嵌入模型是一类能够将文本、图像、语音等非结构化数据转化为低维稠密数值向量（即嵌入向量）的人工智能模型，它通过对数据的语义信息进行编码，让计算机可以高效地理解、计算和比较不同数据之间的关联程度。
嵌入模型的核心作用是把人类可理解的内容转换为机器可处理的数学表示，广泛应用于语义检索、推荐系统、文本聚类、问答匹配以及大模型知识库构建等场景，通过向量之间的相似度计算，实现精准的内容匹配与语义理解。
Ollama 也能运行嵌入模型，生成用于语义搜索、检索和 RAG 的文本嵌入。
你可以将嵌入模型生成的向量存储在向量数据库中、使用余弦相似度进行搜索，或在 RAG 管道中使用。
注意，生成的向量长度取决于模型，通常为 384–1024 维。
推荐的模型如下：
embeddinggemma 是谷歌 DeepMind 于 2025 年推出的轻量级开源文本嵌入模型，核心定位是在手机 / 端侧实现高效、高质量的语义向量生成。
qwen3-embedding 是阿里通义千问 3 系列的文本嵌入模型，核心是把句子 / 文档转成语义向量，用于检索、RAG、匹配、聚类等。
all-minilm 是一款轻量级、高性能的句子嵌入（句向量）模型，核心是小、快、够用。
直接从命令行生成嵌入：
ollama run embeddinggemma "Hello world"注意，如果本地没有下载 embeddinggemma 模型，执行上面命令将会自动下载该模型，然后运行模型，如下：
C:\Users\Administrator> ollama run embeddinggemma "Hello world"
pulling manifest
pulling 0800cbac9c20: 100% ▕██████████████████████████████████████████████████████████▏ 621 MB
pulling 1adbfec9dcf0: 100% ▕██████████████████████████████████████████████████████████▏ 8.4 KB
pulling 45dc10444b87: 100% ▕██████████████████████████████████████████████████████████▏ 34 B
pulling 3901c6a1d7c2: 100% ▕██████████████████████████████████████████████████████████▏ 416 B
verifying sha256 digest
writing manifest
success
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echo "Hello world" | ollama run embeddinggemma嵌入模型输出的是一个 JSON 数组。
需要使用 ollama run 命令运行模型，然后执行下面 cURL：
curl -X POST http://localhost:11434/api/embed \
-H "Content-Type: application/json" \
-d '{
"model": "embeddinggemma",
"input": "The quick brown fox jumps over the lazy dog."
}'运行结果：
C:\Users\Administrator> curl -X POST http://localhost:11434/api/embed -H "Content-Type: application/json" -d "{\"model\": \"embeddinggemma\",\"input\": \"The quick brown fox jumps over the lazy dog.\"}"
{"model":"embeddinggemma","embeddings":[[-0.11036498,0.053970676,0.06899434,-0.022284256,-0.08048941,0.0040779654,0.03508493,0.05144042,0.02992963,-0.028924484,-0.036183856,-0.08594603,0.015506073,-0.04169209,0.010728913,0.032046806,0.007471043,-0.017568296,-0.020182785,0.005392766,0.06946141,0.022528203,0.0075253854,0.021705026,0.038005568,0.0019985917,0.023003371,-0.0038728619,0.016268304,0.01682304,0.055508222,-0.01790423,-0.022862725,0.0028223542,0.06462832,0.04634487,-0.0060931663,-0.08668837,-0.055733923,-0.0716181,-0.04368288,0.0202451,0.0014669674,0.020568611,-0.06606773,-0.0011521348,0.008198328,-0.016938854,-0.063659206,0.066972755,-0.041145578,-0.05151263,-0.07107647,0.023917297,-0.00864156,-0.021327715,-0.05473327,-0.044751327,-0.009433778,0.037573732,-0.077224135,-0.028172206,-0.069507755,0.016577344,-0.030695314,0.005580933,-0.0000891685,0.0466814,0.006982999,0.17074901,-0.0024362025,-0.0072390726,-0.012582305,-0.07567068,0.09463832,-0.05315534,0.0074376217,0.0040037967,0.03343094,0.04192669,0.0010207507,0.051822454,-0.0018194616,-0.0061479984,0.03382246,-0.05267548,0.01850989,0.008914556,0.021512672,-0.034472287,-0.0013797758,0.002254319,-0.05602875,-0.0419878,0.013986861,-0.074751675,0.012140909,0.060660318,-0.03611641,-0.018049935,-0.018746406,-0.0043765157,0.032760125,0.06277453,0.010978261,-0.012369638,-0.06053332,0.012844418,-0.028729659,0.023937784,-0.02147534,-0.016828176,-0.06317908,0.06617891,0.0010720332,-0.004466787,-0.092528455,0.01245762,0.007211532,-0.02148203,-0.037987936,0.014801893,-0.041664157,0.004175011,0.022927621,0.038001552,-0.03534596,-0.044111665,0.01953921,-0.018365636,-0.0044656717,0.07195684,-0.050720964,-0.010448094,-0.015482449,0.024423951,0.006810129,-0.030112904,0.04358506,-0.012126677,-0.0018371685,-0.019950034,-0.011342444,-0.035903607,0.063337855,0.009088733,-0.084279366,-0.0042285398,0.062703274,-0.016289514,-0.0037944075,-0.016497523,-0.0018217412,0.052884508,0.038143672,0.028851282,0.038249265,0.035051584,0.021843268,-0.045606792,-0.056305252,-0.027921066,0.005965839,0.031867858,-0.014354866,0.027402828,0.0027484933,0.022362882,-0.01845192,-0.015130878,-0.033003,-0.0716034,-0.039862767,0.0053746435,-0.016246917,-0.014083807,0.0079061715,-0.019041922,0.06449286,0.009431031,0.0143209305,-0.0030448267,-0.0015728278,-0.039572198,-0.020596202,0.043873686,-0.024948424,0.052324135,-0.024912633,-0.012984435,-0.055218976,-0.07100529,0.002396546,0.012832607,-0.006715981,-0.010123155,0.052214384,0.00049274636,-0.053436358,-0.022741903,-0.02425061,-0.008615368,-0.027826387,0.026712341,-0.04279468,-0.03667105,-0.04443251,-0.028523948,0.0048874286,-0.0064879125,-0.0077694906,0.012373534,-0.023665212,-0.062005337,0.020359773,0.043280948,-0.07465765,-0.04296689,0.011216301,-0.030889126,-0.018028185,0.024177652,0.007726981,-0.024640717,0.029263714,-0.07206011,-0.021252194,0.03454318,-0.052339546,0.03293078,-0.020442847,-0.017772892,0.033256575,-0.034102008,0.011595084,-0.007026003,-0.019704863,0.01099409,-0.002061308,0.04288377,-0.028933086,0.073775604,0.027837884,-0.002260993,0.036327843,0.040432498,-0.01768083,-0.09542869,-0.018469015,-0.034888458,-0.026709901,0.00015626058,-0.009470374,-0.005602624,-0.0127900345,-0.029274816,-0.018265083,0.025537446,0.02859249,-0.009051589,-0.041080788,-0.014059054,-0.016314577,-0.041240692,0.039975677,-0.009146848,0.035203207,0.051928278,-0.06272907,-0.048113134,0.071379155,-0.0379672,-0.019697746,-0.025238892,-0.008989711,0.039690457,0.0733577,0.011572912,0.043814745,0.003236005,0.010253119,0.028318044,0.019068556,0.012180572,0.027155668,0.073971964,-0.013770964,0.009726992,-0.020833591,0.009913446,0.014465937,-0.059483204,-0.029476827,0.04436213,0.025263518,-0.016001018,-0.050535988,-0.0031610553,-0.056822617,-0.039971385,-0.07237077,0.010056447,0.015224328,-0.010931163,0.004613963,-0.037889287,0.071909785,0.016817385,-0.037587848,0.013343286,0.020412656,-0.01471446,0.021562625,-0.0043009897,-0.012645861,0.0050278367,0.0365506,-0.0000784561,-0.031753894,-0.0012163895,-0.0037503794,0.027441615,0.022070676,0.041854113,0.02301173,-0.046404466,0.018080581,-0.08107738,-0.06262087,0.019981435,0.03220134,0.05341628,0.018633867,0.043858543,-0.07735967,-0.06927687,-0.017754685,0.0279647,0.0065417537,-0.013574318,-0.0049634296,-0.023627225,0.01980969,-0.015852017,0.017851757,0.019722695,0.05643326,-0.0071068406,-0.0108984485,-0.021028694,-0.083438344,0.011253921,-0.0063330443,-0.052178286,0.029580437,-0.006544101,-0.042021878,-0.036727235,0.031003512,0.05126763,0.025803298,-0.03048618,-0.0042547127,-0.025062107,0.0036948682,-0.026766518,0.03986134,0.003878541,0.020202529,-0.017546095,-0.058029354,0.008247323,-0.011847796,-0.04004982,0.038076855,-0.017033905,-0.0033235739,0.040601768,-0.0028168194,0.007951209,-0.011075815,0.0039273435,0.024956245,0.024419429,-0.013654462,-0.01030519,0.058933306,-0.017864611,-0.08378403,0.011207318,0.020752126,-0.009704322,-0.031096544,0.023944205,0.012309965,-0.023801789,-0.007302844,0.021919709,-0.06329063,-0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pip install ollama 命令运行依赖库：
import ollama
# 生成文本向量（Embedding）
single = ollama.embed(
model='embeddinggemma', # 使用的向量模型
input='The quick brown fox jumps over the lazy dog.' # 要向量化的文本
)
# 输出向量的维度长度
print(len(single['embeddings'][0])) # 维度长度
print(single)运行结果：
$ python .\ollama_example8.py
768
model='embeddinggemma' created_at=None done=None done_reason=None total_duration=128967700 load_duration=81397000 prompt_eval_count=12 prompt_eval_duration=None eval_count=None eval_duration=None embeddings=[[-0.11036498, 0.053970676, 0.06899434, -0.022284256, -0.08048941, 0.0040779654, 0.03508493, 0.05144042, 0.02992963, -0.028924484, -0.036183856, -0.08594603, 0.015506073, -0.04169209, 0.010728913, 0.032046806, 0.007471043, -0.017568296, -0.020182785, 0.005392766, 0.06946141, 0.022528203, 0.0075253854, 0.021705026, 0.038005568, 0.0019985917, 0.023003371, -0.0038728619, 0.016268304, 0.01682304, 0.055508222, -0.01790423, -0.022862725, 0.0028223542, 0.06462832, 0.04634487, -0.0060931663, -0.08668837, -0.055733923, -0.0716181, -0.04368288, 0.0202451, 0.0014669674, 0.020568611, -0.06606773, -0.0011521348, 0.008198328, -0.016938854, -0.063659206, 0.066972755, -0.041145578, -0.05151263, -0.07107647, 0.023917297, -0.00864156, -0.021327715, -0.05473327, -0.044751327, -0.009433778, 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-0.006544101, -0.042021878, -0.036727235, 0.031003512, 0.05126763, 0.025803298, -0.03048618, -0.0042547127, -0.025062107, 0.0036948682, -0.026766518, 0.03986134, 0.003878541, 0.020202529, -0.017546095, -0.058029354, 0.008247323, -0.011847796, -0.04004982, 0.038076855, -0.017033905, -0.0033235739, 0.040601768, -0.0028168194, 0.007951209, -0.011075815, 0.0039273435, 0.024956245, 0.024419429, -0.013654462, -0.01030519, 0.058933306, -0.017864611, -0.08378403, 0.011207318, 0.020752126, -0.009704322, -0.031096544, 0.023944205, 0.012309965, -0.023801789, -0.007302844, 0.021919709, -0.06329063, -0.04001201, -0.01140921, 0.006986481, -0.010243539, 0.018455628, -0.0030140865, -0.01344526, 0.0056410283, 0.016324395, -0.015951024, -0.056523163, -0.022029227, -0.026852341, -0.007140688, 0.05232074, -0.029077375, -0.034983512, 0.0074313935, -0.0005549734, -0.0425886, -0.042026598, -0.04203879, 0.025128743, -0.0068884646, 0.02027364, 0.011309353, 0.0067055547, -0.004323641, 0.009104468, 0.011672939, 0.0257438, 0.024693886, 0.0087347105, 0.0064783054, 0.09639925, 0.02298001, -0.007827139, 0.0102015585, -0.04204182, 0.03699506, 0.05006732, -0.045544587, 0.054891467, 0.02639103, -0.008371029, 0.017394787, 0.010241153, -0.051601708, 0.053525202, -0.014972491, -0.034154836, 0.040279437, 0.059070837, 0.034704827, -0.04534939, -0.03142095, -0.030334894, -0.007357436, 0.0015195144, 0.018351799, -0.0572426, 0.02654348, 0.04138403, -0.0015171659, -0.0022412443, -0.014399316, -0.034447137, 0.009186495, -0.014511436, -0.052031424, 0.042048823, -0.018257244, -0.002381259, -0.0049890173, 0.021745156, 0.0036433707, 0.016376814, 0.0016728247, -0.01783991, 0.043229114, 0.08734145, -0.01809463, 0.08603129, 0.022397779, 0.005155776, -0.0052799135, -0.010316177, -0.05155979, -0.007570074, -0.00049754337, 0.0034982213, -0.04059447, -0.003888382, 0.01786174, -0.0022544402, -0.06722645, -0.0015285428, -0.047274042, 0.0013227316, -0.02448801, -0.07332514, 0.0031713308, 0.026979923, 0.016981404, 0.034273718, 0.020034766, -0.0048192227, -0.027755415, -0.056495927, 0.033384267, 0.012929872, -0.031897385, 0.020943236, 0.017964965, 0.00087696826, 0.0013843746, -0.006798516, -0.010474431, 0.083615944, 0.0031100942, -0.009651232, 0.026673332, -0.0034575106, 0.042495757, 0.016004788, 0.015245796, 0.003979229, 0.011346277, -0.045250874, 0.06161821, -0.045457643, 0.014363812, 0.02344098, 0.02686236, -0.0372719, -0.013068302, -0.007954607, -0.035013463, 0.040414717, 0.010073487, -0.02566116, 0.02715549, 0.0017940798, -0.026580198, 0.06917855, 0.021503158, 0.017384619, -0.04797934, -0.022939637, -0.06439943, -0.047275554, 0.044317223, -0.01361223, -0.038568404, -0.0027403547, 0.02970877, -0.007576216, -0.05818212, 0.003007985, 0.003901485, 0.09707992, -0.025733432, -0.0015479629, -0.036404707, -0.045776296, 0.02560528, 0.046549696, -0.0007822637, 0.008639762, -0.0005828731, 0.01789793, -0.0360848, -0.0013130403, -0.03550794, -0.012470164, -0.040002417, 0.10348974, 0.01609217, -0.019566327, 0.019865759, 0.032846943, -0.033523574, 0.061336797, 0.014298289, -0.028872157, 0.022225482, 0.03468839, -0.02091806, -0.0013924426, -0.018971538, 0.048420455, -0.04787175, -0.053954467, 0.010028304, 0.012907866, -0.027052661, 0.07187145, 0.03732123, -0.0041972985, 0.013852782, 0.036190435, 0.0037433566, -0.054079566, 0.020870913, -0.027209079, -0.0049226163, -0.036791526, 0.042723667, 0.06376535, 0.026839621, 0.031097779, -0.040294584, 0.029043136, 0.03564742, -0.028629845, -0.016236477, -0.079122275, 0.07766934, 0.054585498, 0.017929342, 0.026101252, -0.005454765, 0.021089759, 0.03690599, -0.0011387316, 0.023712628, 0.0196401, 0.07639788, -0.08188154, -0.03333615, 0.053027585, 0.036258098, -0.009608873, 0.011589603, 0.039249137, 0.017492075, -0.06758352, 0.039820444, -0.0033702708, 0.0062488434, 0.02300003, 0.0069028265, -0.007096924, 0.0076483255, 0.033054348, 0.019986106, -0.024822498, 0.03326411, 0.023175972, -0.015328349, -0.008003501, -0.02355593, 0.011301434, 0.055456065, -0.024852203, -0.04337551, 0.01496405, -0.036418214, -0.000554354, 0.036941104, -0.030620893, 0.006305891, 0.0033570803, 0.053955603, -0.0291719, -0.005546204, 0.022900755, 0.012420988, 0.01882323, 0.01415166, -0.020308819, -0.016054155, -0.08276991, 0.0065516927, -0.02069555, 0.04262871, 0.032596294, 0.006117556, -0.008332288, -0.04179265, 0.030832035, -0.008786083, -0.023866681, 0.019154808, 0.06854753, 0.042001344, -0.031886585, -0.033590935, 0.0024334886, -0.027233312, 0.067048684, -0.034387637, -0.109372064, 0.041209523, -0.059967697, -0.061025023, 0.045516357, 0.03240418, 0.020170372, -0.0054801875, 0.004568837, -0.044772778, -0.0064845225, -0.0029495049, -0.0017798963, 0.024597349, 0.010871305, 0.00076470006, -0.037608087, 0.0047559002, 0.006011998, -0.012875604, -0.045209866, -0.012008299, -0.0013254129, 0.039885916, 0.067376845, 0.022077158, -0.03535826, -0.047090333, 0.026747568, -0.04484238, -0.030934127, 0.05127007, 0.0009347359, -0.054706786, -0.04256496, -0.045838553, -0.007291774, 0.012812481, -0.03073083, 0.05092637, 0.009739617, 0.052830353, 0.015456597, -0.0050602877, 0.05978039, 0.03604302, 0.029877461, -0.016154371, -0.010240419, -0.019689988, 0.024393126, 0.0136109665, -0.055009797, 0.0113726165, -0.048764676, 0.00968195, 0.018692493, 0.02051747, 0.023723487, -0.060147725, 0.012414262, 0.028616397, 0.049563922, 0.006851985, -0.044174124, -0.061355002, 0.055480726, -0.04583805, -0.0029970927, 0.024142873, 0.016185984, -0.032735992, -0.0030076443, -0.051672593, 0.07045297, -0.03124735, 0.028107041]]执行代码前，请先执行 npm install ollama 安装依赖：
import ollama from 'ollama'
const single = await ollama.embed({
model: 'embeddinggemma',
input: 'The quick brown fox jumps over the lazy dog.',
})
console.log(single.embeddings[0].length) // vector length运行结果：
$ node .\ollama_example8.js
768
{
model: 'embeddinggemma',
embeddings: [
[
-0.11036498, 0.053970676, 0.06899434, -0.022284256, -0.08048941,
0.0040779654, 0.03508493, 0.05144042, 0.02992963, -0.028924484,
-0.036183856, -0.08594603, 0.015506073, -0.04169209, 0.010728913,
0.032046806, 0.007471043, -0.017568296, -0.020182785, 0.005392766,
0.06946141, 0.022528203, 0.0075253854, 0.021705026, 0.038005568,
0.0019985917, 0.023003371, -0.0038728619, 0.016268304, 0.01682304,
0.055508222, -0.01790423, -0.022862725, 0.0028223542, 0.06462832,
0.04634487, -0.0060931663, -0.08668837, -0.055733923, -0.0716181,
-0.04368288, 0.0202451, 0.0014669674, 0.020568611, -0.06606773,
-0.0011521348, 0.008198328, -0.016938854, -0.063659206, 0.066972755,
-0.041145578, -0.05151263, -0.07107647, 0.023917297, -0.00864156,
-0.021327715, -0.05473327, -0.044751327, -0.009433778, 0.037573732,
-0.077224135, -0.028172206, -0.069507755, 0.016577344, -0.030695314,
0.005580933, -0.0000891685, 0.0466814, 0.006982999, 0.17074901,
-0.0024362025, -0.0072390726, -0.012582305, -0.07567068, 0.09463832,
-0.05315534, 0.0074376217, 0.0040037967, 0.03343094, 0.04192669,
0.0010207507, 0.051822454, -0.0018194616, -0.0061479984, 0.03382246,
-0.05267548, 0.01850989, 0.008914556, 0.021512672, -0.034472287,
-0.0013797758, 0.002254319, -0.05602875, -0.0419878, 0.013986861,
-0.074751675, 0.012140909, 0.060660318, -0.03611641, -0.018049935,
... 668 more items
]
],
total_duration: 138462500,
load_duration: 88876700,
prompt_eval_count: 12
}注意：/api/embed 端点返回 L2 标准化（单位长度）向量。
将字符串数组传递给 input。
curl -X POST http://localhost:11434/api/embed \
-H "Content-Type: application/json" \
-d '{
"model": "embeddinggemma",
"input": [
"First sentence",
"Second sentence",
"Third sentence"
]
}'运行结果：
C:\Users\Administrator> curl -X POST http://localhost:11434/api/embed -H "Content-Type: application/json" -d "{\"model\": \"embeddinggemma\",\"input\": [\"First sentence\",\"Second sentence\",\"Third sentence\"]}"
{"model":"embeddinggemma","embeddings":[[-0.19222397,0.035262294,...,0.008912927,-0.00021409168],[-0.1455985,0.045831375,...,-0.0418893,-0.015527941],[-0.15585123,0.07278525, ...,0.00996832,0.011739556]],"total_duration":155896900,"load_duration":84787500,"prompt_eval_count":12}import ollama
batch = ollama.embed(
model='embeddinggemma',
input=[
'The quick brown fox jumps over the lazy dog.',
'The five boxing wizards jump quickly.',
'Jackdaws love my big sphinx of quartz.',
]
)
print(len(batch['embeddings'])) # 向量数量运行结果：
$ python .\ollama_example9.py
3
model='embeddinggemma' created_at=None done=None done_reason=None total_duration=185749500 load_duration=87394700 prompt_eval_count=33 prompt_eval_duration=None eval_count=None eval_duration=None embeddings=[[-0.11036498, 0.053970676, ..., -0.03124735, 0.028107041], [-0.1021043, -0.055992533, ..., -0.018857896, -0.0251679], [-0.13213544, 0.008006596, ..., 0.0024478666, 0.052512914]]import ollama from 'ollama'
const batch = await ollama.embed({
model: 'embeddinggemma',
input: [
'The quick brown fox jumps over the lazy dog.',
'The five boxing wizards jump quickly.',
'Jackdaws love my big sphinx of quartz.',
],
})
console.log(batch.embeddings.length) // 向量数量运行结果：
$ node .\ollama_example9.js
3
{
model: 'embeddinggemma',
embeddings: [
[
-0.11036498, 0.053970676, 0.06899434, -0.022284256, -0.08048941,
0.0040779654, 0.03508493, 0.05144042, 0.02992963, -0.028924484,
-0.036183856, -0.08594603, 0.015506073, -0.04169209, 0.010728913,
0.032046806, 0.007471043, -0.017568296, -0.020182785, 0.005392766,
0.06946141, 0.022528203, 0.0075253854, 0.021705026, 0.038005568,
0.0019985917, 0.023003371, -0.0038728619, 0.016268304, 0.01682304,
0.055508222, -0.01790423, -0.022862725, 0.0028223542, 0.06462832,
0.04634487, -0.0060931663, -0.08668837, -0.055733923, -0.0716181,
-0.04368288, 0.0202451, 0.0014669674, 0.020568611, -0.06606773,
-0.0011521348, 0.008198328, -0.016938854, -0.063659206, 0.066972755,
-0.041145578, -0.05151263, -0.07107647, 0.023917297, -0.00864156,
-0.021327715, -0.05473327, -0.044751327, -0.009433778, 0.037573732,
-0.077224135, -0.028172206, -0.069507755, 0.016577344, -0.030695314,
0.005580933, -0.0000891685, 0.0466814, 0.006982999, 0.17074901,
-0.0024362025, -0.0072390726, -0.012582305, -0.07567068, 0.09463832,
-0.05315534, 0.0074376217, 0.0040037967, 0.03343094, 0.04192669,
0.0010207507, 0.051822454, -0.0018194616, -0.0061479984, 0.03382246,
-0.05267548, 0.01850989, 0.008914556, 0.021512672, -0.034472287,
-0.0013797758, 0.002254319, -0.05602875, -0.0419878, 0.013986861,
-0.074751675, 0.012140909, 0.060660318, -0.03611641, -0.018049935,
... 668 more items
],
[
-0.1021043, -0.055992533, 0.0052888356, -0.01506081, -0.007423056,
0.0035323491, -0.018934907, 0.007393962, -0.00864506, -0.10112432,
0.03923387, -0.055499475, -0.020322394, -0.010370304, 0.023008784,
0.06862011, 0.009928923, -0.025206808, -0.046398677, -0.045123007,
0.0046486324, -0.024101047, -0.015042652, 0.021047132, 0.012231394,
0.0025076638, 0.021378767, 0.07829498, 0.058550403, 0.00006889595,
0.047288775, -0.022977468, -0.031922933, 0.017177042, 0.06762574,
0.05879801, 0.05365543, -0.06655763, 0.015392561, -0.049359735,
-0.05042677, 0.021667603, -0.01713863, -0.01878594, -0.039716627,
0.025487334, -0.015635166, -0.015576606, 0.01794494, 0.016878227,
-0.018408043, -0.021361526, -0.050992396, 0.05700022, -0.01473827,
-0.006169947, -0.00736194, 0.046300802, -0.08842091, 0.0031031298,
-0.04435671, -0.02929293, 0.019316496, 0.0066188127, -0.032676563,
0.0031425916, -0.016512563, -0.017464612, 0.018240673, 0.16646186,
-0.042055283, -0.03170875, -0.017588897, -0.038484223, 0.14514576,
0.010372469, -0.041549284, -0.0054184967, 0.018977862, 0.0019390644,
-0.037025895, -0.027408537, -0.043265693, 0.0062456913, 0.034502394,
-0.030917637, 0.02681577, -0.02832421, 0.028032625, -0.010405096,
-0.038512114, -0.04221592, -0.02945318, -0.035069767, 0.008743509,
0.0023213613, 0.02857841, 0.02594996, -0.046538286, -0.014096626,
... 668 more items
],
[
-0.13213544, 0.008006596, 0.012536362, 0.008444433, -0.04039769,
-0.045430955, -0.02818868, 0.04776996, -0.002632574, -0.036779106,
-0.027180726, -0.060608722, 0.046642274, -0.029259205, 0.04693935,
-0.0051844306, 0.014893473, -0.051178932, -0.048276972, 0.0006217969,
-0.011868477, -0.006465729, -0.008868178, 0.0565841, -0.008162769,
0.030062584, -0.009178773, -0.0046556788, 0.035964366, 0.013711405,
-0.015756486, 0.043263745, 0.0018954485, 0.018379059, -0.023307731,
0.046491064, 0.008345392, -0.022747103, 0.034141622, -0.03547982,
-0.0827361, 0.030332252, -0.011482538, -0.032419465, -0.015157166,
-0.0067904606, -0.071306214, -0.06324172, -0.0003438241, 0.034025613,
-0.027688164, -0.06723282, -0.020970752, -0.002839892, -0.038681537,
0.012208122, 0.0012092664, -0.009106925, -0.009353047, 0.0037290258,
-0.004349538, 0.0060666245, 0.015165016, -0.0374544, 0.058963735,
0.0021614756, 0.05245058, 0.023585988, 0.069427244, 0.15011397,
0.003741147, -0.060601417, 0.007187189, 0.015513081, 0.116873845,
0.033678316, 0.016930621, -0.017737467, -0.033225846, 0.04134129,
-0.041457627, 0.08233268, -0.036911476, -0.022150494, 0.06473183,
-0.072936825, 0.027036851, -0.022017784, 0.022079479, -0.009917301,
-0.031363536, 0.034649823, 0.039221503, -0.023123171, 0.028237509,
-0.082189165, 0.030710751, 0.036247496, -0.05331056, 0.019489514,
... 668 more items
]
],
total_duration: 191903400,
load_duration: 92892800,
prompt_eval_count: 33
}注意：
（1）在大多数语义搜索用例中使用余弦相似度。
（2）在索引和查询时使用相同的嵌入模型。
川公网安备51010802032098
