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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
25 lines
722 B
Python
25 lines
722 B
Python
"""Request schema module.
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This module defines the Request model for handling incoming requests
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in the application service layer.
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"""
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from pydantic import BaseModel, ConfigDict, Field
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class Request(BaseModel):
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"""Request model for service endpoints.
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Represents an incoming request with optional metadata and
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extensible fields for various request types.
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The model uses ConfigDict with extra="allow" to support
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dynamically added fields while maintaining type safety.
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Attributes:
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metadata: Request-level metadata for tracking and context.
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"""
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model_config = ConfigDict(extra="allow")
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metadata: dict = Field(default_factory=dict, description="Request metadata for context")
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