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Consolidates the incremental refactor work into a single change set: modular web tools (api/web_tools), native Anthropic request building and SSE block policy, OpenAI conversion and error handling, provider transports and rate limiting, messaging handler and tree queue, safe logging, smoke tests, and broad test coverage.
118 lines
3.8 KiB
Python
118 lines
3.8 KiB
Python
"""NVIDIA NIM settings (fixed values, no env config)."""
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from pydantic import BaseModel, ConfigDict, Field, ValidationInfo, field_validator
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from config.constants import ANTHROPIC_DEFAULT_MAX_OUTPUT_TOKENS
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class NimSettings(BaseModel):
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"""Fixed NVIDIA NIM settings (not configurable via env)."""
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temperature: float = Field(
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1.0, ge=0.0, le=2.0, description="Sampling temperature, must be >=0 and <=2."
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)
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top_p: float = Field(
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1.0, ge=0.0, le=1.0, description="Nucleus sampling probability. [0,1]"
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)
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top_k: int = -1
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max_tokens: int = Field(
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ANTHROPIC_DEFAULT_MAX_OUTPUT_TOKENS,
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ge=1,
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description="Maximum number of tokens in output.",
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)
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presence_penalty: float = Field(0.0, ge=-2.0, le=2.0)
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frequency_penalty: float = Field(0.0, ge=-2.0, le=2.0)
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min_p: float = Field(
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0.0, ge=0.0, le=1.0, description="Minimum probability threshold [0,1]."
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)
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repetition_penalty: float = Field(
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1.0, ge=0.0, description="Penalty for repeated tokens. Must be >=0."
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)
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seed: int | None = None
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stop: str | None = None
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parallel_tool_calls: bool = True
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ignore_eos: bool = False
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min_tokens: int = Field(0, ge=0, description="Minimum tokens in the response.")
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chat_template: str | None = None
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request_id: str | None = None
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model_config = ConfigDict(extra="forbid")
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@field_validator("top_k", mode="before")
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@classmethod
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def validate_top_k(cls, v, info: ValidationInfo):
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if v is None or v == "":
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return -1
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int_v = int(v)
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if int_v < -1:
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raise ValueError(f"{info.field_name} must be -1 or >= 0")
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return int_v
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@field_validator(
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"temperature",
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"top_p",
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"min_p",
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"presence_penalty",
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"frequency_penalty",
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"repetition_penalty",
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mode="before",
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)
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@classmethod
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def validate_float_fields(cls, v, info: ValidationInfo):
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field_defaults = {
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"temperature": 1.0,
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"top_p": 1.0,
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"min_p": 0.0,
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"presence_penalty": 0.0,
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"frequency_penalty": 0.0,
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"repetition_penalty": 1.0,
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}
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if v is None or v == "":
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key = info.field_name or "temperature"
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return field_defaults.get(key, 1.0)
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try:
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val = float(v)
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except (TypeError, ValueError) as err:
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raise ValueError(
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f"{info.field_name} must be a float. Got {type(v).__name__}."
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) from err
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return val
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@field_validator("max_tokens", "min_tokens", mode="before")
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@classmethod
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def validate_int_fields(cls, v, info: ValidationInfo):
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field_defaults = {
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"max_tokens": ANTHROPIC_DEFAULT_MAX_OUTPUT_TOKENS,
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"min_tokens": 0,
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}
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if v is None or v == "":
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key = info.field_name or "max_tokens"
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return field_defaults.get(key, ANTHROPIC_DEFAULT_MAX_OUTPUT_TOKENS)
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try:
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val = int(v)
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except (TypeError, ValueError) as err:
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raise ValueError(
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f"{info.field_name} must be an int. Got {type(v).__name__}."
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) from err
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return val
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@field_validator("seed", mode="before")
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@classmethod
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def parse_optional_int(cls, v, info: ValidationInfo):
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if v == "" or v is None:
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return None
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try:
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return int(v)
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except (TypeError, ValueError) as err:
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raise ValueError(
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f"{info.field_name} must be an int or empty/None."
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) from err
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@field_validator("stop", "chat_template", "request_id", mode="before")
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@classmethod
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def parse_optional_str(cls, v, info: ValidationInfo):
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if v == "":
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return None
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if v is not None and not isinstance(v, str):
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return str(v)
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return v
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