A vector index is designed to efficiently store and manage high-dimensional vectors (or embeddings), enabling fast retrieval of similar vectors based on a chosen similarity metric. Instead of performing an exhaustive search across all stored vectors, the vector index significantly reduces the search space, making nearest-neighbor retrieval more efficient.
Vectors can be viewed as an ordered list of numbers. For example, the vector [1, 2] represents a direction from the origin to the point (1, 2) in a two-dimensional space, and the distance (or magnitude) to that point.

In machine learning and natural language processing (NLP), the term embedding commonly refers to vectors. An embedding is a high-dimensional vector that represents data, often with hundreds of dimensions.
You can create embeddings for both structured data (e.g., text) and unstructured data (e.g., images, graphs) using various models. Some popular models include:
Embeddings are often generated by considering the context of the data entity rather than just the data itself. This is especially relevant in NLP tasks, where the meaning of a word is shaped by its surrounding context.
Consider the word "bank", which has multiple meanings depending on the context:
This contextual information makes embeddings much more powerful for downstream tasks like semantic search, recommendation and classification.
After creating embeddings for entities, they can be imported or stored into Ultipa as properties of type float[] or double[]. By creating vector indexes for these properties, you can perform vector searches.
Vector search is the process of finding vectors most similar to a given query vector, based on a defined similarity measure. Ultipa supports the following similarity measures:



The example graph consists of 10 Book nodes, each containing the properties name, author, summary, and summaryEmbedding. The summaryEmbedding holds the 384-dimensional text embeddings of the summary, generated using the all-MiniLM-L6-v2 model from Hugging Face.
To create the graph, execute each of the following UQL queries sequentially in an empty graphset:
JavaScript// Creates the graph structure create().node_schema("Book") create().node_property(@Book, "title", string).node_property(@Book, "author", string).node_property(@Book, "summary", text).node_property(@Book, "summaryEmbedding", "double[]") // Inserts data insert().into(@Book).nodes([ {_id: 'B1', title: 'Pride and Prejudice', author: 'Jane Austen', summary: 'Elizabeth Bennet navigates love and social class in Regency-era England, clashing with the proud Mr. Darcy before realizing their true feelings for each other. The novel explores themes of marriage, reputation, and personal growth with Austen\'s sharp wit.', summaryEmbedding: [-0.016981, -0.042364, 0.065666, 0.038906, 0.014976, 0.031358, 0.096271, -0.074125, -0.015277, 0.018961, -0.104336, 0.028773, 0.044179, -0.003503, -0.051337, 0.126421, -0.015187, -0.027225, 0.028287, 0.002713, -0.026236, 0.032444, 0.019729, 0.04396, -0.044703, -0.094819, 0.059223, 0.030913, -0.039863, 0.009686, -0.01894, 0.030413, 0.006824, 0.010569, -0.012861, 0.017635, 0.027373, 0.021346, 0.007833, 0.006646, -0.06523, -0.0176, -0.020286, 0.032384, -0.021207, -0.015802, -0.016896, -0.027521, -0.024199, -0.03487, 0.011316, 0.009509, -0.070896, -0.069814, 0.025057, 0.129979, -0.058228, 0.010542, 0.032511, -0.018214, 0.028891, -0.008495, 0.063497, 0.02867, 0.016084, 0.096362, -0.035903, 0.103503, -0.031934, -0.034265, 0.022387, -0.039978, 0.025089, -0.047174, 0.022442, -0.031838, -0.046405, -0.064129, -0.080437, -0.054828, -0.131105, -0.001171, 0.073839, 0.051653, 0.009277, -0.022276, 0.030624, -0.10131, -0.037987, -0.015313, -0.070821, -0.0613, -0.015217, 0.070322, -0.025163, 0.00224, 0.017138, -0.009221, -0.042655, 0.0309, -0.029513, 0.049603, -0.08939, 0.05721, -0.006558, -0.06228, -0.001606, -0.028527, 0.023108, -0.071744, 0.03202, -0.032745, -0.034466, -0.022568, 0.057977, -0.0705, 0.017743, 0.029623, 0.063704, 0.039302, 0.0351, 0.098135, -0.13042, -0.029174, -0.046425, -0.043677, 0.019011, 0.0, -0.049401, 0.019659, 0.00434, 0.103655, 0.03646, -0.006406, 0.01709, 0.010093, -0.035342, -0.021223, -0.02989, 0.004757, -0.04085, -0.049541, -0.028237, 0.010764, -0.023065, 0.019279, 0.043719, 0.022844, 0.005614, 0.047042, -0.00966, -0.028841, -0.141512, -0.046094, 0.093529, 0.076752, 0.051903, 0.005952, -0.009119, 0.010468, 0.020529, -0.117752, -0.025551, -0.020633, -0.035827, -0.045041, 0.021877, 0.104439, -0.102344, 0.061173, 0.051902, -0.033153, -0.108546, 0.025007, 0.072691, 0.077366, 0.011611, 0.04351, -0.025391, -0.05113, 0.007496, 0.034405, -0.02956, 0.068544, 0.001138, 0.003294, 0.053225, -0.050753, 0.111305, -0.119559, 0.030768, -0.083323, 0.027577, 0.056574, 0.018864, -0.054351, -0.049986, -0.049595, -0.053463, 0.13749, 0.00749, -0.015237, 0.026897, 0.05586, -0.053209, -0.045824, 0.002495, -0.041811, -0.088512, -0.045982, 0.001083, 0.0257, -0.025021, -0.056821, 0.079298, -0.04138, 0.05842, 0.12725, 0.075797, -0.063705, 0.02849, -0.059318, -0.056288, -0.0, 0.008574, 0.003689, -0.050495, 0.019619, 0.014629, -0.01671, -0.091144, -0.046019, 0.033841, 0.041957, 0.036199, -0.038631, 0.102301, 0.037733, -0.048338, -0.015469, 0.088624, -0.01908, 0.036952, -0.064097, 0.002211, -0.016333, -0.041148, -0.136672, 0.031455, 0.05582, -0.057396, -0.033742, -0.040174, 0.019922, 0.024373, 0.028434, -0.041116, -0.010636, 0.015855, 0.065905, 0.043568, -0.086017, 0.07593, 0.030394, -0.033286, -0.062302, -2.2e-05, 0.03797, 0.023304, 0.035934, 0.004769, 0.031986, 0.037367, 0.08025, 0.008816, 0.075706, 0.018465, -0.045595, 0.039721, -0.06825, 0.077457, 0.014606, -0.020432, 0.001111, -0.046646, 0.029667, -0.053344, 0.022936, -0.049127, 0.09749, -0.117611, 0.009198, 0.027363, 0.013929, -0.059453, -0.024981, -0.014964, -0.052099, -0.072152, -0.029434, 0.064085, -0.061669, -0.025979, 0.044384, -0.024857, -0.029748, 0.03433, 0.025258, -0.068556, -0.01208, 0.012232, 0.037907, -0.008201, -0.011246, -0.010428, -0.021744, 0.024465, -0.051146, 0.089513, -0.0, -0.088108, -0.059916, -0.068388, -0.033385, -0.011911, 0.046535, -0.013162, 0.038588, 0.013721, 0.082723, -0.063084, -0.020575, 0.003376, -0.028997, 0.024753, 0.066644, 0.130634, -0.075558, 0.01607, 0.009222, 0.08584, -0.006819, -0.008291, -0.028557, -0.043678, 0.028515, 0.039927, -0.062592, -0.011772, 0.066762, 0.027508, 0.049087, -0.044781, -0.011181, 0.01947, 0.042389, -0.043233, 0.150881, 0.056146, 0.058995, 0.006498, 0.0312, -0.048804, 0.042909, 0.055467, -0.010208, -0.039895, 0.025032, 0.003819, -0.007193, 0.067929, -0.006023, 0.100482, 0.045188, -0.026133, 0.041239, -0.00594, 0.043054, -0.029386, 0.037435, -0.055699, 0.083548, 0.020668, -0.081702]}, {_id: 'B2', title: '1984', author: 'George Orwell', summary: 'In a dystopian future, Winston Smith struggles under the oppressive rule of Big Brother, where thought control, surveillance, and propaganda dictate every aspect of life. His rebellion leads to devastating consequences, highlighting themes of totalitarianism and free will.', summaryEmbedding: [-0.008621, 0.091217, -0.022469, -0.022307, 0.025421, 0.106667, 0.001754, -0.065709, -0.022435, 0.057827, -0.014686, 0.083507, 0.017244, -0.024383, -0.042809, 0.035961, -0.047049, -0.020921, -0.105624, 0.020044, -0.047525, -0.023805, 0.014186, 0.047439, -0.098367, 0.044126, 0.017014, 0.044188, -0.009684, -0.001128, -0.052091, -0.021865, 0.052238, 8.1e-05, 0.051572, 0.049274, 0.127821, 0.048921, -0.011751, -0.055761, 0.080929, 0.012329, -0.032541, 0.005175, 0.066084, -0.035489, 0.031082, -0.037632, -0.063821, -0.066129, -0.083817, -0.067446, 0.05262, -0.040641, 0.075976, -0.048971, 0.05027, 0.043345, -0.014722, -0.027574, -0.080322, -0.079703, -0.0453, -0.04559, 0.123172, 0.042333, -0.017341, 0.101922, -0.062573, 0.018903, -0.015795, -0.016941, 0.015264, -0.01361, -0.029711, -0.129424, -0.024845, -0.046623, 0.070202, 0.0443, 0.044555, -0.036263, -0.041683, 0.029205, -0.020685, -0.058189, -0.050959, -0.094882, 0.067173, 0.057603, -0.141696, -0.040523, 0.103626, 0.004706, -0.002419, 0.026994, 0.039842, -0.079611, -0.017373, 0.048091, -0.000586, -0.102408, 0.003314, 0.017276, 0.072057, -0.074581, 0.00571, 0.055517, 0.003812, -0.017347, -0.045918, -0.026583, -0.007081, -0.001156, 0.074983, -0.06314, 0.031952, 0.064857, -0.090344, -0.001005, -0.005211, 0.055158, 0.001642, 0.083283, 0.024727, 0.01225, -0.069203, 0.0, 0.058527, -0.06331, -0.026313, 0.106823, 0.020646, 0.066962, -0.014701, 0.012424, -0.048987, 0.016992, 0.030238, 0.009228, -0.001398, 0.003784, 0.008936, -0.02945, -0.10223, 0.00147, 0.057326, -0.004417, 0.037417, 0.039103, -0.065892, -0.049494, -0.01638, -0.058114, 0.00627, -0.021849, 6.9e-05, 0.03128, -0.008536, 0.13667, -0.079108, -0.003364, 0.062429, -0.021096, -0.076557, 0.02552, 0.072538, 0.018936, -0.06582, -0.002364, -0.042559, -0.046163, 0.02353, 0.025484, 0.134463, -0.015504, 0.008163, 0.015497, 0.032764, -0.014837, -0.021536, -0.026372, -0.019168, -0.018369, 0.012288, 0.022915, -0.038598, 0.013136, 0.038327, -0.033941, 0.029572, 0.038635, 0.010302, -0.096252, -0.063117, 0.023227, -0.033092, 0.116314, 0.05231, 0.036388, -0.001562, -0.031798, -0.018992, 0.052904, -0.07524, 0.066415, -0.099247, -0.013089, 0.023965, 0.021162, 0.022265, 0.004833, -0.001407, 0.03795, 0.055859, -0.092378, 0.026872, 0.004792, -0.003908, -0.093358, 0.039128, -0.020318, -0.091551, -0.0, -0.046535, -0.037969, -0.015275, 0.035466, 0.035897, -0.041856, -0.068587, -0.048424, 0.050674, 0.021732, -0.002393, -0.000446, 0.082799, 0.096639, 0.016579, -0.086886, -0.001073, -0.064376, 0.00767, -0.078391, 0.016434, -0.08163, -0.089212, -0.044454, 0.026081, 0.020704, -0.017052, 0.043146, 0.054896, 0.068385, -0.076187, 0.038646, -0.010708, 0.033081, 0.0199, 0.013365, -0.049776, -0.024593, 0.037183, -0.059185, -0.033034, -0.082648, -0.051082, -0.011112, -0.022965, 0.082457, -0.031499, 0.082153, 0.068979, 0.046753, -0.016227, 0.073926, 0.0606, 0.070905, -0.037076, -0.037028, -0.009335, 0.009383, -0.036358, 0.083562, -0.045985, -0.027577, -0.042756, 0.053987, -0.015786, 0.0039, 0.002802, -0.008193, 0.055457, 0.001805, 0.031887, -0.018394, -0.019132, -0.013288, -0.017759, 0.075113, 0.002179, 0.028745, -0.065958, 0.067536, 0.034431, -0.000787, -0.003597, 0.037126, -0.078908, 0.038574, 0.020738, 0.02015, -0.022434, 0.014085, -0.009719, -0.074353, 0.01077, -0.028893, -0.063277, -0.0, -0.034541, -0.070277, -0.01941, -0.005865, 0.025197, 0.156431, -0.007559, -0.010247, -0.017656, 0.143602, 0.005042, 0.009212, 0.025819, 0.01296, -0.00971, -0.007876, -0.002272, -0.197964, -0.02206, -0.039796, -0.022518, -0.032865, 0.007375, -0.003651, -0.0462, -0.005679, -0.053877, 0.004966, 0.024284, 0.092053, 0.022698, 0.004373, -0.056023, -0.000805, -0.045853, -0.01124, -0.048844, 0.054292, 0.060142, -0.035152, 0.073488, 0.012782, 0.007185, 0.053344, 0.046284, -0.038154, 0.002134, -0.022759, 0.018632, 0.004865, 0.007326, 0.072386, 0.065414, 0.088595, 0.060261, 0.038146, 0.006272, -0.009755, -0.043143, 0.096786, 0.053373, 0.038265, -0.018444, -0.088154]}, {_id: 'B3', title: 'To Kill a Mockingbird', author: 'Harper Lee', summary: 'Set in the racially segregated American South, young Scout Finch learns about justice, morality, and compassion as her father, Atticus, defends a Black man falsely accused of a crime. The novel critiques racial injustice and moral integrity.', summaryEmbedding: [-0.022685, 0.015848, -0.092268, -0.005805, -0.022488, 0.068271, 0.02367, -0.076058, 0.001692, 0.054104, -0.019726, 0.049662, -0.032534, -0.060678, -0.057997, 0.038616, -0.001378, -0.045663, 0.056475, -0.062887, -0.051291, -0.001839, 0.047033, 0.018193, -0.087559, -0.004835, 0.029741, 0.033604, -0.075808, -0.034328, -0.011623, 0.056132, -0.029609, 0.05784, -0.053374, -0.008421, 0.059536, 0.011486, 0.040402, -0.053378, 0.034115, 0.047713, -0.029198, 0.033163, -0.048845, -0.015496, 0.014193, 0.01629, 0.016696, -0.048943, -0.02624, -0.002712, -0.052129, 0.017977, 0.036515, 0.192931, 0.049121, -0.052412, -0.014696, -0.055299, 0.017556, -0.059171, -0.04358, -0.000374, 0.08837, 0.016388, -0.023403, 0.02679, 0.026857, -0.019931, 0.089695, 0.01848, 0.033951, -0.002633, -0.015978, 0.060431, -0.002146, -0.072816, 0.083049, -0.054542, -0.122107, -0.063148, -0.018241, 0.001507, -0.029217, -0.055296, 0.014008, -0.070861, 0.026001, 0.041461, 0.005675, -0.070044, 0.024955, -0.031703, 0.00496, 0.00298, -0.046805, -0.014339, -0.024775, 0.0036, 0.026304, 0.031486, 0.033874, -0.097939, 0.007877, -0.128841, 0.069143, -0.060687, -0.05836, 0.013264, 0.020339, -0.014861, -0.048052, 0.113456, 0.057867, -0.039831, 0.1082, -0.024683, -0.009688, 0.106214, 0.049241, 0.043009, -0.098569, 0.050751, -0.035511, -0.025629, -0.055357, -0.0, 0.000344, -0.040222, 0.00126, -0.05498, 0.094356, 0.01413, -0.000924, 0.000857, -0.036914, 0.024803, -0.05189, -0.039218, -0.027427, 0.05707, 0.022623, 0.043282, -0.060139, -0.031055, 0.007903, -0.055285, -0.052262, 0.08655, -0.068716, -0.102307, -0.022458, -0.005139, -0.008208, -0.001491, -0.03777, 0.02958, 0.018564, 0.078021, 0.018519, 0.001198, 0.054522, 0.024294, -0.031691, -0.00452, 0.028008, 0.055869, 0.014879, -0.007362, 0.017005, 0.023753, 0.047535, -0.041846, -4.3e-05, -0.072499, -0.002898, 0.095969, -0.007599, -0.045246, 0.017599, -0.099306, 0.012444, 0.020177, -0.012894, 0.048299, 0.034884, -0.01905, 0.027787, -0.089254, 0.02116, -0.025526, 0.131431, -0.022242, -0.014277, -0.063793, -0.075555, -0.071412, -0.020671, -0.000502, 0.052591, 0.014231, -0.114578, 0.061905, 0.080356, 0.035511, -0.006664, -0.099888, -0.038341, 0.053163, 0.004475, 0.050597, -0.060631, -0.087168, 0.031206, -0.038886, 0.049127, -0.006717, 0.03515, 0.019972, -0.063002, -0.050685, -0.033951, -0.0, -0.051403, -0.076823, -0.019102, -0.015079, 0.006799, -0.006192, -0.06391, 0.018134, 0.046867, 0.04254, -0.14006, 0.001749, 0.087653, 0.043973, 0.010296, -0.062619, 0.006579, 0.056982, 0.056944, 0.004014, 0.026428, 0.085596, 0.031212, -0.009975, 0.077334, -0.067559, 0.054914, -0.007478, -0.07235, 0.017259, 0.026155, 0.013296, 0.055108, 0.010877, -0.037271, -0.053544, 0.138026, -0.051412, 0.019886, -0.026173, 0.010775, 0.044069, -0.030083, -0.065359, 0.025701, -0.048204, 0.041498, 0.045431, -0.065991, -0.000544, -0.03375, -0.082099, 0.064499, 0.036706, 0.040195, -0.090202, 0.000224, -0.013191, 0.041538, 0.022912, -0.06631, 0.038736, -0.075775, 0.012915, 0.030208, -0.051127, -0.101145, -0.050251, 0.011345, -0.014097, -0.042367, -0.070446, -0.046517, -0.055846, -0.028637, 0.060218, -0.003596, 0.043566, -0.046985, 0.011142, 0.027758, -0.038324, -0.034962, 0.100388, 0.032007, 0.058802, 0.022879, 0.071764, 0.040747, 0.009099, 0.011572, -0.011558, -0.026943, -0.002484, -0.08566, -0.0, 0.001474, 0.034781, 0.018348, 0.047107, -0.003245, 0.093578, 0.002771, -0.052211, 0.006837, 0.099092, -0.090545, -0.076021, 0.099739, -0.053151, -0.004243, 0.003036, 0.078226, -0.021979, -0.037188, 0.087974, 0.052597, 0.091232, 0.084083, 0.01447, 0.008634, 0.004507, -0.041514, -0.074261, -0.052054, 0.060658, 0.011223, 0.134636, 0.075156, -0.001989, -0.086357, -0.040823, 0.065953, 0.10817, 0.076068, 0.011597, -0.021968, -0.014809, -0.009644, -0.044707, 0.013019, -0.01213, 0.01389, 0.038917, 0.010552, 0.005413, -0.023689, 0.034866, 0.02901, -0.035532, 0.040918, -0.019797, 0.00846, -0.031901, -0.01225, -0.050796, 0.1209, 0.048805, -0.049166, -0.012392]}, {_id: 'B4', title: 'The Great Gatsby', author: 'F. Scott Fitzgerald', summary: 'Jay Gatsby, a wealthy but mysterious man, throws lavish parties in an attempt to win back his lost love, Daisy Buchanan. Through the eyes of Nick Carraway, the novel explores themes of the American Dream, class, and the illusions of wealth.', summaryEmbedding: [-0.02481, -0.061498, -0.016459, 0.013824, 0.061165, 0.016651, 0.142201, -0.072818, 0.020808, -0.002779, -0.049229, 0.048879, 0.041182, -0.134283, -0.012917, -0.048263, -0.077979, -0.014902, 0.05853, 0.0264, -0.016752, 0.032369, -0.083641, -0.008015, -0.001195, -0.032516, 0.109321, -0.044194, -0.109634, 0.034549, 0.020258, 0.019063, -0.073458, 0.025434, -0.057394, 0.009432, 0.047803, 0.032902, -0.015933, -0.025801, -0.087016, -0.020839, 0.015638, 0.043783, 0.023102, -0.029874, -0.007379, -0.123993, 0.049499, -0.009779, 0.020158, 0.032399, -0.049439, -0.051735, 0.104907, 0.091621, 0.021406, 0.00272, 0.021135, 0.071739, 0.006356, -0.015859, 0.037401, -0.046381, 0.123268, 0.000124, -0.07015, 0.038322, -0.074761, 0.034271, 0.028509, 0.030658, -0.069122, -0.067256, -0.075874, -0.003074, -0.037567, 0.003349, -0.001743, 0.078347, -0.117151, -0.049647, 0.017345, -0.027313, -0.049133, -0.019061, -0.036887, -0.074609, -0.001043, 0.07469, -0.00765, -0.036249, 0.001913, 0.052002, -0.074449, -0.021182, -0.038632, -0.078172, -0.078873, 0.031107, 0.026107, 0.063358, 0.057954, -0.050606, 0.023983, -0.038648, 0.080144, 0.032675, -0.014176, 0.021173, -0.036661, -0.002756, -0.03222, 0.003514, 0.013504, 0.012299, -0.05979, -0.085184, -0.034601, 0.068569, 0.048107, 0.107345, -0.060121, 0.019972, -0.070312, -0.039605, 0.016218, -0.0, -0.010852, -0.026466, 0.061672, 0.041158, 0.004425, 0.078448, 0.020051, -0.006576, -0.05914, 0.011103, 0.00973, 0.034362, -0.089097, 0.01065, -0.009454, 0.056516, -0.136623, 0.002715, 0.103283, -0.003275, 0.015716, 0.080948, 0.017405, -0.069842, -0.051074, -0.080407, -0.05674, -0.063471, -0.034554, 0.021867, -0.053822, 0.057751, 0.052536, -0.008749, -0.081234, -0.115503, 0.007877, -0.020663, 0.039926, 0.040143, -0.079047, -0.000906, 0.033132, 0.055861, -0.071967, 0.086942, 0.14623, 0.065717, 0.037711, 0.0543, -0.038971, -0.064067, 0.011975, -0.02761, -0.020649, -0.018462, -0.055563, -0.054769, 0.032509, -0.098422, 0.097408, -0.025396, 0.101892, -0.050168, -0.034889, 0.089117, -0.002952, -0.102663, -0.027529, 0.040746, -0.02576, -0.028761, 0.03842, -0.011613, 0.016411, 0.064884, 0.052058, 0.051048, -0.051964, -0.051136, -0.013555, -0.030569, -0.008559, 0.029522, -0.030514, 0.029898, 0.054727, -0.087916, -0.054066, 0.030773, 0.034251, 0.009487, -0.074071, -0.098242, -0.062851, -0.0, 0.024773, -0.034948, 0.012849, -0.027455, 0.088715, -0.033244, -0.003161, -0.045451, 0.032735, 0.015709, -0.124888, 0.019781, 0.089965, -0.017727, 0.008002, -0.087125, 0.039819, -0.09049, 0.01425, -0.012974, 0.037084, 0.048912, 0.032509, -0.104437, 0.006037, -0.013321, 0.015617, -0.02299, -0.07817, -0.024479, 0.04568, 0.010375, -0.010518, 0.026609, -0.048428, -0.019848, 0.026159, -0.014288, -0.010499, -0.05043, -0.03114, -0.062367, 0.001141, 0.025411, -0.010856, -0.020467, -0.043565, 0.044189, 0.055517, 0.024321, 0.002447, 0.045537, 0.017321, 0.08497, -0.023826, 0.009052, 0.036428, 0.027111, 0.031905, 0.114385, -0.073044, 0.008308, -0.014239, 0.020909, 0.030084, -0.018945, 0.00614, 0.013544, 0.022179, 0.008652, -0.025928, -0.091711, 0.053577, 0.000107, -0.014363, 0.131351, 0.026427, 0.010441, 0.002405, 0.015217, 0.056033, 0.026832, 0.046974, -0.004158, -0.050433, 0.033239, -0.014378, 0.007379, 0.018452, 0.021353, -0.062961, -0.05227, 0.032829, -0.049616, -0.050346, -0.0, -0.067956, -0.03242, -0.016346, -0.034438, -0.039518, 0.065123, 0.029656, 0.028142, 0.008457, 0.091718, -0.015256, -0.039197, 0.079874, 0.031033, 0.031567, -0.058562, 0.124938, 0.006887, -0.024335, 0.045624, 0.067157, -0.005782, -0.009172, -0.037202, 0.002565, 0.087897, -0.005818, -0.027061, -0.015172, 0.128069, -0.008347, 0.017995, 0.002395, -0.013229, 0.011757, -0.02203, -0.05814, 0.064249, 0.022116, -0.033141, -0.007192, 0.017367, 0.031594, -0.0118, -0.025942, -0.013397, 0.077393, 0.055882, 0.06671, 0.089779, 0.059657, -0.001718, 0.04319, -0.018253, 0.027701, -0.094625, -0.031938, 0.047505, 0.020611, -0.02551, 0.020609, 0.012773, -0.040688, 0.028274]}, {_id: 'B5', title: 'Moby-Dick', author: 'Herman Melville', summary: 'Ishmael joins a whaling expedition led by the obsessed Captain Ahab, who is determined to hunt the white whale, Moby-Dick. The novel explores themes of fate, obsession, and the limits of human knowledge through rich symbolism and philosophical depth.', summaryEmbedding: [0.024986, 0.089268, 0.002462, 0.056911, -0.017491, 0.006182, 0.069281, -0.011298, -0.058844, 0.095656, -0.033212, -0.048018, 0.019251, 0.007835, -0.014317, 0.036393, 0.051647, -0.066298, 0.007904, -0.009003, 0.025912, 0.082135, -0.004169, -0.071499, -0.068779, -0.033475, 0.043288, -0.098509, -0.061247, -0.027778, 0.038296, -0.017489, 0.049383, 0.03766, -0.001323, 0.018727, 0.081769, -0.0149, 0.050969, -0.023185, 0.051637, 0.05853, 0.00913, 0.058257, -0.077568, -0.028462, -0.021934, -0.013313, 0.027687, 0.0052, -0.087602, -0.0663, -0.010015, -0.110712, 0.09007, -0.026897, 0.03675, -0.056516, 0.035419, -0.129894, 0.027267, -0.026057, 0.034729, 0.005644, 0.102603, -0.047904, -0.009344, 0.022349, -0.040942, 0.006883, 0.011361, 0.008611, -0.004154, -0.016876, -0.01313, -0.052067, -0.006183, -0.035464, 0.055621, -0.024267, -0.135943, -0.106936, -0.013493, 0.018449, 0.016532, 0.013576, -0.012736, -0.046021, -0.011444, -0.055291, 0.017749, -0.147933, -8.1e-05, -0.028001, -0.001052, 0.019796, -0.041573, 0.057494, -0.075127, 0.021072, 0.007211, -0.036711, -0.064611, -0.050708, -0.04879, -0.082788, -0.027285, -0.016139, 0.01136, -0.063803, -0.098311, -0.037908, 0.031846, 0.100425, 0.053012, 0.021356, 0.017865, -0.023734, -0.033644, -0.075537, 0.056319, 0.051129, 0.112451, 0.047294, -0.023279, -0.033195, 0.024845, -0.0, -0.001234, -0.051258, 0.00492, -0.007569, 0.042704, -0.002179, -0.0157, -0.007016, -0.03177, 0.001527, -0.005795, 0.035909, -0.006185, 0.119219, -0.042984, -0.051231, -0.003651, -0.033617, 0.030648, -0.046417, -0.014707, 0.054663, -0.005389, -0.026723, -0.043323, -0.064387, 0.004754, -0.037245, 0.022693, 0.089803, -0.051133, 0.020015, -0.057522, -0.014337, -0.057065, -0.031319, -0.091167, -0.00755, -0.0248, -0.094236, -0.003888, 0.019184, -0.050137, -0.022002, -0.073743, 0.108872, 0.066098, -0.021434, -0.01049, 0.054411, -0.036552, -0.021467, 0.060354, -0.064676, -0.007327, -0.002849, 0.058975, 0.044744, 0.031761, -0.042444, 0.011187, 0.005927, 0.012879, 0.099982, 0.058164, 0.050856, 0.091121, -0.052411, 0.025609, 0.068554, 0.00213, 0.039209, 0.03737, -0.00952, -0.109022, 0.022982, 0.027525, -0.003349, -0.098386, -0.044324, -0.030998, 0.025196, 0.012077, 0.001514, -0.050782, 0.010763, 0.102044, -0.073122, -0.001126, 0.029476, 0.060785, -0.033116, -0.042406, -0.072633, -0.01464, 0.0, 0.032076, -0.036369, 0.004868, -0.032269, 0.009427, -0.036844, 0.058991, 0.070871, -0.021884, -0.094925, -0.059264, -0.059758, 0.025842, 0.012715, 0.112768, -0.028522, 0.03887, 0.034485, 0.055494, -0.074663, 0.009614, -0.036765, -0.01732, -0.069716, 0.07336, 0.071665, -0.025984, 0.000679, -0.077052, -0.027983, -0.026014, 0.103975, 0.029391, -0.053237, -0.075429, 0.063725, 0.040997, 0.055177, 0.028118, -0.017124, -0.017628, -0.000664, -0.006777, -0.037269, -0.04876, 0.037831, -0.034018, 0.115962, -0.024095, -0.000249, -0.005647, 0.039172, 0.106592, -0.085204, 0.061715, 0.042662, -0.021477, -0.032446, 0.039852, 0.001872, -0.060029, -0.042848, 0.041087, 0.032727, -0.040228, 0.002983, 0.008411, -0.090762, -0.009637, 0.017465, -0.003444, -0.073146, -0.052097, 0.03692, 0.013062, 0.046833, -0.096567, 0.026681, -0.039308, 0.040154, -0.054448, -0.004534, 0.018972, 0.11418, 0.032343, 0.008066, -0.023917, 0.089204, 0.046101, -0.017465, -0.041015, -0.053506, -0.024774, 0.033292, 0.012615, -0.0, -0.051662, -0.056536, 0.075332, -0.014211, 0.06104, 0.095765, -0.035307, 0.009331, -0.016606, 0.097363, 0.009923, 0.018415, 0.013492, 0.092102, -0.010562, -0.030544, 0.076898, -0.085053, -0.010554, -0.012726, 0.014894, -0.003037, 0.030773, -0.049188, -0.023961, 0.114537, -0.051036, -0.08396, 0.048463, 0.04152, 0.017938, 0.05466, -0.012975, -0.009366, -0.041479, 0.06124, -0.020477, -0.003307, 0.027755, 0.058596, 0.021437, 0.142451, 0.078608, 0.080473, 0.05182, 0.009803, 0.018128, 0.022249, -0.039128, -0.051696, -0.045714, 0.024123, 0.036962, 0.030822, 0.007994, -0.077624, -0.068565, 0.010505, -0.028864, 0.056541, 0.185368, -0.006059, -0.011937, 0.007561]}, {_id: 'B6', title: 'Crime and Punishment', author: 'Fyodor Dostoevsky', summary: 'Raskolnikov, a destitute student in St. Petersburg, commits murder under the belief that he is above moral law. 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Salinger', summary: 'Teenager Holden Caulfield narrates his journey through New York City after being expelled from prep school, revealing his struggles with identity, alienation, and the transition into adulthood. 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0.030049, 0.005608, 0.021213, 0.017278, 0.00386, -0.00129, 0.025045, 0.038732, -0.112649, -0.068845, 0.091121, -0.008, 0.080029, -0.039866]}, {_id: 'B10', title: 'One Hundred Years of Solitude', author: 'Gabriel García Márquez', summary: 'Following the Buendía family across multiple generations in the fictional town of Macondo, this novel blends history, myth, and magical realism to explore themes of fate, solitude, and the cyclical nature of time.', summaryEmbedding: [0.026657, 0.028431, -0.032534, 0.056663, -0.022202, 0.038214, -0.007063, -0.097044, -0.024331, -0.042871, -0.00251, -0.01553, 0.04323, -0.108958, -0.037939, 0.044918, -0.02538, 0.022936, 0.015863, 0.027742, 0.034097, -0.021096, 0.024324, 0.119196, -0.073228, 0.006559, 0.093397, 0.018486, -0.077086, -0.066561, -0.061422, 0.083535, -0.017346, -0.049387, -0.001638, 0.014735, 0.012934, 0.054124, -0.018105, 0.008935, -0.008215, 0.013462, 0.006916, -0.057713, -0.036141, -0.076769, -0.01255, -0.042816, 0.033945, 0.011049, 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-0.010616, -0.036748, 0.04558, -0.108451, -0.056888, -0.075547, 0.018319, -0.002528, 0.016778, 0.002166, 0.040355, 0.062555, 0.102585, -6e-05, -0.149261, 0.010235, 0.03125, 0.011882, -0.041834, -0.002033, 0.042827, -0.057175, -0.027426, -0.110007, 0.047332, -0.000561, -0.067093, 0.073731, -0.022006, 0.016884, 0.024503, -0.078724, -0.021635, -0.0234, 0.000914, -0.027096, 0.000546, -0.006549, -0.023952, 0.066146, 0.005767, 0.162328, 0.019604, 0.048126, 0.022859, 0.066474, -0.015293, 0.006383, 0.100716, 0.058188, -0.036845, -0.026259, 0.004199, 0.040034, 0.007741, -0.006, 0.03083, 0.016829, -0.018671, -0.063338, -0.016366, -0.052646, -0.035678, -0.024429, 0.059136, 0.047461, 0.003303, 0.057969, -0.068163, -0.095383, 0.001882, 0.062801, 0.057053, 0.041009, -0.070887, -0.064489, -0.0, 0.046044, -0.058527, 0.012656, -0.020662, 0.065134, -0.078622, -0.137736, 0.058991, -0.036417, -0.025577, -0.001395, -0.053878, 0.099823, -0.000594, 0.032634, -0.033411, 0.084393, -0.023813, -0.061469, 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-0.018459, 0.021708, 0.00225, 0.061038, -0.057281, -0.018757, 0.038778, -0.048686, -0.073497, 0.098296, 0.001349, 0.099337, 0.037205, -0.004721, 0.068433, -0.018506, -0.041574, -0.063517, 0.100771, -0.093404, -0.085058, 0.122797, -0.036402, -0.04629, -0.001159, 0.054401, -0.06072, 0.074008, 0.051591, 0.009301, 0.019513, -0.057999, -0.015122, 0.074181, 0.053202, 0.052196, -0.000656, -0.006664, 0.027594, -0.028435, -0.004762, 0.018872, 0.074735, 0.010855, -0.005242]} ])
To retrieve node vector indexes in the current graph:
JavaScriptshow().node_vector_index()
The information about vector indexes is organized into a _nodeVectorIndex table with the following fields:
Field | Description |
|---|---|
name | Vector index name. |
schema | The schema of the vector index. |
properties | The property of the vector index. |
vector_server_name | The vector server that hosts the vector index. |
status | Vector index status, which can be DONE or CREATING. |
config | Vector index configuration, including similarity_function, index_type, and dimensions. |
You can create a vector index using the create().node_vector_index().on() statement for a node property of the float[] or double[] type. The vector index creation runs as a job, you may run show().job("<id?>") afterward to verify the success of the creation.
To create a vector index named summary_embedding for the property summaryEmbedding of Book nodes:
JavaScriptcreate().node_vector_index(@Book.summaryEmbedding, "summary_embedding", { similarity_function: "COSINE", index_type: "FLAT", dimensions: 384 }).on("vector_server_1")
Details
_).Item | Type | Default | Description |
|---|---|---|---|
similarity_function | String | L2 | The similarity function used to assess the similarity of two vectors. Supports L2, COSINE, and IP. Learn more |
index_type | String | FLAT | The method used to organize and search the vectors in the index. Supports FLAT, IVF_FLAT, IVF_SQ8, HNSW, HNSW_SQ, HNSW_PQ, HNSW_PRQ, and SCANN. Learn more |
dimensions | Integer | 128 | The dimensions of the vectors to be indexed. Only vectors that match the configured dimension are indexed. Querying the index with a vector of a different dimension results in an error. |
on() method.You can drop a vector index using the drop().node_vector_index() statement. Dropping a vector index does not affect the actual property values stored in shards.
NOTEA property with a vector index cannot be dropped until the vector index is deleted.
To drop the node vector index summary_embedding:
JavaScriptdrop().node_vector_index("summary_embedding")
You can use a vector index for vector search with the vector.queryNodes() statement.
Syntaxvector.queryNodes("<vectorIndexName>", { limit: <numMostSimNodes>, query_vector: <targetVector> })
Parameters
Params | Description |
|---|---|
vectorIndexName | The name of the vector index to be used. |
<numMostSimNodes> | Number of the nodes to retrieve that have the most similar vectors to <targetVector>. Note that the <targetVector> itself is included. |
<targetVector> | The target vector. |
Returns
_uuid: _uuid of the retrieved node.score: The similarity score between the vector of retrieved node and the target vector.Finds the top two books most similar to Pride and Prejudice by the vector index summary_embedding, return the book names along with the similarity scores between them:
JavaScriptfind().nodes({@Book.title == "Pride and Prejudice"}) as target vector.queryNodes('summary_embedding', { limit: 3, query_vector: target.summaryEmbedding }) as result find().nodes({_uuid == result._uuid}) as book return table(book.title, result.score)
Result:
| book.title | result.score |
|---|---|
| Pride and Prejudice | 1 |
| One Hundred Years of Solitude | 0.39629873633384705 |
| The Great Gatsby | 0.3709701597690582 |
Index Type | Description |
|---|---|
FLAT | A brute-force method where all vectors are stored and compared directly. This method ensures accurate results, but it is computationally expensive and inefficient when dealing with large datasets. |
IVF_FLAT | The IVF (Inverted File) method partitions the vectors into cluster units, and the search only takes place within the most relevant units. IVF_FLAT uses a FLAT approach within each unit, providing faster searches with a slight compromise on accuracy. |
IVF_SQ8 | SQ8 (Scalar Quantization with 8-bit) uses scalar quantization to compress vectors in each unit into an 8-bit representation. It reduces storage requirements and increases search speed at the cost of slightly lower precision. |
HNSW | HNSW (Hierarchical Navigable Small World) is a graph-based indexing method that organizes vectors into a graph structure for fast approximate nearest neighbor search. It is highly efficient, especially for large datasets, and tends to outperform other methods in terms of search speed and recall. |
HNSW_SQ | It combines the HNSW method with SQ (Scalar Quantization) to reduce memory usage while maintaining search efficiency. |
HNSW_PQ | It uses PQ (Product Quantization) to enhance the HNSW method by compressing the vectors, resulting in a more memory-efficient structure with good performance. |
HNSW_PRQ | It combines the benefits of HNSW_PQ with a re-ranking mechanism to improve accuracy after an initial fast search. This method ensures both speed and high precision. |
SCANN | SCANN (Scalable Nearest Neighbor) is an advanced method developed by Google for efficient nearest neighbor search. It utilizes techniques like quantization and partitioning to provide extremely fast retrieval speeds, especially on very large datasets. |