知微
fa45d8aa5f
- health_checklist.json: 192.168.1.122→node122
- ocr_client.py: docstring IP→node122
- docs/market-data-requirements.md: IP→node122
- 所有API调用通过ProxyHandler({})绕过系统代理
Privoxy对node122:18003返回500，直连正常
65 lines
2.5 KiB
Python
65 lines
2.5 KiB
Python
"""Langtrace attribute mapper.
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Produces Langtrace's attribute vocabulary so a span can be ingested by a
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Langtrace backend. Compose it alongside other mappers like any other
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vocabulary.
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Scalar attributes are declared as a flat ``key -> extractor`` table (one lambda
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per mapping operation); the prompt/completion blobs are serialized as a tail.
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"""
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from typing import Callable
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from litellm.integrations.otel.mappers.base import AttributeMap, AttrValue, SpanData
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from litellm.integrations.otel.mappers.utils import (
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collect,
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json_or_none,
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output_messages,
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)
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from litellm.integrations.otel.model.payloads import LLMCallSpanData
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class LangtraceMapper:
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_LLM_CALL_ATTRS: dict[str, Callable[[LLMCallSpanData], AttrValue | None]] = {
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"gen_ai.operation.name": lambda d: "chat",
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"langtrace.service.name": lambda d: d.provider or None,
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"llm.model": lambda d: d.request_model or None,
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"gen_ai.response.model": lambda d: d.response_model or None,
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"gen_ai.response_id": lambda d: d.response_id or None,
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"gen_ai.system_fingerprint": lambda d: d.system_fingerprint or None,
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"llm.temperature": lambda d: d.request_params.temperature,
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"llm.top_p": lambda d: d.request_params.top_p,
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"llm.top_k": lambda d: d.request_params.top_k,
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"llm.max_tokens": lambda d: d.request_params.max_tokens,
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"llm.frequency_penalty": lambda d: d.request_params.frequency_penalty,
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"llm.presence_penalty": lambda d: d.request_params.presence_penalty,
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"llm.stream": lambda d: d.is_streaming,
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"llm.token.counts.prompt": lambda d: d.usage.input_tokens,
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"llm.token.counts.completion": lambda d: d.usage.output_tokens,
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"llm.token.counts.total": lambda d: d.usage.total_tokens,
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}
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_BLOB_ATTRS: dict[str, Callable[[LLMCallSpanData], AttrValue | None]] = {
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"llm.prompts": lambda d: (
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json_or_none(list(d.messages_in)) if d.messages_in else None
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),
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"llm.completions": lambda d: (
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json_or_none(output_messages(d)) if d.choices_out else None
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),
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}
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def map(self, data: SpanData) -> AttributeMap:
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match data:
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case LLMCallSpanData():
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return self._llm_call(data)
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case _:
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return {}
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@classmethod
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def _llm_call(cls, data: LLMCallSpanData) -> AttributeMap:
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return {
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**collect(cls._LLM_CALL_ATTRS, data),
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**collect(cls._BLOB_ATTRS, data),
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}
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