mirror of
https://github.com/coleam00/context-engineering-intro.git
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61 lines
1.6 KiB
Python
61 lines
1.6 KiB
Python
"""
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Flexible provider configuration for LLM models.
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Based on examples/agent/providers.py pattern.
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"""
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from typing import Optional
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from pydantic_ai.providers.openai import OpenAIProvider
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from pydantic_ai.models.openai import OpenAIModel
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from .settings import settings
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def get_llm_model(model_choice: Optional[str] = None) -> OpenAIModel:
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"""
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Get LLM model configuration based on environment variables.
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Args:
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model_choice: Optional override for model choice
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Returns:
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Configured OpenAI-compatible model
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"""
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llm_choice = model_choice or settings.llm_model
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base_url = settings.llm_base_url
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api_key = settings.llm_api_key
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# Create provider based on configuration
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provider = OpenAIProvider(base_url=base_url, api_key=api_key)
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return OpenAIModel(llm_choice, provider=provider)
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def get_model_info() -> dict:
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"""
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Get information about current model configuration.
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Returns:
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Dictionary with model configuration info
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"""
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return {
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"llm_provider": settings.llm_provider,
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"llm_model": settings.llm_model,
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"llm_base_url": settings.llm_base_url,
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"app_env": settings.app_env,
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"debug": settings.debug,
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}
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def validate_llm_configuration() -> bool:
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"""
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Validate that LLM configuration is properly set.
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Returns:
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True if configuration is valid
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"""
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try:
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# Check if we can create a model instance
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get_llm_model()
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return True
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except Exception as e:
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print(f"LLM configuration validation failed: {e}")
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return False |