79 lines
2.7 KiB
Markdown
79 lines
2.7 KiB
Markdown
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# Enhancing Memory Configuration
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Finally, let's enhance our memory configuration to make our agent even more helpful:
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```typescript
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import { LibSQLStore, LibSQLVector } from "@mastra/libsql";
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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",
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}),
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vector: new LibSQLVector({
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id: "learning-memory-vector",
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connectionUrl: "file:../../memory.db",
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}),
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embedder: openai.embedding("text-embedding-3-small"),
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options: {
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// Keep last 20 messages in context
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lastMessages: 20,
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// Enable semantic search to find relevant past conversations
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semanticRecall: {
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topK: 3,
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messageRange: {
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before: 2,
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after: 1,
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},
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},
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// Enable working memory to remember user information
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workingMemory: {
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enabled: true,
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template: `
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<user>
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<first_name></first_name>
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<username></username>
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<preferences></preferences>
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<interests></interests>
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<conversation_style></conversation_style>
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</user>`,
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},
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},
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});
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```
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And update the agent instructions to use this enhanced memory:
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```typescript
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export const personalAssistantAgent = new Agent({
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name: "Personal Assistant",
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instructions: `
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// ... existing instructions ...
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You have access to conversation memory and can remember details about users.
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When you learn something about a user, update their working memory using the appropriate tool.
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This includes:
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- Their interests
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- Their preferences
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- Their conversation style (formal, casual, etc.)
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- Any other relevant information that would help personalize the conversation
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Always maintain a helpful and professional tone.
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Use the stored information to provide more personalized responses.
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`,
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model: openai("gpt-4o"),
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tools: { ...mcpTools },
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memory,
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});
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```
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This enhanced memory configuration gives your agent more sophisticated memory capabilities:
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1. **Conversation History**: The `lastMessages` option keeps the last 20 messages in context, allowing your agent to reference recent conversations.
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2. **Semantic Recall**: The `semanticRecall` option enables your agent to find relevant past conversations using semantic search, even if they happened a long time ago. For `semanticRecall` to work, you need to have a vector store and an embedder configured
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3. **Working Memory**: The `workingMemory` option allows your agent to remember specific information about users, such as their preferences and interests, and use that information to provide more personalized responses.
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By updating your agent's instructions to include information about these memory capabilities, you're helping it understand how to use them effectively to provide a better user experience.
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