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Description
FalkorDB is an exceptionally rapid, multi-tenant graph database that is finely tuned for GraphRAG, ensuring accurate and relevant AI/ML outcomes while minimizing hallucinations and boosting efficiency. By utilizing sparse matrix representations alongside linear algebra, it adeptly processes intricate, interconnected datasets in real-time, leading to a reduction in hallucinations and an increase in the precision of responses generated by large language models. The database is compatible with the OpenCypher query language, enhanced by proprietary features that facilitate expressive and efficient graph data querying. Additionally, it incorporates built-in vector indexing and full-text search functions, which allow for intricate search operations and similarity assessments within a unified database framework. FalkorDB's architecture is designed to support multiple graphs, permitting the existence of several isolated graphs within a single instance, which enhances both security and performance for different tenants. Furthermore, it guarantees high availability through live replication, ensuring that data remains perpetually accessible, even in high-demand scenarios. This combination of features positions FalkorDB as a robust solution for organizations seeking to manage complex graph data effectively.
Description
Current models are costly to train, complicated to implement, challenging to validate, and notoriously susceptible to generating misleading information. At Symbolica, we are reimagining the process of machine learning from its foundation. By leveraging the highly expressive framework of category theory, we create models that can learn and understand algebraic structures. This approach equips our models with a comprehensive and systematic representation of the world that is both explainable and verifiable. Our goal is to empower developers and end users to grasp and articulate the reasons behind model outputs. This level of interpretability and control over the outputs—such as the ability to remove proprietary data from the training set—is essential for applications that are critical to mission success. Additionally, we believe that enhancing transparency in how models derive their conclusions will foster greater trust and collaboration between humans and machines.
API Access
Has API
API Access
Has API
Integrations
Amazon Web Services (AWS)
ChatGPT
Cognee
DSPy
Docker
Gemini
Google Cloud Platform
LangChain
Llama
Mem0
Integrations
Amazon Web Services (AWS)
ChatGPT
Cognee
DSPy
Docker
Gemini
Google Cloud Platform
LangChain
Llama
Mem0
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
FalkorDB
Founded
2023
Country
Israel
Website
www.falkordb.com
Vendor Details
Company Name
Symbolica
Website
www.symbolica.ai
Product Features
Product Features
Artificial Intelligence
Chatbot
For Healthcare
For Sales
For eCommerce
Image Recognition
Machine Learning
Multi-Language
Natural Language Processing
Predictive Analytics
Process/Workflow Automation
Rules-Based Automation
Virtual Personal Assistant (VPA)