import sys
import os
import pandas as pd
from PyQt5
.QtWidgets import *
from PyQt5
.QtCore import *
from PyQt5
.QtGui import *
from ollama import Client
import numpy as np
import re
import torch
import torch
.nn as nn
# LSTM模型类
class LSTMModel(nn
.Module):
def __init__(
self, input_size, hidden_size, num_layers, output_size):
super(LSTMModel,
self)
.__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.lstm = nn
.LSTM(input_size, hidden_size, num_layers, batch_first=True)
self.fc = nn
.Linear(hidden_size, output_size)
def forward(
self, x):
h0 = torch
.zeros(
self.num_layers, x
.size(0),
self.hidden_size)
.to(x
.device)
c0 = torch
.zeros(
self.num_layers, x
.size(0),
self.hidden_size)
.to(x
.device)
out, _ =
self.lstm(x, (h0, c0))
out =
self.fc(out[:, -1, :])
return out
# 向量相似度计算工具
class VectorSimilarity:
@staticmethod
def cosine_similarity(vec1, vec2):
if len(vec1) == 0 or len(vec2) == 0:
return 0
dot_
product = np
.dot(vec1, vec2)
norm_vec1 = np
.linalg
.norm(vec1)
norm_vec2 = np
.linalg
.norm(vec2)
if norm_vec1 == 0 or norm_vec2 == 0:
return 0
return dot_
product / (norm_vec1 * norm_vec2)
@staticmethod
def euclidean_distance(vec1, vec2):
return np
.linalg
.norm(vec1 - vec2)
@staticmethod
def combined_similarity(vec1, vec2):
cosine = VectorSimilarity
.cosine_similarity(vec1, vec2)
euclidean = VectorSimilarity
.euclidean_distance(vec1, vec2)
if euclidean > 0:
euclidean_sim = 1 / (1 + euclidean)
else:
euclidean_sim = 1
return 0
.7 * cosine + 0
.3 * euclidean_sim
# 知识库处理类
class KnowledgeBase:
def __init__(
self):
self.texts = {}
self.vectors = {}
self.metadata = {}
self.similarity_threshold = 0
.3
self.file_paths = {}
def load_from_excel(
self, file_path, kb_type):
try:
df = pd
.read_excel(file_path)
vector_cols = [col for col in df
.columns if col
.startswith('向量化特征_')]
if not vector_cols:
raise ValueError("未找到向量化特征列")
self.texts[kb_type] = []
self.vectors[kb_type] = []
self.metadata[kb_type] = []
for _, row in df
.iterrows():
text = str(row
.get('文本块', ''))
if not text:
continue
vector = row[vector_cols]
.values
.astype(np
.float32)
metadata = {
'来源': row
.get('来源', ''),
'类别': row
.get('类别', ''),
}
self.texts[kb_type]
.append(text)
self.vectors[kb_type]
.append(vector)
self.metadata[kb_type]
.append(metadata)
self._calculate_similarity_threshold(kb_type)
self.file_paths[kb_type] = file_path
return len(
self.texts[kb_type])
except Exception as e:
print(f"加载知识库失败: {e}")
return 0
def _calculate_similarity_threshold(
self, kb_type):
if not
self.vectors[kb_type] or len(
self.vectors[kb_type]) < 2:
return
similarities = []
for i in range(min(100, len(
self.vectors[kb_type]))):
for j in range(i + 1, min(100, len(
self.vectors[kb_type]))):
sim = VectorSimilarity
.combined_similarity(
self.vectors[kb_type][i],
self.vectors[kb_type][j])
similarities
.append(sim)
if similarities:
self.similarity_threshold = max(0
.2, np
.mean(similarities) * 0
.7)
print(f"相似度阈值: {
self.similarity_threshold:
.4f}")
def search(
self, query_vector, kb_type, top_k=10):
if kb_type not in
self.vectors or not
self.vectors[kb_type]:
return []
results = []
for i, vector in enumerate(
self.vectors[kb_type]):
sim = VectorSimilarity
.combined_similarity(query_vector, vector)
if sim >=
self.similarity_threshold:
results
.append((i, sim))
results
.sort(key=lambda x: x[1], reverse=True)
return results[:top_k]
def add_entry(
self, kb_type, text, source, category, vector):
if kb_type not in
self.texts:
self.texts[kb_type] = []
self.vectors[kb_type] = []
self.metadata[kb_type] = []
self.texts[kb_type]
.append(text)
self.vectors[kb_type]
.append(vector)
self.metadata[kb_type]
.append({
'来源': source,
'类别': category
})
self.save_to_excel(kb_type)
def save_to_excel(
self, kb_type):
if kb_type not in
self.file_paths:
return
file_path =
self.file_paths[kb_type]
df = pd
.DataFrame({
'文本块':
self.texts[kb_type],
**{f'向量化特征_{i}': [vec[i] for vec in
self.vectors[kb_type]] for i in range(len(
self.vectors[kb_type][0]))},
'来源': [meta['来源'] for meta in
self.metadata[kb_type]],
'类别': [meta['类别'] for meta in
self.metadata[kb_type]]
})
df
.to_excel(file_path, index=False)
def edit_entry(
self, kb_type, index, text, source, category, vector):
if kb_type in
self.texts and 0 <= index < len(
self.texts[kb_type]):
self.texts[kb_type][index] = text
self.vectors[kb_type][index] = vector
self.metadata[kb_type][index] = {
'来源': source,
'类别': category
}
self.save_to_excel(kb_type)
# AI处理线程
class AIWorker(QThread):
thinking_signal = pyqtSignal(str) # 发送思考过程
answer_signal = pyqtSignal(str) # 发送最终回答
finish_signal = pyqtSignal()
def __init__(
self, client, query, knowledge_base, kb_type):
super()
.__init__()
self.client = client
self.query = query
self.knowledge_base = knowledge_base
self.kb_type = kb_type
def run(
self):
try:
# 发送思考过程开始的信号
self.thinking_signal
.emit("<b>知识库检索中
...</b>")
# 模拟生成查询向量
query_vector = np
.random
.rand(len(
self.knowledge_base
.vectors[
self.kb_type][0]) if
self.knowledge_base
.vectors[
self.kb_type] else 50)
# 执行搜索
search_results =
self.knowledge_base
.search(query_vector,
self.kb_type, top_k=5)
if not search_results:
self.thinking_signal
.emit("<p style=\"color:#888888;\">未找到匹配的知识库内容</p>")
thinking_process = "无"
else:
# 构建HTML格式的思考过程
thinking_html = "<p><b>匹配到的知识库内容：</b></p><ul>"
for i, (idx, sim) in enumerate(search_results):
text =
self.knowledge_base
.texts[
self.kb_type][idx]
metadata =
self.knowledge_base
.metadata[
self.kb_type][idx]
thinking_html += f"<li><b>匹配项 {i + 1} (相似度: {sim:
.4f})</b><br>"
thinking_html += f"<span style=\"color:#6c757d;\">来源: {metadata['来源']} | 类别: {metadata['类别']}</span><br>"
# 提取前300个字符作为摘要，并保留段落结构
summary = text[:300] + ('
...' if len(text) > 300 else '')
# 将换行符转换为<br>标签
summary = summary
.replace('\n', '<br>')
thinking_html += f"{summary}</li><br>"
thinking_html += "</ul>"
self.thinking_signal
.emit(thinking_html)
# 构建LLM提示词中的思考过程
thinking_process = "以下是知识库中的相关参考资料：\n"
for i, (idx, sim) in enumerate(search_results):
text =
self.knowledge_base
.texts[
self.kb_type][idx]
metadata =
self.knowledge_base
.metadata[
self.kb_type][idx]
thinking_process += f"\n[{i + 1}] 相似度: {sim:
.4f}\n"
thinking_process += f"来源: {metadata['来源']} | 类别: {metadata['类别']}\n"
# 提取前200个字符作为摘要
summary = text[:200] + ('
...' if len(text) > 200 else '')
thinking_process += f"{summary}\n\n"
# 构建提示词
prompt = f"""
你是一位专业的涂装工程师。用户问题：{
self.query}。
{thinking_process}
请根据上述资料提供准确、专业的回答。
如果资料不足，请补充涂装领域的通用知识和最佳实践。
请确保回答条理清晰，适当分段分点。
"""
# 发送开始生成回答的信号
self.answer_signal
.emit("<b>正在生成回答
...</b>")
# 调用模型生成回答
answer_html = ""
stream =
self.client
.chat(
model='deepseek-r1:1
.5b',
messages=[{"role": "user", "content": prompt}],
stream=True
)
for chunk in stream:
content = chunk['message']['content']
answer_html += content
# 处理回答内容，添加分段分点格式
formatted_answer =
self._format_answer(answer_html)
self.answer_signal
.emit(formatted_answer)
except Exception as e:
self.answer_signal
.emit(f"<p style=\"color:#FF0000;\">[Error] {str(e)}</p>")
finally:
self.finish_signal
.emit()
def _format_answer(
self, answer):
# 简单的文本格式化处理
# 将段落分隔（空行）转换为<p>标签
paragraphs = answer
.split('\n\n')
formatted = ""
for para in paragraphs:
para = para
.strip()
if not para:
continue
# 处理列表项（如果以数字+点或短横线开头）
if re
.match(r'^\d+\
.', para):
# 有序列表
if not formatted
.endswith('</ol>'):
formatted += '<ol>'
else:
formatted = formatted[:-5] # 移除最后的</ol>标签以便继续添加
# 提取序号和内容
match = re
.match(r'^(\d+\
.)\s*(
.*)', para)
if match:
formatted += f'<li><b>{match
.group(1)}</b> {match
.group(2)}</li>'
else:
formatted += f'<li>{para}</li>'
formatted += '</ol>'
elif re
.match(r'^[-*•]', para):
# 无序列表
if not formatted
.endswith('</ul>'):
formatted += '<ul>'
else:
formatted = formatted[:-5] # 移除最后的</ul>标签
# 提取标记和内容
match = re
.match(r'^[-*•]\s*(
.*)', para)
if match:
formatted += f'<li>{match
.group(1)}</li>'
else:
formatted += f'<li>{para}</li>'
formatted += '</ul>'
else:
# 普通段落
formatted += f'<p>{para}</p>'
return formatted
# 主窗口
class PaintChatWindow(QWidget):
def __init__(
self):
super()
.__init__()
self.knowledge_base = KnowledgeBase()
self.client = Client(host="http://localhost:11435")
self.current_kb_type = None
self.init_ui()
def init_ui(
self):
self.setWindowTitle("涂装知识助手")
self.setGeometry(100, 100, 1200, 800)
# 主布局
main_layout = QVBoxLayout()
# 顶部状态栏
status_bar = QWidget()
status_layout = QHBoxLayout(status_bar)
self.status_label = QLabel("知识库未加载")
self.status_label
.setStyleSheet("color: #888888; font-size: 12px; padding: 5px;")
status_layout
.addWidget(
self.status_label)
main_layout
.addWidget(status_bar)
# 知识库选择区域
kb_selection_layout = QHBoxLayout()
kb_types = ["涂料信息库", "涂装设备库", "涂装工艺库", "涂装环境库", "行业标准与法规库"]
for kb_type in kb_types:
btn = QPushButton(kb_type)
btn
.setStyleSheet("""
QPushButton {
background-color: #6c757d;
color: white;
font-size: 12px;
padding: 5px;
border-radius: 3px;
}
QPushButton:hover { background-color: #5a6268; }
""")
btn
.clicked
.connect(lambda _, t=kb_type:
self.select_knowledge_base(t))
kb_selection_layout
.addWidget(btn)
main_layout
.addLayout(kb_selection_layout)
# 中间内容区域 - 分为思考过程和最终回答两栏
content_splitter = QSplitter(Qt
.Horizontal)
# 思考过程区域
self.thinking_area = QTextEdit()
self.thinking_area
.setReadOnly(True)
self.thinking_area
.setStyleSheet("""
QTextEdit {
background-color: #f8f9fa;
font-family: SimHei, sans-serif;
font-size: 14px;
padding: 15px;
border: 1px solid #e9ecef;
border-radius: 4px;
}
""")
self.thinking_area
.setHtml("<b>思考过程将显示在这里
...</b>")
content_splitter
.addWidget(
self.thinking_area)
# 最终回答区域
self.answer_area = QTextEdit()
self.answer_area
.setReadOnly(True)
self.answer_area
.setStyleSheet("""
QTextEdit {
background-color: #ffffff;
font-family: SimHei, sans-serif;
font-size: 14px;
padding: 15px;
border: 1px solid #e9ecef;
border-radius: 4px;
}
""")
self.answer_area
.setHtml("<b>回答将显示在这里
...</b>")
content_splitter
.addWidget(
self.answer_area)
# 设置两栏的初始大小比例
content_splitter
.setSizes([400, 800])
main_layout
.addWidget(content_splitter)
# 底部控制区域
bottom_layout = QHBoxLayout()
# 文件加载区域
file_layout = QVBoxLayout()
self.file_label = QLabel("未选择知识库文件")
self.file_label
.setStyleSheet("color: #6c757d; font-size: 12px;")
file_layout
.addWidget(
self.file_label)
self.load_btn = QPushButton("加载知识库")
self.load_btn
.setStyleSheet("""
QPushButton {
background-color: #6c757d;
color: white;
font-size: 12px;
padding: 5px;
border-radius: 3px;
}
QPushButton:hover { background-color: #5a6268; }
""")
self.load_btn
.clicked
.connect(
self.load_knowledge_base)
file_layout
.addWidget(
self.load_btn)
bottom_layout
.addLayout(file_layout, 1)
# 输入区域
input_layout = QVBoxLayout()
self.input_box = QTextEdit()
self.input_box
.setMaximumHeight(60)
self.input_box
.setPlaceholderText("输入您的问题
...")
self.input_box
.setStyleSheet("""
QTextEdit {
border: 1px solid #ced4da;
border-radius: 4px;
padding: 8px;
font-family: SimHei, sans-serif;
font-size: 14px;
}
""")
input_layout
.addWidget(
self.input_box)
self.send_btn = QPushButton("提问")
self.send_btn
.setStyleSheet("""
QPushButton {
background-color: #007bff;
color: white;
font-size: 14px;
padding: 8px;
border-radius: 4px;
}
QPushButton:hover { background-color: #0069d9; }
""")
self.send_btn
.clicked
.connect(
self.send_message)
input_layout
.addWidget(
self.send_btn)
bottom_layout
.addLayout(input_layout, 3)
# 添加、查看和编辑按钮
action_layout = QHBoxLayout()
self.add_btn = QPushButton("添加知识库内容")
self.add_btn
.setStyleSheet("""
QPushButton {
background-color: #28a745;
color: white;
font-size: 12px;
padding: 5px;
border-radius: 3px;
}
QPushButton:hover { background-color: #218838; }
""")
self.add_btn
.clicked
.connect(
self.add_knowledge_entry)
action_layout
.addWidget(
self.add_btn)
self.view_btn = QPushButton("查看知识库内容")
self.view_btn
.setStyleSheet("""
QPushButton {
background-color: #17a2b8;
color: white;
font-size: 12px;
padding: 5px;
border-radius: 3px;
}
QPushButton:hover { background-color: #138496; }
""")
self.view_btn
.clicked
.connect(
self.view_knowledge_entries)
action_layout
.addWidget(
self.view_btn)
self.edit_btn = QPushButton("编辑知识库内容")
self.edit_btn
.setStyleSheet("""
QPushButton {
background-color: #ffc107;
color: white;
font-size: 12px;
padding: 5px;
border-radius: 3px;
}
QPushButton:hover { background-color: #e0a800; }
""")
self.edit_btn
.clicked
.connect(
self.edit_knowledge_entry)
action_layout
.addWidget(
self.edit_btn)
bottom_layout
.addLayout(action_layout, 2)
main_layout
.addLayout(bottom_layout)
self.setLayout(main_layout)
def select_knowledge_base(
self, kb_type):
self.current_kb_type = kb_type
if kb_type in
self.knowledge_base
.texts:
self.status_label
.setText(f"当前知识库: {kb_type} ({len(
self.knowledge_base
.texts[kb_type])} 条记录)")
self.status_label
.setStyleSheet("color: #28a745; font-size: 12px; padding: 5px;")
else:
self.status_label
.setText(f"请加载 {kb_type} 知识库")
self.status_label
.setStyleSheet("color: #dc3545; font-size: 12px; padding: 5px;")
def load_knowledge_base(
self):
if not
self.current_kb_type:
QMessageBox
.warning(
self, "警告", "请先选择知识库类型")
return
file_path, _ = QFileDialog
.getOpenFileName(
self, f"选择 {
self.current_kb_type} 文件", "", "Excel Files (*
.xlsx *
.xls)"
)
if file_path:
self.file_label
.setText(f"已加载: {os
.path
.basename(file_path)}")
self.status_label
.setText("正在加载知识库
...")
self.status_label
.setStyleSheet("color: #007bff; font-size: 12px; padding: 5px;")
# 在单独线程中加载知识库
QTimer
.singleShot(0, lambda:
self._load_knowledge_base_thread(file_path,
self.current_kb_type))
def _load_knowledge_base_thread(
self, file_path, kb_type):
try:
count =
self.knowledge_base
.load_from_excel(file_path, kb_type)
if count > 0:
self.status_label
.setText(f"{kb_type} 已加载 ({count} 条记录)")
self.status_label
.setStyleSheet("color: #28a745; font-size: 12px; padding: 5px;")
self.thinking_area
.setHtml(f"<b>{kb_type} 加载成功</b>: 共{count}条知识条目")
self.answer_area
.setHtml("<b>回答将显示在这里
...</b>")
else:
self.status_label
.setText(f"{kb_type} 加载失败")
self.status_label
.setStyleSheet("color: #dc3545; font-size: 12px; padding: 5px;")
except Exception as e:
self.status_label
.setText(f"{kb_type} 加载错误: {str(e)}")
self.status_label
.setStyleSheet("color: #dc3545; font-size: 12px; padding: 5px;")
def send_message(
self):
user_input =
self.input_box
.toPlainText()
.strip()
if not user_input:
return
if not
self.current_kb_type:
QMessageBox
.warning(
self, "警告", "请先选择知识库类型")
return
if
self.current_kb_type not in
self.knowledge_base
.texts or not
self.knowledge_base
.texts[
self.current_kb_type]:
QMessageBox
.warning(
self, "警告", f"请先加载 {
self.current_kb_type} 知识库")
return
# 清空之前的回答和思考过程
self.thinking_area
.setHtml(f"<b>用户问题:</b> {user_input}<br><br><b>思考过程:</b>")
self.answer_area
.setHtml("<b>正在生成回答
...</b>")
self.input_box
.clear()
# 创建AI处理线程
self.ai_worker = AIWorker(
self.client, user_input,
self.knowledge_base,
self.current_kb_type)
self.ai_worker
.thinking_signal
.connect(
self.update_thinking)
self.ai_worker
.answer_signal
.connect(
self.update_answer)
self.ai_worker
.finish_signal
.connect(
self.on_ai_finished)
self.ai_worker
.start()
def update_thinking(
self, message):
# 将思考过程追加到思考区域
cursor =
self.thinking_area
.textCursor()
cursor
.movePosition(QTextCursor
.End)
cursor
.insertHtml(f"<br><br>{message}")
self.thinking_area
.setTextCursor(cursor)
self.thinking_area
.ensureCursorVisible()
def update_answer(
self, message):
# 更新回答区域
self.answer_area
.setHtml(message)
def on_ai_finished(
self):
pass
def add_knowledge_entry(
self):
if not
self.current_kb_type:
QMessageBox
.warning(
self, "警告", "请先选择知识库类型")
return
dialog = QDialog(
self)
dialog
.setWindowTitle(f"添加 {
self.current_kb_type} 条目")
layout = QVBoxLayout()
text_label = QLabel("文本块:")
text_input = QTextEdit()
layout
.addWidget(text_label)
layout
.addWidget(text_input)
source_label = QLabel("来源:")
source_input = QLineEdit()
layout
.addWidget(source_label)
layout
.addWidget(source_input)
category_label = QLabel("类别:")
category_input = QLineEdit()
layout
.addWidget(category_label)
layout
.addWidget(category_input)
vector_label = QLabel("向量化特征 (以逗号分隔):")
vector_input = QLineEdit()
layout
.addWidget(vector_label)
layout
.addWidget(vector_input)
button_box = QDialogButtonBox(QDialogButtonBox
.Ok | QDialogButtonBox
.Cancel)
button_box
.accepted
.connect(dialog
.accept)
button_box
.rejected
.connect(dialog
.reject)
layout
.addWidget(button_box)
dialog
.setLayout(layout)
if dialog
.exec_() == QDialog
.Accepted:
text = text_input
.toPlainText()
source = source_input
.text()
category = category_input
.text()
vector_str = vector_input
.text()
try:
vector = np
.array([float(x) for x in vector_str
.split(',')], dtype=np
.float32)
self.knowledge_base
.add_entry(
self.current_kb_type, text, source, category, vector)
QMessageBox
.information(
self, "成功", "条目已添加到知识库")
except ValueError:
QMessageBox
.warning(
self, "错误", "向量化特征输入无效，请输入有效的浮点数，以逗号分隔")
def view_knowledge_entries(
self):
if not
self.current_kb_type:
QMessageBox
.warning(
self, "警告", "请先选择知识库类型")
return
if
self.current_kb_type not in
self.knowledge_base
.texts or not
self.knowledge_base
.texts[
self.current_kb_type]:
QMessageBox
.warning(
self, "警告", f"请先加载 {
self.current_kb_type} 知识库")
return
dialog = QDialog(
self)
dialog
.setWindowTitle(f"{
self.current_kb_type} 内容")
layout = QVBoxLayout()
list_widget = QListWidget()
for i, text in enumerate(
self.knowledge_base
.texts[
self.current_kb_type]):
metadata =
self.knowledge_base
.metadata[
self.current_kb_type][i]
item_text = f"[{i + 1}] 来源: {metadata['来源']} | 类别: {metadata['类别']}\n{text[:200]}"
list_widget
.addItem(item_text)
layout
.addWidget(list_widget)
dialog
.setLayout(layout)
dialog
.exec_()
def edit_knowledge_entry(
self):
if not
self.current_kb_type:
QMessageBox
.warning(
self, "警告", "请先选择知识库类型")
return
if
self.current_kb_type not in
self.knowledge_base
.texts or not
self.knowledge_base
.texts[
self.current_kb_type]:
QMessageBox
.warning(
self, "警告", f"请先加载 {
self.current_kb_type} 知识库")
return
dialog = QDialog(
self)
dialog
.setWindowTitle(f"编辑 {
self.current_kb_type} 条目")
layout = QVBoxLayout()
index_label = QLabel("请输入要编辑的条目编号 (从1开始):")
index_input = QLineEdit()
layout
.addWidget(index_label)
layout
.addWidget(index_input)
button_box = QDialogButtonBox(QDialogButtonBox
.Ok | QDialogButtonBox
.Cancel)
button_box
.accepted
.connect(dialog
.accept)
button_box
.rejected
.connect(dialog
.reject)
layout
.addWidget(button_box)
dialog
.setLayout(layout)
if dialog
.exec_() == QDialog
.Accepted:
try:
index = int(index_input
.text()) - 1
if 0 <= index < len(
self.knowledge_base
.texts[
self.current_kb_type]):
text =
self.knowledge_base
.texts[
self.current_kb_type][index]
metadata =
self.knowledge_base
.metadata[
self.current_kb_type][index]
vector =
self.knowledge_base
.vectors[
self.current_kb_type][index]
edit_dialog = QDialog(
self)
edit_dialog
.setWindowTitle(f"编辑 {
self.current_kb_type} 条目 {index + 1}")
edit_layout = QVBoxLayout()
text_label = QLabel("文本块:")
text_input = QTextEdit()
text_input
.setPlainText(text)
edit_layout
.addWidget(text_label)
edit_layout
.addWidget(text_input)
source_label = QLabel("来源:")
source_input = QLineEdit()
source_input
.setText(metadata['来源'])
edit_layout
.addWidget(source_label)
edit_layout
.addWidget(source_input)
category_label = QLabel("类别:")
category_input = QLineEdit()
category_input
.setText(metadata['类别'])
edit_layout
.addWidget(category_label)
edit_layout
.addWidget(category_input)
vector_label = QLabel("向量化特征 (以逗号分隔):")
vector_input = QLineEdit()
vector_str = ','
.join([str(x) for x in vector])
vector_input
.setText(vector_str)
edit_layout
.addWidget(vector_label)
edit_layout
.addWidget(vector_input)
edit_button_box = QDialogButtonBox(QDialogButtonBox
.Ok | QDialogButtonBox
.Cancel)
edit_button_box
.accepted
.connect(edit_dialog
.accept)
edit_button_box
.rejected
.connect(edit_dialog
.reject)
edit_layout
.addWidget(edit_button_box)
edit_dialog
.setLayout(edit_layout)
if edit_dialog
.exec_() == QDialog
.Accepted:
new_text = text_input
.toPlainText()
new_source = source_input
.text()
new_category = category_input
.text()
new_vector_str = vector_input
.text()
try:
new_vector = np
.array([float(x) for x in new_vector_str
.split(',')], dtype=np
.float32)
self.knowledge_base
.edit_entry(
self.current_kb_type, index, new_text, new_source, new_category, new_vector)
QMessageBox
.information(
self, "成功", "条目已更新到知识库")
except ValueError:
QMessageBox
.warning(
self, "错误", "向量化特征输入无效，请输入有效的浮点数，以逗号分隔")
else:
QMessageBox
.warning(
self, "错误", "输入的条目编号无效")
except ValueError:
QMessageBox
.warning(
self, "错误", "请输入有效的整数作为条目编号")
if __name__ == "__main__":
app = QApplication(sys
.argv)
app
.setFont(QFont("SimHei"))
window = PaintChatWindow()
window
.show()
sys
.exit(app
.exec_())把这段代码里的“相似度”全部修改为“匹配度”，ollama调用窗口11435修改为11434，其余内容不变，完整代码给我
本文介绍了一种高效算法，用于求解数组中每个位置的元素等于该位置左侧所有元素与右侧所有元素乘积的问题。通过两次遍历实现，先从右到左计算右侧元素的乘积，再结合从左到右的累积乘积得到最终结果。
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