NEXORA
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About this product

RAG boilerplates are usually Python + LangChain + pgvector + Redis + Docker Compose — a weekend to stand up before you write your first eval. This one is Cloudflare-native: Vectorize for vectors, Workers AI for embeddings + rerank, optional Claude for generation, D1 for metadata, KV for cache. Deploys in 5 minutes via `wrangler deploy`. Ships with the eval harness on day one so you can measure context precision and answer faithfulness before shipping to users. Chat UI included — clone the frontend/ dir, set RAG_WORKER_URL, done.

What's included

Cloudflare Worker (Hono) with 3 routes — /ingest (chunk + embed + Vectorize upsert), /query (embed → retrieve → rerank → generate), /eval (golden Q&A → precision + faithfulness scores)
Workers AI wired to `@cf/baai/bge-large-en-v1.5` for embedding + `@cf/baai/bge-reranker-base` for rerank
Generation dual-path — Anthropic Claude when ANTHROPIC_API_KEY is set, falls back to Workers AI Llama 3.1 8B (zero-cost path)
Next.js 15 App Router chat UI + `/api/chat` streaming proxy to the Worker
Eval harness — golden Q&A JSON + runner writes CSV per-question (context precision + faithfulness) + averaged scores
wrangler.jsonc pre-wired for Vectorize/D1/KV/AI bindings — one-shot deploy after IDs paste
Cost model per 1k queries (~$0.66 all-in with Claude, ~$0.06 on Llama fallback)
Deploy runbook + rollback + first-run smoke test + cost monitoring pointer

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