Introduction

📖 Related to the RAG Series: This article provides a deep dive into Qdrant, the vector database used in:

Part 4: ONNX & Qdrant Implementation - Building semantic search
Part 5: Hybrid Search & Auto-Indexing - Production integration

Qdrant (pronounced "quadrant") is an open-source vector database built in Rust. This article covers core concepts, the C# client, performance tuning, and production patterns.

What is Qdrant?

A vector database stores high-dimensional vectors (embeddings) and enables fast similarity search. Unlike traditional databases that find exact matches, Qdrant finds semantically similar items.

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

Key Qdrant features:

HNSW indexing - Sub-linear search times
Filtering - Combine similarity search with metadata filters
gRPC & REST APIs - High-performance access
Distributed deployment - Scale horizontally
Snapshots - Backup and restore

Core Concepts

Collections

A collection is like a table - it holds vectors with a fixed dimensionality and distance metric.

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
    }
);

Distance metrics (docs):

Cosine - Measures angle between vectors (best for text)
Dot - Raw inner product (for pre-normalized vectors)
Euclid - Geometric distance (for spatial data)

Points

A point is a single record containing:

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 });

Filtering

Filtering runs before similarity search - extremely efficient.

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" }
        }}
    }
};

Filter types (docs):

Match.Keyword - Exact string match
Match.Text - Full-text match
Match.Any - Match any in array
Range - Numeric ranges (Gte, Lte, Gt, Lt)
GeoBoundingBox / GeoRadius - Geo filtering

The C# Client

Install the official Qdrant.Client package (GitHub):

dotnet add package Qdrant.Client

Connection Setup

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"
);

Always use gRPC (port 6334) for production - 3-5x faster than REST.

Windows HTTP/2 Fix

On Windows, enable unencrypted HTTP/2 before creating the client:

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

Key Operations

Search

// 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}");
}

Batch Upsert

// 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
);

Delete

// 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 Index Tuning

HNSW (Hierarchical Navigable Small World) is Qdrant's index algorithm.

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

Index Parameters

// 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
);

Search-time accuracy:

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

Tuning guidelines:

Use Case	M	EfConstruct	HnswEf
Fast, low recall	8	64	32
Balanced	16	100	128
High recall	32	200	256

Payload Indexes

Create payload indexes for frequently filtered fields:

// 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
);

Impact: 10-100x faster filtering on large collections.

Quantization

Quantization reduces memory usage:

// 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
        }
    }
);

Trade-off: 4x less memory, ~2% recall loss, 1.5x faster search.

Docker Deployment

# 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:

Security

Enable API key authentication:

environment:
  - QDRANT__SERVICE__API_KEY=your-secret-key

Monitoring

Metrics

Qdrant exposes Prometheus metrics at /metrics:

curl http://localhost:6333/metrics

Key metrics:

qdrant_collections_vector_count - Total vectors
qdrant_rest_responses_duration_seconds - Query latency
qdrant_memory_usage_bytes - Memory consumption

Snapshots

Create backups:

# 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/)

Common Gotchas

1. Port Confusion

6333 = REST API
6334 = gRPC API (use this!)

2. Vector Dimension Mismatch

Error: expected dim: 384, got 768

Your embedding model and collection must match:

all-MiniLM-L6-v2: 384 dimensions
nomic-embed-text: 768 dimensions
OpenAI text-embedding-3-small: 1536 dimensions

3. Slow First Query

HNSW lazy-loads into memory. Warm up after startup:

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

4. Array Filtering

Use Match.Any for array fields:

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

Resources

Official Qdrant Documentation

Overview - Getting started
Concepts - Core concepts
Collections - Creating and managing collections
Points - Working with vectors
Search - Query operations
Filtering - Filter conditions
Indexing - HNSW and payload indexes
Quantization - Memory optimization
Security - Authentication
Monitoring - Metrics and telemetry
Snapshots - Backup and restore

Client Libraries

Qdrant .NET Client - Official C# SDK
NuGet Package - Latest release

Source Code

All code available at: github.com/scottgal/mostlylucidweb

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

Self-Hosted Vector Databases with Qdrant: A Deep Dive