“ydy0615”
17d211bf93
- Introduced a new web search block plugin to handle web search queries and results. - Updated copilot, doc block, and pro block plugins to include web search context in AI completions. - Implemented utility functions for parsing and building web search markdown. - Enhanced API to support web search requests and responses. - Added configuration for web search URL and timeout settings. - Updated size limit checks to account for web search content.
81 lines
2.7 KiB
Python
81 lines
2.7 KiB
Python
from dataclasses import dataclass
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from typing import Any
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from risk_config import RiskConfig
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@dataclass(frozen=True)
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class LLMPolicy:
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job_type: str
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model: str
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profile: str
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max_input_chars: int
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max_output_tokens: int
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temperature: float
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thinking: str | None
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def _normalize_thinking(value: str | None, *, allow_high: bool) -> str | None:
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candidate = (value or "").strip().lower()
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if candidate in {"", "none", "off"}:
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return None
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if candidate not in {"low", "medium", "high"}:
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return "low"
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if candidate == "high" and not allow_high:
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return "medium"
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return candidate
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def resolve_llm_policy(job_type: str, request_payload: dict[str, Any], config: RiskConfig) -> LLMPolicy:
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if job_type == "completion":
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return LLMPolicy(
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job_type=job_type,
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model=config.completion_model,
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profile="completion",
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max_input_chars=config.completion_max_input_chars,
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max_output_tokens=config.completion_max_output_tokens,
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temperature=config.completion_temperature,
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thinking=_normalize_thinking(request_payload.get("model_thinking"), allow_high=False),
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)
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if job_type == "pro_completion":
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return LLMPolicy(
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job_type=job_type,
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model=config.pro_model,
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profile="pro",
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max_input_chars=config.pro_max_input_chars,
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max_output_tokens=config.pro_max_output_tokens,
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temperature=config.pro_temperature,
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thinking=_normalize_thinking(request_payload.get("pro_thinking"), allow_high=True) or "medium",
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)
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if job_type == "web_search":
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return LLMPolicy(
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job_type=job_type,
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model=config.web_search_model,
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profile="completion",
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max_input_chars=config.web_search_max_input_chars,
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max_output_tokens=config.web_search_max_output_tokens,
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temperature=config.web_search_temperature,
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thinking="low",
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)
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if job_type == "compress":
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return LLMPolicy(
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job_type=job_type,
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model=config.completion_model,
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profile="completion",
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max_input_chars=config.compress_max_input_chars,
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max_output_tokens=config.compress_max_output_tokens,
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temperature=0.2,
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thinking="low",
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)
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if job_type == "ocr":
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return LLMPolicy(
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job_type=job_type,
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model=config.vision_model,
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profile="vision",
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max_input_chars=config.ocr_max_input_bytes,
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max_output_tokens=config.completion_max_output_tokens,
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temperature=0.0,
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thinking=None,
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)
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raise ValueError(f"unsupported llm policy job type: {job_type}")
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