# 该文件包含webui通用工具，可以被不同的webui使用 from typing import * from pathlib import Path from io import BytesIO import httpx import asyncio from fastapi.responses import StreamingResponse import contextlib import json import nltk import traceback from loguru import logger from configs.model_config import ( EMBEDDING_MODEL, DEFAULT_VS_TYPE, KB_ROOT_PATH, CB_ROOT_PATH, LLM_MODEL, SCORE_THRESHOLD, VECTOR_SEARCH_TOP_K, SEARCH_ENGINE_TOP_K, NLTK_DATA_PATH, JUPYTER_WORK_PATH, ) from configs.server_config import SANDBOX_SERVER # from configs.server_config import SANDBOX_SERVER from muagent.utils.server_utils import run_async, iter_over_async from muagent.service.kb_api import * from muagent.service.cb_api import * from muagent.chat import LLMChat, SearchChat, KnowledgeChat, CodeChat, AgentChat from muagent.sandbox import PyCodeBox, CodeBoxResponse from muagent.utils.common_utils import file_normalize, get_uploadfile from muagent.codechat.code_crawler.zip_crawler import ZipCrawler from web_crawler.utils.WebCrawler import WebCrawler # nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path def set_httpx_timeout(timeout=60.0): ''' 设置httpx默认timeout到60秒。 httpx默认timeout是5秒，在请求LLM回答时不够用。 ''' httpx._config.DEFAULT_TIMEOUT_CONFIG.connect = timeout httpx._config.DEFAULT_TIMEOUT_CONFIG.read = timeout httpx._config.DEFAULT_TIMEOUT_CONFIG.write = timeout # KB_ROOT_PATH = Path(KB_ROOT_PATH) set_httpx_timeout() class ApiRequest: ''' api.py调用的封装,主要实现: 1. 简化api调用方式 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 ''' def __init__( self, base_url: str = "http://127.0.0.1:7861", sandbox_file_url: str = "http://127.0.0.1:7862", timeout: float = 60.0, no_remote_api: bool = False, # call api view function directly cb_root_path: str = "", ): self.base_url = base_url self.sandbox_file_url = sandbox_file_url self.timeout = timeout self.no_remote_api = no_remote_api self.cb_root_path = cb_root_path self.llmChat = LLMChat() self.searchChat = SearchChat() self.knowledgeChat = KnowledgeChat(kb_root_path=KB_ROOT_PATH) self.codeChat = CodeChat() self.agentChat = AgentChat() # self.codebox = PyCodeBox( # remote_url=self.sandbox_server["url"], # remote_ip=self.sandbox_server["host"], # "http://localhost", # remote_port=self.sandbox_server["port"], # token="mytoken", # do_code_exe=True, # do_remote=self.sandbox_server["do_remote"] # ) # def codebox_chat(self, text: str, file_path: str = None, do_code_exe: bool = None) -> CodeBoxResponse: # return self.codebox.chat(text, file_path, do_code_exe=do_code_exe) def _parse_url(self, url: str) -> str: if (not url.startswith("http") and self.base_url ): part1 = self.sandbox_file_url.strip(" /") \ if "sdfiles" in url else self.base_url.strip(" /") part2 = url.strip(" /") return f"{part1}/{part2}" else: return url def get( self, url: str, params: Union[Dict, List[Tuple], bytes] = None, retry: int = 3, stream: bool = False, **kwargs: Any, ) -> Union[httpx.Response, None]: url = self._parse_url(url) kwargs.setdefault("timeout", self.timeout) while retry > 0: try: if stream: return httpx.stream("GET", url, params=params, **kwargs) else: return httpx.get(url, params=params, **kwargs) except Exception as e: logger.error(e) retry -= 1 async def aget( self, url: str, params: Union[Dict, List[Tuple], bytes] = None, retry: int = 3, stream: bool = False, **kwargs: Any, ) -> Union[httpx.Response, None]: url = self._parse_url(url) kwargs.setdefault("timeout", self.timeout) async with httpx.AsyncClient() as client: while retry > 0: try: if stream: return await client.stream("GET", url, params=params, **kwargs) else: return await client.get(url, params=params, **kwargs) except Exception as e: logger.error(e) retry -= 1 def post( self, url: str, data: Dict = None, json: Dict = None, retry: int = 3, stream: bool = False, **kwargs: Any ) -> Union[httpx.Response, None]: url = self._parse_url(url) kwargs.setdefault("timeout", self.timeout) while retry > 0: try: # return requests.post(url, data=data, json=json, stream=stream, **kwargs) if stream: return httpx.stream("POST", url, data=data, json=json, **kwargs) else: return httpx.post(url, data=data, json=json, **kwargs) except Exception as e: logger.error(e) retry -= 1 async def apost( self, url: str, data: Dict = None, json: Dict = None, retry: int = 3, stream: bool = False, **kwargs: Any ) -> Union[httpx.Response, None]: url = self._parse_url(url) kwargs.setdefault("timeout", self.timeout) async with httpx.AsyncClient() as client: while retry > 0: try: if stream: return await client.stream("POST", url, data=data, json=json, **kwargs) else: return await client.post(url, data=data, json=json, **kwargs) except Exception as e: logger.error(e) retry -= 1 def delete( self, url: str, data: Dict = None, json: Dict = None, retry: int = 3, stream: bool = False, **kwargs: Any ) -> Union[httpx.Response, None]: url = self._parse_url(url) kwargs.setdefault("timeout", self.timeout) while retry > 0: try: if stream: return httpx.stream("DELETE", url, data=data, json=json, **kwargs) else: return httpx.delete(url, data=data, json=json, **kwargs) except Exception as e: logger.error(e) retry -= 1 async def adelete( self, url: str, data: Dict = None, json: Dict = None, retry: int = 3, stream: bool = False, **kwargs: Any ) -> Union[httpx.Response, None]: url = self._parse_url(url) kwargs.setdefault("timeout", self.timeout) async with httpx.AsyncClient() as client: while retry > 0: try: if stream: return await client.stream("DELETE", url, data=data, json=json, **kwargs) else: return await client.delete(url, data=data, json=json, **kwargs) except Exception as e: logger.error(e) retry -= 1 def _fastapi_stream2generator(self, response: StreamingResponse, as_json: bool =False): ''' 将api.py中视图函数返回的StreamingResponse转化为同步生成器 ''' try: loop = asyncio.get_event_loop() except: loop = asyncio.new_event_loop() try: for chunk in iter_over_async(response.body_iterator, loop): if as_json and chunk: yield json.loads(chunk) elif chunk.strip(): yield chunk except Exception as e: logger.error(traceback.format_exc()) def _stream2generator(self, response: str, as_json: bool =False): ''' 将api.py中视图函数返回的StreamingResponse转化为同步生成器 ''' try: if as_json and response: return json.loads(response) elif response.strip(): return response except Exception as e: logger.error(traceback.format_exc()) def _httpx_stream2generator( self, response: contextlib._GeneratorContextManager, as_json: bool = False, ): ''' 将httpx.stream返回的GeneratorContextManager转化为普通生成器 ''' try: with response as r: for chunk in r.iter_text(None): if as_json and chunk: yield json.loads(chunk) elif chunk.strip(): yield chunk except httpx.ConnectError as e: msg = f"无法连接API服务器，请确认 ‘api.py’ 已正常启动。" logger.error(msg) logger.error(e) yield {"code": 500, "msg": msg} 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'）" logger.error(msg) logger.error(e) yield {"code": 500, "msg": msg} except Exception as e: logger.error(e) yield {"code": 500, "msg": str(e)} def chat_chat( self, query: str, 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, api_key: str=os.environ["OPENAI_API_KEY"], api_base_url: str = os.environ["API_BASE_URL"], ): ''' 对应api.py/chat/chat接口 ''' if no_remote_api is None: no_remote_api = self.no_remote_api data = { "query": query, "history": history, "stream": stream, "api_key": api_key, "api_base_url": 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.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, 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, api_key: str=os.environ["OPENAI_API_KEY"], api_base_url: str = os.environ["API_BASE_URL"], ): ''' 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": api_key, "api_base_url": 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, api_key: str=os.environ["OPENAI_API_KEY"], api_base_url: str = os.environ["API_BASE_URL"], ): ''' 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": api_key, "api_base_url": 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, api_key: str=os.environ["OPENAI_API_KEY"], api_base_url: str = os.environ["API_BASE_URL"], ): ''' 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": api_key, "api_base_url": 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 ="", api_key: str=os.environ["OPENAI_API_KEY"], api_base_url: str = os.environ["API_BASE_URL"], ): ''' 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": api_key, "api_base_url": 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="", api_key: str=os.environ["OPENAI_API_KEY"], api_base_url: str = os.environ["API_BASE_URL"], ): ''' 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": api_key, "api_base_url": 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="", api_key: str=os.environ["OPENAI_API_KEY"], api_base_url: str = os.environ["API_BASE_URL"], ): ''' 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": api_key, "api_base_url": 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="", api_key: str=os.environ["OPENAI_API_KEY"], api_base_url: str = os.environ["API_BASE_URL"], ): ''' 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=api_key, api_base_url=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="", api_key: str=os.environ["OPENAI_API_KEY"], api_base_url: str = os.environ["API_BASE_URL"], ): ''' 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": api_key, "api_base_url": 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="", api_key: str=os.environ["OPENAI_API_KEY"], api_base_url: str = os.environ["API_BASE_URL"], ): ''' 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=api_key, api_base_url=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="", api_key: str=os.environ["OPENAI_API_KEY"], api_base_url: str = os.environ["API_BASE_URL"], ): ''' 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": api_key, "api_base_url": 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, api_key: str=os.environ["OPENAI_API_KEY"], api_base_url: str = os.environ["API_BASE_URL"], ): ''' 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": api_key, "api_base_url": 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, api_key: str=os.environ["OPENAI_API_KEY"], api_base_url: str = os.environ["API_BASE_URL"], ): ''' 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": api_key, "api_base_url": 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())