Unreal: Embeddings & Search

Embeddings, vector search, and hybrid search in Unreal Engine. For the concepts behind each step see the guide pages on Embedding Models, Vector Search, and Hybrid & Lexical Search.

KV-store entries and queries are JSON strings; the store takes vectors you produce with Embed, not raw text. embed_dim must match the model — 384 for MiniLM/bge-small, 768 for bge-base.

Embed text

const FString Model = TEXT("in-memory::sentence-transformers/all-MiniLM-L6-v2");

const FString Resp = Atelico->Embed(FString::Printf(TEXT(R"({
    "model": "%s",
    "input": ["The guard saw a thief enter the keep at midnight."]
})"), *Model));
// Parse Resp -> data[0].embedding into a TArray<float>.

// Or a direct cosine similarity between two strings:
float Sim = Atelico->Similarity(Model, TEXT("a knight's blade"), TEXT("a warrior's sword"));

Create a store, insert, query

// 1. Create
Atelico->CreateKvStore(TEXT(R"({
    "store_id": "npc-memory",
    "db_path": "./data/npc-memory",
    "embed_dim": 384
})"));

// 2. Insert — key_embedding is the vector from Embed (EmbeddingCsv = comma-joined floats)
const FString Entries = FString::Printf(TEXT(R"([{
    "id": "mem-001",
    "key_text": "The guard saw a thief enter the keep at midnight.",
    "key_embedding": [%s],
    "facets": {"npc_id": "guard-01", "location": "keep"},
    "priority": 0.8
}])"), *EmbeddingCsv);
Atelico->KvStoreInsert(TEXT("npc-memory"), Entries);

// 3. Query — embed the question first, pass the vector
const FString Query = FString::Printf(TEXT(R"({
    "query_embedding": [%s],
    "facet_filters": {"npc_id": "guard-01"},
    "limit": 5
})"), *QueryEmbeddingCsv);
FString Results = Atelico->KvStoreQuery(TEXT("npc-memory"), Query);
// Results: JSON array of {id, key_text, similarity, priority, combined_score}

uint64 Count = Atelico->KvStoreCount(TEXT("npc-memory"), TEXT(""));

Hybrid (vector + keyword) search

Atelico->KvStoreCreateFtsIndex(TEXT("npc-memory"), TEXT("key_text"));

const FString Hybrid = FString::Printf(TEXT(R"({
    "embeddings": [%s],
    "text": "thief keep",
    "fts_column": "key_text",
    "limit": 5,
    "vector_weight": 0.6,
    "lexical_weight": 0.4
})"), *QueryEmbeddingCsv);
FString Response = Atelico->KvStoreHybridQuery(TEXT("npc-memory"), Hybrid);

See Hybrid & Lexical Search for the scoring details and the lexical-only (KvStoreLexicalQuery) variant.