feat: edu-search-service migriert, voice-service/geo-service entfernt

- edu-search-service von breakpilot-pwa nach breakpilot-lehrer kopiert (ohne vendor)
- opensearch + edu-search-service in docker-compose.yml hinzugefuegt
- voice-service aus docker-compose.yml entfernt (jetzt in breakpilot-core)
- geo-service aus docker-compose.yml entfernt (nicht mehr benoetigt)
- CI/CD: edu-search-service zu Gitea Actions und Woodpecker hinzugefuegt
  (Go lint, test mit go mod download, build, SBOM)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

edu-search-service/internal/embedding/embedding.go

@@ -0,0 +1,332 @@
+package embedding
+
+import (
+	"bytes"
+	"context"
+	"encoding/json"
+	"errors"
+	"fmt"
+	"io"
+	"net/http"
+	"time"
+)
+
+// EmbeddingProvider defines the interface for embedding services
+type EmbeddingProvider interface {
+	// Embed generates embeddings for the given text
+	Embed(ctx context.Context, text string) ([]float32, error)
+
+	// EmbedBatch generates embeddings for multiple texts
+	EmbedBatch(ctx context.Context, texts []string) ([][]float32, error)
+
+	// Dimension returns the embedding vector dimension
+	Dimension() int
+}
+
+// Service wraps an embedding provider
+type Service struct {
+	provider  EmbeddingProvider
+	dimension int
+	enabled   bool
+}
+
+// NewService creates a new embedding service based on configuration
+func NewService(provider, apiKey, model, ollamaURL string, dimension int, enabled bool) (*Service, error) {
+	if !enabled {
+		return &Service{
+			provider:  nil,
+			dimension: dimension,
+			enabled:   false,
+		}, nil
+	}
+
+	var p EmbeddingProvider
+	var err error
+
+	switch provider {
+	case "openai":
+		if apiKey == "" {
+			return nil, errors.New("OpenAI API key required for openai provider")
+		}
+		p = NewOpenAIProvider(apiKey, model, dimension)
+	case "ollama":
+		p, err = NewOllamaProvider(ollamaURL, model, dimension)
+		if err != nil {
+			return nil, err
+		}
+	case "none", "":
+		return &Service{
+			provider:  nil,
+			dimension: dimension,
+			enabled:   false,
+		}, nil
+	default:
+		return nil, fmt.Errorf("unknown embedding provider: %s", provider)
+	}
+
+	return &Service{
+		provider:  p,
+		dimension: dimension,
+		enabled:   true,
+	}, nil
+}
+
+// IsEnabled returns true if semantic search is enabled
+func (s *Service) IsEnabled() bool {
+	return s.enabled && s.provider != nil
+}
+
+// Embed generates embedding for a single text
+func (s *Service) Embed(ctx context.Context, text string) ([]float32, error) {
+	if !s.IsEnabled() {
+		return nil, errors.New("embedding service not enabled")
+	}
+	return s.provider.Embed(ctx, text)
+}
+
+// EmbedBatch generates embeddings for multiple texts
+func (s *Service) EmbedBatch(ctx context.Context, texts []string) ([][]float32, error) {
+	if !s.IsEnabled() {
+		return nil, errors.New("embedding service not enabled")
+	}
+	return s.provider.EmbedBatch(ctx, texts)
+}
+
+// Dimension returns the configured embedding dimension
+func (s *Service) Dimension() int {
+	return s.dimension
+}
+
+// =====================================================
+// OpenAI Embedding Provider
+// =====================================================
+
+// OpenAIProvider implements EmbeddingProvider using OpenAI's API
+type OpenAIProvider struct {
+	apiKey     string
+	model      string
+	dimension  int
+	httpClient *http.Client
+}
+
+// NewOpenAIProvider creates a new OpenAI embedding provider
+func NewOpenAIProvider(apiKey, model string, dimension int) *OpenAIProvider {
+	return &OpenAIProvider{
+		apiKey:    apiKey,
+		model:     model,
+		dimension: dimension,
+		httpClient: &http.Client{
+			Timeout: 60 * time.Second,
+		},
+	}
+}
+
+// openAIEmbeddingRequest represents the OpenAI API request
+type openAIEmbeddingRequest struct {
+	Model      string   `json:"model"`
+	Input      []string `json:"input"`
+	Dimensions int      `json:"dimensions,omitempty"`
+}
+
+// openAIEmbeddingResponse represents the OpenAI API response
+type openAIEmbeddingResponse struct {
+	Data []struct {
+		Embedding []float32 `json:"embedding"`
+		Index     int       `json:"index"`
+	} `json:"data"`
+	Usage struct {
+		PromptTokens int `json:"prompt_tokens"`
+		TotalTokens  int `json:"total_tokens"`
+	} `json:"usage"`
+	Error *struct {
+		Message string `json:"message"`
+		Type    string `json:"type"`
+	} `json:"error,omitempty"`
+}
+
+// Embed generates embedding for a single text
+func (p *OpenAIProvider) Embed(ctx context.Context, text string) ([]float32, error) {
+	embeddings, err := p.EmbedBatch(ctx, []string{text})
+	if err != nil {
+		return nil, err
+	}
+	if len(embeddings) == 0 {
+		return nil, errors.New("no embedding returned")
+	}
+	return embeddings[0], nil
+}
+
+// EmbedBatch generates embeddings for multiple texts
+func (p *OpenAIProvider) EmbedBatch(ctx context.Context, texts []string) ([][]float32, error) {
+	if len(texts) == 0 {
+		return nil, nil
+	}
+
+	// Truncate texts to avoid token limits (max ~8000 tokens per text)
+	truncatedTexts := make([]string, len(texts))
+	for i, text := range texts {
+		if len(text) > 30000 { // Rough estimate: ~4 chars per token
+			truncatedTexts[i] = text[:30000]
+		} else {
+			truncatedTexts[i] = text
+		}
+	}
+
+	reqBody := openAIEmbeddingRequest{
+		Model: p.model,
+		Input: truncatedTexts,
+	}
+
+	// Only set dimensions for models that support it (text-embedding-3-*)
+	if p.model == "text-embedding-3-small" || p.model == "text-embedding-3-large" {
+		reqBody.Dimensions = p.dimension
+	}
+
+	body, err := json.Marshal(reqBody)
+	if err != nil {
+		return nil, fmt.Errorf("failed to marshal request: %w", err)
+	}
+
+	req, err := http.NewRequestWithContext(ctx, "POST", "https://api.openai.com/v1/embeddings", bytes.NewReader(body))
+	if err != nil {
+		return nil, fmt.Errorf("failed to create request: %w", err)
+	}
+
+	req.Header.Set("Authorization", "Bearer "+p.apiKey)
+	req.Header.Set("Content-Type", "application/json")
+
+	resp, err := p.httpClient.Do(req)
+	if err != nil {
+		return nil, fmt.Errorf("failed to call OpenAI API: %w", err)
+	}
+	defer resp.Body.Close()
+
+	respBody, err := io.ReadAll(resp.Body)
+	if err != nil {
+		return nil, fmt.Errorf("failed to read response: %w", err)
+	}
+
+	var apiResp openAIEmbeddingResponse
+	if err := json.Unmarshal(respBody, &apiResp); err != nil {
+		return nil, fmt.Errorf("failed to parse response: %w", err)
+	}
+
+	if apiResp.Error != nil {
+		return nil, fmt.Errorf("OpenAI API error: %s", apiResp.Error.Message)
+	}
+
+	if len(apiResp.Data) != len(texts) {
+		return nil, fmt.Errorf("expected %d embeddings, got %d", len(texts), len(apiResp.Data))
+	}
+
+	// Sort by index to maintain order
+	result := make([][]float32, len(texts))
+	for _, item := range apiResp.Data {
+		result[item.Index] = item.Embedding
+	}
+
+	return result, nil
+}
+
+// Dimension returns the embedding dimension
+func (p *OpenAIProvider) Dimension() int {
+	return p.dimension
+}
+
+// =====================================================
+// Ollama Embedding Provider (for local models)
+// =====================================================
+
+// OllamaProvider implements EmbeddingProvider using Ollama's API
+type OllamaProvider struct {
+	baseURL    string
+	model      string
+	dimension  int
+	httpClient *http.Client
+}
+
+// NewOllamaProvider creates a new Ollama embedding provider
+func NewOllamaProvider(baseURL, model string, dimension int) (*OllamaProvider, error) {
+	return &OllamaProvider{
+		baseURL:   baseURL,
+		model:     model,
+		dimension: dimension,
+		httpClient: &http.Client{
+			Timeout: 120 * time.Second, // Ollama can be slow on first inference
+		},
+	}, nil
+}
+
+// ollamaEmbeddingRequest represents the Ollama API request
+type ollamaEmbeddingRequest struct {
+	Model  string `json:"model"`
+	Prompt string `json:"prompt"`
+}
+
+// ollamaEmbeddingResponse represents the Ollama API response
+type ollamaEmbeddingResponse struct {
+	Embedding []float32 `json:"embedding"`
+}
+
+// Embed generates embedding for a single text
+func (p *OllamaProvider) Embed(ctx context.Context, text string) ([]float32, error) {
+	// Truncate text
+	if len(text) > 30000 {
+		text = text[:30000]
+	}
+
+	reqBody := ollamaEmbeddingRequest{
+		Model:  p.model,
+		Prompt: text,
+	}
+
+	body, err := json.Marshal(reqBody)
+	if err != nil {
+		return nil, fmt.Errorf("failed to marshal request: %w", err)
+	}
+
+	req, err := http.NewRequestWithContext(ctx, "POST", p.baseURL+"/api/embeddings", bytes.NewReader(body))
+	if err != nil {
+		return nil, fmt.Errorf("failed to create request: %w", err)
+	}
+
+	req.Header.Set("Content-Type", "application/json")
+
+	resp, err := p.httpClient.Do(req)
+	if err != nil {
+		return nil, fmt.Errorf("failed to call Ollama API: %w", err)
+	}
+	defer resp.Body.Close()
+
+	if resp.StatusCode != http.StatusOK {
+		respBody, _ := io.ReadAll(resp.Body)
+		return nil, fmt.Errorf("Ollama API error (status %d): %s", resp.StatusCode, string(respBody))
+	}
+
+	var apiResp ollamaEmbeddingResponse
+	if err := json.NewDecoder(resp.Body).Decode(&apiResp); err != nil {
+		return nil, fmt.Errorf("failed to parse response: %w", err)
+	}
+
+	return apiResp.Embedding, nil
+}
+
+// EmbedBatch generates embeddings for multiple texts (sequential for Ollama)
+func (p *OllamaProvider) EmbedBatch(ctx context.Context, texts []string) ([][]float32, error) {
+	result := make([][]float32, len(texts))
+
+	for i, text := range texts {
+		embedding, err := p.Embed(ctx, text)
+		if err != nil {
+			return nil, fmt.Errorf("failed to embed text %d: %w", i, err)
+		}
+		result[i] = embedding
+	}
+
+	return result, nil
+}
+
+// Dimension returns the embedding dimension
+func (p *OllamaProvider) Dimension() int {
+	return p.dimension
+}