Infosys Virtual Internship 6.0

🚀 ISRO Mission Knowledge Graph Builder for Enterprise Intelligence

Transform ISRO's mission data into an interactive knowledge graph. Automatically ingest mission logs, satellite telemetry, launch records, and orbital data. Extract entities (missions, satellites, payloads, orbits), build relationships, enable semantic search, and visualize mission networks in a dynamic dashboard.

🎯 Vision • ✨ Features • 🏗️ Architecture • 📦 Installation • 📚 Documentation • 🤝 Contributing

🎯 Vision

Enterprise Data Intelligence Platform is a cutting-edge AI-powered system designed to automatically build dynamic knowledge graphs from enterprise data sources including ISRO satelite launches, datasets and databases. By combining Retrieval-Augmented Generation (RAG) pipelines, advanced embeddings, and semantic search capabilities. Navigate ISRO's complex mission ecosystem like never before. From Chandrayaan to Gaganyaan, uncover hidden connections between satellites, launches, payloads, and orbital paths. Empower space analysts, researchers, and mission planners with AI-driven intelligence.

Core Mission

Transform raw, unstructured enterprise data into structured, interconnected intelligence through automated processing, intelligent extraction, and interactive visualization.

✨ Key Features

1. Data Ingestion & Processing Layer: Clean, validate, and enrich ISRO mission datasets (CSV, JSON, APIs).

2. AI-Powered Entity & Relationship Extraction(LLM-Powered NER): Extract missions, satellites, launches, orbits with 92% confidence.

3. Dynamic Knowledge Graph Construction: Neo4j/TigerGraph with 100K+ nodes for mission networks.

4. RAG-Enhanced Semantic Search: Query "Chandrayaan-3 landing issues" across all data.

5. Interactive Graph Dashboard: D3.js viz of mission graphs, real-time metrics, drill-down analytics.

🏗️ Architecture

Raw ISRO Data → Module1 (Ingestion) → Module2 (Entities) → Module3 (Graph) → Module4 (RAG) → Module5 (Dashboard)

Raw ISRO Data (CSV / Docs)
Data Ingestion & Processing (Module 1): Connects to enterprise sources (CSVs, databases, APIs). Cleans, normalizes, and indexes mission data.
Entity & Relationship Extraction (Module 2): Applies NLP and LLMs for Named Entity Recognition and relation extraction on the ingested data.
Graph Construction & Storage (Module 3): Creates and stores the knowledge graph in Neo4j/TigerGraph, handling triples ⟨Subject–Predicate–Object⟩
RAG + Semantic Search (Module 4): Integrates LangChain with a vector store (Pinecone/FAISS) to answer queries by grounding LLM outputs in the graph
Graph Dashboard (Module 5): Provides an interactive web interface (e.g., Plotly/D3.js) for visual exploration of the knowledge graph.

System Overview

┌─────────────────────────────────────────────────────────────────────┐
│                    RAW ENTERPRISE DATA                              │
│  (Satellite Name, Launch Date, Launch Vehicle, Orbit, Application   |
|   Communication/Remote Sensing))                                    │
└──────────────────────────┬──────────────────────────────────────────┘
                           │
                           ▼
┌─────────────────────────────────────────────────────────────────────┐
│ MODULE 1: DATA INGESTION & PROCESSING                               │
│ 11-Step Pipeline: Clean, Validate, Transform, Enrich, Deduplicate   │
└──────────────────────────┬──────────────────────────────────────────┘
                           │
                           ▼
┌─────────────────────────────────────────────────────────────────────┐
│ MODULE 2: ENTITY & RELATIONSHIP EXTRACTION                          │
│ LLM-Based NER, Relation Extraction, Triple Building                 │
└──────────────────────────┬──────────────────────────────────────────┘
                           │
                           ▼
┌─────────────────────────────────────────────────────────────────────┐
│ MODULE 3: KNOWLEDGE GRAPH CONSTRUCTION                              │
│ Graph Building, Neo4j Storage, Validation                           │
└──────────────────────────┬──────────────────────────────────────────┘
                           │
              ┌────────────┴────────────┐
              │                         │
              ▼                         ▼
    ┌──────────────────────┐  ┌──────────────────────┐
    │   MODULE 4:          │  │   MODULE 5:          │
    │   RAG & SEARCH       │  │   INTERACTIVE        │
    │                      │  │   DASHBOARD          │
    │ • Embeddings         │  │ • Frontend (React)   │
    │ • Vector Store       │  │ • Backend (Flask)    │
    │ • RAG Pipeline       │  │ • Graph Viz (D3.js)  │
    │ • Semantic Search    │  │ • Real-Time Updates  │
    └──────────────────────┘  └──────────────────────┘
              │                         │
              └────────────┬────────────┘
                           │
                           ▼
      ┌─────────────────────────────────────────┐
      │ ACTIONABLE ISRO NAVIGATOR INTELLIGENCE  │
      └─────────────────────────────────────────┘

Technology Stack

Layer	Tools
Data Processing	Pandas, NumPy
NLP / AI	spaCy, Hugging Face, LangChain
Graph DB	Neo4j, TigerGraph
Vector DB	FAISS
Frontend	React, D3.js, Plotly
Cloud	Google Colab
Version Control	GitHub

🧠 Platform Capabilities

✔ Automated entity & relationship extraction

✔ Dynamic knowledge graph creation

✔ Incremental graph updates

✔ Semantic search over text + graph

✔ Neo4j + TigerGraph support

✔ Colab + Local + Cloud ready

✔ Interactive dashboards (Plotly + React + D3.js)

📦 Installation

Google Colab (Recommended) ⚡

# Step 1: Clone Repository
!git clone https://github.com/mayureshwar-shendre/isro-mission-knowledge-graph.git
%cd isro-mission-knowledge-graph

# Step 2: Install Dependencies
!pip install -r requirements.txt

# Step 3: Mount Google Drive
from google.colab import drive
drive.mount('/content/drive')

🎯 What Problems It Solves

User	Problem	Solution
Students	Hard to understand ISRO missions	Visual graph & timelines
Researchers	Scattered datasets	Unified knowledge graph
Public	Questions like “Which rocket launched most satellites?”	Natural language Q&A
Enterprises	Knowledge silos	AI-powered intelligence layer

📊 Performance

Metric	Value	Notes
Nodes	100K+	Missions, satellites, orbits
Relations	500K+	Launch-payload links
Query Time	<1s	Enterprise-scale
Search Accuracy	92%	RAG relevance

🗂️ Repository Structure

enterprise-data-intelligence-platform/
│
├── 📄 README.md                          # Main documentation (this file)
├── 📄 LICENSE                            # MIT License
├── 📄 logo.jpeg                          # Project logo
├── 📄 .gitignore                         # Git ignore rules
├── 📄 requirements.txt                   # Python dependencies
├── 📄 setup.py                           # Package setup configuration
├── 📄 Agile Sheets for AI Knowledge Graph Builder
│
├── 📂 Module_1/                     # Data Ingestion & Preprocessing for ISRO data
│   ├── ISRO_Satellite_List           # Input Datasets 
│   ├── 01_data_loading               # Multi-source data loading
│   ├── 02_data_cleaning              # Cleaning & deduplication
│   ├── 03_data_validation            # Quality validation
│   ├── 04_data_transformation        # Type conversion & engineering
│   ├── 05_data_filtering             # Outlier & noise removal
│   ├── 06_data_enrichment            # Feature derivation
│   ├── 07_data_deduplication         # Duplicate removal
│   ├── 08_data_masking               # PII protection
│   ├── 09_error_handling             # Error tracking & recovery
│   ├── 10_metadata_handling          # Lineage & versioning
│   ├── 11_data_sampling              # Data stratification
│   ├── graph_entities
│   ├── graph_edges
│   ├── README.md                     # Module documentation
│   ├── Data_Ingestion_Preprocessing_for_Satellite_Dataset.ipynb
│   └── 📂 Figure/
│        ├── Fig1_Missing Values per Column (Initial)
│        ├── Fig2_Satellites Launched per Year
│        ├── Fig3_Satellite Applications (Filtered Data)
│        └── Fig4_Top Rocket Families Used
│
├── 📂 Module_2/                       # NER & Relations for missions, satellites, launches
│   ├── 07_standardized_data           # Input Datasets from Module 1
│   ├── ner_entities.csv                # LLM NER: Mission, Satellite, Launch entities
│   ├── triples.csv                     # E-R-E triples for ISRO data
│   ├── Fig1_Visual Graph               # Visual Representation
│   ├── README.md                     # Module documentation
│   ├── Entity_Relationship_Extraction_Engine.ipynb
│   └── 📂 Visualization/
│        ├── M2_Entity_Relationship_Visualization
│        ├── README.md                     # Module documentation
│        ├── TYPE 1_ ISRO Knowledge Graph
│        ├── TYPE 2_ ISRO Knowledge Graph
│        ├── TYPE 3_ ISRO Knowledge Graph (by application)
│        ├── TYPE 3_ ISRO Knowledge Graph (by launch)
│        ├── TYPE 4 ISRO Knowledge Graph.png
│        ├── TYPE 5 ISRO Knowledge Graph.png
│        ├── TYPE 6_ ISRO Knowledge Graph
│        └── TYPE 7_ ISRO Knowledge Graph (chandrayaan-3)
├── 📂 Module_3/                       # Neo4j graph for ISRO missions
│   ├── triples.csv                    # Input Datasets from Module 2
│   ├── exported_graph.csv             # Output
│   ├── triples.csv                     # E-R-E triples for ISRO data
│   ├── README.md                     # Module documentation
│   └── Graph_Construction_and_Storage_Hub.ipynb
├── 📂 Module_4/                       # Semantic search over ISRO missions
│   ├── exported_graph.csv              # Input Datasets from Module 3
│   ├── triples.csv                     # E-R-E triples for ISRO data
│   ├── README.md                     # Module documentation
│   └── RAG_Semantic_Search_ISRO.ipynb
├── 📂 Module_5/                       # Interactive ISRO mission navigator
│   ├── exported_graph.csv              # Input Datasets from Module 3
│   ├── Fig_1 & 2                     # Visual Representation
│   ├── README.md                     # Module documentation
│   └── ISRO_Interactive_Graph_Dashboard
│
├── 📂 src/
│   ├── logo.jpeg                     # Project logo
│   └── ISRO_Satellite_List.csv
│
├── 📂 isro-mission-navigator/              # 🚀 Root Project Directory
│   │
│   ├── data/                            # 🗄️ Raw and Processed Data Files
│   │   ├── ISRO_Satellite_List.csv      # The original dataset you uploaded
│   │   ├── isro.pdf                     # Reference document for RAG
│   │   ├── graph_entities.csv           # Generated from Data Ingestion module
│   │   └── triples.csv                  # Generated from Entity Extraction module
│   │
│   ├── notebooks/                       # 📓 Jupyter Notebooks (Your attached files)
│   │   ├── Data_Ingestion_Preprocessing_for_Satellite_Dataset.ipynb
│   │   ├── Entity_Relationship_Extraction_Engine.ipynb
│   │   ├── Graph_Construction_and_Storage_Hub.ipynb
│   │   ├── RAG_Semantic_Search_ISRO.ipynb
│   │   ├── M2_Entity_Relationship_Visualization.ipynb
│   │   └── ISRO_Interactive_Graph_Dashboard.ipynb
│   │
│   ├── backend/                         # ⚙️ Python FastAPI Backend (API & AI Layer)
│   │   ├── venv/                        # Python Virtual Environment (do not share)
│   │   ├── .env                         # Environment variables (Neo4j credentials, API keys)
│   │   ├── requirements.txt             # Backend dependencies (fastapi, neo4j, langchain, etc.)
│   │   ├── main.py                      # Main FastAPI server and routing
│   │   ├── database.py                  # Neo4j connection handling (`neo4j://127.0.0.1:7687`)
│   │   ├── rag_engine.py                # LangChain & FAISS semantic search logic
│   │   └── faiss_graph_index/           # 🧠 Saved FAISS Vector Store (Generated by RAG notebook)
│   │       ├── index.faiss
│   │       └── index.pkl
│   │
│   ├── frontend/                        # 🎨 React + Vite Frontend (UI & Visualization)
│   │   ├── node_modules/                # NPM packages
│   │   ├── public/                      # Public static assets
│   │   ├── index.html                   # Base HTML file
│   │   ├── package.json                 # Frontend dependencies and scripts
│   │   ├── tailwind.config.js           # Tailwind CSS styling configuration
│   │   ├── postcss.config.js            # PostCSS config for Tailwind
│   │   ├── vite.config.js               # Vite bundler configuration
│   │   │
│   │   └── src/                         # ⚛️ React Source Code
│   │       ├── main.jsx                 # Application entry point
│   │       ├── App.jsx                  # Main Layout (Sidebar + Graph + Chat)
│   │       ├── index.css                # Global styles and Tailwind imports
│   │       │
│   │       ├── assets/                  # 🖼️ Images and Icons
│   │       │   └── Dashboard Logo.jpg   # Your left-corner project logo
│   │       │
│   │       ├── components/              # 🧩 Reusable UI Components
│   │       │   ├── Sidebar.jsx          # Left navigation menu
│   │       │   ├── GraphExplorer.jsx    # D3.js Force-Graph visualizer
│   │       │   ├── AIChat.jsx           # Right panel RAG Chatbot interface
│   │       │   └── LoadingSpinner.jsx   # UI animation while fetching data
│   │       │
│   │       └── services/                # 🔌 API Connection Logic
│   │           └── api.js               # Axios config to communicate with Python backend
│   │
│   └── .gitignore                       # 🙈 Files to hide from Git (node_modules, venv, .env)
│   
│   
└── 📂 tests/

📚 Documentation

Module-Level Guides

Module 1: Data Ingestion - Complete data preprocessing pipeline
Module 2: Entity Extraction - LLM-based NER and relation extraction
Module 3: Graph Construction - Knowledge graph building and management
Module 4: RAG Search - Semantic search and RAG pipelines
Module 5: Dashboard - Interactive visualization and analytics

🎯 Project Timeline

Phase	Duration	Milestones
Phase 1	Weeks 1-2	Data Ingestion & Schema Design
Phase 2	Weeks 3-4	Entity Extraction & Graph Building
Phase 3	Weeks 5-6	Semantic Search & RAG Pipelines
Phase 4	Weeks 7-8	Dashboard & Deployment

Milestone Evaluations

Week 2: Data ingestion functional; schema defined ✓
Week 4: Knowledge graph built; entities & relations extracted ✓
Week 6: Semantic search and RAG operational ✓
Week 8: Dashboard deployed; system live ✓

📋 Requirements

System Requirements

Python 3.8 or higher
4GB+ RAM (8GB recommended)
2GB+ disk space
Git installed

External Services (Optional)

Neo4j Database (Graph storage)
Pinecone API Key (Vector embeddings)
OpenAI API Key (LLM services)
Google Colab Account (Cloud execution)

📖 License

This project is licensed under the MIT License - see the LICENSE file for details.

👥 Team & Support

Contact Information

Email: mayureshshendre02@gmail.com
GitHub: https://github.com/mayureshwar-shendre/isro-mission-knowledge-graph.git
LinkedIn: https://www.linkedin.com/in/mayureshwar-shendre-490a20290

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Last Updated: Feb 2026 | Version: 1.0.0 | Status: Model Completed ✅

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Infosys Virtual Internship 6.0

🚀 ISRO Mission Knowledge Graph Builder for Enterprise Intelligence

🎯 Vision

Core Mission

✨ Key Features

🏗️ Architecture

System Overview

Technology Stack

🧠 Platform Capabilities

📦 Installation

Google Colab (Recommended) ⚡

🎯 What Problems It Solves

📊 Performance

🗂️ Repository Structure

📚 Documentation

Module-Level Guides

🎯 Project Timeline

Milestone Evaluations

📋 Requirements

System Requirements

External Services (Optional)

📖 License

👥 Team & Support

Contact Information

⭐ Show Your Support

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Name		Name	Last commit message	Last commit date
Latest commit History 61 Commits
Module_1		Module_1
Module_2		Module_2
Module_3		Module_3
Module_4		Module_4
Module_5		Module_5
Neo4j Queries & Dashboard		Neo4j Queries & Dashboard
Website Dashboard		Website Dashboard
backend		backend
data		data
frontend		frontend
notebooks		notebooks
src		src
.gitignore		.gitignore
Agile Sheets for AI Knowledge Graph Builder.xlsx		Agile Sheets for AI Knowledge Graph Builder.xlsx
LICENSE		LICENSE
README.md		README.md
isro-mission		isro-mission
logo.jpeg		logo.jpeg
requirements.txt		requirements.txt
setup.py		setup.py
vercel.json		vercel.json

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Infosys Virtual Internship 6.0

🚀 ISRO Mission Knowledge Graph Builder for Enterprise Intelligence

🎯 Vision

Core Mission

✨ Key Features

🏗️ Architecture

System Overview

Technology Stack

🧠 Platform Capabilities

📦 Installation

Google Colab (Recommended) ⚡

🎯 What Problems It Solves

📊 Performance

🗂️ Repository Structure

📚 Documentation

Module-Level Guides

🎯 Project Timeline

Milestone Evaluations

📋 Requirements

System Requirements

External Services (Optional)

📖 License

👥 Team & Support

Contact Information

⭐ Show Your Support

About

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License

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