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update pay ui

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This commit is contained in:
zzlgreat
2025-12-17 12:32:22 +08:00
parent a5a609e9ba
commit b89aba07e2
1 changed files with 52 additions and 51 deletions
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103
mcp_server.py
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@@ -89,7 +89,7 @@ MODEL_CONFIGS = {
"api_key": "", # 空值 "api_key": "", # 空值
"base_url": "http://111.62.35.50:8000/v1", "base_url": "http://111.62.35.50:8000/v1",
"model": "deepmoney", "model": "deepmoney",
"max_tokens": 65536, # DeepMoney 本地托管，支持 65536 "max_tokens": 32768, # DeepMoney 本地托管，上下文 65536，输出限制 32768
}, },
"gemini-3": { "gemini-3": {
"api_key": "", # 需要配置Google API密钥 "api_key": "", # 需要配置Google API密钥
@@ -99,8 +99,8 @@ MODEL_CONFIGS = {
}, },
} }
# 保持向后兼容的配置（默认使用 deepmoney，本地托管，上下文长） # 默认 LLM 配置（使用 deepmoney，本地托管，上下文长）
KIMI_CONFIG = MODEL_CONFIGS["deepmoney"] LLM_CONFIG = MODEL_CONFIGS["deepmoney"]
DEEPMONEY_CONFIG = MODEL_CONFIGS["deepmoney"] DEEPMONEY_CONFIG = MODEL_CONFIGS["deepmoney"]
# ==================== MCP协议数据模型 ==================== # ==================== MCP协议数据模型 ====================
@@ -1830,17 +1830,18 @@ TOOL_HANDLERS = {
# ==================== Agent系统实现 ==================== # ==================== Agent系统实现 ====================
class MCPAgentIntegrated: class MCPAgentIntegrated:
"""集成版 MCP Agent - 使用 Kimi 和 DeepMoney""" """集成版 MCP Agent - 使用 LLM 进行计划制定和总结"""
def __init__(self): def __init__(self):
# 初始化 Kimi 客户端（计划制定） # 初始化主 LLM 客户端（计划制定 + 总结）
self.kimi_client = OpenAI( self.llm_client = OpenAI(
api_key=KIMI_CONFIG["api_key"], api_key=LLM_CONFIG["api_key"],
base_url=KIMI_CONFIG["base_url"], base_url=LLM_CONFIG["base_url"],
) )
self.kimi_model = KIMI_CONFIG["model"] self.llm_model = LLM_CONFIG["model"]
self.llm_max_tokens = LLM_CONFIG.get("max_tokens", 8192)
# 初始化 DeepMoney 客户端（新闻总结） # 保持 DeepMoney 客户端作为备用
self.deepmoney_client = OpenAI( self.deepmoney_client = OpenAI(
api_key=DEEPMONEY_CONFIG["api_key"], api_key=DEEPMONEY_CONFIG["api_key"],
base_url=DEEPMONEY_CONFIG["base_url"], base_url=DEEPMONEY_CONFIG["base_url"],
@@ -1982,8 +1983,8 @@ class MCPAgentIntegrated:
只返回JSON，不要其他内容。""" 只返回JSON，不要其他内容。"""
async def create_plan(self, user_query: str, tools: List[dict], chat_history: List[dict] = None) -> ExecutionPlan: async def create_plan(self, user_query: str, tools: List[dict], chat_history: List[dict] = None) -> ExecutionPlan:
"""阶段1: 使用 Kimi 创建执行计划（带思考过程和历史上下文）""" """阶段1: 使用 LLM 创建执行计划（带思考过程和历史上下文）"""
logger.info(f"[Planning] Kimi开始制定计划: {user_query}") logger.info(f"[Planning] LLM开始制定计划: {user_query}")
messages = [ messages = [
{"role": "system", "content": self.get_planning_prompt(tools)}, {"role": "system", "content": self.get_planning_prompt(tools)},
@@ -2005,12 +2006,12 @@ class MCPAgentIntegrated:
# 添加当前用户问题 # 添加当前用户问题
messages.append({"role": "user", "content": user_query}) messages.append({"role": "user", "content": user_query})
# 使用配置的模型（默认 deepmoney，支持 65536 上下文） # 使用配置的 LLM 模型
response = self.kimi_client.chat.completions.create( response = self.llm_client.chat.completions.create(
model=self.kimi_model, model=self.llm_model,
messages=messages, messages=messages,
temperature=1.0, temperature=1.0,
max_tokens=65536, max_tokens=self.llm_max_tokens,
) )
choice = response.choices[0] choice = response.choices[0]
@@ -2020,7 +2021,7 @@ class MCPAgentIntegrated:
reasoning_content = "" reasoning_content = ""
if hasattr(message, "reasoning_content"): if hasattr(message, "reasoning_content"):
reasoning_content = getattr(message, "reasoning_content") reasoning_content = getattr(message, "reasoning_content")
logger.info(f"[Planning] Kimi思考过程: {reasoning_content[:200]}...") logger.info(f"[Planning] LLM思考过程: {reasoning_content[:200]}...")
# 提取计划内容 # 提取计划内容
plan_json = message.content.strip() plan_json = message.content.strip()
@@ -2085,7 +2086,7 @@ class MCPAgentIntegrated:
model=self.deepmoney_model, model=self.deepmoney_model,
messages=messages, messages=messages,
temperature=0.7, temperature=0.7,
max_tokens=65536, max_tokens=DEEPMONEY_CONFIG.get("max_tokens", 8192),
) )
summary = response.choices[0].message.content summary = response.choices[0].message.content
@@ -2170,8 +2171,8 @@ class MCPAgentIntegrated:
plan: ExecutionPlan, plan: ExecutionPlan,
step_results: List[StepResult], step_results: List[StepResult],
) -> str: ) -> str:
"""阶段3: 使用 Kimi 生成最终总结""" """阶段3: 使用 LLM 生成最终总结"""
logger.info("[Summary] Kimi生成最终总结") logger.info("[Summary] LLM生成最终总结")
# 收集成功的结果 # 收集成功的结果
successful_results = [r for r in step_results if r.status == "success"] successful_results = [r for r in step_results if r.status == "success"]
@@ -2275,11 +2276,11 @@ class MCPAgentIntegrated:
] ]
try: try:
response = self.kimi_client.chat.completions.create( response = self.llm_client.chat.completions.create(
model=self.kimi_model, # 使用配置的模型（deepmoney） model=self.llm_model,
messages=messages, messages=messages,
temperature=0.7, temperature=0.7,
max_tokens=65536, max_tokens=self.llm_max_tokens,
) )
summary = response.choices[0].message.content summary = response.choices[0].message.content
@@ -2310,13 +2311,13 @@ class MCPAgentIntegrated:
logger.info(f"[Agent] 带有 {len(chat_history)} 条历史消息") logger.info(f"[Agent] 带有 {len(chat_history)} 条历史消息")
try: try:
# 阶段1: Kimi 制定计划（带历史上下文） # 阶段1: LLM 制定计划（带历史上下文）
plan = await self.create_plan(user_query, tools, chat_history) plan = await self.create_plan(user_query, tools, chat_history)
# 阶段2: 执行工具 # 阶段2: 执行工具
step_results = await self.execute_plan(plan, tool_handlers) step_results = await self.execute_plan(plan, tool_handlers)
# 阶段3: Kimi 生成总结 # 阶段3: LLM 生成总结
final_summary = await self.generate_final_summary( final_summary = await self.generate_final_summary(
user_query, plan, step_results user_query, plan, step_results
) )
@@ -2333,8 +2334,8 @@ class MCPAgentIntegrated:
"failed_steps": len([r for r in step_results if r.status == "failed"]), "failed_steps": len([r for r in step_results if r.status == "failed"]),
"total_execution_time": sum(r.execution_time for r in step_results), "total_execution_time": sum(r.execution_time for r in step_results),
"model_used": { "model_used": {
"planning": self.kimi_model, "planning": self.llm_model,
"summarization": self.kimi_model, "summarization": self.llm_model,
"news_summary": self.deepmoney_model, "news_summary": self.deepmoney_model,
}, },
}, },
@@ -2366,7 +2367,7 @@ class MCPAgentIntegrated:
model=self.deepmoney_model, model=self.deepmoney_model,
messages=messages, messages=messages,
temperature=0.3, temperature=0.3,
max_tokens=65536, max_tokens=DEEPMONEY_CONFIG.get("max_tokens", 8192),
) )
title = response.choices[0].message.content.strip() title = response.choices[0].message.content.strip()
@@ -2411,7 +2412,7 @@ class MCPAgentIntegrated:
# 将 cookies 存储为实例属性，供工具调用时使用 # 将 cookies 存储为实例属性，供工具调用时使用
self.cookies = cookies or {} self.cookies = cookies or {}
# 如果传入了自定义模型配置，使用自定义配置，否则使用默认的 Kimi # 如果传入了自定义模型配置，使用自定义配置，否则使用默认 LLM
if model_config: if model_config:
planning_client = OpenAI( planning_client = OpenAI(
api_key=model_config["api_key"], api_key=model_config["api_key"],
@@ -2420,8 +2421,8 @@ class MCPAgentIntegrated:
planning_model = model_config["model"] planning_model = model_config["model"]
logger.info(f"[Agent Stream] 使用自定义模型: {planning_model}") logger.info(f"[Agent Stream] 使用自定义模型: {planning_model}")
else: else:
planning_client = self.kimi_client planning_client = self.llm_client
planning_model = self.kimi_model planning_model = self.llm_model
logger.info(f"[Agent Stream] 使用默认模型: {planning_model}") logger.info(f"[Agent Stream] 使用默认模型: {planning_model}")
try: try:
@@ -2457,8 +2458,8 @@ class MCPAgentIntegrated:
try: try:
# 尝试使用选中的模型流式 API # 尝试使用选中的模型流式 API
# 从模型配置获取 max_tokens，默认 65536（deepmoney） # 从模型配置获取 max_tokens，默认 8192
model_max_tokens = model_config.get("max_tokens", 65536) if model_config else 65536 model_max_tokens = model_config.get("max_tokens", 8192) if model_config else 8192
stream = planning_client.chat.completions.create( stream = planning_client.chat.completions.create(
model=planning_model, model=planning_model,
messages=messages, messages=messages,
@@ -2467,7 +2468,7 @@ class MCPAgentIntegrated:
stream=True, # 启用流式输出 stream=True, # 启用流式输出
) )
# 逐块接收 Kimi 的响应 # 逐块接收 LLM 的响应
for chunk in stream: for chunk in stream:
if chunk.choices[0].delta.content: if chunk.choices[0].delta.content:
content_chunk = chunk.choices[0].delta.content content_chunk = chunk.choices[0].delta.content
@@ -2489,11 +2490,11 @@ class MCPAgentIntegrated:
"content": reasoning_chunk "content": reasoning_chunk
}) })
except Exception as kimi_error: except Exception as llm_error:
# 检查是否是内容风控错误（400） # 检查是否是内容风控错误（400）
error_str = str(kimi_error) error_str = str(llm_error)
if "400" in error_str and ("content_filter" in error_str or "high risk" in error_str): if "400" in error_str and ("content_filter" in error_str or "high risk" in error_str):
logger.warning(f"[Planning] Kimi 内容风控拒绝，切换到 DeepMoney: {error_str}") logger.warning(f"[Planning] LLM 内容风控拒绝，切换到 DeepMoney: {error_str}")
use_fallback = True use_fallback = True
yield self._format_sse("status", { yield self._format_sse("status", {
@@ -2507,7 +2508,7 @@ class MCPAgentIntegrated:
model=self.deepmoney_model, model=self.deepmoney_model,
messages=messages, messages=messages,
temperature=0.7, temperature=0.7,
max_tokens=65536, max_tokens=DEEPMONEY_CONFIG.get("max_tokens", 8192),
) )
plan_content = fallback_response.choices[0].message.content plan_content = fallback_response.choices[0].message.content
@@ -2522,10 +2523,10 @@ class MCPAgentIntegrated:
except Exception as fallback_error: except Exception as fallback_error:
logger.error(f"[Planning] DeepMoney 备选方案也失败: {fallback_error}") logger.error(f"[Planning] DeepMoney 备选方案也失败: {fallback_error}")
raise Exception(f"Kimi 和 DeepMoney 都无法生成计划: {kimi_error}, {fallback_error}") raise Exception(f"LLM 和 DeepMoney 都无法生成计划: {llm_error}, {fallback_error}")
else: else:
# 不是内容风控错误，直接抛出 # 不是内容风控错误，直接抛出
logger.error(f"[Planning] Kimi 调用失败（非风控原因）: {kimi_error}") logger.error(f"[Planning] LLM 调用失败（非风控原因）: {llm_error}")
raise raise
# 解析完整的计划 # 解析完整的计划
@@ -2627,7 +2628,7 @@ class MCPAgentIntegrated:
"execution_time": execution_time, "execution_time": execution_time,
}) })
# 阶段3: Kimi 生成总结（流式） # 阶段3: LLM 生成总结（流式）
yield self._format_sse("status", {"stage": "summarizing", "message": "正在生成最终总结..."}) yield self._format_sse("status", {"stage": "summarizing", "message": "正在生成最终总结..."})
# 收集成功的结果 # 收集成功的结果
@@ -2699,11 +2700,11 @@ class MCPAgentIntegrated:
final_summary = "" final_summary = ""
try: try:
summary_stream = self.kimi_client.chat.completions.create( summary_stream = self.llm_client.chat.completions.create(
model=self.kimi_model, # 使用配置的模型（deepmoney） model=self.llm_model,
messages=messages, messages=messages,
temperature=0.7, temperature=0.7,
max_tokens=65536, max_tokens=self.llm_max_tokens,
stream=True, # 启用流式输出 stream=True, # 启用流式输出
) )
@@ -2720,11 +2721,11 @@ class MCPAgentIntegrated:
logger.info("[Summary] 流式总结完成") logger.info("[Summary] 流式总结完成")
except Exception as kimi_error: except Exception as llm_error:
# 检查是否是内容风控错误（400） # 检查是否是内容风控错误（400）
error_str = str(kimi_error) error_str = str(llm_error)
if "400" in error_str and ("content_filter" in error_str or "high risk" in error_str): if "400" in error_str and ("content_filter" in error_str or "high risk" in error_str):
logger.warning(f"[Summary] Kimi 内容风控拒绝，切换到 DeepMoney: {error_str}") logger.warning(f"[Summary] LLM 内容风控拒绝，切换到 DeepMoney: {error_str}")
yield self._format_sse("status", { yield self._format_sse("status", {
"stage": "summarizing", "stage": "summarizing",
@@ -2737,7 +2738,7 @@ class MCPAgentIntegrated:
model=self.deepmoney_model, model=self.deepmoney_model,
messages=messages, messages=messages,
temperature=0.7, temperature=0.7,
max_tokens=65536, max_tokens=DEEPMONEY_CONFIG.get("max_tokens", 8192),
) )
final_summary = fallback_response.choices[0].message.content final_summary = fallback_response.choices[0].message.content
@@ -2759,7 +2760,7 @@ class MCPAgentIntegrated:
logger.warning("[Summary] 使用降级方案（简单拼接）") logger.warning("[Summary] 使用降级方案（简单拼接）")
else: else:
# 不是内容风控错误，直接抛出 # 不是内容风控错误，直接抛出
logger.error(f"[Summary] Kimi 调用失败（非风控原因）: {kimi_error}") logger.error(f"[Summary] LLM 调用失败（非风控原因）: {llm_error}")
raise raise
# 发送完整的总结和元数据 # 发送完整的总结和元数据
@@ -3681,8 +3682,8 @@ async def stream_role_response(
# 第一次调用：可能触发工具调用 # 第一次调用：可能触发工具调用
tool_calls_made = [] tool_calls_made = []
# 从模型配置获取 max_tokens，默认 65536（deepmoney） # 从模型配置获取 max_tokens，默认 8192
max_tokens = model_config.get("max_tokens", 65536) max_tokens = model_config.get("max_tokens", 8192)
if openai_tools: if openai_tools:
response = client.chat.completions.create( response = client.chat.completions.create(
model=model_config["model"], model=model_config["model"],