amis-rpc-design/libs/amis/scripts/bot/llm/wenxin.py
2023-10-07 19:42:30 +08:00

72 lines
1.9 KiB
Python

from .base import LLM
import requests
from functools import lru_cache
import time
import os
import json
from enum import Enum
from requests.exceptions import HTTPError
base_url = 'https://aip.baidubce.com'
class ModelName(Enum):
ERNIE_BOT = 1
ERNIE_BOT_TURBO = 2
BLOOMZ_7B = 3
def get_ttl_hash(seconds=3600):
"""缓存一小时"""
return round(time.time() / seconds)
@lru_cache(maxsize=1)
def get_token(ttl_hash=None):
"""
根据 ak/sk 获取 access_token
"""
del ttl_hash
ak = os.getenv('WENXIN_AK')
sk = os.getenv('WENXIN_SK')
url = f'{base_url}/oauth/2.0/token?grant_type=client_credentials&client_id={ak}&client_secret={sk}'
response = requests.get(url)
response.raise_for_status()
return response.json()['access_token']
def query(query: str, token: str, model_name: ModelName = ModelName.ERNIE_BOT_TURBO):
"""
参考文档
https://cloud.baidu.com/doc/WENXINWORKSHOP/s/jlil56u11
"""
# 目前暂时写死
user_id = os.getenv('WENXIN_USER_ID')
model_path = "eb-instant"
if model_name == ModelName.BLOOMZ_7B:
model_path = "bloomz_7b1"
elif model_name == ModelName.ERNIE_BOT:
model_path = "completions"
url = f'{base_url}/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/{model_path}?access_token={token}&user_id={user_id}'
messages = [{'role': 'user', 'content': query}]
payload = {
'messages': messages
}
headers = {
'content-type': 'application/json',
}
response = requests.post(url, data=json.dumps(payload), headers=headers)
response.raise_for_status()
return response.json()["result"]
class Wenxin(LLM):
"""文心千帆大模型"""
def generate(self, prompt: str, model_name: ModelName = ModelName.ERNIE_BOT_TURBO) -> str:
try:
return query(prompt, get_token(ttl_hash=get_ttl_hash()), model_name)
except HTTPError as e:
return f'HTTPError: {e}'