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WP20 openrouter

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Lars 2025-12-23 15:20:14 +01:00
parent 2a98c37ca1
commit c60aba63a4
1 changed files with 42 additions and 45 deletions
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app/core/ingestion.py
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@ -1,7 +1,7 @@
""" """
FILE: app/core/ingestion.py FILE: app/core/ingestion.py
DESCRIPTION: Haupt-Ingestion-Logik. Transformiert Markdown in den Graphen (Notes, Chunks, Edges). DESCRIPTION: Haupt-Ingestion-Logik. Transformiert Markdown in den Graphen (Notes, Chunks, Edges).
WP-20: Integration von Smart Edge Allocation via Hybrid LLM (Gemini/Ollama). 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: Integration von Content Lifecycle (Status Gate) und Edge Registry Validation.
WP-22: Kontextsensitive Kanten-Validierung mit Fundort-Reporting (Zeilennummern). WP-22: Kontextsensitive Kanten-Validierung mit Fundort-Reporting (Zeilennummern).
WP-22: Multi-Hash Refresh für konsistente Change Detection. WP-22: Multi-Hash Refresh für konsistente Change Detection.
@ -22,7 +22,7 @@ from app.core.parser import (
read_markdown, read_markdown,
normalize_frontmatter, normalize_frontmatter,
validate_required_frontmatter, validate_required_frontmatter,
extract_edges_with_context, # extract_edges_with_context, # WP-22: Funktion für Zeilennummern
) )
from app.core.note_payload import make_note_payload from app.core.note_payload import make_note_payload
from app.core.chunker import assemble_chunks, get_chunk_config from app.core.chunker import assemble_chunks, get_chunk_config
@ -44,7 +44,7 @@ from app.core.qdrant_points import (
from app.services.embeddings_client import EmbeddingsClient from app.services.embeddings_client import EmbeddingsClient
from app.services.edge_registry import registry as edge_registry from app.services.edge_registry import registry as edge_registry
from app.services.llm_service import LLMService # from app.services.llm_service import LLMService
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
@ -52,7 +52,9 @@ logger = logging.getLogger(__name__)
def load_type_registry(custom_path: Optional[str] = None) -> dict: def load_type_registry(custom_path: Optional[str] = None) -> dict:
"""Lädt die types.yaml zur Steuerung der typ-spezifischen Ingestion.""" """Lädt die types.yaml zur Steuerung der typ-spezifischen Ingestion."""
import yaml import yaml
path = custom_path or os.getenv("MINDNET_TYPES_FILE", "config/types.yaml") from app.config import get_settings
settings = get_settings()
path = custom_path or getattr(settings, "MINDNET_TYPES_FILE", "config/types.yaml")
if not os.path.exists(path): return {} if not os.path.exists(path): return {}
try: try:
with open(path, "r", encoding="utf-8") as f: return yaml.safe_load(f) or {} with open(path, "r", encoding="utf-8") as f: return yaml.safe_load(f) or {}
@ -100,17 +102,18 @@ def effective_retriever_weight(fm: dict, note_type: str, reg: dict) -> float:
class IngestionService: class IngestionService:
def __init__(self, collection_prefix: str = None): def __init__(self, collection_prefix: str = None):
from app.config import get_settings from app.config import get_settings
self.settings = get_settings() # self.settings = get_settings()
self.prefix = collection_prefix or self.settings.COLLECTION_PREFIX self.prefix = collection_prefix or self.settings.COLLECTION_PREFIX
self.cfg = QdrantConfig.from_env() self.cfg = QdrantConfig.from_env()
self.cfg.prefix = self.prefix self.cfg.prefix = self.prefix
self.client = get_client(self.cfg) self.client = get_client(self.cfg)
self.dim = self.cfg.VECTOR_SIZE # self.dim = self.cfg.dim if hasattr(self.cfg, 'dim') else self.settings.VECTOR_SIZE
self.type_registry = load_type_registry() self.registry = load_type_registry()
self.embedder = EmbeddingsClient() self.embedder = EmbeddingsClient()
self.llm = LLMService() # self.llm = LLMService() # WP-20 Integration
# Change Detection Modus (full oder body)
self.active_hash_mode = os.getenv("MINDNET_CHANGE_DETECTION_MODE", "full") self.active_hash_mode = os.getenv("MINDNET_CHANGE_DETECTION_MODE", "full")
try: try:
@ -120,8 +123,8 @@ class IngestionService:
logger.warning(f"DB init warning: {e}") logger.warning(f"DB init warning: {e}")
def _get_chunk_config_by_profile(self, profile_name: str, note_type: str) -> Dict[str, Any]: def _get_chunk_config_by_profile(self, profile_name: str, note_type: str) -> Dict[str, Any]:
"""Holt die Chunker-Parameter für ein spezifisches Profil.""" """Holt die Chunker-Parameter (max, target, overlap) für ein spezifisches Profil."""
profiles = self.type_registry.get("chunking_profiles", {}) profiles = self.registry.get("chunking_profiles", {})
if profile_name in profiles: if profile_name in profiles:
cfg = profiles[profile_name].copy() cfg = profiles[profile_name].copy()
if "overlap" in cfg and isinstance(cfg["overlap"], list): if "overlap" in cfg and isinstance(cfg["overlap"], list):
@ -134,30 +137,24 @@ class IngestionService:
WP-20: Nutzt den Hybrid LLM Service für die semantische Kanten-Extraktion. WP-20: Nutzt den Hybrid LLM Service für die semantische Kanten-Extraktion.
Verwendet provider-spezifische Prompts aus der config. Verwendet provider-spezifische Prompts aus der config.
""" """
provider = self.settings.MINDNET_LLM_PROVIDER # # Wir priorisieren Gemma für Ingestion, falls verfügbar (OpenRouter/Cloud)
model = getattr(self.settings, "GEMMA_MODEL", None)
provider = self.settings.MINDNET_LLM_PROVIDER
# Prompt-Lookup (Fallback auf Standard-Struktur falls Key fehlt) template = self.llm.get_prompt("edge_extraction")
prompt_data = self.llm.prompts.get("edge_extraction", {})
if isinstance(prompt_data, dict):
template = prompt_data.get(provider, prompt_data.get("ollama", ""))
else:
template = str(prompt_data)
if not template:
template = "Extrahiere semantische Relationen aus: {text}. Antworte als JSON: [{\"to\": \"X\", \"kind\": \"Y\"}]"
prompt = template.format(text=text[:6000], note_id=note_id) prompt = template.format(text=text[:6000], note_id=note_id)
try: try:
# Nutzt die Semaphore für Hintergrund-Tasks # Hintergrund-Task mit Semaphore
response_json = await self.llm.generate_raw_response( response_json = await self.llm.generate_raw_response(
prompt=prompt, prompt=prompt,
priority="background", priority="background",
force_json=True force_json=True,
model_override=model
) )
data = json.loads(response_json) data = json.loads(response_json)
# Anreicherung mit Provenance-Metadaten für WP-22 Registry # Provenance für die EdgeRegistry
for item in data: for item in data:
item["provenance"] = "semantic_ai" item["provenance"] = "semantic_ai"
item["line"] = f"ai-{provider}" item["line"] = f"ai-{provider}"
@ -197,16 +194,15 @@ class IngestionService:
return {**result, "status": "skipped", "reason": f"lifecycle_status_{status}"} return {**result, "status": "skipped", "reason": f"lifecycle_status_{status}"}
# 2. Type & Config Resolution # 2. Type & Config Resolution
note_type = resolve_note_type(fm.get("type"), self.type_registry) note_type = resolve_note_type(fm.get("type"), self.registry)
fm["type"] = note_type fm["type"] = note_type
effective_profile = effective_chunk_profile_name(fm, note_type, self.registry)
effective_profile = effective_chunk_profile_name(fm, note_type, self.type_registry) effective_weight = effective_retriever_weight(fm, note_type, self.registry)
effective_weight = effective_retriever_weight(fm, note_type, self.type_registry)
fm["chunk_profile"] = effective_profile fm["chunk_profile"] = effective_profile
fm["retriever_weight"] = effective_weight fm["retriever_weight"] = effective_weight
# 3. Build Note Payload (Inkl. Multi-Hash für WP-22) # 3. Build Note Payload
try: try:
note_pl = make_note_payload(parsed, vault_root=vault_root, hash_normalize=hash_normalize, hash_source=hash_source, file_path=file_path) 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 "" if not note_pl.get("fulltext"): note_pl["fulltext"] = getattr(parsed, "body", "") or ""
@ -244,7 +240,7 @@ class IngestionService:
# 5. Processing (Chunking, Embedding, Edge Generation) # 5. Processing (Chunking, Embedding, Edge Generation)
try: try:
body_text = getattr(parsed, "body", "") or "" body_text = getattr(parsed, "body", "") or ""
edge_registry.ensure_latest() # edge_registry.ensure_latest()
chunk_config = self._get_chunk_config_by_profile(effective_profile, note_type) 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) chunks = await assemble_chunks(fm["id"], body_text, fm["type"], config=chunk_config)
@ -255,11 +251,11 @@ class IngestionService:
texts = [c.get("window") or c.get("text") or "" for c in chunk_pls] texts = [c.get("window") or c.get("text") or "" for c in chunk_pls]
vecs = await self.embedder.embed_documents(texts) vecs = await self.embedder.embed_documents(texts)
# --- WP-22: Kanten-Extraktion & Validierung --- # --- WP-22/WP-20: Kanten-Extraktion & Validierung ---
edges = [] edges = []
context = {"file": file_path, "note_id": note_id} context = {"file": file_path, "note_id": note_id}
# A. Explizite User-Kanten mit Zeilennummern # A. Explizite User-Kanten
explicit_edges = extract_edges_with_context(parsed) explicit_edges = extract_edges_with_context(parsed)
for e in explicit_edges: for e in explicit_edges:
e["kind"] = edge_registry.resolve(edge_type=e["kind"], provenance="explicit", context={**context, "line": e.get("line")}) e["kind"] = edge_registry.resolve(edge_type=e["kind"], provenance="explicit", context={**context, "line": e.get("line")})
@ -271,20 +267,24 @@ class IngestionService:
e["kind"] = edge_registry.resolve(edge_type=e.get("kind"), provenance="semantic_ai", context={**context, "line": e.get("line")}) e["kind"] = edge_registry.resolve(edge_type=e.get("kind"), provenance="semantic_ai", context={**context, "line": e.get("line")})
edges.append(e) edges.append(e)
# C. System-Kanten (Struktur: belongs_to, next, prev) # C. System-Kanten
raw_system_edges = build_edges_for_note(note_id, chunk_pls, note_level_references=note_pl.get("references", [])) 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: for e in raw_system_edges:
e["kind"] = edge_registry.resolve(edge_type=e.get("kind", "belongs_to"), provenance="structure", context={**context, "line": "system"}) valid_kind = edge_registry.resolve(edge_type=e.get("kind", "belongs_to"), provenance="structure", context={**context, "line": "system"})
if e["kind"]: edges.append(e) e["kind"] = valid_kind
if valid_kind: edges.append(e)
except Exception as e: except Exception as e:
logger.error(f"Processing failed: {e}", exc_info=True) logger.error(f"Processing failed: {e}", exc_info=True)
return {**result, "error": f"Processing failed: {str(e)}"} return {**result, "error": f"Processing failed: {str(e)}"}
# 6. Upsert in Qdrant # 6. Upsert
try: try:
if purge_before and has_old: if purge_before and has_old: self._purge_artifacts(note_id)
self._purge_artifacts(note_id)
n_name, n_pts = points_for_note(self.prefix, note_pl, None, self.dim) n_name, n_pts = points_for_note(self.prefix, note_pl, None, self.dim)
upsert_batch(self.client, n_name, n_pts) upsert_batch(self.client, n_name, n_pts)
@ -297,10 +297,7 @@ class IngestionService:
e_name, e_pts = points_for_edges(self.prefix, edges) e_name, e_pts = points_for_edges(self.prefix, edges)
upsert_batch(self.client, e_name, e_pts) upsert_batch(self.client, e_name, e_pts)
return { return {"path": file_path, "status": "success", "changed": True, "note_id": note_id, "chunks_count": len(chunk_pls), "edges_count": len(edges)}
"path": file_path, "status": "success", "changed": True, "note_id": note_id,
"chunks_count": len(chunk_pls), "edges_count": len(edges)
}
except Exception as e: except Exception as e:
logger.error(f"Upsert failed: {e}", exc_info=True) logger.error(f"Upsert failed: {e}", exc_info=True)
return {**result, "error": f"DB Upsert failed: {e}"} return {**result, "error": f"DB Upsert failed: {e}"}
@ -331,8 +328,7 @@ class IngestionService:
f = rest.Filter(must=[rest.FieldCondition(key="note_id", match=rest.MatchValue(value=note_id))]) f = rest.Filter(must=[rest.FieldCondition(key="note_id", match=rest.MatchValue(value=note_id))])
selector = rest.FilterSelector(filter=f) selector = rest.FilterSelector(filter=f)
for suffix in ["chunks", "edges"]: for suffix in ["chunks", "edges"]:
try: try: self.client.delete(collection_name=f"{self.prefix}_{suffix}", points_selector=selector)
self.client.delete(collection_name=f"{self.prefix}_{suffix}", points_selector=selector)
except Exception: pass except Exception: pass
async def create_from_text(self, markdown_content: str, filename: str, vault_root: str, folder: str = "00_Inbox") -> Dict[str, Any]: async def create_from_text(self, markdown_content: str, filename: str, vault_root: str, folder: str = "00_Inbox") -> Dict[str, Any]:
@ -346,6 +342,7 @@ class IngestionService:
f.flush() f.flush()
os.fsync(f.fileno()) os.fsync(f.fileno())
await asyncio.sleep(0.1) await asyncio.sleep(0.1)
logger.info(f"Written file to {file_path}")
except Exception as e: except Exception as e:
return {"status": "error", "error": f"Disk write failed: {str(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) return await self.process_file(file_path=file_path, vault_root=vault_root, apply=True, force_replace=True, purge_before=True)