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PUT
http://api.gu1.ai
/
entities
/
upsert
Upsert
curl --request PUT \
  --url http://api.gu1.ai/entities/upsert \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "entity": {},
  "options": {},
  "options.conflictResolution": {},
  "options.deduplicationStrategy": {},
  "options.createRelationships": true
}
'
{
  "success": true,
  "action": "<string>",
  "entity": {},
  "previousEntity": {},
  "confidence": 123,
  "reasoning": "<string>",
  "conflicts": [
    {}
  ]
}

​
Overview

The upsert endpoint intelligently creates a new person or updates an existing one based on configurable duplicate detection strategies. It automatically handles conflicts and prevents duplicate records using exact matching, fuzzy matching, or AI-powered similarity detection.

​
Endpoint

PUT http://api.gu1.ai/entities/upsert

​
Authentication

Requires a valid API key in the Authorization header: 
Authorization: Bearer YOUR_API_KEY

​
Request Body

​
entity
object
required
The person data (same structure as Create Person endpoint)
​
options
object
Configuration options for upsert behavior
​
options.conflictResolution
enum
How to handle conflicts when an existing person is found:
  • source_wins - New data overwrites existing data
  • target_wins - Keep existing data, ignore new data
  • manual_review - Flag for manual review without updating
  • smart_merge (default) - Intelligently merge both datasets
​
options.deduplicationStrategy
enum
Strategy for detecting duplicate persons:
  • exact_match - Match by externalId and taxId (case-insensitive)
  • fuzzy_match - Similarity matching on name and taxId (80% threshold)
  • ai_similarity - AI-powered semantic similarity detection
  • hybrid (recommended) - Exact match with fuzzy fallback
​
options.createRelationships
boolean
default:"true"
Whether to automatically create relationships between entities

​
Response

​
success
boolean
Indicates if the operation succeeded
​
action
string
The action performed: created or updated
​
entity
object
The final person state after upsert
​
previousEntity
object
The person state before update (null if newly created)
​
confidence
number
Confidence score (0-1) for the duplicate detection match
​
reasoning
string
Explanation of why the person was created/updated
​
conflicts
array
Array of field-level conflicts detected during merge (if any)

​
Examples

​
Simple Upsert (Default Behavior)

curl -X PUT http://api.gu1.ai/entities/upsert \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "entity": {
      "type": "person",
      "externalId": "customer_12345",
      "name": "María González",
      "countryCode": "AR",
      "taxId": "20-12345678-9",
      "entityData": {
        "person": {
          "firstName": "María",
          "lastName": "González",
          "dateOfBirth": "1985-03-15",
          "occupation": "Software Engineer",
          "income": 85000
        }
      }
    }
  }'

​
Upsert with Fuzzy Matching

curl -X PUT http://api.gu1.ai/entities/upsert \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "entity": {
      "type": "person",
      "externalId": "customer_new_123",
      "name": "Maria Gonzales",
      "countryCode": "AR",
      "taxId": "20-12345678-9",
      "entityData": {
        "person": {
          "firstName": "Maria",
          "lastName": "Gonzales"
        }
      }
    },
    "options": {
      "deduplicationStrategy": "fuzzy_match",
      "conflictResolution": "smart_merge"
    }
  }'

​
Response Examples

​
Created New Person

{
  "success": true,
  "action": "created",
  "entity": {
    "id": "550e8400-e29b-41d4-a716-446655440000",
    "externalId": "customer_12345",
    "type": "person",
    "name": "María González",
    ...
  },
  "previousEntity": null,
  "confidence": 1.0,
  "reasoning": "No existing entity found matching criteria. Created new entity.",
  "conflicts": []
}

​
Updated Existing Person

{
  "success": true,
  "action": "updated",
  "entity": {
    "id": "550e8400-e29b-41d4-a716-446655440000",
    "externalId": "customer_12345",
    "type": "person",
    "name": "María González",
    "entityData": {
      "person": {
        "income": 95000
      }
    },
    ...
  },
  "previousEntity": {
    "id": "550e8400-e29b-41d4-a716-446655440000",
    "entityData": {
      "person": {
        "income": 85000
      }
    },
    ...
  },
  "confidence": 1.0,
  "reasoning": "Exact match found on externalId. Updated existing entity with smart merge.",
  "conflicts": [
    {
      "field": "entityData.person.income",
      "oldValue": 85000,
      "newValue": 95000,
      "resolution": "source_wins"
    }
  ]
}

​
Use Cases

​
Data Import from CRM

// Import customer data from CRM, avoiding duplicates
async function importCustomer(crmData) {
  const response = await fetch('http://api.gu1.ai/entities/upsert', {
    method: 'PUT',
    headers: {
      'Authorization': 'Bearer YOUR_API_KEY',
      'Content-Type': 'application/json'
    },
    body: JSON.stringify({
      entity: {
        type: 'person',
        externalId: crmData.customerId,
        name: crmData.fullName,
        countryCode: crmData.country,
        taxId: crmData.taxId,
        entityData: {
          person: {
            firstName: crmData.firstName,
            lastName: crmData.lastName,
            income: crmData.annualIncome
          }
        },
        attributes: {
          source: 'crm_import',
          importDate: new Date().toISOString()
        }
      },
      options: {
        deduplicationStrategy: 'hybrid',
        conflictResolution: 'smart_merge'
      }
    })
  });

  return response.json();
}

​
Progressive Data Enrichment

def enrich_person_data(external_id, new_data):
    """Progressively add data to person as it becomes available"""
    response = requests.put(
        'http://api.gu1.ai/entities/upsert',
        headers={
            'Authorization': 'Bearer YOUR_API_KEY',
            'Content-Type': 'application/json'
        },
        json={
            'entity': {
                'type': 'person',
                'externalId': external_id,
                'name': new_data.get('name'),
                'countryCode': new_data.get('country'),
                'entityData': new_data.get('details', {}),
                'attributes': new_data.get('attributes', {})
            },
            'options': {
                'deduplicationStrategy': 'exact_match',
                'conflictResolution': 'smart_merge'  # Merge new with existing
            }
        }
    )

    result = response.json()
    if result['action'] == 'updated':
        print(f"Enriched existing person with new data")
    return result

​
Best Practices

  1. Choose the Right Strategy:
    • exact_match for clean, structured data with reliable IDs
    • fuzzy_match for user-entered data with potential typos
    • hybrid for most production scenarios
  2. Handle Conflicts Gracefully:
    • Use smart_merge for automatic resolution
    • Use manual_review for critical data
    • Check conflicts array in response for important changes
  3. Monitor Confidence Scores:
    • Scores below 0.7 may indicate weak matches
    • Log low-confidence updates for review

​
Error Responses

​
400 Bad Request

{
  "error": "Invalid tax ID format for country"
}

​
500 Internal Server Error

{
  "error": "Failed to upsert entity"
}

​
Next Steps