Mythosia.VectorDb.InMemory 2.2.0

Mythosia.VectorDb.InMemory

Migration from v1.0.0

v2.0.0 renames logical separation units:

• collection → namespace (terminology update in public API/docs)
• namespace → scope (for second-tier logical isolation)

Package Summary

Provides InMemoryVectorStore, a thread-safe in-memory implementation of IVectorStore using cosine similarity search.
Suitable for development, testing, and small-scale workloads.

Usage

Automatically used as the default vector store in Mythosia.AI.Rag:

// Explicit selection (same as default)
.WithRag(rag => rag
    .AddDocument("docs.txt")
    .UseInMemoryStore()
)

Features

• Thread-safe — Uses ConcurrentDictionary for safe concurrent access
• Cosine similarity — TopK search with configurable result count
• Scope isolation — Filter by scope for multi-tenant scenarios
• Metadata filtering — Filter search results by key-value metadata
• Minimum score — Discard results below a similarity threshold
• Upsert — Single and batch upsert operations
• Namespace-aware operations — Use InNamespace(...) or VectorFilter.Namespace

Standalone Usage

Fluent API (recommended)

using Mythosia.VectorDb;
using Mythosia.VectorDb.InMemory;

var store = new InMemoryVectorStore();
var ns = store.InNamespace("my-namespace");

// Namespace-only
await ns.UpsertAsync(new VectorRecord
{
    Id = "doc-1",
    Content = "Some text content",
    Vector = new float[] { 0.1f, 0.2f, 0.3f },
    Metadata = { ["source"] = "manual.txt" }
});

var results = await ns.SearchAsync(queryVector, topK: 5);

// Namespace + Scope
var scoped = ns.InScope("tenant-1");
await scoped.UpsertAsync(record);   // record.Scope is set automatically
var scopedResults = await scoped.SearchAsync(queryVector);

Direct IVectorStore API

using Mythosia.VectorDb;
using Mythosia.VectorDb.InMemory;

var store = new InMemoryVectorStore();

await store.UpsertAsync(new VectorRecord
{
    Namespace = "my-namespace",
    Id = "doc-1",
    Content = "Some text content",
    Vector = new float[] { 0.1f, 0.2f, 0.3f },
    Metadata = { ["source"] = "manual.txt" }
});

var results = await store.SearchAsync(
    queryVector,
    topK: 5,
    filter: VectorFilter.ByNamespace("my-namespace"));

BM25 Index (v2.1.0)

Bm25Index provides in-memory BM25 keyword search for hybrid retrieval. When UseHybridSearch() is called with InMemoryVectorStore, the RAG pipeline automatically builds a BM25 index alongside the vector store and merges results via RRF.

// Automatic — just enable hybrid search in the builder
var store = await RagStore.BuildAsync(config => config
    .AddText("환불은 14일 이내 가능합니다.", id: "refund")
    .UseLocalEmbedding(512)
    .UseInMemoryStore()
    .UseHybridSearch()     // BM25 index is built automatically
);

Standalone usage:

using Mythosia.VectorDb.InMemory;

var bm25 = new Bm25Index();
bm25.Index("doc1", "machine learning neural network");
bm25.Index("doc2", "cooking recipe pasta");

var results = bm25.Search("machine learning", topK: 5);
// results[0].Id == "doc1", results[0].Score > 0

When hybrid search is used, fused RRF scores are normalized to the [0, 1] range so VectorFilter.MinScore is applied consistently to the final merged score.

Vector Replacement

ReplaceByFilterAsync is available via the IVectorStore default interface method. It performs sequential DeleteByFilterAsync → UpsertBatchAsync (non-transactional, suitable for in-memory usage):

IVectorStore store = new InMemoryVectorStore();

var filter = VectorFilter.ByMetadata("full_path", "/docs/file.md");
filter.Namespace = "default";

await store.ReplaceByFilterAsync(filter, newRecords);

For transactional guarantees (zero query gap), use PostgresStore which wraps both operations in a single database transaction.

Limitations

• Data is not persisted — lost when the process exits
• Not suitable for large-scale production workloads (millions of vectors)
• For persistence or scale, implement a custom IVectorStore (e.g., Qdrant, Chroma, Pinecone)