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* 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>
26 lines
833 B
TypeScript
26 lines
833 B
TypeScript
import apiClient from '../client';
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import endpoints from '../endpoints';
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import type { AvailableModel, Model } from '../../models/types';
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const modelService = {
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getModels: (token: string | null): Promise<Response> =>
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apiClient.get(endpoints.USER.MODELS, token, {}),
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transformModels: (models: AvailableModel[]): Model[] =>
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models.map((model) => ({
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id: model.id,
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value: model.id,
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provider: model.provider,
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display_name: model.display_name,
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description: model.description,
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context_window: model.context_window,
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supported_attachment_types: model.supported_attachment_types,
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supports_tools: model.supports_tools,
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supports_structured_output: model.supports_structured_output,
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supports_streaming: model.supports_streaming,
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})),
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};
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export default modelService;
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