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Integration von payload modulen in die neue Struktur

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Lars 2025-12-27 10:40:44 +01:00
parent 1b7b8091a3
commit a6d37c92d2
3 changed files with 205 additions and 33 deletions
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46
app/core/ingestion/ingestion_chunk_payload.py Normal file
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@ -0,0 +1,46 @@
"""
FILE: app/core/ingestion/ingestion_chunk_payload.py
DESCRIPTION: Baut das JSON-Objekt für mindnet_chunks.
VERSION: 2.4.0
"""
from __future__ import annotations
from typing import Any, Dict, List, Optional
def _as_list(x):
if x is None: return []
return x if isinstance(x, list) else [x]
def make_chunk_payloads(note: Dict[str, Any], note_path: str, chunks_from_chunker: List[Any], **kwargs) -> List[Dict[str, Any]]:
"""Erstellt die Payloads für die Chunks eines Dokuments."""
if isinstance(note, dict) and "frontmatter" in note: fm = note["frontmatter"]
else: fm = note or {}
note_type = fm.get("type") or "concept"
title = fm.get("title") or fm.get("id") or "Untitled"
tags = _as_list(fm.get("tags") or [])
cp = fm.get("chunking_profile") or fm.get("chunk_profile") or "sliding_standard"
rw = float(fm.get("retriever_weight", 1.0))
out: List[Dict[str, Any]] = []
for idx, ch in enumerate(chunks_from_chunker):
text = getattr(ch, "text", "") or ch.get("text", "")
pl: Dict[str, Any] = {
"note_id": getattr(ch, "note_id", None) or fm.get("id"),
"chunk_id": getattr(ch, "id", None),
"title": title,
"index": int(getattr(ch, "index", idx)),
"ord": int(getattr(ch, "index", idx)) + 1,
"type": note_type,
"tags": tags,
"text": text,
"window": getattr(ch, "window", text),
"neighbors_prev": _as_list(getattr(ch, "neighbors_prev", None)),
"neighbors_next": _as_list(getattr(ch, "neighbors_next", None)),
"section": getattr(ch, "section_title", "") or ch.get("section", ""),
"path": note_path,
"source_path": kwargs.get("file_path") or note_path,
"retriever_weight": rw,
"chunk_profile": cp
}
out.append(pl)
return out

82
app/core/ingestion/ingestion_note_payload.py Normal file
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"""
FILE: app/core/ingestion/ingestion_note_payload.py
DESCRIPTION: Baut das JSON-Objekt für mindnet_notes.
FEATURES: Multi-Hash (body/full), Config-Fix für chunking_profile.
VERSION: 2.4.0
"""
from __future__ import annotations
from typing import Any, Dict, Tuple, Optional
import os
import json
import pathlib
import hashlib
import yaml
def _as_dict(x) -> Dict[str, Any]:
if isinstance(x, dict): return dict(x)
out: Dict[str, Any] = {}
for attr in ("frontmatter", "body", "id", "note_id", "title", "path", "tags", "type", "created", "modified", "date"):
if hasattr(x, attr):
val = getattr(x, attr)
if val is not None: out[attr] = val
if not out: out["raw"] = str(x)
return out
def _ensure_list(x) -> list:
if x is None: return []
if isinstance(x, list): return [str(i) for i in x]
if isinstance(x, (set, tuple)): return [str(i) for i in x]
return [str(x)]
def _compute_hash(content: str) -> str:
if not content: return ""
return hashlib.sha256(content.encode("utf-8")).hexdigest()
def _get_hash_source_content(n: Dict[str, Any], mode: str) -> str:
body = str(n.get("body") or "")
if mode == "body": return body
if mode == "full":
fm = n.get("frontmatter") or {}
meta_parts = []
for k in sorted(["title", "type", "status", "tags", "chunking_profile", "chunk_profile", "retriever_weight"]):
val = fm.get(k)
if val is not None: meta_parts.append(f"{k}:{val}")
return f" {'|'.join(meta_parts)}||{body}"
return body
def make_note_payload(note: Any, *args, **kwargs) -> Dict[str, Any]:
"""Baut das Note-Payload inklusive Multi-Hash."""
n = _as_dict(note)
reg = kwargs.get("types_cfg") or {}
hash_source = kwargs.get("hash_source", "parsed")
hash_normalize = kwargs.get("hash_normalize", "canonical")
fm = n.get("frontmatter") or {}
note_type = str(fm.get("type") or n.get("type") or "concept")
# Weights & Profiles
retriever_weight = fm.get("retriever_weight", 1.0)
chunk_profile = fm.get("chunking_profile") or fm.get("chunk_profile") or "sliding_standard"
payload: Dict[str, Any] = {
"note_id": n.get("note_id") or n.get("id") or fm.get("id"),
"title": n.get("title") or fm.get("title") or "",
"type": note_type,
"path": str(n.get("path") or kwargs.get("path") or ""),
"retriever_weight": float(retriever_weight),
"chunk_profile": chunk_profile,
"hashes": {}
}
for mode in ["body", "full"]:
key = f"{mode}:{hash_source}:{hash_normalize}"
payload["hashes"][key] = _compute_hash(_get_hash_source_content(n, mode))
if fm.get("tags") or n.get("tags"): payload["tags"] = _ensure_list(fm.get("tags") or n.get("tags"))
if fm.get("aliases"): payload["aliases"] = _ensure_list(fm.get("aliases"))
for k in ("created", "modified", "date"):
v = fm.get(k) or n.get(k)
if v: payload[k] = str(v)
if n.get("body"): payload["fulltext"] = str(n["body"])
return payload

110
app/core/ingestion/ingestion_processor.py
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@ -1,31 +1,38 @@
"""
FILE: app/core/ingestion/ingestion_processor.py
DESCRIPTION: Orchestriert den Ingestion-Prozess (Parsing -> Chunking -> Validierung -> DB).
WP-14: Modularisiert. Nutzt interne Module für DB, Validierung und Payloads.
WP-15b: Implementiert den Two-Pass Workflow via run_batch.
VERSION: 2.13.2
STATUS: Active
"""
import logging
import asyncio
import os
from typing import Dict, List, Optional, Tuple, Any
# Core Module Imports
from app.core.parser import (
read_markdown, pre_scan_markdown, normalize_frontmatter,
validate_required_frontmatter, NoteContext
)
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
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
# Services
from app.services.embeddings_client import EmbeddingsClient
from app.services.edge_registry import registry as edge_registry
from app.services.llm_service import LLMService
# Package-Interne Imports
# Package-Interne Imports (Refactoring WP-14)
from .ingestion_utils import load_type_registry, resolve_note_type, get_chunk_config_by_profile
from .ingestion_db import fetch_note_payload, artifacts_missing, purge_artifacts
from .ingestion_validation import validate_edge_candidate
from .ingestion_note_payload import make_note_payload
from .ingestion_chunk_payload import make_chunk_payloads
# Fallback für Edges
# Fallback für Edges (Struktur-Verknüpfung)
try:
from app.core.derive_edges import build_edges_for_note
except ImportError:
@ -35,8 +42,10 @@ logger = logging.getLogger(__name__)
class IngestionService:
def __init__(self, collection_prefix: str = None):
"""Initialisiert den Service und stellt die DB-Verbindung bereit."""
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
@ -45,28 +54,37 @@ class IngestionService:
self.registry = load_type_registry()
self.embedder = EmbeddingsClient()
self.llm = LLMService()
self.active_hash_mode = self.settings.CHANGE_DETECTION_MODE
self.batch_cache: Dict[str, NoteContext] = {}
self.batch_cache: Dict[str, NoteContext] = {} # WP-15b LocalBatchCache
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}")
except Exception as e:
logger.warning(f"DB initialization warning: {e}")
async def run_batch(self, file_paths: List[str], vault_root: str) -> List[Dict[str, Any]]:
"""WP-15b: Two-Pass Ingestion Workflow."""
"""
WP-15b: Implementiert den Two-Pass Ingestion Workflow.
Pass 1: Pre-Scan füllt den Context-Cache.
Pass 2: Verarbeitung nutzt den Cache für die semantische Prüfung.
"""
logger.info(f"🔍 [Pass 1] Pre-Scanning {len(file_paths)} files for Context Cache...")
for path in file_paths:
ctx = pre_scan_markdown(path)
if ctx:
self.batch_cache[ctx.note_id] = ctx
self.batch_cache[ctx.title] = ctx
import os
fname = os.path.splitext(os.path.basename(path))[0]
self.batch_cache[fname] = ctx
try:
ctx = pre_scan_markdown(path)
if ctx:
# Mehrfache Indizierung für robusten Look-up (ID, Titel, Dateiname)
self.batch_cache[ctx.note_id] = ctx
self.batch_cache[ctx.title] = ctx
fname = os.path.splitext(os.path.basename(path))[0]
self.batch_cache[fname] = ctx
except Exception as e:
logger.warning(f"⚠️ Pre-scan failed for {path}: {e}")
logger.info(f"🚀 [Pass 2] Semantic Processing of {len(file_paths)} files...")
return [await self.process_file(p, vault_root, apply=True) for p in file_paths]
return [await self.process_file(p, vault_root, apply=True, purge_before=True) for p in file_paths]
async def process_file(self, file_path: str, vault_root: str, **kwargs) -> Dict[str, Any]:
"""Transformiert eine Markdown-Datei in den Graphen."""
@ -78,18 +96,19 @@ class IngestionService:
result = {"path": file_path, "status": "skipped", "changed": False, "error": None}
# 1. Parse & Lifecycle
# 1. Parse & Lifecycle Gate
try:
parsed = read_markdown(file_path)
if not parsed: return {**result, "error": "Empty file"}
fm = normalize_frontmatter(parsed.frontmatter)
validate_required_frontmatter(fm)
except Exception as e: return {**result, "error": f"Validation failed: {str(e)}"}
except Exception as e:
return {**result, "error": f"Validation failed: {str(e)}"}
if fm.get("status", "draft").lower().strip() in ["system", "template", "archive", "hidden"]:
return {**result, "status": "skipped", "reason": "lifecycle_filter"}
# 2. Payload & Change Detection
# 2. Payload & Change Detection (Multi-Hash)
note_type = resolve_note_type(self.registry, fm.get("type"))
note_pl = make_note_payload(parsed, vault_root=vault_root, file_path=file_path, hash_source=hash_source, hash_normalize=hash_normalize)
note_id = note_pl["note_id"]
@ -103,9 +122,10 @@ class IngestionService:
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}
if not apply: return {**result, "status": "dry-run", "changed": True, "note_id": note_id}
if not apply:
return {**result, "status": "dry-run", "changed": True, "note_id": note_id}
# 3. Processing
# 3. Deep Processing (Chunking, Validation, Embedding)
try:
body_text = getattr(parsed, "body", "") or ""
edge_registry.ensure_latest()
@ -113,40 +133,64 @@ class IngestionService:
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
chunks = await assemble_chunks(fm["id"], body_text, note_type, config=chunk_cfg)
for ch in chunks:
filtered = []
for cand in getattr(ch, "candidate_pool", []):
# Nur global_pool Kandidaten erfordern binäre 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: filtered.append(cand)
else:
filtered.append(cand)
ch.candidate_pool = filtered
chunk_pls = make_chunk_payloads(fm, note_pl["path"], chunks, note_text=body_text)
# Payload-Erstellung via interne Module
chunk_pls = make_chunk_payloads(fm, note_pl["path"], chunks, file_path=file_path)
vecs = await self.embedder.embed_documents([c.get("window") or "" for c in chunk_pls]) if chunk_pls else []
# Kanten-Aggregation
edges = build_edges_for_note(note_id, chunk_pls, note_level_references=note_pl.get("references", []))
for e in edges:
e["kind"] = edge_registry.resolve(e.get("kind", "related_to"), provenance=e.get("provenance", "explicit"), context={"file": file_path, "note_id": note_id})
e["kind"] = edge_registry.resolve(
e.get("kind", "related_to"),
provenance=e.get("provenance", "explicit"),
context={"file": file_path, "note_id": note_id}
)
# 4. DB Upsert
if purge_before and old_payload: purge_artifacts(self.client, self.prefix, note_id)
if purge_before and old_payload:
purge_artifacts(self.client, self.prefix, 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: upsert_batch(self.client, f"{self.prefix}_chunks", points_for_chunks(self.prefix, chunk_pls, vecs)[1])
if edges: upsert_batch(self.client, f"{self.prefix}_edges", points_for_edges(self.prefix, edges)[1])
return {"path": file_path, "status": "success", "changed": True, "note_id": note_id, "chunks_count": len(chunk_pls), "edges_count": len(edges)}
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)
if edges:
e_pts = points_for_edges(self.prefix, edges)[1]
upsert_batch(self.client, f"{self.prefix}_edges", 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"Processing failed: {e}", exc_info=True)
return {**result, "error": str(e)}
async def create_from_text(self, markdown_content: str, filename: str, vault_root: str, folder: str = "00_Inbox") -> Dict[str, Any]:
import os
target_dir = os.path.join(vault_root, folder)
os.makedirs(target_dir, exist_ok=True)
file_path = os.path.join(target_dir, filename)
with open(file_path, "w", encoding="utf-8") as f: f.write(markdown_content)
"""Erstellt eine Note aus einem Textstream und triggert die Ingestion."""
target_path = os.path.join(vault_root, folder, filename)
os.makedirs(os.path.dirname(target_path), exist_ok=True)
with open(target_path, "w", encoding="utf-8") as f:
f.write(markdown_content)
await asyncio.sleep(0.1)
return await self.process_file(file_path=file_path, vault_root=vault_root, apply=True, force_replace=True, purge_before=True)
return await self.process_file(file_path=target_path, vault_root=vault_root, apply=True, force_replace=True, purge_before=True)