智能体应用开发：构建各类垂直领域的ai智能体应用

import re

# 假设我们有以下对话历史和问题
dialog_history = ["今天天气怎么样？", "明天会下雨吗？", "北京的天气如何？"]
current_question = "北京明天的天气怎么样？"

# 指代消除和问题扩展
def expand_question(question, history):
    # 这里简单用正则表达式匹配和替换，实际情况可能需要更复杂的NLP处理
    for q in history:
        question = re.sub(r"\b北京\b", q, question, flags=re.IGNORECASE)
    return question

expanded_question = expand_question(current_question, dialog_history)

# Concat查询，假设我们有两个不同的搜索引擎返回的结果
def concat_query(expanded_question):
    # 这里假设search_engine_1和search_engine_2是两个搜索函数
    results_1 = search_engine_1(expanded_question)
    results_2 = search_engine_2(expanded_question)
    # 合并结果
    return results_1 + results_2

concatenated_results = concat_query(expanded_question)

# RRF合并方式，这里我们简单地使用取并集的方式
def rrf_merge(results):
    # 假设result是一个包含多个搜索结果的列表
    merged_results = list(set(results))  # 使用set去重
    return merged_results

rrf_results = rrf_merge(concatenated_results)

# Rerank二次排序，这里我们简单地根据结果的相关性进行排序
def rerank(results):
    # 这里假设我们有一个函数来评估结果的相关性
    ranked_results = sorted(results, key=lambda x: relevance_score(x), reverse=True)
    return ranked_results

reranked_results = rerank(rrf_results)

# 假设的搜索函数和相关性评分函数
def search_engine_1(question):
    # 这里只是一个示例，实际中会调用搜索引擎API
    return ["晴", "多云", "有雨"]

def search_engine_2(question):
    # 这里只是一个示例，实际中会调用另一个搜索引擎API
    return ["有雨", "晴转多云"]

def relevance_score(result):
    # 这里只是一个示例，实际中会根据结果的相关性进行评分
    return len(result)

# 输出最终结果
print("Expanded Question:", expanded_question)
print("Reranked Results:", reranked_results)

import numpy as np

# 假设我们有一组候选答案和它们的初始相关性得分
candidates = ["答案1", "答案2", "答案3"]
initial_scores = np.array([0.7, 0.6, 0.8])

# 定义状态转移矩阵，这里简化为随机选择动作
transition_matrix = np.random.rand(len(candidates), len(candidates))

# 定义奖励函数，这里简化为基于初始得分的随机奖励
def reward_function(state, action):
    # 假设奖励与初始得分成正比
    return initial_scores[action]

# 定义MDP模型
class MDP:
    def __init__(self, states, actions, transition_probabilities, reward_function):
        self.states = states
        self.actions = actions
        self.transition_probabilities = transition_probabilities
        self.reward_function = reward_function

    def step(self, state, action):
        # 执行动作并返回奖励和下一个状态
        next_state = np.random.choice(self.states, p=self.transition_probabilities[state][action])
        reward = self.reward_function(state, action)
        return reward, next_state

# 初始化MDP
mdp = MDP(states=candidates, actions=range(len(candidates)), transition_probabilities=transition_matrix, reward_function=reward_function)

# 简单的策略迭代算法
def policy_iteration(mdp, gamma=0.9, theta=1e-6):
    policy = {s: np.random.choice(mdp.actions) for s in mdp.states}
    V = {s: 0 for s in mdp.states}
    
    while True:
        delta = 0
        for s in mdp.states:
            v = V[s]
            V[s] = max([sum([mdp.transition_probabilities[s][a][i] * (mdp.reward_function(s, a) + gamma * V[i]) for i in mdp.states]) for a in mdp.actions])
            delta = max(delta, abs(v - V[s]))
        if delta < theta:
            break
        policy = {s: np.argmax([sum([mdp.transition_probabilities[s][a][i] * (mdp.reward_function(s, a) + gamma * V[i]) for i in mdp.states]) for a in mdp.actions]) for s in mdp.states}
    return policy, V

# 执行策略迭代
policy, value_function = policy_iteration(mdp)

# 输出最优策略
print("最优策略：", policy)