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SaurabhCodesAI/README.md
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Executive Summary

I am a Developer focused on building systems for complex problem-solving. My flagship project, ENTAERA, is an hybrid research agent that dynamically selects tools, maintains long term memory, and adapts its strategy to optimize for both performance and cost efficiency.

AI Skills Showcase

I am training for end-to-end development of AI systems, from initial API integration and debugging to building robust, multi provider frameworks that solve real world challenges.

Projects Delivered

Autonomous Hybrid Research Agent

A system designed to ingest and synthesize information from multiple sources using a dynamic tool selection mechanism and vector-based memory, integrating both cloud APIs and local open-source models.

πŸ” View Project Details

Key Features

  • Hybrid Provider Integration: Routes requests across Azure, Google Gemini, Perplexity AI, and local models like Llama 3.1 8B.
  • Vector Memory System: FAISS-powered semantic storage and retrieval using all-MiniLM-L6-v2 embeddings.
  • Dynamic Tool Selection: Routes to Google Search, Arxiv, and custom functions based on task requirements.
  • Self-Correction Loop: Implements rule-based feedback for improved tool selection over time.
  • Cost & Rate Limit Management: Strategically utilizes cloud APIs alongside local models to reduce operational costs.
  • Asynchronous Architecture: Built with Python, AsyncIO, and LangChain for concurrent, scalable task execution.

Technical Architecture

β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ β”‚ β”‚ β”‚ β”‚ β”‚ β”‚ User Request β”œβ”€β”€β”€β”€β–Ίβ”‚ Task Planning β”œβ”€β”€β”€β”€β–Ίβ”‚ Tool Selection β”‚ β”‚ β”‚ β”‚ β”‚ β”‚ β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ β–Ό β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ β”‚ β”‚ β”‚ β”‚ β”‚ β”‚ Presentation │◄───── Summarization │◄───── Information β”‚ β”‚ β”‚ β”‚ β”‚ β”‚ Gathering β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

Proven Capabilities

  • Four AI Services Connected: Reliably integrates Azure OpenAI, Google Gemini, Perplexity, and local Ollama.
  • Smart Routing System: Analyzes query type and complexity to select the appropriate AI service while tracking costs.
  • Solid Foundation: Built with professional logging, secure credential management, and comprehensive error handling.

AI-Powered Presentation Generator (Hackathon)

Transforms screenshots, images, and notes into presentations using AI-driven content analysis and theme generation.

πŸ” View Project Details

Key Features

  • Automated Content Extraction: Using Google Gemini Vision API
  • AI Theme Generator: Smart color palette and layout suggestions
  • Drag & Drop Interface: Intuitive content organization
  • Multiple Export Formats: PDF, PPTX, HTML support

Technical Implementation

  • Next.js 14 with App Router for modern UI architecture
  • TypeScript with strict configuration for type safety
  • Tailwind CSS for responsive, accessible design
  • CI/CD Pipeline with health monitoring for production readiness

πŸ› οΈ Technology Stack

Pinned Loading

  1. ENTAERA ENTAERA Public

    A multi-AI integration system that started as curiosity and became a solid foundation for understanding modern AI development

    Python 6 3

  2. snap2slides snap2slides Public

    AI powered tool leveraging Gemini Vision Pro , Slides and API to transform visual notes into structured presentations in seconds.

    TypeScript 3

  3. n8n-Workflow n8n-Workflow Public

    Python 1