import Cerebe from '@cerebe/sdk'

const client = new Cerebe({
  apiKey: 'ck_live_xxx',
  project: 'proj_xxx',     // optional
})

// Store a memory
await client.memory.add({
  content: 'User prefers dark mode',
  sessionId: 'sess_123',
  type: 'semantic',
  importance: 0.8,
})

// Search memories
const results = await client.memory.search({
  query: 'user preferences',
  sessionId: 'sess_123',
})

console.log(results.data)

// Store with full options
await client.memory.add({
  content: 'Learned quadratic formula today',
  sessionId: 'sess_123',
  type: 'episodic',
  importance: 0.9,
  entityId: 'user_42',
  metadata: { subject: 'math' },
})

// Search with filters
const results = await client.memory.search({
  query: 'math concepts',
  sessionId: 'sess_123',
  types: ['episodic', 'semantic'],
  minImportance: 0.5,
  limit: 10,
})

// Harvest memories from conversation
await client.memory.harvest({
  sessionId: 'sess_123',
  transcript: 'User: I find visual explanations helpful...',
  entityId: 'user_42',
})

await client.knowledge.ingest({
  content: 'Photosynthesis converts light energy to chemical energy',
  entityId: 'biology_101',
  source: 'textbook',
})

const graph = await client.knowledge.query({
  query: 'photosynthesis',
  depth: 3,
  limit: 20,
})

// Get presigned upload URL
const { data } = await client.storage.presignedUpload({
  fileName: 'essay.pdf',
  fileType: 'application/pdf',
  fileSize: 102400,
  contentHash: 'sha256_abc123',
  tenantId: 'tenant_1',
})

// Upload directly to the presigned URL, then analyze
await client.storage.analyzeContent({
  uploadId: data.upload_id,
  context: 'student_homework',
})

// Get learning profile
const profile = await client.metaLearning.profile('user_42')

// Get PLRE state
const state = await client.metaLearning.plreState({
  userId: 'user_42',
  sessionId: 'sess_123',
})

// Ingest an agent trace
await client.agents.ingestTrace({
  content: 'Called search tool with query "quadratic formula"',
  sessionId: 'sess_123',
  metadata: { tool: 'search', latency_ms: 120 },
})

// Set working memory with TTL
await client.agents.setWorkingMemory({
  content: 'Current task: help user with algebra homework',
  sessionId: 'sess_123',
  ttlSeconds: 3600,
})

import Cerebe, {
  AuthenticationError,
  RateLimitError,
  NotFoundError,
  ValidationError,
  ServerError,
} from '@cerebe/sdk'

try {
  await client.memory.get('mem_nonexistent')
} catch (error) {
  if (error instanceof NotFoundError) {
    console.log('Memory not found')
  } else if (error instanceof RateLimitError) {
    console.log(`Rate limited, retry after ${error.retryAfter}s`)
  } else if (error instanceof AuthenticationError) {
    console.log('Invalid API key')
  }
}

import type { MemoryType, MemoryAddParams, MemorySearchParams, Memory } from '@cerebe/sdk'