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04: Multi-Source Fusion

Overview

Consolidates customer information from multiple systems (CRM, billing, support, marketing) into a unified profile using intelligent merging. Demonstrates advanced conflict resolution and data quality tracking across heterogeneous sources.

Theme

Customer Data Integration

Strategy

LLM.BALANCED merge with conflict resolution

Key Features

  • ✅ Multi-system data integration
  • ✅ Automatic conflict detection and resolution
  • ✅ Data quality reporting and completeness tracking
  • ✅ Lineage tracking (which systems contributed)
  • ✅ Unified customer view across departments

Data Structure

CustomerProfile

customer_id: str                   # Customer ID
name: str | None                   # Customer name
email: str | None                  # Email address
phone: str | None                  # Phone number
company: str | None                # Company/Organization
job_title: str | None              # Job title
total_spending: float | None       # Lifetime spending
support_tickets: list[str]         # Support ticket IDs
preferred_products: list[str]      # Product preferences
communication_preferences: list    # Preferred channels
data_sources: list[str]            # Which systems provided data
last_updated: str | None           # Last update timestamp

Use Case

Enterprise Data Management: Unify customer data from CRM, billing, support, and marketing systems into a single golden record with intelligent conflict resolution.

Benefits: - Single customer view across departments - Reduced data silos - Automatic conflict resolution - Data quality improvements - Better customer experiences

Running the Example

cd examples/

# Set your OpenAI API key (optional, will fallback without it)
export OPENAI_API_KEY="your-key-here"

python 04_multi_source_fusion.py

Output

Results are stored in temp/customer_unified_profile/: - memory.json: Unified customer profiles - metadata.json: Schema, statistics, and conflict logs

API Requirements 🔄 Optional

This example works better with an OpenAI API key for intelligent conflict resolution, but has graceful fallback without it.

What You'll Learn

  1. Multi-Source Integration: Merging data from multiple heterogeneous sources
  2. Conflict Resolution: Using LLM to intelligently resolve contradictions
  3. Data Quality: Tracking completeness and quality metrics
  4. Lineage Tracking: Recording which systems provided each piece of data
  5. Enterprise Patterns: Building scalable data unification systems

Complexity

⭐⭐⭐⭐ Advanced: This is the most complex example showing enterprise-level data integration patterns.

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