REST API

GoldenMatch includes a local HTTP server for real-time matching, cluster browsing, and data steward review.

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Start the server

goldenmatch serve --file customers.csv --config config.yaml --port 8080

The server loads data, runs initial matching, and exposes endpoints on the specified port.

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Endpoints

Method	Path	Description
GET	/health	Health check
GET	/stats	Dataset statistics
POST	/match	Match a single record
POST	/match/batch	Match multiple records
POST	/explain	Explain why two records match
GET	/clusters	List all clusters
GET	/clusters/<id>	Get cluster detail
GET	/reviews	Review queue (borderline pairs)
GET	/reviews/decisions	List completed review decisions
POST	/reviews/decide	Approve or reject a pair

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Health check

curl http://localhost:8080/health

{"status": "ok"}

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Stats

curl http://localhost:8080/stats

{
    "total_records": 5000,
    "total_clusters": 847,
    "singleton_count": 3200,
    "match_rate": 0.12,
    "avg_cluster_size": 2.1,
    "max_cluster_size": 8
}

---

Match a record

curl -X POST http://localhost:8080/match \
    -H "Content-Type: application/json" \
    -d '{"name": "John Smith", "zip": "10001"}'

{
    "matches": [
        {"row_id": 42, "score": 0.92, "name": "Jon Smyth", "zip": "10001"},
        {"row_id": 108, "score": 0.87, "name": "John Smithe", "zip": "10001"}
    ]
}

Batch match

curl -X POST http://localhost:8080/match/batch \
    -H "Content-Type: application/json" \
    -d '[
        {"name": "John Smith", "zip": "10001"},
        {"name": "Jane Doe", "zip": "90210"}
    ]'

---

Explain a match

curl -X POST http://localhost:8080/explain \
    -H "Content-Type: application/json" \
    -d '{"id_a": 42, "id_b": 108}'

{
    "overall_score": 0.92,
    "explanation": "Strong match: name similarity 0.88 (jaro_winkler), zip exact match.",
    "field_scores": [
        {"field": "name", "scorer": "jaro_winkler", "score": 0.88},
        {"field": "zip", "scorer": "exact", "score": 1.0}
    ]
}

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Clusters

List all clusters

curl http://localhost:8080/clusters

{
    "clusters": [
        {"cluster_id": 1, "size": 3, "confidence": 0.91},
        {"cluster_id": 2, "size": 2, "confidence": 0.87}
    ]
}

Get cluster detail

curl http://localhost:8080/clusters/1

{
    "cluster_id": 1,
    "size": 3,
    "confidence": 0.91,
    "members": [
        {"row_id": 42, "name": "John Smith", "email": "john@example.com"},
        {"row_id": 108, "name": "Jon Smyth", "email": "john@example.com"},
        {"row_id": 215, "name": "J. Smith", "email": "jsmith@example.com"}
    ],
    "pair_scores": {
        "42-108": 0.92,
        "42-215": 0.85,
        "108-215": 0.83
    },
    "bottleneck_pair": [108, 215]
}

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Review queue

The review queue surfaces borderline pairs for data steward approval or rejection.

Get pending reviews

curl http://localhost:8080/reviews

{
    "reviews": [
        {
            "id": "rev_001",
            "id_a": 42,
            "id_b": 300,
            "score": 0.82,
            "record_a": {"name": "John Smith", "zip": "10001"},
            "record_b": {"name": "J. Smithson", "zip": "10001"}
        }
    ]
}

Make a decision

curl -X POST http://localhost:8080/reviews/decide \
    -H "Content-Type: application/json" \
    -d '{"id": "rev_001", "decision": "approve"}'

Decision values: "approve" or "reject".

List completed decisions

curl http://localhost:8080/reviews/decisions

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Python client

GoldenMatch includes a REST client that uses only stdlib urllib (no extra dependencies):

import goldenmatch as gm

client = gm.Client("http://localhost:8080")

# Match a record
matches = client.match({"name": "John Smith", "zip": "10001"})

# List clusters
clusters = client.list_clusters()

# Explain a match
explanation = client.explain(42, 108)

# Get review queue
reviews = client.reviews()

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Docker deployment

docker run --rm -p 8080:8080 -v $(pwd):/data \
    ghcr.io/benzsevern/goldenmatch:latest \
    serve --file /data/customers.csv --port 8080