// MCP: Store user preferences
memory_store_pro(content="I prefer formal communication. My timezone is PST.", about="self")

// MCP: Recall user preferences
memory_recall_pro(query="What are my preferences and working style?", about="self", generate_answer=true)

// Single item — AI extraction with identity hints
await client.memory.memorize({
    content: 'Also extract First Name, Last Name, Company Name, and Job Title if mentioned.\n\nCall with Sarah Chen (VP Eng, Initech). She mentioned they are evaluating SOC2 compliance tools. Main pain point: manual audit prep taking 2 weeks per quarter. Budget approved for Q2.',
    speaker: 'Sales Team',
    email: 'sarah.chen@initech.com',
    enhanced: true,
    tags: ['call-notes', 'sales', 'source:manual'],
});

// Batch sync — per-property control
await client.memory.memorizeBatch({
    source: 'Hubspot',
    mapping: {
        entityType: 'contact',
        email: 'email',
        runName: 'hubspot-contact-sync',
        properties: {
            full_name:    { sourceField: 'firstname', collectionId: 'col_xxx', collectionName: 'Contacts', extractMemories: false },
            job_title:    { sourceField: 'jobtitle',  collectionId: 'col_xxx', collectionName: 'Contacts', extractMemories: false },
            last_notes:   { sourceField: 'notes',     collectionId: 'col_xxx', collectionName: 'Contacts', extractMemories: true },
        },
    },
    rows: crmContacts,  // array of objects from your CRM
});
// ⚠️ memorizeBatch() is async — records land in ~1-2 minutes (EventBridge → Lambda).
// Verify with search() or smartDigest() after processing completes.

Need specific facts about a topic?         → smartRecall()
Need full context about ONE entity?         → smartDigest()
Need to filter/segment a list of records?   → search()
Need organizational rules/guidelines?       → smartGuidelines()

Building a generation prompt?               → smartGuidelines() + smartDigest() + smartRecall()
                                              (governance + entity + task-specific facts)

// Semantic search — find specific facts (recommended)
const results = await client.memory.smartRecall({
    query: 'what pain points did this contact mention?',
    email: 'sarah.chen@initech.com',
    type: 'Contact',
    limit: 10,
    minScore: 0.4,
    include_property_values: true,
});

// Fast recall — skip reflection, ~500ms response
const fast = await client.memory.smartRecall({
    query: 'what do we know about this contact?',
    email: 'sarah.chen@initech.com',
    type: 'Contact',
    fast_mode: true,
});

// Entity digest — compiled context for one person
const digest = await client.memory.smartDigest({
    email: 'sarah.chen@initech.com',
    type: 'Contact',
    token_budget: 2000,
    include_properties: true,
    include_memories: true,
});
// digest.data.compiledContext → ready-to-inject markdown

// Filtered export — find all enterprise contacts
const exported = await client.memory.search({
    type: 'Contact',
    returnRecords: true,
    pageSize: 50,
    groups: [{
        conditions: [
            { field: 'plan_tier', operator: 'EQUALS', value: 'enterprise' },
            { field: 'email', operator: 'IS_SET' },
        ],
    }],
});

// When working on a contact, also pull their company context
const [contactDigest, companyDigest] = await Promise.all([
    client.memory.smartDigest({ email: 'sarah@acme.com', type: 'Contact', token_budget: 1500 }),
    client.memory.smartDigest({ website_url: 'https://acme.com', type: 'Company', token_budget: 1000 }),
]);
// Now you know Sarah AND you know Acme — funding stage, tech stack, team size, etc.

async function assembleContext(email: string, task: string): Promise<string> {
    const sections: string[] = [];

    // 1. Governance — rules and guidelines
    // Use mode: 'fast' for real-time agents (~200ms), 'full' for deep analysis (~3s)
    const governance = await client.ai.smartGuidelines({
        message: `${task} — guidelines, tone, constraints`,
        mode: 'fast', // embedding-only routing, no LLM overhead
    });
    if (governance.data?.compiledContext) {
        sections.push('## Guidelines\n' + governance.data.compiledContext);
    }

    // 2. Entity context — everything about this person
    const digest = await client.memory.smartDigest({
        email,
        type: 'Contact',
        token_budget: 2000,
        include_properties: true,
        include_memories: true,
    });
    if (digest.data?.compiledContext) {
        sections.push('## Recipient Context\n' + digest.data.compiledContext);
    }

    // 3. Task-specific facts — semantic search
    const recalled = await client.memory.smartRecall({
        query: task,
        email,
        type: 'Contact',
        fast_mode: true,
        limit: 10,
        minScore: 0.3,
    });
    if (recalled.data && Array.isArray(recalled.data) && recalled.data.length > 0) {
        sections.push('## Relevant Facts\n' + recalled.data.map((m: any) =>
            `- ${m.text || m.content || JSON.stringify(m)}`
        ).join('\n'));
    }

    return sections.join('\n\n---\n\n');
}

import { Personize } from '@personize/sdk';
const client = new Personize({ secretKey: process.env.PERSONIZE_SECRET_KEY! });

// memorize() — single item
interface MemorizeProOptions {
    content: string;           // The text to memorize
    speaker?: string;          // Who said/wrote it
    timestamp?: string;        // When it happened
    email?: string;            // Match to contact by email
    website_url?: string;      // Match to company by website
    record_id?: string;        // Match to record by ID
    enhanced?: boolean;        // Enable AI extraction (default: false)
    tags?: string[];           // Categorization tags
    max_properties?: number;   // Max properties to extract
    schema?: Record<string, unknown>; // Extraction schema hint
    actionId?: string;         // Target collection ID
}

// memorizeBatch() — batch sync
interface BatchMemorizeOptions {
    source: string;            // Source system label ('Hubspot', 'Salesforce')
    mapping: {
        entityType: string;    // 'contact', 'company'
        email?: string;        // Source field name for email
        website?: string;      // Source field name for website
        runName?: string;      // Tracking label
        properties: Record<string, {
            sourceField: string;       // Source field name in row data
            collectionId: string;      // Target collection ID
            collectionName: string;    // Target collection name
            extractMemories?: boolean; // AI extraction for this property
        }>;
    };
    rows: Record<string, unknown>[]; // Source data rows
    dryRun?: boolean;          // Validate without writing
    chunkSize?: number;        // Rows per chunk (default: 1)
}

// smartRecall() — semantic search (recommended)
interface SmartRecallOptions {
    query: string;             // Natural language query
    limit?: number;            // Max results (default: 10)
    minScore?: number;         // Minimum relevance score (0-1)
    email?: string;            // Scope to one contact
    website_url?: string;      // Scope to one company
    record_id?: string;        // Scope to one record
    type?: string;             // Entity type filter (optional — inferred from email/website_url)
    include_property_values?: boolean; // Include structured properties
    enable_reflection?: boolean;       // AI reflects on results
    generate_answer?: boolean;         // AI generates a direct answer
    fast_mode?: boolean;       // Skip reflection + answer gen, ~500ms (default: false)
    min_score?: number;        // Server-side score filter (in fast_mode, defaults to 0.3)
}

// recall() — direct lookup (simpler, type required)
interface RecallOptions {
    query: string;             // Natural language query
    type: string;              // Entity type — REQUIRED (e.g. 'Contact', 'Company')
    record_id?: string;        // Scope to one record
    email?: string;            // Scope to one contact
    website_url?: string;      // Scope to one company
    filters?: Record<string, unknown>; // Additional filters
}

// smartDigest() — entity context
interface SmartDigestOptions {
    email?: string;            // Contact email
    website_url?: string;      // Company website
    record_id?: string;        // Record ID
    type?: string;             // Entity type ('Contact', 'Company')
    token_budget?: number;     // Max tokens for output (default: 1000)
    max_memories?: number;     // Max memories to include (default: 20)
    include_properties?: boolean; // Include structured properties (default: true)
    include_memories?: boolean;   // Include free-form memories (default: true)
}

┌─────────────────────────────────────────────────────────────┐
│                    PERSONIZE MEMORY                          │
│                                                             │
│  ┌───────────────────┐    ┌──────────────────────────────┐  │
│  │  STRUCTURED DATA  │    │     SEMANTIC MEMORIES         │  │
│  │  (DynamoDB)       │    │     (LanceDB + Vectors)       │  │
│  │                   │    │                              │  │
│  │  Records:         │    │  AI-extracted facts from:    │  │
│  │  ├─ email: "..."  │    │  ├─ Call notes              │  │
│  │  ├─ plan: "pro"   │    │  ├─ Support tickets         │  │
│  │  ├─ title: "VP"   │    │  ├─ Email threads           │  │
│  │  └─ login_count:5 │    │  ├─ Meeting transcripts     │  │
│  │                   │    │  └─ Generated outputs        │  │
│  └───────────────────┘    └──────────────────────────────┘  │
│           │                            │                    │
│           ▼                            ▼                    │
│     search() filters           smartRecall() searches        │
│     memorize() writes          memorizeBatch() writes       │
│     smartDigest() reads both ──────────┘                    │
└─────────────────────────────────────────────────────────────┘

await client.memory.memorize({
    content: `[SIGNAL] Sent "${subject}" via ${channel} (score: ${score}). ${reasoning}`,
    email,
    enhanced: true,
    tags: ['signal:sent', `signal:channel:${channel}`, `signal:type:${eventType}`],
});

// What notifications has Signal sent to this contact?
const sent = await client.memory.smartRecall({
    query: 'notifications sent by signal',
    email: 'jane@acme.com',
    type: 'Contact',
    fast_mode: true,
    limit: 10,
});

// What's pending in the digest queue?
const pending = await client.memory.smartRecall({
    query: 'deferred notifications pending digest',
    email: 'jane@acme.com',
    type: 'Contact',
    fast_mode: true,
    limit: 20,
});