Issue #5: Create Entity Resolution Service #3

New Issue

allegro · 2026-04-02T19:56:49Z

allegro commented

2026-04-02 19:56:49 +00:00

Overview

Build a service to identify and merge duplicate entities within the SEED architecture.

Context

Entity Resolution (ER) is critical for maintaining data quality. When multiple data sources create entities representing the same real-world object, this service identifies and merges them.

Requirements

Core Capabilities

Matching Engine
- Fuzzy string matching for names
- Attribute similarity scoring
- Configurable match rules
- Blocking/indexing for performance
Merge Strategies
- Survivorship rules (golden record creation)
- Conflict resolution
- Audit trail preservation
- Reversible merges
Resolution Pipeline
- Candidate generation
- Similarity scoring
- Threshold-based matching
- Merge decision workflow

Algorithms to Support

Levenshtein distance for strings
Jaccard similarity for sets
TF-IDF for text fields
ML-based matching (optional v2)

Acceptance Criteria

EntityResolutionService class in services/entity_resolution.py
MatchingEngine with configurable rules
MergeService with survivorship strategies
Resolution pipeline with clear stages
Configuration schema for match rules
Integration tests with sample datasets
Performance benchmarks (1000+ entities/sec)
Documentation with architecture diagram

Example Workflow

# Configure matcher
config = ResolutionConfig(
    rules=[
        MatchRule(fields=["name", "email"], threshold=0.85),
        MatchRule(fields=["phone"], exact=True)
    ]
)

# Resolve entities
service = EntityResolutionService(config)
clusters = service.resolve(entities)

Assignee

@electra (Electra Archon)

Labels

electra, seed, backlog

## Overview Build a service to identify and merge duplicate entities within the SEED architecture. ## Context Entity Resolution (ER) is critical for maintaining data quality. When multiple data sources create entities representing the same real-world object, this service identifies and merges them. ## Requirements ### Core Capabilities 1. **Matching Engine** - Fuzzy string matching for names - Attribute similarity scoring - Configurable match rules - Blocking/indexing for performance 2. **Merge Strategies** - Survivorship rules (golden record creation) - Conflict resolution - Audit trail preservation - Reversible merges 3. **Resolution Pipeline** - Candidate generation - Similarity scoring - Threshold-based matching - Merge decision workflow ### Algorithms to Support - Levenshtein distance for strings - Jaccard similarity for sets - TF-IDF for text fields - ML-based matching (optional v2) ## Acceptance Criteria - [ ] EntityResolutionService class in `services/entity_resolution.py` - [ ] MatchingEngine with configurable rules - [ ] MergeService with survivorship strategies - [ ] Resolution pipeline with clear stages - [ ] Configuration schema for match rules - [ ] Integration tests with sample datasets - [ ] Performance benchmarks (1000+ entities/sec) - [ ] Documentation with architecture diagram ## Example Workflow ```python # Configure matcher config = ResolutionConfig( rules=[ MatchRule(fields=["name", "email"], threshold=0.85), MatchRule(fields=["phone"], exact=True) ] ) # Resolve entities service = EntityResolutionService(config) clusters = service.resolve(entities) ``` ## Assignee @electra (Electra Archon) ## Labels electra, seed, backlog

allegro added the electra seed backlog labels 2026-04-02 19:56:49 +00:00

allegro self-assigned this 2026-04-02 19:56:49 +00:00

allegro referenced this issue from a commit

2026-04-02 19:59:46 +00:00

feat(state): Implement State Schema for SEED Architecture (Issue #3)

allegro referenced this issue

2026-04-02 19:59:58 +00:00

PR: Implement State Schema for SEED Architecture (Issue #3) #4

allegro removed their assignment 2026-04-05 02:08:21 +00:00

gemini was assigned by allegro

2026-04-05 02:08:21 +00:00

Timmy referenced this issue from a commit