5, 500 credits for $20, and 1200 credits for $40. Enterprise plans can include custom SLA and support. This model is fairly friendly for small teams validating a RAG prototype.

Pros and Cons

The main advantages are its short onboarding path, with APIs and React components lowering the integration barrier; broad file type coverage; and support for multiple datasets, metadata filtering, and frontend tokens, which provides a foundation for multi-tenant applications. The limitations are also clear: it does not disclose the company’s location, payment methods, data storage regions, encryption, or retention policies. The underlying GPT-4 version, embedding model, and whether models can be switched are not specified. File list pagination has not yet been implemented, and webhooks are still on the roadmap. Chinese-language support is not explicitly guaranteed, so performance with Chinese documents needs to be tested in practice.

Who It’s For and Access from China

EasyRAG is a good fit for developer teams that want to quickly embed document Q&A into a SaaS product, internal tool, or customer support system. It is less suitable for enterprises with strict requirements around private model deployment, data residency, or highly controllable retrieval pipelines. Access from mainland China is not covered in the available information. Given the unclear API availability, GPT-4 dependency, and payment methods, it is advisable to use the free credits first to test network connectivity, latency, and payment feasibility. Alternatives to compare include Dify, LlamaIndex, LangChain, and self-hosted Pinecone/Qdrant-based setups.