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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
28 lines
1 KiB
Python
28 lines
1 KiB
Python
"""Stream chunk schema module.
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This module defines the StreamChunk model for handling streaming
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responses in the application, particularly for LLM outputs.
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"""
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from pydantic import BaseModel, Field
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from ..enumeration import ChunkEnum
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class StreamChunk(BaseModel):
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"""A chunk of streaming response data.
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Represents a single chunk in a streaming response sequence,
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commonly used for LLM outputs that are delivered incrementally.
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Attributes:
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chunk_type: Type identifier for the chunk content.
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chunk: The actual chunk data (string, dict, or list).
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done: Whether this is the final chunk in the stream.
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metadata: Additional metadata about this chunk.
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"""
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chunk_type: ChunkEnum = Field(default=ChunkEnum.CONTENT, description="Type of chunk content")
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chunk: str | dict | list = Field(default="", description="Chunk payload data")
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done: bool = Field(default=False, description="Whether stream is complete")
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metadata: dict = Field(default_factory=dict, description="Chunk metadata")
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