iacore
7e8ba4b291
pptx-translate, chinese-text-normalize, dharma-translation: canonical location now ./skills/ with symlinks in ~/.hermes/skills/. translation-review: merged CSV/XLSX review + .dj comparison workflows into single SKILL.md. Added buddhist-terminology.md and terms-db-alignment.md references from Hermes version.
62 lines
2.4 KiB
Markdown
62 lines
2.4 KiB
Markdown
# Terms Database Alignment
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Batch-align translation glossary entries and body text against the MPI terms database.
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## Setup
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Start the HTTP API server if not running:
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```
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python3 /home/user/documents/mpi/terms-search/server.py &
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```
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Server listens on port 8910.
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## Batch lookup pattern
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Use Python via execute_code to query the API for multiple terms:
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```python
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import urllib.request, json, urllib.parse
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terms = ["三无漏学", "八步三禅", "闻思修", ...]
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for term in terms:
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q = urllib.parse.quote(term)
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resp = urllib.request.urlopen(f"http://localhost:8910/search?q={q}&limit=5", timeout=10)
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data = json.loads(resp.read())
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# Filter to authoritative sources
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author_sources = ["DoT定稿", "内部特色词", "佛教术语", "经论名"]
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relevant = [r for r in data["results"] if r["source"] in author_sources]
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# Compare against current translation, report mismatches
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```
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Or with curl:
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```
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curl -s "http://localhost:8910/search?q=三级修学&limit=5" | python3 -c "import sys,json; ..."
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```
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## Priority ranking
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When the same term has entries in multiple source tables, prefer:
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1. DoT定稿 (highest authority — final translation decisions)
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2. 内部特色词 (MPI internal terminology)
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3. 佛教术语 (general Buddhist terminology)
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4. 经论名 (sutra/shastra titles)
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## Alignment workflow
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1. Extract all Chinese glossary terms from `{% "TERM" ... %}` blocks in the .dj file
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2. Extract body-text domain terms that may not have glossary entries
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3. Batch-query each term against the HTTP API
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4. Filter results to authoritative source tables
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5. Compare DB canonical translation against current file translation
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6. Flag mismatches where DB entry differs materially from current
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7. Apply fixes with `patch` tool — fix both glossary comments AND body text occurrences
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8. Verify with `grep` that no old terms remain
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## Pitfalls
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- `replace_all` can create doubled words when the surrounding context already contains the replacement string (e.g., "The Eight Steps" → "The The Eight Steps"). Prefer targeted single-replacement patches.
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- The `search.py` CLI does not support `src:` or `loc:` filters — use the HTTP API.
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- Start patches from the bottom of the file upward to preserve line numbers.
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- Some DB entries are contextual phrases (e.g., "珍惜法缘" → a full sentence), not standalone term translations. Use standalone term entries where available.
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