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feat: model registry and capabilities for multi-provider support (#2158)
* 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>
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@@ -52,6 +52,10 @@ export const fetchPreviewAnswer = createAsyncThunk<
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}
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if (state.preference) {
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const modelId =
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state.preference.selectedAgent?.default_model_id ||
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state.preference.selectedModel?.id;
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if (API_STREAMING) {
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await handleFetchAnswerSteaming(
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question,
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@@ -120,22 +124,23 @@ export const fetchPreviewAnswer = createAsyncThunk<
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indx,
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state.preference.selectedAgent?.id,
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attachmentIds,
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false, // Don't save preview conversations
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false,
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modelId,
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);
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} else {
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// Non-streaming implementation
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const answer = await handleFetchAnswer(
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question,
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signal,
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state.preference.token,
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state.preference.selectedDocs,
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null, // No conversation ID for previews
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null,
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state.preference.prompt.id,
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state.preference.chunks,
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state.preference.token_limit,
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state.preference.selectedAgent?.id,
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attachmentIds,
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false, // Don't save preview conversations
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false,
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modelId,
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);
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if (answer) {
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