c7e4d59188
Verified catalogue of mobiles and laptops sold in India, collected from real retail listings (FastAPI backend, React frontend, Postgres/pgvector). - REST API under /api/elec (read-only catalogue; admin endpoints need login) - MCP server (FastMCP) at /mcp/ with list_categories, search_products, get_product and price_history tools - Real ratings and reviews read from product pages and search results - Production Dockerfile (requirements-api.txt, no PyTorch) and .env.production.example; remote database only via an explicit ELEC_ALLOW_REMOTE_DB host/name allowlist - docs/API.md: endpoint and MCP reference with live examples Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
53 lines
1.7 KiB
Python
53 lines
1.7 KiB
Python
from __future__ import annotations
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from typing import List, Optional, TYPE_CHECKING
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from app.infrastructure.settings import EMBEDDINGS_MODEL
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if TYPE_CHECKING: # pragma: no cover - typing only, no runtime cost
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from sentence_transformers import SentenceTransformer
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_model_singleton: Optional["SentenceTransformer"] = None
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def get_device() -> str:
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"""Prefer CUDA if available, otherwise CPU.
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Imports torch lazily: on an 8GB RAM / CPU-only laptop there is no
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benefit to importing torch (and paying its startup/memory cost) until
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an embedding is actually requested, so the FastAPI process can boot
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and answer /api/health almost instantly.
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"""
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import torch # local import - see docstring
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return "cuda" if torch.cuda.is_available() else "cpu"
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def get_embedding_model() -> "SentenceTransformer":
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global _model_singleton
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if _model_singleton is None:
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from sentence_transformers import SentenceTransformer # local import - see get_device()
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device = get_device()
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_model_singleton = SentenceTransformer(EMBEDDINGS_MODEL, device=device)
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return _model_singleton
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def embed_texts(texts: List[str]) -> List[List[float]]:
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"""Embed a batch of texts into normalized 384-dim vectors (MiniLM-L6-v2).
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Normalized so that pgvector's cosine-distance operator (`<=>`) behaves
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consistently for the RAG retrieval step in `app.services.vector_store`.
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"""
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if not texts:
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return []
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model = get_embedding_model()
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embeddings = model.encode(
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texts,
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batch_size=32,
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normalize_embeddings=True,
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convert_to_numpy=True,
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show_progress_bar=False,
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)
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return embeddings.tolist()
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