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Nicolas MowenandGitHub
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* Pin ruff * Add python upgrade fixes This enables python upgrade checks in ruff to look for deprecated types and patterns. This namely fixes: - usage of deprecated `Typing` which is now built in - some specific exceptions which are caught and have new aliases Some specific UP checks were also ignored as they are stylistic / unimportant and likely to cause bugs * Remove async blocking calls Use asyncio.to_thread on two remaining blocking calls to fix hanging event thread loop. Enable this specific rule to block it in the future. * Use proper logging mechanism * Correctly format logs * Raise with context When raising an exception include the from context to improve debugging * Cleanup
68 lines
2.3 KiB
Python
68 lines
2.3 KiB
Python
"""Chat API response models."""
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from typing import Any
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from pydantic import BaseModel, Field
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class ToolCallInvocation(BaseModel):
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"""A tool call requested by the LLM (before execution)."""
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id: str = Field(description="Unique identifier for this tool call")
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name: str = Field(description="Tool name to call")
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arguments: dict[str, Any] = Field(description="Arguments for the tool call")
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class ChatMessageResponse(BaseModel):
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"""A message in the chat response."""
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role: str = Field(description="Message role")
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content: str | None = Field(
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default=None, description="Message content (None if tool calls present)"
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)
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reasoning: str | None = Field(
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default=None,
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description="Separated reasoning/thinking trace if the model emitted one",
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)
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tool_calls: list[ToolCallInvocation] | None = Field(
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default=None, description="Tool calls if LLM wants to call tools"
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)
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class ToolCall(BaseModel):
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"""A tool that was executed during the completion, with its response."""
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name: str = Field(description="Tool name that was called")
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arguments: dict[str, Any] = Field(
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default_factory=dict, description="Arguments passed to the tool"
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)
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response: str = Field(
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default="",
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description="The response or result returned from the tool execution",
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)
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class ChatCompletionResponse(BaseModel):
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"""Response from chat completion."""
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message: ChatMessageResponse = Field(description="The assistant's message")
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finish_reason: str = Field(
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description="Reason generation stopped: 'stop', 'tool_calls', 'length', 'error'"
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)
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tool_iterations: int = Field(
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default=0, description="Number of tool call iterations performed"
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)
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tool_calls: list[ToolCall] = Field(
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default_factory=list,
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description="List of tool calls that were executed during this completion",
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)
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messages: list[dict[str, Any]] = Field(
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default_factory=list,
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description=(
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"The full conversation chain, including the system message. Persist "
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"and replay this verbatim on the next request so the prompt prefix "
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"stays byte-identical and the model server's prompt cache keeps "
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"hitting."
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),
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)
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