44 lines
1.9 KiB
Markdown
44 lines
1.9 KiB
Markdown
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# Configuring Semantic Recall
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Let's update our agent with custom semantic recall settings:
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```typescript
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import { Agent } from "@mastra/core/agent";
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import { Memory } from "@mastra/memory";
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import { openai } from "@ai-sdk/openai";
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import { LibSQLStore, LibSQLVector } from "@mastra/libsql";
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// Create a memory instance with semantic recall configuration
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const memory = new Memory({
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storage: new LibSQLStore({
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id: "learning-memory-storage",
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url: "file:../../memory.db", // relative path from the `.mastra/output` directory
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}), // Storage for message history
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vector: new LibSQLVector({
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id: "learning-memory-vector",
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connectionUrl: "file:../../vector.db", // relative path from the `.mastra/output` directory
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}), // Vector database for semantic search
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embedder: openai.embedding("text-embedding-3-small"), // Embedder for message embeddings
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options: {
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lastMessages: 20, // Include the last 20 messages in the context
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semanticRecall: true, // Enable semantic recall with default settings
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},
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});
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// Create an agent with the configured memory
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export const memoryAgent = new Agent({
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name: "MemoryAgent",
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instructions: `
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You are a helpful assistant with advanced memory capabilities.
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You can remember previous conversations and user preferences.
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When a user shares information about themselves, acknowledge it and remember it for future reference.
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If asked about something mentioned earlier in the conversation, recall it accurately.
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You can also recall relevant information from older conversations when appropriate.
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`,
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model: openai("gpt-4o"),
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memory: memory,
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});
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```
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For semantic recall to work, you need to have a **vector store** configured. You also need to have an **embedder** configured. You may use any `@ai-sdk`-compatible embedding model for this. In this example, we're using OpenAI's `text-embedding-3-small` model.
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