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# 带有 Qdrant @: 深底的自定义“-” 矢量数据库

# 一. 导言导言 导言 导言 导言

**与RAG系列有关的📖** 此篇文章为 Qdrant @ ,}提供深度下潜到 QdrantMSK0 中的矢量数据库@:\ {

- [@ 4:ONNX @ I& Qdrant 履行部分](/blog/semantic-search-with-onnx-and-qdrant) 建立语义搜索
- [@ 5:_ 混合搜索@ MS K1\ 自动 @ MASK2 @ Indexing](/blog/rag-hybrid-search-and-indexing) 生产一体化

[解冻](https://qdrant.tech/) ( @propubliced@"{quadrant}") 是一个在 Rust{.}中建的 Open_-}源矢量数据库。

[TOC]

# 什么是 Qdrant?

AA [矢量数据库](https://qdrant.tech/documentation/overview/) 允许快速相似性搜索@.}与找到精确匹配的传统的数据库不同 {,}Qdrant finding *字义相似* .

```mermaid
flowchart LR
    A[Text: 'Docker deployment'] --> B[Embedding Model]
    B --> C["Vector: [0.12, -0.34, 0.56, ...]"]
    C --> D[Qdrant]
    E[Query: 'container setup'] --> F[Embedding Model]
    F --> G["Vector: [0.11, -0.32, 0.58, ...]"]
    G --> H[Similarity Search]
    D --> H
    H --> I[Similar Results]

style B stroke:#6366f1,stroke-width:3px
    style D stroke:#ef4444,stroke-width:3px
    style F stroke:#6366f1,stroke-width:3px
    style H stroke:#10b981,stroke-width:2px
```

**密钥 Qdrant 特性 @:**

- [HNSW 指数化](https://qdrant.tech/documentation/concepts/indexing/) - Sub-线性搜索时间
- [过滤](https://qdrant.tech/documentation/concepts/filtering/) - 将相似搜索与元数据过滤器相结合
- [gRPC +&](https://qdrant.tech/documentation/interfaces/) *-高 -优异访问*
- [分配部署](https://qdrant.tech/documentation/guides/distributed_deployment/) 水平缩放 *\ -
- [抓图](https://qdrant.tech/documentation/concepts/snapshots/) - 备份并恢复

# 核心概念

## 实收款

AA [收藏收藏](https://qdrant.tech/documentation/concepts/collections/) 是像表-}它持有固定维度和距离的矢量

```mermaid
flowchart TB
    subgraph Collection["Collection: blog_posts"]
        A[Vector Size: 384]
        B[Distance: Cosine]
        C[HNSW Index]
    end

subgraph Points
        D[Point 1: slug=docker-intro]
        E[Point 2: slug=kubernetes-basics]
        F[Point N...]
    end

Collection --> Points

style A stroke:#6366f1,stroke-width:2px
    style B stroke:#6366f1,stroke-width:2px
    style C stroke:#f59e0b,stroke-width:2px
    style D stroke:#10b981,stroke-width:2px
    style E stroke:#10b981,stroke-width:2px
```

```csharp
// Create collection - see https://qdrant.tech/documentation/concepts/collections/#create-a-collection
await client.CreateCollectionAsync(
    collectionName: "blog_posts",
    vectorsConfig: new VectorParams
    {
        Size = 384,              // Must match your embedding model
        Distance = Distance.Cosine  // Best for text embeddings
    }
);
```

**远程度量** ([医生数](https://qdrant.tech/documentation/concepts/collections/#distance-metrics)):

- **余弦** - 度量矢量之间的角度 *% MS K1}最佳文本=*
- **点点** - 原始内部产品 @( 预设的- 正常化矢量@)
- **欧元** 空间数据的几何距离 ( 空间数据=)

## 点点

AA [点](https://qdrant.tech/documentation/concepts/points/) 是包含 @:}的单一记录

```mermaid
flowchart LR
    subgraph Point
        A[ID: uuid/int]
        B["Vector: float[384]"]
        C[Payload: JSON metadata]
    end

style A stroke:#8b5cf6,stroke-width:2px
    style B stroke:#f59e0b,stroke-width:2px
    style C stroke:#10b981,stroke-width:2px
```

```csharp
// Upsert points - see https://qdrant.tech/documentation/concepts/points/#upload-points
var point = new PointStruct
{
    Id = new PointId { Uuid = Guid.NewGuid().ToString() },
    Vectors = embedding,  // float[384]
    Payload =
    {
        ["slug"] = "my-post",
        ["title"] = "Vector Databases",
        ["language"] = "en",
        ["categories"] = new[] { "AI", "Databases" },
        ["published"] = DateTimeOffset.UtcNow.ToUnixTimeSeconds()
    }
};

await client.UpsertAsync("blog_posts", points: new[] { point });
```

## 过滤

[过滤](https://qdrant.tech/documentation/concepts/filtering/) 运行中 *之前* 极有效率的 @.

```mermaid
flowchart TB
    A[Search Query] --> B{Apply Filters First}
    B --> C[Language = 'en']
    B --> D[Year >= 2024]
    C --> E[Filtered Subset]
    D --> E
    E --> F[Vector Similarity Search]
    F --> G[Ranked Results]

style B stroke:#ec4899,stroke-width:3px
    style E stroke:#f59e0b,stroke-width:2px
    style F stroke:#6366f1,stroke-width:2px
    style G stroke:#10b981,stroke-width:2px
```

```csharp
// Filter conditions - see https://qdrant.tech/documentation/concepts/filtering/#filtering-conditions
var filter = new Filter
{
    Must =  // AND conditions
    {
        new Condition { Field = new FieldCondition
        {
            Key = "language",
            Match = new Match { Keyword = "en" }
        }},
        new Condition { Field = new FieldCondition
        {
            Key = "published",
            Range = new Range { Gte = 1704067200 }  // 2024-01-01
        }}
    },
    MustNot =  // Exclude conditions
    {
        new Condition { Field = new FieldCondition
        {
            Key = "slug",
            Match = new Match { Keyword = "draft-post" }
        }}
    }
};
```

**过滤器类型** ([医生数](https://qdrant.tech/documentation/concepts/filtering/#match)):

- `Match.Keyword` - 精確的字符串匹配
- `Match.Text` - full-text 匹配
- `Match.Any` - 匹配任意数组
- `Range` -+Numeric 范围 @(_Gte, _Lte,}( GtMS K4+LtMPK5)
- `GeoBoundingBox` / `GeoRadius` - 地理过滤器

# C# 客户端

安装官方 [Qdrant @. @%Client](https://www.nuget.org/packages/Qdrant.Client) 组合包 @( @%[吉特胡布](https://github.com/qdrant/qdrant-dotnet)):

```bash
dotnet add package Qdrant.Client
```

## 连接设置

```csharp
using Qdrant.Client;
using Qdrant.Client.Grpc;

// gRPC client (recommended) - see https://qdrant.tech/documentation/interfaces/#grpc-interface
var client = new QdrantClient(
    host: "localhost",
    port: 6334,  // gRPC port (6333 is REST)
    https: false
);

// With API key - see https://qdrant.tech/documentation/guides/security/
var secureClient = new QdrantClient(
    host: "your-qdrant.cloud",
    port: 6334,
    https: true,
    apiKey: "your-api-key"
);
```

> **总是使用 gRPC** (port @6334)生产@-3-5x 比REST.}快

## Windows HTTP/2 修补

在 Windows\ ,\ 启用未加密的 HTTP\ MPK1\ {} **之前** 创建客户端@:

```csharp
AppContext.SetSwitch("System.Net.Http.SocketsHttpHandler.Http2UnencryptedSupport", true);
```

## 关键业务

### 搜索

```csharp
// Vector search - see https://qdrant.tech/documentation/concepts/search/
var results = await client.SearchAsync(
    collectionName: "blog_posts",
    vector: queryEmbedding,
    limit: 10,
    filter: filter,
    scoreThreshold: 0.5f,  // Minimum similarity
    searchParams: new SearchParams
    {
        HnswEf = 128,  // Search accuracy (higher = better recall)
        Exact = false  // Use approximate search
    },
    withPayload: true
);

foreach (var result in results)
{
    Console.WriteLine($"{result.Payload["title"].StringValue}: {result.Score}");
}
```

### 批次发件夹

```csharp
// Batch operations - see https://qdrant.tech/documentation/concepts/points/#batch-update
var points = documents.Select(doc => new PointStruct
{
    Id = new PointId { Uuid = doc.Id },
    Vectors = doc.Embedding,
    Payload = { ["slug"] = doc.Slug, ["title"] = doc.Title }
}).ToList();

await client.UpsertAsync(
    collectionName: "blog_posts",
    points: points,
    wait: true  // Wait for indexing
);
```

### 删除删除

```csharp
// Delete by filter - see https://qdrant.tech/documentation/concepts/points/#delete-points
await client.DeleteAsync(
    collectionName: "blog_posts",
    filter: new Filter
    {
        Must = { new Condition { Field = new FieldCondition
        {
            Key = "slug",
            Match = new Match { Keyword = "old-post" }
        }}}
    }
);
```

# HNSW 指数图

[HNSW 赫尔辛基](https://qdrant.tech/documentation/concepts/indexing/#vector-index) @ ( @ 历史导航小世界@ MS K1 is Qdrant}'_ 索引算法=.}

```mermaid
flowchart TB
    subgraph "HNSW Graph Layers"
        L2[Layer 2 - Sparse]
        L1[Layer 1 - Medium]
        L0[Layer 0 - Dense]
    end

Q[Query] --> L2
    L2 --> L1
    L1 --> L0
    L0 --> R[Nearest Neighbors]

style L2 stroke:#8b5cf6,stroke-width:2px
    style L1 stroke:#6366f1,stroke-width:2px
    style L0 stroke:#3b82f6,stroke-width:2px
    style Q stroke:#10b981,stroke-width:2px
    style R stroke:#ef4444,stroke-width:2px
```

## 索引参数

```csharp
// HNSW config - see https://qdrant.tech/documentation/concepts/indexing/#hnsw-index
var hnswConfig = new HnswConfigDiff
{
    M = 16,              // Edges per node (16-32 recommended)
    EfConstruct = 100,   // Build-time accuracy (100-200)
    FullScanThreshold = 10000  // Brute force threshold
};

await client.UpdateCollectionAsync(
    collectionName: "blog_posts",
    hnswConfig: hnswConfig
);
```

**搜索@- @ time 准确性 @:}**

```csharp
var searchParams = new SearchParams
{
    HnswEf = 128  // Higher = better recall, slower (64-256)
};
```

**调试指南@:**
“|使用案例”“| M |EfConstruct □|HnswEf|”。
|----------|---|-------------|--------|
@|QFast@,QQ 低调回想起 @MS K2QQ @#8Q} @|Q#64Q @ @MS卡6QZ32|Q#
@|}平衡的| @ 16 @ @ @ I| @ @ *100# #|# @ @ 128 @
@| @高调回想起#| @ 32} @MS K3 200} @| @ @ @256_

# 有效载荷指数

创建创建 [有效有效载荷指数](https://qdrant.tech/documentation/concepts/indexing/#payload-index) 用于常见过滤字段@: @%

```csharp
// Keyword index - see https://qdrant.tech/documentation/concepts/indexing/#payload-index
await client.CreatePayloadIndexAsync(
    collectionName: "blog_posts",
    fieldName: "language",
    schemaType: PayloadSchemaType.Keyword
);

// Integer index for ranges
await client.CreatePayloadIndexAsync(
    collectionName: "blog_posts",
    fieldName: "published",
    schemaType: PayloadSchemaType.Integer
);
```

**影响#:** @ 10-100_x 在大型收藏中快速过滤@.}

# 量化

[量化](https://qdrant.tech/documentation/guides/quantization/) 减少内存使用@: @%

```csharp
// Scalar quantization - see https://qdrant.tech/documentation/guides/quantization/#scalar-quantization
await client.UpdateCollectionAsync(
    collectionName: "blog_posts",
    quantizationConfig: new ScalarQuantization
    {
        Scalar = new ScalarQuantizationConfig
        {
            Type = ScalarType.Int8,  // float32 -> int8
            Quantile = 0.99f,
            AlwaysRam = true
        }
    }
);
```

**贸易 ** @4#x 少一点记忆@,# @ @ ~2% @ recall loss @ ,} *1.5*x 快速搜索=.#

# 嵌入器部署

```yaml
# docker-compose.yml - see https://qdrant.tech/documentation/guides/installation/
services:
  qdrant:
    image: qdrant/qdrant:v1.12.1  # Pin version!
    ports:
      - "6333:6333"  # REST
      - "6334:6334"  # gRPC
    volumes:
      - qdrant_data:/qdrant/storage
    environment:
      - QDRANT__SERVICE__GRPC_PORT=6334
      - QDRANT__SERVICE__HTTP_PORT=6333
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:6333/health"]
      interval: 30s
      timeout: 10s
      retries: 3

volumes:
  qdrant_data:
```

## 安全安保安全

启用 [API 键密钥认证](https://qdrant.tech/documentation/guides/security/):

```yaml
environment:
  - QDRANT__SERVICE__API_KEY=your-secret-key
```

# 监测监测

## 计量数

解冻曝光 [普罗米修斯指标](https://qdrant.tech/documentation/guides/monitoring/) 至 `/metrics`:

```bash
curl http://localhost:6333/metrics
```

密钥 mitus:

- `qdrant_collections_vector_count` *- 矢量总量
- `qdrant_rest_responses_duration_seconds` “- 查询延迟”
- `qdrant_memory_usage_bytes` -内存消耗

## 抓图

创建创建 [备份](https://qdrant.tech/documentation/concepts/snapshots/):

```bash
# Create snapshot
curl -X POST http://localhost:6333/collections/blog_posts/snapshots

# List snapshots
curl http://localhost:6333/collections/blog_posts/snapshots

# Restore (copy snapshot to storage/collections/blog_posts/snapshots/)
```

# 共犯组织

## 港口混凝土

- **6333** -=YTET -伊甸园字幕组=- 翻译:
- **6334** ==GRPC API=(=使用这个!)

## 2. 矢量误配

```
Error: expected dim: 384, got 768
```

您的嵌入模型和收藏必须匹配@ : @

- `all-MiniLM-L6-v2`*: 384度*
- `nomic-embed-text`*: 768度*
- 开放国际 `text-embedding-3-small`*: 1536度*

## “3. 慢速第一查詢”

HNSW 懒惰- 装入内存@. 启动后暖和 @:

```csharp
await client.SearchAsync("blog_posts", new float[384], limit: 1);
```

## 4. 队列过滤

使用 `Match.Any` 用于数组字段@: @%

```csharp
new Match { Any = new RepeatedStrings { Strings = { "AI", "ML" } } }
```

# 资源资源资源 资源和资源资源资源

## 官方 Qdrant 文件

- [概览概览概览](https://qdrant.tech/documentation/overview/) 正在启动
- [概念的概念](https://qdrant.tech/documentation/concepts/) 核心概念
- [实收款](https://qdrant.tech/documentation/concepts/collections/) -建立和管理收藏
- [点点](https://qdrant.tech/documentation/concepts/points/) - 与矢量合作
- [搜索](https://qdrant.tech/documentation/concepts/search/) -查询操作
- [过滤](https://qdrant.tech/documentation/concepts/filtering/) - 过滤条件
- [索引编制](https://qdrant.tech/documentation/concepts/indexing/) NSW和有效载荷指数
- [量化](https://qdrant.tech/documentation/guides/quantization/) - 记忆优化
- [安全安保安全](https://qdrant.tech/documentation/guides/security/) - 認證
- [监测监测](https://qdrant.tech/documentation/guides/monitoring/) 计量和遥测
- [抓图](https://qdrant.tech/documentation/concepts/snapshots/) - 备份并恢复

## 客户图书馆

- [Qdrant @ .@NET 客户端](https://github.com/qdrant/qdrant-dotnet) -=YTET -伊甸园字幕组=- 翻译:
- [NuGet 软件包](https://www.nuget.org/packages/Qdrant.Client) 最新发布

## 相关条款

- [@ 4:ONNX @ I& Qdrant 履行部分](/blog/semantic-search-with-onnx-and-qdrant)
- [@ 5:_ 混合搜索@ MS K1\ 自动 @ MASK2 @ Indexing](/blog/rag-hybrid-search-and-indexing)
- [RAG系列概览](/blog/rag-primer)

## 源代码

所有代码都可用于 @: @% [多数为平质网,](https://github.com/scottgal/mostlylucidweb)

- `Mostlylucid.SemanticSearch/Services/QdrantVectorStoreService.cs` - Qdrant 整合

This is a preview from the server running through my markdig pipeline

AI-Article ASP.NET Qdrant RAG Semantic Search Vector Databases

带有 Qdrant @: 深底的自定义“-” 矢量数据库

Sunday, 23 November 2025

一. 导言导言导言导言导言

与RAG系列有关的📖 此篇文章为 Qdrant @ ,}提供深度下潜到 QdrantMSK0 中的矢量数据库@:\ {

@ 4:ONNX @ I& Qdrant 履行部分建立语义搜索
@ 5:_ 混合搜索@ MS K1\ 自动 @ MASK2 @ Indexing 生产一体化

解冻 ( @propubliced@"") 是一个在 Rust中建的 Open_-}源矢量数据库。

什么是 Qdrant?

AA 矢量数据库允许快速相似性搜索@.}与找到精确匹配的传统的数据库不同 {,}Qdrant finding 字义相似 .

flowchart LR
    A[Text: 'Docker deployment'] --> B[Embedding Model]
    B --> C["Vector: [0.12, -0.34, 0.56, ...]"]
    C --> D[Qdrant]
    E[Query: 'container setup'] --> F[Embedding Model]
    F --> G["Vector: [0.11, -0.32, 0.58, ...]"]
    G --> H[Similarity Search]
    D --> H
    H --> I[Similar Results]

    style B stroke:#6366f1,stroke-width:3px
    style D stroke:#ef4444,stroke-width:3px
    style F stroke:#6366f1,stroke-width:3px
    style H stroke:#10b981,stroke-width:2px

密钥 Qdrant 特性 @:

HNSW 指数化 - Sub-线性搜索时间
过滤 - 将相似搜索与元数据过滤器相结合
gRPC +& -高 -优异访问
分配部署水平缩放 *\ -
抓图 - 备份并恢复

核心概念

实收款

AA 收藏收藏是像表-}它持有固定维度和距离的矢量

flowchart TB
    subgraph Collection["Collection: blog_posts"]
        A[Vector Size: 384]
        B[Distance: Cosine]
        C[HNSW Index]
    end

    subgraph Points
        D[Point 1: slug=docker-intro]
        E[Point 2: slug=kubernetes-basics]
        F[Point N...]
    end

    Collection --> Points

    style A stroke:#6366f1,stroke-width:2px
    style B stroke:#6366f1,stroke-width:2px
    style C stroke:#f59e0b,stroke-width:2px
    style D stroke:#10b981,stroke-width:2px
    style E stroke:#10b981,stroke-width:2px

// Create collection - see https://qdrant.tech/documentation/concepts/collections/#create-a-collection
await client.CreateCollectionAsync(
    collectionName: "blog_posts",
    vectorsConfig: new VectorParams
    {
        Size = 384,              // Must match your embedding model
        Distance = Distance.Cosine  // Best for text embeddings
    }
);

远程度量 (医生数):

余弦 - 度量矢量之间的角度 % MS K1}最佳文本=
点点 - 原始内部产品 @( 预设的- 正常化矢量@)
欧元空间数据的几何距离 ( 空间数据=)

点点

AA 点是包含 @:}的单一记录

flowchart LR
    subgraph Point
        A[ID: uuid/int]
        B["Vector: float[384]"]
        C[Payload: JSON metadata]
    end

    style A stroke:#8b5cf6,stroke-width:2px
    style B stroke:#f59e0b,stroke-width:2px
    style C stroke:#10b981,stroke-width:2px

// Upsert points - see https://qdrant.tech/documentation/concepts/points/#upload-points
var point = new PointStruct
{
    Id = new PointId { Uuid = Guid.NewGuid().ToString() },
    Vectors = embedding,  // float[384]
    Payload =
    {
        ["slug"] = "my-post",
        ["title"] = "Vector Databases",
        ["language"] = "en",
        ["categories"] = new[] { "AI", "Databases" },
        ["published"] = DateTimeOffset.UtcNow.ToUnixTimeSeconds()
    }
};

await client.UpsertAsync("blog_posts", points: new[] { point });

过滤

过滤运行中之前极有效率的 @.

flowchart TB
    A[Search Query] --> B{Apply Filters First}
    B --> C[Language = 'en']
    B --> D[Year >= 2024]
    C --> E[Filtered Subset]
    D --> E
    E --> F[Vector Similarity Search]
    F --> G[Ranked Results]

    style B stroke:#ec4899,stroke-width:3px
    style E stroke:#f59e0b,stroke-width:2px
    style F stroke:#6366f1,stroke-width:2px
    style G stroke:#10b981,stroke-width:2px

// Filter conditions - see https://qdrant.tech/documentation/concepts/filtering/#filtering-conditions
var filter = new Filter
{
    Must =  // AND conditions
    {
        new Condition { Field = new FieldCondition
        {
            Key = "language",
            Match = new Match { Keyword = "en" }
        }},
        new Condition { Field = new FieldCondition
        {
            Key = "published",
            Range = new Range { Gte = 1704067200 }  // 2024-01-01
        }}
    },
    MustNot =  // Exclude conditions
    {
        new Condition { Field = new FieldCondition
        {
            Key = "slug",
            Match = new Match { Keyword = "draft-post" }
        }}
    }
};

过滤器类型 (医生数):

Match.Keyword - 精確的字符串匹配
Match.Text - full-text 匹配
Match.Any - 匹配任意数组
Range -+Numeric 范围 @(_Gte, _Lte,}( GtMS K4+LtMPK5)
GeoBoundingBox / GeoRadius - 地理过滤器

C# 客户端

安装官方 Qdrant @. @%Client 组合包 @( @%吉特胡布):

dotnet add package Qdrant.Client

连接设置

using Qdrant.Client;
using Qdrant.Client.Grpc;

// gRPC client (recommended) - see https://qdrant.tech/documentation/interfaces/#grpc-interface
var client = new QdrantClient(
    host: "localhost",
    port: 6334,  // gRPC port (6333 is REST)
    https: false
);

// With API key - see https://qdrant.tech/documentation/guides/security/
var secureClient = new QdrantClient(
    host: "your-qdrant.cloud",
    port: 6334,
    https: true,
    apiKey: "your-api-key"
);

总是使用 gRPC (port @6334)生产@-3-5x 比REST.}快

Windows HTTP/2 修补

在 Windows\ ,\ 启用未加密的 HTTP\ MPK1\ 之前创建客户端@:

AppContext.SetSwitch("System.Net.Http.SocketsHttpHandler.Http2UnencryptedSupport", true);

关键业务

搜索

// Vector search - see https://qdrant.tech/documentation/concepts/search/
var results = await client.SearchAsync(
    collectionName: "blog_posts",
    vector: queryEmbedding,
    limit: 10,
    filter: filter,
    scoreThreshold: 0.5f,  // Minimum similarity
    searchParams: new SearchParams
    {
        HnswEf = 128,  // Search accuracy (higher = better recall)
        Exact = false  // Use approximate search
    },
    withPayload: true
);

foreach (var result in results)
{
    Console.WriteLine($"{result.Payload["title"].StringValue}: {result.Score}");
}

批次发件夹

// Batch operations - see https://qdrant.tech/documentation/concepts/points/#batch-update
var points = documents.Select(doc => new PointStruct
{
    Id = new PointId { Uuid = doc.Id },
    Vectors = doc.Embedding,
    Payload = { ["slug"] = doc.Slug, ["title"] = doc.Title }
}).ToList();

await client.UpsertAsync(
    collectionName: "blog_posts",
    points: points,
    wait: true  // Wait for indexing
);

删除删除

// Delete by filter - see https://qdrant.tech/documentation/concepts/points/#delete-points
await client.DeleteAsync(
    collectionName: "blog_posts",
    filter: new Filter
    {
        Must = { new Condition { Field = new FieldCondition
        {
            Key = "slug",
            Match = new Match { Keyword = "old-post" }
        }}}
    }
);

HNSW 指数图

HNSW 赫尔辛基 @ ( @ 历史导航小世界@ MS K1 is Qdrant}'_ 索引算法=.}

flowchart TB
    subgraph "HNSW Graph Layers"
        L2[Layer 2 - Sparse]
        L1[Layer 1 - Medium]
        L0[Layer 0 - Dense]
    end

    Q[Query] --> L2
    L2 --> L1
    L1 --> L0
    L0 --> R[Nearest Neighbors]

    style L2 stroke:#8b5cf6,stroke-width:2px
    style L1 stroke:#6366f1,stroke-width:2px
    style L0 stroke:#3b82f6,stroke-width:2px
    style Q stroke:#10b981,stroke-width:2px
    style R stroke:#ef4444,stroke-width:2px

索引参数

// HNSW config - see https://qdrant.tech/documentation/concepts/indexing/#hnsw-index
var hnswConfig = new HnswConfigDiff
{
    M = 16,              // Edges per node (16-32 recommended)
    EfConstruct = 100,   // Build-time accuracy (100-200)
    FullScanThreshold = 10000  // Brute force threshold
};

await client.UpdateCollectionAsync(
    collectionName: "blog_posts",
    hnswConfig: hnswConfig
);

搜索@- @ time 准确性 @:}

var searchParams = new SearchParams
{
    HnswEf = 128  // Higher = better recall, slower (64-256)
};

调试指南@:

“	使用案例”“	M	EfConstruct □	HnswEf
@	QFast@,QQ 低调回想起 @MS K2QQ @#8Q} @	Q#64Q @ @MS卡6QZ32	Q#
@	}平衡的	@ 16 @ @ @ I	@ @ *100# #	# @ @ 128 @
@	@高调回想起#	@ 32} @MS K3 200} @	@ @ @256_

有效载荷指数

创建创建有效有效载荷指数用于常见过滤字段@: @%

// Keyword index - see https://qdrant.tech/documentation/concepts/indexing/#payload-index
await client.CreatePayloadIndexAsync(
    collectionName: "blog_posts",
    fieldName: "language",
    schemaType: PayloadSchemaType.Keyword
);

// Integer index for ranges
await client.CreatePayloadIndexAsync(
    collectionName: "blog_posts",
    fieldName: "published",
    schemaType: PayloadSchemaType.Integer
);

影响#: @ 10-100_x 在大型收藏中快速过滤@.}

量化

量化减少内存使用@: @%

// Scalar quantization - see https://qdrant.tech/documentation/guides/quantization/#scalar-quantization
await client.UpdateCollectionAsync(
    collectionName: "blog_posts",
    quantizationConfig: new ScalarQuantization
    {
        Scalar = new ScalarQuantizationConfig
        {
            Type = ScalarType.Int8,  // float32 -> int8
            Quantile = 0.99f,
            AlwaysRam = true
        }
    }
);

**贸易 ** @4#x 少一点记忆@,# @ @ ~2% @ recall loss @ ,} 1.5x 快速搜索=.#

嵌入器部署

# docker-compose.yml - see https://qdrant.tech/documentation/guides/installation/
services:
  qdrant:
    image: qdrant/qdrant:v1.12.1  # Pin version!
    ports:
      - "6333:6333"  # REST
      - "6334:6334"  # gRPC
    volumes:
      - qdrant_data:/qdrant/storage
    environment:
      - QDRANT__SERVICE__GRPC_PORT=6334
      - QDRANT__SERVICE__HTTP_PORT=6333
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:6333/health"]
      interval: 30s
      timeout: 10s
      retries: 3

volumes:
  qdrant_data:

安全安保安全

启用 API 键密钥认证:

environment:
  - QDRANT__SERVICE__API_KEY=your-secret-key

监测监测

计量数

解冻曝光普罗米修斯指标至 /metrics:

curl http://localhost:6333/metrics

密钥 mitus:

qdrant_collections_vector_count *- 矢量总量
qdrant_rest_responses_duration_seconds “- 查询延迟”
qdrant_memory_usage_bytes -内存消耗

抓图

创建创建备份:

# Create snapshot
curl -X POST http://localhost:6333/collections/blog_posts/snapshots

# List snapshots
curl http://localhost:6333/collections/blog_posts/snapshots

# Restore (copy snapshot to storage/collections/blog_posts/snapshots/)

共犯组织

港口混凝土

6333 -=YTET -伊甸园字幕组=- 翻译:
6334 ==GRPC API=(=使用这个!)

2. 矢量误配

Error: expected dim: 384, got 768

您的嵌入模型和收藏必须匹配@ : @

all-MiniLM-L6-v2: 384度
nomic-embed-text: 768度
开放国际 text-embedding-3-small: 1536度

“3. 慢速第一查詢”

HNSW 懒惰- 装入内存@. 启动后暖和 @:

await client.SearchAsync("blog_posts", new float[384], limit: 1);

4. 队列过滤

使用 Match.Any 用于数组字段@: @%

new Match { Any = new RepeatedStrings { Strings = { "AI", "ML" } } }

资源资源资源资源和资源资源资源

官方 Qdrant 文件

概览概览概览正在启动
概念的概念核心概念
实收款 -建立和管理收藏
点点 - 与矢量合作
搜索 -查询操作
过滤 - 过滤条件
索引编制 NSW和有效载荷指数
量化 - 记忆优化
安全安保安全 - 認證
监测监测计量和遥测
抓图 - 备份并恢复

客户图书馆

Qdrant @ .@NET 客户端 -=YTET -伊甸园字幕组=- 翻译:
NuGet 软件包最新发布

源代码

所有代码都可用于 @: @% 多数为平质网,

Mostlylucid.SemanticSearch/Services/QdrantVectorStoreService.cs - Qdrant 整合

带有 Qdrant @: 深底的自定义“-” 矢量数据库

一. 导言导言 导言 导言 导言

什么是 Qdrant?

核心概念

实收款

点点

过滤

C# 客户端

连接设置

Windows HTTP/2 修补

关键业务

搜索

批次发件夹

删除删除

HNSW 指数图

索引参数

有效载荷指数

量化

嵌入器部署

安全安保安全

监测监测

计量数

抓图

共犯组织

港口混凝土

2. 矢量误配

“3. 慢速第一查詢”

4. 队列过滤

资源资源资源 资源和资源资源资源

官方 Qdrant 文件

客户图书馆

相关条款

源代码

一. 导言导言导言导言导言

资源资源资源资源和资源资源资源