827 lines
26 KiB
Python
827 lines
26 KiB
Python
# 该文件包含webui通用工具，可以被不同的webui使用
|
||
from typing import *
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from pathlib import Path
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||
from configs.model_config import (
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EMBEDDING_MODEL,
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DEFAULT_VS_TYPE,
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||
KB_ROOT_PATH,
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||
LLM_MODEL,
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||
llm_model_dict,
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||
HISTORY_LEN,
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||
SCORE_THRESHOLD,
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||
VECTOR_SEARCH_TOP_K,
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||
SEARCH_ENGINE_TOP_K,
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||
logger,
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||
)
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from configs.server_config import HTTPX_DEFAULT_TIMEOUT
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import httpx
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||
import asyncio
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from server.chat.openai_chat import OpenAiChatMsgIn
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from fastapi.responses import StreamingResponse
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import contextlib
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import json
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from io import BytesIO
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from server.utils import run_async, iter_over_async, set_httpx_timeout, api_address
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||
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from configs.model_config import NLTK_DATA_PATH
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import nltk
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nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path
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from pprint import pprint
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KB_ROOT_PATH = Path(KB_ROOT_PATH)
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set_httpx_timeout()
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class ApiRequest:
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'''
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api.py调用的封装,主要实现:
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1. 简化api调用方式
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2. 实现无api调用(直接运行server.chat.*中的视图函数获取结果),无需启动api.py
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'''
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def __init__(
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self,
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base_url: str = api_address(),
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timeout: float = HTTPX_DEFAULT_TIMEOUT,
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no_remote_api: bool = False, # call api view function directly
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||
):
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self.base_url = base_url
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self.timeout = timeout
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||
self.no_remote_api = no_remote_api
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||
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||
def _parse_url(self, url: str) -> str:
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||
if (not url.startswith("http")
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||
and self.base_url
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||
):
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part1 = self.base_url.strip(" /")
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part2 = url.strip(" /")
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return f"{part1}/{part2}"
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else:
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return url
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def get(
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self,
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url: str,
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params: Union[Dict, List[Tuple], bytes] = None,
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retry: int = 3,
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||
stream: bool = False,
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||
**kwargs: Any,
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||
) -> Union[httpx.Response, None]:
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||
url = self._parse_url(url)
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kwargs.setdefault("timeout", self.timeout)
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while retry > 0:
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||
try:
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if stream:
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return httpx.stream("GET", url, params=params, **kwargs)
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||
else:
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return httpx.get(url, params=params, **kwargs)
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except Exception as e:
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logger.error(f"error when get {url}: {e}")
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retry -= 1
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async def aget(
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self,
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url: str,
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params: Union[Dict, List[Tuple], bytes] = None,
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retry: int = 3,
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stream: bool = False,
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**kwargs: Any,
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) -> Union[httpx.Response, None]:
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url = self._parse_url(url)
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kwargs.setdefault("timeout", self.timeout)
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async with httpx.AsyncClient() as client:
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while retry > 0:
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try:
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if stream:
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return await client.stream("GET", url, params=params, **kwargs)
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else:
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return await client.get(url, params=params, **kwargs)
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except Exception as e:
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logger.error(f"error when aget {url}: {e}")
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retry -= 1
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def post(
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self,
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||
url: str,
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data: Dict = None,
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||
json: Dict = None,
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||
retry: int = 3,
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||
stream: bool = False,
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||
**kwargs: Any
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||
) -> Union[httpx.Response, None]:
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||
url = self._parse_url(url)
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kwargs.setdefault("timeout", self.timeout)
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while retry > 0:
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try:
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# return requests.post(url, data=data, json=json, stream=stream, **kwargs)
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if stream:
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return httpx.stream("POST", url, data=data, json=json, **kwargs)
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else:
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return httpx.post(url, data=data, json=json, **kwargs)
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except Exception as e:
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logger.error(f"error when post {url}: {e}")
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retry -= 1
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async def apost(
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self,
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url: str,
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data: Dict = None,
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||
json: Dict = None,
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retry: int = 3,
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||
stream: bool = False,
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**kwargs: Any
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) -> Union[httpx.Response, None]:
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url = self._parse_url(url)
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kwargs.setdefault("timeout", self.timeout)
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async with httpx.AsyncClient() as client:
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while retry > 0:
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try:
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||
if stream:
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||
return await client.stream("POST", url, data=data, json=json, **kwargs)
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else:
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return await client.post(url, data=data, json=json, **kwargs)
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except Exception as e:
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logger.error(f"error when apost {url}: {e}")
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retry -= 1
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def delete(
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self,
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url: str,
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data: Dict = None,
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||
json: Dict = None,
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retry: int = 3,
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||
stream: bool = False,
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||
**kwargs: Any
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||
) -> Union[httpx.Response, None]:
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url = self._parse_url(url)
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kwargs.setdefault("timeout", self.timeout)
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while retry > 0:
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||
try:
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if stream:
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||
return httpx.stream("DELETE", url, data=data, json=json, **kwargs)
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||
else:
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||
return httpx.delete(url, data=data, json=json, **kwargs)
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except Exception as e:
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logger.error(f"error when delete {url}: {e}")
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retry -= 1
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async def adelete(
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self,
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||
url: str,
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||
data: Dict = None,
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||
json: Dict = None,
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||
retry: int = 3,
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||
stream: bool = False,
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||
**kwargs: Any
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||
) -> Union[httpx.Response, None]:
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||
url = self._parse_url(url)
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kwargs.setdefault("timeout", self.timeout)
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||
async with httpx.AsyncClient() as client:
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||
while retry > 0:
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try:
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||
if stream:
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||
return await client.stream("DELETE", url, data=data, json=json, **kwargs)
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||
else:
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||
return await client.delete(url, data=data, json=json, **kwargs)
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||
except Exception as e:
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||
logger.error(f"error when adelete {url}: {e}")
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retry -= 1
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def _fastapi_stream2generator(self, response: StreamingResponse, as_json: bool =False):
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'''
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将api.py中视图函数返回的StreamingResponse转化为同步生成器
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'''
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try:
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loop = asyncio.get_event_loop()
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||
except:
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loop = asyncio.new_event_loop()
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||
try:
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for chunk in iter_over_async(response.body_iterator, loop):
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if as_json and chunk:
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yield json.loads(chunk)
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elif chunk.strip():
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yield chunk
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||
except Exception as e:
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logger.error(f"error when run fastapi router: {e}")
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||
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||
def _httpx_stream2generator(
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||
self,
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||
response: contextlib._GeneratorContextManager,
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||
as_json: bool = False,
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||
):
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||
'''
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||
将httpx.stream返回的GeneratorContextManager转化为普通生成器
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||
'''
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try:
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with response as r:
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for chunk in r.iter_text(None):
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||
if not chunk: # fastchat api yield empty bytes on start and end
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continue
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if as_json:
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||
try:
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data = json.loads(chunk)
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pprint(data, depth=1)
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yield data
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except Exception as e:
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logger.error(f"接口返回json错误： ‘{chunk}’。错误信息是：{e}。")
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else:
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print(chunk, end="", flush=True)
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yield chunk
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except httpx.ConnectError as e:
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||
msg = f"无法连接API服务器，请确认 ‘api.py’ 已正常启动。({e})"
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||
logger.error(msg)
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logger.error(msg)
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yield {"code": 500, "msg": msg}
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except httpx.ReadTimeout as e:
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msg = f"API通信超时，请确认已启动FastChat与API服务（详见RADME '5. 启动 API 服务或 Web UI'）。（{e}）"
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logger.error(msg)
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yield {"code": 500, "msg": msg}
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||
except Exception as e:
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||
msg = f"API通信遇到错误：{e}"
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||
logger.error(msg)
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||
yield {"code": 500, "msg": msg}
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||
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# 对话相关操作
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||
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||
def chat_fastchat(
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||
self,
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||
messages: List[Dict],
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||
stream: bool = True,
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||
model: str = LLM_MODEL,
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||
temperature: float = 0.7,
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||
max_tokens: int = 1024, # todo:根据message内容自动计算max_tokens
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||
no_remote_api: bool = None,
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||
**kwargs: Any,
|
||
):
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||
'''
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||
对应api.py/chat/fastchat接口
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||
'''
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||
if no_remote_api is None:
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||
no_remote_api = self.no_remote_api
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||
msg = OpenAiChatMsgIn(**{
|
||
"messages": messages,
|
||
"stream": stream,
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||
"model": model,
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||
"temperature": temperature,
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||
"max_tokens": max_tokens,
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||
**kwargs,
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||
})
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||
|
||
if no_remote_api:
|
||
from server.chat.openai_chat import openai_chat
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||
response = openai_chat(msg)
|
||
return self._fastapi_stream2generator(response)
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||
else:
|
||
data = msg.dict(exclude_unset=True, exclude_none=True)
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||
print(f"received input message:")
|
||
pprint(data)
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||
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||
response = self.post(
|
||
"/chat/fastchat",
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||
json=data,
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||
stream=stream,
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||
)
|
||
return self._httpx_stream2generator(response)
|
||
|
||
def chat_chat(
|
||
self,
|
||
query: str,
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||
history: List[Dict] = [],
|
||
stream: bool = True,
|
||
model: str = LLM_MODEL,
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||
no_remote_api: bool = None,
|
||
):
|
||
'''
|
||
对应api.py/chat/chat接口
|
||
'''
|
||
if no_remote_api is None:
|
||
no_remote_api = self.no_remote_api
|
||
|
||
data = {
|
||
"query": query,
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||
"history": history,
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||
"stream": stream,
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||
"model_name": model,
|
||
}
|
||
|
||
print(f"received input message:")
|
||
pprint(data)
|
||
|
||
if no_remote_api:
|
||
from server.chat.chat import chat
|
||
response = chat(**data)
|
||
return self._fastapi_stream2generator(response)
|
||
else:
|
||
response = self.post("/chat/chat", json=data, stream=True)
|
||
return self._httpx_stream2generator(response)
|
||
|
||
def knowledge_base_chat(
|
||
self,
|
||
query: str,
|
||
knowledge_base_name: str,
|
||
top_k: int = VECTOR_SEARCH_TOP_K,
|
||
score_threshold: float = SCORE_THRESHOLD,
|
||
history: List[Dict] = [],
|
||
stream: bool = True,
|
||
model: str = LLM_MODEL,
|
||
no_remote_api: bool = None,
|
||
):
|
||
'''
|
||
对应api.py/chat/knowledge_base_chat接口
|
||
'''
|
||
if no_remote_api is None:
|
||
no_remote_api = self.no_remote_api
|
||
|
||
data = {
|
||
"query": query,
|
||
"knowledge_base_name": knowledge_base_name,
|
||
"top_k": top_k,
|
||
"score_threshold": score_threshold,
|
||
"history": history,
|
||
"stream": stream,
|
||
"model_name": model,
|
||
"local_doc_url": no_remote_api,
|
||
}
|
||
|
||
print(f"received input message:")
|
||
pprint(data)
|
||
|
||
if no_remote_api:
|
||
from server.chat.knowledge_base_chat import knowledge_base_chat
|
||
response = knowledge_base_chat(**data)
|
||
return self._fastapi_stream2generator(response, as_json=True)
|
||
else:
|
||
response = self.post(
|
||
"/chat/knowledge_base_chat",
|
||
json=data,
|
||
stream=True,
|
||
)
|
||
return self._httpx_stream2generator(response, as_json=True)
|
||
|
||
def search_engine_chat(
|
||
self,
|
||
query: str,
|
||
search_engine_name: str,
|
||
top_k: int = SEARCH_ENGINE_TOP_K,
|
||
stream: bool = True,
|
||
model: str = LLM_MODEL,
|
||
no_remote_api: bool = None,
|
||
):
|
||
'''
|
||
对应api.py/chat/search_engine_chat接口
|
||
'''
|
||
if no_remote_api is None:
|
||
no_remote_api = self.no_remote_api
|
||
|
||
data = {
|
||
"query": query,
|
||
"search_engine_name": search_engine_name,
|
||
"top_k": top_k,
|
||
"stream": stream,
|
||
"model_name": model,
|
||
}
|
||
|
||
print(f"received input message:")
|
||
pprint(data)
|
||
|
||
if no_remote_api:
|
||
from server.chat.search_engine_chat import search_engine_chat
|
||
response = search_engine_chat(**data)
|
||
return self._fastapi_stream2generator(response, as_json=True)
|
||
else:
|
||
response = self.post(
|
||
"/chat/search_engine_chat",
|
||
json=data,
|
||
stream=True,
|
||
)
|
||
return self._httpx_stream2generator(response, as_json=True)
|
||
|
||
# 知识库相关操作
|
||
|
||
def _check_httpx_json_response(
|
||
self,
|
||
response: httpx.Response,
|
||
errorMsg: str = f"无法连接API服务器，请确认已执行python server\\api.py",
|
||
) -> Dict:
|
||
'''
|
||
check whether httpx returns correct data with normal Response.
|
||
error in api with streaming support was checked in _httpx_stream2enerator
|
||
'''
|
||
try:
|
||
return response.json()
|
||
except Exception as e:
|
||
msg = "API未能返回正确的JSON。" + (errorMsg or str(e))
|
||
logger.error(msg)
|
||
return {"code": 500, "msg": msg}
|
||
|
||
def list_knowledge_bases(
|
||
self,
|
||
no_remote_api: bool = None,
|
||
):
|
||
'''
|
||
对应api.py/knowledge_base/list_knowledge_bases接口
|
||
'''
|
||
if no_remote_api is None:
|
||
no_remote_api = self.no_remote_api
|
||
|
||
if no_remote_api:
|
||
from server.knowledge_base.kb_api import list_kbs
|
||
response = run_async(list_kbs())
|
||
return response.data
|
||
else:
|
||
response = self.get("/knowledge_base/list_knowledge_bases")
|
||
data = self._check_httpx_json_response(response)
|
||
return data.get("data", [])
|
||
|
||
def create_knowledge_base(
|
||
self,
|
||
knowledge_base_name: str,
|
||
vector_store_type: str = "faiss",
|
||
embed_model: str = EMBEDDING_MODEL,
|
||
no_remote_api: bool = None,
|
||
):
|
||
'''
|
||
对应api.py/knowledge_base/create_knowledge_base接口
|
||
'''
|
||
if no_remote_api is None:
|
||
no_remote_api = self.no_remote_api
|
||
|
||
data = {
|
||
"knowledge_base_name": knowledge_base_name,
|
||
"vector_store_type": vector_store_type,
|
||
"embed_model": embed_model,
|
||
}
|
||
|
||
if no_remote_api:
|
||
from server.knowledge_base.kb_api import create_kb
|
||
response = run_async(create_kb(**data))
|
||
return response.dict()
|
||
else:
|
||
response = self.post(
|
||
"/knowledge_base/create_knowledge_base",
|
||
json=data,
|
||
)
|
||
return self._check_httpx_json_response(response)
|
||
|
||
def delete_knowledge_base(
|
||
self,
|
||
knowledge_base_name: str,
|
||
no_remote_api: bool = None,
|
||
):
|
||
'''
|
||
对应api.py/knowledge_base/delete_knowledge_base接口
|
||
'''
|
||
if no_remote_api is None:
|
||
no_remote_api = self.no_remote_api
|
||
|
||
if no_remote_api:
|
||
from server.knowledge_base.kb_api import delete_kb
|
||
response = run_async(delete_kb(knowledge_base_name))
|
||
return response.dict()
|
||
else:
|
||
response = self.post(
|
||
"/knowledge_base/delete_knowledge_base",
|
||
json=f"{knowledge_base_name}",
|
||
)
|
||
return self._check_httpx_json_response(response)
|
||
|
||
def list_kb_docs(
|
||
self,
|
||
knowledge_base_name: str,
|
||
no_remote_api: bool = None,
|
||
):
|
||
'''
|
||
对应api.py/knowledge_base/list_files接口
|
||
'''
|
||
if no_remote_api is None:
|
||
no_remote_api = self.no_remote_api
|
||
|
||
if no_remote_api:
|
||
from server.knowledge_base.kb_doc_api import list_files
|
||
response = run_async(list_files(knowledge_base_name))
|
||
return response.data
|
||
else:
|
||
response = self.get(
|
||
"/knowledge_base/list_files",
|
||
params={"knowledge_base_name": knowledge_base_name}
|
||
)
|
||
data = self._check_httpx_json_response(response)
|
||
return data.get("data", [])
|
||
|
||
def search_kb_docs(
|
||
self,
|
||
query: str,
|
||
knowledge_base_name: str,
|
||
top_k: int = VECTOR_SEARCH_TOP_K,
|
||
score_threshold: int = SCORE_THRESHOLD,
|
||
no_remote_api: bool = None,
|
||
) -> List:
|
||
'''
|
||
对应api.py/knowledge_base/search_docs接口
|
||
'''
|
||
if no_remote_api is None:
|
||
no_remote_api = self.no_remote_api
|
||
|
||
data = {
|
||
"query": query,
|
||
"knowledge_base_name": knowledge_base_name,
|
||
"top_k": top_k,
|
||
"score_threshold": score_threshold,
|
||
}
|
||
|
||
if no_remote_api:
|
||
from server.knowledge_base.kb_doc_api import search_docs
|
||
return search_docs(**data)
|
||
else:
|
||
response = self.post(
|
||
"/knowledge_base/search_docs",
|
||
json=data,
|
||
)
|
||
data = self._check_httpx_json_response(response)
|
||
return data
|
||
|
||
def upload_kb_docs(
|
||
self,
|
||
files: List[Union[str, Path, bytes]],
|
||
knowledge_base_name: str,
|
||
override: bool = False,
|
||
to_vector_store: bool = True,
|
||
docs: Dict = {},
|
||
not_refresh_vs_cache: bool = False,
|
||
no_remote_api: bool = None,
|
||
):
|
||
'''
|
||
对应api.py/knowledge_base/upload_docs接口
|
||
'''
|
||
if no_remote_api is None:
|
||
no_remote_api = self.no_remote_api
|
||
|
||
def convert_file(file, filename=None):
|
||
if isinstance(file, bytes): # raw bytes
|
||
file = BytesIO(file)
|
||
elif hasattr(file, "read"): # a file io like object
|
||
filename = filename or file.name
|
||
else: # a local path
|
||
file = Path(file).absolute().open("rb")
|
||
filename = filename or file.name
|
||
return filename, file
|
||
|
||
files = [convert_file(file) for file in files]
|
||
data={
|
||
"knowledge_base_name": knowledge_base_name,
|
||
"override": override,
|
||
"to_vector_store": to_vector_store,
|
||
"docs": docs,
|
||
"not_refresh_vs_cache": not_refresh_vs_cache,
|
||
}
|
||
|
||
if no_remote_api:
|
||
from server.knowledge_base.kb_doc_api import upload_docs
|
||
from fastapi import UploadFile
|
||
from tempfile import SpooledTemporaryFile
|
||
|
||
upload_files = []
|
||
for file, filename in files:
|
||
temp_file = SpooledTemporaryFile(max_size=10 * 1024 * 1024)
|
||
temp_file.write(file.read())
|
||
temp_file.seek(0)
|
||
upload_files.append(UploadFile(file=temp_file, filename=filename))
|
||
|
||
response = run_async(upload_docs(upload_files, **data))
|
||
return response.dict()
|
||
else:
|
||
if isinstance(data["docs"], dict):
|
||
data["docs"] = json.dumps(data["docs"], ensure_ascii=False)
|
||
response = self.post(
|
||
"/knowledge_base/upload_docs",
|
||
data=data,
|
||
files=[("files", (filename, file)) for filename, file in files],
|
||
)
|
||
return self._check_httpx_json_response(response)
|
||
|
||
def delete_kb_docs(
|
||
self,
|
||
knowledge_base_name: str,
|
||
file_names: List[str],
|
||
delete_content: bool = False,
|
||
not_refresh_vs_cache: bool = False,
|
||
no_remote_api: bool = None,
|
||
):
|
||
'''
|
||
对应api.py/knowledge_base/delete_docs接口
|
||
'''
|
||
if no_remote_api is None:
|
||
no_remote_api = self.no_remote_api
|
||
|
||
data = {
|
||
"knowledge_base_name": knowledge_base_name,
|
||
"file_names": file_names,
|
||
"delete_content": delete_content,
|
||
"not_refresh_vs_cache": not_refresh_vs_cache,
|
||
}
|
||
|
||
if no_remote_api:
|
||
from server.knowledge_base.kb_doc_api import delete_docs
|
||
response = run_async(delete_docs(**data))
|
||
return response.dict()
|
||
else:
|
||
response = self.post(
|
||
"/knowledge_base/delete_docs",
|
||
json=data,
|
||
)
|
||
return self._check_httpx_json_response(response)
|
||
|
||
def update_kb_docs(
|
||
self,
|
||
knowledge_base_name: str,
|
||
file_names: List[str],
|
||
override_custom_docs: bool = False,
|
||
docs: Dict = {},
|
||
not_refresh_vs_cache: bool = False,
|
||
no_remote_api: bool = None,
|
||
):
|
||
'''
|
||
对应api.py/knowledge_base/update_docs接口
|
||
'''
|
||
if no_remote_api is None:
|
||
no_remote_api = self.no_remote_api
|
||
|
||
data = {
|
||
"knowledge_base_name": knowledge_base_name,
|
||
"file_names": file_names,
|
||
"override_custom_docs": override_custom_docs,
|
||
"docs": docs,
|
||
"not_refresh_vs_cache": not_refresh_vs_cache,
|
||
}
|
||
if no_remote_api:
|
||
from server.knowledge_base.kb_doc_api import update_docs
|
||
response = run_async(update_docs(**data))
|
||
return response.dict()
|
||
else:
|
||
if isinstance(data["docs"], dict):
|
||
data["docs"] = json.dumps(data["docs"], ensure_ascii=False)
|
||
response = self.post(
|
||
"/knowledge_base/update_docs",
|
||
json=data,
|
||
)
|
||
return self._check_httpx_json_response(response)
|
||
|
||
def recreate_vector_store(
|
||
self,
|
||
knowledge_base_name: str,
|
||
allow_empty_kb: bool = True,
|
||
vs_type: str = DEFAULT_VS_TYPE,
|
||
embed_model: str = EMBEDDING_MODEL,
|
||
no_remote_api: bool = None,
|
||
):
|
||
'''
|
||
对应api.py/knowledge_base/recreate_vector_store接口
|
||
'''
|
||
if no_remote_api is None:
|
||
no_remote_api = self.no_remote_api
|
||
|
||
data = {
|
||
"knowledge_base_name": knowledge_base_name,
|
||
"allow_empty_kb": allow_empty_kb,
|
||
"vs_type": vs_type,
|
||
"embed_model": embed_model,
|
||
}
|
||
|
||
if no_remote_api:
|
||
from server.knowledge_base.kb_doc_api import recreate_vector_store
|
||
response = run_async(recreate_vector_store(**data))
|
||
return self._fastapi_stream2generator(response, as_json=True)
|
||
else:
|
||
response = self.post(
|
||
"/knowledge_base/recreate_vector_store",
|
||
json=data,
|
||
stream=True,
|
||
timeout=None,
|
||
)
|
||
return self._httpx_stream2generator(response, as_json=True)
|
||
|
||
def list_running_models(self, controller_address: str = None):
|
||
'''
|
||
获取Fastchat中正运行的模型列表
|
||
'''
|
||
r = self.post(
|
||
"/llm_model/list_models",
|
||
)
|
||
return r.json().get("data", [])
|
||
|
||
def list_config_models(self):
|
||
'''
|
||
获取configs中配置的模型列表
|
||
'''
|
||
return list(llm_model_dict.keys())
|
||
|
||
def stop_llm_model(
|
||
self,
|
||
model_name: str,
|
||
controller_address: str = None,
|
||
):
|
||
'''
|
||
停止某个LLM模型。
|
||
注意：由于Fastchat的实现方式，实际上是把LLM模型所在的model_worker停掉。
|
||
'''
|
||
data = {
|
||
"model_name": model_name,
|
||
"controller_address": controller_address,
|
||
}
|
||
r = self.post(
|
||
"/llm_model/stop",
|
||
json=data,
|
||
)
|
||
return r.json()
|
||
|
||
def change_llm_model(
|
||
self,
|
||
model_name: str,
|
||
new_model_name: str,
|
||
controller_address: str = None,
|
||
):
|
||
'''
|
||
向fastchat controller请求切换LLM模型。
|
||
'''
|
||
if not model_name or not new_model_name:
|
||
return
|
||
|
||
if new_model_name == model_name:
|
||
return {
|
||
"code": 200,
|
||
"msg": "什么都不用做"
|
||
}
|
||
|
||
running_models = self.list_running_models()
|
||
if model_name not in running_models:
|
||
return {
|
||
"code": 500,
|
||
"msg": f"指定的模型'{model_name}'没有运行。当前运行模型：{running_models}"
|
||
}
|
||
|
||
config_models = self.list_config_models()
|
||
if new_model_name not in config_models:
|
||
return {
|
||
"code": 500,
|
||
"msg": f"要切换的模型'{new_model_name}'在configs中没有配置。"
|
||
}
|
||
|
||
data = {
|
||
"model_name": model_name,
|
||
"new_model_name": new_model_name,
|
||
"controller_address": controller_address,
|
||
}
|
||
r = self.post(
|
||
"/llm_model/change",
|
||
json=data,
|
||
timeout=HTTPX_DEFAULT_TIMEOUT, # wait for new worker_model
|
||
)
|
||
return r.json()
|
||
|
||
|
||
def check_error_msg(data: Union[str, dict, list], key: str = "errorMsg") -> str:
|
||
'''
|
||
return error message if error occured when requests API
|
||
'''
|
||
if isinstance(data, dict):
|
||
if key in data:
|
||
return data[key]
|
||
if "code" in data and data["code"] != 200:
|
||
return data["msg"]
|
||
return ""
|
||
|
||
|
||
def check_success_msg(data: Union[str, dict, list], key: str = "msg") -> str:
|
||
'''
|
||
return error message if error occured when requests API
|
||
'''
|
||
if (isinstance(data, dict)
|
||
and key in data
|
||
and "code" in data
|
||
and data["code"] == 200):
|
||
return data[key]
|
||
return ""
|
||
|
||
|
||
if __name__ == "__main__":
|
||
api = ApiRequest(no_remote_api=True)
|
||
|
||
# print(api.chat_fastchat(
|
||
# messages=[{"role": "user", "content": "hello"}]
|
||
# ))
|
||
|
||
# with api.chat_chat("你好") as r:
|
||
# for t in r.iter_text(None):
|
||
# print(t)
|
||
|
||
# r = api.chat_chat("你好", no_remote_api=True)
|
||
# for t in r:
|
||
# print(t)
|
||
|
||
# r = api.duckduckgo_search_chat("室温超导最新研究进展", no_remote_api=True)
|
||
# for t in r:
|
||
# print(t)
|
||
|
||
# print(api.list_knowledge_bases())
|