Run Milvus using Python
This topic describes how to run Milvus using Python.
Through running the example code we provided, you will have a primary understanding of what Milvus is capable of.
Preparations
- Milvus 2.2.16
- Python 3 (3.7.1 or later)
- PyMilvus 2.2.x
Download example code
Download hello_milvus.py
directly or with the following command.
$ wget https://raw.githubusercontent.com/milvus-io/pymilvus/master/examples/hello_milvus.py
Scan the example code
The example code performs the following steps.
- Imports a PyMilvus package:
from pymilvus import (
connections,
utility,
FieldSchema,
CollectionSchema,
DataType,
Collection,
)
- Connects to a server:
connections.connect("default", host="localhost", port="19530")
- Creates a collection:
fields = [
FieldSchema(name="pk", dtype=DataType.INT64, is_primary=True, auto_id=False),
FieldSchema(name="random", dtype=DataType.DOUBLE),
FieldSchema(name="embeddings", dtype=DataType.FLOAT_VECTOR, dim=8)
]
schema = CollectionSchema(fields, "hello_milvus is the simplest demo to introduce the APIs")
hello_milvus = Collection("hello_milvus", schema)
- Inserts vectors in the collection:
import random
entities = [
[i for i in range(3000)], # field pk
[float(random.randrange(-20, -10)) for _ in range(3000)], # field random
[[random.random() for _ in range(8)] for _ in range(3000)], # field embeddings
]
insert_result = hello_milvus.insert(entities)
# After final entity is inserted, it is best to call flush to have no growing segments left in memory
hello_milvus.flush()
- Builds indexes on the entities:
index = {
"index_type": "IVF_FLAT",
"metric_type": "L2",
"params": {"nlist": 128},
}
hello_milvus.create_index("embeddings", index)
- Loads the collection to memory and performs a vector similarity search:
hello_milvus.load()
vectors_to_search = entities[-1][-2:]
search_params = {
"metric_type": "L2",
"params": {"nprobe": 10},
}
result = hello_milvus.search(vectors_to_search, "embeddings", search_params, limit=3, output_fields=["random"])
- Performs a vector query:
result = hello_milvus.query(expr="random > -14", output_fields=["random", "embeddings"])
- Performs a hybrid search:
result = hello_milvus.search(vectors_to_search, "embeddings", search_params, limit=3, expr="random > -12", output_fields=["random"])
- Deletes entities by their primary keys:
expr = f"pk in [{entities[0][0]}, {entities[0][1]}]"
hello_milvus.delete(expr)
- Drops the collection:
utility.drop_collection("hello_milvus")
Run the example code
Execute the following command to run the example code.
$ python3 hello_milvus.py
The returned results and query latency are shown as follows:
=== start connecting to Milvus ===
Does collection hello_milvus exist in Milvus: False
=== Create collection `hello_milvus` ===
=== Start inserting entities ===
Number of entities in Milvus: 3000
=== Start Creating index IVF_FLAT ===
=== Start loading ===
=== Start searching based on vector similarity ===
hit: (distance: 0.0, id: 2998), random field: -11.0
hit: (distance: 0.11455299705266953, id: 1581), random field: -18.0
hit: (distance: 0.1232629269361496, id: 2647), random field: -13.0
hit: (distance: 0.0, id: 2999), random field: -11.0
hit: (distance: 0.10560893267393112, id: 2430), random field: -18.0
hit: (distance: 0.13938161730766296, id: 377), random field: -14.0
search latency = 0.2796s
=== Start querying with `random > -14` ===
query result:
-{'pk': 9, 'random': -13.0, 'embeddings': [0.298433, 0.931987, 0.949756, 0.598713, 0.290125, 0.094323, 0.064444, 0.306993]}
search latency = 0.2970s
=== Start hybrid searching with `random > -12` ===
hit: (distance: 0.0, id: 2998), random field: -11.0
hit: (distance: 0.15773043036460876, id: 472), random field: -11.0
hit: (distance: 0.3273330628871918, id: 2146), random field: -11.0
hit: (distance: 0.0, id: 2999), random field: -11.0
hit: (distance: 0.15844076871871948, id: 2218), random field: -11.0
hit: (distance: 0.1622171700000763, id: 1403), random field: -11.0
search latency = 0.3028s
=== Start deleting with expr `pk in [0, 1]` ===
query before delete by expr=`pk in [0, 1]` -> result:
-{'pk': 0, 'random': -18.0, 'embeddings': [0.142279, 0.414248, 0.378628, 0.971863, 0.535941, 0.107011, 0.207052, 0.98182]}
-{'pk': 1, 'random': -15.0, 'embeddings': [0.57512, 0.358512, 0.439131, 0.862369, 0.083284, 0.294493, 0.004961, 0.180082]}
query after delete by expr=`pk in [0, 1]` -> result: []
=== Drop collection `hello_milvus` ===
Congratulations! You have started Milvus standalone and performed your first vector similarity search.