3a8705ece8
- Replace inline __import__("datetime").timedelta hack with proper import
- Remove unused import random in anki_export.py
- Add error handling for Claude CLI subprocess failures in ai.py
- Fix hardcoded absolute path in stt.py with relative Path resolution
- Fix N+1 DB queries in vocab.get_flashcard_batch and dashboard.get_category_breakdown
by adding db.get_all_word_progress() batch query
- Wire Ollama model and Whisper size settings to actually update config
via ai.set_ollama_model() and stt.set_whisper_size()
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
57 lines
1.6 KiB
Python
57 lines
1.6 KiB
Python
"""Dual AI backend: Ollama (fast/local) and Claude CLI (smart)."""
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import subprocess
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import ollama
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DEFAULT_OLLAMA_MODEL = "qwen2.5:7b"
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_ollama_model = DEFAULT_OLLAMA_MODEL
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def set_ollama_model(model):
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"""Change the Ollama model used for fast queries."""
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global _ollama_model
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_ollama_model = model
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def ask_ollama(prompt, system=None, model=None):
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"""Query Ollama with an optional system prompt."""
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model = model or _ollama_model
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messages = []
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if system:
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messages.append({"role": "system", "content": system})
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messages.append({"role": "user", "content": prompt})
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response = ollama.chat(model=model, messages=messages)
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return response.message.content
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def ask_claude(prompt):
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"""Query Claude via the CLI subprocess."""
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result = subprocess.run(
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["claude", "-p", prompt],
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capture_output=True,
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text=True,
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)
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if result.returncode != 0:
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raise RuntimeError(f"Claude CLI failed (exit {result.returncode}): {result.stderr.strip()}")
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return result.stdout.strip()
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def ask(prompt, system=None, quality="fast"):
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"""Unified interface. quality='fast' uses Ollama, 'smart' uses Claude."""
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if quality == "smart":
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return ask_claude(prompt)
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return ask_ollama(prompt, system=system)
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def chat_ollama(messages, system=None, model=None):
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"""Multi-turn conversation with Ollama."""
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model = model or _ollama_model
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all_messages = []
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if system:
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all_messages.append({"role": "system", "content": system})
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all_messages.extend(messages)
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response = ollama.chat(model=model, messages=all_messages)
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return response.message.content
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