mindsearch codespace版本 (#1759)

LmYjQ · web-flow · commit a240748b9e90 · 2024-08-22T22:40:24.000+08:00
* github mindsearch
diff --git a/docs/L2/MindSearch/readme_github.md b/docs/L2/MindSearch/readme_github.md
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+# MindSearch CPU-only 版在github codespace部署
+
+和[原有的CPU版本](https://github.com/InternLM/Tutorial/blob/camp3/docs/L2/MindSearch/readme.md)相比区别是把internstudio换成了github codespace。
+
+随着硅基流动提供了免费的 InternLM2.5-7B-Chat 服务（免费的 InternLM2.5-7B-Chat 真的很香），MindSearch 的部署与使用也就迎来了纯 CPU 版本，进一步降低了部署门槛。那就让我们来一起看看如何使用硅基流动的 API 来部署 MindSearch 吧。
+
+## 1. 创建开发机 & 环境配置
+
+打开[codespace主页](https://github.com/codespaces)，选择blank template。
+
+![image](https://github.com/user-attachments/assets/27e7a7ef-37e0-4614-89bf-c96044e3c6f3)
+
+浏览器会自动在新的页面打开一个web版的vscode。
+
+<img width="1591" alt="image" src="https://github.com/user-attachments/assets/58727fec-8d83-417d-88e5-eedc631444f2">
+
+接下来的操作就和我们使用vscode基本没差别了。
+
+然后我们新建一个目录用于存放 MindSearch 的相关代码，并把 MindSearch 仓库 clone 下来。在终端中运行下面的命令：
+
+```bash
+mkdir -p /workspaces/mindsearch
+cd /workspaces/mindsearch
+git clone https://github.com/InternLM/MindSearch.git
+cd MindSearch && git checkout b832275 && cd ..
+```
+
+接下来，我们创建一个 conda 环境来安装相关依赖。
+
+```bash
+# 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 /workspaces/mindsearch/MindSearch/requirements.txt
+```
+
+## 2. 获取硅基流动 API Key
+
+因为要使用硅基流动的 API Key，所以接下来便是注册并获取 API Key 了。
+
+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。
+
+![image](https://github.com/user-attachments/assets/7905a2fc-ef30-4e33-b214-274bebdc9251)
+
+## 3. 启动 MindSearch
+
+### 3.1 启动后端
+
+由于硅基流动 API 的相关配置已经集成在了 MindSearch 中，所以我们可以直接执行下面的代码来启动 MindSearch 的后端。
+
+```bash
+export SILICON_API_KEY=第二步中复制的密钥
+conda activate mindsearch
+cd /workspaces/mindsearch/MindSearch
+python -m mindsearch.app --lang cn --model_format internlm_silicon --search_engine DuckDuckGoSearch
+```
+
+### 3.2 启动前端
+
+在后端启动完成后，我们打开新终端运行如下命令来启动 MindSearch 的前端。
+
+```bash
+conda activate mindsearch
+cd /workspaces/mindsearch/MindSearch
+python frontend/mindsearch_gradio.py
+```
+
+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。
+
+<img width="1183" alt="image" src="https://github.com/user-attachments/assets/4ee76ca2-06a5-4145-829a-1310e69c0d83">
+
+
+由于使用codespace，这里我们不需要使用ssh端口转发了，github会自动提示我们打开一个在公网的前端地址。
+
+<img width="600" alt="image" src="https://github.com/user-attachments/assets/545d5827-6ee3-416a-a913-1be09866f29e">
+
+
+然后就可以即刻体验啦。
+
+<img width="1489" alt="image" src="https://github.com/user-attachments/assets/28f5658c-19a6-4a46-9bc9-51f4923a012c">
+
+如果遇到了 timeout 的问题，可以按照 [文档](./readme_gpu.md#2-使用-bing-的接口) 换用 Bing 的搜索接口。
+
+## 4. 部署到 HuggingFace Space
+
+最后，我们来将 MindSearch 部署到 HuggingFace Space。
+
+我们首先打开 https://huggingface.co/spaces ，并点击 Create new Space，如下图所示。
+
+![image](https://github.com/user-attachments/assets/bacbe161-2d21-434e-8f78-5738b076cd74)
+
+在输入 Space name 并选择 License 后，选择配置如下所示。
+
+![image](https://github.com/user-attachments/assets/f4d98e6b-5352-4638-a3da-d090140ce3f6)
+
+然后，我们进入 Settings，配置硅基流动的 API Key。如下图所示。
+
+![image](https://github.com/user-attachments/assets/76947d6c-eeba-4230-ab77-04a98c60d4d3)
+
+选择 New secrets，name 一栏输入 SILICON_API_KEY，value 一栏输入你的 API Key 的内容。
+
+![image](https://github.com/user-attachments/assets/6f4ab268-c5d6-4106-8749-ad282e17ba35)
+
+最后，我们先新建一个目录，准备提交到 HuggingFace Space 的全部文件。
+
+```bash
+# 创建新目录
+mkdir -p /workspaces/mindsearch/mindsearch_deploy
+# 准备复制文件
+cd /workspaces/mindsearch
+cp -r /workspaces/mindsearch/MindSearch/mindsearch /workspaces/mindsearch/mindsearch_deploy
+cp /workspaces/mindsearch/MindSearch/requirements.txt /workspaces/mindsearch/mindsearch_deploy
+# 创建 app.py 作为程序入口
+touch /workspaces/mindsearch/mindsearch_deploy/app.py
+```
+
+其中，app.py 的内容如下：
+
+```python
+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;">🔗 GitHub</a>
+        <a href="https://arxiv.org/abs/2407.20183" style="margin-right: 15px; text-decoration: none; color: #4A90E2;">📄 Arxiv</a>
+        <a href="https://huggingface.co/papers/2407.20183" style="margin-right: 15px; text-decoration: none; color: #4A90E2;">📚 Hugging Face Papers</a>
+        <a href="https://huggingface.co/spaces/internlm/MindSearch" style="text-decoration: none; color: #4A90E2;">🤗 Hugging Face Demo</a>
+    </div>
+    """)
+    with gr.Row():
+        with gr.Column(scale=10):
+            with gr.Row():
+                with gr.Column():
+                    planner = gr.Chatbot(label='planner',
+                                         height=700,
+                                         show_label=True,
+                                         show_copy_button=True,
+                                         bubble_full_width=False,
+                                         render_markdown=True)
+                with gr.Column():
+                    searcher = gr.Chatbot(label='searcher',
+                                          height=700,
+                                          show_label=True,
+                                          show_copy_button=True,
+                                          bubble_full_width=False,
+                                          render_markdown=True)
+            with gr.Row():
+                user_input = gr.Textbox(show_label=False,
+                                        placeholder='帮我搜索一下 InternLM 开源体系',
+                                        lines=5,
+                                        container=False)
+            with gr.Row():
+                with gr.Column(scale=2):
+                    submitBtn = gr.Button('Submit')
+                with gr.Column(scale=1, min_width=20):
+                    emptyBtn = gr.Button('Clear History')
+
+    def user(query, history):
+        return '', history + [[query, '']]
+
+    submitBtn.click(user, [user_input, planner], [user_input, planner],
+                    queue=False).then(predict, [planner, searcher],
+                                      [planner, searcher])
+    emptyBtn.click(rst_mem, [planner, searcher], [planner, searcher],
+                   queue=False)
+
+demo.queue()
+demo.launch(server_name='0.0.0.0',
+            server_port=7860,
+            inbrowser=True,
+            share=True)
+```
+
+在最后，将 /root/mindsearch/mindsearch_deploy 目录下的文件（使用 git）提交到 HuggingFace Space 即可完成部署了。注意将代码提交到huggingface space中需要配置hugginface的token。
