feat(hooks): Improving the hook system #1
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…ar distinctions between Instructor for fast extraction and PydanticAI for agent capabilities, highlighting their respective benefits and linking to the PydanticAI documentation.
Use uv to generate requirements.txt from pyproject.toml to include only core dependencies. This removes optional provider dependencies (cohere, anthropic, groq, etc.) and eliminates dependency conflicts. Users can install provider support via extras: pip install -e ".[anthropic]" 🤖 Generated with Claude Code Co-Authored-By: Claude <noreply@anthropic.com>
Automatically regenerate requirements.txt from pyproject.toml using uv when pyproject.toml changes. This ensures requirements.txt stays in sync with the source configuration. 🤖 Generated with Claude Code Co-Authored-By: Claude <noreply@anthropic.com>
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This PR adds supports for 3 new hooks in instructor:
"cache:hit": emits an event when the cache hits"cache:miss": emits an event when the cache misses"parse:success": emits an event when parsing + Pydantic validation succeeds.Additionally, all callbacks now receive a richer context with dedicated
Eventobjects:CacheHitEvent,CacheMissEvent, etc. This additional context is useful for debugging, monitoring, etc.