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
80 lines
2.7 KiB
Python
80 lines
2.7 KiB
Python
"""Extract vocabulary from English content using 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 an English vocabulary instructor. Analyze the English text below and extract words or phrases that an intermediate English learner (B1-B2 CEFR level) might not know.
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Rules:
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- Extract 5 to 10 items maximum
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- Skip very basic words (go, see, love, etc.) and overly academic/rare words
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- Focus on useful vocabulary: idioms, collocations, phrasal verbs, and mid-level words
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- Each item must appear in the source text
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Return ONLY a valid JSON array. Each element must have:
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- "word": the word or phrase as it appears (string)
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- "pos": part of speech, e.g. "verb", "noun", "adjective", "phrase", "phrasal verb" (string)
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- "definition_en": concise English definition (string)
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- "definition_ko": Korean translation of the definition (string)
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- "example": the sentence from the source text that contains this word/phrase (string)
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Source title: {title}
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Text:
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{text}
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Return only the JSON array, 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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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 extract_vocab(text: str, title: str = "") -> list[dict]:
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"""Extract intermediate-level English vocabulary from text using Gemini Flash.
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Args:
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text: English source text to analyze.
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title: Content title for context.
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Returns:
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List of vocab dicts with keys: word, pos, definition_en, definition_ko, example.
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Returns empty list on failure or if no suitable vocab found.
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"""
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prompt = _PROMPT.format(title=title, text=text[:5000])
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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=2048,
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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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items = json.loads(raw)
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if not isinstance(items, list):
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return []
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return items
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except Exception:
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return []
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