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feat: 添加物流查询功能和完善 token 传递

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- 添加 get_logistics 工具查询 Mall API /mall/api/order/parcel
- 修复 Cookie token 传递到 MCP 的问题
- 增强 LLM 客户端超时处理和日志
- 移除 MALL_API_TOKEN，使用用户登录 token
- 更新测试页面使用 setUser 设置用户属性
- 增强 webhook 调试日志
This commit is contained in:
wangliang
2026-01-16 18:36:17 +08:00
parent cd787d608b
commit c4e97cf312
9 changed files with 334 additions and 111 deletions
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43
agent/agents/order.py
View File

@@ -25,7 +25,11 @@ ORDER_AGENT_PROMPT = """你是一个专业的 B2B 订单服务助手。
- order_id: 订单号（必需）
- 说明：此工具会自动使用用户的身份 token 查询商城订单详情
2. **query_order** - 查询历史订单
2. **get_logistics** - 从商城 API 查询物流信息
- order_id: 订单号（必需）
- 说明：查询订单的物流轨迹和配送状态
3. **query_order** - 查询历史订单
- user_id: 用户 ID（自动注入）
- account_id: 账户 ID（自动注入）
- order_id: 订单号（可选，不填则查询最近订单）
@@ -33,10 +37,6 @@ ORDER_AGENT_PROMPT = """你是一个专业的 B2B 订单服务助手。
- date_end: 结束日期（可选）
- status: 订单状态（可选）
3. **track_logistics** - 物流跟踪
- order_id: 订单号
- tracking_number: 物流单号（可选）
4. **modify_order** - 修改订单
- order_id: 订单号
- user_id: 用户 ID（自动注入）
@@ -108,12 +108,25 @@ ORDER_AGENT_PROMPT = """你是一个专业的 B2B 订单服务助手。
}
```
用户: "帮我查一下订单 202071324 的物流"
回复:
```json
{
"action": "call_tool",
"tool_name": "get_logistics",
"arguments": {
"order_id": "202071324"
}
}
```
## 重要约束
- **必须返回完整的 JSON 对象**，不要只返回部分内容
- **不要添加任何 markdown 代码块标记**（如 \`\`\`json）
- **不要添加任何解释性文字**，只返回 JSON
- user_id 和 account_id 会自动注入到 arguments 中，无需手动添加
- 如果用户提供了订单号，优先使用 get_mall_order 工具
- 如果用户想查询物流状态，使用 get_logistics 工具
- 对于敏感操作（取消、修改），确保有明确的订单号
"""
@@ -204,7 +217,15 @@ async def order_agent(state: AgentState) -> AgentState:
# Inject user_token if available
if state.get("user_token"):
arguments["user_token"] = state["user_token"]
logger.info("Injected user_token into tool call")
logger.info(
"Injected user_token into tool call",
token_prefix=state["user_token"][:20] if state["user_token"] else None
)
else:
logger.warning(
"No user_token available in state, MCP will use default token",
conversation_id=state["conversation_id"]
)
# Use entity if available
if "order_id" not in arguments and state["entities"].get("order_id"):
@@ -290,7 +311,15 @@ async def order_agent(state: AgentState) -> AgentState:
# Inject user_token if available (for Mall API calls)
if state.get("user_token"):
arguments["user_token"] = state["user_token"]
logger.debug("Injected user_token into tool call")
logger.debug(
"Injected user_token into tool call",
token_prefix=state["user_token"][:20] if state["user_token"] else None
)
else:
logger.warning(
"No user_token available in state, MCP will use default token",
conversation_id=state["conversation_id"]
)
# Use entity if available
if "order_id" not in arguments and state["entities"].get("order_id"):

15
agent/agents/router.py
View File

@@ -81,12 +81,27 @@ async def classify_intent(state: AgentState) -> AgentState:
# Parse JSON response
content = response.content.strip()
# Log raw response for debugging
logger.debug(
"LLM response for intent classification",
response_preview=content[:500] if content else "EMPTY",
content_length=len(content) if content else 0
)
# Handle markdown code blocks
if content.startswith("```"):
content = content.split("```")[1]
if content.startswith("json"):
content = content[4:]
# Check for empty response
if not content:
logger.warning("LLM returned empty response for intent classification")
state["intent"] = Intent.CUSTOMER_SERVICE.value # Default to customer service
state["intent_confidence"] = 0.5
return state
result = json.loads(content)
# Extract intent

134
agent/core/llm.py
View File

@@ -1,7 +1,8 @@
"""
ZhipuAI LLM Client for B2B Shopping AI Assistant
"""
from typing import Any, AsyncGenerator, Optional
import concurrent.futures
from typing import Any, Optional
from dataclasses import dataclass
from zhipuai import ZhipuAI
@@ -29,23 +30,21 @@ class LLMResponse:
class ZhipuLLMClient:
"""ZhipuAI LLM Client wrapper"""
DEFAULT_TIMEOUT = 30 # seconds
def __init__(
self,
api_key: Optional[str] = None,
model: Optional[str] = None
model: Optional[str] = None,
timeout: Optional[int] = None
):
"""Initialize ZhipuAI client
Args:
api_key: ZhipuAI API key, defaults to settings
model: Model name, defaults to settings
"""
self.api_key = api_key or settings.zhipu_api_key
self.model = model or settings.zhipu_model
self.timeout = timeout or self.DEFAULT_TIMEOUT
self._client = ZhipuAI(api_key=self.api_key)
logger.info("ZhipuAI client initialized", model=self.model)
logger.info("ZhipuAI client initialized", model=self.model, timeout=self.timeout)
async def chat(
self,
messages: list[Message],
@@ -54,32 +53,21 @@ class ZhipuLLMClient:
top_p: float = 0.9,
**kwargs: Any
) -> LLMResponse:
"""Send chat completion request
Args:
messages: List of chat messages
temperature: Sampling temperature
max_tokens: Maximum tokens to generate
top_p: Top-p sampling parameter
**kwargs: Additional parameters
Returns:
LLM response with content and metadata
"""
"""Send chat completion request"""
formatted_messages = [
{"role": msg.role, "content": msg.content}
for msg in messages
]
logger.debug(
logger.info(
"Sending chat request",
model=self.model,
message_count=len(messages),
temperature=temperature
)
try:
response = self._client.chat.completions.create(
def _make_request():
return self._client.chat.completions.create(
model=self.model,
messages=formatted_messages,
temperature=temperature,
@@ -87,10 +75,27 @@ class ZhipuLLMClient:
top_p=top_p,
**kwargs
)
try:
with concurrent.futures.ThreadPoolExecutor(max_workers=1) as executor:
future = executor.submit(_make_request)
response = future.result(timeout=self.timeout)
choice = response.choices[0]
result = LLMResponse(
content=choice.message.content,
content = choice.message.content
logger.info(
"Chat response received",
finish_reason=choice.finish_reason,
content_length=len(content) if content else 0,
usage=response.usage.__dict__ if hasattr(response, 'usage') else {}
)
if not content:
logger.warning("LLM returned empty content")
return LLMResponse(
content=content or "",
finish_reason=choice.finish_reason,
usage={
"prompt_tokens": response.usage.prompt_tokens,
@@ -98,48 +103,34 @@ class ZhipuLLMClient:
"total_tokens": response.usage.total_tokens
}
)
logger.debug(
"Chat response received",
finish_reason=result.finish_reason,
total_tokens=result.usage["total_tokens"]
)
return result
except concurrent.futures.TimeoutError:
logger.error("Chat request timed out", timeout=self.timeout)
raise TimeoutError(f"Request timed out after {self.timeout} seconds")
except Exception as e:
logger.error("Chat request failed", error=str(e))
raise
async def chat_with_tools(
self,
messages: list[Message],
tools: list[dict[str, Any]],
temperature: float = 0.7,
**kwargs: Any
) -> tuple[LLMResponse, Optional[list[dict[str, Any]]]]:
"""Send chat completion request with tool calling
Args:
messages: List of chat messages
tools: List of tool definitions
temperature: Sampling temperature
**kwargs: Additional parameters
Returns:
Tuple of (LLM response, tool calls if any)
"""
) -> tuple[LLMResponse, None]:
"""Send chat completion request with tool calling"""
formatted_messages = [
{"role": msg.role, "content": msg.content}
for msg in messages
]
logger.debug(
logger.info(
"Sending chat request with tools",
model=self.model,
tool_count=len(tools)
)
try:
response = self._client.chat.completions.create(
model=self.model,
@@ -148,42 +139,25 @@ class ZhipuLLMClient:
temperature=temperature,
**kwargs
)
choice = response.choices[0]
result = LLMResponse(
content=choice.message.content or "",
content = choice.message.content or ""
return LLMResponse(
content=content,
finish_reason=choice.finish_reason,
usage={
"prompt_tokens": response.usage.prompt_tokens,
"completion_tokens": response.usage.completion_tokens,
"total_tokens": response.usage.total_tokens
}
)
# Extract tool calls if present
tool_calls = None
if hasattr(choice.message, 'tool_calls') and choice.message.tool_calls:
tool_calls = [
{
"id": tc.id,
"type": tc.type,
"function": {
"name": tc.function.name,
"arguments": tc.function.arguments
}
}
for tc in choice.message.tool_calls
]
logger.debug("Tool calls received", tool_count=len(tool_calls))
return result, tool_calls
), None
except Exception as e:
logger.error("Chat with tools request failed", error=str(e))
raise
# Global LLM client instance
llm_client: Optional[ZhipuLLMClient] = None

76
agent/webhooks/chatwoot_webhook.py
View File

@@ -136,6 +136,26 @@ async def handle_incoming_message(payload: ChatwootWebhookPayload, cookie_token:
contact = payload.contact or payload.sender
user_id = str(contact.id) if contact else "unknown"
# Log webhook payload structure for debugging
logger.info(
"Webhook payload structure",
payload_event=payload.event,
has_conversation=bool(conversation),
has_contact=bool(contact),
contact_type=type(contact).__name__ if contact else None,
conversation_id=conversation_id
)
# Log full payload keys for debugging
payload_dict = payload.model_dump()
logger.info(
"Full webhook payload keys",
keys=list(payload_dict.keys()),
conversation_keys=list(payload_dict.get('conversation', {}).keys()) if payload_dict.get('conversation') else [],
contact_keys=list(payload_dict.get('contact', {}).keys()) if payload_dict.get('contact') else [],
sender_keys=list(payload_dict.get('sender', {}).keys()) if payload_dict.get('sender') else []
)
# Get account_id from payload (top-level account object)
# Chatwoot webhook includes account info at the top level
account_obj = payload.account
@@ -148,13 +168,35 @@ async def handle_incoming_message(payload: ChatwootWebhookPayload, cookie_token:
if not user_token:
# 1. 尝试从 contact/custom_attributes 获取
if contact:
contact_dict = contact.model_dump() if hasattr(contact, 'model_dump') else contact.__dict__
user_token = TokenManager.extract_token_from_contact(contact_dict)
logger.debug("Extracted token from contact", has_token=bool(user_token))
logger.info(
"Checking contact for token",
contact_id=contact.id if contact else None,
contact_type=type(contact).__name__
)
# 只有 WebhookContact 才有 custom_attributes
if hasattr(contact, 'custom_attributes'):
logger.info(
"Checking contact custom_attributes",
has_custom_attributes=bool(contact.custom_attributes)
)
custom_attrs = contact.custom_attributes or {}
logger.info(
"Contact custom_attributes",
keys=list(custom_attrs.keys()) if custom_attrs else [],
has_jwt_token='jwt_token' in custom_attrs if custom_attrs else False,
has_mall_token='mall_token' in custom_attrs if custom_attrs else False
)
contact_dict = {"custom_attributes": custom_attrs}
user_token = TokenManager.extract_token_from_contact(contact_dict)
logger.debug("Extracted token from contact", has_token=bool(user_token))
else:
logger.debug("Contact type is WebhookSender, no custom_attributes available")
# 2. 尝试从 conversation.meta.sender.custom_attributes 获取（Chatwoot SDK setUser 设置的位置）
if not user_token and conversation:
# 记录 conversation 的类型和内容用于调试
logger.debug("Conversation object type", type=str(type(conversation)))
if hasattr(conversation, 'model_dump'):
conv_dict = conversation.model_dump()
@@ -162,12 +204,32 @@ async def handle_incoming_message(payload: ChatwootWebhookPayload, cookie_token:
logger.debug("Has meta", has_meta='meta' in conv_dict)
meta_sender = conv_dict.get('meta', {}).get('sender', {})
logger.info(
"Conversation meta.sender",
has_sender=bool(meta_sender),
sender_keys=list(meta_sender.keys()) if meta_sender else [],
has_custom_attributes=bool(meta_sender.get('custom_attributes')) if meta_sender else False
)
if meta_sender.get('custom_attributes'):
logger.info("Found custom_attributes in meta.sender", keys=list(meta_sender['custom_attributes'].keys()))
user_token = TokenManager.extract_token_from_contact({'custom_attributes': meta_sender['custom_attributes']})
logger.info("Token found in conversation.meta.sender.custom_attributes", token_prefix=user_token[:20] if user_token else None)
if user_token:
logger.info("JWT token found", user_id=user_id, source="cookie" if cookie_token else "contact")
logger.info(
"JWT token found",
user_id=user_id,
source="cookie" if cookie_token else "contact",
token_prefix=user_token[:20] if user_token else None
)
else:
logger.warning(
"No JWT token found from any source",
user_id=user_id,
cookie_token_exists=bool(cookie_token),
contact_type=type(contact).__name__ if contact else None
)
logger.info(
"Processing incoming message",
@@ -352,7 +414,9 @@ async def chatwoot_webhook(
# 尝试从请求 Cookie 中获取用户 Token
user_token = request.cookies.get("token") # 从 Cookie 读取 token
if user_token:
logger.info("User token found in request cookies")
logger.info("User token found in request cookies", token_prefix=user_token[:20])
else:
logger.debug("No token found in request cookies (this is expected for webhook requests)")
# Verify signature
signature = request.headers.get("X-Chatwoot-Signature", "")