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develop a new shopping crew: Create a shopping_crew that tracks grocery purchases and suggests a shopping list. ## Storage (PostgreSQL, new migration needed) Create migration 008_fridge.sql with these tables: shopping_receipts: id SERIAL PK, telegram_file_id TEXT UNIQUE, store_name TEXT, purchase_datetime TIMESTAMPTZ, currency TEXT DEFAULT 'ILS', grand_total NUMERIC(10,2), item_count INT, uploaded_at TIMESTAMPTZ DEFAULT NOW() shopping_purchases: id SERIAL PK, receipt_id INT FK→shopping_receipts, item TEXT NOT NULL, quantity NUMERIC(8,2) DEFAULT 1, unit TEXT DEFAULT 'unit', unit_price NUMERIC(10,2), total_price NUMERIC(10,2), category TEXT DEFAULT 'Other', store_name TEXT, purchase_datetime TIMESTAMPTZ, created_at TIMESTAMPTZ DEFAULT NOW() CREATE INDEX idx_purchases_item ON shopping_purchases(item); CREATE INDEX idx_purchases_time ON shopping_purchases(purchase_datetime); ## Tool module: src/tools/fridge.py Three functions: 1. analyze_receipt(image_bytes: bytes) -> dict | None - Call Gemini with a multimodal prompt: image + structured JSON instructions - Use google.genai (same pattern as fastlane_crew.py — import genai from google, use client.models.generate_content) - Prompt asks to extract: store_name, purchase_datetime (ISO 8601), currency, grand_total, and items array with fields: item, quantity, unit, unit_price, total_price, category (Produce/Dairy/Meat/Pantry/Frozen/ Beverages/Other) - Request response_mime_type="application/json" - Return parsed dict or None on error 2. save_receipt(result_json: dict, telegram_file_id: str | None = None) -> int - Insert into shopping_receipts, then batch-insert items into shopping_purchases - Normalize item names: lowercase, strip whitespace, collapse multiple spaces - Return count of items saved - This must be ASYNC (uses db pool) 3. get_stock_insights() -> dict - Query shopping_purchases from last 90 days - Group by normalized item name - For items bought >=2 times: if days since last purchase > 14, flag as "overdue" - For items bought in last 3 days: flag as "recent" - Return {"overdue": [(name, days_since, category, avg_interval)], "recent": [(name, days)]} - This must be ASYNC ## Crew module: src/crews/fridge_crew.py MANIFEST: id: fridge_crew name: Fridge & Shopping nickname: קניות description: Tracks grocery purchases and suggests shopping list tools: [] model_tier: fast prompt_key: None trigger_types: ["chat"] The run() function should accept these modes via kwargs: - mode="receipt", image_bytes=bytes → call analyze_receipt, save_receipt, return Hebrew summary - mode="stock" → call get_stock_insights, format Hebrew shopping list (same format as the original: overdue items with days and avg interval, recently bought items) - Default (no mode) → run stock insights Check for duplicate receipts: before saving, query shopping_receipts WHERE telegram_file_id = $1. If exists, return "קבלה זו כבר עובדה." Do NOT create a schedule — this crew is triggered on-demand only. ## Important patterns to follow: - Use the fastlane_crew as a reference for direct Gemini API calls (google.genai, not google.generativeai) - Use urllib.request, not requests - All DB access is async via db._pool_or_raise() - Use logging, not print - from future import annotations at top of every file