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- Introduce BaseAsTokenCounter and EstimatedAsTokenCounter for token estimation - Add AsMsgStat and AsBlockStat schema for message statistics tracking - Implement FileIO class with read/write/append/edit operations - Create file utility functions for safe async file reading and truncation - Add MemorySearch component for semantic search in memory files - Register new component types in ComponentEnum and update imports - Add constants for default host, port, and truncation limits - Create BaseService abstract base class for service implementations - Implement BaseStep with component accessors and lifecycle management - Add proper __all__ exports for all new modules and components
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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