flowchart LR
subgraph Consumers["Consumer Apps"]
direction TB
P[Pomodoro]
U[Unit Converter]
T[Tip Calculator]
end
subgraph Service["Global Chat Service"]
Chat["POST /api/chat"]
end
subgraph External["External Services"]
direction TB
Neon["Neon Postgres + pgvector"]
Vertex["Vertex AI Gemini"]
end
P & U & T --> Chat
Chat -->|RAG + vector search| Neon
Chat -->|completions + embeddings| Vertex
Unified Chat Service
experiment
til
rag
ai
A global ‘chat with your docs’ service.
What It Is
A unified chat service — a frontend component library plus central API for in-product AI chat. Consumer apps all plug into the same backend, which handles RAG via Neon Postgres + pgvector and chat completions via GCP Vertex AI.
Consumers call POST /api/chat with a product_id; the service fetches relevant docs, streams responses, and auto-registers events.
Architecture
Services: Neon Postgres (pgvector) for vector search; GCP Vertex AI (text-embedding-005, Gemini 2.5 Flash) for embeddings and completions; NextAuth with GitHub for doc management at /library. Hosted on Vercel.
RAG flow: Consumer sends POST /api/chat with product_id, user, and messages. The service extracts search terms from the last message, embeds the query, runs cosine similarity search on document_chunks, builds a system prompt with the top 5 chunks, then streams the completion.
Data model: Projects (one per consumer app) → documents (uploaded via /library) → document_chunks (768-dim embeddings). CORS restricted to ALLOWED_ORIGINS.
Try the Apps
The consumer apps below demonstrate distinct styling and RAG tuned to each product’s documentation.
Chat Service Demo
Unit Converter
Minimal Pomodoro
Tip Calculator
Central service + RAG on Neon + Vertex AI. Consumers stay small; the heavy lifting happens once.