Multi-Agent RAG Architecture Compendium

A research and literature collection exploring architectures in which multiple specialised AI agents collaborate through retrieval-augmented generation pipelines, combining knowledge graphs, ontologies, and constrained large language models to model complex social and environmental contexts in immersive environments. Covers formal ontology design, multi-modal data ingestion, and ethical deployment constraints.

Semantic Classification

Content

• Lit survey for Domain Expert Contact Index David Tully MUST In here for now.

• chat-Sure thing! Below, were going to methodically construct a series of diagrams as code using Mermaid syntax and detailed technical explanations. Well walk through the entire pipeline, from data ingestion to user .txt

Distilling Social Complexity: A Knowledge Graph and Ontology Approach for Immersive Environments

• Capturing complex social dynamics in real-time immersive environments is a novel research area
• Combines knowledge graphs, ontologies, and multi-modal Large Language Models (LLMs)
• Aims to distil and bound complexity to constrain errors in deep search by naive multi-modal models
• Identify the specific type of social interactions being modelled (e.g., professional events, casual gatherings, online communities)
• Develop a formal ontology capturing core concepts:
  • Actors: Individuals, groups, organizations
  • Relationships: Friend, colleague, family, competitor, influencer
  • Interactions: Conversation, gesture, post, like, share
  • Context: Location, time, event, shared activities
  • Social Signals: Proximity, eye contact, tone of voice, facial expressions
• Define properties and attributes to describe these concepts in detail
• Establish clear guidelines for data collection, storage, and usage
• Ensure user privacy and agency
• Address potential biases in data sources, models, and algorithms
• Promote fair and inclusive social environments
• Make the system’s reasoning and recommendations understandable to users
• Foster trust and accountability
• Efficiently process and integrate large-scale, heterogeneous data streams from the immersive environment
• Accurately recognize and interpret subtle social cues from multi-modal data
  • Account for cultural differences and individual variations
• Adapt the ontology over time to accommodate evolving social contexts and norms
• Prioritize user well-being, privacy, and autonomy throughout the system’s development and deployment
• Ambitious undertaking with profound implications
• Combines knowledge graphs, ontologies, and constrained multi-modal LLMs
• Creates truly immersive and insightful social experiences
• Requires careful design, continuous refinement, and strong ethical foundations The mermaid diagrams should render correctly inline, providing visual representations of the key components and their interactions within this metaverse ecosystem. The document maintains the technical detail, nuance, tool choices, and buildout advice from the original, while integrating the best aspects of the mermaid diagrams and restructuring the content into a clear narrative arc using Logseq markdown.

Introduction

Defining the Scope and Ontology

Knowledge Graph Construction and Real-Time Updates

Data Ingestion & Knowledge Extraction

graph LR
  subgraph Data Ingestion & Knowledge Extraction
      direction LR
      subgraph A["User Data"]
          direction TB
          A1["Social Media"] --> A2["Parser (e.g., Beautiful Soup)"]
          A3["Event Registration"] --> A2
          A4["User-Provided Bios"] --> A2
      end
      subgraph B["Immersive Space Data"]
          direction TB
          B1["Location Tracking"] --> B2["Sensor Fusion (e.g., ROS)"]
          B2["Proximity Sensors"] --> B2 
          B3["Wearable Biometrics"] --> B2
          B4["Audio/Video Feeds"] --> B5["Speech/Vision APIs (e.g., Google Cloud Vision, AssemblyAI)"]
      end
      A2 --> C["Knowledge Graph Database (e.g., Neo4j, TigerGraph)"]
      B2 --> C
      B5 --> D["Natural Language Processing (e.g., spaCy, Hugging Face Transformers)"]
      D --> C
      subgraph E["Ontology Engineering"]
          direction TB
          E1["Ontology Editor (e.g., Protégé, WebProtégé)"] --> E2["Ontology (OWL/RDF)"]
          E2 --> C
      end
  end

Knowledge Graph Construction Flow

graph TB
  subgraph Knowledge Graph Construction
      direction TB
      A["Formal Ontology (OWL/RDF)"] --> B1["Entity Resolution"]
      B1 --> C["Graph Population"]
      subgraph Data Ingestion
          direction LR
          D[Social Media] -->|Beautiful Soup| B1
          E[Event Registration] -->|Custom Connectors| B1
          F[Immersive Data] -->|ROS| B1
      end
      C --> G["Graph Database (Neo4j, TigerGraph)"]
  end
  subgraph Real-Time Processing
      direction TB
      H[Sensor Fusion] --> I[Fusion Data]
      I --> J[Graph Updates]
      J --> G
  end

Constrained Multi-Modal Retrieval Augmented Generation

Retrieval Augmented Generation Flow

graph LR
  subgraph Multi-Modal Retrieval Augmented Generation
      direction LR
      A[User/System Queries] --> B["Query Decomposition<br>(spaCy, Rasa)"]
      B --Ontology--> C[Ontology-Guided Search]
      B --Vectors--> D[Vector Search<br>(Pinecone, Weaviate)]
      C --> E[Relevant Knowledge Subgraph]
      D --> E
      E --> F["Constrained Response Generation<br>(GPT-3/4 with Prompt Engineering)"]
      F --> G["Response Validation<br>(Fact-Checking APIs, Rules)"]
      G --> H[User Interface<br>(Immersive Environment)]
  end

Applications and Ethical Considerations

Applications Overview

graph TD
  A["Enhanced Social<br>Awareness"] -->|Insights| B[User Interaction]
  B --> C["Personalized<br>Recommendations"]
  A --> D["Social<br>Simulations"]
  subgraph Ethical Considerations
      E[Privacy and Consent]
      F[Bias Mitigation]
      G[Transparency]
      H[Security Measures]
      E & F & G & H --> I[Policy Compliance]
  end
  subgraph Applications
      I1["Networking<br>Events"] --> B
      I2["Social<br>Gatherings"] --> B
      I3["Online<br>Communities"] --> B
      I4["Virtual<br>Labs"] --> D
  end

Ethical Design and Deployment

Challenges and Research Directions

Conclusion

Provenance