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refactor: introduce adapter pattern for AI provider (OpenAI)

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- Add internal/adapters/ai/types.go with neutral shared types
  (Recipe, DayPlan, RecognizedItem, IngredientClassification, etc.)
- Remove types from internal/adapters/openai/ — adapter now uses ai.*
- Define Recognizer interface in recognition package
- Define MenuGenerator interface in menu package
- Define RecipeGenerator interface in recommendation package
- Handler structs now hold interfaces, not *openai.Client
- Add wire.Bind entries for the three new interface bindings

To swap OpenAI for another provider: implement the three interfaces
using ai.* types and change the wire.Bind lines in cmd/server/wire.go.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
dbastrikin
2026-03-15 21:27:04 +02:00
parent 19a985ad49
commit fee240da7d
8 changed files with 217 additions and 194 deletions
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36
backend/internal/adapters/openai/menu.go
View File

@@ -4,34 +4,14 @@ import (
"context"
"fmt"
"sync"
"github.com/food-ai/backend/internal/adapters/ai"
)
// MenuRequest contains parameters for weekly menu generation.
type MenuRequest struct {
UserGoal string
DailyCalories int
Restrictions []string
CuisinePrefs []string
AvailableProducts []string
Lang string // ISO 639-1 target language code, e.g. "en", "ru"
}
// DayPlan is the AI-generated plan for a single day.
type DayPlan struct {
Day int `json:"day"`
Meals []MealEntry `json:"meals"`
}
// MealEntry is a single meal within a day plan.
type MealEntry struct {
MealType string `json:"meal_type"` // breakfast | lunch | dinner
Recipe Recipe `json:"recipe"`
}
// GenerateMenu produces a 7-day × 3-meal plan by issuing three parallel
// GenerateRecipes calls (one per meal type). This avoids token-limit errors
// that arise from requesting 21 full recipes in a single prompt.
func (c *Client) GenerateMenu(ctx context.Context, req MenuRequest) ([]DayPlan, error) {
func (c *Client) GenerateMenu(ctx context.Context, req ai.MenuRequest) ([]ai.DayPlan, error) {
type mealSlot struct {
mealType string
fraction float64 // share of daily calories
@@ -44,7 +24,7 @@ func (c *Client) GenerateMenu(ctx context.Context, req MenuRequest) ([]DayPlan,
}
type mealResult struct {
recipes []Recipe
recipes []ai.Recipe
err error
}
@@ -57,7 +37,7 @@ func (c *Client) GenerateMenu(ctx context.Context, req MenuRequest) ([]DayPlan,
defer wg.Done()
// Scale daily calories to what this meal should contribute.
mealCal := int(float64(req.DailyCalories) * fraction)
r, err := c.GenerateRecipes(ctx, RecipeRequest{
r, err := c.GenerateRecipes(ctx, ai.RecipeRequest{
UserGoal: req.UserGoal,
DailyCalories: mealCal * 3, // prompt divides by 3 internally
Restrictions: req.Restrictions,
@@ -84,11 +64,11 @@ func (c *Client) GenerateMenu(ctx context.Context, req MenuRequest) ([]DayPlan,
}
}
days := make([]DayPlan, 7)
days := make([]ai.DayPlan, 7)
for day := range 7 {
days[day] = DayPlan{
days[day] = ai.DayPlan{
Day: day + 1,
Meals: []MealEntry{
Meals: []ai.MealEntry{
{MealType: slots[0].mealType, Recipe: results[0].recipes[day]},
{MealType: slots[1].mealType, Recipe: results[1].recipes[day]},
{MealType: slots[2].mealType, Recipe: results[2].recipes[day]},

67
backend/internal/adapters/openai/recipe.go
View File

@@ -6,65 +6,10 @@ import (
"fmt"
"strings"
"github.com/food-ai/backend/internal/adapters/ai"
"github.com/food-ai/backend/internal/infra/locale"
)
// RecipeGenerator generates recipes using the Gemini AI.
type RecipeGenerator interface {
GenerateRecipes(ctx context.Context, req RecipeRequest) ([]Recipe, error)
}
// RecipeRequest contains parameters for recipe generation.
type RecipeRequest struct {
UserGoal string // "lose" | "maintain" | "gain"
DailyCalories int
Restrictions []string // e.g. ["gluten_free", "vegetarian"]
CuisinePrefs []string // e.g. ["russian", "asian"]
Count int
AvailableProducts []string // human-readable list of products in user's pantry
Lang string // ISO 639-1 target language code, e.g. "en", "ru"
}
// Recipe is a recipe returned by Gemini.
type Recipe struct {
Title string `json:"title"`
Description string `json:"description"`
Cuisine string `json:"cuisine"`
Difficulty string `json:"difficulty"`
PrepTimeMin int `json:"prep_time_min"`
CookTimeMin int `json:"cook_time_min"`
Servings int `json:"servings"`
ImageQuery string `json:"image_query"`
ImageURL string `json:"image_url"`
Ingredients []Ingredient `json:"ingredients"`
Steps []Step `json:"steps"`
Tags []string `json:"tags"`
Nutrition NutritionInfo `json:"nutrition_per_serving"`
}
// Ingredient is a single ingredient in a recipe.
type Ingredient struct {
Name string `json:"name"`
Amount float64 `json:"amount"`
Unit string `json:"unit"`
}
// Step is a single preparation step.
type Step struct {
Number int `json:"number"`
Description string `json:"description"`
TimerSeconds *int `json:"timer_seconds"`
}
// NutritionInfo contains approximate nutritional information per serving.
type NutritionInfo struct {
Calories float64 `json:"calories"`
ProteinG float64 `json:"protein_g"`
FatG float64 `json:"fat_g"`
CarbsG float64 `json:"carbs_g"`
Approximate bool `json:"approximate"`
}
// goalNames maps internal goal codes to English descriptions used in the prompt.
var goalNames = map[string]string{
"lose": "weight loss",
@@ -72,10 +17,10 @@ var goalNames = map[string]string{
"gain": "muscle gain",
}
// GenerateRecipes generates recipes using the Gemini AI.
// GenerateRecipes generates recipes using the OpenAI API.
// Retries up to maxRetries times only when the response is not valid JSON.
// API-level errors (rate limits, auth, etc.) are returned immediately.
func (c *Client) GenerateRecipes(ctx context.Context, req RecipeRequest) ([]Recipe, error) {
func (c *Client) GenerateRecipes(ctx context.Context, req ai.RecipeRequest) ([]ai.Recipe, error) {
prompt := buildRecipePrompt(req)
messages := []map[string]string{
@@ -111,7 +56,7 @@ func (c *Client) GenerateRecipes(ctx context.Context, req RecipeRequest) ([]Reci
return nil, fmt.Errorf("failed to parse valid JSON after %d attempts: %w", maxRetries, lastErr)
}
func buildRecipePrompt(req RecipeRequest) string {
func buildRecipePrompt(req ai.RecipeRequest) string {
lang := req.Lang
if lang == "" {
lang = "en"
@@ -189,7 +134,7 @@ Return ONLY a valid JSON array without markdown or extra text:
}]`, count, langName, goal, req.DailyCalories, restrictions, cuisines, productsSection, perMealCalories, langName)
}
func parseRecipesJSON(text string) ([]Recipe, error) {
func parseRecipesJSON(text string) ([]ai.Recipe, error) {
text = strings.TrimSpace(text)
if strings.HasPrefix(text, "```") {
text = strings.TrimPrefix(text, "```json")
@@ -198,7 +143,7 @@ func parseRecipesJSON(text string) ([]Recipe, error) {
text = strings.TrimSpace(text)
}
var recipes []Recipe
var recipes []ai.Recipe
if err := json.Unmarshal([]byte(text), &recipes); err != nil {
return nil, err
}

80
backend/internal/adapters/openai/recognition.go
View File

@@ -1,68 +1,16 @@
package openai
import (
"context"
"encoding/json"
"fmt"
"strings"
"context"
"github.com/food-ai/backend/internal/adapters/ai"
)
// RecognizedItem is a food item identified in an image.
type RecognizedItem struct {
Name string `json:"name"`
Quantity float64 `json:"quantity"`
Unit string `json:"unit"`
Category string `json:"category"`
Confidence float64 `json:"confidence"`
}
// UnrecognizedItem is text from a receipt that could not be identified as food.
type UnrecognizedItem struct {
RawText string `json:"raw_text"`
Price float64 `json:"price,omitempty"`
}
// ReceiptResult is the full result of receipt OCR.
type ReceiptResult struct {
Items []RecognizedItem `json:"items"`
Unrecognized []UnrecognizedItem `json:"unrecognized"`
}
// DishResult is the result of dish recognition.
type DishResult struct {
DishName string `json:"dish_name"`
WeightGrams int `json:"weight_grams"`
Calories float64 `json:"calories"`
ProteinG float64 `json:"protein_g"`
FatG float64 `json:"fat_g"`
CarbsG float64 `json:"carbs_g"`
Confidence float64 `json:"confidence"`
SimilarDishes []string `json:"similar_dishes"`
}
// IngredientTranslation holds the localized name and aliases for one language.
type IngredientTranslation struct {
Lang string `json:"lang"`
Name string `json:"name"`
Aliases []string `json:"aliases"`
}
// IngredientClassification is the AI-produced classification of an unknown food item.
type IngredientClassification struct {
CanonicalName string `json:"canonical_name"`
Aliases []string `json:"aliases"` // English aliases
Translations []IngredientTranslation `json:"translations"` // other languages
Category string `json:"category"`
DefaultUnit string `json:"default_unit"`
CaloriesPer100g *float64 `json:"calories_per_100g"`
ProteinPer100g *float64 `json:"protein_per_100g"`
FatPer100g *float64 `json:"fat_per_100g"`
CarbsPer100g *float64 `json:"carbs_per_100g"`
StorageDays int `json:"storage_days"`
}
// RecognizeReceipt uses the vision model to extract food items from a receipt photo.
func (c *Client) RecognizeReceipt(ctx context.Context, imageBase64, mimeType string) (*ReceiptResult, error) {
func (c *Client) RecognizeReceipt(ctx context.Context, imageBase64, mimeType string) (*ai.ReceiptResult, error) {
prompt := `Ты — OCR-система для чеков из продуктовых магазинов.
Проанализируй фото чека и извлеки список продуктов питания.
@@ -91,21 +39,21 @@ func (c *Client) RecognizeReceipt(ctx context.Context, imageBase64, mimeType str
return nil, fmt.Errorf("recognize receipt: %w", err)
}
var result ReceiptResult
var result ai.ReceiptResult
if err := parseJSON(text, &result); err != nil {
return nil, fmt.Errorf("parse receipt result: %w", err)
}
if result.Items == nil {
result.Items = []RecognizedItem{}
result.Items = []ai.RecognizedItem{}
}
if result.Unrecognized == nil {
result.Unrecognized = []UnrecognizedItem{}
result.Unrecognized = []ai.UnrecognizedItem{}
}
return &result, nil
}
// RecognizeProducts uses the vision model to identify food items in a photo (fridge, shelf, etc.).
func (c *Client) RecognizeProducts(ctx context.Context, imageBase64, mimeType string) ([]RecognizedItem, error) {
func (c *Client) RecognizeProducts(ctx context.Context, imageBase64, mimeType string) ([]ai.RecognizedItem, error) {
prompt := `Ты — система распознавания продуктов питания.
Посмотри на фото и определи все видимые продукты питания.
@@ -131,19 +79,19 @@ func (c *Client) RecognizeProducts(ctx context.Context, imageBase64, mimeType st
}
var result struct {
Items []RecognizedItem `json:"items"`
Items []ai.RecognizedItem `json:"items"`
}
if err := parseJSON(text, &result); err != nil {
return nil, fmt.Errorf("parse products result: %w", err)
}
if result.Items == nil {
return []RecognizedItem{}, nil
return []ai.RecognizedItem{}, nil
}
return result.Items, nil
}
// RecognizeDish uses the vision model to identify a dish and estimate its nutritional content.
func (c *Client) RecognizeDish(ctx context.Context, imageBase64, mimeType string) (*DishResult, error) {
func (c *Client) RecognizeDish(ctx context.Context, imageBase64, mimeType string) (*ai.DishResult, error) {
prompt := `Ты — диетолог и кулинарный эксперт.
Посмотри на фото блюда и определи:
@@ -171,7 +119,7 @@ func (c *Client) RecognizeDish(ctx context.Context, imageBase64, mimeType string
return nil, fmt.Errorf("recognize dish: %w", err)
}
var result DishResult
var result ai.DishResult
if err := parseJSON(text, &result); err != nil {
return nil, fmt.Errorf("parse dish result: %w", err)
}
@@ -183,7 +131,7 @@ func (c *Client) RecognizeDish(ctx context.Context, imageBase64, mimeType string
// ClassifyIngredient uses the text model to classify an unknown food item
// and build an ingredient_mappings record for it.
func (c *Client) ClassifyIngredient(ctx context.Context, name string) (*IngredientClassification, error) {
func (c *Client) ClassifyIngredient(ctx context.Context, name string) (*ai.IngredientClassification, error) {
prompt := fmt.Sprintf(`Classify the food product: "%s".
Return ONLY valid JSON without markdown:
{
@@ -209,7 +157,7 @@ Return ONLY valid JSON without markdown:
return nil, fmt.Errorf("classify ingredient: %w", err)
}
var result IngredientClassification
var result ai.IngredientClassification
if err := parseJSON(text, &result); err != nil {
return nil, fmt.Errorf("parse classification: %w", err)
}