Replace the direct Anthropic and OpenAI integrations with a single
provider that talks to Switchboard, an OpenAI-compatible gateway that
routes each request to the best available model. The app no longer pins
a model id anywhere: it sends switchboard/auto and lets the gateway
choose, then logs which model answered and what it cost.
Routing levers are set per feature in src/lib/ai/routing.ts. Three of
those choices came from measuring against the live gateway:
- category and prefer_free are set explicitly on every request. An API
key carries its own routing defaults, and anything left unset inherits
them - drink prompts were being sent to a free coding model.
- Token budgets are generous because the router may pick a reasoning
model, and reasoning tokens come out of the same max_tokens budget as
the answer. At 512 tokens a request returned null content; at 4096 the
same request returned correct JSON.
- No tier lever on text features. tier "cheap" pinned a slow reasoning
model (42-180s, two timeouts and one truncated response in five
trials) and tier "frontier" escalated as far as Opus at $0.02 a call,
while unconstrained routing answered in about a second. Vision keeps
"frontier", where the accuracy is worth a few tenths of a cent.
Gateway failures are mapped to actionable messages rather than passed
through: a 401 relayed as 401 would read as an expired session and
bounce the user to login, and a 429 would collide with the app's own
rate limiter.
Also collapses the key lookup that was duplicated across ten call sites
into getUserProvider(), which fixes a latent bug where a bare findFirst
with no ordering let different features pick different providers.
Existing claude/openai key rows are ignored at runtime and offered for
removal in Settings, so no migration is needed before deploying.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
- Fuzzy ingredient matching for bar inventory against recipes
- AI photo identification API for bottles/labels (drink + bar context)
- Barcode scanner with photo toggle for My Bar
- Barcode scan + photo ID buttons on Add Drink form
- Auto-pull product images from Open Food Facts barcode lookup
- Recipes section on drink detail pages with bar availability
- Dedicated Recipes page in sidebar navigation
- Bar item image support (schema, upload, display)
- Drink detail image upload component
- MinIO image proxy through Next.js rewrites (fixes broken image links)
- Improved category mapping (energy drinks → Mixers, not Spirits)
- Re-process saved recipe ingredients against current bar inventory
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Drink Images: upload/display photos of bottles/cans on drink cards and detail pages
- My Bar: inventory tracker for spirits, liqueurs, mixers, bitters, garnishes, tools
- Bartender: AI-powered cocktail recipe generation, "what can I make" suggestions,
saved recipes. Cross-references bar inventory for ingredient availability.
- Recommend: AI flavor profile analysis, personalized drink recommendations,
"find similar" drinks based on highly-rated favorites
- Navigation: desktop sidebar with all 8 routes, mobile bottom nav with
4 primary items + "More" popup menu
- New Prisma models: BarItem, Recipe, FlavorProfile
- Backup/restore updated to include bar items
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Next.js 14 drink collection tracker with AI-powered search,
menu scanning, ratings, wishlist, sharing, and CSV backup/restore.
Features:
- Auth (credentials + OAuth ready)
- Drink collection with ratings and reviews
- AI search via Claude/OpenAI with search history
- Menu photo scanning with AI extraction
- Wishlist / Try Later system
- Public sharing via slug URLs
- CSV backup and restore (merge/replace modes)
- Docker Compose for Postgres + MinIO + dev server
Security: docker-compose files use env var interpolation
instead of hardcoded secrets.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
