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("""

MindSearch Gradio Demo

""") gr.HTML("""

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).

""") gr.HTML("""
🔗 GitHub 📄 Arxiv 📚 Hugging Face Papers 🤗 Hugging Face Demo
""") 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)