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47 lines
1.4 KiB
Python
47 lines
1.4 KiB
Python
"""
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app/services/embeddings_client.py — Text→Embedding (WP-04)
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Zweck:
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Liefert 384-d Embeddings für Textqueries (lazy load, einmal pro Prozess).
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Standard: Sentence-Transformers (MODEL_NAME aus app.config.Settings).
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Hinweis: Kein Netz-Zugriff; nutzt lokal installierte Modelle.
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Kompatibilität:
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Python 3.12+, sentence-transformers 5.x
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Version:
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0.1.0 (Erstanlage)
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Stand:
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2025-10-07
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Bezug:
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- app/core/retriever.py (nutzt embed_text_if_needed)
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- app/config.py (MODEL_NAME, VECTOR_SIZE)
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Nutzung:
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from app.services.embeddings_client import embed_text
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Änderungsverlauf:
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0.1.0 (2025-10-07) – Erstanlage.
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"""
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from __future__ import annotations
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from typing import List
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from functools import lru_cache
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from app.config import get_settings
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# Lazy import, damit Testläufe ohne Modell-Laden schnell sind
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def _load_model():
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from sentence_transformers import SentenceTransformer # import hier, nicht top-level
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s = get_settings()
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return SentenceTransformer(s.MODEL_NAME, device="cpu")
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@lru_cache(maxsize=1)
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def _cached_model():
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return _load_model()
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def embed_text(text: str) -> List[float]:
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"""
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Erzeugt einen 384-d Vektor (oder laut Settings.VECTOR_SIZE) für den gegebenen Text.
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"""
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if not text or not text.strip():
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raise ValueError("embed_text: leerer Text")
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model = _cached_model()
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vec = model.encode([text], normalize_embeddings=True)[0]
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return vec.astype(float).tolist()
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