> ## Documentation Index
> Fetch the complete documentation index at: https://docs.cognee.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Entity Consolidation
> Rewrite fragmented entity descriptions using LLM analysis of graph neighborhoods
A minimal guide to consolidating entity descriptions in an existing knowledge graph. After ingestion, entity descriptions can be fragmented or repetitive because each one is derived from a single chunk — this lower-level Memify pipeline rewrites each entity's description using the LLM and the entity's full neighborhood context. Use `improve()` for the standard self-improvement flow; use this guide when you specifically want entity-description consolidation.
This pipeline only rewrites description text — it never creates or deletes nodes. To merge near-duplicate `Entity` nodes into one, see [Entity Deduplication](/guides/memify-entity-deduplication).
## Before You Start
* Complete [Quickstart](/getting-started/quickstart) to understand basic operations
* Ensure you have [LLM Providers](/setup-configuration/llm-providers) configured
* Have an existing knowledge graph with `Entity` nodes — the script below builds one with `remember()`
## Code in Action
```python theme={null}
import asyncio
import cognee
from os import path
from cognee.api.v1.visualize.visualize import visualize_graph
from cognee.memify_pipelines.consolidate_entity_descriptions import (
consolidate_entity_descriptions_pipeline,
)
custom_prompt = """
Extract only people and cities as entities.
Connect people to cities with the relationship "lives_in".
Ignore all other entities.
"""
graph_visualization_path_before_enrichment = path.join(
path.dirname(__file__), ".artifacts", "before_consolidate_enrichment_entity_descriptions.html"
)
graph_visualization_path_after_enrichment = path.join(
path.dirname(__file__), ".artifacts", "after_consolidate_enrichment_entity_descriptions.html"
)
async def main():
# Prune data and system metadata before running, only if we want "fresh" state.
await cognee.forget(everything=True)
await cognee.remember(
[
"Alice moved to Paris in 2010, while Bob has always lived in New York.",
"Andreas was born in Venice, but later settled in Lisbon.",
"Diana and Tom were born and raised in Helsinki. Diana currently resides in Berlin, while Tom never moved.",
],
custom_prompt=custom_prompt,
self_improvement=False,
)
await visualize_graph(graph_visualization_path_before_enrichment)
await consolidate_entity_descriptions_pipeline()
await visualize_graph(graph_visualization_path_after_enrichment)
if __name__ == "__main__":
asyncio.run(main())
```
## What Just Happened
### Step 1: Clear Existing Data
```python theme={null}
await cognee.forget(everything=True)
```
Start from a clean state so the before-and-after visualizations only reflect this example run.
### Step 2: Build and Visualize the Graph
```python theme={null}
custom_prompt = """
Extract only people and cities as entities.
Connect people to cities with the relationship "lives_in".
Ignore all other entities.
"""
await cognee.remember(
[
"Alice moved to Paris in 2010, while Bob has always lived in New York.",
"Andreas was born in Venice, but later settled in Lisbon.",
"Diana and Tom were born and raised in Helsinki. Diana currently resides in Berlin, while Tom never moved.",
],
custom_prompt=custom_prompt,
self_improvement=False,
)
await visualize_graph(graph_visualization_path_before_enrichment)
```
Create a focused graph with only people, cities, and `lives_in` relationships, then save a visualization of the graph before consolidation.
### Step 3: Consolidate and Visualize Again
```python theme={null}
await consolidate_entity_descriptions_pipeline()
await visualize_graph(graph_visualization_path_after_enrichment)
```
The pipeline rewrites each existing `Entity` node's `description` in place using LLM analysis of the entity's neighbors and edges — no nodes are created or deleted. Descriptions become more coherent because the LLM sees each entity in the context of its graph neighborhood, not just the original chunk text, and the before and after HTML files make the change easy to inspect.
## Additional Information
* Runnable guide script available on our [GitHub](https://github.com/topoteretes/cognee/blob/main/examples/guides/consolidate_entity_descriptions_example.py)
* Pipeline implementation: [consolidate\_entity\_descriptions.py](https://github.com/topoteretes/cognee/blob/main/cognee/memify_pipelines/consolidate_entity_descriptions.py)
Three tasks run in sequence:
1. **`get_entities_with_neighborhood`** — loads all `Entity` nodes and fetches their edges and neighbor nodes.
2. **`generate_consolidated_entities`** — sends each entity plus neighborhood to the LLM, which returns a refined description.
3. **`add_data_points`** — writes the updated `Entity` objects back to the graph and vector DB.
```python theme={null}
import asyncio
import cognee
from cognee.memify_pipelines.consolidate_entity_descriptions import (
consolidate_entity_descriptions_pipeline,
)
async def main():
await cognee.prune.prune_data()
await cognee.prune.prune_system(metadata=True)
await cognee.add(
[
"Alice moved to Paris in 2010, while Bob has always lived in New York.",
"Andreas was born in Venice, but later settled in Lisbon.",
"Diana and Tom were born and raised in Helsinki. Diana currently resides in Berlin, while Tom never moved.",
]
)
await cognee.cognify()
await consolidate_entity_descriptions_pipeline()
if __name__ == "__main__":
asyncio.run(main())
```
* **No entities found** — the graph must already contain `Entity` nodes. Run `cognee.remember()` first.
* **LLM errors** — verify that your LLM provider is configured. See [LLM Providers](/setup-configuration/llm-providers).
* **Permission errors** — the user must have write access to the target dataset. See [Permissions](/core-concepts/multi-user-mode/permissions-system/datasets).
Merge near-duplicate entity nodes into one canonical node
Understand the current improvement workflow
Bridge session memory and enrich a dataset
Query the enriched graph with specialized search types