app/core/ingestion.py

@@ -3,13 +3,14 @@ app/core/ingestion.py
 
 Zentraler Service für die Transformation von Markdown-Dateien in Qdrant-Objekte (Notes, Chunks, Edges).
 Dient als Shared Logic für:
-1. CLI-Imports (scripts/import_markdown.py)
+1. CLI-Imports (scripts/import_markdown.py) - muss ggf. angepasst werden auf Async!
 2. API-Uploads (WP-11)
 """
 import os
-import json
-from typing import Dict, List, Optional, Tuple, Any, Set
+import logging
+from typing import Dict, List, Optional, Tuple, Any
 
+# Core Module Imports
 from app.core.parser import (
     read_markdown,
     normalize_frontmatter,
@@ -18,10 +19,12 @@ from app.core.parser import (
 from app.core.note_payload import make_note_payload
 from app.core.chunker import assemble_chunks
 from app.core.chunk_payload import make_chunk_payloads
-# Fallback Imports wie im Original-Skript
+
+# Fallback für Edges Import
 try:
     from app.core.derive_edges import build_edges_for_note
 except ImportError:
+    # Fallback falls Dateiname anders ist
     from app.core.edges import build_edges_for_note # type: ignore
 
 from app.core.qdrant import QdrantConfig, get_client, ensure_collections, ensure_payload_indexes
@@ -32,18 +35,16 @@ from app.core.qdrant_points import (
     upsert_batch,
 )
 
-# Optionales Embedding
-try:
-    from app.core.embed import embed_texts
-except ImportError:
-    embed_texts = None
+# WICHTIG: Wir nutzen den API-Client für Embeddings
+from app.services.embeddings_client import EmbeddingsClient
 
-# --- Helper für Type-Registry (ausgelagert aus Script) ---
+logger = logging.getLogger(__name__)
+
+# --- Helper für Type-Registry ---
 def load_type_registry(custom_path: Optional[str] = None) -> dict:
     import yaml
     path = custom_path or os.getenv("MINDNET_TYPES_FILE", "config/types.yaml")
     if not os.path.exists(path):
-        # Fallback auf Root-Ebene (für Tests/CLI)
         if os.path.exists("types.yaml"): 
             path = "types.yaml"
         else:
@@ -58,14 +59,12 @@ def resolve_note_type(requested: Optional[str], reg: dict) -> str:
     types = reg.get("types", {})
     if requested and requested in types:
         return requested
-    return "concept" # Default Fallback
+    return "concept" 
 
 def effective_chunk_profile(note_type: str, reg: dict) -> str:
-    # 1. Specific Type
     t_cfg = reg.get("types", {}).get(note_type, {})
     if t_cfg and t_cfg.get("chunk_profile"):
         return t_cfg.get("chunk_profile")
-    # 2. Defaults
     return reg.get("defaults", {}).get("chunk_profile", "default")
 
 def effective_retriever_weight(note_type: str, reg: dict) -> float:
@@ -79,18 +78,21 @@ class IngestionService:
     def __init__(self, collection_prefix: str = "mindnet"):
         self.prefix = collection_prefix
         self.cfg = QdrantConfig.from_env()
-        self.cfg.prefix = collection_prefix # Override env if needed
+        self.cfg.prefix = collection_prefix 
         self.client = get_client(self.cfg)
         self.dim = self.cfg.dim
         
         # Registry laden
         self.registry = load_type_registry()
         
+        # Embedding Service initialisieren
+        self.embedder = EmbeddingsClient()
+        
         # Init DB Checks
         ensure_collections(self.client, self.prefix, self.dim)
         ensure_payload_indexes(self.client, self.prefix)
 
-    def process_file(
+    async def process_file(
         self, 
         file_path: str, 
         vault_root: str,
@@ -103,8 +105,7 @@ class IngestionService:
         hash_normalize: str = "canonical"
     ) -> Dict[str, Any]:
         """
-        Verarbeitet eine einzelne Datei.
-        Return: Summary Dict (Erfolg, Änderungen, Stats).
+        Verarbeitet eine einzelne Datei (ASYNC).
         """
         result = {
             "path": file_path,
@@ -129,7 +130,6 @@ class IngestionService:
         fm["type"] = note_type
         fm["chunk_profile"] = effective_chunk_profile(note_type, self.registry)
         
-        # Weight Resolution (Frontmatter override > Registry)
         weight = fm.get("retriever_weight")
         if weight is None:
             weight = effective_retriever_weight(note_type, self.registry)
@@ -145,7 +145,6 @@ class IngestionService:
                 hash_source=hash_source,
                 file_path=file_path
             )
-            # Ensure fulltext & weight
             if not note_pl.get("fulltext"):
                 note_pl["fulltext"] = getattr(parsed, "body", "") or ""
             note_pl["retriever_weight"] = fm["retriever_weight"]
@@ -154,21 +153,17 @@ class IngestionService:
         except Exception as e:
              return {**result, "error": f"Payload build failed: {str(e)}"}
 
-        # 4. Change Detection (Hash Check)
-        # Wir holen den alten Payload aus Qdrant, wenn wir nicht forcen
+        # 4. Change Detection
         old_payload = None
         if not force_replace:
             old_payload = self._fetch_note_payload(note_id)
         
         has_old = old_payload is not None
         key_current = f"{hash_mode}:{hash_source}:{hash_normalize}"
-        
         old_hash = (old_payload or {}).get("hashes", {}).get(key_current)
         new_hash = note_pl.get("hashes", {}).get(key_current)
         
         hash_changed = (old_hash != new_hash)
-        
-        # Artefakte prüfen (Chunks/Edges)
         chunks_missing, edges_missing = self._artifacts_missing(note_id)
         
         should_write = force_replace or (not has_old) or hash_changed or chunks_missing or edges_missing
@@ -185,14 +180,29 @@ class IngestionService:
             chunks = assemble_chunks(fm["id"], body_text, fm["type"])
             chunk_pls = make_chunk_payloads(fm, note_pl["path"], chunks, note_text=body_text)
             
-            # Embeddings
+            # --- EMBEDDING FIX ---
             vecs = []
-            if embed_texts and chunk_pls:
+            if chunk_pls:
                 texts = [c.get("window") or c.get("text") or "" for c in chunk_pls]
-                vecs = embed_texts(texts)
-            else:
-                vecs = [[0.0] * self.dim for _ in chunk_pls]
-
+                try:
+                    # Async Aufruf des Embedders
+                    if hasattr(self.embedder, 'embed_documents'):
+                        vecs = await self.embedder.embed_documents(texts)
+                    else:
+                        # Fallback Loop falls Client kein Batch unterstützt
+                        for t in texts:
+                            v = await self.embedder.embed_query(t)
+                            vecs.append(v)
+                            
+                    # Validierung der Dimensionen
+                    if vecs and len(vecs) > 0:
+                        dim_got = len(vecs[0])
+                        if dim_got != self.dim:
+                            raise ValueError(f"Vector dimension mismatch. Expected {self.dim}, got {dim_got}")
+                except Exception as e:
+                    logger.error(f"Embedding generation failed: {e}")
+                    raise RuntimeError(f"Embedding failed: {e}")
+            
             # Edges
             note_refs = note_pl.get("references") or []
             edges = build_edges_for_note(
@@ -208,16 +218,13 @@ class IngestionService:
         if purge_before and has_old:
             self._purge_artifacts(note_id)
 
-        # Upsert Note
         n_name, n_pts = points_for_note(self.prefix, note_pl, None, self.dim)
         upsert_batch(self.client, n_name, n_pts)
 
-        # Upsert Chunks
         if chunk_pls:
             c_name, c_pts = points_for_chunks(self.prefix, chunk_pls, vecs)
             upsert_batch(self.client, c_name, c_pts)
 
-        # Upsert Edges
         if edges:
             e_name, e_pts = points_for_edges(self.prefix, edges)
             upsert_batch(self.client, e_name, e_pts)
@@ -245,12 +252,8 @@ class IngestionService:
         c_col = f"{self.prefix}_chunks"
         e_col = f"{self.prefix}_edges"
         f = rest.Filter(must=[rest.FieldCondition(key="note_id", match=rest.MatchValue(value=note_id))])
-        
-        # Check Chunks
         c_pts, _ = self.client.scroll(collection_name=c_col, scroll_filter=f, limit=1)
-        # Check Edges
         e_pts, _ = self.client.scroll(collection_name=e_col, scroll_filter=f, limit=1)
-        
         return (not bool(c_pts)), (not bool(e_pts))
 
     def _purge_artifacts(self, note_id: str):
@@ -263,7 +266,7 @@ class IngestionService:
             except Exception:
                 pass
     
-    def create_from_text(
+    async def create_from_text(
             self,
             markdown_content: str,
             filename: str,
@@ -272,20 +275,18 @@ class IngestionService:
         ) -> Dict[str, Any]:
             """
             WP-11 Persistence: Schreibt Text sicher und indiziert ihn.
-            Erstellt Verzeichnisse automatisch.
             """
-            # 1. Zielordner vorbereiten
+            # 1. Zielordner
             target_dir = os.path.join(vault_root, folder)
             try:
                 os.makedirs(target_dir, exist_ok=True)
             except Exception as e:
                 return {"status": "error", "error": f"Could not create folder {target_dir}: {e}"}
             
-            # 2. Dateiname bereinigen
+            # 2. Dateiname
             safe_filename = os.path.basename(filename)
             if not safe_filename.endswith(".md"):
                 safe_filename += ".md"
-                
             file_path = os.path.join(target_dir, safe_filename)
             
             # 3. Schreiben
@@ -295,8 +296,8 @@ class IngestionService:
             except Exception as e:
                 return {"status": "error", "error": f"Disk write failed at {file_path}: {str(e)}"}
                 
-            # 4. Indizieren (Single File Upsert)
-            return self.process_file(
+            # 4. Indizieren (Async Aufruf!)
+            return await self.process_file(
                 file_path=file_path,
                 vault_root=vault_root,
                 apply=True,