CreateLink Hybrid RAG Architecture Diagram

Hybrid RAG builds a domain knowledge graph from enterprise private data. It uses graph-enhanced retrieval to boost LLMs' retrieval and generation abilities, improving handling of complex relationships and multi-step reasoning. The system delivers more complete, accurate, timely, and traceable responses, helping users deeply understand and apply private knowledge effectively.

Data Layer

This layer accommodates diverse data structures from enterprise private sources. It leverages LLMs to extract knowledge from unstructured data such as documents, audio, and images, enabling rapid construction of private knowledge graphs. Structured data is efficiently imported into the Galaxybase Graph Database.

Interaction & Application Layer

Intelligent Q&A

Intelligence Analysis

Equipment Operation & Maintenance

Intelligent BI

Enterprise Applications

Mini Programs

Web Pages

Chatbots

Model Layer

Natural Language Understanding

Intent Recognition

Query Rewriting

Knowledge Retrieval

Association Retrieval

Index Retrieval

text2sql

Prompt Engineering

Predefined Templates

Chain of Thought

Response Generation

Intelligent Reply

Form Generation

Large Language Model (LLM)

Multi-Model Support

Scenario-Based Configuration

Galaxybase Graph Database

FAQ

Vertical-Specific Knowledge

Real-Time Intelligence

Knowledge Base Construction

Parse

Process

Parse

Process

Data Sources

Documents

Spreadsheets

Relational Databases

Videos

Application Scenarios

Knowledge Q&A

Hybrid RAG integrates documents from knowledge bases and online-generated data, leveraging the rich semantic information in knowledge graphs to empower traditional knowledge Q&A platforms with the ability to capture deep-level correlations and quickly generate relevant reports or answers. Hybrid RAG can query across multiple documents and literature containing redundant or noisy data, as well as mixed errors or disinformation, providing comprehensive, well-grounded, and traceable answers. It is suitable for rigorous fields such as healthcare and design, which rely heavily on past experience.

Intelligence Analysis

Intelligence data analysis often involves complex and diverse queries, requiring high real-time performance and accuracy. Hybrid RAG can correlate and integrate multi-level, multi-dimensional intelligence data into an intelligence knowledge graph. It performs deep retrieval based on user needs, uncovers hidden relationships, and generates new intelligence reports or insights, suggesting defensive measures.

Personalized Learning Path Planning

Online learning and teaching platforms need to account for individual differences among users, as learning abilities, habits, and career plans vary greatly. Personalized education is essential. Hybrid RAG extracts and integrates key knowledge from textbooks, courseware, academic papers, and video materials to build a knowledge graph. By leveraging the interconnections of knowledge, it provides intelligent knowledge Q&A services and recommends personalized learning resources, enhancing the platform’s intelligence and improving student learning outcomes.

Emergency Response Management

Emergency response involves comprehensive judgment of complex multi-condition scenarios and timely implementation of targeted solutions. GraphRAG provides capabilities for key information discovery and analysis of past use cases. Hybrid RAG supports the integration and updating of various real-time data from different systems, constructing a domain-specific knowledge graph to retrieve and analyze large volumes of historical case data. It generates comprehensive emergency response recommendations for complex events, improving responders’ efficiency, accuracy, and thoroughness in handling incidents, thereby safeguarding lives and property.

Product Advantages

Unifying Fragmented Information Across Long Texts

It overcomes the "needle-in-a-haystack" challenge in retrieving complex and scattered knowledge across enterprise systems by combining semantic vector retrieval with graph-based relationship mining. This hybrid approach accurately identifies and connects key information from long-form documents and structured data sources.

Real-Time Knowledge Updates

By harnessing the robust performance of the Galaxybase graph database, the system ensures that large models are equipped with a "knowledge brain" that is continuously updated in real time with the latest enterprise-specific information. This capability is crucial for maintaining the relevance and accuracy of the knowledge base in line with the rapidly evolving data landscape of enterprises.

Transparent and Traceable Reasoning

The graph store explicitly represents relationships between knowledge entities, making reasoning paths fully visible and source attribution verifiable. This enhances the credibility of answers provided by LLMs and supports auditability and compliance requirements.

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