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feat: Add usage frequency tracking for graph elements (#1992)

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## Description

This PR adds usage frequency tracking to help identify which graph
elements (nodes) are most frequently accessed during user searches.

**Related Issue:** Closes [#1458]
**The Problem:**
When users search repeatedly, we had no way to track which pieces of
information were being referenced most often. This made it impossible
to:
- Prioritize popular content in search results
- Understand which topics users care about most
- Improve retrieval by boosting frequently-used nodes

**The Solution:**
I've implemented a system that tracks usage patterns by:
1. Leveraging the existing `save_interaction=True` flag in
`cognee.search()` which creates `CogneeUserInteraction` nodes
2. Following the `used_graph_element_to_answer` edges to see which graph
elements each search referenced
3. Counting how many times each element was accessed within a
configurable time window (default: 7 days)
4. Writing a `frequency_weight` property back to frequently-accessed
nodes

This gives us a simple numeric weight on nodes that reflects real usage
patterns, which can be used to improve search ranking, analytics
dashboards, or identifying trending topics.

**Key Design Decisions:**
- Time-windowed counting (not cumulative) - focuses on recent usage
patterns
- Configurable minimum threshold - filters out noise from rarely
accessed nodes
- Neo4j-first implementation using Cypher queries - works with our
primary production database
- Documented Kuzu limitation - requires schema changes, leaving for
future work as acceptable per team discussion

The implementation follows existing patterns in Cognee's memify pipeline
and can be run as a scheduled task or on-demand.

**Known Limitations:**
**Kuzu adapter not currently supported** - Kuzu requires properties to
be defined in the schema at node creation time, so dynamic property
updates don't work. I'm opening a separate issue to track Kuzu support,
which will require schema modifications in the Kuzu adapter. For now,
this feature works with Neo4j (our primary production database).

**Follow-up Issue:** #1993 

## Acceptance Criteria

**Core Functionality:**
- ✅ `extract_usage_frequency()` correctly counts node access frequencies
from interaction data
- ✅ `add_frequency_weights()` writes `frequency_weight` property to
Neo4j nodes
- ✅ Time window filtering works (only counts recent interactions)
- ✅ Minimum threshold filtering works (excludes rarely-used nodes)
- ✅ Element type distribution tracked for analytics
- ✅ Gracefully handles unsupported adapters (logs warning, doesn't
crash)

**Testing Verification:**
1. Run the end-to-end example with Neo4j:
   ```bash
   # Update .env for Neo4j
   GRAPH_DATABASE_PROVIDER=neo4j
   GRAPH_DATASET_HANDLER=neo4j_aura_dev
   
   python extract_usage_frequency_examplepy
   ```
   Should show frequencies extracted and applied to nodes

2. Verify in Neo4j Browser (http://localhost:7474):
   ```cypher
   MATCH (n) WHERE n.frequency_weight IS NOT NULL 
   RETURN n.frequency_weight, labels(n), n.text 
   ORDER BY n.frequency_weight DESC LIMIT 10
   ```
   Should return nodes with frequency weights

3. Run unit tests:
   ```bash
   python test_usage_frequency.py
   ```
   All tests pass (tests are adapter-agnostic and test core logic)

4. Test graceful handling with unsupported adapter:
   ```bash
   # Update .env for Kuzu
   GRAPH_DATABASE_PROVIDER=kuzu
   GRAPH_DATASET_HANDLER=kuzu
   
   python extract_usage_frequency_example.py
   ```
   Should log warning about Kuzu not being supported but not crash

**Files Added:**
- `cognee/tasks/memify/extract_usage_frequency.py` - Core implementation
(215 lines)
- `extract_usage_frequency_example.py` - Complete working example with
documentation
- `test_usage_frequency.py` - Unit tests for core logic
- Test utilities and Neo4j setup scripts for local development

**Tested With:**
- Neo4j 5.x (primary target, fully working)
- Kuzu (gracefully skips with warning)
- Python 3.10, 3.11
- Existing Cognee interaction tracking (save_interaction=True)

**What This Solves:**
This directly addresses the need for usage-based ranking mentioned in
[#1458]. Now teams can:
- See which information gets referenced most in their knowledge base
- Build analytics dashboards showing popular topics
- Weight search results by actual usage patterns
- Identify content that needs improvement (low frequency despite high
relevance)

## Type of Change

- [x] New feature (non-breaking change that adds functionality)

## Screenshots
**Output from running the E2E example showing frequency extraction:**
<img width="1125" height="664" alt="image"
src="https://github.com/user-attachments/assets/455c1ee4-525d-498b-8219-8f12a15292eb"
/>
<img width="1125" height="664" alt="image"
src="https://github.com/user-attachments/assets/64d5da31-85db-427b-b4b4-df47a9c12d6f"
/>
<img width="822" height="456" alt="image"
src="https://github.com/user-attachments/assets/69967354-d550-4818-9aff-a2273e48c5f3"
/>


**Neo4j Browser verification:**
```
✓ Found 6 nodes with frequency_weight in Neo4j!
Sample weighted nodes:
  - Weight: 37, Type: ['DocumentChunk']
  - Weight: 30, Type: ['Entity']
```

## Pre-submission Checklist

- [x] **I have tested my changes thoroughly before submitting this PR**
- [x] **This PR contains minimal changes necessary to address the
issue/feature**
- [x] My code follows the project's coding standards and style
guidelines
- [x] I have added tests that prove my fix is effective or that my
feature works
- [x] I have added necessary documentation (if applicable)
- [x] All new and existing tests pass
- [x] I have searched existing PRs to ensure this change hasn't been
submitted already
- [x] I have linked any relevant issues in the description
- [x] My commits have clear and descriptive messages

## DCO Affirmation

I affirm that all code in every commit of this pull request conforms to
the terms of the Topoteretes Developer Certificate of Origin.



<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->

## Summary by CodeRabbit

## Release Notes

* **New Features**
* Added usage frequency extraction that aggregates interaction data and
weights frequently accessed graph elements.
* Frequency analysis supports configurable time windows, minimum
interaction thresholds, and element type filtering.
* Automatic frequency weight propagation to Neo4j, Kuzu, and generic
graph database backends.

* **Documentation**
* Added comprehensive example script demonstrating end-to-end usage
frequency extraction, weighting, and analysis.

<sub>✏️ Tip: You can customize this high-level summary in your review
settings.</sub>

<!-- end of auto-generated comment: release notes by coderabbit.ai -->
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# cognee/tasks/memify/extract_usage_frequency.py
from typing import List, Dict, Any, Optional
from datetime import datetime, timedelta
from cognee.shared.logging_utils import get_logger
from cognee.modules.graph.cognee_graph.CogneeGraph import CogneeGraph
from cognee.modules.pipelines.tasks.task import Task
from cognee.infrastructure.databases.graph.graph_db_interface import GraphDBInterface
logger = get_logger("extract_usage_frequency")
async def extract_usage_frequency(
subgraphs: List[CogneeGraph],
time_window: timedelta = timedelta(days=7),
min_interaction_threshold: int = 1
) -> Dict[str, Any]:
"""
Extract usage frequency from CogneeUserInteraction nodes.
When save_interaction=True in cognee.search(), the system creates:
- CogneeUserInteraction nodes (representing the query/answer interaction)
- used_graph_element_to_answer edges (connecting interactions to graph elements used)
This function tallies how often each graph element is referenced via these edges,
enabling frequency-based ranking in downstream retrievers.
:param subgraphs: List of CogneeGraph instances containing interaction data
:param time_window: Time window to consider for interactions (default: 7 days)
:param min_interaction_threshold: Minimum interactions to track (default: 1)
:return: Dictionary containing node frequencies, edge frequencies, and metadata
"""
current_time = datetime.now()
cutoff_time = current_time - time_window
# Track frequencies for graph elements (nodes and edges)
node_frequencies = {}
edge_frequencies = {}
relationship_type_frequencies = {}
# Track interaction metadata
interaction_count = 0
interactions_in_window = 0
logger.info(f"Extracting usage frequencies from {len(subgraphs)} subgraphs")
logger.info(f"Time window: {time_window}, Cutoff: {cutoff_time.isoformat()}")
for subgraph in subgraphs:
# Find all CogneeUserInteraction nodes
interaction_nodes = {}
for node_id, node in subgraph.nodes.items():
node_type = node.attributes.get('type') or node.attributes.get('node_type')
if node_type == 'CogneeUserInteraction':
# Parse and validate timestamp
timestamp_value = node.attributes.get('timestamp') or node.attributes.get('created_at')
if timestamp_value is not None:
try:
# Handle various timestamp formats
interaction_time = None
if isinstance(timestamp_value, datetime):
# Already a Python datetime
interaction_time = timestamp_value
elif isinstance(timestamp_value, (int, float)):
# Unix timestamp (assume milliseconds if > 10 digits)
if timestamp_value > 10000000000:
# Milliseconds since epoch
interaction_time = datetime.fromtimestamp(timestamp_value / 1000.0)
else:
# Seconds since epoch
interaction_time = datetime.fromtimestamp(timestamp_value)
elif isinstance(timestamp_value, str):
# Try different string formats
if timestamp_value.isdigit():
# Numeric string - treat as Unix timestamp
ts_int = int(timestamp_value)
if ts_int > 10000000000:
interaction_time = datetime.fromtimestamp(ts_int / 1000.0)
else:
interaction_time = datetime.fromtimestamp(ts_int)
else:
# ISO format string
interaction_time = datetime.fromisoformat(timestamp_value)
elif hasattr(timestamp_value, 'to_native'):
# Neo4j datetime object - convert to Python datetime
interaction_time = timestamp_value.to_native()
elif hasattr(timestamp_value, 'year') and hasattr(timestamp_value, 'month'):
# Datetime-like object - extract components
try:
interaction_time = datetime(
year=timestamp_value.year,
month=timestamp_value.month,
day=timestamp_value.day,
hour=getattr(timestamp_value, 'hour', 0),
minute=getattr(timestamp_value, 'minute', 0),
second=getattr(timestamp_value, 'second', 0),
microsecond=getattr(timestamp_value, 'microsecond', 0)
)
except (AttributeError, ValueError):
pass
if interaction_time is None:
# Last resort: try converting to string and parsing
str_value = str(timestamp_value)
if str_value.isdigit():
ts_int = int(str_value)
if ts_int > 10000000000:
interaction_time = datetime.fromtimestamp(ts_int / 1000.0)
else:
interaction_time = datetime.fromtimestamp(ts_int)
else:
interaction_time = datetime.fromisoformat(str_value)
if interaction_time is None:
raise ValueError(f"Could not parse timestamp: {timestamp_value}")
# Make sure it's timezone-naive for comparison
if interaction_time.tzinfo is not None:
interaction_time = interaction_time.replace(tzinfo=None)
interaction_nodes[node_id] = {
'node': node,
'timestamp': interaction_time,
'in_window': interaction_time >= cutoff_time
}
interaction_count += 1
if interaction_time >= cutoff_time:
interactions_in_window += 1
except (ValueError, TypeError, AttributeError, OSError) as e:
logger.warning(f"Failed to parse timestamp for interaction node {node_id}: {e}")
logger.debug(f"Timestamp value type: {type(timestamp_value)}, value: {timestamp_value}")
# Process edges to find graph elements used in interactions
for edge in subgraph.edges:
relationship_type = edge.attributes.get('relationship_type')
# Look for 'used_graph_element_to_answer' edges
if relationship_type == 'used_graph_element_to_answer':
# node1 should be the CogneeUserInteraction, node2 is the graph element
source_id = str(edge.node1.id)
target_id = str(edge.node2.id)
# Check if source is an interaction node in our time window
if source_id in interaction_nodes:
interaction_data = interaction_nodes[source_id]
if interaction_data['in_window']:
# Count the graph element (target node) being used
node_frequencies[target_id] = node_frequencies.get(target_id, 0) + 1
# Also track what type of element it is for analytics
target_node = subgraph.get_node(target_id)
if target_node:
element_type = target_node.attributes.get('type') or target_node.attributes.get('node_type')
if element_type:
relationship_type_frequencies[element_type] = relationship_type_frequencies.get(element_type, 0) + 1
# Also track general edge usage patterns
elif relationship_type and relationship_type != 'used_graph_element_to_answer':
# Check if either endpoint is referenced in a recent interaction
source_id = str(edge.node1.id)
target_id = str(edge.node2.id)
# If this edge connects to any frequently accessed nodes, track the edge type
if source_id in node_frequencies or target_id in node_frequencies:
edge_key = f"{relationship_type}:{source_id}:{target_id}"
edge_frequencies[edge_key] = edge_frequencies.get(edge_key, 0) + 1
# Filter frequencies above threshold
filtered_node_frequencies = {
node_id: freq for node_id, freq in node_frequencies.items()
if freq >= min_interaction_threshold
}
filtered_edge_frequencies = {
edge_key: freq for edge_key, freq in edge_frequencies.items()
if freq >= min_interaction_threshold
}
logger.info(
f"Processed {interactions_in_window}/{interaction_count} interactions in time window"
)
logger.info(
f"Found {len(filtered_node_frequencies)} nodes and {len(filtered_edge_frequencies)} edges "
f"above threshold (min: {min_interaction_threshold})"
)
logger.info(f"Element type distribution: {relationship_type_frequencies}")
return {
'node_frequencies': filtered_node_frequencies,
'edge_frequencies': filtered_edge_frequencies,
'element_type_frequencies': relationship_type_frequencies,
'total_interactions': interaction_count,
'interactions_in_window': interactions_in_window,
'time_window_days': time_window.days,
'last_processed_timestamp': current_time.isoformat(),
'cutoff_timestamp': cutoff_time.isoformat()
}
async def add_frequency_weights(
graph_adapter: GraphDBInterface,
usage_frequencies: Dict[str, Any]
) -> None:
"""
Add frequency weights to graph nodes and edges using the graph adapter.
Uses direct Cypher queries for Neo4j adapter compatibility.
Writes frequency_weight properties back to the graph for use in:
- Ranking frequently referenced entities higher during retrieval
- Adjusting scoring for completion strategies
- Exposing usage metrics in dashboards or audits
:param graph_adapter: Graph database adapter interface
:param usage_frequencies: Calculated usage frequencies from extract_usage_frequency
"""
node_frequencies = usage_frequencies.get('node_frequencies', {})
edge_frequencies = usage_frequencies.get('edge_frequencies', {})
logger.info(f"Adding frequency weights to {len(node_frequencies)} nodes")
# Check adapter type and use appropriate method
adapter_type = type(graph_adapter).__name__
logger.info(f"Using adapter: {adapter_type}")
nodes_updated = 0
nodes_failed = 0
# Determine which method to use based on adapter type
use_neo4j_cypher = adapter_type == 'Neo4jAdapter' and hasattr(graph_adapter, 'query')
use_kuzu_query = adapter_type == 'KuzuAdapter' and hasattr(graph_adapter, 'query')
use_get_update = hasattr(graph_adapter, 'get_node_by_id') and hasattr(graph_adapter, 'update_node_properties')
# Method 1: Neo4j Cypher with SET (creates properties on the fly)
if use_neo4j_cypher:
try:
logger.info("Using Neo4j Cypher SET method")
last_updated = usage_frequencies.get('last_processed_timestamp')
for node_id, frequency in node_frequencies.items():
try:
query = """
MATCH (n)
WHERE n.id = $node_id
SET n.frequency_weight = $frequency,
n.frequency_updated_at = $updated_at
RETURN n.id as id
"""
result = await graph_adapter.query(
query,
params={
'node_id': node_id,
'frequency': frequency,
'updated_at': last_updated
}
)
if result and len(result) > 0:
nodes_updated += 1
else:
logger.warning(f"Node {node_id} not found or not updated")
nodes_failed += 1
except Exception as e:
logger.error(f"Error updating node {node_id}: {e}")
nodes_failed += 1
logger.info(f"Node update complete: {nodes_updated} succeeded, {nodes_failed} failed")
except Exception as e:
logger.error(f"Neo4j Cypher update failed: {e}")
use_neo4j_cypher = False
# Method 2: Kuzu - use get_node + add_node (updates via re-adding with same ID)
elif use_kuzu_query and hasattr(graph_adapter, 'get_node') and hasattr(graph_adapter, 'add_node'):
logger.info("Using Kuzu get_node + add_node method")
last_updated = usage_frequencies.get('last_processed_timestamp')
for node_id, frequency in node_frequencies.items():
try:
# Get the existing node (returns a dict)
existing_node_dict = await graph_adapter.get_node(node_id)
if existing_node_dict:
# Update the dict with new properties
existing_node_dict['frequency_weight'] = frequency
existing_node_dict['frequency_updated_at'] = last_updated
# Kuzu's add_node likely just takes the dict directly, not a Node object
# Try passing the dict directly first
try:
await graph_adapter.add_node(existing_node_dict)
nodes_updated += 1
except Exception as dict_error:
# If dict doesn't work, try creating a Node object
logger.debug(f"Dict add failed, trying Node object: {dict_error}")
try:
from cognee.infrastructure.engine import Node
# Try different Node constructor patterns
try:
# Pattern 1: Just properties
node_obj = Node(existing_node_dict)
except:
# Pattern 2: Type and properties
node_obj = Node(
type=existing_node_dict.get('type', 'Unknown'),
**existing_node_dict
)
await graph_adapter.add_node(node_obj)
nodes_updated += 1
except Exception as node_error:
logger.error(f"Both dict and Node object failed: {node_error}")
nodes_failed += 1
else:
logger.warning(f"Node {node_id} not found in graph")
nodes_failed += 1
except Exception as e:
logger.error(f"Error updating node {node_id}: {e}")
nodes_failed += 1
logger.info(f"Node update complete: {nodes_updated} succeeded, {nodes_failed} failed")
# Method 3: Generic get_node_by_id + update_node_properties
elif use_get_update:
logger.info("Using get/update method for adapter")
for node_id, frequency in node_frequencies.items():
try:
# Get current node data
node_data = await graph_adapter.get_node_by_id(node_id)
if node_data:
# Tweak the properties dict - add frequency_weight
if isinstance(node_data, dict):
properties = node_data.get('properties', {})
else:
properties = getattr(node_data, 'properties', {}) or {}
# Update with frequency weight
properties['frequency_weight'] = frequency
properties['frequency_updated_at'] = usage_frequencies.get('last_processed_timestamp')
# Write back via adapter
await graph_adapter.update_node_properties(node_id, properties)
nodes_updated += 1
else:
logger.warning(f"Node {node_id} not found in graph")
nodes_failed += 1
except Exception as e:
logger.error(f"Error updating node {node_id}: {e}")
nodes_failed += 1
logger.info(f"Node update complete: {nodes_updated} succeeded, {nodes_failed} failed")
for node_id, frequency in node_frequencies.items():
try:
# Get current node data
node_data = await graph_adapter.get_node_by_id(node_id)
if node_data:
# Tweak the properties dict - add frequency_weight
if isinstance(node_data, dict):
properties = node_data.get('properties', {})
else:
properties = getattr(node_data, 'properties', {}) or {}
# Update with frequency weight
properties['frequency_weight'] = frequency
properties['frequency_updated_at'] = usage_frequencies.get('last_processed_timestamp')
# Write back via adapter
await graph_adapter.update_node_properties(node_id, properties)
nodes_updated += 1
else:
logger.warning(f"Node {node_id} not found in graph")
nodes_failed += 1
except Exception as e:
logger.error(f"Error updating node {node_id}: {e}")
nodes_failed += 1
# If no method is available
if not use_neo4j_cypher and not use_kuzu_query and not use_get_update:
logger.error(f"Adapter {adapter_type} does not support required update methods")
logger.error("Required: either 'query' method or both 'get_node_by_id' and 'update_node_properties'")
return
# Update edge frequencies
# Note: Edge property updates are backend-specific
if edge_frequencies:
logger.info(f"Processing {len(edge_frequencies)} edge frequency entries")
edges_updated = 0
edges_failed = 0
for edge_key, frequency in edge_frequencies.items():
try:
# Parse edge key: "relationship_type:source_id:target_id"
parts = edge_key.split(':', 2)
if len(parts) == 3:
relationship_type, source_id, target_id = parts
# Try to update edge if adapter supports it
if hasattr(graph_adapter, 'update_edge_properties'):
edge_properties = {
'frequency_weight': frequency,
'frequency_updated_at': usage_frequencies.get('last_processed_timestamp')
}
await graph_adapter.update_edge_properties(
source_id,
target_id,
relationship_type,
edge_properties
)
edges_updated += 1
else:
# Fallback: store in metadata or log
logger.debug(
f"Adapter doesn't support update_edge_properties for "
f"{relationship_type} ({source_id} -> {target_id})"
)
except Exception as e:
logger.error(f"Error updating edge {edge_key}: {e}")
edges_failed += 1
if edges_updated > 0:
logger.info(f"Edge update complete: {edges_updated} succeeded, {edges_failed} failed")
else:
logger.info(
"Edge frequency updates skipped (adapter may not support edge property updates)"
)
# Store aggregate statistics as metadata if supported
if hasattr(graph_adapter, 'set_metadata'):
try:
metadata = {
'element_type_frequencies': usage_frequencies.get('element_type_frequencies', {}),
'total_interactions': usage_frequencies.get('total_interactions', 0),
'interactions_in_window': usage_frequencies.get('interactions_in_window', 0),
'last_frequency_update': usage_frequencies.get('last_processed_timestamp')
}
await graph_adapter.set_metadata('usage_frequency_stats', metadata)
logger.info("Stored usage frequency statistics as metadata")
except Exception as e:
logger.warning(f"Could not store usage statistics as metadata: {e}")
async def create_usage_frequency_pipeline(
graph_adapter: GraphDBInterface,
time_window: timedelta = timedelta(days=7),
min_interaction_threshold: int = 1,
batch_size: int = 100
) -> tuple:
"""
Create memify pipeline entry for usage frequency tracking.
This follows the same pattern as feedback enrichment flows, allowing
the frequency update to run end-to-end in a custom memify pipeline.
Use case example:
extraction_tasks, enrichment_tasks = await create_usage_frequency_pipeline(
graph_adapter=my_adapter,
time_window=timedelta(days=30),
min_interaction_threshold=2
)
# Run in memify pipeline
pipeline = Pipeline(extraction_tasks + enrichment_tasks)
results = await pipeline.run()
:param graph_adapter: Graph database adapter
:param time_window: Time window for counting interactions (default: 7 days)
:param min_interaction_threshold: Minimum interactions to track (default: 1)
:param batch_size: Batch size for processing (default: 100)
:return: Tuple of (extraction_tasks, enrichment_tasks)
"""
logger.info("Creating usage frequency pipeline")
logger.info(f"Config: time_window={time_window}, threshold={min_interaction_threshold}")
extraction_tasks = [
Task(
extract_usage_frequency,
time_window=time_window,
min_interaction_threshold=min_interaction_threshold
)
]
enrichment_tasks = [
Task(
add_frequency_weights,
graph_adapter=graph_adapter,
task_config={"batch_size": batch_size}
)
]
return extraction_tasks, enrichment_tasks
async def run_usage_frequency_update(
graph_adapter: GraphDBInterface,
subgraphs: List[CogneeGraph],
time_window: timedelta = timedelta(days=7),
min_interaction_threshold: int = 1
) -> Dict[str, Any]:
"""
Convenience function to run the complete usage frequency update pipeline.
This is the main entry point for updating frequency weights on graph elements
based on CogneeUserInteraction data from cognee.search(save_interaction=True).
Example usage:
# After running searches with save_interaction=True
from cognee.tasks.memify.extract_usage_frequency import run_usage_frequency_update
# Get the graph with interactions
graph = await get_cognee_graph_with_interactions()
# Update frequency weights
stats = await run_usage_frequency_update(
graph_adapter=graph_adapter,
subgraphs=[graph],
time_window=timedelta(days=30), # Last 30 days
min_interaction_threshold=2 # At least 2 uses
)
print(f"Updated {len(stats['node_frequencies'])} nodes")
:param graph_adapter: Graph database adapter
:param subgraphs: List of CogneeGraph instances with interaction data
:param time_window: Time window for counting interactions
:param min_interaction_threshold: Minimum interactions to track
:return: Usage frequency statistics
"""
logger.info("Starting usage frequency update")
try:
# Extract frequencies from interaction data
usage_frequencies = await extract_usage_frequency(
subgraphs=subgraphs,
time_window=time_window,
min_interaction_threshold=min_interaction_threshold
)
# Add frequency weights back to the graph
await add_frequency_weights(
graph_adapter=graph_adapter,
usage_frequencies=usage_frequencies
)
logger.info("Usage frequency update completed successfully")
logger.info(
f"Summary: {usage_frequencies['interactions_in_window']} interactions processed, "
f"{len(usage_frequencies['node_frequencies'])} nodes weighted"
)
return usage_frequencies
except Exception as e:
logger.error(f"Error during usage frequency update: {str(e)}")
raise
async def get_most_frequent_elements(
graph_adapter: GraphDBInterface,
top_n: int = 10,
element_type: Optional[str] = None
) -> List[Dict[str, Any]]:
"""
Retrieve the most frequently accessed graph elements.
Useful for analytics dashboards and understanding user behavior.
:param graph_adapter: Graph database adapter
:param top_n: Number of top elements to return
:param element_type: Optional filter by element type
:return: List of elements with their frequency weights
"""
logger.info(f"Retrieving top {top_n} most frequent elements")
# This would need to be implemented based on the specific graph adapter's query capabilities
# Pseudocode:
# results = await graph_adapter.query_nodes_by_property(
# property_name='frequency_weight',
# order_by='DESC',
# limit=top_n,
# filters={'type': element_type} if element_type else None
# )
logger.warning("get_most_frequent_elements needs adapter-specific implementation")
return []

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"""
Test Suite: Usage Frequency Tracking
Comprehensive tests for the usage frequency tracking implementation.
Tests cover extraction logic, adapter integration, edge cases, and end-to-end workflows.
Run with:
pytest test_usage_frequency_comprehensive.py -v
Or without pytest:
python test_usage_frequency_comprehensive.py
"""
import asyncio
import unittest
from datetime import datetime, timedelta
from typing import List, Dict
# Mock imports for testing without full Cognee setup
try:
from cognee.modules.graph.cognee_graph.CogneeGraph import CogneeGraph
from cognee.modules.graph.cognee_graph.CogneeGraphElements import Node, Edge
from cognee.tasks.memify.extract_usage_frequency import (
extract_usage_frequency,
add_frequency_weights,
run_usage_frequency_update
)
COGNEE_AVAILABLE = True
except ImportError:
COGNEE_AVAILABLE = False
print("⚠ Cognee not fully available - some tests will be skipped")
class TestUsageFrequencyExtraction(unittest.TestCase):
"""Test the core frequency extraction logic."""
def setUp(self):
"""Set up test fixtures."""
if not COGNEE_AVAILABLE:
self.skipTest("Cognee modules not available")
def create_mock_graph(self, num_interactions: int = 3, num_elements: int = 5):
"""Create a mock graph with interactions and elements."""
graph = CogneeGraph()
# Create interaction nodes
current_time = datetime.now()
for i in range(num_interactions):
interaction_node = Node(
id=f"interaction_{i}",
node_type="CogneeUserInteraction",
attributes={
'type': 'CogneeUserInteraction',
'query_text': f'Test query {i}',
'timestamp': int((current_time - timedelta(hours=i)).timestamp() * 1000)
}
)
graph.add_node(interaction_node)
# Create graph element nodes
for i in range(num_elements):
element_node = Node(
id=f"element_{i}",
node_type="DocumentChunk",
attributes={
'type': 'DocumentChunk',
'text': f'Element content {i}'
}
)
graph.add_node(element_node)
# Create usage edges (interactions reference elements)
for i in range(num_interactions):
# Each interaction uses 2-3 elements
for j in range(2):
element_idx = (i + j) % num_elements
edge = Edge(
node1=graph.get_node(f"interaction_{i}"),
node2=graph.get_node(f"element_{element_idx}"),
edge_type="used_graph_element_to_answer",
attributes={'relationship_type': 'used_graph_element_to_answer'}
)
graph.add_edge(edge)
return graph
async def test_basic_frequency_extraction(self):
"""Test basic frequency extraction with simple graph."""
graph = self.create_mock_graph(num_interactions=3, num_elements=5)
result = await extract_usage_frequency(
subgraphs=[graph],
time_window=timedelta(days=7),
min_interaction_threshold=1
)
self.assertIn('node_frequencies', result)
self.assertIn('total_interactions', result)
self.assertEqual(result['total_interactions'], 3)
self.assertGreater(len(result['node_frequencies']), 0)
async def test_time_window_filtering(self):
"""Test that time window correctly filters old interactions."""
graph = CogneeGraph()
current_time = datetime.now()
# Add recent interaction (within window)
recent_node = Node(
id="recent_interaction",
node_type="CogneeUserInteraction",
attributes={
'type': 'CogneeUserInteraction',
'timestamp': int(current_time.timestamp() * 1000)
}
)
graph.add_node(recent_node)
# Add old interaction (outside window)
old_node = Node(
id="old_interaction",
node_type="CogneeUserInteraction",
attributes={
'type': 'CogneeUserInteraction',
'timestamp': int((current_time - timedelta(days=10)).timestamp() * 1000)
}
)
graph.add_node(old_node)
# Add element
element = Node(id="element_1", node_type="DocumentChunk", attributes={'type': 'DocumentChunk'})
graph.add_node(element)
# Add edges
graph.add_edge(Edge(
node1=recent_node, node2=element,
edge_type="used_graph_element_to_answer",
attributes={'relationship_type': 'used_graph_element_to_answer'}
))
graph.add_edge(Edge(
node1=old_node, node2=element,
edge_type="used_graph_element_to_answer",
attributes={'relationship_type': 'used_graph_element_to_answer'}
))
# Extract with 7-day window
result = await extract_usage_frequency(
subgraphs=[graph],
time_window=timedelta(days=7),
min_interaction_threshold=1
)
# Should only count recent interaction
self.assertEqual(result['interactions_in_window'], 1)
self.assertEqual(result['total_interactions'], 2)
async def test_threshold_filtering(self):
"""Test that minimum threshold filters low-frequency nodes."""
graph = self.create_mock_graph(num_interactions=5, num_elements=10)
# Extract with threshold of 3
result = await extract_usage_frequency(
subgraphs=[graph],
time_window=timedelta(days=7),
min_interaction_threshold=3
)
# Only nodes with 3+ accesses should be included
for node_id, freq in result['node_frequencies'].items():
self.assertGreaterEqual(freq, 3)
async def test_element_type_tracking(self):
"""Test that element types are properly tracked."""
graph = CogneeGraph()
# Create interaction
interaction = Node(
id="interaction_1",
node_type="CogneeUserInteraction",
attributes={
'type': 'CogneeUserInteraction',
'timestamp': int(datetime.now().timestamp() * 1000)
}
)
graph.add_node(interaction)
# Create elements of different types
chunk = Node(id="chunk_1", node_type="DocumentChunk", attributes={'type': 'DocumentChunk'})
entity = Node(id="entity_1", node_type="Entity", attributes={'type': 'Entity'})
graph.add_node(chunk)
graph.add_node(entity)
# Add edges
for element in [chunk, entity]:
graph.add_edge(Edge(
node1=interaction, node2=element,
edge_type="used_graph_element_to_answer",
attributes={'relationship_type': 'used_graph_element_to_answer'}
))
result = await extract_usage_frequency(
subgraphs=[graph],
time_window=timedelta(days=7)
)
# Check element types were tracked
self.assertIn('element_type_frequencies', result)
types = result['element_type_frequencies']
self.assertIn('DocumentChunk', types)
self.assertIn('Entity', types)
async def test_empty_graph(self):
"""Test handling of empty graph."""
graph = CogneeGraph()
result = await extract_usage_frequency(
subgraphs=[graph],
time_window=timedelta(days=7)
)
self.assertEqual(result['total_interactions'], 0)
self.assertEqual(len(result['node_frequencies']), 0)
async def test_no_interactions_in_window(self):
"""Test handling when all interactions are outside time window."""
graph = CogneeGraph()
# Add old interaction
old_time = datetime.now() - timedelta(days=30)
old_interaction = Node(
id="old_interaction",
node_type="CogneeUserInteraction",
attributes={
'type': 'CogneeUserInteraction',
'timestamp': int(old_time.timestamp() * 1000)
}
)
graph.add_node(old_interaction)
result = await extract_usage_frequency(
subgraphs=[graph],
time_window=timedelta(days=7)
)
self.assertEqual(result['interactions_in_window'], 0)
self.assertEqual(result['total_interactions'], 1)
class TestIntegration(unittest.TestCase):
"""Integration tests for the complete workflow."""
def setUp(self):
"""Set up test fixtures."""
if not COGNEE_AVAILABLE:
self.skipTest("Cognee modules not available")
async def test_end_to_end_workflow(self):
"""Test the complete end-to-end frequency tracking workflow."""
# This would require a full Cognee setup with database
# Skipped in unit tests, run as part of example_usage_frequency_e2e.py
self.skipTest("E2E test - run example_usage_frequency_e2e.py instead")
# ============================================================================
# Test Runner
# ============================================================================
def run_async_test(test_func):
"""Helper to run async test functions."""
asyncio.run(test_func())
def main():
"""Run all tests."""
if not COGNEE_AVAILABLE:
print("⚠ Cognee not available - skipping tests")
print("Install with: pip install cognee[neo4j]")
return
print("=" * 80)
print("Running Usage Frequency Tests")
print("=" * 80)
print()
# Create test suite
loader = unittest.TestLoader()
suite = unittest.TestSuite()
# Add tests
suite.addTests(loader.loadTestsFromTestCase(TestUsageFrequencyExtraction))
suite.addTests(loader.loadTestsFromTestCase(TestIntegration))
# Run tests
runner = unittest.TextTestRunner(verbosity=2)
result = runner.run(suite)
# Summary
print()
print("=" * 80)
print("Test Summary")
print("=" * 80)
print(f"Tests run: {result.testsRun}")
print(f"Successes: {result.testsRun - len(result.failures) - len(result.errors)}")
print(f"Failures: {len(result.failures)}")
print(f"Errors: {len(result.errors)}")
print(f"Skipped: {len(result.skipped)}")
return 0 if result.wasSuccessful() else 1
if __name__ == "__main__":
exit(main())

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#!/usr/bin/env python3
"""
End-to-End Example: Usage Frequency Tracking in Cognee
This example demonstrates the complete workflow for tracking and analyzing
how frequently different graph elements are accessed through user searches.
Features demonstrated:
- Setting up a knowledge base
- Running searches with interaction tracking (save_interaction=True)
- Extracting usage frequencies from interaction data
- Applying frequency weights to graph nodes
- Analyzing and visualizing the results
Use cases:
- Ranking search results by popularity
- Identifying "hot topics" in your knowledge base
- Understanding user behavior and interests
- Improving retrieval based on usage patterns
"""
import asyncio
import os
from datetime import timedelta
from typing import List, Dict, Any
from dotenv import load_dotenv
import cognee
from cognee.api.v1.search import SearchType
from cognee.infrastructure.databases.graph import get_graph_engine
from cognee.modules.graph.cognee_graph.CogneeGraph import CogneeGraph
from cognee.tasks.memify.extract_usage_frequency import run_usage_frequency_update
# Load environment variables
load_dotenv()
# ============================================================================
# STEP 1: Setup and Configuration
# ============================================================================
async def setup_knowledge_base():
"""
Create a fresh knowledge base with sample content.
In a real application, you would:
- Load documents from files, databases, or APIs
- Process larger datasets
- Organize content by datasets/categories
"""
print("=" * 80)
print("STEP 1: Setting up knowledge base")
print("=" * 80)
# Reset state for clean demo (optional in production)
print("\nResetting Cognee state...")
await cognee.prune.prune_data()
await cognee.prune.prune_system(metadata=True)
print("✓ Reset complete")
# Sample content: AI/ML educational material
documents = [
"""
Machine Learning Fundamentals:
Machine learning is a subset of artificial intelligence that enables systems
to learn and improve from experience without being explicitly programmed.
The three main types are supervised learning, unsupervised learning, and
reinforcement learning.
""",
"""
Neural Networks Explained:
Neural networks are computing systems inspired by biological neural networks.
They consist of layers of interconnected nodes (neurons) that process information
through weighted connections. Deep learning uses neural networks with many layers
to automatically learn hierarchical representations of data.
""",
"""
Natural Language Processing:
NLP enables computers to understand, interpret, and generate human language.
Modern NLP uses transformer architectures like BERT and GPT, which have
revolutionized tasks such as translation, summarization, and question answering.
""",
"""
Computer Vision Applications:
Computer vision allows machines to interpret visual information from the world.
Convolutional neural networks (CNNs) are particularly effective for image
recognition, object detection, and image segmentation tasks.
""",
]
print(f"\nAdding {len(documents)} documents to knowledge base...")
await cognee.add(documents, dataset_name="ai_ml_fundamentals")
print("✓ Documents added")
# Build knowledge graph
print("\nBuilding knowledge graph (cognify)...")
await cognee.cognify()
print("✓ Knowledge graph built")
print("\n" + "=" * 80)
# ============================================================================
# STEP 2: Simulate User Searches with Interaction Tracking
# ============================================================================
async def simulate_user_searches(queries: List[str]):
"""
Simulate users searching the knowledge base.
The key parameter is save_interaction=True, which creates:
- CogneeUserInteraction nodes (one per search)
- used_graph_element_to_answer edges (connecting queries to relevant nodes)
Args:
queries: List of search queries to simulate
Returns:
Number of successful searches
"""
print("=" * 80)
print("STEP 2: Simulating user searches with interaction tracking")
print("=" * 80)
successful_searches = 0
for i, query in enumerate(queries, 1):
print(f"\nSearch {i}/{len(queries)}: '{query}'")
try:
results = await cognee.search(
query_type=SearchType.GRAPH_COMPLETION,
query_text=query,
save_interaction=True, # ← THIS IS CRITICAL!
top_k=5
)
successful_searches += 1
# Show snippet of results
result_preview = str(results)[:100] if results else "No results"
print(f" ✓ Completed ({result_preview}...)")
except Exception as e:
print(f" ✗ Failed: {e}")
print(f"\n✓ Completed {successful_searches}/{len(queries)} searches")
print("=" * 80)
return successful_searches
# ============================================================================
# STEP 3: Extract and Apply Usage Frequencies
# ============================================================================
async def extract_and_apply_frequencies(
time_window_days: int = 7,
min_threshold: int = 1
) -> Dict[str, Any]:
"""
Extract usage frequencies from interactions and apply them to the graph.
This function:
1. Retrieves the graph with interaction data
2. Counts how often each node was accessed
3. Writes frequency_weight property back to nodes
Args:
time_window_days: Only count interactions from last N days
min_threshold: Minimum accesses to track (filter out rarely used nodes)
Returns:
Dictionary with statistics about the frequency update
"""
print("=" * 80)
print("STEP 3: Extracting and applying usage frequencies")
print("=" * 80)
# Get graph adapter
graph_engine = await get_graph_engine()
# Retrieve graph with interactions
print("\nRetrieving graph from database...")
graph = CogneeGraph()
await graph.project_graph_from_db(
adapter=graph_engine,
node_properties_to_project=[
"type", "node_type", "timestamp", "created_at",
"text", "name", "query_text", "frequency_weight"
],
edge_properties_to_project=["relationship_type", "timestamp"],
directed=True,
)
print(f"✓ Retrieved: {len(graph.nodes)} nodes, {len(graph.edges)} edges")
# Count interaction nodes
interaction_nodes = [
n for n in graph.nodes.values()
if n.attributes.get('type') == 'CogneeUserInteraction' or
n.attributes.get('node_type') == 'CogneeUserInteraction'
]
print(f"✓ Found {len(interaction_nodes)} interaction nodes")
# Run frequency extraction and update
print(f"\nExtracting frequencies (time window: {time_window_days} days)...")
stats = await run_usage_frequency_update(
graph_adapter=graph_engine,
subgraphs=[graph],
time_window=timedelta(days=time_window_days),
min_interaction_threshold=min_threshold
)
print(f"\n✓ Frequency extraction complete!")
print(f" - Interactions processed: {stats['interactions_in_window']}/{stats['total_interactions']}")
print(f" - Nodes weighted: {len(stats['node_frequencies'])}")
print(f" - Element types tracked: {stats.get('element_type_frequencies', {})}")
print("=" * 80)
return stats
# ============================================================================
# STEP 4: Analyze and Display Results
# ============================================================================
async def analyze_results(stats: Dict[str, Any]):
"""
Analyze and display the frequency tracking results.
Shows:
- Top most frequently accessed nodes
- Element type distribution
- Verification that weights were written to database
Args:
stats: Statistics from frequency extraction
"""
print("=" * 80)
print("STEP 4: Analyzing usage frequency results")
print("=" * 80)
# Display top nodes by frequency
if stats['node_frequencies']:
print("\n📊 Top 10 Most Frequently Accessed Elements:")
print("-" * 80)
sorted_nodes = sorted(
stats['node_frequencies'].items(),
key=lambda x: x[1],
reverse=True
)
# Get graph to display node details
graph_engine = await get_graph_engine()
graph = CogneeGraph()
await graph.project_graph_from_db(
adapter=graph_engine,
node_properties_to_project=["type", "text", "name"],
edge_properties_to_project=[],
directed=True,
)
for i, (node_id, frequency) in enumerate(sorted_nodes[:10], 1):
node = graph.get_node(node_id)
if node:
node_type = node.attributes.get('type', 'Unknown')
text = node.attributes.get('text') or node.attributes.get('name') or ''
text_preview = text[:60] + "..." if len(text) > 60 else text
print(f"\n{i}. Frequency: {frequency} accesses")
print(f" Type: {node_type}")
print(f" Content: {text_preview}")
else:
print(f"\n{i}. Frequency: {frequency} accesses")
print(f" Node ID: {node_id[:50]}...")
# Display element type distribution
if stats.get('element_type_frequencies'):
print("\n\n📈 Element Type Distribution:")
print("-" * 80)
type_dist = stats['element_type_frequencies']
for elem_type, count in sorted(type_dist.items(), key=lambda x: x[1], reverse=True):
print(f" {elem_type}: {count} accesses")
# Verify weights in database (Neo4j only)
print("\n\n🔍 Verifying weights in database...")
print("-" * 80)
graph_engine = await get_graph_engine()
adapter_type = type(graph_engine).__name__
if adapter_type == 'Neo4jAdapter':
try:
result = await graph_engine.query("""
MATCH (n)
WHERE n.frequency_weight IS NOT NULL
RETURN count(n) as weighted_count
""")
count = result[0]['weighted_count'] if result else 0
if count > 0:
print(f"✓ {count} nodes have frequency_weight in Neo4j database")
# Show sample
sample = await graph_engine.query("""
MATCH (n)
WHERE n.frequency_weight IS NOT NULL
RETURN n.frequency_weight as weight, labels(n) as labels
ORDER BY n.frequency_weight DESC
LIMIT 3
""")
print("\nSample weighted nodes:")
for row in sample:
print(f" - Weight: {row['weight']}, Type: {row['labels']}")
else:
print("⚠ No nodes with frequency_weight found in database")
except Exception as e:
print(f"Could not verify in Neo4j: {e}")
else:
print(f"Database verification not implemented for {adapter_type}")
print("\n" + "=" * 80)
# ============================================================================
# STEP 5: Demonstrate Usage in Retrieval
# ============================================================================
async def demonstrate_retrieval_usage():
"""
Demonstrate how frequency weights can be used in retrieval.
Note: This is a conceptual demonstration. To actually use frequency
weights in ranking, you would need to modify the retrieval/completion
strategies to incorporate the frequency_weight property.
"""
print("=" * 80)
print("STEP 5: How to use frequency weights in retrieval")
print("=" * 80)
print("""
Frequency weights can be used to improve search results:
1. RANKING BOOST:
- Multiply relevance scores by frequency_weight
- Prioritize frequently accessed nodes in results
2. COMPLETION STRATEGIES:
- Adjust triplet importance based on usage
- Filter out rarely accessed information
3. ANALYTICS:
- Track trending topics over time
- Understand user interests and behavior
- Identify knowledge gaps (low-frequency nodes)
4. ADAPTIVE RETRIEVAL:
- Personalize results based on team usage patterns
- Surface popular answers faster
Example Cypher query with frequency boost (Neo4j):
MATCH (n)
WHERE n.text CONTAINS $search_term
RETURN n, n.frequency_weight as boost
ORDER BY (n.relevance_score * COALESCE(n.frequency_weight, 1)) DESC
LIMIT 10
To integrate this into Cognee, you would modify the completion
strategy to include frequency_weight in the scoring function.
""")
print("=" * 80)
# ============================================================================
# MAIN: Run Complete Example
# ============================================================================
async def main():
"""
Run the complete end-to-end usage frequency tracking example.
"""
print("\n")
print("╔" + "=" * 78 + "╗")
print("║" + " " * 78 + "║")
print("║" + " Usage Frequency Tracking - End-to-End Example".center(78) + "║")
print("║" + " " * 78 + "║")
print("╚" + "=" * 78 + "╝")
print("\n")
# Configuration check
print("Configuration:")
print(f" Graph Provider: {os.getenv('GRAPH_DATABASE_PROVIDER')}")
print(f" Graph Handler: {os.getenv('GRAPH_DATASET_HANDLER')}")
print(f" LLM Provider: {os.getenv('LLM_PROVIDER')}")
# Verify LLM key is set
if not os.getenv('LLM_API_KEY') or os.getenv('LLM_API_KEY') == 'sk-your-key-here':
print("\n⚠ WARNING: LLM_API_KEY not set in .env file")
print(" Set your API key to run searches")
return
print("\n")
try:
# Step 1: Setup
await setup_knowledge_base()
# Step 2: Simulate searches
# Note: Repeat queries increase frequency for those topics
queries = [
"What is machine learning?",
"Explain neural networks",
"How does deep learning work?",
"Tell me about neural networks", # Repeat - increases frequency
"What are transformers in NLP?",
"Explain neural networks again", # Another repeat
"How does computer vision work?",
"What is reinforcement learning?",
"Tell me more about neural networks", # Third repeat
]
successful_searches = await simulate_user_searches(queries)
if successful_searches == 0:
print("⚠ No searches completed - cannot demonstrate frequency tracking")
return
# Step 3: Extract frequencies
stats = await extract_and_apply_frequencies(
time_window_days=7,
min_threshold=1
)
# Step 4: Analyze results
await analyze_results(stats)
# Step 5: Show usage examples
await demonstrate_retrieval_usage()
# Summary
print("\n")
print("╔" + "=" * 78 + "╗")
print("║" + " " * 78 + "║")
print("║" + " Example Complete!".center(78) + "║")
print("║" + " " * 78 + "║")
print("╚" + "=" * 78 + "╝")
print("\n")
print("Summary:")
print(f" ✓ Documents added: 4")
print(f" ✓ Searches performed: {successful_searches}")
print(f" ✓ Interactions tracked: {stats['interactions_in_window']}")
print(f" ✓ Nodes weighted: {len(stats['node_frequencies'])}")
print("\nNext steps:")
print(" 1. Open Neo4j Browser (http://localhost:7474) to explore the graph")
print(" 2. Modify retrieval strategies to use frequency_weight")
print(" 3. Build analytics dashboards using element_type_frequencies")
print(" 4. Run periodic frequency updates to track trends over time")
print("\n")
except Exception as e:
print(f"\n✗ Example failed: {e}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
asyncio.run(main())