milvus client操作简单示例

1 安装 milvus client库

pip install -U pymilvus -i https://pypi.tuna.tsinghua.edu.cn/simple

2 创建milvus库

创建milvus client之前需要本地或服务器启动milvus数据库

本地数据库连接

from pymilvus import MilvusClient

client = MilvusClient("milvus_demo.db")

远程数据库连接

from pymilvus import MilvusClient

client = MilvusClient(uri="http://localhost:19530", token="root:Milvus")

3 创建collections

if client.has_collection(collection_name="demo_collection"):
    client.drop_collection(collection_name="demo_collection")
client.create_collection(
    collection_name="demo_collection",
    dimension=768,  # The vectors we will use in this demo has 768 dimensions
)

4 准备数据

docs为文档库，vectors表示基于docs计算好的向量数据，这里采用本地定制模型计算。

data = [
    {"id": i, "vector": vectors[i], "text": docs[i], "subject": "history"}
    for i in range(len(vectors))
]

print("Data has", len(data), "entities, each with fields: ", data[0].keys())
print("Vector dim:", len(data[0]["vector"]))

milvus提供了计算向量的库，示例如下

pip install "pymilvus[model]"

from pymilvus import model

# If connection to https://huggingface.co/ failed, uncomment the following path
# import os
# os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'

# This will download a small embedding model "paraphrase-albert-small-v2" (~50MB).
embedding_fn = model.DefaultEmbeddingFunction()

# Text strings to search from.
docs = [
    "Artificial intelligence was founded as an academic discipline in 1956.",
    "Alan Turing was the first person to conduct substantial research in AI.",
    "Born in Maida Vale, London, Turing was raised in southern England.",
]

vectors = embedding_fn.encode_documents(docs)
# The output vector has 768 dimensions, matching the collection that we just created.
print("Dim:", embedding_fn.dim, vectors[0].shape)  # Dim: 768 (768,)

5 插入数据

res = client.insert(collection_name="demo_collection", data=data)
print(res)

{'insert_count': 3, 'ids': [0, 1, 2], 'cost': 0}

6 向量搜索

# query_vectors = embedding_fn.encode_queries(["Who is Alan Turing?"])
# If you don't have the embedding function you can use a fake vector to finish the demo:
# query_vectors = [ [ random.uniform(-1, 1) for _ in range(768) ] ]
query_vectors = [xxx] # 这里query向量已经计算好，采用和准备数据是一样的模型和参数计算。

res = client.search(
    collection_name="demo_collection",  # target collection
    data=query_vectors,  # query vectors
    limit=2,  # number of returned entities
    output_fields=["text", "subject"],  # specifies fields to be returned
)

print(res)

data: ["[{'id': 2, 'distance': 0.5859944820404053, 'entity': {'text': 'Born in Maida Vale, London, Turing was raised in southern England.', 'subject': 'history'}}, {'id': 1, 'distance': 0.5118255615234375, 'entity': {'text': 'Alan Turing was the first person to conduct substantial research in AI.', 'subject': 'history'}}]"] , extra_info: {'cost': 0}

reference

---

quick start with mivus lite.

https://milvus.io/docs/v2.5.x/quickstart.md