NEXUS: Personal Knowledge Base

NEXUS is a high-speed, hybrid query system that transforms your unstructured text documents into a fully queryable, visual knowledge graph.

It uses a Large Language Model (Google's Gemini API) for its world-class accuracy in data extraction and a hyper-fast, custom-built C-Core Engine for instantaneous graph queries.

This "best of both worlds" architecture gives you the intelligence of a massive AI model and the sub-millisecond query speed of pure C.

🏛️ Architecture

NEXUS is built in two distinct phases:

1. Phase 1: Ingestion (Slow, Smart) When you upload a document, the text is sent to the Google Gemini API. The AI reads the text, extracts all entities and relationships, and formats them into a simple graph_data.txt file. This step is "slow" (it can take 30-60 seconds) but only happens once.

   Document.txt --> [Python Backend] --> [Google Gemini API] --> graph_data.txt

2. Phase 2: Querying (Instant, Fast) All subsequent queries use the custom C-Core engine. This compiled program loads the graph_data.txt file into memory and uses highly-optimized C data structures to perform searches. This step is "fast," with most queries returning in less than 1ms.

   User Query --> [Python Backend] --> [C-Core Engine] --> Instant Results

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✨ Features

NEXUS can perform 5 types of advanced queries on your documents:

• Path Finding: Finds the shortest path between two concepts (e.g., "Stack" to "LIFO").
• Topic Discovery: Uses Depth-First Search (DFS) to find all separate, disconnected clusters of information in your graph.
• Visual Mind Map: Generates a visual, interactive graph of how a concept is connected, rendered using Mermaid.js.
• Q&A: Lists all direct, 1-to-1 relationships for a given node.
• Autocomplete: Provides instant search suggestions using a custom-built Trie data structure.

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🛠️ Tech Stack

Component	Technology	Purpose
AI Extraction	Google Gemini API	World-class entity & relation extraction.
Backend	Python Flask	API server to handle requests.
Query Engine	Pure C	Graph algorithms & data structures (DSA).
Frontend	HTML/CSS/JS	User interface for uploading and querying.
Visualization	Mermaid.js	Renders the visual, interactive mind maps.

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🚀 Quick Start

1. Get Your API Key: This project requires a Google Gemini API key.
2. Set Up the Project: Follow the SETUP.md file for detailed installation and compilation steps.
3. Run the Server:

   # (From the 'backend' folder)
   python app.py

4. Open the Frontend: Open the frontend/index.html file in your browser.

Usage

1. Upload: Click "Process Document" and select a .txt file.
2. Wait: Allow 30-60 seconds for the Gemini API to process the text (you only do this once per file).
3. Query: Once the nodes appear in the "Available Nodes" list, use the query interface to explore your new knowledge graph.