Getting Started: Embeddings
info
Embeddings can be used to create a numerical representation of textual data. This numerical representation is useful because it can be used to find similar documents.
Below is an example of how to use the OpenAI embeddings. Embeddings occasionally have different embedding methods for queries versus documents, so the embedding class exposes a embedQuery and embedDocuments method.
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
]
]
*/
Dig deeper
📄️ Integrations
LangChain offers a number of Embeddings implementations that integrate with various model providers. These are:
📄️ Additional Functionality
We offer a number of additional features for chat models. In the examples below, we'll be using the ChatOpenAI model.