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改变api视图函数的sync/async，提高api并发能力： (#1414) 

1. 4个chat类接口改为async
2. 知识库操作，涉及向量库修改的使用async，避免FAISS写入错误；涉及向量库读取的改为sync，提高并发
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liunux4odoo 2023-09-08 12:25:02 +08:00 committed by GitHub
parent 1195eb75eb
commit 775870a516
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7 changed files with 48 additions and 45 deletions
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4
server/chat/chat.py
View File

@ -12,8 +12,8 @@ from typing import List
from server.chat.utils import History
def chat(query: str = Body(..., description="用户输入", examples=["恼羞成怒"]),
history: List[History] = Body([],
async def chat(query: str = Body(..., description="用户输入", examples=["恼羞成怒"]),
history: List[History] = Body([],
description="历史对话",
examples=[[
{"role": "user", "content": "我们来玩成语接龙，我先来，生龙活虎"},

18
server/chat/knowledge_base_chat.py
View File

@ -18,11 +18,11 @@ from urllib.parse import urlencode
from server.knowledge_base.kb_doc_api import search_docs
def knowledge_base_chat(query: str = Body(..., description="用户输入", examples=["你好"]),
knowledge_base_name: str = Body(..., description="知识库名称", examples=["samples"]),
top_k: int = Body(VECTOR_SEARCH_TOP_K, description="匹配向量数"),
score_threshold: float = Body(SCORE_THRESHOLD, description="知识库匹配相关度阈值，取值范围在0-1之间，SCORE越小，相关度越高，取到1相当于不筛选，建议设置在0.5左右", ge=0, le=1),
history: List[History] = Body([],
async def knowledge_base_chat(query: str = Body(..., description="用户输入", examples=["你好"]),
knowledge_base_name: str = Body(..., description="知识库名称", examples=["samples"]),
top_k: int = Body(VECTOR_SEARCH_TOP_K, description="匹配向量数"),
score_threshold: float = Body(SCORE_THRESHOLD, description="知识库匹配相关度阈值，取值范围在0-1之间，SCORE越小，相关度越高，取到1相当于不筛选，建议设置在0.5左右", ge=0, le=1),
history: List[History] = Body([],
description="历史对话",
examples=[[
{"role": "user",
@ -30,10 +30,10 @@ def knowledge_base_chat(query: str = Body(..., description="用户输入", examp
{"role": "assistant",
"content": "虎头虎脑"}]]
),
stream: bool = Body(False, description="流式输出"),
model_name: str = Body(LLM_MODEL, description="LLM 模型名称。"),
local_doc_url: bool = Body(False, description="知识文件返回本地路径(true)或URL(false)"),
request: Request = None,
stream: bool = Body(False, description="流式输出"),
model_name: str = Body(LLM_MODEL, description="LLM 模型名称。"),
local_doc_url: bool = Body(False, description="知识文件返回本地路径(true)或URL(false)"),
request: Request = None,
):
kb = KBServiceFactory.get_service_by_name(knowledge_base_name)
if kb is None:

9
server/chat/openai_chat.py
View File

@ -29,13 +29,13 @@ async def openai_chat(msg: OpenAiChatMsgIn):
print(f"{openai.api_base=}")
print(msg)
def get_response(msg):
async def get_response(msg):
data = msg.dict()
try:
response = openai.ChatCompletion.create(**data)
response = await openai.ChatCompletion.acreate(**data)
if msg.stream:
for data in response:
async for data in response:
if choices := data.choices:
if chunk := choices[0].get("delta", {}).get("content"):
print(chunk, end="", flush=True)
@ -46,8 +46,7 @@ async def openai_chat(msg: OpenAiChatMsgIn):
print(answer)
yield(answer)
except Exception as e:
print(type(e))
logger.error(e)
logger.error(f"获取ChatCompletion时出错：{e}")
return StreamingResponse(
get_response(msg),

34
server/chat/search_engine_chat.py
View File

@ -2,6 +2,7 @@ from langchain.utilities import BingSearchAPIWrapper, DuckDuckGoSearchAPIWrapper
from configs.model_config import BING_SEARCH_URL, BING_SUBSCRIPTION_KEY
from fastapi import Body
from fastapi.responses import StreamingResponse
from fastapi.concurrency import run_in_threadpool
from configs.model_config import (llm_model_dict, LLM_MODEL, SEARCH_ENGINE_TOP_K, PROMPT_TEMPLATE)
from server.chat.utils import wrap_done
from server.utils import BaseResponse
@ -47,29 +48,30 @@ def search_result2docs(search_results):
return docs
def lookup_search_engine(
async def lookup_search_engine(
query: str,
search_engine_name: str,
top_k: int = SEARCH_ENGINE_TOP_K,
):
results = SEARCH_ENGINES[search_engine_name](query, result_len=top_k)
search_engine = SEARCH_ENGINES[search_engine_name]
results = await run_in_threadpool(search_engine, query, result_len=top_k)
docs = search_result2docs(results)
return docs
def search_engine_chat(query: str = Body(..., description="用户输入", examples=["你好"]),
search_engine_name: str = Body(..., description="搜索引擎名称", examples=["duckduckgo"]),
top_k: int = Body(SEARCH_ENGINE_TOP_K, description="检索结果数量"),
history: List[History] = Body([],
description="历史对话",
examples=[[
{"role": "user",
"content": "我们来玩成语接龙，我先来，生龙活虎"},
{"role": "assistant",
"content": "虎头虎脑"}]]
),
stream: bool = Body(False, description="流式输出"),
model_name: str = Body(LLM_MODEL, description="LLM 模型名称。"),
async def search_engine_chat(query: str = Body(..., description="用户输入", examples=["你好"]),
search_engine_name: str = Body(..., description="搜索引擎名称", examples=["duckduckgo"]),
top_k: int = Body(SEARCH_ENGINE_TOP_K, description="检索结果数量"),
history: List[History] = Body([],
description="历史对话",
examples=[[
{"role": "user",
"content": "我们来玩成语接龙，我先来，生龙活虎"},
{"role": "assistant",
"content": "虎头虎脑"}]]
),
stream: bool = Body(False, description="流式输出"),
model_name: str = Body(LLM_MODEL, description="LLM 模型名称。"),
):
if search_engine_name not in SEARCH_ENGINES.keys():
return BaseResponse(code=404, msg=f"未支持搜索引擎 {search_engine_name}")
@ -96,7 +98,7 @@ def search_engine_chat(query: str = Body(..., description="用户输入", exampl
openai_proxy=llm_model_dict[model_name].get("openai_proxy")
)
docs = lookup_search_engine(query, search_engine_name, top_k)
docs = await lookup_search_engine(query, search_engine_name, top_k)
context = "\n".join([doc.page_content for doc in docs])
input_msg = History(role="user", content=PROMPT_TEMPLATE).to_msg_template(False)

2
server/knowledge_base/kb_api.py
View File

@ -7,7 +7,7 @@ from configs.model_config import EMBEDDING_MODEL, logger
from fastapi import Body
async def list_kbs():
def list_kbs():
# Get List of Knowledge Base
return ListResponse(data=list_kbs_from_db())

10
server/knowledge_base/kb_doc_api.py
View File

@ -34,7 +34,7 @@ def search_docs(query: str = Body(..., description="用户输入", examples=["
return data
async def list_files(
def list_files(
knowledge_base_name: str
) -> ListResponse:
if not validate_kb_name(knowledge_base_name):
@ -258,10 +258,10 @@ async def update_docs(
return BaseResponse(code=200, msg=f"更新文档完成", data={"failed_files": failed_files})
async def download_doc(
knowledge_base_name: str = Query(...,description="知识库名称", examples=["samples"]),
file_name: str = Query(...,description="文件名称", examples=["test.txt"]),
preview: bool = Query(False, description="是：浏览器内预览；否：下载"),
def download_doc(
knowledge_base_name: str = Query(...,description="知识库名称", examples=["samples"]),
file_name: str = Query(...,description="文件名称", examples=["test.txt"]),
preview: bool = Query(False, description="是：浏览器内预览；否：下载"),
):
'''
下载知识库文档

16
webui_pages/utils.py
View File

@ -48,6 +48,8 @@ class ApiRequest:
self.base_url = base_url
self.timeout = timeout
self.no_remote_api = no_remote_api
if no_remote_api:
logger.warn("将来可能取消对no_remote_api的支持，更新版本时请注意。")
def _parse_url(self, url: str) -> str:
if (not url.startswith("http")
@ -270,7 +272,7 @@ class ApiRequest:
if no_remote_api:
from server.chat.openai_chat import openai_chat
response = openai_chat(msg)
response = run_async(openai_chat(msg))
return self._fastapi_stream2generator(response)
else:
data = msg.dict(exclude_unset=True, exclude_none=True)
@ -280,7 +282,7 @@ class ApiRequest:
response = self.post(
"/chat/fastchat",
json=data,
stream=stream,
stream=True,
)
return self._httpx_stream2generator(response)
@ -310,7 +312,7 @@ class ApiRequest:
if no_remote_api:
from server.chat.chat import chat
response = chat(**data)
response = run_async(chat(**data))
return self._fastapi_stream2generator(response)
else:
response = self.post("/chat/chat", json=data, stream=True)
@ -349,7 +351,7 @@ class ApiRequest:
if no_remote_api:
from server.chat.knowledge_base_chat import knowledge_base_chat
response = knowledge_base_chat(**data)
response = run_async(knowledge_base_chat(**data))
return self._fastapi_stream2generator(response, as_json=True)
else:
response = self.post(
@ -387,7 +389,7 @@ class ApiRequest:
if no_remote_api:
from server.chat.search_engine_chat import search_engine_chat
response = search_engine_chat(**data)
response = run_async(search_engine_chat(**data))
return self._fastapi_stream2generator(response, as_json=True)
else:
response = self.post(
@ -427,7 +429,7 @@ class ApiRequest:
if no_remote_api:
from server.knowledge_base.kb_api import list_kbs
response = run_async(list_kbs())
response = list_kbs()
return response.data
else:
response = self.get("/knowledge_base/list_knowledge_bases")
@ -499,7 +501,7 @@ class ApiRequest:
if no_remote_api:
from server.knowledge_base.kb_doc_api import list_files
response = run_async(list_files(knowledge_base_name))
response = list_files(knowledge_base_name)
return response.data
else:
response = self.get(