36bec10bd0
Backend (FastAPI + Oracle ADB), Frontend (Next.js), daemon worker. Features: channel/video/restaurant management, semantic search, Google OAuth, user reviews. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
98 lines
2.7 KiB
Python
98 lines
2.7 KiB
Python
"""Vector embedding generation and storage for restaurant semantic search."""
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from __future__ import annotations
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import array
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import os
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import oci
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from oci.generative_ai_inference import GenerativeAiInferenceClient
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from oci.generative_ai_inference.models import (
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EmbedTextDetails,
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OnDemandServingMode,
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)
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from core.db import conn
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def _embed_texts(texts: list[str]) -> list[list[float]]:
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config = oci.config.from_file()
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client = GenerativeAiInferenceClient(
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config,
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service_endpoint=os.environ["OCI_GENAI_ENDPOINT"],
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)
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model_id = os.environ.get("OCI_EMBED_MODEL_ID", "cohere.embed-v4.0")
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compartment_id = os.environ["OCI_COMPARTMENT_ID"]
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details = EmbedTextDetails(
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inputs=texts,
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serving_mode=OnDemandServingMode(model_id=model_id),
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compartment_id=compartment_id,
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input_type="SEARCH_DOCUMENT",
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)
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response = client.embed_text(details)
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return response.data.embeddings
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def _to_vec(embedding: list[float]) -> array.array:
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return array.array("f", embedding)
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def save_restaurant_vectors(restaurant_id: str, chunks: list[str]) -> list[str]:
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"""Embed and store text chunks for a restaurant.
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Returns list of inserted row IDs.
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"""
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if not chunks:
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return []
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embeddings = _embed_texts(chunks)
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inserted: list[str] = []
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sql = """
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INSERT INTO restaurant_vectors (restaurant_id, chunk_text, embedding)
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VALUES (:rid, :chunk, :emb)
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RETURNING id INTO :out_id
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"""
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import oracledb
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with conn() as c:
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cur = c.cursor()
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for chunk, emb in zip(chunks, embeddings):
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out_id = cur.var(oracledb.STRING)
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cur.execute(sql, {
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"rid": restaurant_id,
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"chunk": chunk,
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"emb": _to_vec(emb),
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"out_id": out_id,
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})
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inserted.append(out_id.getvalue()[0])
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return inserted
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def search_similar(query: str, top_k: int = 10) -> list[dict]:
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"""Semantic search: find restaurants similar to query text.
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Returns list of dicts: restaurant_id, chunk_text, distance.
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"""
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embeddings = _embed_texts([query])
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query_vec = _to_vec(embeddings[0])
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sql = """
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SELECT rv.restaurant_id, rv.chunk_text,
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VECTOR_DISTANCE(rv.embedding, :qvec, COSINE) AS dist
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FROM restaurant_vectors rv
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ORDER BY dist
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FETCH FIRST :k ROWS ONLY
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"""
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with conn() as c:
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cur = c.cursor()
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cur.execute(sql, {"qvec": query_vec, "k": top_k})
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return [
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{
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"restaurant_id": r[0],
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"chunk_text": r[1].read() if hasattr(r[1], "read") else r[1],
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"distance": r[2],
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}
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for r in cur.fetchall()
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]
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