Implementation of Advanced Multi-Agent RAG System and Enhancements to Guardrails and Sensitive Information Handling for AIFAQ

[RAWx18]

Overview

This PR introduces two significant updates to the AIFAQ system: a Comprehensive Multi-Agent Architecture for enhanced retrieval-augmented generation (RAG), and a suite of Final Enhancements to Guardrails and Sensitive Information Handling. These updates focus on improving knowledge retrieval efficiency, response adaptability, and the secure handling of sensitive information. The combined enhancements lead to a more robust, context-aware system that aligns with best practices for user data protection and content filtering.

Problem Statement

The AIFAQ system faces two key challenges:

1. Inadequate Contextual Understanding and Knowledge Retrieval: The current single-agent architecture struggles with handling complex technical queries and retrieving the most relevant information from extensive documentation sets.
2. Insecure Handling of Sensitive Information: There are risks related to exposing sensitive data such as personal identifiers, passwords, or other private information, as well as the potential for harmful queries.

These issues impact both the efficiency of the system's knowledge retrieval and the security of the responses generated.

Technical Implementation

1. Multi-Agent Architecture for RAG System

• Comprehensive Agent Framework: Introduced a multi-agent orchestration layer that improves collaboration between specialized agents, enabling efficient query understanding, context-aware retrieval, and accurate document synthesis.

  • Standardized communication protocols between agents.
  • Developed an extensible plugin architecture for future enhancements.

• Enhanced RAG Pipeline:

  • Integrated a query understanding module with advanced intent classification and entity extraction.
  • Implemented hybrid BM25 and semantic search capabilities for multi-stage document retrieval.
  • Context-aware document synthesis with cross-reference validation, ensuring high-quality and consistent responses.

• Full-Stack Integration:

  • Extended backend API to support multi-agent endpoints.
  • Implemented a frontend toggle allowing users to switch seamlessly between standard and enhanced modes.
  • Added metadata visualization for enhanced transparency of the system's decision-making process.

2. Final Enhancements to Guardrails and Sensitive Information Handling

• Guardrails Enhancements:

  • Improved Semantic Matching: Enhanced the detection of cryptocurrency-related queries by including checks for terms such as "bitcoin," "crypto," and "trading." Queries with related terms are blocked, even if the exact terminology isn't used.
  • High-Risk Term Combinations: Added logic to block queries containing high-risk term combinations, e.g., "bypass security steps" and "mine bitcoin profit." This allows the system to be more flexible and proactive in identifying harmful queries.
  • Custom Responses: Tailored responses for hacking, password recommendations, and security bypass topics, offering clear and ethical guidance.

• Sensitive Information Redaction:

  • Post-Filtering Checks: Added advanced regex patterns to detect and redact sensitive information such as credit card numbers, social security numbers (SSNs), IP addresses, and email addresses. These are replaced with [REDACTED].
  • Enhanced Regex Patterns: Improved detection for passwords and phrases that may expose sensitive information, replacing detected strings with [FILTERED].

• Disclaimer Logic:

  • Security Disclaimer: Automatically inserts security disclaimers into responses discussing sensitive topics like "authentication," "passwords," and "security."
  • Blockchain Disclaimer: Automatically appends blockchain-related disclaimers for queries involving terms like "Hyperledger," "distributed ledger," and "smart contract."
  • Technical Disclaimer: Adds technical disclaimers for implementation-focused queries, e.g., "deploy," "install," and "configure."

Testing and Validation

• Test Suite: A comprehensive test suite was added, including 120+ test cases to ensure the functionality of the new system.

• Comparative Evaluation:

  • Response Accuracy: improvement.
  • Query Relevance: improvement.
  • Response Completeness: improvement.
  • Context Retention: improvement.
  • Response Time: reduction in response time while maintaining high-quality responses.

Alignment with LFX Mentorship Goals

This PR directly aligns with the objectives outlined in the LFDT - Multi-Agent RAG project by:

• Successfully implementing a multi-agent architecture for improved FAQ retrieval.
• Enhancing the RAG pipeline with advanced language model integration.
• Developing sophisticated ranking mechanisms to ensure high-relevance responses.
• Substantially improving query understanding and retrieval efficiency.

Additionally, the improvements to sensitive information handling and guardrails align with best practices for data security and content safety, ensuring that user privacy is maintained while optimizing the system's functionality.

Technical Scope

• Files Changed: 54
• Lines Added: 5,728
• Lines Deleted: 700
• Commits: 4
• Checks: 3
• Full End-to-End Implementation: The PR introduces full-stack changes, including backend updates, frontend enhancements, and backend API extensions, with comprehensive documentation provided for future scalability.

Next Steps

The architecture has been designed for extensibility, allowing for future enhancements such as:

• Federated Agent Learning: Agents will evolve based on user interactions to improve overall system intelligence.
• Cross-Domain Knowledge Integration: Enhancements to integrate knowledge from diverse domains will allow for a more comprehensive knowledge base.
• Advanced Visualization: Future iterations will include the ability to generate advanced visualizations to enhance user interaction.
• Multi-Modal Query Processing: Expanding the system’s capabilities to handle multi-modal inputs (e.g., images, voice, etc.) is planned for future versions.

This PR represents a significant advancement in the AIFAQ project's capabilities, improving both its functionality in knowledge retrieval and its ability to handle sensitive information securely.

Conclusion

These updates provide a robust, scalable foundation for the AIFAQ system's future development. The integration of a multi-agent architecture for enhanced knowledge retrieval and the improvements to guardrails and sensitive information handling not only improve the system's overall performance but also ensure its security and ethical alignment with industry standards. These changes position AIFAQ as a leading solution in the FAQ retrieval and AI-driven knowledge systems space.

[RAWx18]

I've been completely immersed in this project for the past several days, dedicating 15-16 hours daily to ensure a comprehensive implementation. As Ryan Madhuwala (RAWx18), I'm genuinely excited about contributing to AIFAQ through the LFX mentorship program. With my experience building production-level projects, I'm committed to supporting this project's growth and would greatly value your mentorship guidance.

[RAWx18]

Added to my PR: Final Enhancements to Guardrails and Sensitive Information Handling

[RAWx18]

Added to my PR: Final Enhancements to Guardrails and Sensitive Information Handling

This PR now also introduces the final set of enhancements to the guardrails system and sensitive information handling. These changes improve the robustness of the filtering mechanism, ensure semantic understanding for blocked topics, and enhance the disclaimer logic. Additionally, sensitive information redaction has been refined to handle edge cases effectively.

[RAWx18]

added new commit to fix the loading when sent query.

[RAWx18]

Hi @bobbi28, @gcapuzzi,could you please take a moment to review my PR when you have time?

This PR introduces two key updates to the AIFAQ system:

1. Comprehensive Multi-Agent Architecture for enhanced retrieval-augmented generation (RAG), improving knowledge retrieval efficiency and response adaptability.
2. Final Enhancements to Guardrails and Sensitive Information Handling, strengthening the system's ability to securely manage user data and filter sensitive content.

These combined improvements result in a more robust, context-aware system that aligns with best practices for data protection and content moderation.

I'm now planning to move forward with UI work for these features, so your feedback on the current implementation would be very helpful—especially regarding any improvements AIFAQ needs under this PR.

Thanks in advance!

[sumana-2705]

Hello @RAWx18, Thanks for your contributions. You can ask feedback in weekly monday meetings of AIFAQ with mentors. The link to join the meets is here

[RAWx18]

Hello @RAWx18, Thanks for your contributions. You can ask feedback in weekly monday meetings of AIFAQ with mentors. The link to join the meets is here

Okay thank you!