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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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40
backend/internal/recognition/handler.go
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@@ -8,7 +8,7 @@ import (
"strings"
"sync"
"github.com/food-ai/backend/internal/adapters/openai"
"github.com/food-ai/backend/internal/adapters/ai"
"github.com/food-ai/backend/internal/infra/middleware"
"github.com/food-ai/backend/internal/ingredient"
)
@@ -21,15 +21,23 @@ type IngredientRepository interface {
UpsertAliases(ctx context.Context, id, lang string, aliases []string) error
}
// Recognizer is the AI provider interface for image-based food recognition.
type Recognizer interface {
RecognizeReceipt(ctx context.Context, imageBase64, mimeType string) (*ai.ReceiptResult, error)
RecognizeProducts(ctx context.Context, imageBase64, mimeType string) ([]ai.RecognizedItem, error)
RecognizeDish(ctx context.Context, imageBase64, mimeType string) (*ai.DishResult, error)
ClassifyIngredient(ctx context.Context, name string) (*ai.IngredientClassification, error)
}
// Handler handles POST /ai/* recognition endpoints.
type Handler struct {
openaiClient *openai.Client
recognizer Recognizer
ingredientRepo IngredientRepository
}
// NewHandler creates a new Handler.
func NewHandler(openaiClient *openai.Client, repo IngredientRepository) *Handler {
return &Handler{openaiClient: openaiClient, ingredientRepo: repo}
func NewHandler(recognizer Recognizer, repo IngredientRepository) *Handler {
return &Handler{recognizer: recognizer, ingredientRepo: repo}
}
// ---------------------------------------------------------------------------
@@ -61,11 +69,11 @@ type EnrichedItem struct {
// ReceiptResponse is the response for POST /ai/recognize-receipt.
type ReceiptResponse struct {
Items []EnrichedItem `json:"items"`
Unrecognized []openai.UnrecognizedItem `json:"unrecognized"`
Unrecognized []ai.UnrecognizedItem `json:"unrecognized"`
}
// DishResponse is the response for POST /ai/recognize-dish.
type DishResponse = openai.DishResult
type DishResponse = ai.DishResult
// ---------------------------------------------------------------------------
// Handlers
@@ -83,7 +91,7 @@ func (h *Handler) RecognizeReceipt(w http.ResponseWriter, r *http.Request) {
return
}
result, err := h.openaiClient.RecognizeReceipt(r.Context(), req.ImageBase64, req.MimeType)
result, err := h.recognizer.RecognizeReceipt(r.Context(), req.ImageBase64, req.MimeType)
if err != nil {
slog.Error("recognize receipt", "err", err)
writeErrorJSON(w, http.StatusServiceUnavailable, "recognition failed, please try again")
@@ -110,13 +118,13 @@ func (h *Handler) RecognizeProducts(w http.ResponseWriter, r *http.Request) {
}
// Process each image in parallel.
allItems := make([][]openai.RecognizedItem, len(req.Images))
allItems := make([][]ai.RecognizedItem, len(req.Images))
var wg sync.WaitGroup
for i, img := range req.Images {
wg.Add(1)
go func(i int, img imageRequest) {
defer wg.Done()
items, err := h.openaiClient.RecognizeProducts(r.Context(), img.ImageBase64, img.MimeType)
items, err := h.recognizer.RecognizeProducts(r.Context(), img.ImageBase64, img.MimeType)
if err != nil {
slog.Warn("recognize products from image", "index", i, "err", err)
return
@@ -140,7 +148,7 @@ func (h *Handler) RecognizeDish(w http.ResponseWriter, r *http.Request) {
return
}
result, err := h.openaiClient.RecognizeDish(r.Context(), req.ImageBase64, req.MimeType)
result, err := h.recognizer.RecognizeDish(r.Context(), req.ImageBase64, req.MimeType)
if err != nil {
slog.Error("recognize dish", "err", err)
writeErrorJSON(w, http.StatusServiceUnavailable, "recognition failed, please try again")
@@ -156,7 +164,7 @@ func (h *Handler) RecognizeDish(w http.ResponseWriter, r *http.Request) {
// enrichItems matches each recognized item against ingredient_mappings.
// Items without a match trigger a Gemini classification call and upsert into the DB.
func (h *Handler) enrichItems(ctx context.Context, items []openai.RecognizedItem) []EnrichedItem {
func (h *Handler) enrichItems(ctx context.Context, items []ai.RecognizedItem) []EnrichedItem {
result := make([]EnrichedItem, 0, len(items))
for _, item := range items {
enriched := EnrichedItem{
@@ -188,7 +196,7 @@ func (h *Handler) enrichItems(ctx context.Context, items []openai.RecognizedItem
}
} else {
// No mapping — ask AI to classify and save for future reuse.
classification, err := h.openaiClient.ClassifyIngredient(ctx, item.Name)
classification, err := h.recognizer.ClassifyIngredient(ctx, item.Name)
if err != nil {
slog.Warn("classify unknown ingredient", "name", item.Name, "err", err)
} else {
@@ -208,7 +216,7 @@ func (h *Handler) enrichItems(ctx context.Context, items []openai.RecognizedItem
}
// saveClassification upserts an AI-produced ingredient classification into the DB.
func (h *Handler) saveClassification(ctx context.Context, c *openai.IngredientClassification) *ingredient.IngredientMapping {
func (h *Handler) saveClassification(ctx context.Context, c *ai.IngredientClassification) *ingredient.IngredientMapping {
if c == nil || c.CanonicalName == "" {
return nil
}
@@ -252,8 +260,8 @@ func (h *Handler) saveClassification(ctx context.Context, c *openai.IngredientCl
// mergeAndDeduplicate combines results from multiple images.
// Items sharing the same name (case-insensitive) have their quantities summed.
func mergeAndDeduplicate(batches [][]openai.RecognizedItem) []openai.RecognizedItem {
seen := make(map[string]*openai.RecognizedItem)
func mergeAndDeduplicate(batches [][]ai.RecognizedItem) []ai.RecognizedItem {
seen := make(map[string]*ai.RecognizedItem)
var order []string
for _, batch := range batches {
@@ -273,7 +281,7 @@ func mergeAndDeduplicate(batches [][]openai.RecognizedItem) []openai.RecognizedI
}
}
result := make([]openai.RecognizedItem, 0, len(order))
result := make([]ai.RecognizedItem, 0, len(order))
for _, key := range order {
result = append(result, *seen[key])
}