feat: 初始化 B2B AI Shopping Assistant 项目
- 配置 Docker Compose 多服务编排 - 实现 Chatwoot + Agent 集成 - 配置 Strapi MCP 知识库 - 支持 7 种语言的 FAQ 系统 - 实现 LangGraph AI 工作流 Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
This commit is contained in:
195
agent/core/llm.py
Normal file
195
agent/core/llm.py
Normal file
@@ -0,0 +1,195 @@
|
||||
"""
|
||||
ZhipuAI LLM Client for B2B Shopping AI Assistant
|
||||
"""
|
||||
from typing import Any, AsyncGenerator, Optional
|
||||
from dataclasses import dataclass
|
||||
|
||||
from zhipuai import ZhipuAI
|
||||
|
||||
from config import settings
|
||||
from utils.logger import get_logger
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
|
||||
@dataclass
|
||||
class Message:
|
||||
"""Chat message structure"""
|
||||
role: str # "system", "user", "assistant"
|
||||
content: str
|
||||
|
||||
|
||||
@dataclass
|
||||
class LLMResponse:
|
||||
"""LLM response structure"""
|
||||
content: str
|
||||
finish_reason: str
|
||||
usage: dict[str, int]
|
||||
|
||||
|
||||
class ZhipuLLMClient:
|
||||
"""ZhipuAI LLM Client wrapper"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
api_key: Optional[str] = None,
|
||||
model: Optional[str] = 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._client = ZhipuAI(api_key=self.api_key)
|
||||
logger.info("ZhipuAI client initialized", model=self.model)
|
||||
|
||||
async def chat(
|
||||
self,
|
||||
messages: list[Message],
|
||||
temperature: float = 0.7,
|
||||
max_tokens: int = 2048,
|
||||
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
|
||||
"""
|
||||
formatted_messages = [
|
||||
{"role": msg.role, "content": msg.content}
|
||||
for msg in messages
|
||||
]
|
||||
|
||||
logger.debug(
|
||||
"Sending chat request",
|
||||
model=self.model,
|
||||
message_count=len(messages),
|
||||
temperature=temperature
|
||||
)
|
||||
|
||||
try:
|
||||
response = self._client.chat.completions.create(
|
||||
model=self.model,
|
||||
messages=formatted_messages,
|
||||
temperature=temperature,
|
||||
max_tokens=max_tokens,
|
||||
top_p=top_p,
|
||||
**kwargs
|
||||
)
|
||||
|
||||
choice = response.choices[0]
|
||||
result = LLMResponse(
|
||||
content=choice.message.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
|
||||
}
|
||||
)
|
||||
|
||||
logger.debug(
|
||||
"Chat response received",
|
||||
finish_reason=result.finish_reason,
|
||||
total_tokens=result.usage["total_tokens"]
|
||||
)
|
||||
|
||||
return result
|
||||
|
||||
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)
|
||||
"""
|
||||
formatted_messages = [
|
||||
{"role": msg.role, "content": msg.content}
|
||||
for msg in messages
|
||||
]
|
||||
|
||||
logger.debug(
|
||||
"Sending chat request with tools",
|
||||
model=self.model,
|
||||
tool_count=len(tools)
|
||||
)
|
||||
|
||||
try:
|
||||
response = self._client.chat.completions.create(
|
||||
model=self.model,
|
||||
messages=formatted_messages,
|
||||
tools=tools,
|
||||
temperature=temperature,
|
||||
**kwargs
|
||||
)
|
||||
|
||||
choice = response.choices[0]
|
||||
result = LLMResponse(
|
||||
content=choice.message.content or "",
|
||||
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
|
||||
|
||||
except Exception as e:
|
||||
logger.error("Chat with tools request failed", error=str(e))
|
||||
raise
|
||||
|
||||
|
||||
# Global LLM client instance
|
||||
llm_client: Optional[ZhipuLLMClient] = None
|
||||
|
||||
|
||||
def get_llm_client() -> ZhipuLLMClient:
|
||||
"""Get or create global LLM client instance"""
|
||||
global llm_client
|
||||
if llm_client is None:
|
||||
llm_client = ZhipuLLMClient()
|
||||
return llm_client
|
||||
Reference in New Issue
Block a user