llm_hub/app/services/zhipu_kb_service.py

86 lines
3.8 KiB
Python

import logging
import time
from zhipuai import ZhipuAI
from app.utils.prompt_repository import PromptRepository
logger = logging.getLogger(__name__)
class ZhipuKbService:
def __init__(self):
self.model_name = "glm-4-plus"
self.app_secret_key = "d54f764a1d67c17d857bd3983b772016.GRjowY0fyiMNurLc"
logger.info("ZhipuKbService initialized with model: %s", self.model_name)
def retrive(self, message, knowledge_id, prompt_template):
logger.info("Starting retrive call with knowledge_id: %s", knowledge_id)
start_time = time.time()
client = ZhipuAI(api_key=self.app_secret_key)
default_prompt = "从文档\n\"\"\"\n{{knowledge}}\n\"\"\"\n中找问题\n\"\"\"\n{{question}}\n\"\"\"\n的答案,找到答案就仅使用文档语句回答问题,找不到答案就用自身知识回答并且告诉用户该信息不是来自文档。\n不要复述问题,直接开始回答。你是一个智能助理,请用该角色的语言风格和对话方式回答问题。"
if prompt_template is None or prompt_template == "":
prompt_template = default_prompt
try:
response = client.chat.completions.create(
model=self.model_name,
messages=[
{"role": "user", "content": message},
],
tools=[
{
"type": "retrieval",
"retrieval": {
"knowledge_id": knowledge_id,
"prompt_template": prompt_template
}
}
],
stream=False,
max_tokens=4095,
temperature=0.01, # default=0.01
top_p=0.1, #default=0.1
)
result = response.choices[0].message.content
end_time = time.time()
logger.info("retrive call completed in %.2f seconds", end_time - start_time)
return result
except Exception as e:
logger.error("Error in retrive: %s", str(e))
raise
def retrive_sse(self, message, knowledge_id, prompt_template=None,system_prompt=None):
logger.info("Starting retrive_sse call with knowledge_id: %s, message:%s", knowledge_id, message)
start_time = time.time()
client = ZhipuAI(api_key=self.app_secret_key)
default_prompt = "从文档\n\"\"\"\n{{knowledge}}\n\"\"\"\n中找问题\n\"\"\"\n{{question}}\n\"\"\"\n的答案,找到答案就仅使用文档语句回答问题,找不到答案就告诉用户知识库中没有该信息。\n不要复述问题,直接开始回答。"
messages = [{"role": "user", "content": message}]
# if system_prompt != None:
# messages.append({"role": "system", "content": system_prompt})
if prompt_template is None or prompt_template == "":
prompt_template = default_prompt
try:
response = client.chat.completions.create(
model=self.model_name,
messages=messages,
tools=[
{
"type": "retrieval",
"retrieval": {
"knowledge_id": knowledge_id,
"prompt_template": prompt_template
}
}
],
stream=True,
max_tokens=4095,
temperature=0.01, # default=0.01
top_p=0.1, #default=0.1
)
for chunk in response:
yield chunk.choices[0].delta.content
end_time = time.time()
logger.info("retrive_sse call completed in %.2f seconds", end_time - start_time)
except Exception as e:
logger.error("Error in retrive_sse: %s", str(e))
raise