Cognee is an AI Memory Python SDK that helps to map out data logically for more accurate AI responses by establishing a knowledge graph, ensuring reliable interactions from LLM agents.
Features
AI Memory Mapping
Cognee constructs a knowledge graph that determines relevant memory types per query, enhancing the accuracy of AI responses by finding hidden connections in your data.
Customizable Storage
Cognee supports various database providers, including vector and graph databases, and allows users to integrate their own by following simple documentation.
RDF-Based Ontologies
Data is structured using RDF-based ontologies, utilizing publicly available rules to make data relationships more intelligent and meaningful.
On-Premise Deployment
Cognee can be run on your own servers, providing full control over your data and reducing third-party risks, which is beneficial for sensitive data.
Handling Large Data Volumes
Cognee's distributed system can efficiently handle and scale to accommodate large volumes of data, such as gigabytes or terabytes.
Actual Reasoning Engines
Cognee allows the use of real reasoners, enabling precise data interpretation and reasoning beyond pattern recognition.
Ontology Support
Allows users to add OWL files that are automagically matched with existing data, grounding large language models with factual information.
Relational Database Import
Enables seamless import of relational databases into Cognee, converting structured data into graphs for AI agent use.
Dreamify - Hyperparameter Tuning System
Turbocharges performance by enhancing prompt management, improving evaluations, and speeding up operations.
Knowledge Graph Query Answering (KGQA)
Enables AI-powered search through knowledge graph query answering, enhancing retrieval-augmented generation and personalized recommendations.
Data Pipelines
Build knowledge graphs by running data pipelines that convert API and relational data into interconnected semantic graphs, enhancing data insights.
AI Memory Enhancement
Provide AI systems like CrewAI agents with memory, enabling context-aware applications and enhancing personalization.
Semantic data structuring
Enables the creation of data representations that emulate human understanding by extracting entities from information and establishing semantic relationships between them.
Integration with LLMs
Seamlessly connects AI models with internal company data, allowing more efficient access and use of the data while keeping it organized and up-to-date.
Flexible ontology design
Allows users to create adaptable entity and relationship models that capture essential structure while remaining flexible to new information.
Graph enrichment steps
Builds both deterministic and advanced non-deterministic graph enrichment methods, incorporating LLM-driven technologies.
Combining technologies
Uses vector embeddings for semantic search and integrates with traditional databases to optimize the knowledge graph's usefulness.
Automated data management
Provides automated processes for data insertion, deletion, and management to maintain up-to-date and accurate knowledge graphs.
Inference and reasoning
Leverages graph structures for deriving new knowledge, identifying patterns, and contextual understanding of data relationships.
Retrieval-Augmented Generation (RAG)
Boosts traditional data retrieval by combining it with natural language processing to generate more coherent and contextually relevant responses.
Enhanced Personalization
Uses knowledge graph technology to match new customer preferences with similar past users, providing more tailored and meaningful recommendations.
Anomaly Detection
Detects discrepancies and anomalies within data inputs to maintain data integrity and consistency through graph-based rules.
Integration with Diverse Data Sources
Facilitates the integration of both structured and unstructured data, using pipelines and tasks to aggregate information into a knowledge graph.