chore: post-deployment cleanup and docs update
- Make AI routing rules DB-driven (read from system_configs, removed from config.py) - Add translation quota tracking to LLM translation (OpenAIProvider) - Add Alibaba MT ECS RAM role support (STS token, no AccessKey needed) - Fix admin sidebar link for AI模型配置 page - Fix Quota.vue API path (quotas → translation-quotas) - Fix login auto-redirect to dashboard - Add provider dropdown selects to AI routing config UI - Clean up stale ai_provider_* system_configs records - Remove OpencodeGo, Spark providers (code + DB) - Update deploy config: nginx port 8000, systemd cwd
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TradeMate Dev
@@ -1,8 +1,6 @@
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from .openai import OpenAIProvider
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from .spark import SparkProvider
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from .sensenova import SensenovaProvider
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from .opencode_go import OpencodeGoProvider
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from .nvidia import NvidiaProvider
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from .alibaba import AlibabaMTProvider
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__all__ = ["OpenAIProvider", "SparkProvider", "SensenovaProvider", "OpencodeGoProvider", "NvidiaProvider", "AlibabaMTProvider"]
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__all__ = ["OpenAIProvider", "SensenovaProvider", "NvidiaProvider", "AlibabaMTProvider"]
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@@ -1,11 +1,13 @@
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from typing import Dict, Any, Optional
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from aliyunsdkcore.client import AcsClient
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from aliyunsdkcore.auth.credentials import StsTokenCredential
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from aliyunsdkalimt.request.v20181012 import TranslateGeneralRequest, TranslateECommerceRequest
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from app.services.translation_quota import TranslationQuotaService
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from app.database import AsyncSessionLocal
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import asyncio
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import json
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import logging
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import os
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logger = logging.getLogger(__name__)
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@@ -16,11 +18,55 @@ ALIBABA_LANG_MAP = {
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"id": "id", "ms": "ms", "tl": "tl", "hi": "hi",
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}
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ECS_METADATA_URL = "http://100.100.100.200/latest/meta-data/ram/security-credentials/"
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def _fetch_ecs_ram_credentials():
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try:
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import urllib.request
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req = urllib.request.Request(ECS_METADATA_URL, method="GET")
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with urllib.request.urlopen(req, timeout=2) as resp:
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role_name = resp.read().decode().strip()
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if not role_name:
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logger.warning("ECS metadata returned empty role name")
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return None
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url = f"{ECS_METADATA_URL}{role_name}"
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req = urllib.request.Request(url, method="GET")
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with urllib.request.urlopen(req, timeout=2) as resp:
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data = json.loads(resp.read().decode())
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if data.get("Code") == "Success":
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logger.info(f"Fetched STS token for role {role_name}, expires {data.get('Expiration')}")
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return (data["AccessKeyId"], data["AccessKeySecret"], data["SecurityToken"])
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else:
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logger.warning(f"ECS metadata returned non-success: {data.get('Code')}")
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except Exception as e:
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logger.debug(f"ECS metadata fetch failed: {e}")
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return None
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def _build_acs_client(access_key_id: str = "", access_key_secret: str = "",
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region_id: str = "cn-hangzhou") -> AcsClient:
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creds = _fetch_ecs_ram_credentials()
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if creds:
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ak, sk, token = creds
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sts_cred = StsTokenCredential(ak, sk, token)
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client = AcsClient(credential=sts_cred, region_id=region_id)
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logger.info("Alibaba MT using ECS RAM role (STS token)")
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return client
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ak = access_key_id or os.getenv("ALIBABA_ACCESS_KEY_ID", "")
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sk = access_key_secret or os.getenv("ALIBABA_ACCESS_KEY_SECRET", "")
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if ak and sk:
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logger.info("Alibaba MT using AccessKey credentials")
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return AcsClient(ak, sk, region_id)
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raise ValueError("No Alibaba Cloud credentials found (neither ECS RAM role nor AccessKey)")
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class AlibabaMTProvider:
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def __init__(self, access_key_id: str, access_key_secret: str,
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def __init__(self, access_key_id: str = "", access_key_secret: str = "",
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region_id: str = "cn-hangzhou"):
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self.client = AcsClient(access_key_id, access_key_secret, region_id)
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self.client = _build_acs_client(access_key_id, access_key_secret, region_id)
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self._name = "alibaba-mt"
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async def translate(self, text: str, source_lang: Optional[str],
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@@ -51,6 +51,20 @@ class OpenAIProvider(AIProvider):
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self._cheap_model = "gpt-4o-mini" if model == "gpt-4o" else model
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async def translate(self, text: str, source_lang: Optional[str], target_lang: str, context: Optional[str] = None) -> Dict[str, Any]:
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from app.services.translation_quota import TranslationQuotaService
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from app.database import AsyncSessionLocal
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async with AsyncSessionLocal() as db:
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quota_svc = TranslationQuotaService(db)
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if not await quota_svc.check_quota("llm"):
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raise Exception("LLM translation quota exhausted or disabled")
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result = await self._do_translate(text, source_lang, target_lang, context)
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if result and result.get("translated_text"):
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await quota_svc.consume("llm", len(text))
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await db.commit()
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return result
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async def _do_translate(self, text: str, source_lang: Optional[str], target_lang: str, context: Optional[str] = None) -> Dict[str, Any]:
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system = SYSTEM_PROMPTS["translate"]
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if context:
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system += f"\nContext: this is about {context}"
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@@ -1,7 +0,0 @@
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from app.ai.providers.openai import OpenAIProvider
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class OpencodeGoProvider(OpenAIProvider):
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def __init__(self, api_key: str, model: str = "deepseek-v4-flash", base_url: str = "https://opencode.ai/zen/go/v1"):
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super().__init__(api_key=api_key, model=model, base_url=base_url)
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self._name = f"opencode-go-{model}"
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@@ -1,90 +0,0 @@
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from typing import Dict, Any, Optional
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import json
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from app.ai.base import AIProvider
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SYSTEM_PROMPTS = {
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"translate": "You are a professional translator specialized in foreign trade. "
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"Translate business terms accurately. Return ONLY the translated text.",
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"reply": "You are an experienced foreign trade sales expert. Write professional, "
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"clear business replies. Return ONLY the reply text.",
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"marketing": "You are a creative copywriter for international trade. "
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"Return ONLY the marketing copy, no explanations.",
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"extract": "Extract structured data from text. Return ONLY valid JSON.",
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}
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class SparkProvider(AIProvider):
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def __init__(self, api_key: str, model: str = "astron-code-latest", base_url: str = None):
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from app.config import settings
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try:
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from openai import AsyncOpenAI
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except ImportError:
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raise ImportError("openai>=1.0 is required for SparkProvider")
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self.client = AsyncOpenAI(
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api_key=api_key,
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base_url=base_url or settings.IFLYTEK_API_BASE,
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)
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self.model = model
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self._name = f"spark-{model}"
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async def translate(self, text: str, source_lang: Optional[str], target_lang: str, context: Optional[str] = None) -> Dict[str, Any]:
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system = SYSTEM_PROMPTS["translate"]
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if context:
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system += f"\nContext: {context}"
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prompt = f"Translate {f'from {source_lang} ' if source_lang and source_lang != 'auto' else ''}to {target_lang}:\n\n{text}"
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content = await self._call(system, prompt)
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return {"translated_text": content, "provider": self.name}
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async def reply(self, inquiry: str, context: Optional[Dict[str, Any]] = None, tone: str = "professional", preference_context: Optional[str] = None) -> Dict[str, Any]:
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system = SYSTEM_PROMPTS["reply"] + f"\nTone: {tone}"
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if preference_context:
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system += f"\nUser preference: {preference_context}"
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ctx = ""
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if context:
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ctx = "\n".join(f"{k}: {v}" for k, v in context.items() if v)
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prompt = f"{ctx}\nCustomer inquiry:\n{inquiry}\n\nWrite a reply:"
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content = await self._call(system, prompt)
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return {"reply": content, "provider": self.name}
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async def generate_marketing(self, product_info: Dict[str, Any], target: str, style: str = "professional", language: str = "en", preference_context: Optional[str] = None) -> Dict[str, Any]:
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system = SYSTEM_PROMPTS["marketing"] + f"\nStyle: {style}\nAudience: {target}\nLanguage: {language}"
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if preference_context:
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system += f"\nUser preference: {preference_context}"
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info = json.dumps(product_info, ensure_ascii=False)
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prompt = f"Product:\n{info}\n\nGenerate marketing copy:"
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content = await self._call(system, prompt, max_tokens=1500)
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return {"content": content, "provider": self.name}
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async def extract_info(self, text: str, schema: Dict[str, Any]) -> Dict[str, Any]:
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system = SYSTEM_PROMPTS["extract"]
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prompt = f"Schema:\n{json.dumps(schema, indent=2)}\n\nText:\n{text}\n\nJSON:"
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content = await self._call(system, prompt, response_format={"type": "json_object"})
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try:
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data = json.loads(content)
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return {"data": data, "confidence": 0.9, "provider": self.name}
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except json.JSONDecodeError:
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return {"data": {}, "confidence": 0.0, "provider": self.name}
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async def _call(self, system: str, prompt: str, max_tokens: int = 1000, response_format: Optional[Dict] = None) -> str:
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kwargs = {
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"model": self.model,
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"messages": [
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{"role": "system", "content": system},
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{"role": "user", "content": prompt},
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],
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"max_tokens": max_tokens,
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"temperature": 0.7,
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}
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if response_format:
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kwargs["response_format"] = response_format
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resp = await self.client.chat.completions.create(**kwargs)
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return resp.choices[0].message.content
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@property
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def name(self) -> str:
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return self._name
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@property
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def cost_per_1k_tokens(self) -> float:
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return 0.0
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