FastRAG

Introduction to FastRAG

FastRAG is a production-ready Next.js starter kit for building retrieval-augmented generation (RAG) applications. It provides preconfigured ingestion, embedding, and retrieval workflows so developers can focus on product logic rather than infrastructure. The project integrates a modern stack, including Next.js, LangChain, Pinecone, OpenAI, Vercel, React, and Tailwind.

Designed for developers who need a maintainable codebase, FastRAG exposes clear API routes and sensible defaults. It supports document ingestion from PDFs and URLs, citation-aware responses, streaming output, and a responsive chat interface suitable for both desktop and mobile. Version 1.3 adds a web scraping engine and a mobile-ready UI.

Key Takeaways

• Production-ready Next.js 14 boilerplate for RAG applications
• Multi-file PDF ingestion with drag-and-drop, automatic chunking, and preserved citations
• Web scraping engine (Cheerio + LangChain) for ingesting and cleaning website content
• Citation highlighting in the UI to show exact source passages
• Streaming responses via the Vercel AI SDK with a responsive, mobile-friendly chat interface
• Markdown and code block rendering in chat
• Smart vector configuration (1024-dimensional) to help reduce Pinecone storage usage
• Developer-first design: full access to API routes to customize scrapers, vector stores, and prompts; live demo available (no API key required; uploads up to 10MB per file)

How FastRAG Works

FastRAG ingests content from PDFs (via drag and drop) and URLs (via a web scraping pipeline). For websites, it uses Cheerio and LangChain to fetch, clean, and structure HTML content. For documents, it parses PDF files and maintains file-level citations. Documents are split using a recursive text splitter to preserve context, then converted into vector embeddings and stored in Pinecone.

During a chat session, the application retrieves relevant chunks from the vector store and constructs responses with citations. The Vercel AI SDK enables streaming output for a real-time typing experience. The UI renders Markdown and code blocks, and the layout is responsive for mobile devices. Developers can modify the pages/api routes to swap the vector database, adjust the splitter settings, or change prompting strategies as needed.

Core Benefits and Applications

FastRAG provides a practical foundation for developers building knowledge assistants, documentation search tools, and support bots. Its ingestion and scraping pipelines support internal wikis, technical docs, product manuals, and other reference materials.

Common applications include:

• Internal knowledge bases for engineering or operations teams
• Documentation Q&A for product, API, or onboarding guides
• Customer support assistants grounded in company policies and help articles
• Content summarization and citation-backed answers for research workflows

By offering a prebuilt pipeline for ingestion, embedding, retrieval, and a streaming chat UI, FastRAG reduces initial setup time and lowers maintenance overhead, while allowing full customization of data sources, vector storage, and response logic.