向量数据库基准2026：存储与检索的极致优化

# Milvus
results = collection.search(
    data=[query_vector],
    anns_field="embedding",
    param={"metric_type": "L2"},
    limit=10,
    expr='category == "tech" and year > 2025'
)

# Qdrant
results = client.search(
    collection_name="docs",
    query_vector=query_vector,
    query_filter=Filter(
        must=[
            FieldCondition(key="category", match=MatchValue(value="tech")),
            FieldCondition(key="year", range=Range(gt=2025))
        ]
    )
)

# Chroma - 最简单
import chromadb
client = chromadb.Client()
collection = client.create_collection("docs")
collection.add(embeddings=[...], documents=[...])
results = collection.query(query_embeddings=[...], n_results=5)

# Qdrant
from qdrant_client import QdrantClient
client = QdrantClient(host="localhost", port=6333)
client.create_collection("docs", vectors_config=VectorParams(size=1024))
client.upsert(collection_name="docs", points=[...])
results = client.search(collection_name="docs", query_vector=...)

# Milvus
from pymilvus import connections, Collection
connections.connect(host="localhost", port="19530")
collection = Collection("docs")
collection.insert([...])
results = collection.search([...])