Initial commit: Tasteby - YouTube restaurant map service

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>

backend/core/extractor.py

@@ -0,0 +1,128 @@
+"""LLM-based restaurant info extraction from video transcripts.
+
+Uses OCI GenAI (Gemini Flash) to extract structured restaurant data.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import os
+import re
+
+import oci
+from oci.generative_ai_inference import GenerativeAiInferenceClient
+from oci.generative_ai_inference.models import (
+    ChatDetails,
+    GenericChatRequest,
+    OnDemandServingMode,
+    TextContent,
+    UserMessage,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def _get_client() -> GenerativeAiInferenceClient:
+    config = oci.config.from_file()
+    endpoint = os.environ.get("OCI_CHAT_ENDPOINT") or os.environ["OCI_GENAI_ENDPOINT"]
+    return GenerativeAiInferenceClient(config, service_endpoint=endpoint)
+
+
+def _llm(prompt: str, max_tokens: int = 4096) -> str:
+    client = _get_client()
+    req = GenericChatRequest(
+        messages=[UserMessage(content=[TextContent(text=prompt)])],
+        max_tokens=max_tokens,
+        temperature=0,
+    )
+    det = ChatDetails(
+        compartment_id=os.environ["OCI_COMPARTMENT_ID"],
+        serving_mode=OnDemandServingMode(model_id=os.environ["OCI_CHAT_MODEL_ID"]),
+        chat_request=req,
+    )
+    resp = client.chat(det)
+    return resp.data.chat_response.choices[0].message.content[0].text.strip()
+
+
+def _parse_json(raw: str) -> dict | list:
+    raw = re.sub(r"^```(?:json)?\s*|\s*```$", "", raw, flags=re.MULTILINE).strip()
+    raw = re.sub(r",\s*([}\]])", r"\1", raw)
+    try:
+        return json.loads(raw)
+    except json.JSONDecodeError:
+        pass
+    try:
+        return json.JSONDecoder(strict=False).decode(raw)
+    except json.JSONDecodeError:
+        pass
+    # recover truncated array
+    if raw.lstrip().startswith("["):
+        decoder = json.JSONDecoder(strict=False)
+        items: list = []
+        idx = raw.index("[") + 1
+        while idx < len(raw):
+            while idx < len(raw) and raw[idx] in " \t\n\r,":
+                idx += 1
+            if idx >= len(raw) or raw[idx] == "]":
+                break
+            try:
+                obj, end = decoder.raw_decode(raw, idx)
+                items.append(obj)
+                idx = end
+            except json.JSONDecodeError:
+                break
+        if items:
+            return items
+    raise ValueError(f"JSON parse failed: {raw[:80]!r}")
+
+
+_EXTRACT_PROMPT = """\
+다음은 유튜브 먹방/맛집 영상의 자막입니다.
+이 영상에서 언급된 모든 식당 정보를 추출하세요.
+
+규칙:
+- 식당이 없으면 빈 배열 [] 반환
+- 각 식당에 대해 아래 필드를 JSON 배열로 반환
+- 확실하지 않은 정보는 null
+- 추가 설명 없이 JSON만 반환
+
+필드:
+- name: 식당 이름 (string, 필수)
+- address: 주소 또는 위치 힌트 (string | null)
+- region: 지역 (예: 서울 강남, 부산 해운대) (string | null)
+- cuisine_type: 음식 종류 (예: 한식, 일식, 중식, 양식, 카페) (string | null)
+- price_range: 가격대 (예: 1만원대, 2-3만원) (string | null)
+- foods_mentioned: 언급된 메뉴들 (string[])
+- evaluation: 평가 내용 (string | null)
+- guests: 함께한 게스트 (string[])
+
+영상 제목: {title}
+자막:
+{transcript}
+
+JSON 배열:"""
+
+
+def extract_restaurants(title: str, transcript: str) -> tuple[list[dict], str]:
+    """Extract restaurant info from a video transcript using LLM.
+
+    Returns (list of restaurant dicts, raw LLM response text).
+    """
+    # Truncate very long transcripts
+    if len(transcript) > 8000:
+        transcript = transcript[:7000] + "\n...(중략)...\n" + transcript[-1000:]
+
+    prompt = _EXTRACT_PROMPT.format(title=title, transcript=transcript)
+
+    try:
+        raw = _llm(prompt, max_tokens=4096)
+        result = _parse_json(raw)
+        if isinstance(result, list):
+            return result, raw
+        if isinstance(result, dict):
+            return [result], raw
+        return [], raw
+    except Exception as e:
+        logger.error("Restaurant extraction failed: %s", e)
+        return [], ""