Santhosh P — doc2site

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║  SANTHOSH  ·  AI/ML Engineering Runtime  ·  ACTIVE    ║
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$ whoami

┌─────────────────────────────────────────────────────────────────────┐
│  IDENTITY BLOCK                                                     │
│  ─────────────────────────────────────────────────────────────────  │
│  Role    :  AI / ML Engineer                                        │
│  Focus   :  Agentic systems · RAG pipelines · Production MLOps      │
│  Output  :  Deployed systems — not prototypes                       │
│  Mantra  :  "Make it work. Make it right. Make it fast."            │
└─────────────────────────────────────────────────────────────────────┘

`$ developer-os --describe`

A self-operating system for turning ambiguous problems into deployed AI infrastructure.

╔════════════════════════════════════════════════════════════════════════╗
║                  SANTHOSH ENGINEER OS  ·  RUNTIME SPEC               ║
╠══════════════╦═════════════════════════════════════════════════════════╣
║   INPUT      ║  Problem statements · Research papers · Broken systems ║
╠══════════════╬═════════════════════════════════════════════════════════╣
║  PROCESSOR   ║  Agentic reasoning (LangGraph)                         ║
║              ║  System design thinking → microservice decomposition   ║
║              ║  Architecture-first, code-second approach              ║
╠══════════════╬═════════════════════════════════════════════════════════╣
║   MEMORY     ║  Qdrant / ChromaDB vector stores                       ║
║              ║  BM25 sparse retrieval + dense embedding fusion        ║
║              ║  Past project patterns as reusable mental models       ║
╠══════════════╬═════════════════════════════════════════════════════════╣
║   OUTPUT     ║  Containerized · CI/CD-wired · Security-hardened       ║
║              ║  JSON-logged · Rate-limited · Production-ready         ║
╠══════════════╬═════════════════════════════════════════════════════════╣
║  OS TRAITS   ║  ✓ Docker from commit one                              ║
║              ║  ✓ GitHub Actions in every repo                        ║
║              ║  ✓ Agents / API / ML as separate bounded services      ║
║              ║  ✓ Security baked in — never bolted on                 ║
╚══════════════╩═════════════════════════════════════════════════════════╝

`$ skills --deep`

LOADING MODULE REGISTRY...  ████████████████████████  100%

[MODULE 01] AI CORE ENGINE

Component	Tech	Status
Agent Orchestration	LangGraph · LangChain	🟢 ACTIVE
Vision & Detection	PyTorch · OpenCV · Transformers	🟢 ACTIVE
STT · Multi-language	Whisper · Sarvam AI Saaras v3	🟢 ACTIVE
TTS Synthesis	edge-tts · Neural Voices	🟢 ACTIVE

[MODULE 02] DATA & RETRIEVAL LAYER

Component	Tech	Status
Vector Store	Qdrant · ChromaDB · FAISS	🟢 ACTIVE
Sparse Retrieval	BM25 · Reciprocal Rank Fusion	🟢 ACTIVE
Document Ingestion	MarkItDown · PDF · DOCX · PPTX	🟢 ACTIVE
Embedding Models	MiniLM-L6 · HuggingFace Hub	🟢 ACTIVE

[MODULE 03] BACKEND SYSTEMS

Component	Tech	Status
API Framework	FastAPI	🟢 ACTIVE
Primary Language	Python	🟢 ACTIVE
Relational Store	PostgreSQL	🟢 ACTIVE
Object Storage	MinIO	🟢 ACTIVE

[MODULE 04] INFRASTRUCTURE & DEVOPS

Component	Tech	Status
Containerization	Docker · Docker Compose	🟢 ACTIVE
CI / CD Pipelines	GitHub Actions	🟢 ACTIVE
Security Scanning	Bandit · detect-secrets · ruff	🟢 ACTIVE
Log Monitoring	Dozzle · JSON stdout	🟢 ACTIVE

[MODULE 05] FRONTEND & UX

Component	Tech	Status
Web Framework	Next.js · TypeScript	🟢 ACTIVE
Rapid Prototyping	Gradio	🟢 ACTIVE
Static Generation	Node.js · GitHub Pages	🟢 ACTIVE
Runtime	Linux	🟢 ACTIVE

`$ projects --list --verbose`

SCANNING PROJECT REGISTRY...  4 SYSTEMS FOUND

`[SYS-01]` 🕵️ deepfake-agentic-ai

Problem: Deepfake media is proliferating faster than detection tooling can scale. Existing solutions are monolithic and brittle — one model, one failure point, no auditability.

Solution: Forensic deepfake detection architected as a production microservices mesh.

PIPELINE FLOW:

  [UPLOAD]──►[API Gateway]──►[Agent Orchestrator]──►[ML Service]
                                      │                    │
                               [ChromaDB]◄──────────[RetinaFace]
                                      │             [Xception CNN]
                               [PostgreSQL]         [Transformers]
                                      │
                               [MinIO Object Store]──►[Auto-Expiry 30d]

AUDIT TRAIL:
pending → temp_stored → processing → processed → deleted

Stack:  Python · FastAPI · LangGraph · Docker Compose
        PostgreSQL · ChromaDB · MinIO · RetinaFace · Xception
CI/CD:  GitHub Actions  ·  network audit  ·  logging validation

Architecture: Multi-Dockerfile services (Dockerfile.api / Dockerfile.agents / Dockerfile.ml) sharing a single docker-compose.yml. JSON-structured logging across every module with live Dozzle viewer.

`[SYS-02]` 🧠 rag-multimodal-assistant

Problem: Enterprise manuals exist in a graveyard of PDFs and PPTX files. Users either search manually or get generic LLM hallucinations. No voice support for Indic languages. No agentic troubleshooting.

Solution: Production RAG system with hybrid search, voice layer, and LangGraph-powered diagnostic engine.

INGESTION PIPELINE:

  [PDF·DOCX·PPTX·XLSX·TXT]
         │
   [MarkItDown Parser]
         │
   [Chunker + Overlap Windows]
         │
   [MiniLM-L6 Embeddings]
         │
   [Qdrant Vector Store]


RETRIEVAL ENGINE (3-level priority):

  [Query]──►[Exact Match]──►[Family Match]──►[Global Match]
                │                │                │
           [Dense Vec]      [BM25 Sparse]    [RRF Fusion]──►[LLM]


VOICE LAYER:

  [English Audio]──►[Whisper STT]──►[Response]──►[edge-tts]
  [Indic Audio]───►[Sarvam Saaras v3]──►[Response]──►[edge-tts]


SECURITY STACK:

  slowapi rate limiting · MIME-type guards · regex prompt injection
  isolated RAG prompts · 24-test suite (unit + integration + security)

Stack:  Python · FastAPI · LangGraph · Qdrant · BM25 · MiniLM-L6
        Whisper · Sarvam AI · edge-tts · Next.js · TypeScript · Docker
CI/CD:  GitHub Actions  ·  ruff  ·  black  ·  bandit  ·  detect-secrets

Architecture: Full-stack monorepo (backend/ + frontend/). Prompt injection filtered at both the regex layer and the LLM system prompt level. Admin upload panel in Next.js.

`[SYS-03]` 🛒 shoppyai

Problem: E-commerce discovery still relies on keyword search. Users describe what they want in natural language — the product catalog doesn't speak that.

Solution: LLM-powered natural language product discovery deployed publicly on Hugging Face Spaces.

ARCHITECTURE:

  [User Query (NL)]──►[Gradio UI Layer (app.py)]
                              │
                       [Inference Logic]──►[LLM API]
                              │
                       [Data Layer]──►[FAISS / ChromaDB ready]
                              │
                       [Docker Container]──►[HF Spaces]

Stack:  Python · Gradio · Transformers / LLM APIs · Docker · HF Spaces
CI/CD:  GitHub Actions

Architecture: Prompt-orchestration-first design, modular for async inference and vector DB integration with zero structural changes.

`[SYS-04]` 📄 doc2site

Problem: Markdown documentation has no navigable web presence without a full CMS or framework overhead.

Solution: Zero-config static site generator with incremental builds and auto-deploy on push.

CI/CD PIPELINE:

  [Markdown Source]
         │
   [Node.js Build]──►[HTML + Dark Mode + Search]
         │
   [GitHub Actions]──►[GitHub Pages] (on every push)
         │
   [Incremental]──►[Only changed files regenerated]

Stack:  JavaScript · Node.js · GitHub Actions · GitHub Pages

`$ analytics --dashboard`

╔══════════════════════════════════════════════════════════════╗
║              SYSTEM METRICS  ·  GITHUB TELEMETRY            ║
╚══════════════════════════════════════════════════════════════╝

  UPTIME (Activity)    ██████████████████░░  88%
  THROUGHPUT (Commits) █████████████████░░░  84%
  INFRA COVERAGE       ████████████████████  100%  (Docker on all projects)
  CI/CD COVERAGE       ████████████████████  100%  (Actions on all repos)
  SECURITY POSTURE     ██████████████████░░  Bandit · detect-secrets enabled

`$ standards --list`

Every system in this portfolio ships with the same non-negotiables.

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║  ENGINEERING INVARIANTS                                       ║
╠═══════════════════════════════════════════════════════════════╣
║  [✓]  Containerized from commit one — Docker / Compose       ║
║  [✓]  CI/CD in every repo — lint · security · deploy         ║
║  [✓]  JSON structured logging — stdout, never print()        ║
║  [✓]  Security-first APIs — rate limit · MIME guard · scan   ║
║  [✓]  Separation of concerns — agents ≠ API ≠ ML             ║
╚═══════════════════════════════════════════════════════════════╝

`$ connect --open-to-work`

// Open to roles in AI engineering, MLOps, and agentic systems.
// I build production systems, not demos. Let's build.

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║  SANTHOSH.OS  ·  SYSTEM HALT  ·  All services nominal.  ✓      ║
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$ developer-os --describe

$ skills --deep

$ projects --list --verbose

[SYS-01] 🕵️ deepfake-agentic-ai

[SYS-02] 🧠 rag-multimodal-assistant

[SYS-03] 🛒 shoppyai

[SYS-04] 📄 doc2site

$ analytics --dashboard

$ standards --list

$ connect --open-to-work