入门: 嵌入
info
嵌入可以用来创建文本数据的数字表示。这种数字表示很有用，因为它可以用来找到相似的文档。
以下是如何使用OpenAI嵌入的示例。嵌入有时在查询和文档方面具有不同的嵌入方法，因此嵌入类公开了embedQuery和embedDocuments方法。
import { OpenAIEmbeddings } from "langchain/embeddings/openai";
/* Create instance */
const embeddings = new OpenAIEmbeddings();
/* Embed queries */
const res = await embeddings.embedQuery("Hello world");
/*
[
-0.004845875, 0.004899438, -0.016358767, -0.024475135, -0.017341806,
0.012571548, -0.019156644, 0.009036391, -0.010227379, -0.026945334,
0.022861943, 0.010321903, -0.023479493, -0.0066544134, 0.007977734,
0.0026371893, 0.025206111, -0.012048521, 0.012943339, 0.013094575,
-0.010580265, -0.003509951, 0.004070787, 0.008639394, -0.020631202,
-0.0019203906, 0.012161949, -0.019194454, 0.030373365, -0.031028723,
0.0036170771, -0.007813894, -0.0060778237, -0.017820721, 0.0048647798,
-0.015640393, 0.001373733, -0.015552171, 0.019534737, -0.016169721,
0.007316074, 0.008273906, 0.011418369, -0.01390117, -0.033347685,
0.011248227, 0.0042503807, -0.012792102, -0.0014595914, 0.028356876,
0.025407761, 0.00076445413, -0.016308354, 0.017455231, -0.016396577,
0.008557475, -0.03312083, 0.031104341, 0.032389853, -0.02132437,
0.003324056, 0.0055610985, -0.0078012915, 0.006090427, 0.0062038545,
0.0169133, 0.0036391325, 0.0076815626, -0.018841568, 0.026037913,
0.024550753, 0.0055264398, -0.0015824712, -0.0047765584, 0.018425668,
0.0030656934, -0.0113742575, -0.0020322427, 0.005069579, 0.0022701253,
0.036095154, -0.027449455, -0.008475555, 0.015388331, 0.018917186,
0.0018999106, -0.003349262, 0.020895867, -0.014480911, -0.025042271,
0.012546342, 0.013850759, 0.0069253794, 0.008588983, -0.015199285,
-0.0029585673, -0.008759124, 0.016749462, 0.004111747, -0.04804285,
... 1436 more items
]
*/
/* Embed documents */
const documentRes = await embeddings.embedDocuments(["Hello world", "Bye bye"]);
/*
[
[
-0.0047852774, 0.0048640342, -0.01645707, -0.024395779, -0.017263541,
0.012512918, -0.019191515, 0.009053908, -0.010213212, -0.026890801,
0.022883644, 0.010251015, -0.023589306, -0.006584088, 0.007989113,
0.002720268, 0.025088841, -0.012153786, 0.012928754, 0.013054766,
-0.010395928, -0.0035566676, 0.0040008575, 0.008600268, -0.020678446,
-0.0019106456, 0.012178987, -0.019241918, 0.030444318, -0.03102397,
0.0035692686, -0.007749692, -0.00604854, -0.01781799, 0.004860884,
-0.015612794, 0.0014097509, -0.015637996, 0.019443536, -0.01612944,
0.0072960514, 0.008316742, 0.011548932, -0.013987249, -0.03336778,
0.011341013, 0.00425603, -0.0126578305, -0.0013861238, 0.028302127,
0.025466874, 0.0007029065, -0.016318457, 0.017427357, -0.016394064,
0.008499459, -0.033241767, 0.031200387, 0.03238489, -0.0212833,
0.0032416396, 0.005443686, -0.007749692, 0.0060201874, 0.006281661,
0.016923312, 0.003528315, 0.0076740854, -0.01881348, 0.026109532,
0.024660403, 0.005472039, -0.0016712243, -0.0048136297, 0.018397642,
0.003011669, -0.011385117, -0.0020193304, 0.005138109, 0.0022335495,
0.03603922, -0.027495656, -0.008575066, 0.015436378, 0.018851284,
0.0018019609, -0.0034338066, 0.02094307, -0.014503895, -0.024950229,
0.012632628, 0.013735226, 0.0069936244, 0.008575066, -0.015196957,
-0.0030541976, -0.008745181, 0.016746895, 0.0040481114, -0.048010286,
... 1436 more items
],
[
-0.009446913, -0.013253193, 0.013174579, 0.0057552797, -0.038993083,
0.0077763423, -0.0260478, -0.0114384955, -0.0022683728, -0.016509168,
0.041797023, 0.01787183, 0.00552271, -0.0049789557, 0.018146982,
-0.01542166, 0.033752076, 0.006112323, 0.023872782, -0.016535373,
-0.006623321, 0.016116094, -0.0061090477, -0.0044155475, -0.016627092,
-0.022077737, -0.0009286407, -0.02156674, 0.011890532, -0.026283644,
0.02630985, 0.011942943, -0.026126415, -0.018264906, -0.014045896,
-0.024187243, -0.019037955, -0.005037917, 0.020780588, -0.0049527506,
0.002399398, 0.020767486, 0.0080908025, -0.019666875, -0.027934562,
0.017688395, 0.015225122, 0.0046186363, -0.0045007137, 0.024265857,
0.03244183, 0.0038848957, -0.03244183, -0.018893827, -0.0018065092,
0.023440398, -0.021763276, 0.015120302, -0.01568371, -0.010861984,
0.011739853, -0.024501702, -0.005214801, 0.022955606, 0.001315165,
-0.00492327, 0.0020358032, -0.003468891, -0.031079166, 0.0055259857,
0.0028547104, 0.012087069, 0.007992534, -0.0076256637, 0.008110457,
0.002998838, -0.024265857, 0.006977089, -0.015185814, -0.0069115767,
0.006466091, -0.029428247, -0.036241557, 0.036713246, 0.032284595,
-0.0021144184, -0.014255536, 0.011228855, -0.027227025, -0.021619149,
0.00038242966, 0.02245771, -0.0014748519, 0.01573612, 0.0041010873,
0.006256451, -0.007992534, 0.038547598, 0.024658933, -0.012958387,
... 1436 more items
]
]
*/
更深入地了解
📄️ 集成
LangChain提供了许多与各种模型提供商集成的嵌入实现。这些是:
📄️ 附加功能
我们为聊天模型提供了许多附加功能。在下面的示例中，我们将使用“OpenAI嵌入”模型。