锦中融合门户系统

# 示例：使用Hugging Face Transformers库进行意图识别

from transformers import pipeline

# 加载预训练模型

classifier = pipeline("text-classification", model="bert-base-uncased")

# 输入用户查询

user_input = "我想查询今天的课程安排"

# 进行意图分类

result = classifier(user_input)

print(f"意图识别结果: {result[0]['label']}, 置信度: {result[0]['score']:.4f}")

# 示例：使用Spacy进行实体识别

import spacy

nlp = spacy.load("en_core_web_sm")

text = "我需要在明天下午三点去图书馆参加讲座"

doc = nlp(text)

for ent in doc.ents:

print(f"实体: {ent.text}, 类型: {ent.label_}")

# 示例：简单对话状态管理

class DialogueState:

def __init__(self):

self.history = []

def add_message(self, message):

self.history.append(message)

def get_history(self):

return self.history

# 使用示例

ds = DialogueState()

ds.add_message("用户: 我想查询今天的课程安排")

ds.add_message("系统: 今天有数学和英语两门课")

print(ds.get_history())

# 示例：使用T5模型生成回复

from transformers import T5Tokenizer, T5ForConditionalGeneration

tokenizer = T5Tokenizer.from_pretrained("t5-small")

model = T5ForConditionalGeneration.from_pretrained("t5-small")

input_text = "用户问: 今天的课程安排是什么？"

inputs = tokenizer(input_text, return_tensors="pt")

outputs = model.generate(inputs["input_ids"], max_length=50, num_beams=5, early_stopping=True)

response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(f"系统回复: {response}")

# 示例：使用Python爬取网页并清洗数据

import requests

from bs4 import BeautifulSoup

url = "https://www.example.edu/course-schedule"

response = requests.get(url)

soup = BeautifulSoup(response.text, "html.parser")

# 提取所有课程信息

course_elements = soup.find_all("div", class_="course-info")

courses = [element.get_text(strip=True) for element in course_elements]

print(courses)

# 示例：使用OpenIE进行关系抽取

from py2neo import Graph

from openie import OpenIE

graph = Graph("http://localhost:7474", auth=("neo4j", "password"))

ie = OpenIE()

text = "张三教授计算机科学课程"

relations = ie.annotate(text)

for relation in relations:

print(relation)

# 将关系存入Neo4j图数据库

graph.create(f"CREATE (a:Person {{name: '{relation['subject']}'}})-[:TEACHES]->(b:Course {{title: '{relation['object']}'}})")

# 示例：结合知识库进行问答

def answer_question(question, knowledge_base):

# 检索知识库

results = knowledge_base.search(question)

if results:

return results[0]["answer"]

else:

return "暂时没有找到相关信息，请尝试其他问题。"

# 假设knowledge_base是一个包含问答对的字典

knowledge_base = {

"今天的课程安排": "今天有数学和英语两门课",

"图书馆开放时间": "图书馆每天上午8点到晚上10点开放"

}

user_query = "今天的课程安排是什么？"

print(answer_question(user_query, knowledge_base))