文章目录
闯关任务
任务描述：MindSearch CPU-only 版部署
任务文档：MindSearch CPU-only 版部署
完成结果
- 按照教程，将 MindSearch 部署到 HuggingFace，并提供截图。
新建一个目录用于存放 MindSearch 的相关代码，并把 MindSearch 仓库 clone 下来：
mkdir -p /root/mindsearch
cd /root/mindsearch
git clone https://github.com/InternLM/MindSearch.git
cd MindSearch && git checkout b832275 && cd ..
环境配置：
# 创建环境
conda create -n mindsearch python=3.10 -y
# 激活环境
conda activate mindsearch
# 安装依赖
pip install -r /root/mindsearch/MindSearch/requirements.txt
获取硅基流动 API Key：
首先，我们打开 https://account.siliconflow.cn/login 来注册硅基流动的账号（如果注册过，则直接登录即可）。
在完成注册后，打开 https://cloud.siliconflow.cn/account/ak 来准备 API Key。首先创建新 API 密钥，然后点击密钥进行复制，以备后续使用。
启动 MindSearch：
(1) 启动后端；
export SILICON_API_KEY=第二步中复制的密钥
conda activate mindsearch
cd /root/mindsearch/MindSearch
python -m mindsearch.app --lang cn --model_format internlm_silicon --search_engine DuckDuckGoSearch
(2) 启动前端；
conda activate mindsearch
cd /root/mindsearch/MindSearch
python frontend/mindsearch_gradio.py
(3) 我们把 8002 端口和 7882 端口都映射到本地；
ssh -CNg -L 8002:127.0.0.1:8002 -L 7882:127.0.0.1:7882 [email protected] -p <你的 SSH 端口号>
(4) 在本地浏览器中打开 localhost:7882 即可体验；
部署到github codespace：
(1) 打开codespace主页，选择blank template；
(2) 浏览器会自动在新的页面打开一个web版的vscode；
(3) 按照上述过程clone MindSearch仓库、配置环境和获取硅基流动 API Key；
(4) 启动后端和启动前端；
前后端都启动后，我们应该可以看到github自动为这两个进程做端口转发；
(5) 不需要使用ssh端口转发，github会自动提示打开一个在公网的前端地址即可体验；
部署到 HuggingFace Space：
(1) 首先打开 https://huggingface.co/spaces ，并点击 Create new Space，如下图所示；
(2) 在输入 Space name 并选择 License 后，选择配置如下所示；
(3) 进入 Settings，配置硅基流动的 API Key，如下图所示；
(4) 选择 New secrets，name 一栏输入 SILICON_API_KEY，value 一栏输入你的 API Key 的内容；
(5) 先新建一个目录，准备提交到 HuggingFace Space 的全部文件。
# 创建新目录
mkdir -p /root/mindsearch/mindsearch_deploy
# 准备复制文件
cd /root/mindsearch
cp -r /root/mindsearch/MindSearch/mindsearch /root/mindsearch/mindsearch_deploy
cp /root/mindsearch/MindSearch/requirements.txt /root/mindsearch/mindsearch_deploy
# 创建 app.py 作为程序入口
touch /root/mindsearch/mindsearch_deploy/app.py
app.py文件的内容如下：
import json
import os
import gradio as gr
import requests
from lagent.schema import AgentStatusCode
os.system("python -m mindsearch.app --lang cn --model_format internlm_silicon &")
PLANNER_HISTORY = []
SEARCHER_HISTORY = []
def rst_mem(history_planner: list, history_searcher: list):
'''
Reset the chatbot memory.
'''
history_planner = []
history_searcher = []
if PLANNER_HISTORY:
PLANNER_HISTORY.clear()
return history_planner, history_searcher
def format_response(gr_history, agent_return):
if agent_return['state'] in [
AgentStatusCode.STREAM_ING, AgentStatusCode.ANSWER_ING
]:
gr_history[-1][1] = agent_return['response']
elif agent_return['state'] == AgentStatusCode.PLUGIN_START:
thought = gr_history[-1][1].split('```')[0]
if agent_return['response'].startswith('```'):
gr_history[-1][1] = thought + '\n' + agent_return['response']
elif agent_return['state'] == AgentStatusCode.PLUGIN_END:
thought = gr_history[-1][1].split('```')[0]
if isinstance(agent_return['response'], dict):
gr_history[-1][
1] = thought + '\n' + f'```json\n{json.dumps(agent_return["response"], ensure_ascii=False, indent=4)}\n```' # noqa: E501
elif agent_return['state'] == AgentStatusCode.PLUGIN_RETURN:
assert agent_return['inner_steps'][-1]['role'] == 'environment'
item = agent_return['inner_steps'][-1]
gr_history.append([
None,
f"```json\n{json.dumps(item['content'], ensure_ascii=False, indent=4)}\n```"
])
gr_history.append([None, ''])
return
def predict(history_planner, history_searcher):
def streaming(raw_response):
for chunk in raw_response.iter_lines(chunk_size=8192,
decode_unicode=False,
delimiter=b'\n'):
if chunk:
decoded = chunk.decode('utf-8')
if decoded == '\r':
continue
if decoded[:6] == 'data: ':
decoded = decoded[6:]
elif decoded.startswith(': ping - '):
continue
response = json.loads(decoded)
yield (response['response'], response['current_node'])
global PLANNER_HISTORY
PLANNER_HISTORY.append(dict(role='user', content=history_planner[-1][0]))
new_search_turn = True
url = 'http://localhost:8002/solve'
headers = {'Content-Type': 'application/json'}
data = {'inputs': PLANNER_HISTORY}
raw_response = requests.post(url,
headers=headers,
data=json.dumps(data),
timeout=20,
stream=True)
for resp in streaming(raw_response):
agent_return, node_name = resp
if node_name:
if node_name in ['root', 'response']:
continue
agent_return = agent_return['nodes'][node_name]['detail']
if new_search_turn:
history_searcher.append([agent_return['content'], ''])
new_search_turn = False
format_response(history_searcher, agent_return)
if agent_return['state'] == AgentStatusCode.END:
new_search_turn = True
yield history_planner, history_searcher
else:
new_search_turn = True
format_response(history_planner, agent_return)
if agent_return['state'] == AgentStatusCode.END:
PLANNER_HISTORY = agent_return['inner_steps']
yield history_planner, history_searcher
return history_planner, history_searcher
with gr.Blocks() as demo:
gr.HTML("""<h1 align="center">MindSearch Gradio Demo</h1>""")
gr.HTML("""<p style="text-align: center; font-family: Arial, sans-serif;">MindSearch is an open-source AI Search Engine Framework with Perplexity.ai Pro performance. You can deploy your own Perplexity.ai-style search engine using either closed-source LLMs (GPT, Claude) or open-source LLMs (InternLM2.5-7b-chat).</p>""")
gr.HTML("""
<div style="text-align: center; font-size: 16px;">
<a href="https://github.com/InternLM/MindSearch" style="margin-right: 15px; text-decoration: none; color: #4A90E2;">
标签：MindSearch,进阶,return,gr,agent,书生,mindsearch,history
From： https://blog.csdn.net/wocsdn111/article/details/141434094