Agentic Research System

A state-of-the-art Retrieval-Augmented Generation (RAG) research workspace powered by LangGraph multi-agent orchestration and RAGAS compliance auditing.

It features a high-fidelity React frontend dashboard with real-time graph execution playbacks, a robust FastAPI backend, and an intelligent client-side fallback simulation for offline/static hosting (such as GitHub Pages).

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Key Features

• Multi-Agent Orchestration (LangGraph):
  • RetrievalAgent: Handles FAISS query matching and dynamic query expansion.
  • ReasoningAgent: Synthesizes and refines grounded responses.
  • ValidationAgent: Audits drafts for hallucinations and enforces faithfulness, triggering self-correction routing loops back to reasoning if claims are ungrounded.
• FAISS Vector Database: Fast local chunking, embedding, and semantic similarity searching.
• Premium Dark-Theme Interface: A sleek React dashboard built with Vite, showcasing real-time trace playbacks and interactive SVG flowcharts representing agent states.
• RAGAS Compliance Auditing: Interactive Evaluation Studio running batch audits of faithfulness, answer relevance, and context precision.
• Hybrid Execution Modes: Seamless transition between Live LLM Engine (OpenAI/Anthropic) and Local Demo/Simulation fallback.

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Architecture Flow

graph TD
    Query[User Query] --> RetrievalAgent[RetrievalAgent: Query Expansion & FAISS Search]
    RetrievalAgent --> ReasoningAgent[ReasoningAgent: Synthesize Draft]
    ReasoningAgent --> ValidationAgent[ValidationAgent: Hallucination & Faithfulness Audit]
    ValidationAgent -- Grounding Failed (Reject) --> ReasoningAgent
    ValidationAgent -- Grounding Passed (Approve) --> FinalAnswer[Final Grounded Answer]

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Getting Started

Prerequisites

• Python 3.10+
• Node.js 18+

Setup & Running the Backend

1. Navigate to the backend directory:

   cd backend

2. Create a virtual environment and activate it:

   python3 -m venv venv
   source venv/bin/activate

3. Install dependencies:

   pip install -r requirements.txt

4. Run the backend: To run imports correctly, execute from the project root folder:

   cd ..
   python3 -m backend.main

   The FastAPI server will start on http://localhost:8000.

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Setup & Running the Frontend

1. Navigate to the frontend directory:

   cd frontend

2. Install dependencies:

   npm install

3. Run the development server:

   npm run dev

   The dashboard will run on http://localhost:5173.

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Offline & Demo Mode

If the backend is not running, the frontend automatically enters Offline Fallback / Demo Mode.

• Supports interactive document management simulations.
• Employs a smart rule-based simulator matching key search terms (e.g., Deep Learning, RAG, LangGraph, Hallucination, Evaluation, Scheduling) to run realistic self-correction loops and return accurate final grounded answers in the UI.
• Accessible live at: https://Zura16.github.io/Agentic-Research-System