pyr0ball
01aae2eec8
Recipe corpus (#108): - Add _MAIN_INGREDIENT_SIGNALS to tag_inferrer.py (Chicken/Beef/Pork/Fish/Pasta/ Vegetables/Eggs/Legumes/Grains/Cheese) — infers main:* tags from ingredient names - Update browser_domains.py main_ingredient categories to use main:* tag queries instead of raw food terms; recipe_browser_fts now has full 3.19M row coverage (was ~1.2K before backfill) Bug fixes: - Fix community posts response shape (#96): add total/page/page_size fields - Fix export endpoint arg types (#92) - Fix household invite store leak (#93) - Fix receipts endpoint issues - Fix saved_recipes endpoint - Add session endpoint (app/api/endpoints/session.py) Shopping list: - Add migration 033_shopping_list.sql - Add shopping schemas (app/models/schemas/shopping.py) - Add ShoppingView.vue, ShoppingItemRow.vue, shopping.ts store Frontend: - InventoryList, RecipesView, RecipeDetailPanel polish - App.vue routing updates for shopping view Docs: - Add user-facing docs under docs/ (getting-started, user-guide, reference) - Add screenshots
74 lines
2.4 KiB
Markdown
74 lines
2.4 KiB
Markdown
# LLM Backend Setup (Optional)
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An LLM backend unlocks **receipt OCR**, **recipe suggestions (L3–L4)**, and **style auto-classification**. Everything else works without one.
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You can use any OpenAI-compatible inference server: Ollama, vLLM, LM Studio, a local llama.cpp server, or a commercial API.
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## BYOK — Bring Your Own Key
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BYOK means you provide your own LLM backend. Paid AI features are unlocked at **any tier** when a valid backend is configured. You pay for your own inference; Kiwi just uses it.
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## Choosing a backend
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| Backend | Best for | Notes |
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|---------|----------|-------|
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| **Ollama** | Local, easy setup | Recommended for getting started |
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| **vLLM** | Local, high throughput | Better for faster hardware |
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| **OpenAI API** | No local GPU | Requires paid API key |
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| **Anthropic API** | No local GPU | Requires paid API key |
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## Ollama setup (recommended)
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```bash
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# Install Ollama
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curl -fsSL https://ollama.ai/install.sh | sh
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# Pull a model — llama3.1 8B works well for recipe tasks
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ollama pull llama3.1
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# Verify it's running
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ollama list
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```
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In your Kiwi `.env`:
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```bash
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LLM_BACKEND=ollama
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LLM_BASE_URL=http://host.docker.internal:11434
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LLM_MODEL=llama3.1
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```
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!!! note "Docker networking"
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Use `host.docker.internal` instead of `localhost` when Ollama is running on your host and Kiwi is in Docker.
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## OpenAI-compatible API
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```bash
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LLM_BACKEND=openai
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LLM_BASE_URL=https://api.openai.com/v1
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LLM_API_KEY=sk-your-key-here
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LLM_MODEL=gpt-4o-mini
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```
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## Verify the connection
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In the Kiwi **Settings** page, the LLM status indicator shows whether the backend is reachable. A green checkmark means OCR and L3–L4 recipe suggestions are active.
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## What LLM is used for
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| Feature | LLM required |
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|---------|-------------|
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| Receipt OCR (line-item extraction) | Yes |
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| Recipe suggestions L1 (pantry match) | No |
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| Recipe suggestions L2 (substitution) | No |
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| Recipe suggestions L3 (style templates) | Yes |
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| Recipe suggestions L4 (full generation) | Yes |
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| Style auto-classifier | Yes |
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L1 and L2 suggestions use deterministic matching — they work without any LLM configured. See [Recipe Engine](../reference/recipe-engine.md) for the full algorithm breakdown.
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## Model recommendations
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- **Receipt OCR**: any model with vision capability (LLaVA, GPT-4o, etc.)
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- **Recipe suggestions**: 7B–13B instruction-tuned models work well; larger models produce more creative L4 output
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- **Style classification**: small models handle this fine (3B+)
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