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Core ConceptsMemory ProcessingCognee - Memory Processing

Memory Processing

Memory Processing encompasses the computational workflows and organizational structures that transform raw data into structured, queryable knowledge within Cognee’s knowledge graphs.

Overview

Once your data has been ingested and converted into memory through the Data to Memory process, Memory Processing takes over to organize, analyze, and structure that information into meaningful patterns and relationships.

Key Components

Memory Processing consists of three core elements that work together to transform your data:

Tasks

Individual processing units that perform specific operations on your data, from text analysis to relationship extraction. Learn more about:

  • How tasks process data chunks
  • Built-in task types
  • Creating custom tasks
  • Task execution patterns

Pipelines

Coordinated workflows that chain together multiple tasks to create comprehensive data processing sequences. Explore:

  • Pipeline architecture
  • Default pipelines
  • Custom pipeline creation
  • Pipeline optimization

DataPoints

The fundamental units of information that carry metadata, relationships, and structured content through the processing pipeline. Understand:

  • DataPoint structure
  • Relationship modeling
  • Metadata handling
  • Graph node creation

Processing Flow

The Memory Processing system follows a structured approach:

  1. Task Execution: Individual processing operations are performed on data chunks
  2. Pipeline Coordination: Multiple tasks are orchestrated in sequence or parallel
  3. DataPoint Creation: Results are structured into queryable data points with rich metadata
  4. Relationship Mapping: Connections and dependencies between data points are established

Integration Points

Memory Processing integrates seamlessly with:

  • Data to Memory: Receives processed data chunks for further analysis
  • Search Memory: Provides structured data points for efficient querying
  • Graph Generation: Creates the relationships and connections that power knowledge graphs

Next Steps

  • Learn about Tasks and how they process your data
  • Understand Pipelines for workflow orchestration
  • Explore DataPoints for data structuring

This systematic approach ensures that your data is not just stored, but actively processed and structured for maximum utility and insight generation.

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