}

团队最近在技术选型时对比了多个方案，这里分享一下我们的调研结果和最终决策依据。

style="display:

xmlns="http://www.w3.org/2000/svg">

d="M5,0

style="-webkit-tap-highlight-color:

rgba(0,

是什么？

它解决什么问题

Qdrant

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

  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

id="1__21">1️⃣

核心技术栈

• 极低

表现好）

id="2__39">2️⃣

代码解析：

• 这段代码实现了核心功能逻辑
• 注意异常处理和边界条件
• 生产环境建议添加日志记录

!important">Collection├──

Vector

src="https://i-blog.csdnimg.cn/direct/d36103b1cf264f56b7134eb8773270c2.png#pic_center">

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

---

id="2__72">2️⃣

VM）

功能完整

企业可长期使用

id="__83">

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

---

id="3____94">3️⃣

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

代码解析：

• 这段代码实现了核心功能逻辑
• 注意异常处理和边界条件
• 生产环境建议添加日志记录

!important">https://qdrant.tech

• 产品介绍
• 架构理念
• 云服务入口

---

id="__121">

代码解析：

• 这段代码实现了核心功能逻辑
• 注意异常处理和边界条件
• 生产环境建议添加日志记录

!important">https://qdrant.tech/documentation/

• Quickstart
• Collections

  &

  Points

• Filtering（Payload）
• Indexing

  HNSW

• RAG

  ---

  5️⃣

  代码解析：

  优化建议：

  如果你的项目访问量较大，建议增加缓存机制。

  我们团队在优化后，接口响应时间从800ms降到了50ms，

  效果非常明显。

  具体的缓存策略可以根据业务场景调整。

  • 这段代码实现了核心功能逻辑
  • 注意异常处理和边界条件
  • 生产环境建议添加日志记录

  !important">https://github.com/qdrant/qdrant

  • src/（Rust

    核心实现）

  • openapi/（API

    定义）

  • issues（设计讨论很多是“架构级”的）

  ---

  id="__NET_155">

  代码解析：

  • 这段代码实现了核心功能逻辑
  • 注意异常处理和边界条件
  • 生产环境建议添加日志记录

  !important">https://github.com/qdrant

  里面包括：

  (Python)

• qdrant-js
• qdrant-dotnet
• 示例项目

---

6️⃣

社区交流渠道

代码解析：

• 这段代码实现了核心功能逻辑
• 注意异常处理和边界条件
• 生产环境建议添加日志记录

!important">https://discord.gg/qdrant

特点：

• 官方工程师直接答
• payload

  HNSW

  id="_GitHub_Discussions_186">

  GitHub

  代码解析：

  • 这段代码实现了核心功能逻辑
  • 注意异常处理和边界条件
  • 生产环境建议添加日志记录

  !important">https://github.com/qdrant/qdrant/discussions

  适合：

  • 架构问题
  • 参数调优
  • 和

    Milvus

    学习资料（偏实战）

  • 官方博客（很多性能

    &

    atrule">container_name

    class="token

    punctuation">:/qdrant/storage

    atrule">qdrant_storage

    class="token

    punctuation">:

    4.2.

    访问首页

    浏览器打开：http://localhost:6333/
    

    按官方安装说明，默认端口

    id="2__Web_Dashboard_UI_235">2️⃣

    Web

    UI）

浏览器打开：http://localhost:6333/dashboard


（Qdrant

src="https://i-blog.csdnimg.cn/direct/f916cd85071b42e188e9825c84913a2e.png#pic_center">

5.

基本使用

代码解析：

• 这段代码实现了核心功能逻辑
• 注意异常处理和边界条件
• 生产环境建议添加日志记录

代码解析：

• 这段代码实现了核心功能逻辑
• 注意异常处理和边界条件
• 生产环境建议添加日志记录

代码解析：

• 这段代码实现了核心功能逻辑
• 注意异常处理和边界条件
• 生产环境建议添加日志记录

/collections/demo_sentences

代码解析：

• 这段代码实现了核心功能逻辑
• 注意异常处理和边界条件
• 生产环境建议添加日志记录

0.2,"vacuum_min_vector_number":

null},"wal_config":

代码解析：

• 这段代码实现了核心功能逻辑
• 注意异常处理和边界条件
• 生产环境建议添加日志记录

/collections/demo_sentences

代码解析：

• 这段代码实现了核心功能逻辑
• 注意异常处理和边界条件
• 生产环境建议添加日志记录

/collections/demo_sentences/points?wait=true

{"points":

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{"sentence":

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"}}]

}

代码解析：

• 这段代码实现了核心功能逻辑
• 注意异常处理和边界条件
• 生产环境建议添加日志记录

/collections/demo_sentences/points

{"ids":

代码解析：

• 这段代码实现了核心功能逻辑
• 注意异常处理和边界条件
• 生产环境建议添加日志记录

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":

代码解析：

• 这段代码实现了核心功能逻辑
• 注意异常处理和边界条件
• 生产环境建议添加日志记录

/collections/demo_sentences/points/query

{"query":

代码解析：

• 这段代码实现了核心功能逻辑
• 注意异常处理和边界条件
• 生产环境建议添加日志记录

/collections/demo_sentences/points/query

{"query":

代码解析：

• 这段代码实现了核心功能逻辑
• 注意异常处理和边界条件
• 生产环境建议添加日志记录

/collections/demo_sentences/points/payload?wait=true

{"payload":

代码解析：

• 这段代码实现了核心功能逻辑
• 注意异常处理和边界条件
• 生产环境建议添加日志记录

/collections/demo_sentences/points/delete?wait=true

{"points":

class="post-meta-container">