一、Qdrant 是什么？它解决什么问题
Qdrant 是一个 专为向量相似度搜索（Vector Similarity Search）设计的高性能数据库，核心目标是：
在海量高维向量中，快速、可过滤、可扩展地找到"语义最相近"的数据
它并不是传统意义上的关系型数据库，而是：
- 原生向量数据库（Native Vector DB）
- 面向 Embedding / ANN（Approximate Nearest Neighbor）
- 强调 低延迟、高召回率、强过滤能力
一句话定位：
Qdrant = "工程化程度很高、偏生产可用的向量检索引擎"
二、Qdrant 的整体架构
1️⃣ 核心技术栈
| 层级 | 技术 |
|---|---|
| 语言 | Rust（非常关键） |
| 存储 | mmap + RocksDB / 自研存储 |
| 索引 | HNSW |
| API | REST / gRPC |
| 部署 | 单机 / 集群 / 云原生 |
👉 Rust 带来的直接结果是：
- 极低 GC 干扰
- 稳定延迟（P99 表现好）
- 高并发下更可控
2️⃣ 逻辑架构（概念）
Collection
├── Vector Index (HNSW)
├── Payload Index (倒排 / 数值索引)
├── Vector Storage
└── Metadata StorageCollection 是一切的核心单位（类似 PG 的 table / Milvus 的 collection）
3. 相关资料信息
3.1. 官网地址
1️⃣ 背后公司
- 公司名：Qdrant Solutions
- 总部：欧洲（最早在德国 / 奥地利一带）
- 不是云厂商附属产品，是纯向量数据库公司
2️⃣ 商业模式是什么？
Qdrant 走的是非常清晰的 "Open Core + 托管服务" 路线：
🔹 免费 & 开源
- 自部署（Docker / k8s / VM）
- 功能完整
- 企业可长期使用
🔹 商业部分（你可完全不用）
-
Qdrant Cloud
-
托管、SLA、自动扩缩容、备份
-
按量付费
👉 不买云服务，不影响你任何功能
3️⃣ 会不会"以后突然闭源 / 锁死"？
从目前信号判断：
- Apache 2.0（不是 AGPL）
- 核心一直在 GitHub 主仓
- 商业收入点在云托管，不在功能封锁
👉 风险远低于很多新兴 AI Infra 项目
4️⃣ 官方核心入口（必收藏）
🌐 官方网站
https://qdrant.tech- 产品介绍
- 架构理念
- 云服务入口
📘 官方文档（质量很高）
https://qdrant.tech/documentation/重点推荐你看的几块：
- Quickstart
- Collections & Points
- Filtering（Payload）
- Indexing / HNSW
- RAG 示例
👉 文档是工程师写的，不是市场文案。
5️⃣ GitHub（真正的技术社区）
⭐ 核心仓库
https://github.com/qdrant/qdrant你可以重点关注：
src/（Rust 核心实现）openapi/（API 定义）issues（设计讨论很多是"架构级"的）
🔌 官方客户端仓库（含 .NET）
https://github.com/qdrant里面包括：
- qdrant-client (Python)
- qdrant-js
- qdrant-dotnet
- 示例项目
6️⃣ 社区交流渠道
💬 Discord（最活跃）
https://discord.gg/qdrant特点：
- 官方工程师直接答
- payload / HNSW / 性能问题都有人聊
- 很多 RAG 实战讨论
🧠 GitHub Discussions
https://github.com/qdrant/qdrant/discussions适合：
- 架构问题
- 参数调优
- 和 Milvus / FAISS 的对比
7️⃣ 学习资料（偏实战）
- 官方博客（很多性能 & 架构文章）
- RAG 示例（LangChain / LlamaIndex）
- 各语言 SDK 示例
4. 部署和基本使用
4.1. docker部署
yaml
services:
qdrant:
image: qdrant/qdrant
container_name: qdrant
ports:
- "6333:6333"
- "6334:6334"
volumes:
- qdrant_storage:/qdrant/storage
volumes:
qdrant_storage:4.2. 访问
1️⃣ 访问首页
浏览器打开：http://localhost:6333/
按官方安装说明，默认端口 6333，访问应该能看到 welcome 信息。
2️⃣ 访问 Web Dashboard（内置 UI）
浏览器打开：http://localhost:6333/dashboard
（Qdrant 提供 dashboard 路由在社区/issue 中经常被提到；Docker 下通常可用。）
5. 基本使用
5.1. 列出所有集合
GET /collections
{
"result": {
"collections": [
{
"name": "demo_sentences"
}
]
},
"status": "ok",
"time": 0.001410125
}5.2. 查看集合信息
GET /collections/demo_sentences
{
"result": {
"status": "green",
"optimizer_status": "ok",
"indexed_vectors_count": 0,
"points_count": 1,
"segments_count": 2,
"config": {
"params": {
"vectors": {
"size": 1024,
"distance": "Cosine"
},
"shard_number": 1,
"replication_factor": 1,
"write_consistency_factor": 1,
"on_disk_payload": true
},
"hnsw_config": {
"m": 16,
"ef_construct": 100,
"full_scan_threshold": 10000,
"max_indexing_threads": 0,
"on_disk": false
},
"optimizer_config": {
"deleted_threshold": 0.2,
"vacuum_min_vector_number": 1000,
"default_segment_number": 0,
"max_segment_size": null,
"memmap_threshold": null,
"indexing_threshold": 10000,
"flush_interval_sec": 5,
"max_optimization_threads": null
},
"wal_config": {
"wal_capacity_mb": 32,
"wal_segments_ahead": 0,
"wal_retain_closed": 1
},
"quantization_config": null
},
"payload_schema": {}
},
"status": "ok",
"time": 0.007238713
}5.3. 删除集合
DELETE /collections/demo_sentences5.4. 插入数据
PUT /collections/demo_sentences/points?wait=true
{
"points": [
{
"id": 1,
"vector": [
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],
"payload": {
"sentence": "C# 是一种在数据科学和机器学习领域广泛使用的编程语言。",
"created_at": "2026-01-26T08:55:02.109973+00:00"
}
}
]
}5.5. 按 id 取回
POST /collections/demo_sentences/points
{
"ids": [1, 2, 3],
"with_payload": true,
"with_vector": false
}
{
"result": [
{
"id": 1,
"payload": {
"sentence": "C# 是一种在数据科学和机器学习领域广泛使用的编程语言。",
"created_at": "2026-01-26T08:55:02.109973+00:00"
}
}
],
"status": "ok",
"time": 0.000386568
}5.6. 最简单：topK 最近邻
POST /collections/demo_sentences/points/query
{
"query": [0.1, 0.2, 0.3],
"limit": 5,
"with_payload": true
}5.7. 向量检索 + 过滤
POST /collections/demo_sentences/points/query
{
"query": [0.1, 0.2, 0.3],
"limit": 10,
"filter": {
"must": [
{ "key": "tag", "match": { "value": "sports" } }
],
"must_not": [
{ "key": "is_deleted", "match": { "value": true } }
]
},
"with_payload": true
}5.8. 仅更新 payload
POST /collections/demo_sentences/points/payload?wait=true
{
"payload": { "tag": "sports" },
"points": [1, 2, 3]
}5.9. 删除点
POST /collections/demo_sentences/points/delete?wait=true
{
"points": [1, 2, 3]
}