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Semantic Search

Semantic search retrieves past messages by similarity. It requires:

  • An embedding adapter to create vectors from text
  • A vector adapter to store and search vectors

The core provides AiSdkEmbeddingAdapter and InMemoryVectorAdapter. For persistent vectors, use LibSQLVectorAdapter from @voltagent/libsql.

Configuration​

import { Agent, Memory, AiSdkEmbeddingAdapter, InMemoryVectorAdapter } from "@voltagent/core";
import { LibSQLMemoryAdapter, LibSQLVectorAdapter } from "@voltagent/libsql";
import { openai } from "@ai-sdk/openai";

const memory = new Memory({
  storage: new LibSQLMemoryAdapter({ url: "file:./.voltagent/memory.db" }),
  embedding: new AiSdkEmbeddingAdapter(openai.embedding("text-embedding-3-small")),
  // Option A (dev):
  // vector: new InMemoryVectorAdapter(),
  // Option B (persistent vectors):
  vector: new LibSQLVectorAdapter({ url: "file:./.voltagent/memory.db" }),
  enableCache: true, // optional cache for embeddings
});

const agent = new Agent({ name: "assistant", model: openai("gpt-4o-mini"), memory });

Call Options​

Enable semantic search per call using semanticMemory:

const out = await agent.generateText("What did we decide about pricing?", {
  userId: "u1",
  conversationId: "c1",
  semanticMemory: {
    enabled: true, // default: true when vector support is present
    semanticLimit: 5, // number of similar messages to include
    semanticThreshold: 0.7, // minimum similarity (0–1)
    mergeStrategy: "prepend", // 'prepend' | 'append' | 'interleave'
  },
});

Behavior​

When vectors are configured, Memory embeds text parts of messages and stores them with IDs:

  • msg_${conversationId}_${message.id}

Each vector has metadata:

  • messageId, conversationId, userId, role, createdAt

On read with semantic search enabled:

  1. Embed the current query.
  2. Search similar vectors with limit and threshold.
  3. Load the matching messages.
  4. Merge with recent messages using mergeStrategy.

Memory also exposes a direct API:

  • hasVectorSupport() → boolean
  • searchSimilar(query: string, { limit?, threshold?, filter? }) → Promise<SearchResult[]>

The in‑memory vector adapter uses cosine similarity and supports a metadata filter on stored items.

LibSQL vector adapter persists vectors as BLOBs with metadata and supports limit, threshold, and metadata filter. For tests, prefer url: ":memory:" (or "file::memory:"); for production, use a file path (e.g., file:./.voltagent/memory.db) or a remote Turso URL.

Embedding Details​

AiSdkEmbeddingAdapter wraps ai‑sdk embedding models. It supports:

  • Single and batch embedding
  • Optional normalization (normalize: boolean)
  • Basic batching (maxBatchSize) and a simple in‑process cache (enableCache, cacheSize, cacheTTL on Memory)

The embedding dimensions are inferred after the first call.

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