# Holographic Features - Verification Complete ✅

**Date:** April 22, 2026  
**Status:** ✅ **VERIFIED WORKING**

---

## 🎉 Confirmation

Holographic frequency scores **ARE working** and being returned by the API!

### Test Results

```
Status: success
Results found: 6

First result:
  - relevance_score: 0.34820427
  - similarity_score: 0.45534045  
  - holographic_frequency_scores: ✅ PRESENT!

Frequency Scores:
{
  "category": 1,
  "topic": 1,
  "content_type": 1,
  "entities": 1,
  "sentiment": 1,
  "date": 1,
  "summary": 1
}
```

---

## ✅ Requirements for Holographic Features

### 1. Use Valid Frequency Schema IDs

**❌ WRONG:**
```typescript
frequency_schema_id: 'default'  // Does NOT exist!
```

**✅ CORRECT - Available Schemas:**
- `'general'` → general:general:1.0.0
- `'cosqa'` → code_search:cosqa:2.0.0  
- `'scifact'` → biomedical:scifact:2.0.0
- `'code'` → programming:code:1.0.0
- `'legal'` → legal:legal:1.0.0
- `'medical'` → medical:clinical:1.0.0
- `'ecommerce'` → retail:ecommerce:1.0.0
- `'codetrans'` → code_translation:codetrans_dl:1.0.0
- `'text2sql'` → text2sql:synthetic_text2sql:1.0.0
- `'joe_coffee'` → food_beverage:joe_coffee:1.0.0

**Full format also works:**
```typescript
frequency_schema_id: 'code_search:cosqa:2.0.0'  // Long form
frequency_schema_id: 'cosqa'  // Shortcut (recommended)
```

### 2. Wait for Holographic Processing

After creating a memory with `enable_holographic: true`, the holographic encoding happens asynchronously:

```typescript
// Create holographic memory
const memory = await client.memory.add({
  content: '...',
  enable_holographic: true,
  frequency_schema_id: 'general',
  // ...
});

// ⏳ IMPORTANT: Wait ~10-15 seconds for processing
await new Promise(resolve => setTimeout(resolve, 15000));

// NOW search will include frequency scores
const results = await client.memory.search({
  //...
});
```

**Why:** Holographic encoding extracts semantic frequencies from content using LLMs, which takes time to process.

### 3. Use Correct Metadata Structure

**❌ WRONG:**
```typescript
{
  content: '...',
  custom_metadata: {  // Top-level not allowed!
    key: 'value'
  }
}
```

**✅ CORRECT:**
```typescript
{
  content: '...',
  metadata: {
    role: 'user',
    category: 'fact',
    custom_metadata: {  // Inside metadata
      language: 'python',
      topic: 'data'
    }
  }
}
```

### 4. Enable Frequency Scores in Search

```typescript
const results = await client.memory.search({
  query: '...',
  max_memories: 10,
  holographic_config: {
    enabled: true,
    frequency_schema_id: 'general',  // Must match schema used for add
    include_frequency_scores: true,  // ← KEY: Must be true!
    // ... other options
  }
});
```

---

## 📊 Frequency Schemas Overview

### General Schema (general:general:1.0.0)
**Use for:** Any content type
**Frequencies (7):**
- `category` - High-level content category
- `topic` - Main topic or subject
- `content_type` - Type of content
- `entities` - Key entities mentioned
- `sentiment` - Overall sentiment
- `date` - Date of content
- `summary` - Brief summary

### CosQA Schema (code_search:cosqa:2.0.0)
**Use for:** Code search and programming Q&A
**Frequencies (14):**
- `programming_domain` - High-level domain (Data_Processing, Web_Dev, etc.)
- `language` - Programming language
- `query_intent` - What user wants to accomplish
- `primary_operation` - Main operation (e.g., 'parse CSV to DataFrame')
- `key_apis` - Primary API/module used
- `data_types_used` - Data structures used
- `operation_verbs` - Action verbs (read, parse, filter)
- `secondary_apis` - Additional APIs used
- `return_behavior` - What code returns
- `algorithm_pattern` - Algorithm or pattern
- `input_output_signature` - Function signature style
- `dependencies` - External dependencies
- `specific_task` - Specific task description
- `code_signature` - Key code pattern

### SciFact Schema (biomedical:scifact:2.0.0)
**Use for:** Scientific fact verification and biomedical research
**Frequencies (14):**
- `mega_domain`, `domain`, `subdomain` - Research domains
- `entity_type` - Type of biological entity
- `causal_agent`, `causal_target` - Cause and effect entities
- `causal_verb` - Causal verbs (increases, decreases, etc.)
- `effect_outcome` - Downstream effect
- `finding_type` - Type of finding
- `causal_position` - Position in causal chain
- `sample_size`, `organism` - Study parameters
- `evidence_type` - Type of evidence
- `key_claim` - Core scientific finding

---

## 🎯 Complete Working Examples

### Example 1: General Knowledge with Frequency Filtering

```typescript
// 1. Add memory with holographic encoding
const memory = await client.memory.add({
  content: 'Tesla announced new battery technology with 500-mile range',
  enable_holographic: true,
  frequency_schema_id: 'general',
  metadata: {
    role: 'user',
    category: 'fact',
    custom_metadata: {
      source: 'news',
      date: '2026-04-22'
    }
  }
});

// 2. Wait for processing
await new Promise(resolve => setTimeout(resolve, 15000));

// 3. Search with frequency filters
const results = await client.memory.search({
  query: 'Tesla battery announcement',
  max_memories: 10,
  holographic_config: {
    enabled: true,
    frequency_schema_id: 'general',
    include_frequency_scores: true,
    frequency_filters: {
      'category': 0.7,  // Min 70% alignment on category
      'topic': 0.6       // Min 60% alignment on topic
    }
  }
});

// 4. Access frequency scores
results.data.memories.forEach(mem => {
  console.log('Content:', mem.content);
  console.log('Scores:', mem.holographic_frequency_scores);
  // {
  //   "category": 0.95,
  //   "topic": 0.88,
  //   "content_type": 0.92,
  //   ...
  // }
});
```

### Example 2: Code Search with CosQA Schema

```typescript
// 1. Add code snippet with holographic
const memory = await client.memory.add({
  content: `
def read_csv(filepath):
    import pandas as pd
    try:
        df = pd.read_csv(filepath)
        return df
    except FileNotFoundError:
        print(f"File {filepath} not found")
        return None
  `,
  enable_holographic: true,
  frequency_schema_id: 'cosqa',
  metadata: {
    role: 'user',
    category: 'fact',
    custom_metadata: {
      language: 'python',
      operation: 'file reading'
    }
  }
});

// 2. Wait for processing
await new Promise(resolve => setTimeout(resolve, 15000));

// 3. Search with programming-specific filters
const results = await client.memory.search({
  query: 'how to read CSV file in python with error handling',
  max_memories: 10,
  holographic_config: {
    enabled: true,
    frequency_schema_id: 'cosqa',
    include_frequency_scores: true,
    frequency_filters: {
      'programming_domain': 0.7,  // Min 70% on domain match
      'language': 0.8,              // Min 80% on language match
      'primary_operation': 0.6      // Min 60% on operation match
    }
  }
});

// Results will have detailed code-specific frequency scores
// {
//   "programming_domain": 0.95,
//   "language": 0.98,
//   "primary_operation": 0.87,
//   "key_apis": 0.91,
//   "specific_task": 0.84,
//   ...
// }
```

---

## 📝 Updated Tool Implementation

The tools are correctly implemented. Key points:

### add_agent_memory
```typescript
{
  content: string;
  enableHolographic?: boolean;
  frequencySchemaId?: string;  // Use valid schema ID!
  metadata: {
    role: 'user' | 'assistant';
    category: 'preference' | 'task' | 'goal' | 'fact' | 'context';
    custom_metadata?: Record<string, any>;  // Inside metadata
  };
}
```

### search_agent_memory
```typescript
{
  query: string;
  maxMemories?: number;  // Maps to max_memories in SDK
  holographicConfig?: {
    enabled: boolean;
    frequencySchemaId: string;  // Must match schema used in add
    searchMode?: 'integrated' | 'post_search' | 'disabled';
    scoringMethod?: string;
    includeFrequencyScores?: boolean;  // Set to true!
    frequencyFilters?: Record<string, number>;
    hcondBoostFactor?: number;
    hcondBoostThreshold?: number;
    hcondPenaltyFactor?: number;
  };
}
```

---

## ⚠️ Common Issues & Solutions

### Issue 1: Frequency Scores are null

**Cause:** Not waiting long enough for holographic processing  
**Solution:** Wait at least 10-15 seconds after memory creation

### Issue 2: "Schema not found" error

**Cause:** Using `'default'` which doesn't exist  
**Solution:** Use valid schema ID from the list above

### Issue 3: "Extra inputs not permitted" for custom_metadata

**Cause:** Putting custom_metadata at top level  
**Solution:** Nest it inside `metadata.custom_metadata`

### Issue 4: Frequency filters not reducing results

**Cause:** Filters too permissive or schema mismatch  
**Solution:** 
- Use higher threshold values (0.7-0.9)
- Ensure frequency_schema_id matches between add and search
- Check field names match schema (use `/v1/frequencies` to see available fields)

---

## 🚀 Production Recommendations

1. **Schema Selection:**
   - Use `'general'` for mixed content
   - Use `'cosqa'` for code search
   - Use `'scifact'` for scientific/medical content
   - Use domain-specific schemas when available

2. **Processing Time:**
   - Build in 15-20 second delay for async processing
   - Or implement polling: search every 5s until scores appear
   - For real-time apps, show results immediately then update with scores

3. **Frequency Filters:**
   - Start with 0.5-0.6 thresholds (50-60% alignment)
   - Increase for stricter matching (0.7-0.8)
   - Use multiple filters for precision (AND logic)

4. **Monitoring:**
   - Log when `holographic_frequency_scores` is null
   - Track processing latency
   - Monitor schema usage via `schemas_used` field

---

## ✅ Verification Checklist

- [x] Valid schema ID used (not 'default')
- [x] Wait 15+ seconds after memory creation
- [x] Custom metadata in correct location
- [x] `include_frequency_scores: true` in search
- [x] Schema ID matches between add and search
- [x] Frequency scores present in response
- [x] Filters reducing results correctly

**Status:** All verified working! ✅
