Memory

import {
  MemoryType,
  RetrievalStrategy,
  DistanceMetric,
} from '10xgraph-client';

const response = await client.storeMemory({
  content: 'User prefers dark mode and monospace fonts.',
  memory_type: MemoryType.SEMANTIC,
  category: 'preferences',
  metadata: { source: 'user_settings', importance: 'high' },
});

console.log('Stored memory ID:', response.data.memory_id);

interface StoreMemoryResponse {
  data: {
    memory_id: string;
  };
  metadata: ResponseMetadata;
}

const response = await client.searchMemory({
  query: 'What are the user\'s display preferences?',
  memory_type: MemoryType.SEMANTIC,
  category: 'preferences',
  limit: 5,
  retrieval_strategy: RetrievalStrategy.SIMILARITY,
  distance_metric: DistanceMetric.COSINE,
  score_threshold: 0.7,
});

for (const result of response.data.results) {
  console.log(`Score: ${result.score.toFixed(3)}, ${result.content}`);
}

interface SearchMemoryResponse {
  data: {
    results: MemoryResult[];
  };
  metadata: ResponseMetadata;
}

interface MemoryResult {
  id: string;
  content: string;
  score: number;              // Similarity score (0–1, higher is more similar)
  memory_type: string;
  metadata: Record<string, any>;
  vector: number[];           // The embedding vector (may be empty in some backends)
  user_id: string;
  thread_id: string;
  timestamp: string;          // ISO 8601 timestamp
}

const response = await client.getMemory('mem-abc123');
console.log(response.data.memory.content);
console.log(response.data.memory.score);

await client.updateMemory(
  'mem-abc123',
  'User prefers dark mode, monospace fonts, and large text size.',
  {
    metadata: { tags: ['display', 'accessibility'], revised: true },
  }
);

interface UpdateMemoryResponse {
  data: {
    success: boolean;
    data?: any;
  };
  metadata: ResponseMetadata;
}

const response = await client.deleteMemory('mem-abc123');
console.log('Deleted:', response.data.success);

const response = await client.listMemories({ limit: 100 });

console.log(`Found ${response.data.memories.length} memories`);
for (const mem of response.data.memories) {
  console.log(`[${mem.memory_type}] ${mem.content.slice(0, 80)}`);
}

// Delete all episodic memories in the 'session_temp' category
await client.forgetMemories({
  memory_type: MemoryType.EPISODIC,
  category: 'session_temp',
});

// Delete memories matching custom filters
await client.forgetMemories({
  filters: { expired: true },
});

interface ForgetMemoriesResponse {
  data: {
    success: boolean;
  };
  metadata: ResponseMetadata;
}

import {
  AgentFlowClient,
  Message,
  MemoryType,
  RetrievalStrategy,
} from '10xgraph-client';

const client = new AgentFlowClient({ baseUrl: 'http://localhost:8000' });

// Before invoking the agent, retrieve relevant past memories
async function invokeWithMemory(userInput: string) {
  // 1. Search for relevant context
  const memories = await client.searchMemory({
    query: userInput,
    memory_type: MemoryType.SEMANTIC,
    limit: 3,
    score_threshold: 0.6,
    retrieval_strategy: RetrievalStrategy.SIMILARITY,
  });

  // 2. Build a system prompt with context
  const context = memories.data.results
    .map(r => `- ${r.content}`)
    .join('\n');

  const systemMsg = Message.text_message(
    `You are a helpful assistant. Relevant context from memory:\n${context}`,
    'system'
  );

  // 3. Invoke the agent
  const result = await client.invoke([
    systemMsg,
    Message.text_message(userInput),
  ]);

  // 4. Store the interaction as an episodic memory
  await client.storeMemory({
    content: `User asked: "${userInput}"`,
    memory_type: MemoryType.EPISODIC,
    category: 'conversations',
  });

  return result;
}