---
title: Configurable Graph Threshold
description: "Configure the graph store threshold parameter to control how strictly nodes are matched during data ingestion."
---

## Overview

The graph store threshold parameter controls how strictly nodes are matched during graph data ingestion based on embedding similarity. This feature allows you to customize the matching behavior to prevent false matches or enable entity merging based on your specific use case.

## Configuration

Add the `threshold` parameter to your graph store configuration:

```python
from mem0 import Memory

config = {
    "graph_store": {
        "provider": "neo4j",  # or memgraph, neptune, kuzu
        "config": {
            "url": "bolt://localhost:7687",
            "username": "neo4j",
            "password": "password"
        },
        "threshold": 0.7  # Default value, range: 0.0 to 1.0
    }
}

memory = Memory.from_config(config)
```

## Parameters

| Parameter | Type | Default | Range | Description |
|-----------|------|---------|-------|-------------|
| `threshold` | float | 0.7 | 0.0 - 1.0 | Minimum embedding similarity score required to match existing nodes during graph ingestion |

## Use Cases

### Strict Matching (UUIDs, IDs)

Use higher thresholds (0.95-0.99) when working with identifiers that should remain distinct:

```python
config = {
    "graph_store": {
        "provider": "neo4j",
        "config": {...},
        "threshold": 0.95  # Strict matching
    }
}
```

**Example:** Prevents UUID collisions like `MXxBUE18QVBQTElDQVRJT058MjM3MTM4NjI5` being matched with `MXxBUE18QVBQTElDQVRJT058MjA2OTYxMzM`

### Permissive Matching (Natural Language)

Use lower thresholds (0.6-0.7) when entity variations should be merged:

```python
config = {
    "graph_store": {
        "threshold": 0.6  # Permissive matching
    }
}
```

**Example:** Merges similar entities like "Bob" and "Robert" as the same person.

## Threshold Guidelines

| Use Case | Recommended Threshold | Behavior |
|----------|----------------------|----------|
| UUIDs, IDs, Keys | 0.95 - 0.99 | Prevent false matches between similar identifiers |
| Structured Data | 0.85 - 0.9 | Balanced precision and recall |
| General Purpose | 0.7 - 0.8 | Default recommendation |
| Natural Language | 0.6 - 0.7 | Allow entity variations to merge |

## Examples

### Example 1: Preventing Data Loss with UUIDs

```python
from mem0 import Memory

config = {
    "graph_store": {
        "provider": "neo4j",
        "config": {
            "url": "bolt://localhost:7687",
            "username": "neo4j",
            "password": "password"
        },
        "threshold": 0.98  # Very strict for UUIDs
    }
}

memory = Memory.from_config(config)

# These UUIDs create separate nodes instead of being incorrectly merged
memory.add(
    [{"role": "user", "content": "MXxBUE18QVBQTElDQVRJT058MjM3MTM4NjI5 relates to Project A"}],
    user_id="user1"
)

memory.add(
    [{"role": "user", "content": "MXxBUE18QVBQTElDQVRJT058MjA2OTYxMzM relates to Project B"}],
    user_id="user1"
)
```

### Example 2: Merging Entity Variations

```python
config = {
    "graph_store": {
        "provider": "neo4j",
        "config": {...},
        "threshold": 0.6  # More permissive
    }
}

memory = Memory.from_config(config)

# These will be merged as the same entity
memory.add([{"role": "user", "content": "Bob works at Google"}], user_id="user1")
memory.add([{"role": "user", "content": "Robert works at Google"}], user_id="user1")
```

### Example 3: Different Thresholds for Different Clients

```python
# Client 1: Strict matching for transactional data
memory_strict = Memory.from_config({
    "graph_store": {"threshold": 0.95}
})

# Client 2: Permissive matching for conversational data
memory_permissive = Memory.from_config({
    "graph_store": {"threshold": 0.6}
})
```

## Supported Graph Providers

The threshold parameter works with all graph store providers:

- ✅ Neo4j
- ✅ Memgraph
- ✅ Kuzu
- ✅ Neptune (both Analytics and DB)

## How It Works

When adding a relation to the graph:

1. **Embedding Generation**: The system generates embeddings for source and destination entities
2. **Node Search**: Searches for existing nodes with similar embeddings
3. **Threshold Comparison**: Compares similarity scores against the configured threshold
4. **Decision**:
   - If similarity ≥ threshold: Uses the existing node
   - If similarity < threshold: Creates a new node

```python
# Pseudocode
if node_similarity >= threshold:
    use_existing_node()
else:
    create_new_node()
```

## Troubleshooting

### Issue: Duplicate nodes being created

**Symptom**: Expected nodes to merge but they're created separately

**Solution**: Lower the threshold
```python
config = {"graph_store": {"threshold": 0.6}}
```

### Issue: Unrelated entities being merged

**Symptom**: Different entities incorrectly matched as the same node

**Solution**: Raise the threshold
```python
config = {"graph_store": {"threshold": 0.95}}
```

### Issue: Validation error

**Symptom**: `ValidationError: threshold must be between 0.0 and 1.0`

**Solution**: Ensure threshold is in valid range
```python
config = {"graph_store": {"threshold": 0.7}}  # Valid: 0.0 ≤ x ≤ 1.0
```

## Backward Compatibility

- **Default Value**: 0.7 (maintains existing behavior)
- **Optional Parameter**: Existing code works without any changes
- **No Breaking Changes**: Graceful fallback if not specified

## Related

- [Graph Memory](/platform/features/graph-memory)
- [Issue #3590](https://github.com/mem0ai/mem0/issues/3590)
