{
    "id": "openjarvis-intelligence-stack",
    "name": "OpenJarvis — Full Intelligence Stack",
    "description": "The everything-intelligent stack. OpenJarvis as the central brain with dual inference engines (vLLM + Ollama), full memory hierarchy (Qdrant, ChromaDB, LanceDB, Redis, MariaDB), all RAG/OCR/document tools, audio I/O (Whisper + LuxTTS + Parakeet), computer vision, knowledge graphs, semantic search, and Obsidian knowledge base. 30+ tools working in concert.",
    "version": "1.0",
    "author": "ai-lsc",
    "tags": ["openjarvis", "intelligence", "multi-modal", "rag", "vision", "audio", "knowledge-graph", "memory", "full-stack", "local-first"],
    "endpoints": {
        "openjarvis": "http://localhost:17070",
        "ollama_base": "http://localhost:11434/v1",
        "vllm_base": "http://localhost:8000",
        "litellm_base": "http://localhost:4000",
        "qdrant": "http://localhost:6333",
        "chromadb": "http://localhost:8000",
        "lancedb": "http://localhost:8100",
        "redis": "localhost:6379",
        "mariadb": "localhost:3306",
        "elasticsearch": "http://localhost:9200",
        "meilisearch": "http://localhost:7700",
        "openwebui": "http://localhost:3000"
    },
    "tools": [
        "openjarvis",
        "ollama",
        "vllm",
        "litellm",
        "qdrant",
        "chromadb",
        "lancedb",
        "redis",
        "mariadb",
        "turbovec",
        "graphrag",
        "elasticsearch",
        "meilisearch",
        "airweave",
        "markitdown",
        "opendataloader",
        "opendataloader_pdf",
        "docling",
        "whisper",
        "luxtts",
        "parakeet",
        "deep_eye",
        "understand_anything",
        "fabric",
        "openwebui",
        "openhands",
        "obsidian",
        "aider",
        "hermes_agent",
        "glances",
        "langchain"
    ],
    "notes": {
        "architecture": "OpenJarvis sits at L11 (User Interfaces) as the central brain. It orchestrates all layers below: L5/L6 inference engines, L7 data/knowledge pipelines, L8 automation, L10 intelligent routing, and L13 knowledge management. Every tool in this stack feeds into or is controlled by OpenJarvis.",
        "inference_tier": "Ollama at 11434 handles lightweight and medium models (8B-32B). vLLM at 8000 serves heavy models (70B+) with PagedAttention for max throughput. LiteLLM at 4000 provides a unified OpenAI-compatible API over both backends — OpenJarvis and all sub-agents route through LiteLLM for seamless model switching.",
        "memory_hierarchy": "Five-tier memory system: (1) Redis 6379 = hot cache / pub-sub / session state (ms latency). (2) Qdrant 6333 = semantic vector memory for RAG and agent recall (us). (3) ChromaDB = document chunk embeddings (dedicated RAG pipeline). (4) LanceDB = fast local vector DB for code and small corpus. (5) MariaDB 3306 = persistent structured storage (audit logs, user prefs, conversation history, task state). TurboVec provides accelerated embedding generation across all vector stores.",
        "rag_document_pipeline": "Documents enter through four ingestion paths: MarkItDown converts Office docs to Markdown. OpenDataLoader handles web scraping and structured data. Docling performs deep PDF extraction with layout analysis. OpenDataLoader PDF specializes in scanned/image PDFs. All output flows to Fabric for text transformation, then into vector stores via TurboVec embeddings.",
        "audio_i_o": "Whisper (OpenAI) provides speech-to-text for voice commands and meeting transcription. LuxTTS generates natural speech output for responses and notifications. Parakeet is a lightweight alternative TTS for quick alerts. Together they give OpenJarvis full voice I/O capability.",
        "vision": "Deep Eye provides computer vision — image description, object detection, scene understanding. Understand Anything handles universal document understanding (charts, diagrams, mixed content). These feed into the RAG pipeline so OpenJarvis can reason about visual content.",
        "knowledge_graph_search": "GraphRag builds knowledge graphs from document collections — entities, relationships, community detection. Elasticsearch at 9200 provides full-text search with BM25 ranking. Meilisearch at 7700 provides typo-tolerant instant search. Airweave syncs data across all stores in real-time.",
        "knowledge_management": "Obsidian serves as the human-facing knowledge base — markdown notes, bi-directional links, graph view. OpenJarvis can read/write to the Obsidian vault, making the LLM's knowledge graph accessible to humans through a familiar note-taking interface. Logseq and Joplin are alternative knowledge tools in the stack.",
        "coding_agents": "OpenHands provides autonomous software engineering. Aider handles interactive pair programming. Hermes Agent runs persistent background agent tasks. All three route through the same LiteLLM/Ollama inference path.",
        "monitoring": "Glances provides real-time system resource monitoring. OpenJarvis dashboard exposes all service health, log aggregation, and resource metrics in one view.",
        "recommended_models": "Primary reasoning: qwen2.5:72b or llama3.1:70b (via vLLM). Fast tasks: qwen2.5:14b, mistral-nemo:12b (via Ollama). Coding: qwen2.5-coder:32b. Embeddings: nomic-embed-text (fast), bge-large (quality). Vision: llava:13b. Audio: whisper-large-v3.",
        "setup": "Start services in order: (1) Redis, MariaDB, Elasticsearch (infrastructure). (2) Qdrant, ChromaDB, LanceDB, Meilisearch (memory/search). (3) Ollama, then vLLM for heavy models. (4) LiteLLM pointing at both backends. (5) TurboVec for embedding acceleration. (6) Document tools (Docling, MarkItDown). (7) Audio tools (Whisper, LuxTTS). (8) OpenJarvis as the central brain. (9) Agent tools (OpenHands, Aider, Hermes Agent). (10) Obsidian for knowledge base. AI-LSC manages all of this through the stack template.",
        "resource_requirements": "Minimum: 32GB RAM, 12GB VRAM (6B models). Recommended: 64GB RAM, 24GB VRAM (32B models). Full stack: 128GB RAM, 2x 24GB VRAM (70B models + vLLM + embedding). MariaDB needs 4GB. Elasticsearch needs 4GB. Redis needs 2GB. Plan 16GB+ RAM just for infrastructure services.",
        "tips": "Don't start everything at once — use AI-LSC's service manager to bring up tiers incrementally. The inference tier (Ollama + vLLM + LiteLLM) is the foundation. Memory services (Redis + Qdrant + MariaDB) come next. Then document/audio tools. OpenJarvis last. Use the LiteLLM model list to hot-swap models per task without restarting downstream tools."
    }
}
