Quick Start

This tutorial guides you through

Obtaining a database endpoint from Zilliz Cloud,
Connecting to the database,
Creating a collection,
Setting index type,
Importing a dataset, and
Conducting an Approximate Nearest Neighbor (ANN) search.

Before you start

We will use the Milvus Python SDK (PyMilvus) throughout this guide. If Python is not your programming language of choice, there are also SDKs available in JAVA, Go, and Node.js as well.

Zilliz Cloud will always upgrade all hosted databases to the latest Milvus version. Therefore, you should install or upgrade PyMilvus to a compatible version. Before installing PyMilvus, ensure your Python version is above 3.7. To install PyMilvus of a recommended version, run the following command:

python -m pip install pymilvus==2.2.2

If you already have PyMilvus installed and need to upgrade to the recommended version, run the following command:

python -m pip install --upgrade pymilvus==2.2.2

For details on the recommended version of PyMilvus for use with a specific version of Milvus, refer to Which PyMilvus version should I use?

Obtain endpoint URI from Zilliz Cloud

Once you sign in to Zilliz Cloud, create a database in the default project. Do remember the user name and password displayed in the prompted Save Name and Password dialog box for database access later.

Create the first database

Click the database name to view its details. In the Database Details tab, you can find the public endpoint URI in the Cloud Endpoint section.

Get endpoint

To enable Internet access to your database endpoint, add your IP address to Network Access in the Summary section.

Add network access whitelist

Connect to the database

Use the endpoint URI and your database credentials to connect to the database instance.

# Run `python3` in your terminal to operate in the Python interactive mode.
from pymilvus import connections

connections.connect(
  alias="default", 
  uri='endpoint', # Endpoint URI obtained from Zilliz Cloud
  secure=True,
  user='user', # Username specified when you created this database
  password='password' # Password specified when you created this database
)

Create a collection

A collection is similar to a table in a relational database. You can create a collection by setting its name, schema, and optional description. If you’d like to learn more about the schema of the example dataset, refer to Example Dataset Overview.

from pymilvus import FieldSchema, CollectionSchema, DataType, Collection

# Define the fields in the collection first.
fields = [
    FieldSchema(name="id", dtype=DataType.INT64, is_primary=True),
    FieldSchema(name="title", dtype=DataType.VARCHAR, max_length=512),   
    FieldSchema(name="title_vector", dtype=DataType.FLOAT_VECTOR, dim=768),
    FieldSchema(name="link", dtype=DataType.VARCHAR, max_length=512),
    FieldSchema(name="reading_time", dtype=DataType.INT64),
    FieldSchema(name="publication", dtype=DataType.VARCHAR, max_length=512),
    FieldSchema(name="claps", dtype=DataType.INT64),
    FieldSchema(name="responses", dtype=DataType.INT64)
]

# Build the schema
schema = CollectionSchema(
    fields,
    description="Schema of Medium articles"
)

# Reference the fields in the collection to be created as the schema.
collection = Collection(
    name="medium_articles_2020", 
    description="Medium articles published between Jan 2020 to August 2020 in prominent publications",
    schema=schema
)

Once your collection is created, you can view it in the Collections tab of the database details page.

View collections

Set index type

Configure the index parameters of the vector field title_vector. In the index parameters, metric_type defines how similarity is calculated. The following example sets metric_type to L2 (Euclidean distance).

After setting the index parameters, load the collection. Existing data and data inserted in the future will be automatically available for search.

index_params = {
    "index_type": "AUTOINDEX",
    "metric_type": "L2",
    "params": {}
}

# Set 'field_name' to the field to be indexed 
collection.create_index("title_vector", index_params)

# Load the collection for the following operations.
collection.load()

Import a dataset

To import a dataset, you need to upload the file containing the dataset to Zilliz Cloud. For your convenience, a sample JSON file has been uploaded to a public storage bucket.

For a database deployed on Amazon Web Service (AWS), Click here to copy the S3 URL.
For a database deployed on Google Cloud Platform (GCP), Click here to copy the Google Cloud Storage (GCS) URL.

Click the name of the collection to view its details. In the Collection tab, click Import Now in the Import Data to a Collection area. You can select either Import a local file or Import a file from S3 in the dialog box.

To import a local file, download the sample JSON file, click upload a file to upload the file, and click Import.

Import a local file
To import a file from cloud storage bucket, enter the copied URL in S3 File Path / GCS File Path and click Import.

Import a file from S3

Conduct an ANN search

In the dataset, values in the field title_vector are vector embeddings of the corresponding titles. This example illustrates how to conduct an ANN search among these vectors.

from pymilvus import Collection

# Reference the collection created.
collection = Collection("medium_articles_2020")

# Ensure that the collection has been loaded before searches.
collection.load()

# Set search parameters.
#
# metric_type: Indicates the algorithm used to measure vector 
#              similarity. 
#              Possible values are L2 and IP.
# params:      Optional customizable settings for searches  
#              using AUTOINDEX, a proprietary index method.
# - level:     Indicates the precision level of the search 
#              results. It defaults to 1.
#              Possible values are 1, 2, 3. A greater value 
#              indicates a more accurate result at slower 
#              performance.
search_params = {
    "metric_type": "L2", 
    "params": {"level": 1}
}

# Define the search conditions.
# The vector specified in 'data' is the query vector.
# The search returns the top 10 nearest neighbors of the query
# vector.
results = collection.search(
    data = [[0.041732933, 0.013779674, -0.027564144, -0.013061441, 0.009748648, 0.00082446384, -0.00071647146, 0.048612226, -0.04836573, -0.04567751, 0.018008126, 0.0063936645, -0.011913628, 0.030776596, -0.018274948, 0.019929802, 0.020547243, 0.032735646, -0.031652678, -0.033816382, -0.051087562, -0.033748355, 0.0039493158, 0.009246126, -0.060236514, -0.017136049, 0.028754413, -0.008433934, 0.011168004, -0.012391256, -0.011225835, 0.031775184, 0.002929508, -0.007448661, -0.005337719, -0.010999258, -0.01515909, -0.005130484, 0.0060212007, 0.0034560722, -0.022935811, -0.04970116, -0.0155887455, 0.06627353, -0.006052789, -0.051570725, -0.109865054, 0.033205193, 0.00041118253, 0.0029823708, 0.036160238, -0.011256539, 0.00023560718, 0.058322437, 0.022275906, 0.015206677, -0.02884609, 0.0016338055, 0.0049200393, 0.014388571, -0.0049061654, -0.04664761, -0.027454877, 0.017526226, -0.005100602, 0.018090058, 0.02700998, 0.04031944, -0.0097965, -0.03674761, -0.0043163053, -0.023320708, 0.012654851, -0.014262311, -0.008081833, -0.018334744, 0.0014025003, -0.003053399, -0.002636383, -0.022398386, -0.004725274, 0.00036367847, -0.012368711, 0.0014739085, 0.03450414, 0.009684024, 0.017912658, 0.06594397, 0.021381201, 0.029343689, -0.0069561847, 0.026152428, 0.04635037, 0.014746184, -0.002119602, 0.034359712, -0.013705124, 0.010691518, 0.04060854, 0.013679299, -0.018990282, 0.035340093, 0.007353945, -0.035990074, 0.013126987, -0.032933377, -0.001756877, -0.0049658176, -0.03380879, -0.07024137, -0.0130426735, 0.010533265, -0.023091802, -0.004645729, -0.03344451, 0.04759929, 0.025985204, -0.040710885, -0.016681142, -0.024664842, -0.025170377, 0.08839205, -0.023733815, 0.019494494, 0.0055427826, 0.045460507, 0.07066554, 0.022181382, 0.018302314, 0.026806992, -0.006066003, 0.046525814, -0.04066389, 0.019001767, 0.021242762, -0.020784091, -0.031635042, 0.04573943, 0.02515421, -0.050663553, -0.05183343, -0.046468202, -0.07910535, 0.017036669, 0.021445233, 0.04277428, -0.020235524, -0.055314954, 0.00904601, -0.01104365, 0.03069203, -0.00821997, -0.035594665, 0.024322856, -0.0068963314, 0.009003657, 0.00398102, -0.008596356, 0.014772055, 0.02740991, 0.025503553, 0.0038213644, -0.0047855405, -0.034888722, 0.030553816, -0.008325959, 0.030010607, 0.023729775, 0.016138833, -0.022967983, -0.08616877, -0.02460819, -0.008210168, -0.06444098, 0.018750126, -0.03335763, 0.022024624, 0.032374356, 0.023870794, 0.021288997, -0.026617877, 0.020435361, -0.003692393, -0.024113296, 0.044870164, -0.030451361, 0.013022849, 0.002278627, -0.027616743, -0.012087787, -0.033232547, -0.022974484, 0.02801226, -0.029057292, 0.060317725, -0.02312559, 0.015558754, 0.073630534, 0.02490823, -0.0140531305, -0.043771528, 0.040756326, 0.01667925, -0.0046050115, -0.08938058, 0.10560781, 0.015044094, 0.003613817, 0.013523503, -0.011039813, 0.06396795, 0.013428416, -0.025031878, -0.014972648, -0.015970055, 0.037022553, -0.013759925, 0.013363354, 0.0039748577, -0.0040822625, 0.018209668, -0.057496265, 0.034993384, 0.07075411, 0.023498386, 0.085871644, 0.028646072, 0.007590898, 0.07037031, -0.05005178, 0.010477505, -0.014106617, 0.013402172, 0.007472563, -0.03131418, 0.020552127, -0.031878896, -0.04170217, -0.03153583, 0.03458349, 0.03366634, 0.021306382, -0.037176874, 0.029069472, 0.014662372, 0.0024123765, -0.025403008, -0.0372993, -0.049923114, -0.014209514, -0.015524425, 0.036377322, 0.04259327, -0.029715618, 0.02657093, -0.0062432447, -0.0024253451, -0.021287171, 0.010478781, -0.029322306, -0.021203341, 0.047209084, 0.025337176, 0.018471811, -0.008709492, -0.047414266, -0.06227469, -0.05713435, 0.02141101, 0.024481304, 0.07176469, 0.0211379, -0.049316987, -0.124073654, 0.0049275495, -0.02461509, -0.02738388, 0.04825289, -0.05069646, 0.012640115, -0.0061352802, 0.034599125, 0.02799496, -0.01511028, -0.046418104, 0.011309801, 0.016673129, -0.033531003, -0.049203333, -0.027218347, -0.03528408, 0.008881575, 0.010736325, 0.034232814, 0.012807507, -0.0100207105, 0.0067757815, 0.009538357, 0.026212366, -0.036120333, -0.019764563, 0.006527411, -0.016437015, -0.009759148, -0.042246807, 0.012492151, 0.0066206953, 0.010672299, -0.44499892, -0.036189068, -0.015703931, -0.031111298, -0.020329623, 0.0047888453, 0.090396516, -0.041484866, 0.033830352, -0.0033847596, 0.06065415, 0.030880837, 0.05558494, 0.022805553, 0.009607596, 0.006682602, 0.036806617, 0.02406229, 0.034229457, -0.0105605405, 0.034754273, 0.02436426, -0.03849325, 0.021132406, -0.01251386, 0.022090863, -0.029137045, 0.0064384523, -0.03175176, -0.0070441505, 0.016025176, -0.023172623, 0.00076795724, -0.024106828, -0.045440633, -0.0074440194, 0.00035374766, 0.024374487, 0.0058897804, -0.012461025, -0.029086761, 0.0029477053, -0.022914894, -0.032369837, 0.020743662, 0.024116345, 0.0020526652, 0.0008596536, -0.000583463, 0.061080184, 0.020812698, -0.0235381, 0.08112197, 0.05689626, -0.003070104, -0.010714772, -0.004864459, 0.027089117, -0.030910335, 0.0017404438, -0.014978656, 0.0127020255, 0.01878998, -0.051732827, -0.0037475713, 0.013033434, -0.023682894, -0.03219574, 0.03736345, 0.0058930484, -0.054040316, 0.047637977, 0.012636436, -0.05820182, 0.013828813, -0.057893142, -0.012405234, 0.030266648, -0.0029184038, -0.021839319, -0.045179468, -0.013123978, -0.021320488, 0.0015718226, 0.020244086, -0.014414709, 0.009535103, -0.004497577, -0.02577227, -0.0085017495, 0.029090486, 0.009356506, 0.0055838437, 0.021151636, 0.039531752, 0.07814674, 0.043186333, -0.0077368533, 0.028967595, 0.025058193, 0.05432941, -0.04383656, -0.027070394, -0.080263995, -0.03616516, -0.026129462, -0.0033627374, 0.035040155, 0.015231506, -0.06372076, 0.046391208, 0.0049725454, 0.003783345, -0.057800908, 0.061461, -0.017880175, 0.022820404, 0.048944063, 0.04725843, -0.013392871, 0.05023065, 0.0069421427, -0.019561166, 0.012953843, 0.06227977, -0.02114757, -0.003334329, 0.023241237, -0.061053444, -0.023145229, 0.016086273, 0.0774039, 0.008069459, -0.0013532874, -0.016790181, -0.027246375, -0.03254919, 0.033754334, 0.00037142826, -0.02387325, 0.0057056695, 0.0084914565, -0.051856343, 0.029254, 0.005583839, 0.011591886, -0.033027634, -0.004170374, 0.018334484, -0.0030969654, 0.0024489106, 0.0030196267, 0.023012564, 0.020529047, 0.00010772953, 0.0017700809, 0.029260442, -0.018829526, -0.024797931, -0.039499596, 0.008108761, -0.013099816, -0.11726566, -0.005652353, -0.008117937, -0.012961832, 0.0152542135, -0.06429504, 0.0184562, 0.058997117, -0.027178442, -0.019294549, -0.01587592, 0.0048053437, 0.043830805, 0.011232237, -0.026841154, -0.0007282251, -0.00862919, -0.008405325, 0.019370917, -0.008112641, -0.014931766, 0.065622255, 0.0149185015, 0.013089685, -0.0028022556, -0.028629888, -0.048105706, 0.009296162, 0.010251239, 0.030800395, 0.028263845, -0.011021621, -0.034127586, 0.014709971, -0.0075270324, 0.010737263, 0.020517904, -0.012932179, 0.007153817, 0.03736311, -0.03391106, 0.03028614, 0.012531187, -0.046059456, -0.0043963846, 0.028799629, -0.06663413, -0.009447025, -0.019833198, -0.036111858, -0.01901045, 0.040701825, 0.0060573653, 0.027482377, -0.019782187, -0.020186251, 0.028398912, 0.027108852, 0.026535714, -0.000995191, -0.020599326, -0.005658084, -0.017271476, 0.026300041, -0.006992451, -0.08593853, 0.03675959, 0.0029454317, -0.040927384, -0.035480253, 0.016498009, -0.03406521, -0.026182177, -0.0007024827, 0.019500641, 0.0047998386, -0.02416359, 0.0019833131, 0.0033488963, 0.037788488, -0.009154958, -0.043469638, -0.024896, -0.017234193, 0.044996973, -0.06303135, -0.051730774, 0.04041444, 0.0075959326, -0.03901764, -0.019851806, -0.008242245, 0.06107143, 0.030118924, -0.016167669, -0.028161867, -0.0025679746, -0.021713274, 0.025275888, -0.012819265, -0.036431268, 0.017991759, 0.040626206, -0.0036572467, -0.0005935883, -0.0037468506, 0.034460746, -0.0182785, -0.00431203, -0.044755403, 0.016463224, 0.041199315, -0.0093387, 0.03919184, -0.01151653, -0.016965209, 0.006347649, 0.021104146, 0.060276803, -0.026659148, 0.026461488, -0.032700688, 0.0012274865, -0.024675943, -0.003006079, -0.009607032, 0.010597691, 0.0043017124, -0.01908524, 0.006748306, -0.03049305, -0.017481703, 0.036747415, 0.036634356, 0.0007106319, 0.045647435, -0.020883067, -0.0593661, -0.03929885, 0.042825453, 0.016104022, -0.03222858, 0.031112716, 0.020407677, -0.013276762, 0.03657825, -0.033871554, 0.004176301, 0.009538976, -0.009995692, 0.0042660628, 0.050545394, -0.018142857, 0.005219403, 0.0006711967, -0.014264284, 0.031044828, -0.01827481, 0.012488852, 0.031393733, 0.050390214, -0.014484084, -0.054758117, 0.055042055, -0.005506624, -0.0066648237, 0.010891078, 0.012446279, 0.061687976, 0.018091502, 0.0026527622, 0.0321537, -0.02469515, 0.01772019, 0.006846163, -0.07471038, -0.024433741, 0.02483875, 0.0497063, 0.0043456135, 0.056550737, 0.035752796, -0.02430349, 0.036570627, -0.027576203, -0.012418993, 0.023442797, -0.03433812, 0.01953399, -0.028003592, -0.021168072, 0.019414881, -0.014712576, -0.0003938545, 0.021453558, -0.023197332, -0.004455581, -0.08799191, 0.0010808896, 0.009281116, -0.0051161298, 0.031497046, 0.034916095, -0.023042161, 0.030799815, 0.017298799, 0.0015253434, 0.013728047, 0.0035838438, 0.016767647, -0.022243451, 0.013371096, 0.053564783, -0.008776885, -0.013133307, 0.015577713, -0.027008705, 0.009490815, -0.04103532, -0.012426461, -0.0050485474, -0.04323231, -0.013291623, -0.01660157, -0.055480026, 0.017622838, 0.017476618, -0.009798125, 0.038226977, -0.03127579, 0.019329516, 0.033461004, -0.0039813113, -0.039526325, 0.03884973, -0.011381027, -0.023257744, 0.03033401, 0.0029607012, -0.0006490531, -0.0347344, 0.029701462, -0.04153701, 0.028073426, -0.025427297, 0.009756264, -0.048082624, 0.021743972, 0.057197016, 0.024082556, -0.013968224, 0.044379756, -0.029081704, 0.003487999, 0.042621125, -0.04339743, -0.027005397, -0.02944044, -0.024172144, -0.07388652, 0.05952364, 0.02561452, -0.010255158, -0.015288555, 0.045012463, 0.012403602, -0.021197597, 0.025847573, -0.016983166, 0.03021369, -0.02920852, 0.035140667, -0.010627725, -0.020431923, 0.03191218, 0.0046844087, 0.056356475, -0.00012615003, -0.0052536936, -0.058609407, 0.009710908, 0.00041168949, -0.22300485, -0.0077232462, 0.0029359192, -0.028645728, -0.021156758, 0.029606635, -0.026473567, -0.0019432966, 0.023867624, 0.021946864, -0.00082128344, 0.01897284, -0.017976845, -0.015677344, -0.0026336901, 0.030096486]],
    anns_field="title_vector",
    param=search_params,
    limit = 10
)

print(results[0])

The expected results are as follows:

["['(distance: 0.0, id: 0)', '(distance: 0.29999837279319763, id: 3177)', '(distance: 0.36103835701942444, id: 5607)', '(distance: 0.37674015760421753, id: 5641)', '(distance: 0.4162980318069458, id: 3441)', '(distance: 0.436093807220459, id: 938)', '(distance: 0.4586222767829895, id: 5780)', '(distance: 0.46275582909584045, id: 3072)', '(distance: 0.48078256845474243, id: 4328)', '(distance: 0.48886314034461975, id: 4275)']"]

Next steps

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