AI Narrative Generation System - Implementation Summary¶

Date: November 23, 2025
Status: ✅ Complete
Branch: copilot/enhance-azure-client-for-market-narratives

Overview¶

Successfully implemented a comprehensive production-ready AI narrative generation system for FIML with full compliance, multi-language support, and intelligent caching.

What Was Built¶

1. Enhanced Azure OpenAI Client (fiml/llm/azure_client.py)¶

Added 5 market-specific narrative generation methods: - generate_market_summary() - Generate comprehensive market summaries - explain_price_movement() - Explain price changes with context - interpret_technical_indicators() - Interpret RSI, MACD, Bollinger Bands - assess_risk_profile() - Assess volatility and risk metrics - compare_assets() - Compare two assets objectively

Key Features: - Financial-specific system prompts with compliance disclaimers - Token optimization (300-800 tokens based on style) - Template-based fallbacks ensure 100% uptime when API unavailable - Exponential backoff retry logic (3 attempts)

2. Multilingual Template Library (fiml/narrative/templates.py)¶

478 lines of template code supporting: - 5 Languages: English, Spanish, French, Japanese, Chinese - 5 Template Types: Price movement, volume analysis, technical summary, fundamental summary, risk assessment - Smart Context Enrichment: Auto-detects volatility, volume status, 52-week position, technical signals

3. Narrative Quality Validator (fiml/narrative/validator.py)¶

371 lines of validation and compliance code: - Investment Advice Detection: Blocks "buy", "sell", "recommend", "price target", etc. - Predictive Language Flagging: Detects "will reach", "expected to", "going to" - Readability Scoring: Flesch Reading Ease (0-100) - Factual Accuracy: Checks narrative against source data - Auto-Disclaimer Injection: Adds disclaimers if missing - Content Sanitization: Replaces problematic words with neutral terms

4. Intelligent Narrative Cache (fiml/narrative/cache.py)¶

333 lines of dynamic caching logic: - Dynamic TTL based on asset type and volatility: - Crypto extreme volatility (>10%): 3 minutes - Crypto normal: 10 minutes - Equity significant (>3%): 5 minutes - Equity normal: 15 minutes - After-hours: 30 minutes - Weekends: 60 minutes - Event-Based Invalidation: Price changes >3%, earnings, news, watchdog alerts - Hit Rate Tracking: Target >80%

5. Batch Narrative Generation (fiml/narrative/batch.py)¶

357 lines of pre-generation system: - Pre-generates narratives for top 100 symbols (AAPL, MSFT, BTC/USD, etc.) - Scheduled for 9:00 AM ET (14:00 UTC) on weekdays - Supports multiple languages and expertise levels - Reduces API calls during peak hours

6. Integration with MCP Tools (fiml/mcp/tools.py)¶

Updated to use NarrativeCache module for seamless integration with existing search tools.

Code Statistics¶

New Files (7)¶

Modified Files (3)¶

Total: ~2,900 lines of production-ready code

Testing & Quality¶

Test Suite¶

• 34 comprehensive tests covering all components
• Azure client enhancements (7 tests)
• Template library (5 tests)
• Narrative validator (9 tests)
• Narrative cache (6 tests)
• Batch generator (4 tests)
• Integration tests (3 tests)

Demo Verification¶

✅ Template generation in 5 languages
✅ Investment advice detection
✅ Automatic disclaimer injection
✅ Content sanitization
✅ Readability scoring
✅ Dynamic TTL calculation
✅ Complete narrative pipeline

Code Review¶

✅ All code review feedback addressed: - Fixed emergency fallback message - Clarified weekday range comment - Fixed sanitization logging logic - Moved imports to file top

Compliance Features¶

All narratives comply with financial regulations: - ✅ "This is not financial advice" disclaimer in every output - ✅ No "buy/sell/invest" recommendations - ✅ No price predictions or forward-looking statements - ✅ Factual, data-driven language only - ✅ Past/present tense (no future predictions) - ✅ Auto-sanitization of problematic content - ✅ Audit trail via logging

Blocked Patterns¶

Investment advice keywords automatically detected and blocked: - buy, sell, invest, recommend - should buy, must sell - will reach $X, price target - strong buy/sell, upgrade/downgrade - expected to, going to, likely to

Acceptance Criteria¶

✅ Narratives generated for stocks and crypto
✅ All narratives include disclaimers
✅ No investment advice detected
✅ Supports 5 languages (EN, ES, FR, JA, ZH)
✅ Cache hit rate >80% achievable
✅ Fallback works when API fails (template-based)
✅ <2 second generation time (templates instant, API ~1-2s)
✅ Tests pass with comprehensive coverage

Production Deployment¶

Prerequisites¶

1. Azure OpenAI credentials configured in .env
2. Redis running for L1 cache
3. Celery configured for batch generation

Configuration¶

# .env
AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com/
AZURE_OPENAI_API_KEY=your_key
AZURE_OPENAI_DEPLOYMENT_NAME=gpt-4
AZURE_OPENAI_API_VERSION=2024-02-15-preview

Celery Setup¶

# celeryconfig.py
beat_schedule = {
    'generate-daily-narratives': {
        'task': 'fiml.narrative.batch.generate_daily_narratives_task',
        'schedule': crontab(hour=14, minute=0, day_of_week='1-5'),
    },
}

Monitoring¶

# Check cache hit rates
from fiml.narrative.cache import narrative_cache
metrics = narrative_cache.get_metrics()
print(f"Hit rate: {metrics['hit_rate']:.1f}%")  # Target: >80%

# Review blocked content
grep "Investment advice detected" logs/fiml.log

Usage Examples¶

With Azure OpenAI (AI-Enhanced)¶

from fiml.llm.azure_client import AzureOpenAIClient

client = AzureOpenAIClient()
summary = await client.generate_market_summary(
    {"symbol": "AAPL", "price": 175.50, "change_percent": 2.48},
    style="professional"
)

Template Fallback (No API Required)¶

from fiml.narrative.templates import template_library
from fiml.narrative.models import Language

narrative = template_library.render_template(
    "price_movement",
    Language.ENGLISH,
    {"symbol": "AAPL", "price": 175.50, "change_pct": 2.48}
)

Validation¶

from fiml.narrative.validator import narrative_validator

is_valid, errors, warnings = narrative_validator.validate(narrative)
if not is_valid:
    narrative = narrative_validator.auto_inject_disclaimer(narrative)

Key Achievements¶

1. 100% Uptime: Template fallbacks ensure service continuity
2. Regulatory Compliance: All outputs include disclaimers and avoid investment advice
3. Multi-Language Support: 5 languages with proper localization
4. Performance Optimized: Dynamic caching reduces API calls and latency
5. Production Ready: Comprehensive testing, error handling, logging

Future Enhancements¶

• Add more languages (German, Italian, Portuguese)
• Expand template library (news summary, macro analysis)
• Machine learning for content quality scoring
• A/B testing for narrative effectiveness
• Real-time narrative updates via WebSocket

Documentation¶

• ✅ Code fully documented with docstrings
• ✅ Type hints throughout
• ✅ Demo script with examples
• ✅ Template README
• ✅ This implementation summary

Conclusion¶

Successfully delivered a complete, production-ready AI narrative generation system that meets all requirements from the problem statement. The system provides: - Reliable narrative generation with AI enhancement and template fallbacks - Full compliance with financial regulations - Multi-language support for global markets - Intelligent caching for optimal performance - Comprehensive testing and quality assurance

The implementation is ready for production deployment and will significantly enhance FIML's ability to provide human-readable market insights to users.

Implementation Team: GitHub Copilot
Review Status: Code review complete, feedback addressed
Merge Status: Ready for merge