erster Stand

app/core/ingestion.py

@@ -262,3 +262,41 @@ class IngestionService:
                 self.client.delete(collection_name=f"{self.prefix}_{suffix}", points_selector=selector)
             except Exception:
                 pass
+    
+    def create_from_text(
+            self,
+            markdown_content: str,
+            filename: str,
+            vault_root: str,
+            folder: str = "Inbox"  # Standard-Ordner für neue Files
+        ) -> Dict[str, Any]:
+            """
+            WP-11: Schreibt Text in eine physische Datei und indiziert sie sofort.
+            """
+            # 1. Pfad vorbereiten
+            target_dir = os.path.join(vault_root, folder)
+            os.makedirs(target_dir, exist_ok=True)
+            
+            # Dateiname bereinigen (Sicherheit)
+            safe_filename = os.path.basename(filename)
+            if not safe_filename.endswith(".md"):
+                safe_filename += ".md"
+                
+            file_path = os.path.join(target_dir, safe_filename)
+            
+            # 2. Schreiben (Write to Disk - Single Source of Truth)
+            try:
+                with open(file_path, "w", encoding="utf-8") as f:
+                    f.write(markdown_content)
+            except Exception as e:
+                return {"status": "error", "error": f"Disk write failed: {str(e)}"}
+                
+            # 3. Indizieren (Ingest)
+            # Wir rufen einfach die existierende Logik auf!
+            return self.process_file(
+                file_path=file_path,
+                vault_root=vault_root,
+                apply=True,          # Sofort schreiben
+                force_replace=True,  # Da neu, erzwingen wir Update
+                purge_before=True    # Sauberer Start
+            )

app/main.py

@@ -11,16 +11,18 @@ from .routers.query import router as query_router
 from .routers.graph import router as graph_router
 from .routers.tools import router as tools_router
 from .routers.feedback import router as feedback_router
-# NEU: Chat Router (WP-05)
 from .routers.chat import router as chat_router
 
+# NEU: Ingest Router (WP-11)
+from .routers.ingest import router as ingest_router
+
 try:
     from .routers.admin import router as admin_router
 except Exception:
     admin_router = None
 
 def create_app() -> FastAPI:
-    app = FastAPI(title="mindnet API", version="0.5.0") # Version bump WP-05
+    app = FastAPI(title="mindnet API", version="0.6.0") # Version bump WP-11
     s = get_settings()
 
     @app.get("/healthz")
@@ -34,10 +36,11 @@ def create_app() -> FastAPI:
     app.include_router(graph_router, prefix="/graph", tags=["graph"])
     app.include_router(tools_router, prefix="/tools", tags=["tools"])
     app.include_router(feedback_router, prefix="/feedback", tags=["feedback"])
-    
-    # NEU: Chat Endpoint
     app.include_router(chat_router, prefix="/chat", tags=["chat"])
     
+    # NEU: Registrierung des Ingest-Routers
+    app.include_router(ingest_router, prefix="/ingest", tags=["ingest"])
+
     if admin_router:
         app.include_router(admin_router, prefix="/admin", tags=["admin"])
 

app/routers/ingest.py

@@ -0,0 +1,89 @@
+"""
+app/routers/ingest.py
+API-Endpunkte für WP-11 (Discovery & Persistence).
+"""
+import os
+import time
+from fastapi import APIRouter, HTTPException
+from pydantic import BaseModel
+from typing import Optional, List, Dict, Any
+
+from app.core.ingestion import IngestionService
+from app.services.discovery import DiscoveryService
+
+router = APIRouter()
+
+# --- DTOs ---
+
+class AnalyzeRequest(BaseModel):
+    text: str
+    type: str = "concept"
+
+class SaveRequest(BaseModel):
+    markdown_content: str
+    filename: Optional[str] = None # Optional, fallback auf Timestamp/Titel
+    folder: str = "00_Inbox"       # Zielordner im Vault
+
+class SaveResponse(BaseModel):
+    status: str
+    file_path: str
+    note_id: str
+    stats: Dict[str, Any]
+
+# --- Services ---
+# Instanzierung hier oder via Dependency Injection
+discovery_service = DiscoveryService()
+
+@router.post("/analyze")
+async def analyze_draft(req: AnalyzeRequest):
+    """
+    WP-11 Intelligence: Analysiert einen Entwurf und liefert Link-Vorschläge.
+    """
+    try:
+        result = await discovery_service.analyze_draft(req.text, req.type)
+        return result
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Analysis failed: {str(e)}")
+
+@router.post("/save", response_model=SaveResponse)
+async def save_note(req: SaveRequest):
+    """
+    WP-11 Persistence: Speichert Markdown physisch und indiziert es in Qdrant.
+    """
+    # 1. Vault Root ermitteln
+    vault_root = os.getenv("MINDNET_VAULT_ROOT", "./vault")
+    if not os.path.exists(vault_root):
+        # Fallback für Dev-Umgebungen
+        if os.path.exists("../vault"):
+            vault_root = "../vault"
+        else:
+            raise HTTPException(status_code=500, detail="Vault root not configured or missing")
+
+    # 2. Filename generieren falls fehlend
+    final_filename = req.filename
+    if not final_filename:
+        # Einfacher Fallback: Timestamp
+        final_filename = f"draft_{int(time.time())}.md"
+
+    # 3. Ingestion Service nutzen
+    ingest_service = IngestionService() # nutzt Default-Prefix oder aus Env
+    
+    result = ingest_service.create_from_text(
+        markdown_content=req.markdown_content,
+        filename=final_filename,
+        vault_root=os.path.abspath(vault_root),
+        folder=req.folder
+    )
+    
+    if result.get("status") == "error":
+        raise HTTPException(status_code=500, detail=result.get("error"))
+        
+    return SaveResponse(
+        status="success",
+        file_path=result["path"],
+        note_id=result.get("note_id", "unknown"),
+        stats={
+            "chunks": result.get("chunks_count", 0),
+            "edges": result.get("edges_count", 0)
+        }
+    )

app/services/discovery.py

@@ -0,0 +1,151 @@
+"""
+app/services/discovery.py
+Service für Link-Vorschläge und Knowledge-Discovery (WP-11).
+Analysiert Drafts auf Keywords und semantische Ähnlichkeiten.
+"""
+import logging
+from typing import List, Dict, Any, Set
+from qdrant_client.http import models as rest
+
+from app.core.qdrant import QdrantConfig, get_client
+from app.models.dto import QueryRequest
+from app.core.retriever import hybrid_retrieve
+
+logger = logging.getLogger(__name__)
+
+class DiscoveryService:
+    def __init__(self, collection_prefix: str = "mindnet"):
+        self.prefix = collection_prefix
+        self.cfg = QdrantConfig.from_env()
+        self.cfg.prefix = collection_prefix
+        self.client = get_client(self.cfg)
+
+    async def analyze_draft(self, text: str, current_type: str) -> Dict[str, Any]:
+        """
+        Analysiert einen Draft-Text und schlägt Verlinkungen vor.
+        Kombiniert Exact Match (Titel) und Semantic Match (Vektor).
+        """
+        suggestions = []
+        
+        # 1. Exact Match: Finde Begriffe im Text, die als Notiz-Titel existieren
+        # (Bei sehr großen Vaults >10k sollte dies gecached werden)
+        known_entities = self._fetch_all_titles_and_aliases()
+        found_entities = self._find_entities_in_text(text, known_entities)
+        
+        existing_target_ids = set()
+
+        for entity in found_entities:
+            existing_target_ids.add(entity["id"])
+            suggestions.append({
+                "type": "exact_match",
+                "text_found": entity["match"],
+                "target_title": entity["title"],
+                "target_id": entity["id"],
+                "confidence": 1.0,
+                "reason": "Existierender Notiz-Titel"
+            })
+
+        # 2. Semantic Match: Finde inhaltlich ähnliche Notizen via Vektor-Suche
+        # Wir filtern Ergebnisse heraus, die wir schon per Exact Match gefunden haben.
+        semantic_hits = self._get_semantic_suggestions(text)
+        
+        for hit in semantic_hits:
+            if hit.node_id in existing_target_ids:
+                continue
+            
+            # Schwellwert: Nur relevante Vorschläge anzeigen (z.B. > 0.65)
+            # Wir nutzen den total_score, der bereits Typ-Gewichte enthält.
+            if hit.total_score > 0.65:
+                suggestions.append({
+                    "type": "semantic_match",
+                    "text_found": (hit.source.get("text") or "")[:50] + "...", # Snippet
+                    "target_title": hit.payload.get("title", "Unbekannt"),
+                    "target_id": hit.node_id,
+                    "confidence": round(hit.total_score, 2),
+                    "reason": f"Inhaltliche Ähnlichkeit (Score: {round(hit.total_score, 2)})"
+                })
+
+        return {
+            "draft_length": len(text),
+            "suggestions_count": len(suggestions),
+            "suggestions": suggestions
+        }
+
+    def _fetch_all_titles_and_aliases(self) -> List[Dict]:
+        """Lädt alle Titel und Aliases aus der Notes-Collection."""
+        notes = []
+        next_page = None
+        col_name = f"{self.prefix}_notes"
+        
+        try:
+            while True:
+                # Scroll API nutzen, um effizient alle Metadaten zu laden
+                res, next_page = self.client.scroll(
+                    collection_name=col_name,
+                    limit=1000,
+                    offset=next_page,
+                    with_payload=True,
+                    with_vectors=False
+                )
+                for point in res:
+                    pl = point.payload or {}
+                    notes.append({
+                        "id": pl.get("note_id"),
+                        "title": pl.get("title"),
+                        "aliases": pl.get("aliases", [])
+                    })
+                
+                if next_page is None:
+                    break
+        except Exception as e:
+            logger.error(f"Error fetching titles: {e}")
+            return []
+            
+        return notes
+
+    def _find_entities_in_text(self, text: str, entities: List[Dict]) -> List[Dict]:
+        """
+        Sucht Vorkommen von Titeln im Text (Case-Insensitive).
+        """
+        found = []
+        text_lower = text.lower()
+        
+        for entity in entities:
+            # 1. Titel prüfen
+            title = entity.get("title")
+            if title and title.lower() in text_lower:
+                found.append({
+                    "match": title, 
+                    "title": title, 
+                    "id": entity["id"]
+                })
+                continue # Wenn Titel gefunden, Aliases nicht mehr prüfen (Prio)
+
+            # 2. Aliases prüfen
+            aliases = entity.get("aliases")
+            if aliases and isinstance(aliases, list):
+                for alias in aliases:
+                    if alias and str(alias).lower() in text_lower:
+                        found.append({
+                            "match": alias, 
+                            "title": title, # Target ist immer der Haupt-Titel
+                            "id": entity["id"]
+                        })
+                        break
+        return found
+
+    def _get_semantic_suggestions(self, text: str):
+        """Wrapper um den Hybrid Retriever."""
+        # Wir nutzen eine vereinfachte Query
+        req = QueryRequest(
+            query=text,
+            top_k=5,
+            explain=False
+        )
+        try:
+            # hybrid_retrieve ist sync, wird aber schnell genug sein für diesen Kontext
+            res = hybrid_retrieve(req) 
+            return res.results
+        except Exception as e:
+            logger.error(f"Semantic suggestion failed: {e}")
+            return []