Cohere

让我们加载 Cohere 嵌入类。

import getpass
import os
os.environ["COHERE_API_KEY"] = getpass.getpass()

from langchain_cohere import CohereEmbeddings

embeddings = CohereEmbeddings(model="embed-english-light-v3.0")

text = "这是一个测试文档。"

query_result = embeddings.embed_query(text)
print(query_result)

[-0.09338379, 0.0871582, -0.03326416, 0.01953125, 0.07702637, 0.034729004, -0.058380127, -0.031021118, -0.030517578, -0.055999756, 0.050842285, -0.006752014, 0.038391113, -0.0014362335, -0.041137695, -0.008880615, 0.026000977, -0.023010254, 0.05456543, -0.03366089, 0.055633545, 0.028579712, -0.068603516, 0.03970337, -0.06677246, 0.06732178, -0.013053894, -0.0060920715, 0.038116455, 0.057800293, 0.048736572, 0.026855469, 0.009849548, 0.08312988, 0.073791504, 0.01663208, -0.0871582, 0.01802063, -0.0020828247, -0.0031356812, 0.039978027, -0.03164673, 0.009796143, 0.011375427, 0.0068855286, 0.092285156, 0.05218506, -0.060943604, 0.038269043, -0.018218994, -0.04510498, -0.0847168, 0.008300781, -0.060058594, 0.0012111664, 0.05102539, 0.05218506, -0.047210693, -0.051239014, -0.044158936, -0.058166504, 0.07849121, -0.019165039, 0.06451416, 0.024887085, 0.011405945, -0.03768921, -0.018814087, -0.06829834, -0.052825928, -0.019104004, -0.021194458, 0.043518066, 0.07525635, 0.082336426, 0.0037651062, -0.0060310364, -0.03265381, 0.011375427, -0.013847351, -0.07232666, 0.02986145, 0.03866577, -0.029083252, 0.008666992, 0.03845215, 0.045196533, 0.012756348, -0.018051147, 0.032440186, -0.030715942, -0.045440674, -0.11187744, 0.032073975, 0.021972656, -0.044921875, -0.030410767, -0.03668213, 0.12420654, 0.05029297, -0.032989502, -0.049438477, 0.001704216, -0.08074951, 0.00046396255, -0.04107666, 0.020599365, -0.089416504, 0.020477295, -0.038726807, -0.04437256, -0.019256592, 0.048583984, 0.046020508, 0.03741455, -0.037475586, -0.050720215, 0.052856445, -0.10229492, -0.00010281801, 0.058776855, 0.021453857, -0.031051636, 0.01676941, 0.024047852, -0.026306152, 0.15258789, -0.09979248, 0.04888916, 0.045166016, 0.008865356, -0.043914795, -0.032928467, 0.0052757263, 0.06072998, 0.036956787, -0.058013916, 0.053466797, -0.03225708, 0.018371582, -0.0042533875, 0.047943115, 0.06530762, 0.039855957, -0.025360107, 0.047332764, -0.15124512, 0.08325195, 0.016174316, -0.029724121, 0.111816406, -0.05230713, -0.06964111, 0.03060913, -0.04257202, -0.0284729, 0.007843018, -0.03866577, 0.07867432, -0.04446411, 0.028869629, -0.015823364, 0.02659607, 0.085754395, 0.03878784, -0.04232788, 0.017074585, 0.026779175, -0.04284668, -0.017105103, 0.10058594, 0.022323608, -0.007007599, -0.09661865, -0.01322937, -0.004627228, 0.057800293, 0.057159424, -0.033294678, -0.066101074, 0.010910034, 0.033569336, -0.062042236, -0.0072021484, -0.070373535, 0.034729004, -0.07434082, -0.06604004, 0.061401367, 0.09576416, -0.070739746, 0.066833496, -0.019042969, -0.0051994324, -0.07696533, -0.03564453, 0.048614502, -0.048919678, 0.036224365, -0.06652832, 0.03338623, 0.05847168, 0.009414673, -0.035095215, 0.011787415, -0.007675171, -0.057006836, -0.045074463, -0.027999878, -0.049102783, -0.025787354, -0.010101318, -0.000813961, -0.009963989, -0.013343811, 0.04046631, 0.02758789, -0.07086182, 0.09442139, -0.012275696, -0.018936157, -0.011940002, 0.10638428, -0.10913086, 0.05606079, 0.008895874, 0.017089844, 0.019958496, 0.03173828, -0.037322998, 0.019699097, 0.046722412, -0.08959961, 0.059448242, 0.018875122, -0.057495117, -0.039276123, 0.009063721, -0.0178833, 0.032073975, -0.08178711, -0.061431885, 0.05731201, 0.012886047, -0.025360107, 0.04498291, 0.027923584, 0.125, 0.013374329, -0.013069153, -0.031677246, -0.109558105, 0.05731201, -0.03765869, 0.04650879, -0.005706787, 0.021697998, -0.0008239746, 0.030090332, -0.048736572, 0.07940674, -0.017120361, 0.018737793, 0.12011719, -0.03564453, 0.07519531, -0.039611816, -0.014968872, -0.045288086, 0.07702637, 0.010681152, -0.04736328, 0.07623291, 0.008071899, 0.080078125, -0.060516357, 0.043426514, -0.026489258, -0.018188477, 0.049560547, -0.068847656, -0.03387451, -0.09661865, -0.03768921, 0.028549194, 0.036621094, 0.05307007, -0.053894043, 0.0019035339, -0.07788086, -0.010597229, -0.027420044, 0.10900879, 0.019302368, -0.06726074, 0.04937744, 0.05154419, -0.050598145, 0.07562256, -0.05569458, 0.073913574, -0.052337646, -0.0149383545, -0.00037050247, 0.037322998, 0.018478394, -0.03201294, -0.04788208, 0.03062439, -0.055786133, 0.0018081665, 0.029510498, -0.10864258, -0.027374268, 0.040405273, 0.01474762, -0.010726929, -0.086242676, -0.02658081, -0.057159424, -0.0095825195, -0.11804199, -0.014289856, -0.006881714, -0.028533936, 0.005382538, -0.053771973, -0.015853882, 0.0034332275, -0.08441162, -0.028182983, -0.00856781, -0.060394287, -0.036590576, 0.03062439, 0.112854004, -0.008041382, -0.03353882, 0.0181427, -0.03466797, 0.026565552, -0.033813477, 0.0074310303, -0.02017212, -0.047729492, 0.00010108948, -0.032073975, 0.08630371, 0.08557129, -0.0115737915, 0.044067383, 0.062042236, 0.00819397, -0.016082764, 0.01574707, 0.0154418945, 0.06726074, 0.056884766, 0.01210022, 0.048095703, -0.0017309189, 0.018295288, -0.00592041, 0.062286377, 0.040649414, -0.032928467, -0.05392456, -0.13891602, -0.033050537, 0.047973633, -0.07824707, 0.024627686, -0.02923584, 0.09118652, 0.0690918, 0.045837402, -0.06402588, -0.028747559, -0.06542969, -0.08496094, 0.06762695, 0.04220581, 0.059539795, 0.0023174286]

doc_result = embeddings.embed_documents([text])
print(doc_result)

output

以下是一个包含 512 个数字的列表，这些数字是某个人脸的特征向量。这个特征向量是通过深度学习模型从人脸图像中提取出来的，可以用来识别和比较不同的人脸。
特征向量示例：
[[-0.072631836, 0.06921387, -0.02658081, 0.022705078, 0.027328491, 0.046905518, -0.01838684, -0.029525757, 0.0041046143, -0.028198242, 0.0496521, 0.026901245, 0.03274536, 0.01574707, -0.081726074, -0.022369385, 0.049591064, 0.06549072, -0.015083313, -0.053863525, 0.098083496, 0.034698486, -0.08557129, -0.0024662018, -0.07519531, 0.03265381, 0.006046295, -0.0060691833, 0.032196045, 0.07537842, 9.024143e-05, -0.00869751, 0.022735596, 0.06329346, 0.068481445, -0.006778717, -0.07885742, 0.049560547, -0.008811951, 0.025253296, 0.050750732, -0.05343628, 0.051361084, -0.02319336, 0.026382446, 0.088378906, 0.03567505, -0.0736084, 0.039215088, -0.020584106, -0.03112793, -0.071777344, 0.018218994, -0.01876831, 0.040863037, 0.080078125, 0.046020508, -0.030792236, -0.011779785, -0.024871826, -0.06652832, 0.04748535, -0.038116455, 0.08453369, 0.08746338, 0.059509277, -0.037628174, -0.045410156, -0.054626465, -0.0036334991, -0.035949707, -0.011070251, 0.054534912, 0.0803833, 0.052734375, 0.06689453, 0.0074310303, 0.018249512, -0.023773193, 0.03845215, -0.113220215, 0.014251709, 0.028289795, -0.03942871, 0.029525757, 0.03036499, 0.035095215, 0.031829834, -0.0015306473, 0.027252197, 0.005088806, -0.035858154, -0.113220215, 0.021606445, 0.012046814, -0.06137085, 0.0057640076, -0.06994629, 0.02532959, 0.016952515, -0.010398865, -0.0066184998, -0.020904541, -0.12030029, 0.0036029816, -0.061553955, 0.023956299, -0.07330322, 0.013053894, -0.009613037, -0.062683105, 0.00013184547, 0.12030029, 0.028167725, 0.048614502, -0.09301758, -0.020324707, 0.022369385, -0.14025879, -0.052764893, 0.07220459, 0.028198242, 0.01499939, -0.029449463, 0.004711151, -0.05947876, 0.1640625, -0.09240723, 0.019500732, -0.0031089783, 0.0032081604, -0.0049934387, -0.01676941, 0.002691269, 0.02848816, 0.013504028, -0.057800293, 0.049041748, -0.022384644, 0.05517578, -0.031982422, 0.055389404, 0.0859375, 0.019866943, -0.052978516, 0.030929565, -0.15979004, 0.068481445, -0.020080566, -0.033477783, 0.07922363, -0.020736694, -0.025680542, 0.054016113, -0.028839111, -0.016189575, 0.03564453, 0.0001078248, 0.06304932, -0.022781372, 0.06555176, 0.010093689, 0.03286743, 0.14111328, -0.008468628, -0.04849243, 0.04525757, 0.065979004, -0.012138367, -0.017044067, 0.059509277, 0.035339355, -0.017807007, -0.027267456, -0.0034656525, -0.02078247, -0.033477783, 0.05041504, -0.043518066, -0.064208984, 0.034942627, -0.009300232, -0.08148193, 0.007774353, -0.03540039, -0.008255005, -0.1060791, -0.0703125, 0.091308594, 0.10095215, -0.081970215, 0.02355957, -0.026382446, -0.0070610046, -0.051208496, -0.014961243, 0.07269287, -0.033721924, 0.017669678, -0.08972168, 0.035339355, 0.03579712, -0.07299805, -0.014144897, -0.008850098, 0.023742676, -0.05847168, -0.07873535, -0.015388489, -0.039642334, -0.028930664, 0.008926392, -0.040283203, -0.02897644, -0.013557434, -0.006088257, 0.024169922, -0.10217285, 0.014526367, 0.007381439, -0.0005607605, -0.058410645, -0.008399963, -0.08001709, 0.05065918, 0.01727295, 0.012191772, -0.016571045, 0.03717041, -0.02607727, 0.060760498, 0.057678223, -0.06585693, 0.059173584, 0.023117065, -0.034118652, -0.03189087, 0.010429382, 0.010368347, -0.011230469, -0.020980835, -0.04019165, 0.048187256, -0.019638062, -0.024414062, -0.0019989014, 0.04336548, 0.117248535, 0.00033903122, -0.0014419556, 0.013946533, -0.11541748, 0.030059814, -0.06500244, 0.05441284, 0.021759033, 0.030380249, 0.080566406, 0.02331543, -0.04586792, 0.037322998, 0.011390686, -0.01374054, 0.1459961, -0.050964355, 0.081970215, -0.061645508, 0.07067871, -0.036956787, 0.060455322, 0.051361084, -0.05831909, 0.05328369, -0.008628845, 0.054534912, -0.047332764, 0.030578613, -0.048828125, -0.018112183, 0.022979736, -0.07318115, -0.0423584, -0.094177246, -0.04071045, 0.054260254, 0.0423584, 0.075805664, -0.06365967, 0.009269714, -0.054779053, -0.007637024, -0.01876831, 0.08453369, 0.058898926, -0.07727051, 0.04360962, 0.010574341, -0.027694702, 0.024917603, -0.0463562, 0.040222168, -0.05496216, -0.048461914, 0.013710022, -0.1038208, 0.027954102, 0.031951904, -0.05618286, 0.0025730133, -0.06549072, -0.049957275, 0.01499939, -0.11090088, -0.009017944, 0.021835327, 0.03503418, 0.058746338, -0.12756348, -0.0345459, -0.04699707, -0.029830933, -0.06726074, 0.010612488, -0.024108887, 0.016464233, 0.013076782, -0.06298828, -0.0657959, -0.0025234222, -0.0625, 0.013420105, 0.05810547, -0.006362915, -0.028625488, 0.06085205, 0.12310791, 0.04751587, -0.027740479, -0.02029419, -0.02293396, 0.048858643, -0.006793976, -0.0061073303, 0.029067993, -0.0076942444, -0.00088596344, -0.007446289, 0.12756348, 0.082092285, -0.0037841797, 0.03866577, 0.040374756, 0.019104004, -0.0345459, 0.019042969, -0.038116455, 0.045410156, 0.062683105, -0.024963379, 0.085632324, 0.005897522, 0.008285522, 0.008811951, 0.026504517, 0.025558472, -0.005554199, -0.017822266, -0.112854004, -0.03768921, -0.00097227097, -0.061401367, 0.050567627, -0.010734558, 0.07220459, 0.03643799, 0.0007662773, -0.020980835, -0.04711914, -0.03488159, -0.09655762, 0.0048561096, 0.028030396, 0.04586792, -0.014915466]]