mindnet/app/core/ingestion.py

"""
FILE: app/core/ingestion.py
DESCRIPTION: Haupt-Ingestion-Logik. Transformiert Markdown in den Graphen (Notes, Chunks, Edges).
             WP-20: Integration von Smart Edge Allocation via Hybrid LLM (Gemini/Gemma/OpenRouter).
             WP-22: Integration von Content Lifecycle (Status Gate) und Edge Registry Validation.
             WP-22: Kontextsensitive Kanten-Validierung mit Fundort-Reporting (Zeilennummern).
             WP-22: Multi-Hash Refresh für konsistente Change Detection.
FIX: Robuste Verarbeitung von LLM-Antworten (Dict vs String) zur Vermeidung von Item-Assignment-Errors.
VERSION: 2.11.5
STATUS: Active
DEPENDENCIES: app.core.parser, app.core.note_payload, app.core.chunker, app.services.llm_service, app.services.edge_registry
EXTERNAL_CONFIG: config/types.yaml, config/prompts.yaml
"""
import os
import json
import logging
import asyncio
import time
from typing import Dict, List, Optional, Tuple, Any

# Core Module Imports
from app.core.parser import (
    read_markdown,
    normalize_frontmatter,
    validate_required_frontmatter,
    extract_edges_with_context, 
)
from app.core.note_payload import make_note_payload
from app.core.chunker import assemble_chunks, get_chunk_config
from app.core.chunk_payload import make_chunk_payloads

# Fallback für Edges
try:
    from app.core.derive_edges import build_edges_for_note
except ImportError:
    def build_edges_for_note(*args, **kwargs): return []

from app.core.qdrant import QdrantConfig, get_client, ensure_collections, ensure_payload_indexes
from app.core.qdrant_points import (
    points_for_chunks,
    points_for_note,
    points_for_edges,
    upsert_batch,
)

from app.services.embeddings_client import EmbeddingsClient
from app.services.edge_registry import registry as edge_registry
from app.services.llm_service import LLMService 

logger = logging.getLogger(__name__)

# --- Helper ---
def load_type_registry(custom_path: Optional[str] = None) -> dict:
    """Lädt die types.yaml zur Steuerung der typ-spezifischen Ingestion."""
    import yaml
    from app.config import get_settings
    settings = get_settings()
    path = custom_path or settings.MINDNET_TYPES_FILE
    if not os.path.exists(path): return {}
    try:
        with open(path, "r", encoding="utf-8") as f: return yaml.safe_load(f) or {}
    except Exception: return {}

def resolve_note_type(requested: Optional[str], reg: dict) -> str:
    """Bestimmt den finalen Notiz-Typ (Fallback auf 'concept')."""
    types = reg.get("types", {})
    if requested and requested in types: return requested
    return "concept" 

def effective_chunk_profile_name(fm: dict, note_type: str, reg: dict) -> str:
    """Ermittelt den Namen des zu nutzenden Chunk-Profils."""
    override = fm.get("chunking_profile") or fm.get("chunk_profile")
    if override and isinstance(override, str): return override
    t_cfg = reg.get("types", {}).get(note_type, {})
    if t_cfg:
        cp = t_cfg.get("chunking_profile") or t_cfg.get("chunk_profile")
        if cp: return cp
    return reg.get("defaults", {}).get("chunking_profile", "sliding_standard")

def effective_retriever_weight(fm: dict, note_type: str, reg: dict) -> float:
    """Ermittelt das effektive retriever_weight für das Scoring."""
    override = fm.get("retriever_weight")
    if override is not None:
        try: return float(override)
        except: pass
    t_cfg = reg.get("types", {}).get(note_type, {})
    if t_cfg and "retriever_weight" in t_cfg: return float(t_cfg["retriever_weight"])
    return float(reg.get("defaults", {}).get("retriever_weight", 1.0))


class IngestionService:
    def __init__(self, collection_prefix: str = None):
        from app.config import get_settings
        self.settings = get_settings()
        
        self.prefix = collection_prefix or self.settings.COLLECTION_PREFIX
        self.cfg = QdrantConfig.from_env()
        self.cfg.prefix = self.prefix 
        self.client = get_client(self.cfg)
        self.dim = self.settings.VECTOR_SIZE
        self.registry = load_type_registry()
        self.embedder = EmbeddingsClient()
        self.llm = LLMService() 
        
        self.active_hash_mode = self.settings.CHANGE_DETECTION_MODE
        
        try:
            ensure_collections(self.client, self.prefix, self.dim)
            ensure_payload_indexes(self.client, self.prefix)
        except Exception as e:
            logger.warning(f"DB init warning: {e}")

    def _get_chunk_config_by_profile(self, profile_name: str, note_type: str) -> Dict[str, Any]:
        """Holt die Chunker-Parameter für ein spezifisches Profil."""
        profiles = self.registry.get("chunking_profiles", {})
        if profile_name in profiles:
            cfg = profiles[profile_name].copy()
            if "overlap" in cfg and isinstance(cfg["overlap"], list): 
                cfg["overlap"] = tuple(cfg["overlap"])
            return cfg
        return get_chunk_config(note_type)

    async def _perform_smart_edge_allocation(self, text: str, note_id: str) -> List[Dict]:
        """
        WP-20: Nutzt den Hybrid LLM Service für die semantische Kanten-Extraktion.
        QUOTEN-SCHUTZ: Bevorzugt OpenRouter (Gemma 2), um Gemini-Tageslimits zu schonen.
        """
        provider = "openrouter" if self.settings.OPENROUTER_API_KEY else self.settings.MINDNET_LLM_PROVIDER
        model = self.settings.GEMMA_MODEL
        
        logger.info(f"🚀 [Ingestion] Turbo-Mode: Extracting edges for '{note_id}' using {model} on {provider}")
        
        # WP-22: Hole valide Typen für das Prompt-Template
        edge_registry.ensure_latest()
        valid_types_str = ", ".join(sorted(list(edge_registry.valid_types)))
        
        template = self.llm.get_prompt("edge_extraction", provider)
        
        try:
            # Befülle das Template (v2.5.0 erwartet valid_types)
            prompt = template.format(
                text=text[:6000], 
                note_id=note_id,
                valid_types=valid_types_str
            )

            response_json = await self.llm.generate_raw_response(
                prompt=prompt,
                priority="background",
                force_json=True,
                provider=provider,
                model_override=model
            )
            
            # Robustes Parsing (WP-20 Fix für 'str' object assignment error)
            raw_data = json.loads(response_json)
            processed_edges = []
            
            # Das LLM liefert manchmal ein Dict mit einem Key statt einer Liste
            if isinstance(raw_data, dict):
                logger.debug(f"ℹ️ [Ingestion] LLM returned dict for {note_id}, attempting recovery.")
                for key in ["edges", "links", "results", "kanten"]:
                    if key in raw_data and isinstance(raw_data[key], list):
                        raw_data = raw_data[key]
                        break
            
            if not isinstance(raw_data, list):
                logger.warning(f"⚠️ [Ingestion] LLM output for {note_id} is not a list: {type(raw_data)}")
                return []

            for item in raw_data:
                # Fall 1: Element ist bereits ein Dict (Idealfall)
                if isinstance(item, dict) and "to" in item:
                    item["provenance"] = "semantic_ai"
                    item["line"] = f"ai-{provider}"
                    processed_edges.append(item)
                
                # Fall 2: Element ist ein String (z.B. "kind:target") -> Umwandlung
                elif isinstance(item, str) and ":" in item:
                    parts = item.split(":", 1)
                    processed_edges.append({
                        "to": parts[1].strip(),
                        "kind": parts[0].strip(),
                        "provenance": "semantic_ai",
                        "line": f"ai-{provider}"
                    })
                else:
                    logger.debug(f"⏩ [Ingestion] Skipping unparseable AI edge: {item}")
                    
            return processed_edges

        except Exception as e:
            logger.warning(f"⚠️ [Ingestion] Smart Edge Allocation failed for {note_id} on {provider}: {e}")
            return []

    async def process_file(
        self, 
        file_path: str, 
        vault_root: str,
        force_replace: bool = False,
        apply: bool = False,
        purge_before: bool = False,
        note_scope_refs: bool = False,
        hash_source: str = "parsed",
        hash_normalize: str = "canonical"
    ) -> Dict[str, Any]:
        """Verarbeitet eine Markdown-Datei und schreibt sie in den Graphen."""
        result = {"path": file_path, "status": "skipped", "changed": False, "error": None}

        # 1. Parse & Frontmatter Validation
        try:
            parsed = read_markdown(file_path)
            if not parsed: return {**result, "error": "Empty or unreadable file"}
            fm = normalize_frontmatter(parsed.frontmatter)
            validate_required_frontmatter(fm)
        except Exception as e:
            logger.error(f"Validation failed for {file_path}: {e}")
            return {**result, "error": f"Validation failed: {str(e)}"}

        # --- WP-22: Content Lifecycle Gate ---
        status = fm.get("status", "draft").lower().strip()
        if status in ["system", "template", "archive", "hidden"]:
            return {**result, "status": "skipped", "reason": f"lifecycle_status_{status}"}

        # 2. Type & Config Resolution
        note_type = resolve_note_type(fm.get("type"), self.registry)
        fm["type"] = note_type
        effective_profile = effective_chunk_profile_name(fm, note_type, self.registry)
        effective_weight = effective_retriever_weight(fm, note_type, self.registry)
        
        fm["chunk_profile"] = effective_profile 
        fm["retriever_weight"] = effective_weight

        # 3. Build Note Payload
        try:
            note_pl = make_note_payload(parsed, vault_root=vault_root, hash_normalize=hash_normalize, hash_source=hash_source, file_path=file_path)
            if not note_pl.get("fulltext"): note_pl["fulltext"] = getattr(parsed, "body", "") or ""
            note_pl["retriever_weight"] = effective_weight
            note_pl["chunk_profile"] = effective_profile
            note_pl["status"] = status
            note_id = note_pl["note_id"]
        except Exception as e:
             return {**result, "error": f"Payload build failed: {str(e)}"}

        # 4. Change Detection (WP-22 Multi-Hash)
        old_payload = None
        if not force_replace:
            old_payload = self._fetch_note_payload(note_id)
        
        has_old = old_payload is not None
        check_key = f"{self.active_hash_mode}:{hash_source}:{hash_normalize}"
        
        old_hashes = (old_payload or {}).get("hashes", {})
        old_hash = old_hashes.get(check_key) if isinstance(old_hashes, dict) else None
        new_hash = note_pl.get("hashes", {}).get(check_key)
        
        hash_changed = (old_hash != new_hash)
        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

        if not should_write:
            return {**result, "status": "unchanged", "note_id": note_id}

        if not apply:
            return {**result, "status": "dry-run", "changed": True, "note_id": note_id}

        # 5. Processing (Chunking, Embedding, Edge Generation)
        try:
            body_text = getattr(parsed, "body", "") or ""
            
            # WP-22 STABILITY PATCH: Prüfen, ob ensure_latest existiert
            if hasattr(edge_registry, "ensure_latest"):
                edge_registry.ensure_latest()

            chunk_config = self._get_chunk_config_by_profile(effective_profile, note_type)
            chunks = await assemble_chunks(fm["id"], body_text, fm["type"], config=chunk_config)
            chunk_pls = make_chunk_payloads(fm, note_pl["path"], chunks, note_text=body_text)
            
            vecs = []
            if chunk_pls:
                texts = [c.get("window") or c.get("text") or "" for c in chunk_pls]
                vecs = await self.embedder.embed_documents(texts)
            
            # --- WP-22/WP-20: Kanten-Extraktion & Validierung ---
            edges = []
            context = {"file": file_path, "note_id": note_id}

            # A. Explizite User-Kanten
            explicit_edges = extract_edges_with_context(parsed)
            for e in explicit_edges:
                e["kind"] = edge_registry.resolve(edge_type=e["kind"], provenance="explicit", context={**context, "line": e.get("line")})
                edges.append(e)

            # B. WP-20: Smart AI Edges (Hybrid Turbo Acceleration)
            ai_edges = await self._perform_smart_edge_allocation(body_text, note_id)
            for e in ai_edges:
                # Validierung gegen EdgeRegistry (Vermeidet 'Transition' etc.)
                valid_kind = edge_registry.resolve(edge_type=e.get("kind"), provenance="semantic_ai", context={**context, "line": e.get("line")})
                e["kind"] = valid_kind
                edges.append(e)

            # C. System-Kanten (Struktur)
            try:
                raw_system_edges = build_edges_for_note(note_id, chunk_pls, note_level_references=note_pl.get("references", []), include_note_scope_refs=note_scope_refs)
            except TypeError:
                raw_system_edges = build_edges_for_note(note_id, chunk_pls)
            
            for e in raw_system_edges:
                valid_kind = edge_registry.resolve(edge_type=e.get("kind", "belongs_to"), provenance="structure", context={**context, "line": "system"})
                if valid_kind:
                    e["kind"] = valid_kind
                    edges.append(e)

        except Exception as e:
            logger.error(f"Processing failed for {file_path}: {e}", exc_info=True)
            return {**result, "error": f"Processing failed: {str(e)}"}

        # 6. Upsert
        try:
            if purge_before and has_old: self._purge_artifacts(note_id)

            n_name, n_pts = points_for_note(self.prefix, note_pl, None, self.dim)
            upsert_batch(self.client, n_name, n_pts)

            if chunk_pls and vecs:
                c_name, c_pts = points_for_chunks(self.prefix, chunk_pls, vecs)
                upsert_batch(self.client, c_name, c_pts)
            
            if edges:
                e_name, e_pts = points_for_edges(self.prefix, edges)
                upsert_batch(self.client, e_name, e_pts)

            return {"path": file_path, "status": "success", "changed": True, "note_id": note_id, "chunks_count": len(chunk_pls), "edges_count": len(edges)}
        except Exception as e:
            logger.error(f"Upsert failed for {note_id}: {e}", exc_info=True)
            return {**result, "error": f"DB Upsert failed: {e}"}
            
    def _fetch_note_payload(self, note_id: str) -> Optional[dict]:
        from qdrant_client.http import models as rest
        col = f"{self.prefix}_notes"
        try:
            f = rest.Filter(must=[rest.FieldCondition(key="note_id", match=rest.MatchValue(value=note_id))])
            pts, _ = self.client.scroll(collection_name=col, scroll_filter=f, limit=1, with_payload=True)
            return pts[0].payload if pts else None
        except: return None

    def _artifacts_missing(self, note_id: str) -> Tuple[bool, bool]:
        from qdrant_client.http import models as rest
        try:
            f = rest.Filter(must=[rest.FieldCondition(key="note_id", match=rest.MatchValue(value=note_id))])
            c_pts, _ = self.client.scroll(collection_name=f"{self.prefix}_chunks", scroll_filter=f, limit=1)
            e_pts, _ = self.client.scroll(collection_name=f"{self.prefix}_edges", scroll_filter=f, limit=1)
            return (not bool(c_pts)), (not bool(e_pts))
        except: return True, True

    def _purge_artifacts(self, note_id: str):
        from qdrant_client.http import models as rest
        f = rest.Filter(must=[rest.FieldCondition(key="note_id", match=rest.MatchValue(value=note_id))])
        selector = rest.FilterSelector(filter=f)
        for suffix in ["chunks", "edges"]:
            try: self.client.delete(collection_name=f"{self.prefix}_{suffix}", points_selector=selector)
            except Exception: pass
    
    async def create_from_text(self, markdown_content: str, filename: str, vault_root: str, folder: str = "00_Inbox") -> Dict[str, Any]:
        """Hilfsmethode zur Erstellung einer Note aus einem Textstream."""
        target_dir = os.path.join(vault_root, folder)
        os.makedirs(target_dir, exist_ok=True)
        file_path = os.path.join(target_dir, filename)
        try:
            with open(file_path, "w", encoding="utf-8") as f:
                f.write(markdown_content)
                f.flush()
                os.fsync(f.fileno())
            await asyncio.sleep(0.1) 
            logger.info(f"Written file to {file_path}")
        except Exception as e:
            return {"status": "error", "error": f"Disk write failed: {str(e)}"}
        return await self.process_file(file_path=file_path, vault_root=vault_root, apply=True, force_replace=True, purge_before=True)