基础RAG实现，最佳入门选择（二）

初次创建embeddings向量

安装相关依赖

pip install sentence-transformers torch protobuf sentencepiece
pip install sentence-transformers torch protobuf sentencepiece
pip install sentence-transformers torch protobuf sentencepiece

代码

from sentence_transformers import SentenceTransformer
import torch
​
def create_embeddings(text, model="paraphrase-multilingual-MiniLM-L12-v2"):
    """
    为给定文本创建嵌入向量
​
    参数:
    text (str): 需要创建嵌入向量的输入文本
    model (str): 用于创建嵌入向量的模型名称，默认为 "paraphrase-multilingual-MiniLM-L12-v2"
​
    返回:
    torch.Tensor: 包含嵌入向量的张量
    """
    try:
        # 初始化模型
        model = SentenceTransformer(model)
        
        # 创建文本的嵌入向量
        embeddings = model.encode(text)
        
        return embeddings
    except Exception as e:
        print(f"创建嵌入向量时发生错误: {str(e)}")
        raise
​
# 示例使用
if __name__ == "__main__":
    try:
        # 示例文本
        text_chunks = [
            "这是一个测试句子。",
            "这是另一个测试句子。"
        ]
        
        # 创建嵌入向量
        embeddings = create_embeddings(text_chunks)
        
        # 打印结果
        print("嵌入向量的形状:", embeddings.shape)
        print("嵌入向量:", embeddings)
    except Exception as e:
        print(f"程序运行出错: {str(e)}")
​
​

执行效果

基于智谱AI嵌入模型的语义搜索功能

0. create_embeddings(text) ：调用智谱AI的embedding-2模型，将输入文本转换为嵌入向量（embedding）。
1. semantic_search(query, text_chunks, embeddings, k=5) ：计算查询与文本块之间的余弦相似度，返回最相关的k个文本块。
2. main() ：定义示例文本块并为其生成嵌入，执行查询并输出最相关的结果。

import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
from zhipuai import ZhipuAI

# 配置智谱AI API密钥
ZHIPU_API_KEY = "0d16323780a243e0a261a50ad622262d.xxxx"  # 替换为你的智谱AI API Key

def create_embeddings(text):
    """
    使用智谱AI创建文本的嵌入向量
    
    Args:
        text (str): 需要创建嵌入向量的文本
        
    Returns:
        list: 文本的嵌入向量
    """
    try:
        client = ZhipuAI(api_key=ZHIPU_API_KEY)
        response = client.embeddings.create(
            model="embedding-2",  # 使用智谱AI的embedding-2模型
            input=text
        )
        # 打印响应格式以便调试
        print(f"API响应类型: {type(response)}")
        print(f"API响应内容: {response}")
        
        # 根据响应格式获取嵌入向量
        if hasattr(response, 'data'):
            return response.data[0].embedding
        elif hasattr(response, 'embedding'):
            return response.embedding
        else:
            raise ValueError("无法从API响应中获取嵌入向量")
            
    except Exception as e:
        print(f"创建嵌入向量时出错: {str(e)}")
        raise

def semantic_search(query, text_chunks, embeddings, k=5):
    """
    使用语义搜索在文本块中查找最相关的内容

    Args:
        query (str): 搜索查询
        text_chunks (List[str]): 要搜索的文本块列表
        embeddings (List[list]): 文本块的嵌入向量列表
        k (int): 返回的最相关文本块数量，默认为5

    Returns:
        List[str]: 基于查询返回的最相关的k个文本块
    """
    # 为查询创建嵌入向量
    query_embedding = create_embeddings(query)
    similarity_scores = []  # 初始化存储相似度分数的列表

    # 计算查询嵌入向量与每个文本块嵌入向量之间的相似度分数
    for i, chunk_embedding in enumerate(embeddings):
        similarity_score = cosine_similarity(
            np.array(query_embedding).reshape(1, -1), 
            np.array(chunk_embedding).reshape(1, -1)
        )[0][0]
        similarity_scores.append((i, similarity_score))  # 添加索引和相似度分数

    # 按相似度分数降序排序
    similarity_scores.sort(key=lambda x: x[1], reverse=True)
    # 获取最相似的k个文本块的索引
    top_indices = [index for index, _ in similarity_scores[:k]]
    # 返回最相关的k个文本块
    return [text_chunks[index] for index in top_indices]

def main():
    """
    主函数：用于测试语义搜索功能
    """
    try:
        # 示例文本块
        text_chunks = [
            "人工智能正在改变我们的生活方式",
            "机器学习是人工智能的一个重要分支",
            "深度学习在图像识别领域取得了重大突破",
            "自然语言处理技术使机器能够理解人类语言",
            "计算机视觉技术让机器能够'看见'并理解图像"
        ]
        
        print("正在创建文本嵌入向量...")
        # 为文本块创建嵌入向量
        embeddings = []
        for chunk in text_chunks:
            print(f"\n处理文本: {chunk}")
            embedding = create_embeddings(chunk)
            embeddings.append(embedding)
        
        # 测试查询
        query = "什么是机器学习？"
        print(f"\n执行查询: {query}")
        
        # 执行语义搜索
        results = semantic_search(query, text_chunks, embeddings, k=2)
        
        # 打印结果
        print("\n最相关的文本块:")
        for i, result in enumerate(results, 1):
            print(f"{i}. {result}")
            
    except Exception as e:
        print(f"程序执行出错: {str(e)}")

if __name__ == "__main__":
    main()

返回结果

E:\AworkNew2025\all-rag-techniques\venv\Scripts\python.exe E:\AworkNew2025\all-rag-techniques\test\RAG04.py 
正在创建文本嵌入向量...

处理文本: 人工智能正在改变我们的生活方式
API响应类型: <class 'zhipuai.types.embeddings.EmbeddingsResponded'>
API响应内容: EmbeddingsResponded(object='list', data=[Embedding(object='embedding', index=0, embedding=[-0.040483367, 0.04265946, -0.04440628, -0.040421568, -0.023671854, -0.010280027, -0.0044765756, -0.0119311, 0.014968291, -0.026824702, -0.040870316, -0.019036043, -0.01329304, 0.051257383, -0.007196653, -0.01872651, -0.009806797, -0.0105997715, -0.026533859, 0.024069374, -0.003682119, 0.04471735, -0.017939555, -0.032278974, 0.04980713, 0.012489125, -0.007725015, -0.004877706, 0.04770769, -0.07133253, 0.024202365, -0.0092263855, -0.018188888, -0.0217557, -0.051787212, -0.02847703, -0.0115772365, 0.06495931, -0.02382171, 0.005030721, -0.055211157, -0.035890266, 0.031462952, 0.0005080568, 0.065448344, -0.004615475, -0.009311114, -0.05969915, -0.013829998, -0.056745857, -0.015153038, -0.031599857, 0.009487858, -0.017896023, 0.058272507, -0.0370002, 0.014670343, -0.05223245, -0.017886825, 0.02722662, 0.018186275, 0.01635498, -0.046459936, 0.025984336, -0.020833239, -0.040917236, 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处理文本: 机器学习是人工智能的一个重要分支
API响应类型: <class 'zhipuai.types.embeddings.EmbeddingsResponded'>
API响应内容: EmbeddingsResponded(object='list', data=[Embedding(object='embedding', index=0, embedding=[0.0032774527, 0.044587824, 0.007937388, -0.016013587, 0.037512787, -0.046530876, 0.016799824, -0.00039179847, 0.01112296, 0.0263018, -0.0262702, -0.007463252, -0.023811884, 0.059200566, -0.020111289, -0.0021348638, -0.01793518, 0.026927304, -0.00037970196, -0.01051042, -0.019707708, 0.039681297, -0.029712357, -0.02519463, 0.022068974, -0.05175961, -0.0076312725, 0.040030964, 0.04597552, -0.059259735, 0.0077906833, -0.001963219, -0.023676883, -0.009573251, 0.029179465, 0.010196561, 0.035344116, 0.007813898, -0.0042310352, 0.04527177, -0.014923096, -0.029355936, 0.044411525, -0.019042097, 0.07443466, -0.0035089727, 0.085220486, -0.017672816, -0.04464011, 0.024052525, -0.0060036285, -0.034703955, -0.0078025516, -0.00076275336, 0.019230021, -0.019526785, 0.030454183, -0.008674875, -0.03743806, 0.025009342, 0.027852755, 0.0013795455, -0.03364578, 0.0104585085, -0.054345734, -0.037203696, 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total_tokens=10))

处理文本: 深度学习在图像识别领域取得了重大突破
API响应类型: <class 'zhipuai.types.embeddings.EmbeddingsResponded'>
API响应内容: EmbeddingsResponded(object='list', data=[Embedding(object='embedding', index=0, embedding=[-0.009022754, 0.01461842, 0.014050749, 0.019079506, 0.022143774, -0.051804833, 0.028764352, -0.008793819, 0.07096166, 0.0038920443, -0.07076976, -0.057463612, -0.015015602, 0.034613732, -0.0049069882, 0.058731556, -0.0085280705, -0.0015769231, 0.0240925, -0.04533474, 0.006145017, 0.016274137, -0.015769385, -0.0075555965, 0.02197632, -0.0076160445, 0.036380444, -0.0018174513, 0.050465025, -0.020754876, 0.0015422676, -0.020222537, -0.0117696095, -0.00085508195, 0.021179462, -0.0006780605, 0.036410656, 0.05416321, 0.013720035, -0.022841707, -0.053622544, -0.014449534, 0.027037421, -0.005198127, -0.008109515, -0.009290396, 0.027746093, 0.013375944, -0.059572585, 0.019841572, -0.00698352, 0.02640503, 0.0024895906, 0.024511693, 0.051674604, 0.019666005, 0.03885558, -0.008465217, -0.008382933, 0.013770437, 0.0065727397, 0.028276967, -0.0927804, 0.0013949492, -0.055035513, -0.014457555, 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completion_tokens=0, total_tokens=11))

处理文本: 自然语言处理技术使机器能够理解人类语言
API响应类型: <class 'zhipuai.types.embeddings.EmbeddingsResponded'>
API响应内容: EmbeddingsResponded(object='list', data=[Embedding(object='embedding', index=0, embedding=[-0.01824848, 0.005297184, 0.022955732, -0.017698267, 0.05086688, -0.059060834, -0.031920813, 0.04172005, 0.02849854, 0.028516743, -0.011810722, -0.052262943, -0.014852996, 0.013402161, -0.015669739, 0.029687896, 0.009215286, -0.0031817253, -0.010832212, -0.032158703, -0.035635732, 0.031304833, 0.03495606, -0.05291413, 0.052237045, -0.030987682, 0.011471445, 0.0037531168, 0.02042221, -0.089548714, 0.046413165, 0.0215097, 0.015260854, -0.0073041674, -0.003070405, 0.007215059, 0.054022666, 0.06650789, 0.00028062714, 0.032549627, -0.026326923, 0.025727035, 0.021365864, -0.025333013, 0.015279301, -0.015115829, 0.04617972, -0.020764757, -0.02382325, -0.00913538, 0.003127713, -0.008004766, -0.016116321, 0.060592722, 0.03973158, 0.019906402, -0.01912034, -0.017401118, -0.020370902, -0.00063837087, 0.04116655, 0.058104485, -0.056017913, 0.0077768, -0.043496735, -0.05614046, -0.039265465, 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completion_tokens=0, total_tokens=12))

处理文本: 计算机视觉技术让机器能够'看见'并理解图像
API响应类型: <class 'zhipuai.types.embeddings.EmbeddingsResponded'>
API响应内容: EmbeddingsResponded(object='list', data=[Embedding(object='embedding', index=0, embedding=[-0.0042652297, 0.0663053, -0.0044417386, 0.01428896, 0.0010466815, -0.03384332, -0.022530537, 0.04283225, 0.031604588, -0.01721605, -0.06765889, -0.033155717, -0.025094314, 0.06471553, 0.017790508, 0.008888654, -0.02695026, 0.0013464927, -0.0135686435, -0.059066765, -0.039874915, 0.042449463, -0.04119524, -0.037139542, 0.026032947, -0.05598561, 0.02475911, 0.0011741514, 0.04491457, -0.042290192, 0.04080299, -0.015160501, 0.030587172, 0.022643453, -0.010563716, 0.009092049, 0.03137147, 0.04339191, -0.011069108, 0.04670331, -0.030417118, 0.0140404925, 0.041020207, -0.008528018, 0.011407627, -0.027320359, 0.016447881, -0.0076391497, -0.06904515, -0.013229366, 0.009484501, 0.010489942, -0.0037487126, 0.08189811, 0.043279316, 0.013514149, 0.015847811, -0.022916554, 0.035533622, -0.012361899, 0.0680761, 0.02385979, -0.07438911, -0.013109459, -0.025241213, -0.031015852, -0.04786424, 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执行查询: 什么是机器学习？
API响应类型: <class 'zhipuai.types.embeddings.EmbeddingsResponded'>
API响应内容: EmbeddingsResponded(object='list', data=[Embedding(object='embedding', index=0, embedding=[0.0018496081, -0.00083098863, -0.016484153, -0.008043244, 0.024124246, -0.021182446, 0.016661765, 0.020148227, 0.017531803, 0.0034398383, -0.02959654, -0.013137895, -0.020599466, 0.079962045, 0.016094862, -0.03029398, 0.00505072, 0.034582265, 0.0009974443, -0.065946124, -0.03366133, 0.019357515, -0.0055826465, -0.032422703, 0.01680616, -0.034951113, 0.020722331, 0.0070960564, 0.05999956, -0.06317737, 0.0432636, 0.021475837, 0.008540774, -0.0010802707, 0.024452426, -0.0018407467, -0.0030198179, 0.009774467, -0.022213707, 0.05184508, -0.027221315, -0.022880627, 0.04792357, -0.025952207, 0.012832222, -0.028277215, 0.042465616, -0.034636836, -0.038788766, 0.033058725, -0.007001647, -0.033691246, 0.028397081, 0.036392193, -0.0033946962, 0.017846469, -0.00838132, -0.030104725, -0.014481664, 0.012145779, 0.027005624, 0.035710894, -0.034235932, 0.00092652964, -0.056459203, -0.071871296, 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