Data to Memory
Data to Memory is the foundational process that transforms your raw data into Cognee’s structured memory system, making it ready for processing, analysis, and intelligent querying.
Overview
The Data to Memory process takes unstructured information from various sources and converts it into a format that Cognee can understand, process, and build knowledge graphs from. This is the critical first step that enables all subsequent memory processing and search capabilities.
The Conversion Process
Input Sources
Cognee can ingest data from multiple sources:
- Documents: PDFs, text files, Word documents
- Web Content: Web pages, articles, documentation sites
- Databases: Relational and NoSQL databases
- APIs: REST APIs, GraphQL endpoints
- Real-time Streams: Live data feeds and event streams
Memory Formation
During the Data to Memory process, your raw data undergoes several transformations:
- Data Ingestion: Raw data is imported from various sources using specialized connectors
- Format Normalization: Different data formats are standardized into common structures
- Content Extraction: Meaningful content is extracted from complex formats
- Initial Structuring: Data is organized into preliminary structures ready for processing
Key Components
Data Ingestion
The data ingestion system handles the complex task of importing data from diverse sources while maintaining data integrity and relationships. Learn about:
- Data import strategies
- Source connectors
- Data validation
- Error handling
Node Sets
Node Sets provide powerful tagging and organization capabilities that help manage the growing complexity of your knowledge base. Explore:
- Node set organization
- Tagging strategies
- Content grouping
- Access control
Ontologies
Ontologies define the semantic structure and relationships that guide how your data is organized. Understand:
- Semantic structures
- Relationship types
- Knowledge organization
- Schema definition
Chunking
Chunking breaks down large datasets into manageable pieces, optimizing them for efficient processing. Learn about:
- Chunk sizing
- Content splitting
- Overlap strategies
- Metadata preservation
Memory Readiness
Once your data has completed the Data to Memory process, it becomes “memory-ready” - structured, tagged, and prepared for the next stage of processing. This memory-ready data can then be:
- Processed through computational workflows in Memory Processing
- Queried and searched through Search Memory
- Visualized and explored through various interfaces
Next Steps
- Explore Node Sets for organization
- Understand Ontologies for structure
- Master Chunking for processing
The Data to Memory process ensures that regardless of your data’s original format or source, it becomes part of a unified, intelligent memory system that can grow and adapt with your needs.