WP4d #16
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@ -1,7 +1,8 @@
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"""
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FILE: app/core/chunking/chunking_processor.py
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DESCRIPTION: Der zentrale Orchestrator für das Chunking-System.
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AUDIT v3.3.3: Wiederherstellung der "Gold-Standard" Qualität.
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AUDIT v3.3.4: Wiederherstellung der "Gold-Standard" Qualität.
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- Fix: Synchronisierung der Parameter (context_prefix) für alle Strategien.
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- Integriert physikalische Kanten-Injektion (Propagierung).
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- Stellt H1-Kontext-Fenster sicher.
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- Baut den Candidate-Pool für die WP-15b Ingestion auf.
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@ -30,16 +31,19 @@ async def assemble_chunks(note_id: str, md_text: str, note_type: str, config: Op
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fm, body_text = extract_frontmatter_from_text(md_text)
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blocks, doc_title = parse_blocks(md_text)
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# Vorbereitung des H1-Präfix für die Embedding-Fenster
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# Vorbereitung des H1-Präfix für die Embedding-Fenster (Breadcrumbs)
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h1_prefix = f"# {doc_title}" if doc_title else ""
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# 2. Anwendung der Splitting-Strategie
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# Wir übergeben den Dokument-Titel/Präfix für die Window-Bildung.
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# Alle Strategien nutzen nun einheitlich context_prefix für die Window-Bildung.
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if config.get("strategy") == "by_heading":
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chunks = await asyncio.to_thread(strategy_by_heading, blocks, config, note_id, doc_title)
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chunks = await asyncio.to_thread(
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strategy_by_heading, blocks, config, note_id, context_prefix=h1_prefix
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)
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else:
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# sliding_window nutzt nun den context_prefix für das Window-Feld.
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chunks = await asyncio.to_thread(strategy_sliding_window, blocks, config, note_id, context_prefix=h1_prefix)
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chunks = await asyncio.to_thread(
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strategy_sliding_window, blocks, config, note_id, context_prefix=h1_prefix
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)
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if not chunks:
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return []
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@ -52,6 +56,7 @@ async def assemble_chunks(note_id: str, md_text: str, note_type: str, config: Op
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# Zuerst die explizit im Text vorhandenen Kanten sammeln.
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for ch in chunks:
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# Wir extrahieren aus dem bereits (durch Propagation) angereicherten Text.
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# ch.candidate_pool wird im Modell-Konstruktor als leere Liste initialisiert.
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for e_str in parse_edges_robust(ch.text):
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parts = e_str.split(':', 1)
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if len(parts) == 2:
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@ -71,7 +76,7 @@ async def assemble_chunks(note_id: str, md_text: str, note_type: str, config: Op
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parts = e_str.split(':', 1)
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if len(parts) == 2:
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k, t = parts
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# Diese Kanten werden als "Global Pool" markiert für die spätere KI-Prüfung.
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# Diese Kanten werden als "global_pool" markiert für die spätere KI-Prüfung.
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for ch in chunks:
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ch.candidate_pool.append({"kind": k, "to": t, "provenance": "global_pool"})
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@ -80,6 +85,7 @@ async def assemble_chunks(note_id: str, md_text: str, note_type: str, config: Op
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seen = set()
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unique = []
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for c in ch.candidate_pool:
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# Eindeutigkeit über Typ, Ziel und Herkunft (Provenance)
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key = (c["kind"], c["to"], c["provenance"])
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if key not in seen:
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seen.add(key)
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|
|
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@ -1,7 +1,8 @@
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"""
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FILE: app/core/chunking/chunking_propagation.py
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DESCRIPTION: Injiziert Sektions-Kanten physisch in den Text (Embedding-Enrichment).
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Fix v3.3.5: Erkennt Wikilink-Targets, um Dopplungen zu verhindern.
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Fix v3.3.6: Nutzt robustes Parsing zur Erkennung vorhandener Kanten,
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um Dopplungen direkt hinter [!edge] Callouts format-agnostisch zu verhindern.
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"""
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from typing import List, Dict, Set
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from .chunking_models import Chunk
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@ -34,15 +35,19 @@ def propagate_section_edges(chunks: List[Chunk]) -> List[Chunk]:
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if not edges_to_add:
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continue
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injections = []
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for e_str in edges_to_add:
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kind, target = e_str.split(':', 1)
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# Vorhandene Kanten (Typ:Ziel) in DIESEM Chunk ermitteln,
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# um Dopplungen (z.B. durch Callouts) zu vermeiden.
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existing_edges = parse_edges_robust(ch.text)
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# DER FIX: Wir prüfen, ob das Ziel (target) bereits im Text vorkommt.
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# Wir suchen nach [[target]] (Callout-Stil) oder |target]] (Rel-Stil).
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if f"[[{target}]]" in ch.text or f"|{target}]]" in ch.text:
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injections = []
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# Sortierung für deterministische Ergebnisse
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for e_str in sorted(list(edges_to_add)):
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# Wenn die Kante (Typ + Ziel) bereits vorhanden ist (egal welches Format),
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# überspringen wir die Injektion für diesen Chunk.
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if e_str in existing_edges:
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continue
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kind, target = e_str.split(':', 1)
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injections.append(f"[[rel:{kind}|{target}]]")
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if injections:
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|
|
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|
@ -1,25 +1,29 @@
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"""
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FILE: app/core/chunking/chunking_strategies.py
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DESCRIPTION: Strategien für atomares Sektions-Chunking v3.9.8.
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DESCRIPTION: Strategien für atomares Sektions-Chunking v3.9.9.
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Implementiert das 'Pack-and-Carry-Over' Verfahren nach Regel 1-3.
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- Keine redundante Kanten-Injektion.
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- Strikte Einhaltung von Sektionsgrenzen via Look-Ahead.
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- Fix: Synchronisierung der Parameter mit dem Orchestrator (context_prefix).
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"""
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from typing import List, Dict, Any, Optional
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from .chunking_models import RawBlock, Chunk
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from .chunking_utils import estimate_tokens
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from .chunking_parser import split_sentences
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def _create_win(doc_title: str, sec_title: Optional[str], text: str) -> str:
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def _create_win(context_prefix: str, sec_title: Optional[str], text: str) -> str:
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"""Baut den Breadcrumb-Kontext für das Embedding-Fenster."""
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parts = [doc_title] if doc_title else []
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if sec_title and sec_title != doc_title: parts.append(sec_title)
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parts = [context_prefix] if context_prefix else []
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# Verhindert Dopplung, falls der Context-Prefix (H1) bereits den Sektionsnamen enthält
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if sec_title and f"# {sec_title}" != context_prefix and sec_title not in (context_prefix or ""):
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parts.append(sec_title)
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prefix = " > ".join(parts)
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return f"{prefix}\n{text}".strip() if prefix else text
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def strategy_by_heading(blocks: List[RawBlock], config: Dict[str, Any], note_id: str, doc_title: str = "") -> List[Chunk]:
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def strategy_by_heading(blocks: List[RawBlock], config: Dict[str, Any], note_id: str, context_prefix: str = "") -> List[Chunk]:
|
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"""
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Universelle Heading-Strategie mit Carry-Over Logik.
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Synchronisiert auf context_prefix für Kompatibilität mit dem Orchestrator.
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"""
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smart_edge = config.get("enable_smart_edge_allocation", True)
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strict = config.get("strict_heading_split", False)
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|
|
@ -34,7 +38,7 @@ def strategy_by_heading(blocks: List[RawBlock], config: Dict[str, Any], note_id:
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def _emit(txt, title, path):
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"""Schreibt den finalen Chunk ohne Text-Modifikationen."""
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idx = len(chunks)
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win = _create_win(doc_title, title, txt)
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win = _create_win(context_prefix, title, txt)
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chunks.append(Chunk(
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id=f"{note_id}#c{idx:02d}", note_id=note_id, index=idx,
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text=txt, window=win, token_count=estimate_tokens(txt),
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|
|
@ -139,7 +143,7 @@ def strategy_by_heading(blocks: List[RawBlock], config: Dict[str, Any], note_id:
|
|||
|
||||
return chunks
|
||||
|
||||
def strategy_sliding_window(blocks: List[RawBlock], config: Dict[str, Any], note_id: str, doc_title: str = "") -> List[Chunk]:
|
||||
def strategy_sliding_window(blocks: List[RawBlock], config: Dict[str, Any], note_id: str, context_prefix: str = "") -> List[Chunk]:
|
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"""Standard-Sliding-Window für flache Texte ohne Sektionsfokus."""
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target = config.get("target", 400); max_tokens = config.get("max", 600)
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chunks: List[Chunk] = []; buf: List[RawBlock] = []
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|
|
@ -149,14 +153,14 @@ def strategy_sliding_window(blocks: List[RawBlock], config: Dict[str, Any], note
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curr_tokens = sum(estimate_tokens(x.text) for x in buf) if buf else 0
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if curr_tokens + b_tokens > max_tokens and buf:
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txt = "\n\n".join([x.text for x in buf]); idx = len(chunks)
|
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win = _create_win(doc_title, buf[0].section_title, txt)
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win = _create_win(context_prefix, buf[0].section_title, txt)
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chunks.append(Chunk(id=f"{note_id}#c{idx:02d}", note_id=note_id, index=idx, text=txt, window=win, token_count=curr_tokens, section_title=buf[0].section_title, section_path=buf[0].section_path, neighbors_prev=None, neighbors_next=None))
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||||
buf = []
|
||||
buf.append(b)
|
||||
|
||||
if buf:
|
||||
txt = "\n\n".join([x.text for x in buf]); idx = len(chunks)
|
||||
win = _create_win(doc_title, buf[0].section_title, txt)
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win = _create_win(context_prefix, buf[0].section_title, txt)
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||||
chunks.append(Chunk(id=f"{note_id}#c{idx:02d}", note_id=note_id, index=idx, text=txt, window=win, token_count=estimate_tokens(txt), section_title=buf[0].section_title, section_path=buf[0].section_path, neighbors_prev=None, neighbors_next=None))
|
||||
|
||||
return chunks
|
||||
|
|
@ -54,6 +54,7 @@ def _get_hash_source_content(n: Dict[str, Any], mode: str) -> str:
|
|||
fm = n.get("frontmatter") or {}
|
||||
meta_parts = []
|
||||
# Wir inkludieren alle Felder, die das Chunking oder Retrieval beeinflussen
|
||||
# Jede Änderung hier führt nun zwingend zu einem neuen Full-Hash
|
||||
keys = [
|
||||
"title", "type", "status", "tags",
|
||||
"chunking_profile", "chunk_profile",
|
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|
|
@ -143,6 +144,7 @@ def make_note_payload(note: Any, *args, **kwargs) -> Dict[str, Any]:
|
|||
}
|
||||
|
||||
# --- MULTI-HASH ---
|
||||
# Generiert Hashes für Change Detection (WP-15b)
|
||||
for mode in ["body", "full"]:
|
||||
content = _get_hash_source_content(n, mode)
|
||||
payload["hashes"][f"{mode}:{hash_source}:{hash_normalize}"] = _compute_hash(content)
|
||||
|
|
|
|||
|
|
@ -4,8 +4,8 @@ DESCRIPTION: Der zentrale IngestionService (Orchestrator).
|
|||
WP-14: Modularisierung der Datenbank-Ebene (app.core.database).
|
||||
WP-15b: Two-Pass Workflow mit globalem Kontext-Cache.
|
||||
WP-20/22: Cloud-Resilienz und Content-Lifecycle integriert.
|
||||
AUDIT v2.13.10: Umstellung auf app.core.database Infrastruktur.
|
||||
VERSION: 2.13.10
|
||||
AUDIT v2.13.11: Synchronisierung mit Atomic-Chunking v3.9.9.
|
||||
VERSION: 2.13.11
|
||||
STATUS: Active
|
||||
"""
|
||||
import logging
|
||||
|
|
@ -60,6 +60,7 @@ class IngestionService:
|
|||
self.embedder = EmbeddingsClient()
|
||||
self.llm = LLMService()
|
||||
|
||||
# Festlegen, welcher Hash für die Change-Detection maßgeblich ist
|
||||
self.active_hash_mode = self.settings.CHANGE_DETECTION_MODE
|
||||
self.batch_cache: Dict[str, NoteContext] = {} # WP-15b LocalBatchCache
|
||||
|
||||
|
|
@ -130,12 +131,18 @@ class IngestionService:
|
|||
)
|
||||
note_id = note_pl["note_id"]
|
||||
|
||||
# Abgleich mit der Datenbank (Qdrant)
|
||||
old_payload = None if force_replace else fetch_note_payload(self.client, self.prefix, note_id)
|
||||
|
||||
# Prüfung gegen den konfigurierten Hash-Modus (body vs. full)
|
||||
check_key = f"{self.active_hash_mode}:{hash_source}:{hash_normalize}"
|
||||
old_hash = (old_payload or {}).get("hashes", {}).get(check_key)
|
||||
new_hash = note_pl.get("hashes", {}).get(check_key)
|
||||
|
||||
# Check ob Chunks oder Kanten in der DB fehlen (Reparatur-Modus)
|
||||
c_miss, e_miss = artifacts_missing(self.client, self.prefix, note_id)
|
||||
|
||||
# Wenn Hash identisch und Artefakte vorhanden -> Skip
|
||||
if not (force_replace or not old_payload or old_hash != new_hash or c_miss or e_miss):
|
||||
return {**result, "status": "unchanged", "note_id": note_id}
|
||||
|
||||
|
|
@ -146,36 +153,46 @@ class IngestionService:
|
|||
try:
|
||||
body_text = getattr(parsed, "body", "") or ""
|
||||
edge_registry.ensure_latest()
|
||||
|
||||
# Profil-Auflösung via Registry
|
||||
profile = fm.get("chunk_profile") or fm.get("chunking_profile") or "sliding_standard"
|
||||
chunk_cfg = get_chunk_config_by_profile(self.registry, profile, note_type)
|
||||
enable_smart = chunk_cfg.get("enable_smart_edge_allocation", False)
|
||||
|
||||
# WP-15b: Chunker-Aufruf bereitet Candidate-Pool vor
|
||||
# WP-15b: Chunker-Aufruf bereitet den Candidate-Pool pro Chunk vor.
|
||||
# assemble_chunks (v3.3.4) führt intern auch die Propagierung durch.
|
||||
chunks = await assemble_chunks(note_id, body_text, note_type, config=chunk_cfg)
|
||||
|
||||
# Semantische Kanten-Validierung (Smart Edge Allocation)
|
||||
for ch in chunks:
|
||||
filtered = []
|
||||
for cand in getattr(ch, "candidate_pool", []):
|
||||
# WP-15b: Nur global_pool Kandidaten erfordern binäre Validierung
|
||||
# Nur global_pool Kandidaten (aus dem Pool am Ende) erfordern KI-Validierung
|
||||
if cand.get("provenance") == "global_pool" and enable_smart:
|
||||
if await validate_edge_candidate(ch.text, cand, self.batch_cache, self.llm, self.settings.MINDNET_LLM_PROVIDER):
|
||||
filtered.append(cand)
|
||||
else:
|
||||
# Explizite Kanten (Wikilinks/Callouts) werden ungeprüft übernommen
|
||||
filtered.append(cand)
|
||||
ch.candidate_pool = filtered
|
||||
|
||||
# Payload-Erstellung via interne Module
|
||||
# Payload-Erstellung für die Chunks
|
||||
chunk_pls = make_chunk_payloads(
|
||||
fm, note_pl["path"], chunks, file_path=file_path,
|
||||
types_cfg=self.registry
|
||||
)
|
||||
|
||||
# Vektorisierung der Fenster-Texte
|
||||
vecs = await self.embedder.embed_documents([c.get("window") or "" for c in chunk_pls]) if chunk_pls else []
|
||||
|
||||
# Kanten-Aggregation
|
||||
# Aggregation aller finalen Kanten (Edges)
|
||||
edges = build_edges_for_note(
|
||||
note_id, chunk_pls,
|
||||
note_level_references=note_pl.get("references", []),
|
||||
include_note_scope_refs=note_scope_refs
|
||||
)
|
||||
|
||||
# Kanten-Typen via Registry validieren/auflösen
|
||||
for e in edges:
|
||||
e["kind"] = edge_registry.resolve(
|
||||
e.get("kind", "related_to"),
|
||||
|
|
@ -184,16 +201,20 @@ class IngestionService:
|
|||
)
|
||||
|
||||
# 4. DB Upsert via modularisierter Points-Logik
|
||||
# WICHTIG: Wenn sich der Inhalt geändert hat, löschen wir erst alle alten Fragmente.
|
||||
if purge_before and old_payload:
|
||||
purge_artifacts(self.client, self.prefix, note_id)
|
||||
|
||||
# Speichern der Haupt-Note
|
||||
n_name, n_pts = points_for_note(self.prefix, note_pl, None, self.dim)
|
||||
upsert_batch(self.client, n_name, n_pts)
|
||||
|
||||
# Speichern der Chunks
|
||||
if chunk_pls and vecs:
|
||||
c_pts = points_for_chunks(self.prefix, chunk_pls, vecs)[1]
|
||||
upsert_batch(self.client, f"{self.prefix}_chunks", c_pts)
|
||||
|
||||
# Speichern der Kanten
|
||||
if edges:
|
||||
e_pts = points_for_edges(self.prefix, edges)[1]
|
||||
upsert_batch(self.client, f"{self.prefix}_edges", e_pts)
|
||||
|
|
@ -217,4 +238,5 @@ class IngestionService:
|
|||
with open(target_path, "w", encoding="utf-8") as f:
|
||||
f.write(markdown_content)
|
||||
await asyncio.sleep(0.1)
|
||||
# Triggert sofortigen Import mit force_replace/purge_before
|
||||
return await self.process_file(file_path=target_path, vault_root=vault_root, apply=True, force_replace=True, purge_before=True)
|
||||
Loading…
Reference in New Issue
Block a user