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"text":"用户偏好",
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"text":"##### 多维向量\n* 维度：可以认为是大模型计算出来的基于语义上不同维度的值。\n* 例：广州这夏天，真是让我这东北人没法活。\n\t* 维度一：情感[-1, 1]：强烈得分 0.9 \n\t* 维度二：温度[-1, 1]：热得分 0.88\n\t* 维度三：色温[-1, 1]：暖色 得分 0.91\n\t* 维度四：科技[-1, 1]：不相关得分 0.001\n\t* 维度五：喜好[-1, 1]：厌恶得分 -0.82\n\n##### 向量的距离\n* 在二维空间里，向量的距离是平面上一个线段的距离\n\t* 这个距离越近，两个点的x，y的坐标就越近\n* 在三维空间里，向量的距离是立体空间上两个点的连线\n\t* 这个距离越近，两个点的x，y，z的坐标就越近\n* 在四维、五维，只要是空间，对应维度的向量，都有一个距离。距离越近，相近度越大",
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"text":"##### 感受\n前例：\n* 广州这夏天，真是让我这东北人没法活。\n\n对比例子：\n1. 机房服务器的风扇是真的吵。\n2. 机房没了空调，热得没法呆人了。\n\n相对来说，2描述的场景和前例更加相似。",
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"text":"#### 公益租赁平台 - AI中介\n个性化推荐房源：\n1. 用户当前的位置，推荐周边\n2. 用户搜索记录，小区，户型，价位，配套情况，交通偏好",
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"text":"#### 公益租赁平台 - 健康咨询\n个性化膳食推荐：\n1. 用户体质评测，匹配食材\n2. 用户搜索记录，食材偏好，料理方式偏好",
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"text":"房屋属性：位置，周边，设施，价格，户型 ....",
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},
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"text":"* BAAI/bge-m3\n* BAAI/bge-large-zh-v1.5\n* BAAI/bge-large-en-v1.5",
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"text":"\n| **度量方法** | **核心特性** | | **典型应用场景** | **案例参考** |\n| --------- | ------------------- | --- | ----------------------------------------------------- | --------- |\n| **L1距离** | 计算各维度绝对差之和，路径类似网格行走 | | 1. 特征选择（稀疏性）
2. 路径规划（网格状移动）
3. 鲁棒性要求高的数据分析 | 城市导航、异常检测 |\n| **L2距离** | 计算多维空间中的直线距离 | | 1. 图像特征匹配
2. 地理位置计算
3. 需要精确几何距离的场景 | 人脸识别、三维建模 |\n| **余弦相似度** | 计算向量夹角的余弦值，忽略向量长度 | | 1. 文本语义相似度
2. 用户兴趣推荐（忽略强度差异）
3. 高维稀疏数据（如NLP词向量） | 文档检索、广告推荐 |\n",
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"text":"传统数据库扩展\n* Sqlite-vec\n* postgres-vector",
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"x":-312,
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"text":"传统数据库扩展\n* Redis\n* MongoDB",
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"text":"服务\n* Pinecone\n* Milvus",
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"text":"本地文件\n* chromaDB\n* lanceDB",
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"text":"```sql\ncreate virtual table vec_examples using vec0(\n sample_embedding float[5]\n);\ninsert into vec_examples(rowid, sample_embedding)\n values\n (1, '[-0.200, 0.250, 0.341, -0.211, 0.645'),\n (2, '[0.443, -0.501, 0.355, -0.771, 0.707'),\n (3, '[0.716, -0.927, 0.134, 0.052, -0.669'),\n (4, '[-0.710, 0.330, 0.656, 0.041, -0.990');\n-- KNN style query\nselect rowid,distance from vec_examples\nwhere sample_embedding match '[0.890, 0.544, 0.825, 0.961, 0.358]'\norder by distance\nlimit 2;\n\n/*\n┌───────┬──────────────────┐\n│ rowid │ distance │\n├───────┼──────────────────┤\n│ 2 │ 2.38687372207642 │\n│ 1 │ 2.38978505134583 │\n└───────┴──────────────────┘\n*/\n```\n",
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"text":"* 膳食：适应人群，适应体质，所用食材，料理方式\n* 方案：面向人群，调养目标 ...",
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"x":249,
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},
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"id":"9a1235e1dcfd91d5",
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"text":"* 文本-Embedding\n* 图-Embedding\n* 声音-Embedding\n",
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},
{
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"text":"* text-embedding-ada-002\n* text-embedding-3-small\n* text-embedding-3-large",
"type":"text",
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"x":108,
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},
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