ChakshuGautam

Solution → Document Mapping

1. SI Enablement Package

Solution	Docs
1.1 RnR of SI and Platform team	D
1.2 Onboarding Guide for SIs	D, H
1.3.1.1 Documentation and Examples	C, D
1.3.1.2 Auto-generated config files	C, B
1.3.1.3 Progressive Disclosure of configs	C, E

ChakshuGautam

Country Ready Starter Pack

1. SI Enablement Package 1.1. Docs that define RnR of SI and Platform team 1.2. Onboarding Guide for SIs 1.3. What do SIs do? 1.3.1. Configure 1.3.1.1. Documentation and Examples 1.3.1.2. Auto-generated config files for common usecases 1.3.1.3. Progressive Disclosure of configs based on complexity

ChakshuGautam

MECE Knowledge Graph System with Continuous Refactoring

A self-improving knowledge graph system that maintains MECE (Mutually Exclusive, Collectively Exhaustive) compliance through automated refactoring, semantic search, and event-driven monitoring.

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Core Architecture

Data Structures

ChakshuGautam

MECE-Compliant Knowledge Graph Construction: A Multi-Mechanism Approach

Abstract

We present a novel agentic framework for constructing Mutually Exclusive, Collectively Exhaustive (MECE) knowledge graphs from unstructured government scheme data. The system employs four complementary mechanisms—vector-based semantic search, dynamic synonym mapping, strict naming conventions, and post-processing consolidation—to achieve 53.6% entity compression while maintaining complete graph connectivity. Our approach demonstrates significant improvements over baseline methods, reducing duplicate entities by 60% and achieving 95+ quality scores in MECE compliance metrics.

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1. Introduction

ChakshuGautam

Continuous Graph Refactoring Guide

🎯 Your Question Answered: YES!

Q: Do entity types and predicates improve with more data? A: YES! With the new refactoring tools, the graph continuously evolves to be more MECE and generic.

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🔄 Continuous Improvement Workflow

ChakshuGautam

MECE Knowledge Graph Implementation - Pseudocode

Overview

An agentic system that builds MECE-compliant knowledge graphs using 4 complementary mechanisms to prevent duplicate entities and ensure atomic, mutually exclusive concepts.

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Architecture

ChakshuGautam

NIPUN Bharat Load Test Report - 500 VU

Test Duration: 10m 30s | VUs: 500 constant | Date: Dec 26, 2025
Total Requests: 185,548 | Throughput: 294.5 req/s | Failures: 1.04%

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Executive Summary

| Metric | Value | Status |

ChakshuGautam

# SDCRS Deployment Environment Variables
# ========================================

# Database Configuration
POSTGRES_USER=egov
POSTGRES_PASSWORD=egov_password_change_me
POSTGRES_DB=egov

# Kafka Configuration
KAFKA_BROKER=kafka:9092

ChakshuGautam

/**
 * Claude Code System Prompts
 * Extracted from @anthropic-ai/claude-code v2.0.69
 * Source: https://unpkg.com/@anthropic-ai/claude-code/cli.js (line 188088)
 *
 * These prompts are validated server-side by Anthropic.
 * OAuth tokens will be rejected without the correct system prompt prefix.
 */

// =============================================================================

ChakshuGautam

/**
 * Claude Code OAuth Configuration
 * Extracted and deobfuscated from @anthropic-ai/claude-code v2.0.69
 * Source: https://unpkg.com/@anthropic-ai/claude-code/cli.js
 */

// =============================================================================
// Original Source (cli.js lines 58770-58810, prettified)
// =============================================================================