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RAG Knowledge Base Applications

RAG knowledge base applications combine information retrieval with large language models (LLM). It effectively alleviates model hallucination problems, improves knowledge update speed, and enhances content generation transparency, making large models more practical and trustworthy in real applications.
RAG Knowledge Base Applications

What is RAG Technology?

索引

  • RAG Concept
    RAG Concept
  • Risks and Challenges of RAG
  • RAG Knowledge Base Application Governance Solution
What is RAG Technology?

RAG Technology Overview

Retrieval-Augmented Generation (RAG) is a technology that combines information retrieval with large language models (LLM). Before generating answers, RAG retrieves relevant information from external knowledge bases to provide the latest, accurate and traceable content. This approach effectively alleviates model hallucination problems, improves knowledge update speed, and enhances content generation transparency, making large models more practical and trustworthy in real applications.

Risks and Challenges of RAG

Components in the AI supply chain, such as open source models and training datasets, may have been tampered with by attackers, leading to controlled model outputs, malicious code execution, or malware distribution.

Pre-development

  • Components in the AI supply chain, such as open source models and training datasets, may have been tampered with by attackers, leading to controlled model outputs, malicious code execution, or malware distribution.

Post-deployment

  • Deployed RAG applications are vulnerable to harmful input and model output threats. Attackers may design specific prompts to increase computational resource consumption, raise operational costs, and affect model performance for other users. They may also steal sensitive information from connected vector databases through prompt injection.

Security Risks

  • Although RAG technology aims to improve model accuracy and relevance, AI applications may still generate incorrect, harmful, or content that violates data security and privacy requirements. These issues may stem from user prompts or unintentionally generated errors.
  • Although RAG technology aims to improve model accuracy and relevance, AI applications may still generate incorrect, harmful, or content that violates data security and privacy requirements. These issues may stem from user prompts or unintentionally generated errors.

RAG Knowledge Base Application Governance Solution

As RAG technology is widely applied across various industries, its security and trustworthiness issues are gradually becoming prominent. To ensure the healthy development of RAG applications, GenTel has proposed a comprehensive security and trust solution. This solution starts from multiple dimensions including usability assessment, knowledge base compliance checking, and sensitive content protection, aiming to improve the reliability and security of RAG applications in practical use, ensuring they meet user needs while complying with social and legal norms.

Prevent Agent Risk Content Output

Preventing Agent risk content output is one of the core functions of this solution. The platform can detect and block inappropriate content generated by Agents through real-time monitoring and filtering technology, including malicious, illegal, biased, false information, etc. Using multi-level security models and natural language processing technology, the platform can accurately identify potential risks, ensure generated content complies with ethical and legal norms, and effectively reduce the risk of non-compliant content output.

Protection Against High Resource Consumption Attacks

To ensure Agent stability and efficient operation, protection against high resource consumption attacks is crucial. The platform can identify and prevent denial of service attacks (DDoS), resource consumption attacks, and other behaviors. Through dynamic resource allocation, load balancing and other technical means, it reduces the burden of malicious attacks on the system. Through intelligent protection measures, the platform can maintain normal service operation in the face of malicious traffic attacks and ensure the user experience of other users.

Provide User Access Permission Management

The user access permission management function aims to flexibly limit users' access to content provided by intelligent agents based on different user roles or needs. The platform ensures that users can only access content related to their roles by defining role permissions, avoiding leakage of sensitive or inappropriate information. Administrators can set permissions for different types of users, flexibly control the scope of services they can access, including query, interaction, data operation and other functions, ensuring the security and compliance of platform operations. This function can provide personalized and efficient permission management according to different application scenarios, enhancing user experience and security protection.