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3f7de867cc
* feat: Implement model registry and capabilities for multi-provider support - Added ModelRegistry to manage available models and their capabilities. - Introduced ModelProvider enum for different LLM providers. - Created ModelCapabilities dataclass to define model features. - Implemented methods to load models based on API keys and settings. - Added utility functions for model management in model_utils.py. - Updated settings.py to include provider-specific API keys. - Refactored LLM classes (Anthropic, OpenAI, Google, etc.) to utilize new model registry. - Enhanced utility functions to handle token limits and model validation. - Improved code structure and logging for better maintainability. * feat: Add model selection feature with API integration and UI component * feat: Add model selection and default model functionality in agent management * test: Update assertions and formatting in stream processing tests * refactor(llm): Standardize model identifier to model_id * fix tests --------- Co-authored-by: Alex <a@tushynski.me>
21 lines
540 B
Python
21 lines
540 B
Python
from application.agents.classic_agent import ClassicAgent
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from application.agents.react_agent import ReActAgent
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import logging
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logger = logging.getLogger(__name__)
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class AgentCreator:
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agents = {
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"classic": ClassicAgent,
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"react": ReActAgent,
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}
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@classmethod
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def create_agent(cls, type, *args, **kwargs):
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agent_class = cls.agents.get(type.lower())
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if not agent_class:
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raise ValueError(f"No agent class found for type {type}")
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return agent_class(*args, **kwargs)
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