Yijia-Xiao
8a22594607
Adds a cross-provider temperature config (and TRADINGAGENTS_TEMPERATURE), forwarded to every LLM client when set, so runs can be made less variable on models that honor it. Adds a README "Reproducibility" section that separates the sources of run-to-run variation, what users can control (temperature, non-reasoning model, pinned date), and what is inherent to LLM-driven analysis, and notes that the identity and verified-data fixes already removed the "different companies / fabricated prices" variance. #178 #168
69 lines
2.3 KiB
Python
69 lines
2.3 KiB
Python
import re
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from typing import Any, Optional
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from langchain_anthropic import ChatAnthropic
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from .base_client import BaseLLMClient, normalize_content
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from .validators import validate_model
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_PASSTHROUGH_KWARGS = (
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"timeout", "max_retries", "api_key", "max_tokens", "temperature",
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"callbacks", "http_client", "http_async_client", "effort",
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)
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# Anthropic's extended-thinking ``effort`` parameter is accepted by Opus 4.5+
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# and Sonnet 4.5+ only. Haiku (any version shipped to date) 400s with
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# ``"This model does not support the effort parameter"`` (#831). Future
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# ``claude-{opus,sonnet}-X-Y`` releases inherit effort support via the
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# forward-compat pattern below; future Haiku stays excluded by default.
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_EFFORT_EXACT = {
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"claude-mythos-preview", # non-standard preview name; effort-capable
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}
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_EFFORT_PATTERN = re.compile(r"^claude-(opus|sonnet)-\d+-\d+$")
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def _supports_effort(model: str) -> bool:
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"""Whether Anthropic accepts the ``effort`` parameter for this model."""
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model_lc = model.lower()
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return model_lc in _EFFORT_EXACT or bool(_EFFORT_PATTERN.match(model_lc))
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class NormalizedChatAnthropic(ChatAnthropic):
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"""ChatAnthropic with normalized content output.
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Claude models with extended thinking or tool use return content as a
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list of typed blocks. This normalizes to string for consistent
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downstream handling.
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"""
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def invoke(self, input, config=None, **kwargs):
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return normalize_content(super().invoke(input, config, **kwargs))
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class AnthropicClient(BaseLLMClient):
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"""Client for Anthropic Claude models."""
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def __init__(self, model: str, base_url: Optional[str] = None, **kwargs):
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super().__init__(model, base_url, **kwargs)
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def get_llm(self) -> Any:
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"""Return configured ChatAnthropic instance."""
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self.warn_if_unknown_model()
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llm_kwargs = {"model": self.model}
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if self.base_url:
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llm_kwargs["base_url"] = self.base_url
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for key in _PASSTHROUGH_KWARGS:
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if key not in self.kwargs:
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continue
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if key == "effort" and not _supports_effort(self.model):
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continue
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llm_kwargs[key] = self.kwargs[key]
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return NormalizedChatAnthropic(**llm_kwargs)
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def validate_model(self) -> bool:
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"""Validate model for Anthropic."""
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return validate_model("anthropic", self.model)
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