Knowledge Library MCP Architecture

This layer ensures seamless user interaction, bridging human queries with the system’s backend capabilities. It sets the stage for chat-based insights, a core feature of KL MCP.

- Browser Interface: Users access the system through a web browser, ensuring broad compatibility and ease of use. This interface supports standard web interactions, making KL MCP accessible without specialized software.

- FluentUI Chat Interface: Built with FluentUI, a Microsoft design system, this chat interface provides a polished, intuitive experience for users to query the system and receive insights. It supports both text input and speech-to-text functionality, catering to diverse user preferences.

- Speech-to-Text and Text Input: The system accepts voice commands via speech-to-text, alongside traditional text input, enhancing accessibility for users who prefer verbal queries, such as asking for specific SEC filings during a meeting.

MCP ensures that KL MCP isn’t just a keyword search tool—it’s a context-aware system that delivers relevant, precise results by understanding the relationships between data and queries.

- MCP Component: This protocol manages the context of data interactions, ensuring that queries are processed with relevant background information. It helps the system understand the intent behind a user’s request, such as distinguishing between a request for Tesla’s 10-K filing versus a workflow document.

- Document Upload and Search Interface: Users can upload documents directly through this interface, which connects to the MCP for context-aware processing. The search interface allows users to query the system, leveraging MCP to refine results based on the data’s context.

These services form the operational backbone, ensuring KL MCP runs smoothly, scales effectively, and remains reliable under load—a critical feature for professionals relying on it for time-sensitive tasks like audits.

- API Service: Manages API calls between the user interface and backend systems, ensuring smooth communication for data retrieval and processing.

- Orchestration: Coordinates the workflow between different components, such as routing a user query to the appropriate agent (e.g., SECBot for filings).

- Content Discovery: Identifies relevant content within the system, aiding in the discovery of documents or data points that match user queries.

- Resilience Patterns: Implements strategies to ensure system stability, such as retry mechanisms or failover processes, maintaining uptime and reliability.

- Health Monitoring: Tracks the system’s performance and health, alerting administrators to issues like server overloads or API failures.

This layer bridges raw data with actionable insights, leveraging Azure AI to process and organize information for downstream use by agents and chat interfaces.

- Knowledge API: A central API that interfaces with Azure AI services, handling requests for data retrieval, vectorization, and agent coordination.

- Document Service: Processes and manages documents, converting them into formats suitable for search and analysis (e.g., extracting text from PDFs).

- Vector Service: Converts data into vector representations, enabling semantic search capabilities that go beyond keyword matching to understand meaning and context.

- Agent Service: Manages the deployment and operation of specialized agents (e.g., TSLA 10-K Agent), ensuring they respond efficiently to user queries.

These agents embody KL MCP’s bot-driven approach (e.g., DocBot, SECBot), specializing in specific data types or tasks. They enable the system to handle diverse datasets with precision, a key strength highlighted in your social posts.

- TSLA 10-K Agent: Focuses on Tesla’s 10-K filings, extracting and processing data from these annual reports.

- TSLA 10-K PDF Agent: Specifically handles PDF versions of Tesla’s 10-Ks, likely using OCR to extract text and images for analysis.

- TSLA 10-K Table Agent: Targets tabular data within Tesla’s 10-K filings, such as financial tables, ensuring structured data is accessible for analysis.

- MCP Azure Agent: Oversees the integration of the MCP framework within Azure, ensuring context-aware processing across all agents.

- go-mcp-metasearch and go-mcp-brave: These components, written in Go (Golang), extend search capabilities—metasearch aggregates results from multiple sources and integrate with the Brave search engine for web-based data.

These components show KL MCP’s versatility, extending its utility beyond internal data management to external applications, making it a valuable tool for broader financial and research workflows.

- Financial Analyst App: An external application that leverages KL MCP’s insights for financial analysis, such as evaluating company performance based on SEC filings.

- Company Research: Supports research tasks, pulling data from filings, web sources, and real-time feeds for comprehensive company profiles.

- Azure AI Services (Multi-Agent RAG): Utilizes Azure’s Multi-Agent Retrieval-Augmented Generation (RAG) framework, enhancing KL MCP’s ability to combine retrieved data with generative AI for richer insights.

- Real-Time Audio: Supports real-time audio processing for transcribing meetings or analyzing audio-based financial reports.

This infrastructure ensures KL MCP can manage and access diverse data types efficiently, supporting its ability to process up to 10,000 files and deliver fast, accurate results.- Vector Store: Stores vectorized representations of data, enabling semantic search for text, images, and other formats.

- Document Store: Houses raw documents (e.g., PDFs, Excel files), serving as the primary repository for unprocessed data.

- Web Search API: Integrates web search capabilities, allowing KL MCP to pull in external data, such as news articles or market updates.

- Image API: Processes images using OCR and labeling to extract text or metadata, supporting KL MCP’s multi-modal capabilities.

- AIbot AI Store and AIbot AI Vector: Additional storage components for AI-generated data or vectorized outputs, enhancing search and retrieval efficiency.

8. Data Flow and Interactions

The flow ensures a seamless user experience, from query to insight, with each component playing a specialized role in the pipeline.

- User to Integration Layer: Queries from the browser or chat interface are routed through the MCP and web frontend to the integration layer.

- Integration to Agents: The Knowledge API assigns tasks to appropriate agents (e.g., TSLA 10-K Agent for a filing query).

- Agents to Storage: Agents pull data from vector stores, document stores, or external APIs, processing it for relevance.

- External Components: Processed data feeds into applications like the Financial Analyst App or supports real-time audio analysis.

- Return Path: Insights are sent back through the integration layer to the user’s chat interface, completing the loop.

The architecture supports KL MCP’s core mission: to streamline data retrieval and analysis for professionals. It handles SEC filings (e.g., Tesla’s 10-Ks), processes tables and images, and integrates real-time data via APIs, enabling chat-based insights for financial analysis or company research. The use of Azure AI Multi-Agent RAG, specialized agents, and vectorized storage aligns with the core features—specialized bot-driven search, multi-modal integration, on-the-fly analysis, and resource-smart design. For example:

- Bot-Driven Search: Agents like TSLA 10-K Agent and SECBot reflect the hierarchical, targeted retrieval approach.

- Multi-Modal Integration: Vector stores and image APIs support text, tables, and images, unified in the chat interface.

- On-the-Fly Analysis: The integration layer’s connection to Azure AI services enables live computations, though not explicitly shown in the diagram.

- Resource-Smart Design: The use of Azure’s infrastructure and a lean C client ensures efficiency.