Frequently Asked Questions

Product Information & Overview

What is FalkorDB's GraphRAG-SDK?

GraphRAG-SDK is a specialized toolkit for building Graph Retrieval-Augmented Generation (GraphRAG) systems. It integrates knowledge graphs, ontology management, and large language models (LLMs) to deliver accurate, efficient, and customizable RAG workflows. Source

What is the primary purpose of GraphRAG-SDK?

The primary purpose of GraphRAG-SDK is to enable development teams to build accurate, multi-tenant RAG solutions powered by low-latency, scalable graph database technology. It is designed for handling complex, interconnected data in real-time, reducing hallucinations and improving LLM response accuracy. Source

Is GraphRAG-SDK open source?

Yes, GraphRAG-SDK is open source, allowing developers to freely use, modify, and contribute to the toolkit. Source

What types of data does GraphRAG-SDK handle?

GraphRAG-SDK can ingest both structured and unstructured data, including file formats like PDF, CSV, HTML, TXT, JSON, and URLs. Source

Can GraphRAG-SDK be deployed in the cloud or on-premises?

Yes, FalkorDB instances can be deployed via cloud infrastructure or containerized environments, providing flexibility for different organizational needs. Source

Does GraphRAG-SDK support multi-tenancy?

Yes, FalkorDB's GraphRAG-SDK supports multi-tenancy, enabling robust management of multiple tenants and isolated data environments. Source

What is ontology discovery in GraphRAG-SDK?

Ontology discovery in GraphRAG-SDK uses generative AI to detect and construct ontologies from datasets, allowing users to review, modify, and optimize graph structures automatically. Source

How does GraphRAG-SDK construct knowledge graphs?

GraphRAG-SDK leverages generative AI capabilities to efficiently construct knowledge graphs from diverse data formats, streamlining the ETL process. Source

What is multi-agent orchestration in GraphRAG-SDK?

Multi-agent orchestration allows users to configure domain-specific Knowledge Graph agents for targeted analysis, implement orchestration layers for automated coordination, and execute sophisticated queries through agent collaboration. Source

How does GraphRAG-SDK help reduce hallucinations in LLMs?

By integrating accurate knowledge graphs and ontology management, GraphRAG-SDK provides structured context to LLMs, resulting in fewer hallucinations and more reliable responses. Source

Features & Capabilities

What are the key features of GraphRAG-SDK?

Key features include ontology discovery, knowledge graph construction, multi-agent orchestration, support for structured and unstructured data, and optimized AI workflows. Source

Does GraphRAG-SDK support automated ETL processes?

Yes, GraphRAG-SDK streamlines ETL processes for ingesting diverse data formats, enabling efficient transformation into knowledge graphs. Source

Can I define custom parameters for ontology detection?

Yes, users can define custom parameters to scope and control ontology detection processes, optimizing graph structures for their specific needs. Source

Does GraphRAG-SDK offer integration with LLMs?

Yes, GraphRAG-SDK is optimized for AI applications and integrates with large language models to deliver accurate retrieval-augmented generation workflows. Source

How does GraphRAG-SDK enable multi-agent collaboration?

GraphRAG-SDK allows orchestration of specialized agents for complex knowledge operations, enabling automated coordination and sophisticated query execution. Source

Is GraphRAG-SDK optimized for AI workflows?

Yes, GraphRAG-SDK is specifically optimized for AI workflows, including GraphRAG and agent memory use cases, supporting intelligent agents and chatbots with real-time adaptability. Source

Does GraphRAG-SDK support regulatory compliance workflows?

Yes, GraphRAG-SDK helps organizations stay ahead of financial regulations by mapping regulations to workflows, identifying compliance gaps, and providing actionable recommendations. Source

Can GraphRAG-SDK be used for fraud detection?

Yes, GraphRAG-SDK enables real-time pattern detection across transaction networks, helping organizations trace fund flows, identify shell companies, and flag suspicious patterns. Source

Use Cases & Benefits

What are practical use cases for GraphRAG-SDK?

Practical use cases include regulatory compliance analysis, AML network analysis, and financial product recommendation engines. These use cases leverage knowledge graphs for precise insights, compliance mapping, fraud detection, and personalized recommendations. Source

How does GraphRAG-SDK help with regulatory compliance?

GraphRAG-SDK uses NLP-driven analysis to organize regulatory mandates into knowledge graphs, maps regulations to workflows, identifies compliance gaps, and suggests improvements for policies and risk management. Source

How does GraphRAG-SDK support AML network analysis?

GraphRAG-SDK visualizes transaction networks, uncovers hidden relationships, detects shell companies, and flags suspicious patterns using graph-based analysis, refining detection and scaling AML efforts. Source

How does GraphRAG-SDK enable financial product recommendations?

GraphRAG-SDK structures financial records and life events into knowledge graphs, maps product relationships, and leverages graph reasoning to deliver transparent, explainable, and tailored recommendations. Source

Who can benefit from using GraphRAG-SDK?

GraphRAG-SDK is ideal for highly technical teams, enterprises, SaaS providers, and organizations managing complex, interconnected data in real-time, especially those requiring accurate AI workflows and compliance solutions. Source

What business impact can GraphRAG-SDK deliver?

GraphRAG-SDK enables improved scalability, enhanced trust and reliability, reduced alert fatigue in cybersecurity, faster time-to-market, and support for advanced AI applications, empowering organizations to unlock actionable insights and achieve strategic goals. Source

Are there customer success stories for GraphRAG-SDK?

Yes, FalkorDB has been successfully implemented by organizations such as AdaptX, XR.Voyage, and Virtuous AI. These customers have leveraged FalkorDB's technology for clinical data analysis, immersive experience platforms, and ethical AI development. Case Studies

Technical Requirements & Documentation

Where can I find technical documentation for GraphRAG-SDK?

Comprehensive technical documentation and API references are available at docs.falkordb.com, including guides for setup and advanced configurations. Source

Does GraphRAG-SDK provide an API?

Yes, FalkorDB provides an API with references and guides available in the official documentation, supporting integration into developer workflows. Source

How easy is it to start using GraphRAG-SDK?

Getting started is straightforward: sign up for FalkorDB Cloud, launch a free instance, run locally via Docker, or schedule a demo. Documentation and community support are available for onboarding. Source

How long does it take to implement GraphRAG-SDK?

FalkorDB is built for rapid deployment, enabling teams to go from concept to enterprise-grade solutions in weeks, not months. Source

What integrations are available for GraphRAG-SDK?

FalkorDB supports integrations with frameworks like Graphiti (by ZEP), g.v(), Cognee, LangChain, and LlamaIndex, enhancing AI agent memory, visualization, and LLM interfaces. Source

Is community support available for GraphRAG-SDK?

Yes, community support is available via Discord and GitHub Discussions, providing real-time help and collaborative problem-solving. Discord, GitHub

Security & Compliance

Is FalkorDB SOC 2 Type II compliant?

Yes, FalkorDB is SOC 2 Type II compliant, meeting rigorous standards for security, availability, processing integrity, confidentiality, and privacy. Source

What security features does FalkorDB offer?

FalkorDB protects against unauthorized access, ensures system availability, delivers accurate data processing, safeguards sensitive information, and complies with privacy regulations. Source

Competition & Comparison

How does FalkorDB compare to Neo4j?

FalkorDB offers up to 496x faster latency and 6x better memory efficiency than Neo4j, includes multi-tenancy in all plans, and supports flexible horizontal scaling. Source

How does FalkorDB compare to AWS Neptune?

FalkorDB provides better latency performance, supports multi-tenancy, is open source, and offers highly efficient vector search compared to AWS Neptune. Source

How does FalkorDB compare to TigerGraph?

FalkorDB delivers faster latency, more efficient memory usage, and flexible horizontal scaling compared to TigerGraph's limited scaling and moderate memory efficiency. Source

How does FalkorDB compare to ArangoDB?

FalkorDB demonstrates superior latency and memory efficiency, flexible horizontal scaling, and robust multi-tenancy compared to ArangoDB. Source

Pricing & Plans

What pricing plans are available for FalkorDB?

FalkorDB offers four plans: FREE (for MVPs with community support), STARTUP (from /1GB/month, includes TLS and backups), PRO (from 0/8GB/month, includes cluster deployment and high availability), and ENTERPRISE (custom pricing with VPC, custom backups, and 24/7 support). Source

What features are included in the FREE plan?

The FREE plan is designed for building powerful MVPs and includes community support. Source

What features are included in the STARTUP plan?

The STARTUP plan starts from /1GB/month and includes TLS and automated backups. Source

What features are included in the PRO plan?

The PRO plan starts from 0/8GB/month and includes advanced features like cluster deployment and high availability. Source

What features are included in the ENTERPRISE plan?

The ENTERPRISE plan offers tailored pricing and includes enterprise-grade features such as VPC, custom backups, and 24/7 support. Source

FalkorDB Header Menu

Get GraphRAG Right with Our SDK

A specialized toolkit for building Graph Retrieval-Augmented Generation (GraphRAG) systems, integrating knowledge graphs, ontology management, and large language models (LLMs) to deliver accurate, efficient, and customizable RAG workflows.

Start Free
  • OPEN-SOURCE
  • STRUCTURED & UNSTRUCTURED DATA
  • OPTIMIZED FOR AI
GraphRAG-SDK Logo
ONTOLOGY DISCOVERY
KNOWLEDGE GRAPH CONSTRUCTION
MULTI-AGENT RAG

Remove the barrier of ontology creation

Transform raw data into structured knowledge models automatically.
  • Use generative AI to detect and construct ontologies from your datasets
  • Review, modify and iterate on detected ontologies to optimize graph structures
  • Define custom parameters to scope and control ontology detection processes

Handle structured and unstructured data

Ingest diverse data formats through a streamlined ETL process
  • Process multiple file formats including PDF, CSV, HTML, TXT, JSON and URLs
  • Deploy FalkorDB instances via cloud infrastructure or containerized environments
  • Utilize genAI capabilities to construct knowledge graphs efficiently

Handle structured and unstructured data

Ingest diverse data formats through a streamlined ETL process
  • Process multiple file formats including PDF, CSV, HTML, TXT, JSON and URLs
  • Deploy FalkorDB instances via cloud infrastructure or containerized environments
  • Utilize genAI capabilities to construct knowledge graphs efficiently

Use multi-agent Orchestration

Coordinate specialized agents for complex knowledge operations
  • Configure domain-specific Knowledge Graph agents for targeted analysis
  • Implement orchestration layer for automated agent coordination and planning
  • Execute sophisticated queries through multi-agent collaboration
Regulatory Compliance Analyzer
Practical Use Case

Regulatory Compliance Analyzer

Stay ahead of financial regulations with GraphRAG-SDK.

  • Extract Key Requirements: NLP-driven analysis organizes regulatory mandates into a knowledge graph for precise insights.
  • Map Regulations to Processes: Link policies to workflows, ensuring compliance across departments.
  • Identify Compliance Gaps: Query relationships to spot misalignments and track updates.
  • Suggest Improvements: Get data-driven recommendations to refine policies, training, and risk management.
AML Network Analyzer
Practical Use Case

AML Network Analyzer

Detect and analyze financial crime with GraphRAG-SDK.

 

  • Trace Fund Flows: Visualize transaction networks across institutions to track money movement with clarity.
  • Identify Shell Companies: Uncover hidden relationships and detect illicit entities within financial ecosystems.
  • Flag Suspicious Patterns: Use graph-based analysis to detect anomalies, refine detection over time, and scale AML efforts effectively.
Financial Product Recommendation Engine
Practical Use Case

Financial Product Recommendation Engine

Deliver precise financial recommendations using GraphRAG-SDK.

 

  • Analyze Customer Data: Structure financial records and life events into knowledge graphs for relevant, personalized insights.
  • Map Product Relationships: Clearly visualize connections between products, customer segments, and risk profiles.
  • Tailored Recommendations: Leverage graph reasoning to offer transparent, explainable suggestions, boosting trust and satisfaction.

Build fast and accurate GenAI apps with GraphRAG SDK at scale

FalkorDB offers an accurate, multi-tenant RAG solution based on our low-latency, scalable graph database technology. It’s ideal for highly technical teams that handle complex, interconnected data in real-time, resulting in fewer hallucinations and more accurate responses from LLMs.

Book a demo
Try Free