781 lines
25 KiB
Python
781 lines
25 KiB
Python
# 该文件封装了对api.py的请求，可以被不同的webui使用
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# 通过ApiRequest和AsyncApiRequest支持同步/异步调用
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import base64
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import contextlib
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import json
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import logging
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import os
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from io import BytesIO
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from pathlib import Path
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from typing import *
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import httpx
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from chatchat.settings import Settings
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from chatchat.server.utils import api_address, get_httpx_client, set_httpx_config, get_default_embedding
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from chatchat.utils import build_logger
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logger = build_logger()
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set_httpx_config()
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class ApiRequest:
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"""
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api.py调用的封装（同步模式）,简化api调用方式
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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 = Settings.basic_settings.HTTPX_DEFAULT_TIMEOUT,
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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._use_async = False
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self._client = None
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@property
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def client(self):
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if self._client is None or self._client.is_closed:
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self._client = get_httpx_client(
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base_url=self.base_url, use_async=self._use_async, timeout=self.timeout
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)
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return self._client
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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, Iterator[httpx.Response], None]:
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while retry > 0:
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try:
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if stream:
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return self.client.stream("GET", url, params=params, **kwargs)
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else:
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return self.client.get(url, params=params, **kwargs)
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except Exception as e:
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msg = f"error when get {url}: {e}"
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logger.error(f"{e.__class__.__name__}: {msg}")
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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, Iterator[httpx.Response], None]:
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while retry > 0:
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try:
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# print(kwargs)
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if stream:
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return self.client.stream(
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"POST", url, data=data, json=json, **kwargs
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)
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else:
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return self.client.post(url, data=data, json=json, **kwargs)
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except Exception as e:
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msg = f"error when post {url}: {e}"
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logger.error(f"{e.__class__.__name__}: {msg}")
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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, Iterator[httpx.Response], None]:
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while retry > 0:
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try:
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if stream:
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return self.client.stream(
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"DELETE", url, data=data, json=json, **kwargs
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)
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else:
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return self.client.delete(url, data=data, json=json, **kwargs)
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except Exception as e:
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msg = f"error when delete {url}: {e}"
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logger.error(f"{e.__class__.__name__}: {msg}")
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retry -= 1
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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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async def ret_async(response, as_json):
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try:
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async with response as r:
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chunk_cache = ""
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async for chunk in r.aiter_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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if chunk.startswith("data: "):
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data = json.loads(chunk_cache + chunk[6:-2])
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elif chunk.startswith(":"): # skip sse comment line
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continue
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else:
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data = json.loads(chunk_cache + chunk)
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chunk_cache = ""
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yield data
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except Exception as e:
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msg = f"接口返回json错误： ‘{chunk}’。错误信息是：{e}。"
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logger.error(f"{e.__class__.__name__}: {msg}")
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if chunk.startswith("data: "):
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chunk_cache += chunk[6:-2]
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elif chunk.startswith(":"): # skip sse comment line
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continue
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else:
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chunk_cache += chunk
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continue
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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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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Wiki '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(f"{e.__class__.__name__}: {msg}")
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yield {"code": 500, "msg": msg}
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def ret_sync(response, as_json):
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try:
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with response as r:
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chunk_cache = ""
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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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if chunk.startswith("data: "):
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data = json.loads(chunk_cache + chunk[6:-2])
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elif chunk.startswith(":"): # skip sse comment line
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continue
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else:
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data = json.loads(chunk_cache + chunk)
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chunk_cache = ""
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yield data
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except Exception as e:
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msg = f"接口返回json错误： ‘{chunk}’。错误信息是：{e}。"
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logger.error(f"{e.__class__.__name__}: {msg}")
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if chunk.startswith("data: "):
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chunk_cache += chunk[6:-2]
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elif chunk.startswith(":"): # skip sse comment line
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continue
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else:
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chunk_cache += chunk
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continue
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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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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Wiki '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(f"{e.__class__.__name__}: {msg}")
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yield {"code": 500, "msg": msg}
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if self._use_async:
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return ret_async(response, as_json)
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else:
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return ret_sync(response, as_json)
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def _get_response_value(
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self,
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response: httpx.Response,
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as_json: bool = False,
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value_func: Callable = None,
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):
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"""
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转换同步或异步请求返回的响应
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`as_json`: 返回json
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`value_func`: 用户可以自定义返回值，该函数接受response或json
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"""
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def to_json(r):
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try:
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return r.json()
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except Exception as e:
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msg = "API未能返回正确的JSON。" + str(e)
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logger.error(f"{e.__class__.__name__}: {msg}")
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return {"code": 500, "msg": msg, "data": None}
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if value_func is None:
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value_func = lambda r: r
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async def ret_async(response):
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if as_json:
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return value_func(to_json(await response))
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else:
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return value_func(await response)
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if self._use_async:
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return ret_async(response)
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else:
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if as_json:
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return value_func(to_json(response))
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else:
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return value_func(response)
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# 服务器信息
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def get_server_configs(self, **kwargs) -> Dict:
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response = self.post("/server/configs", **kwargs)
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return self._get_response_value(response, as_json=True)
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def get_prompt_template(
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self,
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type: str = "llm_chat",
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name: str = "default",
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**kwargs,
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) -> str:
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data = {
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"type": type,
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"name": name,
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}
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response = self.post("/server/get_prompt_template", json=data, **kwargs)
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return self._get_response_value(response, value_func=lambda r: r.text)
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#LLM对话
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def chat_completion(
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self,
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query: str,
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conversation_id: str = None,
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history_len: int = -1,
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history: List[Dict] = [],
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stream: bool = True,
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model: str = Settings.model_settings.DEFAULT_LLM_MODEL,
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temperature: float = 0.6,
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max_tokens: int = None,
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prompt_name: str = "default",
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**kwargs,
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):
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'''
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对应api.py/chat/completion接口
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'''
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data = {
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"query": query,
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"conversation_id": conversation_id,
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"history_len": history_len,
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"history": history,
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"stream": stream,
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"model_name": model,
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"temperature": temperature,
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"max_tokens": max_tokens,
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"prompt_name": prompt_name,
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}
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# print(f"received input message:")
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# pprint(data)
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logger.info(f"chat_completion:/chat/llm_chat")
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response = self.post("/chat/llm_chat", json=data, stream=True, **kwargs)
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return self._httpx_stream2generator(response, as_json=True)
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# 对话相关操作
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def chat_chat(
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self,
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query: str,
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metadata: dict,
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conversation_id: str = None,
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history_len: int = -1,
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history: List[Dict] = [],
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stream: bool = True,
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chat_model_config: Dict = None,
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tool_config: Dict = None,
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**kwargs,
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):
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"""
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对应api.py/chat/chat接口
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"""
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data = {
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"query": query,
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"metadata": metadata,
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"conversation_id": conversation_id,
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"history_len": history_len,
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"history": history,
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"stream": stream,
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"chat_model_config": chat_model_config,
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"tool_config": tool_config,
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}
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# print(f"received input message:")
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# pprint(data)
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response = self.post("/chat/chat", json=data, stream=True, **kwargs)
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return self._httpx_stream2generator(response, as_json=True)
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def upload_temp_docs(
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self,
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files: List[Union[str, Path, bytes]],
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knowledge_id: str = None,
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chunk_size=Settings.kb_settings.CHUNK_SIZE,
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chunk_overlap=Settings.kb_settings.OVERLAP_SIZE,
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zh_title_enhance=Settings.kb_settings.ZH_TITLE_ENHANCE,
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):
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"""
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对应api.py/knowledge_base/upload_temp_docs接口
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"""
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def convert_file(file, filename=None):
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if isinstance(file, bytes): # raw bytes
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file = BytesIO(file)
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elif hasattr(file, "read"): # a file io like object
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filename = filename or file.name
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else: # a local path
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file = Path(file).absolute().open("rb")
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filename = filename or os.path.split(file.name)[-1]
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return filename, file
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files = [convert_file(file) for file in files]
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data = {
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"knowledge_id": knowledge_id,
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"chunk_size": chunk_size,
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"chunk_overlap": chunk_overlap,
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"zh_title_enhance": zh_title_enhance,
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}
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response = self.post(
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"/knowledge_base/upload_temp_docs",
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data=data,
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files=[("files", (filename, file)) for filename, file in files],
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)
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return self._get_response_value(response, as_json=True)
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def file_chat(
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self,
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query: str,
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knowledge_id: str,
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top_k: int = Settings.kb_settings.VECTOR_SEARCH_TOP_K,
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score_threshold: float = Settings.kb_settings.SCORE_THRESHOLD,
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history: List[Dict] = [],
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stream: bool = True,
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model: str = None,
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temperature: float = 0.9,
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max_tokens: int = None,
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prompt_name: str = "default",
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):
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"""
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对应api.py/chat/file_chat接口
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"""
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data = {
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"query": query,
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"knowledge_id": knowledge_id,
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"top_k": top_k,
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"score_threshold": score_threshold,
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"history": history,
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"stream": stream,
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"model_name": model,
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"temperature": temperature,
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"max_tokens": max_tokens,
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"prompt_name": prompt_name,
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}
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response = self.post(
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"/chat/file_chat",
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json=data,
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stream=True,
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)
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return self._httpx_stream2generator(response, as_json=True)
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# 知识库相关操作
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def list_knowledge_bases(
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self,
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):
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"""
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对应api.py/knowledge_base/list_knowledge_bases接口
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"""
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response = self.get("/knowledge_base/list_knowledge_bases")
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return self._get_response_value(
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response, as_json=True, value_func=lambda r: r.get("data", [])
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)
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def create_knowledge_base(
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self,
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knowledge_base_name: str,
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vector_store_type: str = Settings.kb_settings.DEFAULT_VS_TYPE,
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embed_model: str = get_default_embedding(),
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):
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"""
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对应api.py/knowledge_base/create_knowledge_base接口
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"""
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data = {
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"knowledge_base_name": knowledge_base_name,
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"vector_store_type": vector_store_type,
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"embed_model": embed_model,
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}
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response = self.post(
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"/knowledge_base/create_knowledge_base",
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json=data,
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)
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return self._get_response_value(response, as_json=True)
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def delete_knowledge_base(
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self,
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knowledge_base_name: str,
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):
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"""
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对应api.py/knowledge_base/delete_knowledge_base接口
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"""
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response = self.post(
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"/knowledge_base/delete_knowledge_base",
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json=f"{knowledge_base_name}",
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)
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return self._get_response_value(response, as_json=True)
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def list_kb_docs(
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self,
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knowledge_base_name: str,
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):
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"""
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对应api.py/knowledge_base/list_files接口
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"""
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response = self.get(
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"/knowledge_base/list_files",
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params={"knowledge_base_name": knowledge_base_name},
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)
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return self._get_response_value(
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response, as_json=True, value_func=lambda r: r.get("data", [])
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)
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def search_kb_docs(
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self,
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knowledge_base_name: str,
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query: str = "",
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top_k: int = Settings.kb_settings.VECTOR_SEARCH_TOP_K,
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score_threshold: int = Settings.kb_settings.SCORE_THRESHOLD,
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file_name: str = "",
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metadata: dict = {},
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) -> List:
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"""
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对应api.py/knowledge_base/search_docs接口
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"""
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data = {
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"query": query,
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"knowledge_base_name": knowledge_base_name,
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"top_k": top_k,
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"score_threshold": score_threshold,
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"file_name": file_name,
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"metadata": metadata,
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}
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response = self.post(
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"/knowledge_base/search_docs",
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json=data,
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)
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return self._get_response_value(response, as_json=True)
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def upload_kb_docs(
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self,
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files: List[Union[str, Path, bytes]],
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knowledge_base_name: str,
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override: bool = False,
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to_vector_store: bool = True,
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chunk_size=Settings.kb_settings.CHUNK_SIZE,
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chunk_overlap=Settings.kb_settings.OVERLAP_SIZE,
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zh_title_enhance=Settings.kb_settings.ZH_TITLE_ENHANCE,
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docs: Dict = {},
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not_refresh_vs_cache: bool = False,
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):
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"""
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对应api.py/knowledge_base/upload_docs接口
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"""
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def convert_file(file, filename=None):
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if isinstance(file, bytes): # raw bytes
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file = BytesIO(file)
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elif hasattr(file, "read"): # a file io like object
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||
filename = filename or file.name
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else: # a local path
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file = Path(file).absolute().open("rb")
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filename = filename or os.path.split(file.name)[-1]
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return filename, file
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files = [convert_file(file) for file in files]
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data = {
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"knowledge_base_name": knowledge_base_name,
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"override": override,
|
||
"to_vector_store": to_vector_store,
|
||
"chunk_size": chunk_size,
|
||
"chunk_overlap": chunk_overlap,
|
||
"zh_title_enhance": zh_title_enhance,
|
||
"docs": docs,
|
||
"not_refresh_vs_cache": not_refresh_vs_cache,
|
||
}
|
||
|
||
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._get_response_value(response, as_json=True)
|
||
|
||
def delete_kb_docs(
|
||
self,
|
||
knowledge_base_name: str,
|
||
file_names: List[str],
|
||
delete_content: bool = False,
|
||
not_refresh_vs_cache: bool = False,
|
||
):
|
||
"""
|
||
对应api.py/knowledge_base/delete_docs接口
|
||
"""
|
||
data = {
|
||
"knowledge_base_name": knowledge_base_name,
|
||
"file_names": file_names,
|
||
"delete_content": delete_content,
|
||
"not_refresh_vs_cache": not_refresh_vs_cache,
|
||
}
|
||
|
||
response = self.post(
|
||
"/knowledge_base/delete_docs",
|
||
json=data,
|
||
)
|
||
return self._get_response_value(response, as_json=True)
|
||
|
||
def update_kb_info(self, knowledge_base_name, kb_info):
|
||
"""
|
||
对应api.py/knowledge_base/update_info接口
|
||
"""
|
||
data = {
|
||
"knowledge_base_name": knowledge_base_name,
|
||
"kb_info": kb_info,
|
||
}
|
||
|
||
response = self.post(
|
||
"/knowledge_base/update_info",
|
||
json=data,
|
||
)
|
||
return self._get_response_value(response, as_json=True)
|
||
|
||
def update_kb_docs(
|
||
self,
|
||
knowledge_base_name: str,
|
||
file_names: List[str],
|
||
override_custom_docs: bool = False,
|
||
chunk_size=Settings.kb_settings.CHUNK_SIZE,
|
||
chunk_overlap=Settings.kb_settings.OVERLAP_SIZE,
|
||
zh_title_enhance=Settings.kb_settings.ZH_TITLE_ENHANCE,
|
||
docs: Dict = {},
|
||
not_refresh_vs_cache: bool = False,
|
||
):
|
||
"""
|
||
对应api.py/knowledge_base/update_docs接口
|
||
"""
|
||
data = {
|
||
"knowledge_base_name": knowledge_base_name,
|
||
"file_names": file_names,
|
||
"override_custom_docs": override_custom_docs,
|
||
"chunk_size": chunk_size,
|
||
"chunk_overlap": chunk_overlap,
|
||
"zh_title_enhance": zh_title_enhance,
|
||
"docs": docs,
|
||
"not_refresh_vs_cache": not_refresh_vs_cache,
|
||
}
|
||
logger.info(f"update_kb_docs:{file_names}")
|
||
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._get_response_value(response, as_json=True)
|
||
|
||
def recreate_vector_store(
|
||
self,
|
||
knowledge_base_name: str,
|
||
allow_empty_kb: bool = True,
|
||
vs_type: str = Settings.kb_settings.DEFAULT_VS_TYPE,
|
||
embed_model: str = get_default_embedding(),
|
||
chunk_size=Settings.kb_settings.CHUNK_SIZE,
|
||
chunk_overlap=Settings.kb_settings.OVERLAP_SIZE,
|
||
zh_title_enhance=Settings.kb_settings.ZH_TITLE_ENHANCE,
|
||
):
|
||
"""
|
||
对应api.py/knowledge_base/recreate_vector_store接口
|
||
"""
|
||
data = {
|
||
"knowledge_base_name": knowledge_base_name,
|
||
"allow_empty_kb": allow_empty_kb,
|
||
"vs_type": vs_type,
|
||
"embed_model": embed_model,
|
||
"chunk_size": chunk_size,
|
||
"chunk_overlap": chunk_overlap,
|
||
"zh_title_enhance": zh_title_enhance,
|
||
}
|
||
|
||
response = self.post(
|
||
"/knowledge_base/recreate_vector_store",
|
||
json=data,
|
||
stream=True,
|
||
timeout=None,
|
||
)
|
||
return self._httpx_stream2generator(response, as_json=True)
|
||
|
||
def embed_texts(
|
||
self,
|
||
texts: List[str],
|
||
embed_model: str = get_default_embedding(),
|
||
to_query: bool = False,
|
||
) -> List[List[float]]:
|
||
"""
|
||
对文本进行向量化，可选模型包括本地 embed_models 和支持 embeddings 的在线模型
|
||
"""
|
||
data = {
|
||
"texts": texts,
|
||
"embed_model": embed_model,
|
||
"to_query": to_query,
|
||
}
|
||
resp = self.post(
|
||
"/other/embed_texts",
|
||
json=data,
|
||
)
|
||
return self._get_response_value(
|
||
resp, as_json=True, value_func=lambda r: r.get("data")
|
||
)
|
||
|
||
def chat_feedback(
|
||
self,
|
||
message_id: str,
|
||
score: int,
|
||
reason: str = "",
|
||
) -> int:
|
||
"""
|
||
反馈对话评价
|
||
"""
|
||
data = {
|
||
"message_id": message_id,
|
||
"score": score,
|
||
"reason": reason,
|
||
}
|
||
resp = self.post("/chat/feedback", json=data)
|
||
return self._get_response_value(resp)
|
||
|
||
def list_tools(self) -> Dict:
|
||
"""
|
||
列出所有工具
|
||
"""
|
||
resp = self.get("/tools")
|
||
return self._get_response_value(
|
||
resp, as_json=True, value_func=lambda r: r.get("data", {})
|
||
)
|
||
|
||
def call_tool(
|
||
self,
|
||
name: str,
|
||
tool_input: Dict = {},
|
||
):
|
||
"""
|
||
调用工具
|
||
"""
|
||
data = {
|
||
"name": name,
|
||
"tool_input": tool_input,
|
||
}
|
||
resp = self.post("/tools/call", json=data)
|
||
return self._get_response_value(
|
||
resp, as_json=True, value_func=lambda r: r.get("data")
|
||
)
|
||
|
||
|
||
class AsyncApiRequest(ApiRequest):
|
||
def __init__(
|
||
self, base_url: str = api_address(), timeout: float = Settings.basic_settings.HTTPX_DEFAULT_TIMEOUT
|
||
):
|
||
super().__init__(base_url, timeout)
|
||
self._use_async = True
|
||
|
||
|
||
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 ""
|
||
|
||
|
||
def get_img_base64(file_name: str) -> str:
|
||
"""
|
||
get_img_base64 used in streamlit.
|
||
absolute local path not working on windows.
|
||
"""
|
||
image = f"{Settings.basic_settings.IMG_DIR}/{file_name}"
|
||
# 读取图片
|
||
with open(image, "rb") as f:
|
||
buffer = BytesIO(f.read())
|
||
base_str = base64.b64encode(buffer.getvalue()).decode()
|
||
return f"data:image/png;base64,{base_str}"
|
||
|
||
|
||
if __name__ == "__main__":
|
||
api = ApiRequest()
|
||
aapi = AsyncApiRequest()
|
||
|
||
# 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())
|