AI Features Overview

Last Updated: 2026-01-30 Status: ✅ Production

Overview

Bayit+ integrates advanced AI capabilities across the platform to enhance content discovery, personalization, and user experience. The AI features are powered by state-of-the-art language models including Anthropic Claude (Sonnet 4.5, Haiku 3.5) and OpenAI GPT-4.

AI Feature Categories

Category	Features	Status
Beta 500 Program	AI Search, AI Recommendations, Auto Catch-Up	✅ Production
AI Agent System	50+ tools for content management and automation	✅ Production
LLM Search	VOD Search, EPG Search, Vector Search	✅ Production
Specialized AI	Chess AI, Onboarding AI, Live Dubbing	✅ Production
Olorin Ecosystem	Content intelligence, cultural context, partner APIs	✅ Production

Beta 500 Program

The Beta 500 program is a closed beta providing 500 AI credits to selected users for testing AI features.

Features

1. AI Search

Endpoint: POST /api/v1/beta/search

Natural language search powered by Claude Sonnet 4.5. Users can search using conversational queries instead of keywords.

Examples:

"Show me movies about family reunions in Israel"
"I want to watch something funny for kids"
"Find documentaries about Jewish history"

Cost: 10 credits per search

Key Features:

Natural language understanding
Context-aware results
Multi-language support (10 languages)
Semantic search with vector embeddings

2. AI Recommendations

Endpoint: GET /api/v1/beta/recommendations

Personalized content recommendations based on viewing history, preferences, and contextual factors.

Cost: 5 credits per recommendation request

Key Features:

Personalized to user's viewing history
Time-aware (morning ritual, evening content)
Cultural context integration
Diversity in recommendations

3. Auto Catch-Up

Endpoint: GET /api/v1/live/{channel_id}/catchup

AI-generated summaries of missed live content, allowing users to quickly understand what happened.

Cost: 15 credits per summary

Key Features:

Real-time transcript analysis
Key moments extraction
Speaker identification
Topic tracking
Multi-turn conversation support

Credit System

Initial Grant: 500 credits per Beta 500 user

Credit Costs:

Feature	Cost per Request
AI Search	10 credits
AI Recommendations	5 credits
Auto Catch-Up	15 credits

Credit Balance:

Real-time tracking via /api/v1/beta/credits/balance
Low credit warnings at 50, 20, and 10 credits
Insufficient credit error handling with graceful degradation

Access Requirements

Enrollment:

Receive beta invitation email
Sign up with Google OAuth
Automatic enrollment and credit grant

Rate Limits:

Beta 500 users: 120 requests/minute
AI Search: 10 searches/minute
AI Recommendations: 20 requests/minute
Auto Catch-Up: 5 requests/minute

AI Agent System

The AI Agent System provides 50+ tools for automated content management, metadata enrichment, and system maintenance.

Architecture

Components:

Agent Loop: Orchestrates tool execution with max iterations
Tool Dispatcher: Routes tool calls to appropriate executors
Issue Tracker: Logs problems and findings
Logger: Structured logging with context

Tool Categories (13 categories, 50+ tools):

Search & Discovery (5 tools) - VOD search, EPG search, keyword search
Metadata Management (8 tools) - TMDB integration, classification, titles
Content Auditing (6 tools) - Stream validation, duplicate detection, integrity checks
Taxonomy Management (4 tools) - Category management, tag updates
Subtitle Operations (5 tools) - Subtitle generation, translation, validation
Podcast Management (6 tools) - Episode sync, metadata enrichment
Database Operations (4 tools) - Queries, updates, aggregations
File Operations (3 tools) - File reading, writing, validation
External APIs (4 tools) - TMDB, OpenSubtitles, ElevenLabs
Content Intelligence (3 tools) - Cultural context, recommendations
Quality Assurance (2 tools) - Test execution, validation
Reporting (2 tools) - Report generation, statistics
System Maintenance (2 tools) - Cache clearing, index rebuilding

Use Cases

Library Auditing:

python

from app.services.ai_agent.agent import AIAgent

agent = AIAgent(model="claude-sonnet-4-5")
result = await agent.execute_comprehensive_audit(
    scope="all_content",
    checks=["metadata", "streams", "duplicates"]
)

Metadata Enrichment:

python

result = await agent.enrich_metadata(
    content_id="123",
    sources=["tmdb", "olorin_context"]
)

Stream Validation:

python

result = await agent.validate_streams(
    section="movies",
    check_playability=True,
    auto_fix=True
)

Configuration

Environment Variables:

bash

ANTHROPIC_API_KEY=<secret-manager>  # Claude API key
AI_AGENT_MAX_ITERATIONS=20          # Max tool calls per run
AI_AGENT_BUDGET_LIMIT=5.0           # Max cost per run ($)
AI_AGENT_DRY_RUN=false              # Dry run mode (no writes)

API Endpoints:

POST /api/v1/admin/ai-agent/audit - Library audit
POST /api/v1/admin/ai-agent/enrich - Metadata enrichment
POST /api/v1/admin/ai-agent/validate - Stream validation
GET /api/v1/admin/ai-agent/status - Agent status

LLM Search Services

VOD Search

Service: LLMVODSearchServiceModel: Claude Haiku 3.5 (fast, cost-effective)

Natural language search for video-on-demand content (movies, series, audiobooks).

Features:

Multi-language query support
Semantic search with vector embeddings
Faceted filtering (genre, year, language, rating)
Result ranking by relevance

Example:

python

from app.services.llm_search_service import LLMVODSearchService

service = LLMVODSearchService()
results = await service.search(
    query="Israeli comedy movies from the 2020s",
    limit=20,
    filters={"section": "movies", "language": "he"}
)

EPG Search

Service: LLMEPGSearchServiceModel: Claude Haiku 3.5

Natural language search for electronic program guide (live TV schedules).

Features:

Time-aware search ("what's on now", "tonight at 8pm")
Channel-specific queries
Program type filtering (news, sports, movies)

Example:

python

from app.services.llm_search_service import LLMEPGSearchService

service = LLMEPGSearchService()
results = await service.search(
    query="What news shows are on tonight?",
    time_range="tonight"
)

Vector Search

Service: OlorinVectorSearchEngine: MongoDB Atlas Vector Search

Semantic search using content embeddings for similarity-based retrieval.

Features:

Content embedding generation (OpenAI ada-002)
Cosine similarity matching
Hybrid search (vector + keyword)
Real-time index updates

Example:

python

from app.services.olorin.search import OlorinVectorSearch

service = OlorinVectorSearch()
results = await service.vector_search(
    query_embedding=query_vector,
    limit=10,
    min_score=0.7
)

Specialized AI Features

Chess AI

Service: ChessServiceModel: Claude Sonnet 4.5

AI-powered chess opponent with difficulty levels and move analysis.

Features:

Multiple difficulty levels (beginner to expert)
Move validation and suggestions
Game analysis and commentary
Opening book integration

API:

POST /api/v1/chess/move - Make a move
GET /api/v1/chess/analysis - Get position analysis
POST /api/v1/chess/hint - Request move hint

Onboarding AI

Service: OnboardingAIServiceModel: Claude Haiku 3.5

Interactive AI-guided onboarding for new users.

Features:

Personalized content recommendations
User preference learning
Interactive Q&A
Tutorial generation

Live Dubbing

Service: LiveDubbingServiceEngine: ElevenLabs TTS + Google Speech-to-Text

Real-time dubbing of live TV channels from Hebrew to other languages.

Features:

Real-time transcription (Google STT)
Translation (Google Translate)
Text-to-speech (ElevenLabs)
Speaker voice matching
Latency optimization (❤️ seconds)

Supported Languages:

Hebrew → English
Hebrew → Spanish
Hebrew → French
Hebrew → Russian

Olorin Ecosystem AI Services

Content Intelligence

Service: OlorinContextService

Cultural context detection and content classification for Israeli and Jewish content.

Features:

Jewish holiday detection
Israeli culture context
Hebrew language nuances
Religious content classification
Kosher content filtering

Partner AI Integration

Service: OlorinPartnerService

AI-powered APIs for content partners and third-party integrations.

Features:

Content recommendation API
Metadata enrichment API
Search API
User behavior analytics
Custom model fine-tuning

LLM Model Selection Guide

Model Comparison

Model	Speed	Cost	Use Case
Claude Sonnet 4.5	Medium	$15/M tokens	Complex reasoning, comprehensive analysis
Claude Haiku 3.5	Fast	$1/M tokens	Search, quick queries, high-volume operations
GPT-4 Turbo	Medium	$10/M tokens	Alternative for specific tasks
OpenAI Embedding	Fast	$0.13/M tokens	Vector embeddings for search

Model Selection Criteria

Use Claude Sonnet 4.5 for:

AI Agent comprehensive audits
Complex content analysis
Multi-step reasoning tasks
High-accuracy requirements

Use Claude Haiku 3.5 for:

Beta 500 AI Search (cost-effective)
EPG/VOD search services
High-volume operations
Real-time applications

Use OpenAI Embedding for:

Vector search indexing
Semantic similarity
Content clustering
Recommendation systems

Cost Optimization Strategies

1. Caching:

python

from functools import lru_cache

@lru_cache(maxsize=1000)
async def cached_search(query: str):
    return await llm_service.search(query)

2. Batch Operations:

python

# Process multiple items in single request
results = await llm_service.batch_process([
    item1, item2, item3
])

3. Token Limits:

python

# Limit context size
context = content[:4000]  # ~1000 tokens
result = await llm_service.analyze(context)

4. Prompt Optimization:

python

# Use concise prompts
prompt = f"Classify: {title}"  # Instead of verbose instructions

5. Model Downgrading:

python

# Use Haiku for simple tasks
if task_complexity == "simple":
    model = "claude-haiku-3-5"
else:
    model = "claude-sonnet-4-5"

6. Result Caching:

python

# Cache search results
cache_key = f"search:{query_hash}"
if cached := await redis.get(cache_key):
    return cached
result = await llm_service.search(query)
await redis.setex(cache_key, 3600, result)

Monitoring and Observability

Metrics

Prometheus Metrics:

bayit_ai_requests_total - Total AI requests by feature
bayit_ai_request_duration_seconds - Request latency histogram
bayit_ai_credits_used_total - Credits consumed by user
bayit_ai_errors_total - Error count by type
bayit_ai_cost_dollars_total - Total AI cost

Grafana Dashboards:

AI Features Overview
Beta 500 Credit Usage
LLM Model Performance
Error Rate and Latency

Logging

Structured Logging:

python

from app.core.logging_config import get_logger

logger = get_logger(__name__)

logger.info("AI search executed", extra={
    "user_id": user_id,
    "query": query,
    "credits_used": 10,
    "results_count": len(results),
    "duration_ms": duration
})

Alerts

Critical Alerts:

Credit balance below 10 for user
AI service error rate > 5%
Request latency p95 > 2 seconds
Daily cost > budget threshold

Security and Privacy

Data Protection

User Data:

Queries encrypted in transit (HTTPS)
No query logging without consent
Opt-out available for AI features
GDPR compliance

API Keys:

Stored in Google Cloud Secret Manager
Rotated quarterly
Never logged or exposed
Rate-limited per service account

Rate Limiting

Global Limits:

1000 AI requests/minute (platform-wide)
100 concurrent AI operations

Per-User Limits:

Tier	Requests/Minute
Free	60
Beta 500	120
Premium	300
Admin	Unlimited

AI API Reference - Complete API documentation
Beta 500 User Manual - User guide for Beta 500
Credit System - Credit architecture and metering
LLM Configuration - Model setup and configuration
AI Agent System - Agent architecture and tools
AI Troubleshooting - Common issues and solutions

Document Status: ✅ Complete Last Updated: 2026-01-30 Maintained by: AI Team Next Review: 2026-03-30

AI Features Overview ​

Overview ​

AI Feature Categories ​

Beta 500 Program ​

Features ​

1. AI Search ​

2. AI Recommendations ​

3. Auto Catch-Up ​

Credit System ​

Access Requirements ​

AI Agent System ​

Architecture ​

Use Cases ​

Configuration ​

LLM Search Services ​

VOD Search ​

EPG Search ​

Vector Search ​

Specialized AI Features ​

Chess AI ​

Onboarding AI ​

Live Dubbing ​

Olorin Ecosystem AI Services ​

Content Intelligence ​

Partner AI Integration ​

LLM Model Selection Guide ​

Model Comparison ​

Model Selection Criteria ​

Cost Optimization Strategies ​

Monitoring and Observability ​

Metrics ​

Logging ​

Alerts ​

Security and Privacy ​

Data Protection ​

Rate Limiting ​

Related Documentation ​

AI Features Overview

Overview

AI Feature Categories

Beta 500 Program

Features

1. AI Search

2. AI Recommendations

3. Auto Catch-Up

Credit System

Access Requirements

AI Agent System

Architecture

Use Cases

Configuration

LLM Search Services

VOD Search

EPG Search

Vector Search

Specialized AI Features

Chess AI

Onboarding AI

Live Dubbing

Olorin Ecosystem AI Services

Content Intelligence

Partner AI Integration

LLM Model Selection Guide

Model Comparison

Model Selection Criteria

Cost Optimization Strategies

Monitoring and Observability

Metrics

Logging

Alerts

Security and Privacy

Data Protection

Rate Limiting

Related Documentation