NeuroLens-Knowledge-Retrieval-Engine

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NeuroLens-Knowledge-Retrieval-Engine

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NeuroLens is a premium, state-of-the-art Document Retrieval-Augmented Generation (RAG) system. It combines an immersive, holographic, sci-fi-themed React frontend with a high-performance FastAPI backend.

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# NeuroLens Knowledge Retrieval Engine

![NeuroLens Banner](https://raw.githubusercontent.com/udityamerit/NeuroLens-Knowledge-Retrieval-Engine/main/banner.png)

**NeuroLens** is a premium, state-of-the-art Document Retrieval-Augmented Generation (RAG) system. It combines an immersive, holographic, sci-fi-themed React frontend with a high-performance FastAPI backend.

NeuroLens allows users to upload local documents (PDF, DOCX, TXT), index them into a local **FAISS vector database** using **SentenceTransformers embeddings**, and query them interactively with contextual history. It integrates seamlessly with multiple LLM API providers like **Groq**, **OpenAI**, and **Hugging Face Hub** for synthesizing intelligent, citation-backed answers.

---

## 🚀 Key Features

*   **Immersive Holographic UI:** Custom-built glassmorphism design with interactive, physics-based neural synapse canvas animations responding to user interactions.
*   **Local Document Ingestion:** Drag-and-drop support for PDF, Word documents (.docx), and Plain Text (.txt) files.
*   **URL Content Fetching:** Paste any webpage URL to extract and index its content as a knowledge source.
*   **Vector Search & Embeddings:** Employs LangChain, `sentence-transformers/all-MiniLM-L6-v2` embeddings, and a `FAISS` index to run sub-millisecond semantic retrieval locally.
*   **Smart Conversational Rephrasing:** Evaluates active user query history to construct self-contained, standalone search terms before running semantic retrieval.
*   **Multi-Provider Integration:** Custom-configured settings supporting Groq (Llama-3.3, etc.), OpenAI (GPT-4o, etc.), and Hugging Face Hub inference API pipelines.
*   **Granular Citation Support:** Displays glowing, interactive source citations mapping exact text passages and PDF pages matching the generated answers.
*   **Camera OCR Scanner:** Capture documents with your camera and extract text for indexing.
*   **Chat Without Documents:** Ask general questions or chat directly without uploading any files.
*   **Lightweight Deployment & Storage:** Features automatic temporary file cleanups and vector store index serialization locally.

---

## 🛠️ Architecture Overview

NeuroLens is designed as a decoupled client-server architecture.

1.  **Frontend (React/Vite):** Renders the modular interface components (Sidebar, Chat Panel, Settings, and Author modals). Manages local settings configuration, document ingestion status, and query dispatching.
2.  **Backend (FastAPI):** Exposes RESTful API endpoints for file uploads, query execution, document list registry, and database clearing.
3.  **RAG Engine (LangChain/FAISS):** Oversees text extraction, recursive character splitting (size: 800, overlap: 150), vector search matching, and API orchestration with LLM providers.

### System Architecture

```mermaid
graph TD
    classDef client fill:#0d1b2a,stroke:#00f5d4,stroke-width:2px,color:#fff;
    classDef server fill:#1b263b,stroke:#9d4edd,stroke-width:2px,color:#fff;
    classDef database fill:#0f172a,stroke:#3a0ca3,stroke-width:2px,color:#fff;
    classDef external fill:#2b2d42,stroke:#ef233c,stroke-width:2px,color:#fff;

subgraph Client [Client - React/Vite App]
        UI[Interactive UI]:::client
        UploadSide[Sidebar File Upload]:::client
        ChatPanel[Chat Panel]:::client
        SettingsModal[Model & Settings Panel]:::client
    end

subgraph Server [Backend - FastAPI & Python]
 API[FastAPI Router]:::server
 RAGEngine[RAG Engine Manager]:::server
 Parser[Doc Parsers pypdf / docx / txt]:::server
 Splitter[RecursiveCharacterTextSplitter Size: 800, Overlap: 150]:::server
 Embeddings[SentenceTransformers all-MiniLM-L6-v2]:::server
 end

subgraph DB [Vector Storage]
        FAISS[FAISS Index]:::database
    end

subgraph External [External Services]
 LLM[LLM APIs Groq / OpenAI / HF]:::external
 end

%% Flow connections
    UI --> UploadSide
    UI --> ChatPanel
    UI --> SettingsModal

API --> RAGEngine
    RAGEngine --> Parser
    Parser --> Splitter
    Splitter --> Embeddings
    Embeddings -->|Index & Search Chunks| FAISS
    RAGEngine -->|Generates Answer| LLM
```

### RAG Query Pipeline Flow

```mermaid
sequenceDiagram
    autonumber
    actor User as User (UI)
    participant API as FastAPI Backend
    participant RAG as RAG Engine
    participant VS as FAISS Vector Store
    participant LLM as LLM Provider (Groq/OpenAI/HF)

User->>API: Send Question + Chat History + LLM Settings (POST /api/query)
    API->>RAG: Invoke query_llm()
    alt Has Chat History
        RAG->>LLM: Rewrite Query as Standalone (temperature = 0)
        LLM-->>RAG: Return Standalone Search Query
    end
    RAG->>VS: Perform Similarity Search (k matching chunks)
    VS-->>RAG: Return Top-k Chunks + Metadata
    RAG->>RAG: Format System Prompt with Context Chunks
    RAG->>LLM: Invoke Model with Context + Question + History
    LLM-->>RAG: Synthesized RAG Response
    RAG-->>API: Return Answer & Sources JSON
    API-->>User: Render Response with glowing source citation tags
```

---

## ⚡ Setup & Installation

### Prerequisites

*   **Node.js** (v18 or higher)
*   **Python** (v3.10 or higher)

### Environment Configurations

Create a `.env` file in the project's root folder to configure backend LLM credentials (optional; you can also paste them directly into the frontend Settings UI):

```env
GROQ_API_KEY=your_groq_api_key
OPENAI_API_KEY=your_openai_api_key
HF_TOKEN=your_hugging_face_token
```

### Automated Start (Windows)

Simply run the double-click helper batch script:

```bash
run.bat
```

This will automatically create a virtual environment, install Python/Node dependencies, and start both the FastAPI backend (on port `8000`) and the Vite development server (on port `5173`).

---

## 📦 Manual Setup

### 1. Backend

Navigate to the `backend` folder:

```bash
cd backend
```

Create a virtual environment and activate it:

```bash
python -m venv venv
# On Windows
venv\Scripts\activate
# On macOS/Linux
source venv/bin/activate
```

Install requirements:

```bash
pip install -r requirements.txt
```

Launch the FastAPI server:

```bash
python app.py
```

### 2. Frontend

Navigate to the `frontend` folder:

```bash
cd frontend
```

Install dependencies:

```bash
npm install
```

Launch Vite local development server:

```bash
npm run dev
```

---

## 🌐 Production Deployment

The frontend of this application is pre-configured for GitHub Pages deployment. To deploy your own instance:

1. Make sure the `base` property in `frontend/vite.config.js` matches your GitHub repository name:
 ```javascript
 export default defineConfig({
 plugins: [react()],
 base: '/<repository-name>/',
 })
 ```
2. Push to `main`/`master` to trigger the automated GitHub Actions deployment workflow defined in `.github/workflows/deploy.yml`.

---

## 📄 License

This project is licensed under the MIT License. See [LICENSE](LICENSE) for details.

---

Built with ❤️ by <a href="https://udityanarayantiwari.netlify.app/">Uditya Narayan Tiwari</a> &nbsp;|&nbsp;
 <a href="https://github.com/udityamerit">GitHub</a> &nbsp;|&nbsp;
 <a href="https://www.linkedin.com/in/uditya-narayan-tiwari-562332289/">LinkedIn</a>

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