1187 lines
40 KiB
Python
1187 lines
40 KiB
Python
# 该文件包含webui通用工具，可以被不同的webui使用
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from typing import *
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from pathlib import Path
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from io import BytesIO
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import httpx
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import asyncio
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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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import nltk
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import traceback
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from loguru import logger
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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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CB_ROOT_PATH,
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LLM_MODEL,
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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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NLTK_DATA_PATH,
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JUPYTER_WORK_PATH,
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)
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from configs.server_config import SANDBOX_SERVER
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# from configs.server_config import SANDBOX_SERVER
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from coagent.utils.server_utils import run_async, iter_over_async
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from coagent.service.kb_api import *
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from coagent.service.cb_api import *
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from coagent.chat import LLMChat, SearchChat, KnowledgeChat, CodeChat, AgentChat
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from coagent.sandbox import PyCodeBox, CodeBoxResponse
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from coagent.utils.common_utils import file_normalize, get_uploadfile
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from coagent.codechat.code_crawler.zip_crawler import ZipCrawler
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from web_crawler.utils.WebCrawler import WebCrawler
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# nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path
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def set_httpx_timeout(timeout=60.0):
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'''
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设置httpx默认timeout到60秒。
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httpx默认timeout是5秒，在请求LLM回答时不够用。
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'''
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httpx._config.DEFAULT_TIMEOUT_CONFIG.connect = timeout
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httpx._config.DEFAULT_TIMEOUT_CONFIG.read = timeout
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httpx._config.DEFAULT_TIMEOUT_CONFIG.write = timeout
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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 = "http://127.0.0.1:7861",
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sandbox_file_url: str = "http://127.0.0.1:7862",
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timeout: float = 60.0,
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no_remote_api: bool = False, # call api view function directly
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cb_root_path: str = "",
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):
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self.base_url = base_url
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self.sandbox_file_url = sandbox_file_url
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self.timeout = timeout
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self.no_remote_api = no_remote_api
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self.cb_root_path = cb_root_path
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self.llmChat = LLMChat()
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self.searchChat = SearchChat()
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self.knowledgeChat = KnowledgeChat(kb_root_path=KB_ROOT_PATH)
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self.codeChat = CodeChat()
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self.agentChat = AgentChat()
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# self.codebox = PyCodeBox(
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# remote_url=self.sandbox_server["url"],
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# remote_ip=self.sandbox_server["host"], # "http://localhost",
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# remote_port=self.sandbox_server["port"],
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# token="mytoken",
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# do_code_exe=True,
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# do_remote=self.sandbox_server["do_remote"]
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# )
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# def codebox_chat(self, text: str, file_path: str = None, do_code_exe: bool = None) -> CodeBoxResponse:
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# return self.codebox.chat(text, file_path, do_code_exe=do_code_exe)
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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.sandbox_file_url.strip(" /") \
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if "sdfiles" in url else 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(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(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(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(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(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(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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||
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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(traceback.format_exc())
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||
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||
def _stream2generator(self, response: str, 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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||
if as_json and response:
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||
return json.loads(response)
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||
elif response.strip():
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||
return response
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||
except Exception as e:
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||
logger.error(traceback.format_exc())
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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 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 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。"
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||
logger.error(msg)
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logger.error(e)
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yield {"code": 500, "msg": msg}
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||
except httpx.ReadTimeout as e:
|
||
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'）"
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logger.error(msg)
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||
logger.error(e)
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||
yield {"code": 500, "msg": msg}
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||
except Exception as e:
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||
logger.error(e)
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||
yield {"code": 500, "msg": str(e)}
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||
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||
def chat_chat(
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||
self,
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||
query: str,
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||
history: List[Dict] = [],
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||
stream: bool = True,
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||
no_remote_api: bool = None,
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||
embed_model: str="", embed_model_path: str="", model_device: str="", embed_engine: str="",
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||
llm_model: str ="", temperature: float= 0.2
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||
):
|
||
'''
|
||
对应api.py/chat/chat接口
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||
'''
|
||
if no_remote_api is None:
|
||
no_remote_api = self.no_remote_api
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||
|
||
data = {
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||
"query": query,
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||
"history": history,
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||
"stream": stream,
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||
"api_key": os.environ["OPENAI_API_KEY"],
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||
"api_base_url": os.environ["API_BASE_URL"],
|
||
"embed_model": embed_model,
|
||
"embed_model_path": embed_model_path,
|
||
"embed_engine": embed_engine,
|
||
"model_name": llm_model,
|
||
"temperature": temperature,
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||
"model_device": model_device,
|
||
"temperature": temperature,
|
||
}
|
||
|
||
if no_remote_api:
|
||
response = self.llmChat.chat(**data)
|
||
return self._fastapi_stream2generator(response, as_json=True)
|
||
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 = 5,
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||
score_threshold: float = 1.0,
|
||
history: List[Dict] = [],
|
||
stream: bool = True,
|
||
no_remote_api: bool = None,
|
||
embed_model: str="", embed_model_path: str="", model_device: str="", embed_engine: str="",
|
||
llm_model: str ="", temperature: float= 0.2
|
||
):
|
||
'''
|
||
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,
|
||
"engine_name": knowledge_base_name,
|
||
"top_k": top_k,
|
||
"score_threshold": score_threshold,
|
||
"history": history,
|
||
"stream": stream,
|
||
"local_doc_url": no_remote_api,
|
||
"api_key": os.environ["OPENAI_API_KEY"],
|
||
"api_base_url": os.environ["API_BASE_URL"],
|
||
"embed_model": embed_model,
|
||
"embed_model_path": embed_model_path,
|
||
"embed_engine": embed_engine,
|
||
"model_name": llm_model,
|
||
"temperature": temperature,
|
||
"model_device": model_device,
|
||
"temperature": temperature,
|
||
}
|
||
|
||
if no_remote_api:
|
||
response = self.knowledgeChat.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,
|
||
history: List[Dict] = [],
|
||
stream: bool = True,
|
||
no_remote_api: bool = None,
|
||
embed_model: str="", embed_model_path: str="", model_device: str="", embed_engine: str="",
|
||
llm_model: str ="", temperature: float= 0.2
|
||
):
|
||
'''
|
||
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,
|
||
"engine_name": search_engine_name,
|
||
"top_k": top_k,
|
||
"history": history,
|
||
"stream": stream,
|
||
"api_key": os.environ["OPENAI_API_KEY"],
|
||
"api_base_url": os.environ["API_BASE_URL"],
|
||
"embed_model": embed_model,
|
||
"embed_model_path": embed_model_path,
|
||
"embed_engine": embed_engine,
|
||
"model_name": llm_model,
|
||
"temperature": temperature,
|
||
"model_device": model_device,
|
||
"temperature": temperature,
|
||
}
|
||
|
||
if no_remote_api:
|
||
response = self.searchChat.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)
|
||
|
||
def code_base_chat(
|
||
self,
|
||
query: str,
|
||
code_base_name: str,
|
||
code_limit: int = 1,
|
||
history: List[Dict] = [],
|
||
cb_search_type: str = 'tag',
|
||
stream: bool = True,
|
||
no_remote_api: bool = None,
|
||
embed_model: str="", embed_model_path: str="", model_device: str="", embed_engine: str="",
|
||
llm_model: str ="", temperature: float= 0.2
|
||
):
|
||
'''
|
||
对应api.py/chat/knowledge_base_chat接口
|
||
'''
|
||
if no_remote_api is None:
|
||
no_remote_api = self.no_remote_api
|
||
|
||
cb_search_type = {
|
||
'基于 cypher': 'cypher',
|
||
'基于标签': 'tag',
|
||
'基于描述': 'description',
|
||
'tag': 'tag',
|
||
'description': 'description',
|
||
'cypher': 'cypher'
|
||
}.get(cb_search_type, 'tag')
|
||
|
||
|
||
data = {
|
||
"query": query,
|
||
"history": history,
|
||
"engine_name": code_base_name,
|
||
"code_limit": code_limit,
|
||
"cb_search_type": cb_search_type,
|
||
"stream": stream,
|
||
"local_doc_url": no_remote_api,
|
||
"api_key": os.environ["OPENAI_API_KEY"],
|
||
"api_base_url": os.environ["API_BASE_URL"],
|
||
"embed_model": embed_model,
|
||
"embed_model_path": embed_model_path,
|
||
"embed_engine": embed_engine,
|
||
"model_name": llm_model,
|
||
"temperature": temperature,
|
||
"model_device": model_device,
|
||
}
|
||
logger.info('data={}'.format(data))
|
||
|
||
if no_remote_api:
|
||
# logger.info('history_node_list before={}'.format(self.codeChat.history_node_list))
|
||
response = self.codeChat.chat(**data)
|
||
# logger.info('history_node_list after={}'.format(self.codeChat.history_node_list))
|
||
return self._fastapi_stream2generator(response, as_json=True)
|
||
else:
|
||
response = self.post(
|
||
"/chat/code_chat",
|
||
json=data,
|
||
stream=True,
|
||
)
|
||
return self._httpx_stream2generator(response, as_json=True)
|
||
|
||
def agent_chat(
|
||
self,
|
||
query: str,
|
||
phase_name: str,
|
||
doc_engine_name: str,
|
||
code_engine_name: str,
|
||
search_engine_name: str,
|
||
top_k: int = 3,
|
||
score_threshold: float = 1.0,
|
||
history: List[Dict] = [],
|
||
stream: bool = True,
|
||
local_doc_url: bool = False,
|
||
do_search: bool = False,
|
||
do_doc_retrieval: bool = False,
|
||
do_code_retrieval: bool = False,
|
||
do_tool_retrieval: bool = False,
|
||
choose_tools: List[str] = [],
|
||
custom_phase_configs = {},
|
||
custom_chain_configs = {},
|
||
custom_role_configs = {},
|
||
no_remote_api: bool = None,
|
||
history_node_list: List[str] = [],
|
||
isDetailed: bool = False,
|
||
upload_file: Union[str, Path, bytes] = "",
|
||
kb_root_path: str =KB_ROOT_PATH,
|
||
embed_model: str="", embed_model_path: str="",
|
||
model_device: str="", embed_engine: str="",
|
||
temperature: float=0.2, model_name:str ="",
|
||
):
|
||
'''
|
||
对应api.py/chat/chat接口
|
||
'''
|
||
if no_remote_api is None:
|
||
no_remote_api = self.no_remote_api
|
||
|
||
data = {
|
||
"query": query,
|
||
"phase_name": phase_name,
|
||
"chain_name": "",
|
||
"history": history,
|
||
"doc_engine_name": doc_engine_name,
|
||
"code_engine_name": code_engine_name,
|
||
"search_engine_name": search_engine_name,
|
||
"top_k": top_k,
|
||
"score_threshold": score_threshold,
|
||
"stream": stream,
|
||
"local_doc_url": local_doc_url,
|
||
"do_search": do_search,
|
||
"do_doc_retrieval": do_doc_retrieval,
|
||
"do_code_retrieval": do_code_retrieval,
|
||
"do_tool_retrieval": do_tool_retrieval,
|
||
"custom_phase_configs": custom_phase_configs,
|
||
"custom_chain_configs": custom_phase_configs,
|
||
"custom_role_configs": custom_role_configs,
|
||
"choose_tools": choose_tools,
|
||
"history_node_list": history_node_list,
|
||
"isDetailed": isDetailed,
|
||
"upload_file": upload_file,
|
||
"kb_root_path": kb_root_path,
|
||
"api_key": os.environ["OPENAI_API_KEY"],
|
||
"api_base_url": os.environ["API_BASE_URL"],
|
||
"embed_model": embed_model,
|
||
"embed_model_path": embed_model_path,
|
||
"embed_engine": embed_engine,
|
||
"model_device": model_device,
|
||
"model_name": model_name,
|
||
"temperature": temperature,
|
||
"jupyter_work_path": JUPYTER_WORK_PATH,
|
||
"sandbox_server": SANDBOX_SERVER,
|
||
}
|
||
if no_remote_api:
|
||
response = self.agentChat.chat(**data)
|
||
return self._fastapi_stream2generator(response, as_json=True)
|
||
else:
|
||
response = self.post("/chat/data_chat", json=data, stream=True)
|
||
return self._httpx_stream2generator(response)
|
||
|
||
def agent_achat(
|
||
self,
|
||
query: str,
|
||
phase_name: str,
|
||
doc_engine_name: str,
|
||
code_engine_name: str,
|
||
cb_search_type: str,
|
||
search_engine_name: str,
|
||
top_k: int = 3,
|
||
score_threshold: float = 1.0,
|
||
history: List[Dict] = [],
|
||
stream: bool = True,
|
||
local_doc_url: bool = False,
|
||
do_search: bool = False,
|
||
do_doc_retrieval: bool = False,
|
||
do_code_retrieval: bool = False,
|
||
do_tool_retrieval: bool = False,
|
||
choose_tools: List[str] = [],
|
||
custom_phase_configs = {},
|
||
custom_chain_configs = {},
|
||
custom_role_configs = {},
|
||
no_remote_api: bool = None,
|
||
history_node_list: List[str] = [],
|
||
isDetailed: bool = False,
|
||
upload_file: Union[str, Path, bytes] = "",
|
||
kb_root_path: str =KB_ROOT_PATH,
|
||
embed_model: str="", embed_model_path: str="",
|
||
model_device: str="", embed_engine: str="",
|
||
temperature: float=0.2, model_name: str="",
|
||
):
|
||
'''
|
||
对应api.py/chat/chat接口
|
||
'''
|
||
if no_remote_api is None:
|
||
no_remote_api = self.no_remote_api
|
||
|
||
data = {
|
||
"query": query,
|
||
"phase_name": phase_name,
|
||
"chain_name": "",
|
||
"history": history,
|
||
"doc_engine_name": doc_engine_name,
|
||
"code_engine_name": code_engine_name,
|
||
"cb_search_type": cb_search_type,
|
||
"search_engine_name": search_engine_name,
|
||
"top_k": top_k,
|
||
"score_threshold": score_threshold,
|
||
"stream": stream,
|
||
"local_doc_url": local_doc_url,
|
||
"do_search": do_search,
|
||
"do_doc_retrieval": do_doc_retrieval,
|
||
"do_code_retrieval": do_code_retrieval,
|
||
"do_tool_retrieval": do_tool_retrieval,
|
||
"custom_phase_configs": custom_phase_configs,
|
||
"custom_chain_configs": custom_chain_configs,
|
||
"custom_role_configs": custom_role_configs,
|
||
"choose_tools": choose_tools,
|
||
"history_node_list": history_node_list,
|
||
"isDetailed": isDetailed,
|
||
"upload_file": upload_file,
|
||
"kb_root_path": kb_root_path,
|
||
"api_key": os.environ["OPENAI_API_KEY"],
|
||
"api_base_url": os.environ["API_BASE_URL"],
|
||
"embed_model": embed_model,
|
||
"embed_model_path": embed_model_path,
|
||
"embed_engine": embed_engine,
|
||
"model_device": model_device,
|
||
"model_name": model_name,
|
||
"temperature": temperature,
|
||
"jupyter_work_path": JUPYTER_WORK_PATH,
|
||
"sandbox_server": SANDBOX_SERVER,
|
||
}
|
||
|
||
if no_remote_api:
|
||
for response in self.agentChat.achat(**data):
|
||
yield self._stream2generator(response, as_json=True)
|
||
else:
|
||
response = self.post("/chat/data_chat", json=data, stream=True)
|
||
yield self._httpx_stream2generator(response)
|
||
|
||
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:
|
||
logger.error(e)
|
||
return {"code": 500, "msg": errorMsg or str(e)}
|
||
|
||
def _check_httpx_file_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.content
|
||
except Exception as e:
|
||
logger.error(e)
|
||
return {"code": 500, "msg": errorMsg or str(e)}
|
||
|
||
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:
|
||
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",
|
||
no_remote_api: bool = None,
|
||
kb_root_path: str =KB_ROOT_PATH,
|
||
embed_model: str="", embed_model_path: str="",
|
||
embedding_device: str="", embed_engine: str=""
|
||
):
|
||
'''
|
||
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,
|
||
"kb_root_path": kb_root_path,
|
||
"api_key": os.environ["OPENAI_API_KEY"],
|
||
"api_base_url": os.environ["API_BASE_URL"],
|
||
"embed_model": embed_model,
|
||
"embed_model_path": embed_model_path,
|
||
"model_device": embedding_device,
|
||
"embed_engine": embed_engine
|
||
}
|
||
|
||
if no_remote_api:
|
||
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,
|
||
kb_root_path: str =KB_ROOT_PATH,
|
||
):
|
||
'''
|
||
对应api.py/knowledge_base/delete_knowledge_base接口
|
||
'''
|
||
if no_remote_api is None:
|
||
no_remote_api = self.no_remote_api
|
||
data = {
|
||
"knowledge_base_name": knowledge_base_name,
|
||
"kb_root_path": kb_root_path,
|
||
}
|
||
if no_remote_api:
|
||
response = run_async(delete_kb(**data))
|
||
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_docs接口
|
||
'''
|
||
if no_remote_api is None:
|
||
no_remote_api = self.no_remote_api
|
||
|
||
if no_remote_api:
|
||
response = run_async(list_docs(knowledge_base_name, kb_root_path=KB_ROOT_PATH))
|
||
return response.data
|
||
else:
|
||
response = self.get(
|
||
"/knowledge_base/list_docs",
|
||
params={"knowledge_base_name": knowledge_base_name}
|
||
)
|
||
data = self._check_httpx_json_response(response)
|
||
return data.get("data", [])
|
||
|
||
def upload_kb_doc(
|
||
self,
|
||
file: Union[str, Path, bytes],
|
||
knowledge_base_name: str,
|
||
filename: str = None,
|
||
override: bool = False,
|
||
not_refresh_vs_cache: bool = False,
|
||
no_remote_api: bool = None,
|
||
kb_root_path: str = KB_ROOT_PATH,
|
||
embed_model: str="", embed_model_path: str="",
|
||
model_device: str="", embed_engine: str=""
|
||
):
|
||
'''
|
||
对应api.py/knowledge_base/upload_docs接口
|
||
'''
|
||
if no_remote_api is None:
|
||
no_remote_api = self.no_remote_api
|
||
|
||
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
|
||
|
||
if no_remote_api:
|
||
from fastapi import UploadFile
|
||
from tempfile import SpooledTemporaryFile
|
||
|
||
temp_file = SpooledTemporaryFile(max_size=10 * 1024 * 1024)
|
||
temp_file.write(file.read())
|
||
temp_file.seek(0)
|
||
response = run_async(upload_doc(
|
||
UploadFile(file=temp_file, filename=filename),
|
||
knowledge_base_name,
|
||
override,
|
||
not_refresh_vs_cache,
|
||
kb_root_path=kb_root_path,
|
||
api_key=os.environ["OPENAI_API_KEY"],
|
||
api_base_url=os.environ["API_BASE_URL"],
|
||
embed_model=embed_model,
|
||
embed_model_path=embed_model_path,
|
||
model_device=model_device,
|
||
embed_engine=embed_engine
|
||
))
|
||
return response.dict()
|
||
else:
|
||
response = self.post(
|
||
"/knowledge_base/upload_doc",
|
||
data={
|
||
"knowledge_base_name": knowledge_base_name,
|
||
"override": override,
|
||
"not_refresh_vs_cache": not_refresh_vs_cache,
|
||
},
|
||
files={"file": (filename, file)},
|
||
)
|
||
return self._check_httpx_json_response(response)
|
||
|
||
def delete_kb_doc(
|
||
self,
|
||
knowledge_base_name: str,
|
||
doc_name: str,
|
||
delete_content: bool = False,
|
||
not_refresh_vs_cache: bool = False,
|
||
no_remote_api: bool = None,
|
||
kb_root_path: str = KB_ROOT_PATH,
|
||
embed_model: str="", embed_model_path: str="",
|
||
model_device: str="", embed_engine: str=""
|
||
):
|
||
'''
|
||
对应api.py/knowledge_base/delete_doc接口
|
||
'''
|
||
if no_remote_api is None:
|
||
no_remote_api = self.no_remote_api
|
||
|
||
data = {
|
||
"knowledge_base_name": knowledge_base_name,
|
||
"doc_name": doc_name,
|
||
"delete_content": delete_content,
|
||
"not_refresh_vs_cache": not_refresh_vs_cache,
|
||
"kb_root_path": kb_root_path,
|
||
"api_key": os.environ["OPENAI_API_KEY"],
|
||
"api_base_url": os.environ["API_BASE_URL"],
|
||
"embed_model": embed_model,
|
||
"embed_model_path": embed_model_path,
|
||
"model_device": model_device,
|
||
"embed_engine": embed_engine
|
||
}
|
||
|
||
if no_remote_api:
|
||
response = run_async(delete_doc(**data))
|
||
return response.dict()
|
||
else:
|
||
response = self.post(
|
||
"/knowledge_base/delete_doc",
|
||
json=data,
|
||
)
|
||
return self._check_httpx_json_response(response)
|
||
|
||
def update_kb_doc(
|
||
self,
|
||
knowledge_base_name: str,
|
||
file_name: str,
|
||
not_refresh_vs_cache: bool = False,
|
||
no_remote_api: bool = None,
|
||
embed_model: str="", embed_model_path: str="",
|
||
model_device: str="", embed_engine: str=""
|
||
):
|
||
'''
|
||
对应api.py/knowledge_base/update_doc接口
|
||
'''
|
||
if no_remote_api is None:
|
||
no_remote_api = self.no_remote_api
|
||
|
||
if no_remote_api:
|
||
response = run_async(update_doc(
|
||
knowledge_base_name, file_name, not_refresh_vs_cache, kb_root_path=KB_ROOT_PATH,
|
||
api_key=os.environ["OPENAI_API_KEY"],
|
||
api_base_url=os.environ["API_BASE_URL"],
|
||
embed_model=embed_model,
|
||
embed_model_path=embed_model_path,
|
||
model_device=model_device,
|
||
embed_engine=embed_engine))
|
||
return response.dict()
|
||
else:
|
||
response = self.post(
|
||
"/knowledge_base/update_doc",
|
||
json={
|
||
"knowledge_base_name": knowledge_base_name,
|
||
"file_name": file_name,
|
||
"not_refresh_vs_cache": not_refresh_vs_cache,
|
||
},
|
||
)
|
||
return self._check_httpx_json_response(response)
|
||
|
||
def recreate_vector_store(
|
||
self,
|
||
knowledge_base_name: str,
|
||
allow_empty_kb: bool = True,
|
||
vs_type: str = "faiss",
|
||
no_remote_api: bool = None,
|
||
kb_root_path: str =KB_ROOT_PATH,
|
||
embed_model: str="", embed_model_path: str="",
|
||
embedding_device: str="", embed_engine: str=""
|
||
):
|
||
'''
|
||
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,
|
||
"kb_root_path": kb_root_path,
|
||
"api_key": os.environ["OPENAI_API_KEY"],
|
||
"api_base_url": os.environ["API_BASE_URL"],
|
||
"embed_model": embed_model,
|
||
"embed_model_path": embed_model_path,
|
||
"model_device": embedding_device,
|
||
"embed_engine": embed_engine
|
||
}
|
||
|
||
if no_remote_api:
|
||
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 web_crawl(
|
||
self,
|
||
base_url: str,
|
||
html_dir: str,
|
||
text_dir: str,
|
||
do_dfs: bool = False,
|
||
reptile_lib: str = "requests",
|
||
method: str = "get",
|
||
time_sleep: float = 2,
|
||
no_remote_api: bool = None
|
||
):
|
||
'''
|
||
根据url来检索
|
||
'''
|
||
async def _web_crawl(html_dir, text_dir, base_url, reptile_lib, method, time_sleep, do_dfs):
|
||
wc = WebCrawler()
|
||
try:
|
||
if not do_dfs:
|
||
wc.webcrawler_single(html_dir=html_dir,
|
||
text_dir=text_dir,
|
||
base_url=base_url,
|
||
reptile_lib=reptile_lib,
|
||
method=method,
|
||
time_sleep=time_sleep
|
||
)
|
||
else:
|
||
wc.webcrawler_1_degree(html_dir=html_dir,
|
||
text_dir=text_dir,
|
||
base_url=base_url,
|
||
reptile_lib=reptile_lib,
|
||
method=method,
|
||
time_sleep=time_sleep
|
||
)
|
||
return {"status": 200, "response": "success"}
|
||
except Exception as e:
|
||
return {"status": 500, "response": str(e)}
|
||
|
||
if no_remote_api is None:
|
||
no_remote_api = self.no_remote_api
|
||
|
||
data = {
|
||
"base_url": base_url,
|
||
"html_dir": html_dir,
|
||
"text_dir": text_dir,
|
||
"do_dfs": do_dfs,
|
||
"reptile_lib": reptile_lib,
|
||
"method": method,
|
||
"time_sleep": time_sleep,
|
||
}
|
||
if no_remote_api:
|
||
response = run_async(_web_crawl(**data))
|
||
return response
|
||
else:
|
||
raise Exception("not impletenion")
|
||
|
||
def web_sd_upload(self, file: str = None, filename: str = None):
|
||
'''对应file_service/sd_upload_file'''
|
||
file, filename = file_normalize(file, filename)
|
||
response = self.post(
|
||
"/sdfiles/upload",
|
||
files={"file": (filename, file)},
|
||
)
|
||
return self._check_httpx_json_response(response)
|
||
|
||
def web_sd_download(self, filename: str, save_filename: str = None):
|
||
'''对应file_service/sd_download_file'''
|
||
save_filename = save_filename or filename
|
||
response = self.get(
|
||
f"/sdfiles/download",
|
||
params={"filename": filename, "save_filename": save_filename}
|
||
)
|
||
# logger.debug(f"response: {response.json()}")
|
||
if filename:
|
||
file_content, _ = file_normalize(response.json()["data"])
|
||
return file_content, save_filename
|
||
return "", save_filename
|
||
|
||
def web_sd_delete(self, filename: str):
|
||
'''对应file_service/sd_delete_file'''
|
||
response = self.get(
|
||
f"/sdfiles/delete",
|
||
params={"filename": filename}
|
||
)
|
||
return self._check_httpx_json_response(response)
|
||
|
||
def web_sd_list_files(self, ):
|
||
'''对应对应file_service/sd_list_files接口'''
|
||
response = self.get("/sdfiles/list",)
|
||
return self._check_httpx_json_response(response)
|
||
|
||
# code base 相关操作
|
||
def create_code_base(self, cb_name, zip_file, do_interpret: bool, no_remote_api: bool = None,
|
||
embed_model: str="", embed_model_path: str="", embedding_device: str="", embed_engine: str="",
|
||
llm_model: str ="", temperature: float= 0.2
|
||
):
|
||
'''
|
||
创建 code_base
|
||
@param cb_name:
|
||
@param zip_path:
|
||
@return:
|
||
'''
|
||
if no_remote_api is None:
|
||
no_remote_api = self.no_remote_api
|
||
|
||
# mkdir
|
||
# cb_root_path = CB_ROOT_PATH
|
||
mkdir_dir = [
|
||
self.cb_root_path,
|
||
self.cb_root_path + os.sep + cb_name,
|
||
raw_code_path := self.cb_root_path + os.sep + cb_name + os.sep + 'raw_code'
|
||
]
|
||
for dir in mkdir_dir:
|
||
os.makedirs(dir, exist_ok=True)
|
||
|
||
data = {
|
||
"zip_file": zip_file,
|
||
"cb_name": cb_name,
|
||
"code_path": raw_code_path,
|
||
"do_interpret": do_interpret,
|
||
"api_key": os.environ["OPENAI_API_KEY"],
|
||
"api_base_url": os.environ["API_BASE_URL"],
|
||
"embed_model": embed_model,
|
||
"embed_model_path": embed_model_path,
|
||
"embed_engine": embed_engine,
|
||
"model_name": llm_model,
|
||
"temperature": temperature,
|
||
"model_device": embedding_device,
|
||
}
|
||
logger.info('create cb data={}'.format(data))
|
||
|
||
if no_remote_api:
|
||
response = run_async(create_cb(**data))
|
||
return response.dict()
|
||
else:
|
||
response = self.post(
|
||
"/code_base/create_code_base",
|
||
json=data,
|
||
)
|
||
logger.info('response={}'.format(response.json()))
|
||
return self._check_httpx_json_response(response)
|
||
|
||
def delete_code_base(self, cb_name: str, no_remote_api: bool = None,
|
||
embed_model: str="", embed_model_path: str="", embedding_device: str="", embed_engine: str="",
|
||
llm_model: str ="", temperature: float= 0.2
|
||
):
|
||
'''
|
||
删除 code_base
|
||
@param cb_name:
|
||
@return:
|
||
'''
|
||
if no_remote_api is None:
|
||
no_remote_api = self.no_remote_api
|
||
data = {
|
||
"cb_name": cb_name,
|
||
"api_key": os.environ["OPENAI_API_KEY"],
|
||
"api_base_url": os.environ["API_BASE_URL"],
|
||
"embed_model": embed_model,
|
||
"embed_model_path": embed_model_path,
|
||
"embed_engine": embed_engine,
|
||
"model_name": llm_model,
|
||
"temperature": temperature,
|
||
"model_device": embedding_device
|
||
}
|
||
if no_remote_api:
|
||
response = run_async(delete_cb(**data))
|
||
return response.dict()
|
||
else:
|
||
response = self.post(
|
||
"/code_base/delete_code_base",
|
||
json=cb_name
|
||
)
|
||
logger.info(response.json())
|
||
return self._check_httpx_json_response(response)
|
||
|
||
def list_cb(self, no_remote_api: bool = None):
|
||
'''
|
||
列举 code_base
|
||
@return:
|
||
'''
|
||
if no_remote_api is None:
|
||
no_remote_api = self.no_remote_api
|
||
|
||
if no_remote_api:
|
||
response = run_async(list_cbs())
|
||
return response.data
|
||
else:
|
||
response = self.get("/code_base/list_code_bases")
|
||
data = self._check_httpx_json_response(response)
|
||
return data.get("data", [])
|
||
|
||
|
||
|
||
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())
|