147 lines
5.1 KiB
Python
147 lines
5.1 KiB
Python
import json
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import streamlit as st
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import torch
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from transformers import AutoModel, AutoTokenizer
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# from api import get_docs
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from server.knowledge_base.kb_doc_api import search_docs
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# 设置页面标题、图标和布局
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st.set_page_config(
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page_title="ChatGLM3-6B 演示",
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page_icon=":robot:",
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layout="wide"
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)
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# 设置为模型ID或本地文件夹路径
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model_path = "THUDM/chatglm3-6b-32k"
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def get_docs(query, top_k, score_threshold, knowledge_base_name):
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docs = search_docs(query, knowledge_base_name, top_k, score_threshold)
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context1 = "\n".join([doc.page_content for doc in docs])
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return context1
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@st.cache_resource
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def get_model():
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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model = AutoModel.from_pretrained(model_path, trust_remote_code=True).cuda()
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# 多显卡支持,使用下面两行代替上面一行,将num_gpus改为你实际的显卡数量
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# from utils import load_model_on_gpus
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# model = load_model_on_gpus("THUDM/chatglm3-6b", num_gpus=2)
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model = model.eval()
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return tokenizer, model
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# 加载Chatglm3的model和tokenizer
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tokenizer, model = get_model()
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system_prompt = """你是一个农业领域的AI助手，你需要通过已知信息和历史对话来回答人类的问题。
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特别注意：已知信息不一定是正确答案，还要根据对话的历史问题和答案进行思考回答，如果找不到答案，请用自己的知识进行回答，不可以乱回答！！！
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答案使用中文。\n"""
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# 初始化历史记录和past key values
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if "history" not in st.session_state:
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st.session_state.history = [{'role': 'system', 'content': system_prompt}]
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if "past_key_values" not in st.session_state:
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st.session_state.past_key_values = None
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if "all_prompt_text" not in st.session_state:
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st.session_state.all_prompt_text = []
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# 设置max_length、top_p和temperature
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max_length = st.sidebar.slider("max_length", 0, 32768, 32768, step=1024)
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top_p = st.sidebar.slider("top_p", 0.0, 1.0, 0.8, step=0.01)
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temperature = st.sidebar.slider("temperature", 0.0, 1.0, 0.6, step=0.01)
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# 清理会话历史
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buttonClean = st.sidebar.button("清理会话历史", key="clean")
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if buttonClean:
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st.session_state.history = []
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st.session_state.past_key_values = None
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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st.rerun()
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# 渲染聊天历史记录
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for i, message in enumerate(st.session_state.history):
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if message["role"] == "user":
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with st.chat_message(name="user", avatar="user"):
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st.markdown(message["content"])
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else:
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with st.chat_message(name="assistant", avatar="assistant"):
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st.markdown(message["content"])
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# 输入框和输出框
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with st.chat_message(name="user", avatar="user"):
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input_placeholder = st.empty()
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with st.chat_message(name="assistant", avatar="assistant"):
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message_placeholder = st.empty()
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def build_prompt(prompt_text, new_query):
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knowledge_base_name = 'agriculture'
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context = get_docs(new_query, 3, 1, knowledge_base_name)
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print("query：", new_query)
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print("context：", context)
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prompt = f"\n\n<已知信息>：\n{context}\n</已知信息>\n\n"
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prompt += f"\n\n<问题>：{prompt_text}</问题>\n\n"
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return prompt
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# 获取用户输入
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prompt_text = st.chat_input("请输入您的问题")
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# 如果用户输入了内容,则生成回复
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if prompt_text:
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input_placeholder.markdown(prompt_text)
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history = st.session_state.history
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with open('a.json', 'w') as f:
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json.dump(history, f, ensure_ascii=False)
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past_key_values = st.session_state.past_key_values
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new_query = ""
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assistants = []
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# 历史问题
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st.session_state.all_prompt_text.append(prompt_text)
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# 历史助手
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for h in history:
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# 助手
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if h['role'] == 'assistant':
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assistants.append(h['content'])
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# 将历史问题和历史助手组合成新问题
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for i in range(len(st.session_state.all_prompt_text)):
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new_query += st.session_state.all_prompt_text[i]
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if i < len(st.session_state.all_prompt_text) - 1:
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new_query += ","
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else:
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new_query += "?"
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if i < len(assistants):
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new_query += assistants[i]
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history = []
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for k, v in enumerate(assistants):
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a = assistants[k]
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q = st.session_state.all_prompt_text[k]
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history.append({'role': 'user', 'content': q})
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history.append({'role': 'user', 'content': a})
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prompt = build_prompt(prompt_text, new_query)
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# TODO
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# 控制历史信息、滑动窗口的长度！
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for response, history, past_key_values in model.stream_chat(
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tokenizer,
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prompt,
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history,
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past_key_values=past_key_values,
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max_length=max_length,
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top_p=top_p,
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temperature=temperature,
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return_past_key_values=True,
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):
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message_placeholder.markdown(response)
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# 历史assistant
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# print(history)
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# 更新历史记录和past key values
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st.session_state.history = history
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st.session_state.past_key_values = past_key_values
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