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- 报告渲染 Go/No-Go 上线评估横幅（HTML 彩色 banner + Markdown 引用块） - 抽取 scored_llm 共享模块：llm_score / fluency 直连调用与评分解析收敛 - 网关新增 chat_with_usage / embed_with_usage，规则按次归集 llm_usage - 引擎分岗位用量归集（judge/generator/embedding/moderation）写入 RunSummary.eval_usage_by_purpose，并发下不做总量差值 - cost_tracking 重构：data/model_pricing.json 覆盖 + 默认计价表， 删除从未有数据支撑的 Turn 维度成本函数（偏差说明见 PR） - 报告 summary 增加 eval_cost 分岗位成本段并在 Markdown 渲染
118 lines
4.7 KiB
Python
118 lines
4.7 KiB
Python
"""Shared multi-protocol model transport used by evaluation features."""
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from typing import Any
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import httpx
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from agenteval.model_protocols import ProtocolAdapterError, get_protocol_adapter
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from agenteval.services.model_configs import ModelRuntimeConfig
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class ModelGatewayError(RuntimeError):
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pass
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class ModelGateway:
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def __init__(self, timeout: float = 60.0, transport: httpx.AsyncBaseTransport | None = None):
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self.timeout = timeout
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self.transport = transport
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# 评测侧 LLM 调用的累计 token 用量（引擎结束时写入 run summary）
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self.total_usage = {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0}
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async def _post(self, config: ModelRuntimeConfig, payload: dict[str, Any]) -> dict[str, Any]:
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adapter = self._adapter(config)
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try:
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async with httpx.AsyncClient(timeout=self.timeout, transport=self.transport) as client:
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response = await client.post(
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config.endpoint_url,
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headers=adapter.headers(config.api_key),
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json=payload,
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)
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response.raise_for_status()
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data = response.json()
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if not isinstance(data, dict):
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raise ModelGatewayError("模型接口返回格式不是 JSON 对象")
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return data
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except ModelGatewayError:
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raise
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except httpx.HTTPStatusError as exc:
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detail = exc.response.text[:300]
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raise ModelGatewayError(f"模型接口返回 HTTP {exc.response.status_code}: {detail}") from exc
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except Exception as exc:
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raise ModelGatewayError(f"模型接口调用失败: {exc}") from exc
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@staticmethod
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def _adapter(config: ModelRuntimeConfig):
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try:
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return get_protocol_adapter(config.provider)
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except ProtocolAdapterError as exc:
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raise ModelGatewayError(str(exc)) from exc
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async def chat(
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self,
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config: ModelRuntimeConfig,
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messages: list[dict[str, str]],
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temperature: float = 0.2,
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) -> str:
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content, _ = await self.chat_with_usage(config, messages, temperature)
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return content
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async def chat_with_usage(
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self,
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config: ModelRuntimeConfig,
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messages: list[dict[str, str]],
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temperature: float = 0.2,
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) -> tuple[str, dict[str, int] | None]:
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"""Like chat(), but also returns this call's token usage (None if omitted)."""
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adapter = self._adapter(config)
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try:
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payload = adapter.chat_payload(config.model_name, messages, temperature)
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data = await self._post(config, payload)
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usage = adapter.parse_usage(data)
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self._accumulate(usage)
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return adapter.parse_chat(data), usage
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except ProtocolAdapterError as exc:
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raise ModelGatewayError(str(exc)) from exc
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async def embed(self, config: ModelRuntimeConfig, inputs: str | list[str]) -> list[list[float]]:
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vectors, _ = await self.embed_with_usage(config, inputs)
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return vectors
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async def embed_with_usage(
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self, config: ModelRuntimeConfig, inputs: str | list[str]
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) -> tuple[list[list[float]], dict[str, int] | None]:
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adapter = self._adapter(config)
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try:
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payload = adapter.embedding_payload(config.model_name, inputs)
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data = await self._post(config, payload)
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usage = adapter.parse_usage(data)
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self._accumulate(usage)
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return adapter.parse_embeddings(data), usage
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except ProtocolAdapterError as exc:
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raise ModelGatewayError(str(exc)) from exc
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def _accumulate(self, usage: dict[str, int] | None) -> None:
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if not usage:
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return
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for key in self.total_usage:
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self.total_usage[key] += int(usage.get(key) or 0)
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async def moderate(self, config: ModelRuntimeConfig, text: str) -> dict[str, Any]:
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adapter = self._adapter(config)
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try:
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payload = adapter.moderation_payload(config.model_name, text)
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return adapter.parse_moderation(await self._post(config, payload))
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except ProtocolAdapterError as exc:
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raise ModelGatewayError(str(exc)) from exc
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async def test_connection(self, config: ModelRuntimeConfig) -> str:
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if config.capability.value == "chat":
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await self.chat(config, [{"role": "user", "content": "回复 OK"}], temperature=0)
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elif config.capability.value == "embedding":
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vectors = await self.embed(config, "connection test")
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if not vectors or not vectors[0]:
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raise ModelGatewayError("Embedding 接口未返回向量")
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else:
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await self.moderate(config, "connection test")
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return "连接成功"
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