Modular-LLM-Architecture

Exploring the cognitive architecture and design of a multi-agent system leveraging Large Language Models (LLMs) for scalable workflows.

Architecture Diagram

The diagram illustrates the key components of the system, including the Conversation Manager, Router, LLM Manager, and specialized agents.

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Core Features

• Modular multi-agent design.
• Dynamic LLM integration with support for multiple models.
• Separation of concerns for improved maintainability.

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Introduction

In the era of Large Language Models (LLMs), designing systems that handle diverse tasks with scalability and flexibility is a growing challenge. The cognitive architecture presented here leverages modular design principles to build a robust multi-agent system, featuring clear separation of responsibilities and dynamic LLM integration. This blog explores the architectural details, using the attached diagram as a guide.

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Core Components of the Architecture

1. Conversation Manager (Brain and Orchestrator):

   • The central "brain" of the system. It processes user inputs and orchestrates tasks by delegating them to specialized agents through the Router.
   • Maintains state for multi-step conversations, ensuring continuity and coherence.

2. Router (Task Assignment):

   • A lightweight component that assigns tasks to the appropriate agent based on their expertise.
   • Decouples task routing from task execution for better maintainability.

3. LLMManager (Flexible LLM Integration):

   • Centralizes interaction with LLMs, enabling seamless integration of multiple models.
   • Supports dynamic model selection and configuration changes, such as adjusting temperature or choosing specific LLMs for tasks.
   • Ideas for caching and small-model fallback mechanisms have been proposed but are not yet implemented.

Specialized Agents

Each agent is designed with a specific role, ensuring a clear separation of concerns:

• SQL Agent (Natural Language to SQL): Converts user prompts into SQL queries, currently interacting with the Database Schema via prompt injection to fetch metadata. In production, this will transition to a graph-based Retrieval-Augmented Generation (RAG) system for enhanced flexibility and scalability.

• SQL Debugging Agent: Debugs and corrects SQL queries, ensuring accurate execution. It refines queries up to three times, addressing issues like spelling errors or incomplete logic, to ensure meaningful outputs.

• Analyser Agent (Data Analysis): Analyzes structured or unstructured data to provide insights. Future enhancements include integrating LLM results with business data from platforms like Slack, Microsoft Teams, and OneDrive.

• Charting Agent: Writes Vega specifications for data visualization.

• Vega Debugging Agent: Corrects and validates Vega JSON specifications for accurate visual rendering.

• Knowledge Agent: Retrieves answers from document repositories, relying on the Document RAG for context. It addresses business-specific queries like defining terms such as 'churn' at Peacock, ensuring concise and clear summaries.

• Follow-Up Agent: Engages with users to provide iterative feedback and refine responses. It also suggests 2-3 similar analyses to help users explore related questions.

• Clarification Agent: Ensures the highest level of accuracy by detecting ambiguity and prompting for clarification. For instance, it can inquire, "What do you consider a top show? Should it be based on total hours viewed, other metrics, or specific time periods like a calendar month?"

• Note Agent: Streamlines analyses by capturing preferences like focusing on Android devices in New York state. This eliminates repetitive specifications, ensuring appropriate SQL queries and tailored visualizations.

• Summary Agent (Result Compilation): Summarizes outputs from various agents, compiling user-friendly reports. It always runs at the end, providing concise summaries to ensure clarity.

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Key Features of the Design

1. Modularity and Maintainability:

   • Each component focuses on a single responsibility, improving maintainability and scalability.
   • The Router and Conversation Manager allow seamless integration of new agents.

2. Flexible LLM Integration:

   • The LLMManager supports a plug-and-play approach, enabling dynamic LLM selection.
   • Designed for integration with vector databases and embedding techniques for advanced retrieval functionalities.

3. Separation of Concerns:

   • Clear division of tasks between routing, orchestration, and execution layers prevents overlap and reduces complexity.

4. Resilience:

   • Built-in mechanisms for retries, error handling, and debugging ensure reliable performance under diverse scenarios.

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Workflow in Action

1. User Input: The Conversation Manager receives a query, such as "What are the top 5 most viewed shows across all regions?"
2. Routing: The Router assigns the task to the SQL Agent. Before this step, the Clarification Agent intervenes to detect ambiguity and refines the user’s intent by asking up to three questions.
3. LLM and Database Integration: The SQL Agent interacts with the LLMManager to generate a SQL query, retrieves metadata from the Database Schema, and sends the query to the Query Executer.
4. Validation and Debugging: If the SQL Agent encounters challenges like errors or empty results, the SQL Debugging Agent provides assistance, refining the query as needed.
5. Visualization: The Vega Agent generates a JSON specification for visualization, enabling insightful and interactive outputs.
6. Summarization: The Summary Agent compiles findings into concise reports, limited to three sentences for clarity.
7. Follow-Up: The Follow-Up Agent recommends 2-3 similar analyses to assist users in exploring related questions.

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Real-World Applications

• Business Intelligence (BI): Generating dynamic dashboards and actionable insights from natural language queries.
• Data Exploration: Allowing non-technical users to interact with databases intuitively.
• Knowledge Management: Streamlining access to enterprise knowledge repositories.

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Challenges and Future Directions

• Scalability: Ensuring the system scales effectively with increasing user demands.
• Security: Safeguarding sensitive data by leveraging open-source and small models where feasible to minimize exposure.
• Agent Expansion: Adding more specialized agents for domain-specific tasks.

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Conclusion

This cognitive architecture highlights the strengths of modular design and multi-agent systems in effectively utilizing LLMs for complex workflows. By integrating flexible LLM capabilities and maintaining a clear separation of responsibilities, it establishes a robust framework for building scalable and efficient AI-driven solutions.

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Suggestions for Moving Forward

1. Enhanced Task Queue Features: Implement advanced parallelism techniques and error recovery using task retry logic for more resilient execution.
2. Enhanced Agent Collaboration: Introduce inter-agent communication protocols to improve collaboration and reduce redundant processing.
3. Observation and Evaluation Tools: Incorporate tools like Arize.ai or LangSmith to monitor and evaluate system performance.
4. Dynamic Feedback Mechanisms: Implement advanced user feedback loops to refine agent outputs in real time.
5. Agent Response Evaluation: Integrate solutions like Galileo or similar platforms to evaluate the performance of agent responses for comprehensive insights.
6. Memory Enhancements: Use tools like LangGraph to improve system memory and context recall.
7. Scalability Optimization: Focus on performance for large-scale deployments, leveraging platforms like Predibase for enhanced scalability and adaptability.