Introduction
Cognee is an open source tool and platform that transforms your raw data into intelligent, searchable memory. It combines vector search with graph databases to make your data both searchable by meaning and connected by relationships.Dual storage architecture gives you both semantic search and structural reasoning
Main operations handle the complete workflow from ingestion to search: add, cognify, memify, search.
Table of Contents
Architecture
Architecture
Cognee uses three complementary storage systems, each playing a different role:
- Relational store — Tracks documents, chunks, and provenance (where data came from and how it’s linked)
- Vector store — Holds embeddings for semantic similarity (numerical representations that find conceptually related content)
- Graph store — Captures entities and relationships in a knowledge graph (nodes and edges that show connections between concepts)
Building Blocks
Building Blocks
Cognee’s processing system is built from three fundamental components:
- DataPoints — Structured data units that become graph nodes, carrying both content and metadata for indexing
- Tasks — Individual processing units that transform data, from text analysis to relationship extraction
- Pipelines — Orchestration of Tasks into coordinated workflows, like assembly lines for data transformation
- Use built-in Tasks for common operations
- Create custom Tasks for domain-specific logic by extending DataPoints
- Compose Tasks into Pipelines that match your workflow
Main Operations
Main Operations
Cognee provides four main operations that users interact with:
- Add — Ingest and prepare data for processing, handling various file formats and data sources
- Cognify — Create knowledge graphs from processed data through cognitive processing and entity extraction
- Memify — Optional semantic enrichment of the graph for enhanced understanding (coming soon)
- Search — Query and retrieve information using semantic similarity, graph traversal, or hybrid approaches
Further Concepts
Further Concepts
Beyond the core workflow, Cognee offers advanced features for sophisticated knowledge management:
- Node Sets — Tagging and organization system that helps categorize and filter your knowledge base content
- Ontologies — External knowledge grounding through RDF/XML ontologies that connect your data to established knowledge structures
- Organization — Managing growing knowledge bases with systematic tagging
- Knowledge grounding — Connecting your data to external, validated knowledge sources
- Domain expertise — Leveraging existing ontologies for specialized fields like medicine, finance, or research