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fix: report provider test upstream errors

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cls_宁波本机
2026-06-22 00:38:04 +08:00
parent 8407232dc6
commit f180fff020
6 changed files with 311 additions and 184 deletions
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backend-fastapi/ai_platform/providers/base.py
+8
View File
@@ -348,6 +348,14 @@ class BaseLLMProvider(ABC):
模型列表，格式与 get_default_models 一致
"""
return []
async def fetch_models_from_api_strict(self) -> List[Dict[str, Any]]:
"""
严格拉取模型列表，用于连接测试。
子类可在这里保留上游异常，让调用方给出明确错误原因。
"""
return await self.fetch_models_from_api()
@classmethod
def get_default_models(cls) -> List[Dict[str, Any]]:
backend-fastapi/ai_platform/providers/claude_provider.py
+38 -34
View File
@@ -409,6 +409,15 @@ class ClaudeProvider(BaseLLMProvider):
async def fetch_models_from_api(self) -> List[Dict[str, Any]]:
"""通过 Anthropic /v1/models 端点在线拉取模型列表"""
base_url = (self.api_base or self.DEFAULT_API_BASE).rstrip('/')
try:
return await self.fetch_models_from_api_strict()
except Exception as e:
logger.warning(f'在线拉取 Anthropic 模型列表失败 ({base_url}): {e}')
return []
async def fetch_models_from_api_strict(self) -> List[Dict[str, Any]]:
"""通过 Anthropic 端点严格拉取模型列表，失败时保留上游异常"""
import httpx
base_url = (self.api_base or self.DEFAULT_API_BASE).rstrip('/')
@@ -417,43 +426,38 @@ class ClaudeProvider(BaseLLMProvider):
'anthropic-version': '2023-06-01',
}
try:
async with httpx.AsyncClient(timeout=15) as client:
resp = await client.get(f'{base_url}/v1/models', headers=headers)
resp.raise_for_status()
data = resp.json()
async with httpx.AsyncClient(timeout=15) as client:
resp = await client.get(f'{base_url}/v1/models', headers=headers)
resp.raise_for_status()
data = resp.json()
model_list = data.get('data', [])
if not model_list:
return []
results = []
for item in model_list:
model_id = item.get('id', '')
display = item.get('display_name', model_id)
if not model_id:
continue
results.append({
'model_name': model_id,
'display_name': display,
'model_type': 'chat',
'max_tokens': 4096,
'context_window': 200000,
'supports_vision': True,
'supports_function_call': True,
'input_price': 0,
'output_price': 0,
})
results.sort(key=lambda m: m['model_name'])
logger.info(f'从 Anthropic ({base_url}) 拉取到 {len(results)} 个模型')
return results
except Exception as e:
logger.warning(f'在线拉取 Anthropic 模型列表失败 ({base_url}): {e}')
model_list = data.get('data', [])
if not model_list:
return []
results = []
for item in model_list:
model_id = item.get('id', '')
display = item.get('display_name', model_id)
if not model_id:
continue
results.append({
'model_name': model_id,
'display_name': display,
'model_type': 'chat',
'max_tokens': 4096,
'context_window': 200000,
'supports_vision': True,
'supports_function_call': True,
'input_price': 0,
'output_price': 0,
})
results.sort(key=lambda m: m['model_name'])
logger.info(f'从 Anthropic ({base_url}) 拉取到 {len(results)} 个模型')
return results
@classmethod
def get_default_models(cls) -> List[Dict[str, Any]]:
"""获取默认模型列表"""
backend-fastapi/ai_platform/providers/ollama_provider.py
+47 -39
View File
@@ -376,6 +376,19 @@ class OllamaProvider(BaseLLMProvider):
async def fetch_models_from_api(self) -> List[Dict[str, Any]]:
"""通过 /api/tags 端点拉取 Ollama 本地已安装的模型"""
base_url = (self.api_base or 'http://localhost:11434').rstrip('/')
# Ollama 的 /api/tags 在根路径，去掉 /v1
if base_url.endswith('/v1'):
base_url = base_url[:-3]
try:
return await self.fetch_models_from_api_strict()
except Exception as e:
logger.warning(f'在线拉取 Ollama 模型列表失败 ({base_url}): {e}')
return []
async def fetch_models_from_api_strict(self) -> List[Dict[str, Any]]:
"""通过 Ollama /api/tags 严格拉取模型列表，失败时保留上游异常"""
import httpx
base_url = (self.api_base or 'http://localhost:11434').rstrip('/')
@@ -383,50 +396,45 @@ class OllamaProvider(BaseLLMProvider):
if base_url.endswith('/v1'):
base_url = base_url[:-3]
try:
async with httpx.AsyncClient(timeout=10) as client:
resp = await client.get(f'{base_url}/api/tags')
resp.raise_for_status()
data = resp.json()
async with httpx.AsyncClient(timeout=10) as client:
resp = await client.get(f'{base_url}/api/tags')
resp.raise_for_status()
data = resp.json()
results = []
for item in data.get('models', []):
name = item.get('name', '')
if not name:
continue
results = []
for item in data.get('models', []):
name = item.get('name', '')
if not name:
continue
name_lower = name.lower()
if any(kw in name_lower for kw in self._RERANK_KEYWORDS):
model_type = 'rerank'
elif any(kw in name_lower for kw in self._EMBEDDING_KEYWORDS):
model_type = 'embedding'
else:
model_type = 'chat'
name_lower = name.lower()
if any(kw in name_lower for kw in self._RERANK_KEYWORDS):
model_type = 'rerank'
elif any(kw in name_lower for kw in self._EMBEDDING_KEYWORDS):
model_type = 'embedding'
else:
model_type = 'chat'
# 从 size 推算显示名称
size_gb = round(item.get('size', 0) / (1024 ** 3), 1)
display = f'{name} ({size_gb}GB)' if size_gb > 0 else name
# 从 size 推算显示名称
size_gb = round(item.get('size', 0) / (1024 ** 3), 1)
display = f'{name} ({size_gb}GB)' if size_gb > 0 else name
results.append({
'model_name': name,
'display_name': display,
'model_type': model_type,
'max_tokens': 4096,
'context_window': 4096,
'supports_vision': False,
'supports_function_call': model_type == 'chat',
'input_price': 0,
'output_price': 0,
})
results.append({
'model_name': name,
'display_name': display,
'model_type': model_type,
'max_tokens': 4096,
'context_window': 4096,
'supports_vision': False,
'supports_function_call': model_type == 'chat',
'input_price': 0,
'output_price': 0,
})
type_order = {'chat': 0, 'embedding': 1, 'rerank': 2}
results.sort(key=lambda m: (type_order.get(m['model_type'], 9), m['model_name']))
logger.info(f'从 Ollama ({base_url}) 拉取到 {len(results)} 个本地模型')
return results
except Exception as e:
logger.warning(f'在线拉取 Ollama 模型列表失败 ({base_url}): {e}')
return []
type_order = {'chat': 0, 'embedding': 1, 'rerank': 2}
results.sort(key=lambda m: (type_order.get(m['model_type'], 9), m['model_name']))
logger.info(f'从 Ollama ({base_url}) 拉取到 {len(results)} 个本地模型')
return results
@classmethod
def get_default_models(cls) -> List[Dict[str, Any]]:
backend-fastapi/ai_platform/providers/openai_provider.py
+59 -55
View File
@@ -387,6 +387,15 @@ class OpenAIProvider(BaseLLMProvider):
适用于所有 OpenAI 兼容的提供商
"""
base_url = (self.api_base or self.DEFAULT_API_BASE).rstrip('/')
try:
return await self.fetch_models_from_api_strict()
except Exception as e:
logger.warning(f'在线拉取模型列表失败 ({base_url}): {e}')
return []
async def fetch_models_from_api_strict(self) -> List[Dict[str, Any]]:
"""通过 /models 端点严格拉取模型列表，失败时保留上游异常"""
import httpx
base_url = (self.api_base or self.DEFAULT_API_BASE).rstrip('/')
@@ -394,64 +403,59 @@ class OpenAIProvider(BaseLLMProvider):
if self.api_key:
headers['Authorization'] = f'Bearer {self.api_key}'
try:
async with httpx.AsyncClient(timeout=15) as client:
resp = await client.get(f'{base_url}/models', headers=headers)
resp.raise_for_status()
data = resp.json()
async with httpx.AsyncClient(timeout=15) as client:
resp = await client.get(f'{base_url}/models', headers=headers)
resp.raise_for_status()
data = resp.json()
model_list = data.get('data', [])
if not model_list:
return []
results = []
for item in model_list:
model_id = item.get('id', '')
if not model_id:
continue
model_lower = model_id.lower()
# 跳过非文本模型
if any(kw in model_lower for kw in self._SKIP_KEYWORDS):
continue
# 推断模型类型
if any(kw in model_lower for kw in self._RERANK_KEYWORDS):
model_type = 'rerank'
elif any(kw in model_lower for kw in self._EMBEDDING_KEYWORDS):
model_type = 'embedding'
else:
model_type = 'chat'
results.append({
'model_name': model_id,
'display_name': model_id,
'model_type': model_type,
'max_tokens': 4096,
'context_window': 4096,
'supports_vision': False,
'supports_function_call': model_type == 'chat',
'input_price': 0,
'output_price': 0,
})
# 补充默认列表中的 embedding/rerank 模型（API 可能不返回这些类型）
fetched_names = {m['model_name'] for m in results}
for dm in self.__class__.get_default_models():
if dm['model_type'] in ('embedding', 'rerank') and dm['model_name'] not in fetched_names:
results.append(dm)
# 按类型排序：chat → embedding → rerank
type_order = {'chat': 0, 'embedding': 1, 'rerank': 2}
results.sort(key=lambda m: (type_order.get(m['model_type'], 9), m['model_name']))
logger.info(f'从 {base_url} 拉取到 {len(results)} 个模型')
return results
except Exception as e:
logger.warning(f'在线拉取模型列表失败 ({base_url}): {e}')
model_list = data.get('data', [])
if not model_list:
return []
results = []
for item in model_list:
model_id = item.get('id', '')
if not model_id:
continue
model_lower = model_id.lower()
# 跳过非文本模型
if any(kw in model_lower for kw in self._SKIP_KEYWORDS):
continue
# 推断模型类型
if any(kw in model_lower for kw in self._RERANK_KEYWORDS):
model_type = 'rerank'
elif any(kw in model_lower for kw in self._EMBEDDING_KEYWORDS):
model_type = 'embedding'
else:
model_type = 'chat'
results.append({
'model_name': model_id,
'display_name': model_id,
'model_type': model_type,
'max_tokens': 4096,
'context_window': 4096,
'supports_vision': False,
'supports_function_call': model_type == 'chat',
'input_price': 0,
'output_price': 0,
})
# 补充默认列表中的 embedding/rerank 模型（API 可能不返回这些类型）
fetched_names = {m['model_name'] for m in results}
for dm in self.__class__.get_default_models():
if dm['model_type'] in ('embedding', 'rerank') and dm['model_name'] not in fetched_names:
results.append(dm)
# 按类型排序：chat → embedding → rerank
type_order = {'chat': 0, 'embedding': 1, 'rerank': 2}
results.sort(key=lambda m: (type_order.get(m['model_type'], 9), m['model_name']))
logger.info(f'从 {base_url} 拉取到 {len(results)} 个模型')
return results
@classmethod
def get_default_models(cls) -> List[Dict[str, Any]]:
"""获取默认模型列表"""
backend-fastapi/ai_platform/providers/qwen_provider.py
+57 -53
View File
@@ -378,6 +378,15 @@ class QwenProvider(BaseLLMProvider):
async def fetch_models_from_api(self) -> List[Dict[str, Any]]:
"""通过 DashScope 兼容的 /v1/models 端点在线拉取模型列表"""
base_url = (self.api_base or self.DEFAULT_API_BASE).rstrip('/')
try:
return await self.fetch_models_from_api_strict()
except Exception as e:
logger.warning(f'在线拉取模型列表失败 ({base_url}): {e}')
return []
async def fetch_models_from_api_strict(self) -> List[Dict[str, Any]]:
"""通过 DashScope 兼容端点严格拉取模型列表，失败时保留上游异常"""
import httpx
base_url = (self.api_base or self.DEFAULT_API_BASE).rstrip('/')
@@ -385,62 +394,57 @@ class QwenProvider(BaseLLMProvider):
if self.api_key:
headers['Authorization'] = f'Bearer {self.api_key}'
try:
async with httpx.AsyncClient(timeout=15) as client:
resp = await client.get(f'{base_url}/models', headers=headers)
resp.raise_for_status()
data = resp.json()
async with httpx.AsyncClient(timeout=15) as client:
resp = await client.get(f'{base_url}/models', headers=headers)
resp.raise_for_status()
data = resp.json()
model_list = data.get('data', [])
if not model_list:
return []
results = []
for item in model_list:
model_id = item.get('id', '')
if not model_id:
continue
model_lower = model_id.lower()
if any(kw in model_lower for kw in self._SKIP_KEYWORDS):
continue
if any(kw in model_lower for kw in self._RERANK_KEYWORDS):
model_type = 'rerank'
elif any(kw in model_lower for kw in self._EMBEDDING_KEYWORDS):
model_type = 'embedding'
else:
model_type = 'chat'
results.append({
'model_name': model_id,
'display_name': model_id,
'model_type': model_type,
'max_tokens': 4096,
'context_window': 4096,
'supports_vision': False,
'supports_function_call': model_type == 'chat',
'input_price': 0,
'output_price': 0,
})
# DashScope /v1/models 可能只返回 chat 模型，
# 将默认列表中的 embedding/rerank 模型合并进去
fetched_names = {m['model_name'] for m in results}
for dm in self.__class__.get_default_models():
if dm['model_type'] in ('embedding', 'rerank') and dm['model_name'] not in fetched_names:
results.append(dm)
type_order = {'chat': 0, 'embedding': 1, 'rerank': 2}
results.sort(key=lambda m: (type_order.get(m['model_type'], 9), m['model_name']))
logger.info(f'从 {base_url} 拉取到 {len(results)} 个模型（含补充的 embedding/rerank）')
return results
except Exception as e:
logger.warning(f'在线拉取模型列表失败 ({base_url}): {e}')
model_list = data.get('data', [])
if not model_list:
return []
results = []
for item in model_list:
model_id = item.get('id', '')
if not model_id:
continue
model_lower = model_id.lower()
if any(kw in model_lower for kw in self._SKIP_KEYWORDS):
continue
if any(kw in model_lower for kw in self._RERANK_KEYWORDS):
model_type = 'rerank'
elif any(kw in model_lower for kw in self._EMBEDDING_KEYWORDS):
model_type = 'embedding'
else:
model_type = 'chat'
results.append({
'model_name': model_id,
'display_name': model_id,
'model_type': model_type,
'max_tokens': 4096,
'context_window': 4096,
'supports_vision': False,
'supports_function_call': model_type == 'chat',
'input_price': 0,
'output_price': 0,
})
# DashScope /v1/models 可能只返回 chat 模型，
# 将默认列表中的 embedding/rerank 模型合并进去
fetched_names = {m['model_name'] for m in results}
for dm in self.__class__.get_default_models():
if dm['model_type'] in ('embedding', 'rerank') and dm['model_name'] not in fetched_names:
results.append(dm)
type_order = {'chat': 0, 'embedding': 1, 'rerank': 2}
results.sort(key=lambda m: (type_order.get(m['model_type'], 9), m['model_name']))
logger.info(f'从 {base_url} 拉取到 {len(results)} 个模型（含补充的 embedding/rerank）')
return results
@classmethod
def get_default_models(cls) -> List[Dict[str, Any]]:
"""获取默认模型列表"""