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
85 lines
3.2 KiB
Python
85 lines
3.2 KiB
Python
"""Langfuse OTLP attribute mapper.
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Langfuse ingests OTLP spans and reads from its own vendor namespace
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(``langfuse.observation.*``, ``langfuse.trace.*``). Compose this mapper after
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``GenAIMapper`` to send canonical + Langfuse-flavored spans simultaneously.
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Every attribute is declared as a ``key -> extractor`` table entry (one callable
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per mapping operation): ``_LLM_CALL_ATTRS`` for scalars and ``_BLOB_ATTRS`` for
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the JSON-serialized payloads. ``_llm_call`` just applies both tables.
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"""
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import json
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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_if,
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output_messages,
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serialize_messages,
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)
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from litellm.integrations.otel.model.payloads import (
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LLMCallSpanData,
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LLMRequestParams,
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LLMUsage,
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)
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class LangfuseMapper:
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_LLM_CALL_ATTRS: dict[str, Callable[[LLMCallSpanData], AttrValue | None]] = {
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"langfuse.observation.type": lambda d: "generation",
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"langfuse.observation.model.name": lambda d: d.request_model or None,
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"langfuse.observation.metadata.provider": lambda d: d.provider or None,
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"langfuse.observation.id": lambda d: d.identity.call_id or None,
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"langfuse.trace.metadata.team_id": lambda d: d.identity.team_id or None,
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"langfuse.trace.metadata.team_alias": lambda d: d.identity.team_alias or None,
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}
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# Sub-tables folded into their respective JSON blobs.
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_MODEL_PARAMS: dict[str, Callable[[LLMRequestParams], AttrValue | None]] = {
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"temperature": lambda rp: rp.temperature,
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"top_p": lambda rp: rp.top_p,
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"max_tokens": lambda rp: rp.max_tokens,
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"frequency_penalty": lambda rp: rp.frequency_penalty,
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"presence_penalty": lambda rp: rp.presence_penalty,
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"seed": lambda rp: rp.seed,
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}
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_USAGE_FIELDS: dict[str, Callable[[LLMUsage], AttrValue | None]] = {
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"input": lambda u: u.input_tokens,
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"output": lambda u: u.output_tokens,
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"total": lambda u: u.total_tokens,
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}
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# JSON-payload attributes: each builder returns the serialized blob or None.
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_BLOB_ATTRS: dict[str, Callable[[LLMCallSpanData], AttrValue | None]] = {
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"langfuse.observation.model.parameters": lambda d: json_if(
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collect(LangfuseMapper._MODEL_PARAMS, d.request_params)
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),
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"langfuse.observation.input": lambda d: serialize_messages(d.messages_in),
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"langfuse.observation.output": lambda d: serialize_messages(output_messages(d)),
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"langfuse.observation.usage_details": lambda d: json_if(
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collect(LangfuseMapper._USAGE_FIELDS, d.usage)
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),
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"langfuse.observation.cost_details": lambda d: (
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json.dumps({"total": d.response_cost})
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if d.response_cost is not None
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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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