RAG Chatbot Template for Next.js
A Next.js RAG chatbot template with document ingestion, embeddings, vector search, reranking, and citation rendering. pgvector-based. WCAG AA accessible.
Starting at $47$7940% OFF· 7-day money-back guarantee
RAG (Retrieval-Augmented Generation) is what separates a chatbot from a generic LLM wrapper. Real RAG handles document ingestion at scale, semantic chunking, embedding generation, vector storage, hybrid retrieval, and reranking — then renders answers with clickable citations to the source. This Next.js RAG chatbot template is being built with all of that done correctly from day one.
Key Features
Document Ingestion
PDF, DOCX, HTML, Markdown via Unstructured.io. Async processing.
Semantic Chunking
Chunk by semantic boundaries, not fixed size. Better retrieval quality.
Hybrid Retrieval
Keyword + semantic search combined. Reranking via Cohere or OpenAI.
pgvector Storage
Postgres extension for vector embeddings. Cheaper than Pinecone, easier than Weaviate.
Citation Rendering
Inline [1] [2] references with source preview on hover. Clickable to underlying document.
WCAG AA Accessible
Keyboard-navigable, screen-reader-tested, color-contrast verified.
How thefrontkit Compares
| Feature | thefrontkit | Typical Alternatives |
|---|---|---|
| Production-Ready | Yes | Often demo-quality |
| WCAG AA Accessible | Built-in from day one | Usually missing |
| Source Ownership | You own the code | Often locked SaaS |
| Pricing Model | One-time payment | Per-seat monthly |
Ready to Ship Faster?
Skip the boilerplate and start building what matters. Production-ready, accessible, and token-synced.
View AI Chat UI Kit & Chatbot Template →Related Resources
Need it built, not just a kit?
The team behind thefrontkit does done-for-you launches and custom builds. Fixed quotes, most projects live in 2-4 weeks, from $1999.
Frequently Asked Questions
pgvector is free, runs in your existing Postgres, and is fast enough for under 10M vectors. Easier to operate. Switch to Pinecone or Weaviate at scale.
Hybrid retrieval + reranking is the production-quality default in 2026. Far better than top-K vector search alone.
Yes. OpenAI is the default; the embedding layer is pluggable for Voyage AI, Cohere, or self-hosted models.
PDF, DOCX, HTML, Markdown, plain text via Unstructured.io or LlamaParse.
Yes. It ships today as the AI Chat UI Kit & Chatbot Template, with the full source.