RAG Qdrant 高级用法

# Qdrant 高级用法指南 ## 分布式部署 ### 集群设置 Qdrant 使用 Raft 共识进行分布式协调。 yaml # 用于 3 节点集群的 docker-compose.yml version: '3.8' services: qdrant-node-1: image: qdrant/qdrant:latest ports: - "6333:6333" - "6334:6334" - "6335:6335" volumes: - ./node1_storage:/qdrant/storage environment: - QDRANT__CLUSTER__ENABLED=true - QDRANT__CLUSTER__P2P__PORT=6335 - QDRANT__SERVICE__HTTP_PORT=6333 - QDRANT__SERVICE__GRPC_PORT=6334 qdrant-node-2: image: qdrant/qdrant:latest ports: - "6343:6333" - "6344:6334" - "6345:6335" volumes: - ./node2_storage:/qdrant/storage environment: - QDRANT__CLUSTER__ENABLED=true - QDRANT__CLUSTER__P2P__PORT=6335 - QDRANT__CLUSTER__BOOTSTRAP=http://qdrant-node-1:6335 depends_on: - qdrant-node-1 qdrant-node-3: image: qdrant/qdrant:latest ports: - "6353:6333" - "6354:6334" - "6355:6335" volumes: - ./node3_storage:/qdrant/storage environment: - QDRANT__CLUSTER__ENABLED=true - QDRANT__CLUSTER__P2P__PORT=6335 - QDRANT__CLUSTER__BOOTSTRAP=http://qdrant-node-1:6335 depends_on: - qdrant-node-1 ### 分片配置 python from qdrant_client import QdrantClient from qdrant_client.models import VectorParams, Distance, ShardingMethod client = QdrantClient(host="localhost", port=6333) # 创建分片集合 client.create_collection( collection_name="large_collection", vectors_config=VectorParams(size=384, distance=Distance.COSINE), shard_number=6, # 分片数量 replication_factor=2, # 每个分片的副本数 write_consistency_factor=1 # 写入所需的确认数 ) # 检查集群状态 cluster_info = client.get_cluster_info() print(f"Peers: {cluster_info.peers}") print(f"Raft state: {cluster_info.raft_info}") ### 复制与一致性 python from qdrant_client.models import WriteOrdering # 强一致性写入 client.upsert( collection_name="critical_data", points=points, ordering=WriteOrdering.STRONG # 等待所有副本 ) # 最终一致性（更快） client.upsert( collection_name="logs", points=points, ordering=WriteOrdering.WEAK # 主节点确认后返回 ) # 从特定分片读取 results = client.search( collection_name="documents", query_vector=query, consistency="m