88 lines
3.2 KiB
Python
88 lines
3.2 KiB
Python
import logging
|
|
import time
|
|
from zhipuai import ZhipuAI
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
class ZhipuAlltoolService:
|
|
def __init__(self):
|
|
self.model_name = "glm-4-alltools"
|
|
self.app_secret_key = "d54f764a1d67c17d857bd3983b772016.GRjowY0fyiMNurLc"
|
|
logger.info("ZhipuAlltoolService initialized with model: %s", self.model_name)
|
|
|
|
def func_call_classify(self, message):
|
|
logger.info("Starting web_search call")
|
|
start_time = time.time()
|
|
client = ZhipuAI(api_key=self.app_secret_key)
|
|
tools = [
|
|
{
|
|
"type": "function",
|
|
"function": {
|
|
"name": "classify_user_input",
|
|
"description": "根据用户输入，判断用户意图，返回意图类型",
|
|
"parameters": {
|
|
"type": "object",
|
|
"properties": {
|
|
"category": {
|
|
"type": "string",
|
|
"description": "用户的意图有以下选项:web_search,retrive_knowledge,generate_report",
|
|
}
|
|
},
|
|
"required": ["category"],
|
|
},
|
|
}
|
|
}
|
|
]
|
|
messages = [
|
|
{
|
|
"role": "user",
|
|
"content": f"判断以下用户输入的意图并将其分类返回意图类型：{message}"
|
|
}
|
|
]
|
|
try:
|
|
response = client.chat.completions.create(
|
|
model="glm-4-flash", # 填写需要调用的模型名称
|
|
messages= messages,
|
|
tools= tools,
|
|
tool_choice="auto"
|
|
)
|
|
print(response)
|
|
return response.choices[0].message.tool_calls[0].function.arguments
|
|
except Exception as e:
|
|
logger.error("Error in web_search call: %s", str(e))
|
|
raise e
|
|
|
|
def web_search_sse(self, message):
|
|
logger.info("Starting web_search_sse call")
|
|
start_time = time.time()
|
|
client = ZhipuAI(api_key=self.app_secret_key)
|
|
try:
|
|
response = client.chat.completions.create(
|
|
model="glm-4-alltools", # 填写需要调用的模型名称
|
|
messages=[
|
|
{
|
|
"role": "user",
|
|
"content":[
|
|
{
|
|
"type":"text",
|
|
"text":message
|
|
}
|
|
]
|
|
}
|
|
],
|
|
stream=True,
|
|
tools=[
|
|
{
|
|
"type": "web_browser"
|
|
}
|
|
]
|
|
)
|
|
for chunk in response:
|
|
# print(chunk)
|
|
print("content: ",chunk.choices[0].delta.content)
|
|
print("tool_calls: ",chunk.choices[0].delta.tool_calls)
|
|
logger.info("content: %s", str(chunk))
|
|
yield chunk.choices[0].delta.content
|
|
except Exception as e:
|
|
logger.error("Error in web_search_sse call: %s", str(e))
|
|
raise e |