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# IntelliCore: Enterprise AI Knowledge Management System IntelliCore is a production-grade Enterprise Knowledge Management System (KMS) built with a multi-agentic RAG architecture. It leverages the Model Context Protocol (MCP) concepts and LangGraph to provide accurate, reliable, and observable enterprise policy information to employees and administrators. ## 🚀 Overview IntelliCore streamlines policy management operations by providing an intelligent interface for policy retrieval, automated gap detection, and employee support. It features a state-of-the-art RAG pipeline that can process both text and visual data (charts, tables) from enterprise documents. ### Key Concepts Covered - **RAG (Retrieval-Augmented Generation)**: Implements a high-performance retrieval system using **ChromaDB** as a vector store and **Sentence-Transformers** for embedding generation. The system supports multi-modal ingestion, where text and visual elements (like charts or tables) are extracted from PDFs via **Vision AI**, vectorized, and then retrieved based on semantic similarity. - **Agentic Framework**: Built with **LangGraph** for structured, stateful multi-agent workflows. - **Multi-Agent Systems**: Features specialized agents for retrieval, QA, and validation. - **Model Context Protocol (MCP)**: Adheres to the principles of the **Model Context Protocol** by decoupling model reasoning from data sources. - **Guardrails**: Implementation of input validation and output hallucination checks. - **Observability**: Real-time logging of queries, confidence scores, and system metrics via MongoDB. --- ## 🏗️ Architecture IntelliCore follows a modern decoupled architecture designed for scalability and observability. <img src="assets/architecture_dia.png" width="80%" alt="Architecture Diagram"> ### Tech Stack Summary | Component | Technology | Description | | :--- | :--- | :--- | | **Frontend** | React, Vite, Tailwind CSS | Modern, responsive dashboard and chat interface. | | **Backend** | FastAPI (Python) | High-performance API orchestrating the AI agents. | | **Orchestration** | LangGraph, MCP | Stateful multi-agent workflows and tool standardization. | | **Vector Store** | ChromaDB | Local vector database for sub-second semantic search. | | **Primary DB** | MongoDB | Persistent storage for user data, logs, and analytics. | | **LLMs** | Llama 3.3 70B (Groq) | High-speed inference for reasoning and generation. | | **Vision AI** | Llama 3.2 Vision | Processing of visual elements in policy documents. | --- ## 📂 Project Structure ```text . ├── assets/ # Project screenshots and diagrams ├── backend/ # FastAPI server and AI logic │ ├── mcp/ # Model Context Protocol tool implementations │ ├── rag/ # RAG pipeline and vectorization logic │ ├── tests/ # Backend unit and integration tests │ └── main.py # Entry point for the backend ├── frontend/ # React application │ ├── src/ # Component and page source code │ └── public/ # Static assets └── README.md # Project documentation ``` --- ## 🧠 Deep Dive: Core Concepts ### 1. Retrieval-Augmented Generation (RAG) In IntelliCore, RAG is the engine that transforms static documents into a dynamic knowledge base. It ensures the AI doesn't rely on its training data, but instead "looks up" specific enterprise policies before answering. #### I. The Multi-Modal Ingestion Pipeline 1. **Extraction (Text & Vision):** Uses `PyMuPDF` for text and **Llama 3.2 Vision** for charts, tables, and diagrams. 2. **Chunking:** Documents are broken into overlapping chunks (500-1000 characters) to maintain semantic continuity. 3. **Vectorization:** Chunks are converted into 768-dimensional embeddings using `all-MiniLM-L6-v2`. #### II. Semantic Storage (ChromaDB) Vectors are stored in **ChromaDB**. It uses mathematical proximity (Cosine Similarity) rather than keyword matching, allowing for sub-second retrieval across thousands of pages. #### III. The Retrieval Process - **Query Embedding:** User questions are converted into vectors in real-time. - **Top-K Fetching:** The system retrieves the top 10 most relevant chunks based on semantic similarity. #### IV. Augmented Generation (Groundedness) Retrieved chunks are injected into the LLM prompt as **context**, forcing the model to answer based ONLY on the provided documents. ### 2. Model Context Protocol (MCP) IntelliCore uses MCP to decouple the AI’s **reasoning** (LLM) from its **capabilities** (tools and data). #### I. Tool Decoupling The LLM acts as an orchestrator. It knows it needs to "retrieve information," but the MCP layer handles the specific implementation (talking to ChromaDB). #### II. The Standardized Tool Registry Centralized in `backend/mcp/tools.py`, all capabilities follow a consistent schema: - **Retriever:** Data sourcing via RAG. - **QA Tool:** Knowledge extraction with grounded logic. - **Summarizer:** Information compression. - **Vision Analyzer:** Interpretation of visual document elements. #### III. Operational Flow 1. **Selection:** Agents identify the best tool for the query. 2. **Invocation:** Agents send structured parameters to the tool. 3. **Response:** Tools return consistent JSON with output and metadata (confidence scores). ### 3. Agentic Framework Built using **LangGraph**, the framework provides a stateful, multi-agent orchestration system following a "Self-Correcting RAG" pattern. #### I. The Orchestration Layer Uses a **State Graph** that allows the system to loop back and correct itself if a "hallucination" is detected. #### II. The Specialist Agents - **🛡️ Gatekeeper (Safety):** Blocks restricted requests (e.g., salary/password queries). - **🔍 Searcher (Retrieval):** Fetches relevant document segments from ChromaDB. - **✍️ Writer (Generation):** Drafts responses using Llama 3.3 70B via Groq. - **✅ Validator (Verification):** Fact-checks the Writer's response against source documents. #### III. The Workflow (Self-Correction Loop) 1. **Safety Check** → If safe, proceed. 2. **Retrieval** → Fetch documents. 3. **Generation** → Writer creates a draft. 4. **Validation** → If the Validator detects a hallucination, it sends a critique back to the Writer for a retry (up to 2 retries). ### 4. Multi Agent System #### I. State Management (`AgentState`) All agents communicate through a shared "Global Memory": ```python class AgentState(TypedDict): query: str # User's input documents: List[str] # Context from ChromaDB generation: str # Current draft steps: List[str] # Trace of agent actions critique: str # Feedback for self-correction hallucination_retries: int # Loop prevention counter ``` #### II. Operational Phases - **Phase A (Gatekeeping):** Scans for restricted keywords. - **Phase B (Retrieval):** populates the state with document chunks. - **Phase C (Generation):** Chooses the best tool (QA, Summarizer, etc.) to draft a response. - **Phase D (Validation):** Ensures **Faithfulness** and **Answer Relevance**. - **Phase E (Self-Correction):** Loops back to the Writer with specific feedback if validation fails. --- ## 🛠️ Setup Instructions ### Prerequisites - Python 3.9+ - Node.js 18+ - MongoDB (Running locally or via Atlas) - Groq API Key ### Backend Setup 1. Navigate to the `backend` directory. 2. Create a `.env` file with the following: ```env GROQ_API_KEY=your_api_key_here MONGO_URI=mongodb://localhost:27017 DATABASE_NAME=policy_kms_db ``` 3. Install dependencies: ```bash pip install -r requirements.txt ``` 4. Start the server: ```bash python main.py ``` ### Frontend Setup 1. Navigate to the `frontend` directory. 2. Install dependencies: ```bash npm install ``` 3. Start the development server: ```bash npm run dev ``` ## 📖 Usage Guide IntelliCore provides distinct interfaces for employees and policy administrators. ### 1. Employee Journey * **Access the Assistant**: Navigate to the Employee Portal and use the chat interface to ask natural language questions about company policies. * **View Policy Alerts**: Check the 'Alerts' tab for real-time notifications about new or updated policies relevant to your department. * **History Management**: Access previous conversations through the sidebar to resume or reference past queries. ### 2. Administrator Journey * **Document Ingestion**: Use the 'Knowledge Base' section to upload PDFs or images. The system will automatically process and vectorize the content. * **Gap Detection**: Monitor the 'Policy Gaps' dashboard to see what questions are being asked that the current documentation doesn't cover. * **Observability**: Use the 'Hallucination Guard' and 'Live Logs' to ensure the AI's responses are accurate and grounded in your specific documents. --- ## ✨ Core Features ### 👥 Employee Platform - **Intelligent Policy Assistant**: A conversational interface to ask complex questions about company policies using natural language. - **Contextual Retrieval**: Answers are automatically personalized based on the user's department and employment status. - **Traceable Answers**: Transparency into the AI's reasoning, showing exactly which document chunks and tools (Searcher, QA) were used. - **Session Persistence**: Complete chat history management, allowing employees to start new threads or resume past conversations. - **Real-time Policy Alerts**: Instant notifications when new policies relevant to the user's department are published. ### 🛡️ Policy Administrator Dashboard - **Multimodal Document Ingestion**: Seamlessly upload PDF policies or document screenshots. The system uses **Vision AI** to OCR and vectorize content. - **Policy Gap Analytics**: Automatically identifies "Knowledge Gaps"—questions employees are asking that are not yet covered by existing documentation. - **System Observability**: - **Live Query Logs**: Real-time monitoring of all employee-AI interactions. - **Health Metrics**: Tracking of system uptime, query success rates, and hallucination frequency. - **Hallucination Guard**: A dedicated view to review and analyze cases where the **Validator Agent** intervened to prevent AI errors. - **Knowledge Base Management**: Full CRUD operations for policy documents and vectorized knowledge chunks. --- ## 📸 Implementation Screenshots ### 🏠 Landing Page <img src="assets/landing.png" width="100%" alt="Landing Page"> ### 👥 Employee Portal <img src="assets/emp_chat.png" width="49%" alt="Employee Chat"> <img src="assets/emp_alerts.png" width="49%" alt="Policy Alerts"> <img src="assets/emp_profile.png" width="49%" alt="Employee Profile"> <img src="assets/emp_feedback.png" width="49%" alt="Employee Feedback"> ### 🛡️ Policy Administrator Dashboard <img src="assets/admin_dashboard.png" width="49%" alt="Admin Dashboard"> <img src="assets/admin_bot.png" width="49%" alt="Enterprise Policy Assistant"> <img src="assets/admin_policygap.png" width="49%" alt="Policy Gap Detection"> <img src="assets/admin_hallunication.png" width="49%" alt="Hallucination Guard"> <img src="assets/admin_emp_list.png" width="49%" alt="Employee Management"> <img src="assets/admin_vfeed.png" width="49%" alt="View Feedback"> <img src="assets/admin_pro.png" width="49%" alt="Admin Profile"> --- ## 🏁 Conclusion IntelliCore represents a leap forward in enterprise knowledge accessibility. By combining the precision of **RAG**, the flexibility of the **Model Context Protocol**, and the reasoning power of **Llama 3.3**, it transforms static policy documents into a dynamic, interactive, and observable knowledge asset. It not only empowers employees with instant, accurate information but also provides administrators with the analytical tools needed to maintain a robust and comprehensive knowledge base. ---

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