milvus入门使用

插入数据后的效果：

 代码如下：

import configparser
from pymilvus import connections, Collection, DataType, FieldSchema, CollectionSchema
import numpy as np

def create_collection():
    # Define the schema
    fields = [
        FieldSchema(name="sentence_id", dtype=DataType.INT64, is_primary=True, auto_id=True),
        FieldSchema(name="sentence", dtype=DataType.VARCHAR, max_length=512),
        FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, dim=128)
    ]
    schema = CollectionSchema(fields, description="Sentence collection")

    # Create the collection
    collection = Collection(name="sentence_collection", schema=schema)
    return collection

def insert_data(collection):
    sentences = [
        "这是第一句。",
        "这是第二句。",
        "这是第三句。"
    ]
    
    embeddings = np.random.rand(len(sentences), 128).tolist()  # Generate 128-dimensional vectors
    
    entities = [
        sentences,
        embeddings
    ]

    insert_result = collection.insert(entities)
    print(f"Inserted {len(insert_result.primary_keys)} records into collection.")

def create_index(collection):
    index_params = {
        "index_type": "IVF_FLAT",
        "params": {"nlist": 128},
        "metric_type": "L2"
    }
    collection.create_index(field_name="embedding", index_params=index_params)
    print("Index created.")

def search_data(collection, query_sentence):
    query_embedding = np.random.rand(1, 128).tolist()  # Generate a vector for the query sentence

    search_params = {"metric_type": "L2", "params": {"nprobe": 10}}
    
    results = collection.search(
        data=query_embedding,
        anns_field="embedding",
        param=search_params,
        limit=3,
        expr=None,
        output_fields=["sentence"]
    )
    
    for hits in results:
        for hit in hits:
            print(f"Match found: {hit.id} with distance: {hit.distance}, sentence: {hit.entity.get('sentence')}")

if __name__ == '__main__':
    # Connect to Milvus
    cfp = configparser.RawConfigParser()
    cfp.read('config.ini')
    milvus_uri = cfp.get('example', 'uri')
    token = cfp.get('example', 'token')
    connections.connect("default",
                        uri=milvus_uri,
                        token=token)
    print(f"Connecting to DB: {milvus_uri}")
    
    # Create collection
    collection = create_collection()

    # Insert data
    insert_data(collection)
    
    # Create index
    create_index(collection)
    
    # Load the collection into memory
    collection.load()
    
    # Search data
    search_data(collection, "这是一个查询句子。")

　　

运行效果：

python hello_zilliz_vectordb.py
Connecting to DB: https://in03-ca69f49bb65709f.api.gcp-us-west1.zillizcloud.com
Inserted 3 records into collection.
Index created.
Match found: 450140263656791260 with distance: 19.557846069335938, sentence: 这是第二句。
Match found: 450140263656791261 with distance: 20.327802658081055, sentence: 这是第三句。
Match found: 450140263656791259 with distance: 20.40052032470703, sentence: 这是第一句。

 

注意事项：

1. 向量转换：上面的代码使用了随机向量来模拟句子向量。在实际应用中，您需要使用 NLP 模型（例如中文 BERT）来将中文句子转换为向量。
2. 字符编码：确保在读取和处理中文文本时使用正确的字符编码（通常是 UTF-8）。