开源向量数据库：Apache 2.0，可自建可托管

# pip install -U "pymilvus[milvus-lite]"
import random
from pymilvus import MilvusClient

client = MilvusClient("milvus_demo.db")  # Milvus Lite: data lives in this local file
client.create_collection(collection_name="demo", dimension=768)

docs = ["Milvus is an open-source vector database.", "It runs on a laptop or on Kubernetes."]
vecs = [[random.random() for _ in range(768)] for _ in docs]  # swap in your embedding model
client.insert(collection_name="demo",
              data=[{"id": i, "vector": v, "text": t} for i, (v, t) in enumerate(zip(vecs, docs))])

print(client.search(collection_name="demo", data=[vecs[0]], limit=2, output_fields=["text"]))