锦中融合门户系统

import jieba

from transformers import pipeline

# 初始化问答模型

qa_pipeline = pipeline("question-answering")

def answer_question(question, context):

result = qa_pipeline(question=question, context=context)

return result['answer']

# 示例上下文

context = "天津是中国北方的重要城市，拥有丰富的历史文化和现代化的城市建设。"

# 用户提问

question = "天津有什么特点？"

# 获取答案

answer = answer_question(question, context)

print("问题：", question)

print("答案：", answer)

from neo4j import GraphDatabase

# 连接到Neo4j数据库

uri = "bolt://localhost:7687"

user = "neo4j"

password = "your_password"

driver = GraphDatabase.driver(uri, auth=(user, password))

def query_knowledge_graph(question):

# 简单的意图识别（这里仅做示例）

if "天津" in question:

query = """

MATCH (city:City {name: '天津'})

RETURN city.description

"""

with driver.session() as session:

result = session.run(query)

for record in result:

return record["city.description"]

return "没有找到相关信息。"

# 示例问题

question = "天津有什么特色？"

# 查询知识图谱

answer = query_knowledge_graph(question)

print("问题：", question)

print("答案：", answer)

class DialogueState:

def __init__(self):

self.context = {}

def update_context(self, key, value):

self.context[key] = value

def get_context(self, key):

return self.context.get(key, None)

# 初始化对话状态

dialogue_state = DialogueState()

def process_question(question):

# 假设用户之前提到过“天津”

previous_city = dialogue_state.get_context("city")

if previous_city:

# 使用之前的上下文进行问答

context = f"{previous_city} 是中国北方的重要城市，拥有丰富的历史文化和现代化的城市建设。"

answer = answer_question(question, context)

return answer

else:

# 如果没有上下文，使用默认内容

context = "天津是中国北方的重要城市，拥有丰富的历史文化和现代化的城市建设。"

answer = answer_question(question, context)

return answer

# 模拟多轮对话

dialogue_state.update_context("city", "天津")

question1 = "天津有什么特点？"

answer1 = process_question(question1)

print("问题：", question1)

print("答案：", answer1)

question2 = "那天津的历史文化有哪些？"

answer2 = process_question(question2)

print("问题：", question2)

print("答案：", answer2)

from transformers import AutoTokenizer, AutoModelForQuestionAnswering, Trainer, TrainingArguments

from datasets import load_dataset

# 加载数据集

dataset = load_dataset("squad")

# 加载预训练模型和分词器

model_name = "bert-base-uncased"

tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModelForQuestionAnswering.from_pretrained(model_name)

# 数据预处理

def preprocess_function(examples):

questions = [q.strip() for q in examples["question"]]

contexts = [c.strip() for c in examples["context"]]

answers = [a["text"] for a in examples["answers"]]

start_positions = [a["answer_start"] for a in examples["answers"]]

# 对输入进行编码

inputs = tokenizer(

questions,

contexts,

truncation="only_second",

padding="max_length",

max_length=512,

return_offsets_mapping=True,

)

# 处理答案位置

labels = []

for i, offset in enumerate(inputs["offset_mapping"]):

sample_answers = answers[i]

if isinstance(sample_answers, list) and len(sample_answers) > 0:

answer = sample_answers[0]

answer_start = start_positions[i]

answer_end = answer_start + len(answer)

# 找到对应的token位置

for j, (start, end) in enumerate(offset):

if start <= answer_start and end >= answer_end:

labels.append(j)

break

else:

labels.append(0)

else:

labels.append(0)

inputs["labels"] = labels

return inputs

# 应用预处理

tokenized_datasets = dataset.map(preprocess_function, batched=True)

# 设置训练参数

training_args = TrainingArguments(

output_dir="./results",

evaluation_strategy="epoch",

learning_rate=2e-5,

per_device_train_batch_size=16,

num_train_epochs=3,

weight_decay=0.01,

)

# 初始化Trainer

trainer = Trainer(

model=model,

args=training_args,

train_dataset=tokenized_datasets["train"],

eval_dataset=tokenized_datasets["validation"],

)

# 开始训练

trainer.train()