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ChatManager 還原: - OpenClaw (188:8088) 負責 RCA 仲裁，不改用 Gemini (未經批准) - NemoClaw (NVIDIA NIM nemotron-mini-4b) 負責補充/評論 - 雙 AI 並行執行，OpenClaw 30s / NemoClaw 50s timeout - 支援 @openclaw / @nemo 指定對象 nvidia_provider.py 修 timeout 根因: - NVIDIA_TIMEOUT 從硬編碼 30.0 改為讀 NEMOTRON_TIMEOUT_SECONDS (45s) - Memory 記載 NIM 免費 tier 延遲 11-45s，30s 硬編碼導致慢請求全超時 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
903 lines
31 KiB
Python
903 lines
31 KiB
Python
"""
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NVIDIA Nemotron Provider - ADR-036
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==================================
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2026-03-29 ogt: Nemotron Tool Calling 整合 (83.3% 精準度)
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專門處理 Tool Calling 任務，提供高精準度的 K8s 操作決策。
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設計原則:
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1. OpenAI 相容格式 - 與 Nemotron API 對接
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2. Pydantic 強制驗證 - 所有回應必須通過 Schema 驗證
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3. Fallback 機制 - 失敗時降級到 Gemini/Claude
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4. HITL 高風險保護 - DELETE 等操作需人工審核
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版本: v1.0
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建立: 2026-03-29 (台北時區)
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建立者: Claude Code
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"""
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from __future__ import annotations
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import asyncio
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import json
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import random
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import time
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from enum import Enum
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from typing import Any, Protocol, runtime_checkable # 2026-03-29 ogt: P2-1 Protocol
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import httpx
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import structlog
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from prometheus_client import Counter, Histogram # 2026-03-29 ogt: P3-3 Prometheus
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from src.core.config import get_settings
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from src.core.telemetry import get_tracer # 2026-03-29 ogt: P1-2 OTEL 追蹤
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from src.models.nvidia import (
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NvidiaProviderResult,
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NvidiaResponse,
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ToolCallValidationResult,
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ToolDefinition,
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)
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from src.services.langfuse_client import ( # 2026-03-29 ogt: P1-1 Langfuse 整合
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LangfuseTraceContext,
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)
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logger = structlog.get_logger(__name__)
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settings = get_settings()
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# OTEL Tracer (P1-2 修復)
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_tracer = get_tracer("nvidia_provider")
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# =============================================================================
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# Protocol 定義 (P2-1 修復)
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# =============================================================================
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@runtime_checkable
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class INvidiaProvider(Protocol):
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"""
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NVIDIA Provider Interface - P2-1 修復
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2026-03-29 ogt: 定義 NvidiaProvider 介面，支援 DI 和測試替換
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使用方式:
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```python
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def process_tool_call(provider: INvidiaProvider):
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result = await provider.tool_call(messages, tools)
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```
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"""
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async def tool_call(
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self,
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messages: list[dict[str, Any]],
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tools: list[ToolDefinition | dict[str, Any]],
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model: str = ...,
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temperature: float = ...,
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max_tokens: int = ...,
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) -> NvidiaProviderResult:
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"""執行 Tool Calling 請求"""
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...
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def is_high_risk_tool(self, tool_name: str) -> bool:
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"""檢查是否為高風險 Tool"""
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...
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def get_high_risk_tools(
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self, tool_calls: list[ToolCallValidationResult]
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) -> list[ToolCallValidationResult]:
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"""篩選高風險 Tool Calls"""
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...
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async def close(self) -> None:
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"""關閉資源"""
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...
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async def chat(
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self,
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prompt: str,
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model: str = ...,
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temperature: float = ...,
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max_tokens: int = ...,
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) -> tuple[str, bool, int, float]:
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"""
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一般對話 (非 Tool Calling) - 2026-03-29 ogt 新增
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Returns:
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tuple: (response_text, success, total_tokens, cost_usd)
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"""
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...
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# =============================================================================
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# 常量定義
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# =============================================================================
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# NVIDIA NIM API Endpoint
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NVIDIA_API_URL = "https://integrate.api.nvidia.com/v1/chat/completions"
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# 預設模型 (2026-03-31 ogt: 恢復為 nemotron-mini-4b-instruct)
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NVIDIA_DEFAULT_MODEL = "nvidia/nemotron-mini-4b-instruct"
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# 請求超時 (秒)
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# 2026-04-01 ogt: 設為 30s (平衡點)
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# 2026-04-03 ogt: 改從 config 讀取，與 NEMOTRON_TIMEOUT_SECONDS=45 對齊
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# Memory 記載 NIM 免費 tier 延遲 11-45s，30s 硬編碼導致慢請求全超時
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def _get_nvidia_timeout() -> float:
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try:
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from src.core.config import get_settings
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return float(get_settings().NEMOTRON_TIMEOUT_SECONDS)
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except Exception:
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return 45.0
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NVIDIA_TIMEOUT = _get_nvidia_timeout()
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# 重試次數
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MAX_RETRIES = 2
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# =============================================================================
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# P3-1: Circuit Breaker 配置 (2026-03-29 ogt)
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# =============================================================================
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# Circuit Breaker 閾值
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CIRCUIT_BREAKER_FAILURE_THRESHOLD = 3 # 連續失敗次數觸發斷路
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CIRCUIT_BREAKER_RECOVERY_TIMEOUT = 60 # 斷路後等待恢復時間 (秒)
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CIRCUIT_BREAKER_HALF_OPEN_REQUESTS = 1 # 半開狀態允許的測試請求數
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# P3-2: 指數退避配置
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RETRY_BASE_DELAY = 1.0 # 基礎延遲 (秒)
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RETRY_MAX_DELAY = 30.0 # 最大延遲 (秒)
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RETRY_EXPONENTIAL_BASE = 2 # 指數基數
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# =============================================================================
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# P3-3: Prometheus Metrics (2026-03-29 ogt)
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# =============================================================================
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NVIDIA_REQUESTS_TOTAL = Counter(
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"nvidia_tool_call_requests_total",
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"Total NVIDIA Tool Calling requests",
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["status", "tool_name"],
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)
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NVIDIA_LATENCY_HISTOGRAM = Histogram(
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"nvidia_tool_call_latency_seconds",
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"NVIDIA Tool Calling latency in seconds",
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buckets=[1, 5, 10, 15, 20, 30, 45, 60],
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)
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NVIDIA_CIRCUIT_BREAKER_STATE = Counter(
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"nvidia_circuit_breaker_state_changes_total",
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"Circuit breaker state changes",
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["from_state", "to_state"],
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)
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# =============================================================================
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# P3-1: Circuit Breaker 狀態機 (2026-03-29 ogt)
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# =============================================================================
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class CircuitState(Enum):
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"""Circuit Breaker 狀態"""
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CLOSED = "closed" # 正常運作
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OPEN = "open" # 斷路，拒絕請求
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HALF_OPEN = "half_open" # 測試恢復
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class CircuitBreaker:
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"""
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Circuit Breaker 實作 - P3-1 優化
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防止連鎖故障，當 NVIDIA API 連續失敗時自動斷路。
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狀態轉換:
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CLOSED → (連續失敗 >= 3) → OPEN
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OPEN → (等待 60s) → HALF_OPEN
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HALF_OPEN → (成功) → CLOSED
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HALF_OPEN → (失敗) → OPEN
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"""
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def __init__(
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self,
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failure_threshold: int = CIRCUIT_BREAKER_FAILURE_THRESHOLD,
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recovery_timeout: float = CIRCUIT_BREAKER_RECOVERY_TIMEOUT,
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):
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self._state = CircuitState.CLOSED
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self._failure_count = 0
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self._last_failure_time: float = 0
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self._failure_threshold = failure_threshold
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self._recovery_timeout = recovery_timeout
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@property
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def state(self) -> CircuitState:
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"""取得當前狀態 (含自動轉換檢查)"""
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if self._state == CircuitState.OPEN:
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# 檢查是否應該轉為 HALF_OPEN
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if time.time() - self._last_failure_time >= self._recovery_timeout:
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self._transition_to(CircuitState.HALF_OPEN)
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return self._state
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def _transition_to(self, new_state: CircuitState) -> None:
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"""狀態轉換 (含 Prometheus 記錄)"""
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old_state = self._state
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if old_state != new_state:
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NVIDIA_CIRCUIT_BREAKER_STATE.labels(
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from_state=old_state.value, to_state=new_state.value
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).inc()
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logger.info(
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"circuit_breaker_state_change",
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from_state=old_state.value,
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to_state=new_state.value,
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)
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self._state = new_state
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def can_execute(self) -> bool:
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"""是否允許執行請求"""
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state = self.state # 觸發自動狀態檢查
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if state == CircuitState.CLOSED:
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return True
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if state == CircuitState.HALF_OPEN:
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return True # 允許測試請求
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return False # OPEN 狀態拒絕
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def record_success(self) -> None:
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"""記錄成功"""
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if self._state == CircuitState.HALF_OPEN:
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self._transition_to(CircuitState.CLOSED)
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self._failure_count = 0
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def record_failure(self) -> None:
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"""記錄失敗"""
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self._failure_count += 1
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self._last_failure_time = time.time()
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if self._state == CircuitState.HALF_OPEN:
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# HALF_OPEN 失敗，重新斷路
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self._transition_to(CircuitState.OPEN)
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elif self._failure_count >= self._failure_threshold:
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# 連續失敗達閾值，斷路
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self._transition_to(CircuitState.OPEN)
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# 高風險 Tool 清單 (需要 HITL 審核)
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HIGH_RISK_TOOLS: set[str] = {
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"delete_pod",
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"delete_deployment",
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"delete_namespace",
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"delete_service",
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"delete_configmap",
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"delete_secret",
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"scale_to_zero",
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"drain_node",
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"cordon_node",
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"delete_pvc",
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"delete_pv",
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}
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# =============================================================================
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# NvidiaProvider 類別
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# =============================================================================
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class NvidiaProvider:
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"""
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NVIDIA Nemotron Provider
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專門處理 Tool Calling 任務，提供 83.3% 精準度的 K8s 操作決策。
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使用方式:
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```python
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provider = NvidiaProvider()
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result = await provider.tool_call(
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messages=[{"role": "user", "content": "重啟 awoooi-api pod"}],
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tools=[restart_tool, scale_tool],
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)
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if result.success:
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for tc in result.tool_calls:
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if tc.valid:
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execute_tool(tc.tool_name, tc.arguments)
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```
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"""
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def __init__(self, api_key: str | None = None):
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"""
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初始化 NvidiaProvider
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Args:
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api_key: NVIDIA API Key (預設從 settings 取得)
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2026-03-29 ogt: P3-1 加入 Circuit Breaker
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"""
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self._api_key = api_key or settings.NVIDIA_API_KEY
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self._client: httpx.AsyncClient | None = None
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self._circuit_breaker = CircuitBreaker() # P3-1: Circuit Breaker
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async def _get_client(self) -> httpx.AsyncClient:
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"""取得或建立 HTTP Client"""
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if self._client is None or self._client.is_closed:
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self._client = httpx.AsyncClient(
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timeout=httpx.Timeout(NVIDIA_TIMEOUT, connect=10.0),
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limits=httpx.Limits(max_connections=10, max_keepalive_connections=5),
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)
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return self._client
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async def close(self) -> None:
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"""關閉 HTTP Client"""
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if self._client and not self._client.is_closed:
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await self._client.aclose()
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self._client = None
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async def tool_call(
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self,
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messages: list[dict[str, Any]],
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tools: list[ToolDefinition | dict[str, Any]],
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model: str = NVIDIA_DEFAULT_MODEL,
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temperature: float = 0.0,
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max_tokens: int = 1024,
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) -> NvidiaProviderResult:
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"""
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執行 Tool Calling 請求
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Args:
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messages: 對話訊息列表
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tools: 可用 Tool 定義列表
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model: 模型名稱
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temperature: 溫度 (0.0 最確定性)
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max_tokens: 最大輸出 Token
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Returns:
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NvidiaProviderResult: 包含驗證後的 Tool Calls
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2026-03-29 ogt: P1-1/P1-2 修復 - 加入 OTEL + Langfuse 追蹤
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2026-03-29 ogt: P3-1/P3-2/P3-3 - Circuit Breaker + 指數退避 + Prometheus
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"""
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start_time = time.perf_counter()
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# P1-2: OTEL Span 包裝整個 Tool Calling 流程
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with _tracer.start_as_current_span("nvidia_tool_call") as span:
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span.set_attribute("ai.provider", "nvidia")
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span.set_attribute("ai.model", model)
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span.set_attribute("ai.tool_count", len(tools))
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# P3-1: Circuit Breaker 檢查
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if not self._circuit_breaker.can_execute():
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span.set_attribute("ai.error", "circuit_breaker_open")
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NVIDIA_REQUESTS_TOTAL.labels(status="circuit_open", tool_name="").inc()
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logger.warning(
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"nvidia_circuit_breaker_open",
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state=self._circuit_breaker.state.value,
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)
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return NvidiaProviderResult(
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success=False,
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error="Circuit Breaker OPEN - NVIDIA API 暫時不可用",
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fallback_triggered=True,
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)
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# 檢查 API Key
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if not self._api_key:
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span.set_attribute("ai.error", "api_key_not_set")
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NVIDIA_REQUESTS_TOTAL.labels(status="error", tool_name="").inc()
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return NvidiaProviderResult(
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success=False,
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error="NVIDIA_API_KEY 未設定",
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fallback_triggered=True,
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)
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# 轉換 tools 為 dict 格式
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tools_data = []
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tool_names = []
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for tool in tools:
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if isinstance(tool, ToolDefinition):
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tools_data.append(tool.model_dump())
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tool_names.append(tool.function.get("name", "unknown"))
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else:
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tools_data.append(tool)
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tool_names.append(tool.get("function", {}).get("name", "unknown"))
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span.set_attribute("ai.tool_names", ",".join(tool_names))
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# 建立請求
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request_body = {
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"model": model,
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"messages": messages,
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"tools": tools_data,
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"tool_choice": "auto",
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"temperature": temperature,
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"max_tokens": max_tokens,
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}
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# P1-1: Langfuse 追蹤
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with LangfuseTraceContext(
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name="nvidia_tool_call",
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metadata={"model": model, "tool_count": len(tools)},
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) as langfuse_ctx:
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# 執行請求 (含 P3-2 指數退避重試)
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response_data: dict | None = None
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last_error: str | None = None
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for attempt in range(MAX_RETRIES + 1):
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try:
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response_data = await self._send_request(request_body)
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self._circuit_breaker.record_success() # P3-1
|
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break
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except Exception as e:
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last_error = str(e)
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span.set_attribute(f"ai.retry.{attempt}", last_error)
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logger.warning(
|
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"nvidia_request_retry",
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attempt=attempt + 1,
|
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max_retries=MAX_RETRIES,
|
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error=last_error,
|
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)
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if attempt == MAX_RETRIES:
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self._circuit_breaker.record_failure() # P3-1
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break
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# P3-2: 指數退避 (含 jitter)
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delay = min(
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RETRY_BASE_DELAY * (RETRY_EXPONENTIAL_BASE ** attempt),
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RETRY_MAX_DELAY,
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)
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jitter = random.uniform(0, delay * 0.1) # 10% jitter
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await asyncio.sleep(delay + jitter)
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latency_ms = (time.perf_counter() - start_time) * 1000
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latency_seconds = latency_ms / 1000
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span.set_attribute("ai.latency_ms", round(latency_ms, 2))
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NVIDIA_LATENCY_HISTOGRAM.observe(latency_seconds) # P3-3
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# 請求失敗
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if response_data is None:
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span.set_attribute("ai.success", False)
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span.set_attribute("ai.error", last_error or "unknown")
|
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NVIDIA_REQUESTS_TOTAL.labels(status="error", tool_name="").inc()
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logger.error(
|
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"nvidia_request_failed",
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error=last_error,
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latency_ms=round(latency_ms, 2),
|
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)
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return NvidiaProviderResult(
|
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success=False,
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error=last_error,
|
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latency_ms=latency_ms,
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fallback_triggered=True,
|
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)
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|
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# 解析回應
|
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try:
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nvidia_response = NvidiaResponse.model_validate(response_data)
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except Exception as e:
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span.set_attribute("ai.success", False)
|
||
span.set_attribute("ai.error", f"parse_failed: {e}")
|
||
logger.error(
|
||
"nvidia_response_parse_failed",
|
||
error=str(e),
|
||
raw_response=str(response_data)[:500],
|
||
)
|
||
return NvidiaProviderResult(
|
||
success=False,
|
||
error=f"回應解析失敗: {e}",
|
||
latency_ms=latency_ms,
|
||
fallback_triggered=True,
|
||
)
|
||
|
||
# 驗證 Tool Calls
|
||
tool_calls = self._validate_tool_calls(nvidia_response)
|
||
|
||
# 統計
|
||
usage = nvidia_response.usage
|
||
prompt_tokens = usage.prompt_tokens if usage else 0
|
||
completion_tokens = usage.completion_tokens if usage else 0
|
||
total_tokens = usage.total_tokens if usage else 0
|
||
|
||
# P1-2: OTEL 屬性
|
||
span.set_attribute("ai.success", True)
|
||
span.set_attribute("ai.tool_call_count", len(tool_calls))
|
||
span.set_attribute(
|
||
"ai.valid_count", sum(1 for tc in tool_calls if tc.valid)
|
||
)
|
||
span.set_attribute("ai.prompt_tokens", prompt_tokens)
|
||
span.set_attribute("ai.completion_tokens", completion_tokens)
|
||
span.set_attribute("ai.total_tokens", total_tokens)
|
||
|
||
# P1-1: Langfuse Generation 記錄
|
||
if langfuse_ctx and hasattr(langfuse_ctx, "generation"):
|
||
try:
|
||
langfuse_ctx.generation(
|
||
name="nvidia_nemotron",
|
||
model=model,
|
||
input={"messages": messages, "tools": tool_names},
|
||
output={
|
||
"tool_calls": [
|
||
{"name": tc.tool_name, "args": tc.arguments}
|
||
for tc in tool_calls
|
||
if tc.valid
|
||
]
|
||
},
|
||
usage={"input": prompt_tokens, "output": completion_tokens},
|
||
metadata={
|
||
"latency_ms": round(latency_ms, 2),
|
||
"valid_count": sum(1 for tc in tool_calls if tc.valid),
|
||
},
|
||
)
|
||
except Exception as le:
|
||
logger.warning("langfuse_generation_failed_safe", error=str(le))
|
||
|
||
# P3-3: Prometheus 成功指標
|
||
for tc in tool_calls:
|
||
if tc.valid and tc.tool_name:
|
||
NVIDIA_REQUESTS_TOTAL.labels(
|
||
status="success", tool_name=tc.tool_name
|
||
).inc()
|
||
|
||
logger.info(
|
||
"nvidia_tool_call_completed",
|
||
success=True,
|
||
tool_call_count=len(tool_calls),
|
||
valid_count=sum(1 for tc in tool_calls if tc.valid),
|
||
latency_ms=round(latency_ms, 2),
|
||
prompt_tokens=prompt_tokens,
|
||
completion_tokens=completion_tokens,
|
||
)
|
||
|
||
return NvidiaProviderResult(
|
||
success=True,
|
||
tool_calls=tool_calls,
|
||
usage=usage,
|
||
latency_ms=latency_ms,
|
||
fallback_triggered=False,
|
||
)
|
||
|
||
async def _send_request(self, request_body: dict) -> dict:
|
||
"""
|
||
發送 HTTP 請求到 NVIDIA API
|
||
|
||
Args:
|
||
request_body: 請求內容
|
||
|
||
Returns:
|
||
API 回應 (dict)
|
||
|
||
Raises:
|
||
Exception: 請求失敗
|
||
"""
|
||
client = await self._get_client()
|
||
|
||
headers = {
|
||
"Authorization": f"Bearer {self._api_key}",
|
||
"Content-Type": "application/json",
|
||
}
|
||
|
||
response = await client.post(
|
||
NVIDIA_API_URL,
|
||
headers=headers,
|
||
json=request_body,
|
||
)
|
||
|
||
if response.status_code != 200:
|
||
error_text = response.text[:500]
|
||
raise Exception(
|
||
f"NVIDIA API 錯誤: {response.status_code} - {error_text}"
|
||
)
|
||
|
||
return response.json()
|
||
|
||
def _validate_tool_calls(
|
||
self, response: NvidiaResponse
|
||
) -> list[ToolCallValidationResult]:
|
||
"""
|
||
驗證 Tool Calls
|
||
|
||
Args:
|
||
response: NVIDIA API 回應
|
||
|
||
Returns:
|
||
驗證後的 Tool Call 結果列表
|
||
"""
|
||
results: list[ToolCallValidationResult] = []
|
||
|
||
if not response.choices:
|
||
return results
|
||
|
||
message = response.choices[0].message
|
||
if not message.tool_calls:
|
||
return results
|
||
|
||
for tc in message.tool_calls:
|
||
try:
|
||
# 解析 arguments JSON
|
||
arguments = json.loads(tc.function.arguments)
|
||
|
||
results.append(
|
||
ToolCallValidationResult(
|
||
valid=True,
|
||
tool_name=tc.function.name,
|
||
arguments=arguments,
|
||
)
|
||
)
|
||
except json.JSONDecodeError as e:
|
||
results.append(
|
||
ToolCallValidationResult(
|
||
valid=False,
|
||
tool_name=tc.function.name,
|
||
error=f"Arguments JSON 解析失敗: {e}",
|
||
raw_response=tc.function.arguments,
|
||
)
|
||
)
|
||
except Exception as e:
|
||
results.append(
|
||
ToolCallValidationResult(
|
||
valid=False,
|
||
error=f"驗證失敗: {e}",
|
||
)
|
||
)
|
||
|
||
return results
|
||
|
||
def is_high_risk_tool(self, tool_name: str) -> bool:
|
||
"""
|
||
檢查是否為高風險 Tool
|
||
|
||
Args:
|
||
tool_name: Tool 名稱
|
||
|
||
Returns:
|
||
是否需要 HITL 審核
|
||
"""
|
||
return tool_name.lower() in HIGH_RISK_TOOLS
|
||
|
||
def get_high_risk_tools(
|
||
self, tool_calls: list[ToolCallValidationResult]
|
||
) -> list[ToolCallValidationResult]:
|
||
"""
|
||
篩選高風險 Tool Calls
|
||
|
||
Args:
|
||
tool_calls: Tool Call 結果列表
|
||
|
||
Returns:
|
||
高風險 Tool Calls
|
||
"""
|
||
return [
|
||
tc
|
||
for tc in tool_calls
|
||
if tc.valid and tc.tool_name and self.is_high_risk_tool(tc.tool_name)
|
||
]
|
||
|
||
async def chat(
|
||
self,
|
||
prompt: str,
|
||
model: str | None = None,
|
||
temperature: float = 0.1,
|
||
max_tokens: int = 2048,
|
||
use_json_mode: bool = False,
|
||
) -> tuple[str, bool, int, float]:
|
||
"""
|
||
一般對話 (非 Tool Calling) - 用於 RCA 分析或自由對話
|
||
|
||
2026-03-29 ogt: 新增，符合模組化規範
|
||
2026-03-31 ogt: 新增 use_json_mode 參數 (Phase 22 修復)
|
||
- RCA/分析場景: use_json_mode=True (結構化輸出)
|
||
- 對話場景: use_json_mode=False (預設，自然語言回應)
|
||
|
||
Args:
|
||
prompt: 對話內容
|
||
model: 模型名稱 (預設從 ModelRegistry 取得)
|
||
temperature: 溫度
|
||
max_tokens: 最大輸出 Token
|
||
|
||
Returns:
|
||
tuple: (response_text, success, total_tokens, cost_usd)
|
||
"""
|
||
start_time = time.perf_counter()
|
||
|
||
# OTEL Span
|
||
with _tracer.start_as_current_span("nvidia_chat") as span:
|
||
span.set_attribute("ai.provider", "nvidia")
|
||
|
||
# Circuit Breaker 檢查
|
||
if not self._circuit_breaker.can_execute():
|
||
span.set_attribute("ai.error", "circuit_breaker_open")
|
||
NVIDIA_REQUESTS_TOTAL.labels(status="circuit_open", tool_name="chat").inc()
|
||
logger.warning("nvidia_chat_circuit_breaker_open")
|
||
return "Circuit Breaker OPEN - NVIDIA API 暫時不可用", False, 0, 0.0
|
||
|
||
# 檢查 API Key
|
||
if not self._api_key:
|
||
span.set_attribute("ai.error", "api_key_not_set")
|
||
return "NVIDIA_API_KEY not configured", False, 0, 0.0
|
||
|
||
# 從 ModelRegistry 取得模型
|
||
from src.services.model_registry import get_model_registry
|
||
registry = get_model_registry()
|
||
model_name = model or registry.get_model("nvidia", "rca")
|
||
|
||
span.set_attribute("ai.model", model_name)
|
||
|
||
logger.info(
|
||
"nvidia_chat_request_start",
|
||
model=model_name,
|
||
prompt_length=len(prompt),
|
||
)
|
||
|
||
# Langfuse 追蹤
|
||
with LangfuseTraceContext(
|
||
name="nvidia_chat",
|
||
metadata={"model": model_name, "task": "rca"},
|
||
) as langfuse_ctx:
|
||
try:
|
||
client = await self._get_client()
|
||
|
||
response = await client.post(
|
||
NVIDIA_API_URL,
|
||
headers={
|
||
"Authorization": f"Bearer {self._api_key}",
|
||
"Content-Type": "application/json",
|
||
},
|
||
json={
|
||
"model": model_name,
|
||
"messages": [{"role": "user", "content": prompt}],
|
||
"temperature": temperature,
|
||
"max_tokens": max_tokens,
|
||
**({"response_format": {"type": "json_object"}} if use_json_mode else {}),
|
||
},
|
||
)
|
||
response.raise_for_status()
|
||
data = response.json()
|
||
|
||
self._circuit_breaker.record_success()
|
||
|
||
text = data["choices"][0]["message"]["content"]
|
||
|
||
# Token 用量
|
||
usage = data.get("usage", {})
|
||
prompt_tokens = usage.get("prompt_tokens", 0)
|
||
completion_tokens = usage.get("completion_tokens", 0)
|
||
total_tokens = usage.get("total_tokens", prompt_tokens + completion_tokens)
|
||
|
||
# NVIDIA NIM 免費 tier = $0
|
||
cost_usd = 0.0
|
||
|
||
latency_ms = (time.perf_counter() - start_time) * 1000
|
||
span.set_attribute("ai.latency_ms", latency_ms)
|
||
span.set_attribute("ai.total_tokens", total_tokens)
|
||
|
||
# Prometheus
|
||
NVIDIA_REQUESTS_TOTAL.labels(status="success", tool_name="chat").inc()
|
||
NVIDIA_LATENCY_HISTOGRAM.observe(latency_ms / 1000)
|
||
|
||
# Langfuse
|
||
if langfuse_ctx and hasattr(langfuse_ctx, "generation"):
|
||
try:
|
||
langfuse_ctx.generation(
|
||
name="nvidia_chat",
|
||
model=model_name,
|
||
input=prompt[:500],
|
||
output=text[:500],
|
||
metadata={
|
||
"total_tokens": total_tokens,
|
||
"cost_usd": cost_usd,
|
||
"latency_ms": round(latency_ms, 2),
|
||
},
|
||
)
|
||
except Exception as le:
|
||
logger.warning("langfuse_chat_generation_failed_safe", error=str(le))
|
||
|
||
logger.info(
|
||
"nvidia_chat_response_received",
|
||
model=model_name,
|
||
response_length=len(text),
|
||
prompt_tokens=prompt_tokens,
|
||
completion_tokens=completion_tokens,
|
||
total_tokens=total_tokens,
|
||
latency_ms=round(latency_ms, 2),
|
||
)
|
||
|
||
return text, True, total_tokens, cost_usd
|
||
|
||
except httpx.TimeoutException as e:
|
||
# 2026-04-01 ogt: timeout 不計入 circuit breaker
|
||
# Nemo free tier 偶爾慢是正常的，下次請求仍應優先嘗試
|
||
# 只有硬性錯誤 (auth/rate limit) 才應斷路
|
||
NVIDIA_REQUESTS_TOTAL.labels(status="timeout", tool_name="chat").inc()
|
||
logger.warning("nvidia_chat_timeout", error=str(e))
|
||
return f"Timeout: {e}", False, 0, 0.0
|
||
|
||
except httpx.HTTPStatusError as e:
|
||
# 2026-03-31 ogt: 記錄完整響應體以診斷 400 錯誤
|
||
self._circuit_breaker.record_failure() # 硬性錯誤才斷路
|
||
NVIDIA_REQUESTS_TOTAL.labels(status="error", tool_name="chat").inc()
|
||
response_text = e.response.text if e.response else "No response body"
|
||
logger.warning(
|
||
"nvidia_chat_failed",
|
||
error=str(e),
|
||
error_type="HTTPStatusError",
|
||
status_code=e.response.status_code if e.response else None,
|
||
response_body=response_text[:500], # 截斷避免日誌過大
|
||
)
|
||
return str(e), False, 0, 0.0
|
||
|
||
except Exception as e:
|
||
self._circuit_breaker.record_failure()
|
||
NVIDIA_REQUESTS_TOTAL.labels(status="error", tool_name="chat").inc()
|
||
import traceback
|
||
logger.warning(
|
||
"nvidia_chat_failed",
|
||
error=str(e),
|
||
error_type=type(e).__name__,
|
||
stacktrace=traceback.format_exc()
|
||
)
|
||
return str(e), False, 0, 0.0
|
||
|
||
|
||
# =============================================================================
|
||
# 單例與工廠函數
|
||
# =============================================================================
|
||
|
||
_provider: NvidiaProvider | None = None
|
||
|
||
|
||
def get_nvidia_provider() -> NvidiaProvider:
|
||
"""取得 NvidiaProvider 單例"""
|
||
global _provider
|
||
if _provider is None:
|
||
_provider = NvidiaProvider()
|
||
return _provider
|
||
|
||
|
||
def reset_nvidia_provider() -> None:
|
||
"""重置單例 (用於測試)"""
|
||
global _provider
|
||
_provider = None
|
||
|
||
|
||
# =============================================================================
|
||
# 便捷函數
|
||
# =============================================================================
|
||
|
||
|
||
async def nvidia_tool_call(
|
||
messages: list[dict[str, Any]],
|
||
tools: list[ToolDefinition | dict[str, Any]],
|
||
**kwargs,
|
||
) -> NvidiaProviderResult:
|
||
"""
|
||
便捷函數: 執行 NVIDIA Tool Calling
|
||
|
||
Args:
|
||
messages: 對話訊息列表
|
||
tools: 可用 Tool 定義列表
|
||
**kwargs: 其他參數 (model, temperature, max_tokens)
|
||
|
||
Returns:
|
||
NvidiaProviderResult
|
||
"""
|
||
provider = get_nvidia_provider()
|
||
return await provider.tool_call(messages, tools, **kwargs)
|
||
|
||
|
||
def create_tool_definition(
|
||
name: str,
|
||
description: str,
|
||
parameters: dict[str, Any],
|
||
) -> ToolDefinition:
|
||
"""
|
||
建立 Tool 定義
|
||
|
||
Args:
|
||
name: Tool 名稱
|
||
description: Tool 描述
|
||
parameters: JSON Schema 參數定義
|
||
|
||
Returns:
|
||
ToolDefinition
|
||
"""
|
||
return ToolDefinition(
|
||
type="function",
|
||
function={
|
||
"name": name,
|
||
"description": description,
|
||
"parameters": parameters,
|
||
},
|
||
)
|