Initial commit: my-brain-importer RAG knowledge management agent

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-03-10 21:07:23 +01:00
commit a3bcac55fb
12 changed files with 880 additions and 0 deletions
--- a/internal/brain/ingest_json.go
+++ b/internal/brain/ingest_json.go
@@ -0,0 +1,99 @@
+// ingest_json.go – Importiert KI-Bildbeschreibungen aus einer JSON-Datei in Qdrant
+package brain
+
+import (
+	"context"
+	"encoding/json"
+	"fmt"
+	"log"
+	"os"
+
+	pb "github.com/qdrant/go-client/qdrant"
+	openai "github.com/sashabaranov/go-openai"
+	"google.golang.org/grpc/metadata"
+
+	"my-brain-importer/internal/config"
+)
+
+// ImageEntry entspricht der JSON-Ausgabe von analyze-images.go
+type ImageEntry struct {
+	FilePath    string `json:"file_path"`
+	FileName    string `json:"file_name"`
+	Description string `json:"description"`
+}
+
+// RunIngestJSON importiert Bildbeschreibungen aus einer JSON-Datei in Qdrant.
+func RunIngestJSON(inputFile string) {
+	fmt.Printf("📂 Lade \"%s\"...\n", inputFile)
+	raw, err := os.ReadFile(inputFile)
+	if err != nil {
+		log.Fatalf("❌ Datei nicht gefunden: %v", err)
+	}
+
+	var entries []ImageEntry
+	if err := json.Unmarshal(raw, &entries); err != nil {
+		log.Fatalf("❌ JSON Fehler: %v", err)
+	}
+	if len(entries) == 0 {
+		log.Fatal("❌ Keine Einträge in JSON")
+	}
+	fmt.Printf("✅ %d Einträge geladen\n\n", len(entries))
+
+	ctx := context.Background()
+	ctx = metadata.AppendToOutgoingContext(ctx, "api-key", config.Cfg.Qdrant.APIKey)
+
+	conn := config.NewQdrantConn()
+	defer conn.Close()
+
+	ensureCollection(ctx, pb.NewCollectionsClient(conn))
+	pointsClient := pb.NewPointsClient(conn)
+	embClient := config.NewEmbeddingClient()
+
+	fmt.Printf("🤖 Embedding: %s (%s)\n\n", config.Cfg.Embedding.Model, config.Cfg.Embedding.URL)
+
+	success := 0
+	for i, entry := range entries {
+		fmt.Printf("[%d/%d] 🔄 %s\n", i+1, len(entries), entry.FileName)
+
+		embResp, err := embClient.CreateEmbeddings(ctx, openai.EmbeddingRequest{
+			Input: []string{entry.Description},
+			Model: openai.EmbeddingModel(config.Cfg.Embedding.Model),
+		})
+		if err != nil {
+			log.Printf("   ❌ Embedding Fehler: %v\n", err)
+			continue
+		}
+
+		_, err = pointsClient.Upsert(ctx, &pb.UpsertPoints{
+			CollectionName: config.Cfg.Qdrant.Collection,
+			Points: []*pb.PointStruct{
+				{
+					Id: &pb.PointId{
+						PointIdOptions: &pb.PointId_Uuid{
+							Uuid: generateID(entry.Description, entry.FileName),
+						},
+					},
+					Vectors: &pb.Vectors{
+						VectorsOptions: &pb.Vectors_Vector{
+							Vector: &pb.Vector{Data: embResp.Data[0].Embedding},
+						},
+					},
+					Payload: map[string]*pb.Value{
+						"text":   {Kind: &pb.Value_StringValue{StringValue: entry.Description}},
+						"source": {Kind: &pb.Value_StringValue{StringValue: entry.FileName}},
+						"path":   {Kind: &pb.Value_StringValue{StringValue: entry.FilePath}},
+						"type":   {Kind: &pb.Value_StringValue{StringValue: "image"}},
+					},
+				},
+			},
+		})
+		if err != nil {
+			log.Printf("   ❌ Speichern Fehler: %v\n", err)
+		} else {
+			success++
+		}
+	}
+
+	fmt.Printf("\n✅ Fertig: %d von %d Bildern importiert\n", success, len(entries))
+	fmt.Printf("🌐 Dashboard: http://%s:6333/dashboard\n", config.Cfg.Qdrant.Host)
+}