Backend Setup

In-Memory

The simplest backend — no external dependencies. Data is held in RAM and lost when the process exits. Good for development, tests, and demos.

dotnet add package Mythosia.VectorDb.InMemory

using Mythosia.VectorDb.InMemory;

var store = new InMemoryVectorStore();

Built-in hybrid search: RRF (Reciprocal Rank Fusion) merges cosine similarity and BM25 keyword scores.

Diagnostics

// List all stored records
var all = await store.ListAllRecordsAsync();
Console.WriteLine($"Total: {store.GetTotalRecordCount()}");

// Inspect raw similarity scores
var scored = await store.ScoredListAsync(queryVector);
foreach (var r in scored)
    Console.WriteLine($"[{r.Score:F3}] {r.Record.Content[..60]}");

---

Qdrant

Production-grade vector database with native hybrid search. Runs as a standalone service via Docker or Qdrant Cloud.

dotnet add package Mythosia.VectorDb.Qdrant

# Start Qdrant locally
docker run -p 6333:6333 -p 6334:6334 qdrant/qdrant

using Mythosia.VectorDb.Qdrant;

var store = new QdrantStore(new QdrantOptions
{
    Host             = "localhost",
    Port             = 6334,           // gRPC port
    CollectionName   = "my-docs",
    Dimension        = 1536,           // Must match your embedding model
    AutoCreateCollection = true        // Creates collection on first upsert
});

All Options

new QdrantOptions
{
    Host                   = "localhost",
    Port                   = 6334,
    UseTls                 = false,
    ApiKey                 = null,             // Required for Qdrant Cloud

    CollectionName         = "my-collection",  // Required
    Dimension              = 1536,             // Required

    DistanceStrategy       = QdrantDistanceStrategy.Cosine,
    HybridFusionStrategy   = QdrantHybridFusionStrategy.Rrf,
    AutoCreateCollection   = true,

    // Add extra payload indexes for faster server-side filtering
    AdditionalPayloadIndexes = new List<QdrantIndexOption>
    {
        new QdrantIndexOption { Field = "meta.language", SchemaType = PayloadSchemaType.Keyword },
        new QdrantIndexOption { Field = "meta.date",     SchemaType = PayloadSchemaType.Integer }
    }
}

Distance Strategies

Value	Description
Cosine	Cosine similarity — best for normalized embeddings (default)
Euclidean	L2 distance — lower distance = more similar
DotProduct	Dot product — use with unit-normalized vectors

Hybrid Fusion Strategies

Value	Description
Rrf	Reciprocal Rank Fusion — robust rank-based merging (default)
Dbsf	Distribution-Based Score Fusion — merges by score distribution

Qdrant Cloud

new QdrantOptions
{
    Host           = "your-cluster.cloud.qdrant.io",
    Port           = 6334,
    UseTls         = true,
    ApiKey         = "your-qdrant-cloud-key",
    CollectionName = "production",
    Dimension      = 1536
}

Using an External QdrantClient

If you already have a configured QdrantClient (e.g., from a DI container), pass it directly:

var store = new QdrantStore(options, existingQdrantClient);

The store will not dispose the externally provided client.

All vector stores implement IDisposable. When you create a store with the standard constructor, call Dispose() (or use using) to release internal resources.

---

Pinecone

Fully managed serverless vector database. No infrastructure to manage.

dotnet add package Mythosia.VectorDb.Pinecone

using Mythosia.VectorDb.Pinecone;

var store = new PineconeStore(new PineconeOptions
{
    IndexHost = "https://my-index-xxxx.svc.us-east1-gcp.pinecone.io",
    ApiKey    = "your-api-key"
});

Auto-Create Index

If you don't have an index yet, let the SDK create it:

new PineconeOptions
{
    ApiKey          = "your-api-key",
    AutoCreateIndex = true,
    IndexName       = "my-index",
    Dimension       = 1536,
    Cloud           = "aws",          // "aws", "gcp", or "azure"
    Region          = "us-east-1"
}

When AutoCreateIndex is enabled, the index is created with dotproduct metric — required for hybrid (sparse + dense) search.

All Options

new PineconeOptions
{
    IndexHost              = "https://...",   // Required (or use AutoCreateIndex)
    ApiKey                 = "...",           // Required
    Namespace              = "production",    // Optional: applied to all operations

    UpsertBatchSize        = 100,             // Records per batch upsert request
    RequestTimeoutSeconds  = 100,

    AutoCreateIndex        = false,
    IndexName              = null,
    Dimension              = 0,
    Cloud                  = null,
    Region                 = null,
    ControlPlaneHost       = "https://api.pinecone.io"
}

Using an External HttpClient

If you already have a configured HttpClient (e.g., from IHttpClientFactory):

var store = new PineconeStore(options, existingHttpClient);

The store will not dispose the externally provided client.

---

PostgreSQL (pgvector)

Uses the pgvector extension to add vector similarity search to a standard PostgreSQL database.

dotnet add package Mythosia.VectorDb.Postgres

Prerequisites

-- Run once on your PostgreSQL server
CREATE EXTENSION IF NOT EXISTS vector;
CREATE EXTENSION IF NOT EXISTS pg_trgm;  -- Only if using Trigram text search

Or let the SDK handle it automatically with EnsureSchema = true.

using Mythosia.VectorDb.Postgres;

var store = new PostgresStore(new PostgresOptions
{
    ConnectionString = "Host=localhost;Port=5432;Database=mydb;Username=user;Password=pass;",
    Dimension        = 1536,
    EnsureSchema     = true    // Auto-creates extension, table, and indexes
});

Index Types

Type	Class	When to Use
HNSW	HnswIndexOptions	Default. Fast approximate search. Best for most use cases.
IVFFlat	IvfFlatIndexOptions	Lower memory. Good for large static datasets.
None	NoIndexOptions	Sequential scan. Use only for tiny datasets.

// HNSW (default)
new PostgresOptions
{
    // ...
    Index = new HnswIndexOptions
    {
        M              = 16,   // Max neighbor connections per node
        EfConstruction = 64,   // Search scope during index build (higher = better quality)
        EfSearch       = 40    // Runtime search scope (higher = better recall, slower)
    }
}

// IVFFlat
new PostgresOptions
{
    // ...
    Index = new IvfFlatIndexOptions
    {
        Lists  = 100,  // Number of inverted lists
        Probes = 10    // How many lists to probe at query time
    }
}

// No index (sequential scan)
new PostgresOptions { Index = new NoIndexOptions() }

Text Search Modes

Used for the keyword side of hybrid search:

Mode	Best For
TsVector	Standard full-text search — English, most Western languages
Trigram	CJK languages (Korean, Chinese, Japanese), fuzzy matching

new PostgresOptions
{
    TextSearchMode   = TextSearchMode.Trigram,
    TextSearchConfig = "simple"     // PostgreSQL text search configuration
}

Distance Strategies

Value	Postgres Operator	Notes
Cosine	<=>	1 − cosine similarity (default)
Euclidean	<->	L2 distance
InnerProduct	<#>	Negative dot product — use with unit-normalized vectors

Runtime Search Profile

Fine-tune recall vs. latency at query time:

var opts = new HnswSearchRuntimeOptions
{
    Profile = SearchProfile.HighRecall,  // Fast | Balanced | HighRecall
    EfSearch = 80                        // Override HNSW ef_search directly
};

var results = await store.SearchAsync(queryVector, topK: 5, filter: null, runtimeOptions: opts);

All Options

new PostgresOptions
{
    ConnectionString  = "...",
    Dimension         = 1536,

    SchemaName        = "public",
    TableName         = "vectors",

    EnsureSchema      = false,
    DistanceStrategy  = DistanceStrategy.Cosine,
    Index             = new HnswIndexOptions(),

    TextSearchConfig  = "simple",
    TextSearchMode    = TextSearchMode.TsVector,

    FailFastOnIndexCreationFailure = true
}