a9db6a8771
- core/vocab.py: extract B1-B2 level vocabulary from English content via Gemini Flash - core/anki.py: register vocab cards to AnkiConnect (English::Vocabulary deck) - core/enricher.py: add language detection field + summary_ko (Korean summary) - core/obsidian.py: render Korean + English summary in note - daemon/worker.py: call vocab extraction and Anki registration for English content
100 lines
3.5 KiB
Python
100 lines
3.5 KiB
Python
"""LLM-based content enrichment via OCI GenAI Gemini Flash."""
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import json
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import os
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import re
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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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ChatDetails,
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GenericChatRequest,
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OnDemandServingMode,
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TextContent,
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UserMessage,
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)
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_PROMPT = """\
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You are a knowledge extraction assistant. Analyze the content below and return ONLY a valid JSON object with these fields:
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- "title": concise descriptive title for this content (string)
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- "summary": 3-5 sentence summary capturing key insights, written in English (string)
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- "summary_ko": the same summary translated into Korean (string)
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- "tags": list of 3-7 relevant keywords or topics (string[])
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- "author": author or creator name, or null if not found (string | null)
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- "date": publication date in ISO 8601 format (YYYY-MM-DD), or null if not found (string | null)
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- "content_type": one of "youtube", "article", "documentation", "news", "forum", "code", "other" (string)
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- "language": primary language of the content, ISO 639-1 code, e.g. "en", "ko", "ja" (string)
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Content type: {content_type}
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Source URL: {url}
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Content:
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{text}
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Return only the JSON object, no markdown, no explanation."""
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def _get_client() -> GenerativeAiInferenceClient:
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config = oci.config.from_file()
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# Gemini models live in us-ashburn-1; use OCI_CHAT_ENDPOINT if set,
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# otherwise fall back to OCI_GENAI_ENDPOINT.
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endpoint = os.environ.get("OCI_CHAT_ENDPOINT") or os.environ["OCI_GENAI_ENDPOINT"]
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return GenerativeAiInferenceClient(config, service_endpoint=endpoint)
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def enrich(content_type: str, title: str, url: str, text: str) -> dict:
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"""Extract structured metadata from content using Gemini Flash.
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Args:
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content_type: One of 'youtube', 'url', 'text'.
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title: Initial title hint (may be empty).
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url: Source URL (empty for plain text).
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text: The full content text to analyze.
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Returns:
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Dict with keys: title, summary, tags, author, date, content_type.
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Falls back to minimal defaults on LLM failure.
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"""
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prompt = _PROMPT.format(
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content_type=content_type,
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url=url or "(none)",
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text=text[:6000],
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)
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try:
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client = _get_client()
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req = GenericChatRequest(
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messages=[UserMessage(content=[TextContent(text=prompt)])],
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max_tokens=1024,
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temperature=0,
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)
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det = ChatDetails(
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compartment_id=os.environ["OCI_COMPARTMENT_ID"],
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serving_mode=OnDemandServingMode(model_id=os.environ["OCI_CHAT_MODEL_ID"]),
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chat_request=req,
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)
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response = client.chat(det)
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raw = response.data.chat_response.choices[0].message.content[0].text.strip()
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raw = re.sub(r"^```(?:json)?\s*|\s*```$", "", raw, flags=re.MULTILINE)
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metadata = json.loads(raw)
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except Exception as exc:
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metadata = {
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"title": title or url or text[:80],
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"summary": text[:300],
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"tags": [],
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"author": None,
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"date": None,
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"content_type": content_type,
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"_error": str(exc),
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}
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# Ensure required keys exist
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metadata.setdefault("title", title or url or text[:80])
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metadata.setdefault("summary", "")
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metadata.setdefault("tags", [])
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metadata.setdefault("author", None)
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metadata.setdefault("date", None)
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metadata.setdefault("content_type", content_type)
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metadata.setdefault("language", "en")
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return metadata
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