aigc-content-vector/BertQuery.py

from BertDemo import collection, text_to_vector, List, Doc
import pandas as pd

# 创建一个缓存字典，用于存储查询结果
# query_cache = {}


def vector_to_tuple(vector):
    # 将列表转换为元组，以便作为字典的键使用
    return tuple(vector)


def queryCollection(vector) -> List[Doc]:
    # 如果向量已经查询过，则直接返回结果
    vector_tuple = vector_to_tuple(vector)

    # if vector_tuple in query_cache:
    #     return query_cache[vector_tuple]

    # 根据向量进行相似性检索 + 条件过滤
    ret = collection.query(
        vector=vector,   # 向量检索，也可设置主键检索
        topk=10,
        # filter='playCount > 1000',
        include_vector=True
    )
    if ret is None or ret.code != 0:
        print('查询失败')
        return None
    # query_cache[vector_tuple] = ret.output
    return ret.output


def calculate_ros(row):
    print(f"title={row['title']} ")
    vector = text_to_vector(row['title'])
    docs = queryCollection(vector)
    sumRos = 0
    for doc in docs:
        sumRos += doc.fields['rntHeadCount'] / doc.fields['shareCount']

    try:
        ros = sumRos / len(docs)
    except:
        ros = 0

    print(f"预测ROS={ros}")
    print("=====================================")
    return ros


def calculate_return(row):
    print(f"title={row['title']} ")
    vector = text_to_vector(row['title'])
    docs = queryCollection(vector)
    sumHeadCount = 0
    for doc in docs:
        sumHeadCount += doc.fields['rntHeadCount']

    try:
        headCount = sumHeadCount / len(docs)
    except:
        headCount = 0

    print(f"预测回流人数={headCount}")
    print("=====================================")
    return headCount


def calculate_and_export(filename):
    # 读取表格数据
    df = pd.read_excel(filename)

    # 应用计算函数并创建新的列
    df['实际ROS(回流人数/分享次数)'] = df.apply(lambda row: row['回流人数'] /
                                      row['分享次数'], axis=1)
    df['预测ROS'] = df.apply(calculate_ros, axis=1)
    df['实际回流人数'] = df['回流人数']
    df['预测回流人数'] = df.apply(calculate_return, axis=1)

    # 将结果保存回表格
    df.to_excel('videos-result.xlsx', index=False)