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Merge pull request #741 from SuanmoSuanyangTechnology/feature/agent-tool_xjn

Browse Source 
fix(app)
This commit is contained in:
Mark
2026-04-01 16:08:32 +08:00
committed by GitHub
parent 68fdf5d76f 258c19f9e0
commit 75bb96d4e7
30 changed files with 584 additions and 130 deletions
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1
api/app/controllers/ontology_controller.py
View File

@@ -163,6 +163,7 @@ def _get_ontology_service(
api_key=api_key_config.api_key,
base_url=api_key_config.api_base,
is_omni=api_key_config.is_omni,
support_thinking="thinking" in (api_key_config.capability or []),
max_retries=3,
timeout=60.0
)

6
api/app/controllers/public_share_controller.py
View File

@@ -453,6 +453,9 @@ async def chat(
# 流式返回
agent_config = agent_config_4_app_release(release)
if not (agent_config.model_parameters.get("deep_thinking", False) and payload.thinking):
agent_config.model_parameters["deep_thinking"] = False
if payload.stream:
async def event_generator():
async for event in app_chat_service.agnet_chat_stream(
@@ -634,7 +637,8 @@ async def config_query(
"app_type": release.app.type,
"variables": release.config.get("variables"),
"memory": release.config.get("memory", {}).get("enabled"),
"features": release.config.get("features")
"features": release.config.get("features"),
"model_parameters": release.config.get("model_parameters")
}
elif release.app.type == AppType.MULTI_AGENT:
content = {

5
api/app/controllers/service/app_api_controller.py
View File

@@ -144,6 +144,11 @@ async def chat(
# print(app.current_release.default_model_config_id)
agent_config = agent_config_4_app_release(app.current_release)
# print(agent_config.default_model_config_id)
# thinking 开关：仅当 agent 配置了 deep_thinking 且请求 thinking=True 时才启用
if not (agent_config.model_parameters.get("deep_thinking", False) and payload.thinking):
agent_config.model_parameters["deep_thinking"] = False
# 流式返回
if payload.stream:
async def event_generator():

2
api/app/controllers/service/end_user_api_controller.py
View File

@@ -42,7 +42,7 @@ async def create_end_user(
payload = CreateEndUserRequest(**body)
workspace_id = api_key_auth.workspace_id
logger.info(f"Create end user request - other_id: {payload.other_id}, workspace_id: {workspace_id}")
logger.info("Create end user request - other_id: %s, workspace_id: %s", payload.other_id, workspace_id)
# Resolve memory_config_id: explicit > workspace default
memory_config_id = None

91
api/app/core/agent/langchain_agent.py
View File

@@ -37,7 +37,10 @@ class LangChainAgent:
tools: Optional[Sequence[BaseTool]] = None,
streaming: bool = False,
max_iterations: Optional[int] = None, # 最大迭代次数（None 表示自动计算）
max_tool_consecutive_calls: int = 3 # 单个工具最大连续调用次数
max_tool_consecutive_calls: int = 3, # 单个工具最大连续调用次数
deep_thinking: bool = False, # 是否启用深度思考模式
thinking_budget_tokens: Optional[int] = None, # 深度思考 token 预算
capability: Optional[List[str]] = None # 模型能力列表，用于校验是否支持深度思考
):
"""初始化 LangChain Agent
@@ -60,6 +63,7 @@ class LangChainAgent:
self.streaming = streaming
self.is_omni = is_omni
self.max_tool_consecutive_calls = max_tool_consecutive_calls
self.deep_thinking = deep_thinking and ("thinking" in (capability or []))
# 工具调用计数器：记录每个工具的连续调用次数
self.tool_call_counter: Dict[str, int] = {}
@@ -82,6 +86,13 @@ class LangChainAgent:
f"auto_calculated={max_iterations is None}"
)
# 根据 capability 校验是否真正支持深度思考
actual_deep_thinking = self.deep_thinking
if deep_thinking and not actual_deep_thinking:
logger.warning(
f"模型 {model_name} 不支持深度思考（capability 中无 'thinking'），已自动关闭 deep_thinking"
)
# 创建 RedBearLLM（支持多提供商）
model_config = RedBearModelConfig(
model_name=model_name,
@@ -89,10 +100,13 @@ class LangChainAgent:
api_key=api_key,
base_url=api_base,
is_omni=is_omni,
deep_thinking=actual_deep_thinking,
thinking_budget_tokens=thinking_budget_tokens if actual_deep_thinking else None,
support_thinking="thinking" in (capability or []),
extra_params={
"temperature": temperature,
"max_tokens": max_tokens,
"streaming": streaming # 使用参数控制流式
"streaming": streaming
}
)
@@ -249,6 +263,33 @@ class LangChainAgent:
return messages
@staticmethod
def _extract_tokens_from_message(msg) -> int:
"""从 AIMessage 或类似对象中提取 total_tokens，兼容多种 provider 格式
支持的格式：
- response_metadata.token_usage.total_tokens (OpenAI/ChatOpenAI)
- response_metadata.usage.total_tokens (部分 provider)
- usage_metadata.total_tokens (LangChain 新版)
"""
total = 0
# 1. response_metadata
response_meta = getattr(msg, "response_metadata", None)
if response_meta and isinstance(response_meta, dict):
# 尝试 token_usage 路径
token_usage = response_meta.get("token_usage") or response_meta.get("usage", {})
if isinstance(token_usage, dict):
total = token_usage.get("total_tokens", 0)
# 2. usage_metadata（LangChain 新版 AIMessage 属性）
if not total:
usage_meta = getattr(msg, "usage_metadata", None)
if usage_meta:
if isinstance(usage_meta, dict):
total = usage_meta.get("total_tokens", 0)
else:
total = getattr(usage_meta, "total_tokens", 0)
return total or 0
def _build_multimodal_content(self, text: str, files: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
"""
构建多模态消息内容
@@ -283,6 +324,17 @@ class LangChainAgent:
return content_parts
@staticmethod
def _extract_reasoning_content(msg) -> str:
"""从 AIMessage 中提取深度思考内容（reasoning_content）
所有 provider 统一通过 additional_kwargs.reasoning_content 传递：
- DeepSeek-R1 / QwQ: 原生字段
- Volcano (Doubao-thinking): 由 VolcanoChatOpenAI 从 delta.reasoning_content 注入
"""
additional = getattr(msg, "additional_kwargs", None) or {}
return additional.get("reasoning_content") or additional.get("reasoning", "")
async def chat(
self,
message: str,
@@ -348,6 +400,7 @@ class LangChainAgent:
logger.debug(f"输出消息数量: {len(output_messages)}")
total_tokens = 0
reasoning_content = ""
for msg in reversed(output_messages):
if isinstance(msg, AIMessage):
logger.debug(f"找到 AI 消息，content 类型: {type(msg.content)}")
@@ -382,8 +435,8 @@ class LangChainAgent:
else:
content = str(msg.content)
logger.debug(f"转换为字符串: {content[:100]}...")
response_meta = msg.response_metadata if hasattr(msg, 'response_metadata') else None
total_tokens = response_meta.get("token_usage", {}).get("total_tokens", 0) if response_meta else 0
total_tokens = self._extract_tokens_from_message(msg)
reasoning_content = self._extract_reasoning_content(msg) if self.deep_thinking else ""
break
logger.info(f"最终提取的内容长度: {len(content)}")
@@ -399,6 +452,8 @@ class LangChainAgent:
"total_tokens": total_tokens
}
}
if reasoning_content:
response["reasoning_content"] = reasoning_content
logger.debug(
"Agent 调用完成",
@@ -420,7 +475,7 @@ class LangChainAgent:
history: Optional[List[Dict[str, str]]] = None,
context: Optional[str] = None,
files: Optional[List[Dict[str, Any]]] = None
) -> AsyncGenerator[str, None]:
) -> AsyncGenerator[str | int, None]:
"""执行流式对话
Args:
@@ -431,6 +486,8 @@ class LangChainAgent:
Yields:
str: 消息内容块
int: token 统计
Dict: 深度思考内容 {"type": "reasoning", "content": "..."}
"""
logger.info("=" * 80)
logger.info(" chat_stream 方法开始执行")
@@ -451,6 +508,7 @@ class LangChainAgent:
# 统一使用 agent 的 astream_events 实现流式输出
logger.debug("使用 Agent astream_events 实现流式输出")
full_content = ''
full_reasoning = ''
try:
last_event = {}
async for event in self.agent.astream_events(
@@ -467,6 +525,13 @@ class LangChainAgent:
# LLM 流式输出
chunk = event.get("data", {}).get("chunk")
if chunk and hasattr(chunk, "content"):
# 提取深度思考内容（仅在启用深度思考时）
if self.deep_thinking:
reasoning_chunk = self._extract_reasoning_content(chunk)
if reasoning_chunk:
full_reasoning += reasoning_chunk
yield {"type": "reasoning", "content": reasoning_chunk}
# 处理多模态响应：content 可能是字符串或列表
chunk_content = chunk.content
if isinstance(chunk_content, str) and chunk_content:
@@ -497,6 +562,13 @@ class LangChainAgent:
chunk = event.get("data", {}).get("chunk")
if chunk:
if hasattr(chunk, "content"):
# 提取深度思考内容（仅在启用深度思考时）
if self.deep_thinking:
reasoning_chunk = self._extract_reasoning_content(chunk)
if reasoning_chunk:
full_reasoning += reasoning_chunk
yield {"type": "reasoning", "content": reasoning_chunk}
chunk_content = chunk.content
if isinstance(chunk_content, str) and chunk_content:
full_content += chunk_content
@@ -535,12 +607,9 @@ class LangChainAgent:
output_messages = last_event.get("data", {}).get("output", {}).get("messages", [])
for msg in reversed(output_messages):
if isinstance(msg, AIMessage):
response_meta = msg.response_metadata if hasattr(msg, 'response_metadata') else None
total_tokens = response_meta.get("token_usage", {}).get(
"total_tokens",
0
) if response_meta else 0
yield total_tokens
stream_total_tokens = self._extract_tokens_from_message(msg)
logger.info(f"流式 token 统计: total_tokens={stream_total_tokens}")
yield stream_total_tokens
break
except Exception as e:

3
api/app/core/memory/src/search.py
View File

@@ -758,8 +758,7 @@ async def run_hybrid_search(
model_name=embedder_config_dict["model_name"],
provider=embedder_config_dict["provider"],
api_key=embedder_config_dict["api_key"],
base_url=embedder_config_dict["base_url"],
type="llm"
base_url=embedder_config_dict["base_url"]
)
config_load_time = time.time() - config_load_start
logger.info(f"[PERF] Config loading took {config_load_time:.4f}s")

75
api/app/core/models/base.py
View File

@@ -14,6 +14,7 @@ from pydantic import BaseModel, Field
from app.core.error_codes import BizCode
from app.core.exceptions import BusinessException
from app.models.models_model import ModelProvider, ModelType
from app.core.models.volcano_chat import VolcanoChatOpenAI
T = TypeVar("T")
@@ -25,6 +26,9 @@ class RedBearModelConfig(BaseModel):
api_key: str
base_url: Optional[str] = None
is_omni: bool = False # 是否为 Omni 模型
deep_thinking: bool = False # 是否启用深度思考模式
thinking_budget_tokens: Optional[int] = None # 深度思考 token 预算
support_thinking: bool = False # 模型是否支持 enable_thinking 参数（capability 含 thinking）
# 请求超时时间（秒）- 默认120秒以支持复杂的LLM调用，可通过环境变量 LLM_TIMEOUT 配置
timeout: float = Field(default_factory=lambda: float(os.getenv("LLM_TIMEOUT", "120.0")))
# 最大重试次数 - 默认2次以避免过长等待，可通过环境变量 LLM_MAX_RETRIES 配置
@@ -44,7 +48,7 @@ class RedBearModelFactory:
# 打印供应商信息用于调试
from app.core.logging_config import get_business_logger
logger = get_business_logger()
logger.debug(f"获取模型参数 - Provider: {provider}, Model: {config.model_name}, is_omni: {config.is_omni}")
logger.debug(f"获取模型参数 - Provider: {provider}, Model: {config.model_name}, is_omni: {config.is_omni}, deep_thinking: {config.deep_thinking}")
# dashscope 的 omni 模型使用 OpenAI 兼容模式
if provider == ModelProvider.DASHSCOPE and config.is_omni:
@@ -58,7 +62,7 @@ class RedBearModelFactory:
write=60.0,
pool=10.0,
)
return {
params: Dict[str, Any] = {
"model": config.model_name,
"base_url": config.base_url,
"api_key": config.api_key,
@@ -66,6 +70,21 @@ class RedBearModelFactory:
"max_retries": config.max_retries,
**config.extra_params
}
# 流式模式下启用 stream_usage 以获取 token 统计
is_streaming = bool(config.extra_params.get("streaming"))
if is_streaming:
params["stream_usage"] = True
# 只有支持 thinking 的模型才传 enable_thinking
if config.support_thinking:
model_kwargs: Dict[str, Any] = config.extra_params.get("model_kwargs", {})
if is_streaming:
model_kwargs["enable_thinking"] = config.deep_thinking
if config.deep_thinking and config.thinking_budget_tokens:
model_kwargs["thinking_budget"] = config.thinking_budget_tokens
else:
model_kwargs["enable_thinking"] = False
params["model_kwargs"] = model_kwargs
return params
if provider in [ModelProvider.OPENAI, ModelProvider.XINFERENCE, ModelProvider.GPUSTACK, ModelProvider.OLLAMA, ModelProvider.VOLCANO]:
# 使用 httpx.Timeout 对象来设置详细的超时配置
@@ -78,7 +97,7 @@ class RedBearModelFactory:
write=60.0, # 写入超时：60秒
pool=10.0, # 连接池超时：10秒
)
return {
params: Dict[str, Any] = {
"model": config.model_name,
"base_url": config.base_url,
"api_key": config.api_key,
@@ -86,16 +105,47 @@ class RedBearModelFactory:
"max_retries": config.max_retries,
**config.extra_params
}
# 流式模式下启用 stream_usage 以获取 token 统计
if config.extra_params.get("streaming"):
params["stream_usage"] = True
# 深度思考模式
is_streaming = bool(config.extra_params.get("streaming"))
if is_streaming:
if provider == ModelProvider.VOLCANO:
# 火山引擎深度思考仅流式调用支持，非流式时不传 thinking 参数
thinking_config: Dict[str, Any] = {
"type": "enabled" if config.deep_thinking else "disabled"
}
if config.deep_thinking and config.thinking_budget_tokens:
thinking_config["budget_tokens"] = config.thinking_budget_tokens
params["extra_body"] = {"thinking": thinking_config}
else:
# 始终显式传递 enable_thinking，不支持该参数的模型（如 DeepSeek-R1）会直接忽略
model_kwargs: Dict[str, Any] = config.extra_params.get("model_kwargs", {})
model_kwargs["enable_thinking"] = config.deep_thinking
if config.deep_thinking and config.thinking_budget_tokens:
model_kwargs["thinking_budget"] = config.thinking_budget_tokens
params["model_kwargs"] = model_kwargs
return params
elif provider == ModelProvider.DASHSCOPE:
# DashScope (通义千问) 使用自己的参数格式
# 注意: DashScopeEmbeddings 不支持 timeout 和 base_url 参数
# 只支持: model, dashscope_api_key, max_retries, client
return {
params = {
"model": config.model_name,
"dashscope_api_key": config.api_key,
"max_retries": config.max_retries,
**config.extra_params
}
# 只有支持 thinking 的模型才传 enable_thinking
if config.support_thinking:
is_streaming = bool(config.extra_params.get("streaming"))
model_kwargs: Dict[str, Any] = config.extra_params.get("model_kwargs", {})
if is_streaming:
model_kwargs["enable_thinking"] = config.deep_thinking
if config.deep_thinking and config.thinking_budget_tokens:
model_kwargs["thinking_budget"] = config.thinking_budget_tokens
else:
model_kwargs["enable_thinking"] = False
params["model_kwargs"] = model_kwargs
return params
elif provider == ModelProvider.BEDROCK:
# Bedrock 使用 AWS 凭证
# api_key 格式: "access_key_id:secret_access_key" 或只是 access_key_id
@@ -134,6 +184,13 @@ class RedBearModelFactory:
elif "region_name" not in params:
params["region_name"] = "us-east-1" # 默认区域
# 深度思考模式：Claude 3.7 Sonnet 等支持思考的模型
# 通过 additional_model_request_fields 传递 thinking 块，关闭时不传（Bedrock 无 disabled 选项）
if config.deep_thinking:
budget = config.thinking_budget_tokens or 10000
params["additional_model_request_fields"] = {
"thinking": {"type": "enabled", "budget_tokens": budget}
}
return params
else:
raise BusinessException(f"不支持的提供商: {provider}", code=BizCode.PROVIDER_NOT_SUPPORTED)
@@ -160,7 +217,9 @@ def get_provider_llm_class(config: RedBearModelConfig, type: ModelType = ModelTy
# dashscope 的 omni 模型使用 OpenAI 兼容模式
if provider == ModelProvider.DASHSCOPE and config.is_omni:
return ChatOpenAI
if provider in [ModelProvider.OPENAI, ModelProvider.XINFERENCE, ModelProvider.GPUSTACK, ModelProvider.VOLCANO]:
if provider == ModelProvider.VOLCANO:
return VolcanoChatOpenAI
if provider in [ModelProvider.OPENAI, ModelProvider.XINFERENCE, ModelProvider.GPUSTACK]:
if type == ModelType.LLM:
return OpenAI
elif type == ModelType.CHAT:

17
api/app/core/models/scripts/bedrock_models.yaml
View File

@@ -11,6 +11,7 @@ models:
tags:
- 大语言模型
logo: bedrock
- name: amazon nova
type: llm
provider: bedrock
@@ -27,6 +28,7 @@ models:
- stream-tool-call
- vision
logo: bedrock
- name: anthropic claude
type: llm
provider: bedrock
@@ -35,6 +37,7 @@ models:
is_official: true
capability:
- vision
- thinking
is_omni: false
tags:
- 大语言模型
@@ -44,6 +47,7 @@ models:
- stream-tool-call
- document
logo: bedrock
- name: cohere
type: llm
provider: bedrock
@@ -58,6 +62,7 @@ models:
- tool-call
- stream-tool-call
logo: bedrock
- name: deepseek
type: llm
provider: bedrock
@@ -66,6 +71,7 @@ models:
is_official: true
capability:
- vision
- thinking
is_omni: false
tags:
- 大语言模型
@@ -74,6 +80,7 @@ models:
- tool-call
- stream-tool-call
logo: bedrock
- name: meta
type: llm
provider: bedrock
@@ -87,6 +94,7 @@ models:
- agent-thought
- tool-call
logo: bedrock
- name: mistral
type: llm
provider: bedrock
@@ -100,6 +108,7 @@ models:
- agent-thought
- tool-call
logo: bedrock
- name: openai
type: llm
provider: bedrock
@@ -114,6 +123,7 @@ models:
- tool-call
- stream-tool-call
logo: bedrock
- name: qwen
type: llm
provider: bedrock
@@ -128,6 +138,7 @@ models:
- tool-call
- stream-tool-call
logo: bedrock
- name: amazon.rerank-v1:0
type: rerank
provider: bedrock
@@ -139,6 +150,7 @@ models:
tags:
- 重排序模型
logo: bedrock
- name: cohere.rerank-v3-5:0
type: rerank
provider: bedrock
@@ -150,6 +162,7 @@ models:
tags:
- 重排序模型
logo: bedrock
- name: amazon.nova-2-multimodal-embeddings-v1:0
type: embedding
provider: bedrock
@@ -163,6 +176,7 @@ models:
- 文本嵌入模型
- vision
logo: bedrock
- name: amazon.titan-embed-text-v1
type: embedding
provider: bedrock
@@ -174,6 +188,7 @@ models:
tags:
- 文本嵌入模型
logo: bedrock
- name: amazon.titan-embed-text-v2:0
type: embedding
provider: bedrock
@@ -185,6 +200,7 @@ models:
tags:
- 文本嵌入模型
logo: bedrock
- name: cohere.embed-english-v3
type: embedding
provider: bedrock
@@ -196,6 +212,7 @@ models:
tags:
- 文本嵌入模型
logo: bedrock
- name: cohere.embed-multilingual-v3
type: embedding
provider: bedrock

225
api/app/core/models/scripts/dashscope_models.yaml
View File

@@ -6,36 +6,42 @@ models:
description: DeepSeek-R1-Distill-Qwen-14B大语言模型，支持智能体思考，32000上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
- agent-thought
logo: dashscope
- name: deepseek-r1-distill-qwen-32b
type: llm
provider: dashscope
description: DeepSeek-R1-Distill-Qwen-32B大语言模型，支持智能体思考，32000上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
- agent-thought
logo: dashscope
- name: deepseek-r1
type: llm
provider: dashscope
description: DeepSeek-R1大语言模型，支持智能体思考，131072超大上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
- agent-thought
logo: dashscope
- name: deepseek-v3.1
type: llm
provider: dashscope
@@ -48,6 +54,7 @@ models:
- 大语言模型
- agent-thought
logo: dashscope
- name: deepseek-v3.2-exp
type: llm
provider: dashscope
@@ -60,6 +67,7 @@ models:
- 大语言模型
- agent-thought
logo: dashscope
- name: deepseek-v3.2
type: llm
provider: dashscope
@@ -72,6 +80,7 @@ models:
- 大语言模型
- agent-thought
logo: dashscope
- name: deepseek-v3
type: llm
provider: dashscope
@@ -84,6 +93,7 @@ models:
- 大语言模型
- agent-thought
logo: dashscope
- name: farui-plus
type: llm
provider: dashscope
@@ -98,6 +108,7 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: glm-4.7
type: llm
provider: dashscope
@@ -112,6 +123,7 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qvq-max-latest
type: llm
provider: dashscope
@@ -119,7 +131,8 @@ models:
is_deprecated: false
is_official: true
capability:
- vision
- vision
- thinking
is_omni: false
tags:
- 大语言模型
@@ -127,6 +140,7 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qvq-max
type: llm
provider: dashscope
@@ -134,7 +148,8 @@ models:
is_deprecated: false
is_official: true
capability:
- vision
- vision
- thinking
is_omni: false
tags:
- 大语言模型
@@ -142,6 +157,7 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen-coder-turbo-0919
type: llm
provider: dashscope
@@ -155,13 +171,15 @@ models:
- 代码模型
- agent-thought
logo: dashscope
- name: qwen-max-latest
type: llm
provider: dashscope
description: qwen-max-latest大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式，支持联网搜索
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
@@ -169,6 +187,7 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen-max-longcontext
type: llm
provider: dashscope
@@ -183,13 +202,15 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen-max
type: llm
provider: dashscope
description: qwen-max大语言模型，支持多工具调用、智能体思考、流式工具调用，32768上下文窗口，对话模式，支持联网搜索
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
@@ -197,6 +218,7 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen-mt-plus
type: llm
provider: dashscope
@@ -210,6 +232,7 @@ models:
- 翻译模型
- agent-thought
logo: dashscope
- name: qwen-mt-turbo
type: llm
provider: dashscope
@@ -223,6 +246,7 @@ models:
- 翻译模型
- agent-thought
logo: dashscope
- name: qwen-plus-0112
type: llm
provider: dashscope
@@ -237,6 +261,7 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen-plus-0125
type: llm
provider: dashscope
@@ -251,6 +276,7 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen-plus-0723
type: llm
provider: dashscope
@@ -265,6 +291,7 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen-plus-0806
type: llm
provider: dashscope
@@ -279,6 +306,7 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen-plus-0919
type: llm
provider: dashscope
@@ -293,6 +321,7 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen-plus-1125
type: llm
provider: dashscope
@@ -307,6 +336,7 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen-plus-1127
type: llm
provider: dashscope
@@ -321,6 +351,7 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen-plus-1220
type: llm
provider: dashscope
@@ -335,6 +366,7 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen-vl-max
type: chat
provider: dashscope
@@ -342,8 +374,8 @@ models:
is_deprecated: false
is_official: true
capability:
- vision
- video
- vision
- video
is_omni: false
tags:
- 大语言模型
@@ -352,6 +384,7 @@ models:
- agent-thought
- video
logo: dashscope
- name: qwen-vl-plus-0809
type: chat
provider: dashscope
@@ -359,8 +392,8 @@ models:
is_deprecated: true
is_official: true
capability:
- vision
- video
- vision
- video
is_omni: false
tags:
- 大语言模型
@@ -369,6 +402,7 @@ models:
- agent-thought
- video
logo: dashscope
- name: qwen-vl-plus-2025-01-02
type: chat
provider: dashscope
@@ -376,8 +410,8 @@ models:
is_deprecated: false
is_official: true
capability:
- vision
- video
- vision
- video
is_omni: false
tags:
- 大语言模型
@@ -386,6 +420,7 @@ models:
- agent-thought
- video
logo: dashscope
- name: qwen-vl-plus-2025-01-25
type: chat
provider: dashscope
@@ -393,8 +428,8 @@ models:
is_deprecated: false
is_official: true
capability:
- vision
- video
- vision
- video
is_omni: false
tags:
- 大语言模型
@@ -403,6 +438,7 @@ models:
- agent-thought
- video
logo: dashscope
- name: qwen-vl-plus-latest
type: chat
provider: dashscope
@@ -410,8 +446,8 @@ models:
is_deprecated: false
is_official: true
capability:
- vision
- video
- vision
- video
is_omni: false
tags:
- 大语言模型
@@ -420,6 +456,7 @@ models:
- agent-thought
- video
logo: dashscope
- name: qwen-vl-plus
type: chat
provider: dashscope
@@ -427,8 +464,8 @@ models:
is_deprecated: false
is_official: true
capability:
- vision
- video
- vision
- video
is_omni: false
tags:
- 大语言模型
@@ -437,6 +474,7 @@ models:
- agent-thought
- video
logo: dashscope
- name: qwen2.5-0.5b-instruct
type: llm
provider: dashscope
@@ -451,13 +489,15 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen3-14b
type: llm
provider: dashscope
description: qwen3-14b大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
@@ -465,13 +505,15 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen3-235b-a22b-instruct-2507
type: llm
provider: dashscope
description: qwen3-235b-a22b-instruct-2507大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
@@ -479,13 +521,15 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen3-235b-a22b-thinking-2507
type: llm
provider: dashscope
description: qwen3-235b-a22b-thinking-2507大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
@@ -493,13 +537,15 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen3-235b-a22b
type: llm
provider: dashscope
description: qwen3-235b-a22b大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
@@ -507,13 +553,15 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen3-30b-a3b-instruct-2507
type: llm
provider: dashscope
description: qwen3-30b-a3b-instruct-2507大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
@@ -521,13 +569,15 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen3-30b-a3b
type: llm
provider: dashscope
description: qwen3-30b-a3b大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
@@ -535,13 +585,15 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen3-32b
type: llm
provider: dashscope
description: qwen3-32b大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
@@ -549,13 +601,15 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen3-4b
type: llm
provider: dashscope
description: qwen3-4b大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
@@ -563,13 +617,15 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen3-8b
type: llm
provider: dashscope
description: qwen3-8b大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
@@ -577,65 +633,75 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen3-coder-30b-a3b-instruct
type: llm
provider: dashscope
description: qwen3-coder-30b-a3b-instruct大语言模型，支持智能体思考，262144上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
- 代码模型
- agent-thought
logo: dashscope
- name: qwen3-coder-480b-a35b-instruct
type: llm
provider: dashscope
description: qwen3-coder-480b-a35b-instruct大语言模型，支持智能体思考，262144上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
- 代码模型
- agent-thought
logo: dashscope
- name: qwen3-coder-plus-2025-09-23
type: llm
provider: dashscope
description: qwen3-coder-plus-2025-09-23大语言模型，支持智能体思考，1000000上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
- 代码模型
- agent-thought
logo: dashscope
- name: qwen3-coder-plus
type: llm
provider: dashscope
description: qwen3-coder-plus大语言模型，支持智能体思考，1000000上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
- 代码模型
- agent-thought
logo: dashscope
- name: qwen3-max-2025-09-23
type: llm
provider: dashscope
description: qwen3-max-2025-09-23大语言模型，支持多工具调用、智能体思考、流式工具调用，262144上下文窗口，对话模式，支持联网搜索
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
@@ -644,13 +710,15 @@ models:
- stream-tool-call
- 联网搜索
logo: dashscope
- name: qwen3-max-2026-01-23
type: llm
provider: dashscope
description: qwen3-max-2026-01-23大语言模型，支持多工具调用、智能体思考、流式工具调用，262144上下文窗口，对话模式，支持联网搜索
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
@@ -659,13 +727,15 @@ models:
- stream-tool-call
- 联网搜索
logo: dashscope
- name: qwen3-max-preview
type: llm
provider: dashscope
description: qwen3-max-preview大语言模型，支持多工具调用、智能体思考、流式工具调用，262144上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
@@ -673,13 +743,15 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen3-max
type: llm
provider: dashscope
description: qwen3-max大语言模型，支持多工具调用、智能体思考、流式工具调用，262144上下文窗口，对话模式，支持联网搜索
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
@@ -688,13 +760,15 @@ models:
- stream-tool-call
- 联网搜索
logo: dashscope
- name: qwen3-next-80b-a3b-instruct
type: llm
provider: dashscope
description: qwen3-next-80b-a3b-instruct大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
@@ -702,13 +776,15 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen3-next-80b-a3b-thinking
type: llm
provider: dashscope
description: qwen3-next-80b-a3b-thinking大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
@@ -716,6 +792,7 @@ models:
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwen3-omni-flash-2025-12-01
type: llm
provider: dashscope
@@ -723,9 +800,10 @@ models:
is_deprecated: false
is_official: true
capability:
- vision
- video
- audio
- vision
- video
- audio
- thinking
is_omni: true
tags:
- 大语言模型
@@ -735,6 +813,7 @@ models:
- video
- audio
logo: dashscope
- name: qwen3-vl-235b-a22b-instruct
type: chat
provider: dashscope
@@ -742,8 +821,9 @@ models:
is_deprecated: false
is_official: true
capability:
- vision
- video
- vision
- video
- thinking
is_omni: false
tags:
- 大语言模型
@@ -754,6 +834,7 @@ models:
- vision
- video
logo: dashscope
- name: qwen3-vl-235b-a22b-thinking
type: chat
provider: dashscope
@@ -761,8 +842,9 @@ models:
is_deprecated: false
is_official: true
capability:
- vision
- video
- vision
- video
- thinking
is_omni: false
tags:
- 大语言模型
@@ -773,6 +855,7 @@ models:
- vision
- video
logo: dashscope
- name: qwen3-vl-30b-a3b-instruct
type: chat
provider: dashscope
@@ -780,8 +863,9 @@ models:
is_deprecated: false
is_official: true
capability:
- vision
- video
- vision
- video
- thinking
is_omni: false
tags:
- 大语言模型
@@ -792,6 +876,7 @@ models:
- vision
- video
logo: dashscope
- name: qwen3-vl-30b-a3b-thinking
type: chat
provider: dashscope
@@ -799,8 +884,9 @@ models:
is_deprecated: false
is_official: true
capability:
- vision
- video
- vision
- video
- thinking
is_omni: false
tags:
- 大语言模型
@@ -811,6 +897,7 @@ models:
- vision
- video
logo: dashscope
- name: qwen3-vl-flash
type: chat
provider: dashscope
@@ -818,8 +905,9 @@ models:
is_deprecated: false
is_official: true
capability:
- vision
- video
- vision
- video
- thinking
is_omni: false
tags:
- 大语言模型
@@ -830,6 +918,7 @@ models:
- vision
- video
logo: dashscope
- name: qwen3-vl-plus-2025-09-23
type: chat
provider: dashscope
@@ -837,8 +926,9 @@ models:
is_deprecated: false
is_official: true
capability:
- vision
- video
- vision
- video
- thinking
is_omni: false
tags:
- 大语言模型
@@ -847,6 +937,7 @@ models:
- agent-thought
- video
logo: dashscope
- name: qwen3-vl-plus
type: chat
provider: dashscope
@@ -854,8 +945,9 @@ models:
is_deprecated: false
is_official: true
capability:
- vision
- video
- vision
- video
- thinking
is_omni: false
tags:
- 大语言模型
@@ -864,45 +956,52 @@ models:
- agent-thought
- video
logo: dashscope
- name: qwq-32b
type: llm
provider: dashscope
description: qwq-32b大语言模型，支持智能体思考、流式工具调用，131072上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwq-plus-0305
type: llm
provider: dashscope
description: qwq-plus-0305大语言模型，支持智能体思考、流式工具调用，131072上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
- agent-thought
- stream-tool-call
logo: dashscope
- name: qwq-plus
type: llm
provider: dashscope
description: qwq-plus大语言模型，支持智能体思考、流式工具调用，131072上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
- agent-thought
- stream-tool-call
logo: dashscope
- name: gte-rerank-v2
type: rerank
provider: dashscope
@@ -914,6 +1013,7 @@ models:
tags:
- 重排序模型
logo: dashscope
- name: gte-rerank
type: rerank
provider: dashscope
@@ -925,6 +1025,7 @@ models:
tags:
- 重排序模型
logo: dashscope
- name: multimodal-embedding-v1
type: embedding
provider: dashscope
@@ -932,13 +1033,14 @@ models:
is_deprecated: false
is_official: true
capability:
- vision
- vision
is_omni: false
tags:
- 嵌入模型
- 多模态模型
- vision
logo: dashscope
- name: text-embedding-v1
type: embedding
provider: dashscope
@@ -951,6 +1053,7 @@ models:
- 嵌入模型
- 文本嵌入
logo: dashscope
- name: text-embedding-v2
type: embedding
provider: dashscope
@@ -963,6 +1066,7 @@ models:
- 嵌入模型
- 文本嵌入
logo: dashscope
- name: text-embedding-v3
type: embedding
provider: dashscope
@@ -975,6 +1079,7 @@ models:
- 嵌入模型
- 文本嵌入
logo: dashscope
- name: text-embedding-v4
type: embedding
provider: dashscope
@@ -986,4 +1091,4 @@ models:
tags:
- 嵌入模型
- 文本嵌入
logo: dashscope
logo: dashscope

36
api/app/core/models/scripts/openai_models.yaml
View File

@@ -20,6 +20,7 @@ models:
- audio
- video
logo: openai
- name: gpt-3.5-turbo-0125
type: llm
provider: openai
@@ -34,6 +35,7 @@ models:
- agent-thought
- stream-tool-call
logo: openai
- name: gpt-3.5-turbo-1106
type: llm
provider: openai
@@ -48,6 +50,7 @@ models:
- agent-thought
- stream-tool-call
logo: openai
- name: gpt-3.5-turbo-16k
type: llm
provider: openai
@@ -62,6 +65,7 @@ models:
- agent-thought
- stream-tool-call
logo: openai
- name: gpt-3.5-turbo-instruct
type: llm
provider: openai
@@ -73,6 +77,7 @@ models:
tags:
- 大语言模型
logo: openai
- name: gpt-3.5-turbo
type: llm
provider: openai
@@ -87,6 +92,7 @@ models:
- agent-thought
- stream-tool-call
logo: openai
- name: gpt-4-0125-preview
type: llm
provider: openai
@@ -101,6 +107,7 @@ models:
- agent-thought
- stream-tool-call
logo: openai
- name: gpt-4-1106-preview
type: llm
provider: openai
@@ -115,6 +122,7 @@ models:
- agent-thought
- stream-tool-call
logo: openai
- name: gpt-4-turbo-2024-04-09
type: llm
provider: openai
@@ -131,6 +139,7 @@ models:
- stream-tool-call
- vision
logo: openai
- name: gpt-4-turbo-preview
type: llm
provider: openai
@@ -145,6 +154,7 @@ models:
- agent-thought
- stream-tool-call
logo: openai
- name: gpt-4-turbo
type: llm
provider: openai
@@ -161,6 +171,7 @@ models:
- stream-tool-call
- vision
logo: openai
- name: o1-preview
type: llm
provider: openai
@@ -173,6 +184,7 @@ models:
- 大语言模型
- agent-thought
logo: openai
- name: o1
type: llm
provider: openai
@@ -181,6 +193,7 @@ models:
is_official: true
capability:
- vision
- thinking
is_omni: false
tags:
- 大语言模型
@@ -190,6 +203,7 @@ models:
- vision
- structured-output
logo: openai
- name: o3-2025-04-16
type: llm
provider: openai
@@ -198,6 +212,7 @@ models:
is_official: true
capability:
- vision
- thinking
is_omni: false
tags:
- 大语言模型
@@ -207,13 +222,15 @@ models:
- stream-tool-call
- structured-output
logo: openai
- name: o3-mini-2025-01-31
type: llm
provider: openai
description: o3-mini-2025-01-31大语言模型，支持智能体思考、工具调用、流式工具调用、结构化输出，200000上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
@@ -222,13 +239,15 @@ models:
- stream-tool-call
- structured-output
logo: openai
- name: o3-mini
type: llm
provider: openai
description: o3-mini大语言模型，支持智能体思考、工具调用、流式工具调用、结构化输出，200000上下文窗口，对话模式
is_deprecated: false
is_official: true
capability: []
capability:
- thinking
is_omni: false
tags:
- 大语言模型
@@ -237,6 +256,7 @@ models:
- stream-tool-call
- structured-output
logo: openai
- name: o3-pro-2025-06-10
type: llm
provider: openai
@@ -245,6 +265,7 @@ models:
is_official: true
capability:
- vision
- thinking
is_omni: false
tags:
- 大语言模型
@@ -253,6 +274,7 @@ models:
- vision
- structured-output
logo: openai
- name: o3-pro
type: llm
provider: openai
@@ -261,6 +283,7 @@ models:
is_official: true
capability:
- vision
- thinking
is_omni: false
tags:
- 大语言模型
@@ -269,6 +292,7 @@ models:
- vision
- structured-output
logo: openai
- name: o3
type: llm
provider: openai
@@ -277,6 +301,7 @@ models:
is_official: true
capability:
- vision
- thinking
is_omni: false
tags:
- 大语言模型
@@ -286,6 +311,7 @@ models:
- stream-tool-call
- structured-output
logo: openai
- name: o4-mini-2025-04-16
type: llm
provider: openai
@@ -294,6 +320,7 @@ models:
is_official: true
capability:
- vision
- thinking
is_omni: false
tags:
- 大语言模型
@@ -303,6 +330,7 @@ models:
- stream-tool-call
- structured-output
logo: openai
- name: o4-mini
type: llm
provider: openai
@@ -311,6 +339,7 @@ models:
is_official: true
capability:
- vision
- thinking
is_omni: false
tags:
- 大语言模型
@@ -320,6 +349,7 @@ models:
- stream-tool-call
- structured-output
logo: openai
- name: text-embedding-3-large
type: embedding
provider: openai
@@ -331,6 +361,7 @@ models:
tags:
- 文本向量模型
logo: openai
- name: text-embedding-3-small
type: embedding
provider: openai
@@ -342,6 +373,7 @@ models:
tags:
- 文本向量模型
logo: openai
- name: text-embedding-ada-002
type: embedding
provider: openai

9
api/app/core/models/scripts/volcano_models.yaml
View File

@@ -10,6 +10,7 @@ models:
capability:
- vision
- video
- thinking
is_omni: false
tags:
- 大语言模型
@@ -24,6 +25,7 @@ models:
capability:
- vision
- video
- thinking
is_omni: false
tags:
- 大语言模型
@@ -38,6 +40,7 @@ models:
capability:
- vision
- video
- thinking
is_omni: false
tags:
- 大语言模型
@@ -52,6 +55,7 @@ models:
capability:
- vision
- video
- thinking
is_omni: false
tags:
- 大语言模型
@@ -82,6 +86,7 @@ models:
capability:
- vision
- video
- thinking
is_omni: false
tags:
- 大语言模型
@@ -96,6 +101,7 @@ models:
capability:
- vision
- video
- thinking
is_omni: false
tags:
- 大语言模型
@@ -110,6 +116,7 @@ models:
capability:
- vision
- video
- thinking
is_omni: false
tags:
- 大语言模型
@@ -124,6 +131,7 @@ models:
capability:
- vision
- video
- thinking
is_omni: false
tags:
- 大语言模型
@@ -139,6 +147,7 @@ models:
capability:
- vision
- video
- thinking
is_omni: false
tags:
- 大语言模型

38
api/app/core/models/volcano_chat.py Normal file
View File

@@ -0,0 +1,38 @@
"""
火山引擎 ChatOpenAI 扩展
ChatOpenAI 在解析流式 SSE 时只取 delta.content，会丢弃 delta.reasoning_content。
此类仅重写 _convert_chunk_to_generation_chunk，将 reasoning_content 补入 additional_kwargs。
"""
from __future__ import annotations
from typing import Any, Optional
from langchain_core.outputs import ChatGenerationChunk
from langchain_openai import ChatOpenAI
class VolcanoChatOpenAI(ChatOpenAI):
"""火山引擎 Chat 模型，支持深度思考内容（reasoning_content）的流式透传。"""
def _convert_chunk_to_generation_chunk(
self,
chunk: dict,
default_chunk_class: type,
base_generation_info: Optional[dict],
) -> Optional[ChatGenerationChunk]:
gen_chunk = super()._convert_chunk_to_generation_chunk(
chunk, default_chunk_class, base_generation_info
)
if gen_chunk is None:
return None
# 从原始 chunk 中提取 reasoning_content
choices = chunk.get("choices") or chunk.get("chunk", {}).get("choices", [])
if choices:
delta = choices[0].get("delta") or {}
reasoning: Any = delta.get("reasoning_content")
if reasoning:
gen_chunk.message.additional_kwargs["reasoning_content"] = reasoning
return gen_chunk

10
api/app/core/tools/mcp/client.py
View File

@@ -99,7 +99,7 @@ class SimpleMCPClient:
# 建立 SSE 连接
response = await self._session.get(self.server_url)
if response.status not in (200, 202):
if not (200 <= response.status < 300):
error_text = await response.text()
raise MCPConnectionError(f"SSE 连接失败 {response.status}: {error_text}")
@@ -190,9 +190,7 @@ class SimpleMCPClient:
try:
async with self._session.post(self._endpoint_url, json=request) as response:
# MCP SSE 协议：POST 请求返回 200 或 202 均为正常
# 202 Accepted 表示请求已接受，结果通过 SSE 流异步返回
if response.status not in (200, 202):
if not (200 <= response.status < 300):
error_text = await response.text()
raise MCPConnectionError(f"请求失败 {response.status}: {error_text}")
@@ -207,7 +205,7 @@ class SimpleMCPClient:
raise MCPConnectionError("endpoint URL 未初始化")
async with self._session.post(self._endpoint_url, json=notification) as response:
if response.status not in (200, 202):
if not (200 <= response.status < 300):
logger.warning(f"通知发送失败: {response.status}")
async def _initialize_modelscope_session(self):
@@ -225,7 +223,7 @@ class SimpleMCPClient:
try:
async with self._session.post(self.server_url, json=init_request) as response:
if response.status != 200:
if not (200 <= response.status < 300):
error_text = await response.text()
raise MCPConnectionError(f"初始化失败 {response.status}: {error_text}")

3
api/app/core/workflow/nodes/llm/node.py
View File

@@ -135,7 +135,8 @@ class LLMNode(BaseNode):
api_key=model_info.api_key,
base_url=model_info.api_base,
extra_params=extra_params,
is_omni=model_info.is_omni
is_omni=model_info.is_omni,
support_thinking="thinking" in (model_info.capability or []),
),
type=model_info.model_type
)

4
api/app/core/workflow/nodes/parameter_extractor/node.py
View File

@@ -109,6 +109,7 @@ class ParameterExtractorNode(BaseNode):
api_key = api_config.api_key
api_base = api_config.api_base
is_omni = api_config.is_omni
capability = api_config.capability
model_type = config.type
llm = RedBearLLM(
@@ -117,7 +118,8 @@ class ParameterExtractorNode(BaseNode):
provider=provider,
api_key=api_key,
base_url=api_base,
is_omni=is_omni
is_omni=is_omni,
support_thinking="thinking" in (capability or []),
),
type=ModelType(model_type)
)

4
api/app/core/workflow/nodes/question_classifier/node.py
View File

@@ -62,6 +62,7 @@ class QuestionClassifierNode(BaseNode):
api_key = api_config.api_key
base_url = api_config.api_base
is_omni = api_config.is_omni
capability = api_config.capability
model_type = config.type
return RedBearLLM(
@@ -70,7 +71,8 @@ class QuestionClassifierNode(BaseNode):
provider=provider,
api_key=api_key,
base_url=base_url,
is_omni=is_omni
is_omni=is_omni,
support_thinking="thinking" in (capability or []),
),
type=ModelType(model_type)
)

2
api/app/models/models_model.py
View File

@@ -81,7 +81,7 @@ class ModelConfig(BaseModel):
# 模型配置参数
capability = Column(ARRAY(String), default=list, nullable=False, server_default=text("'{}'::varchar[]"),
comment="模型能力列表（如['vision', 'audio', 'video']）")
comment="模型能力列表（如['vision', 'audio', 'video', 'thinking']）")
is_omni = Column(Boolean, default=False, nullable=False, server_default="false", comment="是否为Omni模型（使用特殊API调用）")
config = Column(JSON, comment="模型配置参数")
# - temperature : 控制生成文本的随机性。值越高，输出越随机、越有创造性；值越低，输出越确定、越保守。

3
api/app/schemas/app_schema.py
View File

@@ -241,6 +241,8 @@ class ModelParameters(BaseModel):
presence_penalty: float = Field(default=0.0, ge=-2.0, le=2.0, description="存在惩罚")
n: int = Field(default=1, ge=1, le=10, description="生成的回复数量")
stop: Optional[List[str]] = Field(default=None, description="停止序列")
deep_thinking: bool = Field(default=False, description="是否启用深度思考模式（需模型支持，如 DeepSeek-R1、QwQ 等）")
thinking_budget_tokens: Optional[int] = Field(default=None, ge=1024, le=131072, description="深度思考 token 预算（仅部分模型支持）")
class VariableDefinition(BaseModel):
@@ -612,6 +614,7 @@ class AppChatRequest(BaseModel):
user_id: Optional[str] = Field(default=None, description="用户ID（用于会话管理）")
variables: Optional[Dict[str, Any]] = Field(default=None, description="自定义变量参数值")
stream: bool = Field(default=False, description="是否流式返回")
thinking: bool = Field(default=False, description="是否启用深度思考（需Agent配置支持）")
files: List[FileInput] = Field(default_factory=list, description="附件列表（支持多文件）")

1
api/app/schemas/conversation_schema.py
View File

@@ -31,6 +31,7 @@ class ChatRequest(BaseModel):
stream: bool = Field(default=False, description="是否流式返回")
web_search: bool = Field(default=False, description="是否启用网络搜索")
memory: bool = Field(default=True, description="是否启用记忆功能")
thinking: bool = Field(default=False, description="是否启用深度思考（需Agent配置支持）")
files: Optional[List[FileInput]] = Field(default=None, description="附件列表（支持多文件）")

26
api/app/services/app_chat_service.py
View File

@@ -117,7 +117,9 @@ class AppChatService:
max_tokens=model_parameters.get("max_tokens", 2000),
system_prompt=system_prompt,
tools=tools,
deep_thinking=model_parameters.get("deep_thinking", False),
thinking_budget_tokens=model_parameters.get("thinking_budget_tokens"),
capability=api_key_obj.capability or [],
)
model_info = ModelInfo(
@@ -205,7 +207,8 @@ class AppChatService:
"model": api_key_obj.model_name,
"usage": result.get("usage", {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0}),
"audio_url": None,
"citations": filtered_citations
"citations": filtered_citations,
"reasoning_content": result.get("reasoning_content")
}
if files:
for f in files:
@@ -258,6 +261,7 @@ class AppChatService:
"conversation_id": conversation_id,
"message_id": str(message_id),
"message": result["content"],
"reasoning_content": result.get("reasoning_content"),
"usage": result.get("usage", {
"prompt_tokens": 0,
"completion_tokens": 0,
@@ -354,7 +358,10 @@ class AppChatService:
max_tokens=model_parameters.get("max_tokens", 2000),
system_prompt=system_prompt,
tools=tools,
streaming=True
streaming=True,
deep_thinking=model_parameters.get("deep_thinking", False),
thinking_budget_tokens=model_parameters.get("thinking_budget_tokens"),
capability=api_key_obj.capability or [],
)
model_info = ModelInfo(
@@ -403,6 +410,7 @@ class AppChatService:
# 流式调用 Agent（支持多模态），同时并行启动 TTS
full_content = ""
full_reasoning = ""
total_tokens = 0
text_queue: asyncio.Queue = asyncio.Queue()
@@ -426,6 +434,9 @@ class AppChatService:
):
if isinstance(chunk, int):
total_tokens = chunk
elif isinstance(chunk, dict) and chunk.get("type") == "reasoning":
full_reasoning += chunk['content']
yield f"event: reasoning\ndata: {json.dumps({'content': chunk['content']}, ensure_ascii=False)}\n\n"
else:
full_content += chunk
yield f"event: message\ndata: {json.dumps({'content': chunk}, ensure_ascii=False)}\n\n"
@@ -472,7 +483,8 @@ class AppChatService:
"model": api_key_obj.model_name,
"usage": {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": total_tokens},
"audio_url": None,
"citations": filtered_citations
"citations": filtered_citations,
"reasoning_content": full_reasoning or None
}
if files:
@@ -652,13 +664,13 @@ class AppChatService:
storage_type=storage_type,
user_rag_memory_id=user_rag_memory_id
):
if "sub_usage" in event:
# 拦截 sub_usage 事件，累加 token
if "event: sub_usage" in event:
if "data:" in event:
try:
data_line = event.split("data: ", 1)[1].strip()
data = json.loads(data_line)
if "total_tokens" in data:
total_tokens += data["total_tokens"]
total_tokens += data.get("total_tokens", 0)
except:
pass
else:

4
api/app/services/conversation_service.py
View File

@@ -534,6 +534,7 @@ class ConversationService:
api_key = api_config.api_key
api_base = api_config.api_base
is_omni = api_config.is_omni
capability = api_config.capability
model_type = config.type
llm = RedBearLLM(
@@ -542,7 +543,8 @@ class ConversationService:
provider=provider,
api_key=api_key,
base_url=api_base,
is_omni=is_omni
is_omni=is_omni,
support_thinking="thinking" in (capability or []),
),
type=ModelType(model_type)
)

41
api/app/services/draft_run_service.py
View File

@@ -458,7 +458,7 @@ class AgentRunService:
statement = opening["statement"]
suggested_questions = opening["suggested_questions"]
# 如果有变量，进行替换（仅支持 {{var_name}} 格式）
if variables:
for var_name, var_value in variables.items():
@@ -595,6 +595,9 @@ class AgentRunService:
max_tokens=effective_params.get("max_tokens", 2000),
system_prompt=system_prompt,
tools=tools,
deep_thinking=effective_params.get("deep_thinking", False),
thinking_budget_tokens=effective_params.get("thinking_budget_tokens"),
capability=api_key_config.get("capability", []),
)
# 5. 处理会话ID（创建或验证），新会话时写入开场白
@@ -689,7 +692,8 @@ class AgentRunService:
"prompt_tokens": 0,
"completion_tokens": 0,
"total_tokens": 0
})
}),
"reasoning_content": result.get("reasoning_content")
},
files=files,
processed_files=processed_files,
@@ -701,6 +705,7 @@ class AgentRunService:
response = {
"message": result["content"],
"reasoning_content": result.get("reasoning_content"),
"conversation_id": conversation_id,
"usage": result.get("usage", {
"prompt_tokens": 0,
@@ -838,7 +843,10 @@ class AgentRunService:
max_tokens=effective_params.get("max_tokens", 2000),
system_prompt=system_prompt,
tools=tools,
streaming=True
streaming=True,
deep_thinking=effective_params.get("deep_thinking", False),
thinking_budget_tokens=effective_params.get("thinking_budget_tokens"),
capability=api_key_config.get("capability", []),
)
# 5. 处理会话ID（创建或验证），新会话时写入开场白
@@ -898,6 +906,7 @@ class AgentRunService:
# 9. 流式调用 Agent（支持多模态），同时并行启动 TTS
full_content = ""
full_reasoning = ""
total_tokens = 0
# 启动流式 TTS（文本边输出边合成）
@@ -916,6 +925,9 @@ class AgentRunService:
):
if isinstance(chunk, int):
total_tokens = chunk
elif isinstance(chunk, dict) and chunk.get("type") == "reasoning":
full_reasoning += chunk["content"]
yield self._format_sse_event("reasoning", {"content": chunk["content"]})
else:
full_content += chunk
yield self._format_sse_event("message", {"content": chunk})
@@ -944,7 +956,8 @@ class AgentRunService:
app_id=agent_config.app_id,
user_id=user_id,
meta_data={
"usage": {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": total_tokens}
"usage": {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": total_tokens},
"reasoning_content": full_reasoning or None
},
files=files,
processed_files=processed_files,
@@ -1665,7 +1678,7 @@ class AgentRunService:
"""从 text_queue 取文本按句子切分后喂给 synthesizer"""
import re
buf = ""
sentence_end = re.compile(r'[\u3002\uff01\uff1f\.!?\n]')
sentence_end = re.compile(r'[\u3002\uff01\uff1f.!?\n]')
while True:
chunk = await text_queue.get()
if chunk is None:
@@ -1894,6 +1907,7 @@ class AgentRunService:
"conversation_id": result['conversation_id'],
"parameters_used": model_info["parameters"],
"message": result.get("message"),
"reasoning_content": result.get("reasoning_content"),
"usage": usage,
"elapsed_time": elapsed,
"tokens_per_second": (
@@ -2012,7 +2026,7 @@ class AgentRunService:
# 需要从 ModelApiKey 获取实际的模型名称，或者在 ModelConfig 中添加 model 字段
return None
def _with_parameters(self, agent_config: AgentConfig, parameters: Dict[str, Any]) -> AgentConfig:
def _with_parameters(self, agent_config: AgentConfig, parameters: Dict[str, Any]) -> tuple[AgentConfig, Any]:
"""创建一个带有覆盖参数的 agent_config（浅拷贝，只修改 model_parameters）
Args:
@@ -2110,6 +2124,7 @@ class AgentRunService:
start_time = time.time()
full_content = ""
full_reasoning = ""
returned_conversation_id = model_conversation_id
audio_url = None
audio_status = None
@@ -2168,6 +2183,18 @@ class AgentRunService:
"content": chunk
}))
# 转发深度思考事件（带模型标识）
if event_type == "reasoning" and event_data:
reasoning_chunk = event_data.get("content", "")
full_reasoning += reasoning_chunk
await event_queue.put(self._format_sse_event("model_reasoning", {
"model_index": idx,
"model_config_id": model_config_id,
"label": model_label,
"conversation_id": returned_conversation_id,
"content": event_data.get("content", "")
}))
# 从 end 事件中提取 features 输出字段
if event_type == "end" and event_data:
audio_url = event_data.get("audio_url")
@@ -2199,6 +2226,7 @@ class AgentRunService:
"conversation_id": returned_conversation_id,
"parameters_used": model_info["parameters"],
"message": full_content,
"reasoning_content": full_reasoning or None,
"elapsed_time": elapsed,
"audio_url": audio_url,
"audio_status": audio_status,
@@ -2351,6 +2379,7 @@ class AgentRunService:
"label": r["label"],
"conversation_id": r.get("conversation_id"),
"message": r.get("message"),
"reasoning_content": r.get("reasoning_content"),
"elapsed_time": r.get("elapsed_time", 0),
"audio_url": r.get("audio_url"),
"audio_status": r.get("audio_status"),

1
api/app/services/llm_router.py
View File

@@ -415,6 +415,7 @@ class LLMRouter:
api_key=api_key_config.api_key,
base_url=api_key_config.api_base,
is_omni=api_key_config.is_omni,
support_thinking="thinking" in (api_key_config.capability or []),
temperature=0.3,
max_tokens=500
)

12
api/app/services/master_agent_router.py
View File

@@ -393,6 +393,7 @@ class MasterAgentRouter:
api_key=api_key_config.api_key,
base_url=api_key_config.api_base,
is_omni=api_key_config.is_omni,
support_thinking="thinking" in (api_key_config.capability or []),
extra_params = extra_params
)
@@ -403,6 +404,17 @@ class MasterAgentRouter:
response = await llm.ainvoke(prompt)
ModelApiKeyService.record_api_key_usage(self.db, api_key_config.id)
# 提取 token 消耗
self._last_routing_tokens = 0
if hasattr(response, 'usage_metadata') and response.usage_metadata:
um = response.usage_metadata
self._last_routing_tokens = um.get("total_tokens", 0) if isinstance(um, dict) else getattr(um, "total_tokens", 0)
elif hasattr(response, 'response_metadata') and response.response_metadata:
token_usage = response.response_metadata.get("token_usage") or response.response_metadata.get("usage", {})
if isinstance(token_usage, dict):
self._last_routing_tokens = token_usage.get("total_tokens", 0)
logger.info(f"Master Agent 路由 token 消耗: {self._last_routing_tokens}")
# 提取响应内容
if hasattr(response, 'content'):
return response.content

3
api/app/services/memory_perceptual_service.py
View File

@@ -232,7 +232,8 @@ class MemoryPerceptualService:
provider=model_config.provider,
api_key=model_config.api_key,
base_url=model_config.api_base,
is_omni=model_config.is_omni
is_omni=model_config.is_omni,
support_thinking="thinking" in (model_config.capability or []),
)
)
return llm, model_config

10
api/app/services/model_parameter_merger.py
View File

@@ -45,12 +45,20 @@ class ModelParameterMerger:
"frequency_penalty": 0.0,
"presence_penalty": 0.0,
"n": 1,
"stop": None
"stop": None,
"deep_thinking": False,
"thinking_budget_tokens": None
}
# 合并参数：默认值 -> 模型配置 -> Agent 配置
merged = default_params.copy()
# Pydantic 对象转为 dict
if model_config_params and hasattr(model_config_params, 'model_dump'):
model_config_params = model_config_params.model_dump()
if agent_config_params and hasattr(agent_config_params, 'model_dump'):
agent_config_params = agent_config_params.model_dump()
# 应用模型配置参数
if model_config_params:
for key in default_params:

32
api/app/services/model_service.py
View File

@@ -85,15 +85,16 @@ class ModelConfigService:
@staticmethod
async def validate_model_config(
db: Session,
*,
model_name: str,
provider: str,
api_key: str,
api_base: Optional[str] = None,
model_type: str = "llm",
test_message: str = "Hello",
is_omni: bool = False
db: Session,
*,
model_name: str,
provider: str,
api_key: str,
api_base: Optional[str] = None,
model_type: str = "llm",
test_message: str = "Hello",
is_omni: bool = False,
capability: Optional[list] = None
) -> Dict[str, Any]:
"""验证模型配置是否有效
@@ -124,6 +125,7 @@ class ModelConfigService:
api_key=api_key,
base_url=api_base,
is_omni=is_omni,
support_thinking="thinking" in (capability or []),
temperature=0.7,
max_tokens=100
)
@@ -320,7 +322,8 @@ class ModelConfigService:
api_base=api_key_data.api_base,
model_type=model_data.type,
test_message="Hello",
is_omni=model_data.is_omni
is_omni=model_data.is_omni,
capability=model_data.capability
)
if not validation_result["valid"]:
raise BusinessException(
@@ -590,7 +593,8 @@ class ModelApiKeyService:
api_base=data.api_base,
model_type=model_config.type,
test_message="Hello",
is_omni=data.is_omni
is_omni=data.is_omni,
capability=model_config.capability
)
if not validation_result["valid"]:
# 记录验证失败的模型，但不抛出异常
@@ -675,7 +679,8 @@ class ModelApiKeyService:
api_base=api_key_data.api_base,
model_type=model_config.type,
test_message="Hello",
is_omni=api_key_data.is_omni
is_omni=api_key_data.is_omni,
capability=model_config.capability
)
if not validation_result["valid"]:
raise BusinessException(
@@ -707,7 +712,8 @@ class ModelApiKeyService:
api_base=api_key_data.api_base or existing_api_key.api_base,
model_type=model_config.type,
test_message="Hello",
is_omni=model_config.is_omni
is_omni=model_config.is_omni,
capability=model_config.capability
)
if not validation_result["valid"]:
raise BusinessException(

36
api/app/services/multi_agent_orchestrator.py
View File

@@ -287,6 +287,11 @@ class MultiAgentOrchestrator:
sub_conversation_id = None
total_tokens = 0
# 累加 Master Agent 路由决策消耗的 token
total_tokens += task_analysis.get("routing_tokens", 0)
# 累加 Master Agent 整合消耗的 token
total_tokens += getattr(self, '_last_merge_tokens', 0)
if isinstance(results, dict):
sub_conversation_id = results.get("conversation_id") or results.get("result", {}).get("conversation_id")
# 提取 token 信息
@@ -358,12 +363,16 @@ class MultiAgentOrchestrator:
variables=variables
)
# 获取路由决策消耗的 token
routing_tokens = getattr(self.router, '_last_routing_tokens', 0)
logger.info(
"Master Agent 分析完成",
extra={
"selected_agent": routing_decision.get("selected_agent_id"),
"confidence": routing_decision.get("confidence"),
"strategy": routing_decision.get("strategy")
"strategy": routing_decision.get("strategy"),
"routing_tokens": routing_tokens
}
)
@@ -372,7 +381,8 @@ class MultiAgentOrchestrator:
"variables": variables or {},
"sub_agents": self.config.sub_agents,
"initial_context": variables or {},
"routing_decision": routing_decision
"routing_decision": routing_decision,
"routing_tokens": routing_tokens
}
async def _execute_sequential(
@@ -1032,6 +1042,11 @@ class MultiAgentOrchestrator:
# 5. 流式执行子 Agent
sub_conversation_id = None
# Master Agent 路由决策消耗的 token，通过 sub_usage 事件发送给上层
routing_tokens = task_analysis.get("routing_tokens", 0)
if routing_tokens > 0:
yield self._format_sse_event("sub_usage", {"total_tokens": routing_tokens})
async for event in self._execute_sub_agent_stream(
agent_data["config"],
message,
@@ -1054,6 +1069,7 @@ class MultiAgentOrchestrator:
except:
pass
# 直接透传所有事件（包括 sub_usage），累加统一由上层处理
yield event
# 6. 如果有会话 ID，发送一个包含它的事件
@@ -2600,6 +2616,7 @@ class MultiAgentOrchestrator:
api_key=api_key_config.api_key,
base_url=api_key_config.api_base,
is_omni=api_key_config.is_omni,
support_thinking="thinking" in (api_key_config.capability or []),
temperature=0.7, # 整合任务使用中等温度
max_tokens=2000
)
@@ -2612,6 +2629,17 @@ class MultiAgentOrchestrator:
ModelApiKeyService.record_api_key_usage(self.db, api_key_config.id)
# 提取整合消耗的 token
merge_tokens = 0
if hasattr(response, 'usage_metadata') and response.usage_metadata:
um = response.usage_metadata
merge_tokens = um.get("total_tokens", 0) if isinstance(um, dict) else getattr(um, "total_tokens", 0)
elif hasattr(response, 'response_metadata') and response.response_metadata:
token_usage = response.response_metadata.get("token_usage") or response.response_metadata.get("usage", {})
if isinstance(token_usage, dict):
merge_tokens = token_usage.get("total_tokens", 0)
self._last_merge_tokens = merge_tokens
# 提取响应内容
if hasattr(response, 'content'):
merged_response = response.content
@@ -2621,7 +2649,8 @@ class MultiAgentOrchestrator:
logger.info(
"Master Agent 整合完成",
extra={
"merged_length": len(merged_response)
"merged_length": len(merged_response),
"merge_tokens": merge_tokens
}
)
@@ -2766,6 +2795,7 @@ class MultiAgentOrchestrator:
api_key=api_key_config.api_key,
base_url=api_key_config.api_base,
is_omni=api_key_config.is_omni,
support_thinking="thinking" in (api_key_config.capability or []),
temperature=0.7,
max_tokens=2000,
extra_params={"streaming": True} # 启用流式输出

3
api/app/services/prompt_optimizer_service.py
View File

@@ -185,7 +185,8 @@ class PromptOptimizerService:
provider=api_config.provider,
api_key=api_config.api_key,
base_url=api_config.api_base,
is_omni=api_config.is_omni
is_omni=api_config.is_omni,
support_thinking="thinking" in (api_config.capability or []),
), type=ModelType(model_config.type))
try:
prompt_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'prompt')

11
api/app/services/shared_chat_service.py
View File

@@ -248,7 +248,9 @@ class SharedChatService:
max_tokens=model_parameters.get("max_tokens", 2000),
system_prompt=system_prompt,
tools=tools,
deep_thinking=model_parameters.get("deep_thinking", False),
thinking_budget_tokens=model_parameters.get("thinking_budget_tokens"),
capability=api_key_obj.capability or [],
)
# 加载历史消息
@@ -450,7 +452,10 @@ class SharedChatService:
max_tokens=model_parameters.get("max_tokens", 2000),
system_prompt=system_prompt,
tools=tools,
streaming=True
streaming=True,
deep_thinking=model_parameters.get("deep_thinking", False),
thinking_budget_tokens=model_parameters.get("thinking_budget_tokens"),
capability=api_key_obj.capability or [],
)
# 加载历史消息
@@ -479,6 +484,8 @@ class SharedChatService:
):
if isinstance(chunk, int):
total_tokens = chunk
elif isinstance(chunk, dict) and chunk.get("type") == "reasoning":
yield f"event: reasoning\ndata: {json.dumps({'content': chunk['content']}, ensure_ascii=False)}\n\n"
else:
full_content += chunk
# 发送消息块事件