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- Implement BaseComponent with async lifecycle and context management - Add ApplicationContext for managing component initialization and registry - Create Application class for orchestrating job execution and lifecycle - Add AS LLM components with OpenAI chat model wrapper - Implement AS LLM formatter components with OpenAI formatter - Add client implementations including base, HTTP and ReMe clients - Create embedding model base class with caching and batching support - Implement file store base class with vector and full-text search - Add file watcher components for monitoring file system changes - Create job components for executing workflows - Implement service components for exposing jobs via different protocols - Add configuration schema with ApplicationConfig and ComponentConfig - Include utility modules for case conversion, chunking, logging and similarity - Register component types and create component registry system
27 lines
997 B
Python
27 lines
997 B
Python
"""Response schema module.
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This module defines the standardized data structure for model output
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responses used throughout the application.
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"""
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from typing import Any
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from pydantic import BaseModel, Field, ConfigDict
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class Response(BaseModel):
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"""Represents a structured response with result, status, and metadata.
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This model provides a consistent interface for returning results
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from operations, LLM calls, and service endpoints.
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Attributes:
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answer: The main response content, typically a string or structured data.
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success: Whether the operation completed successfully.
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metadata: Additional context and diagnostic information.
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"""
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model_config = ConfigDict(extra="allow")
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answer: str | Any = Field(default="", description="Response content or result data")
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success: bool = Field(default=True, description="Operation success status")
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metadata: dict = Field(default_factory=dict, description="Additional context and diagnostics")
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