[{"_1":2,"_424":-5,"_425":-5},"loaderData",{"_3":4,"_22":23,"_247":248},"root",{"_5":6,"_7":8},"toasts",[],"visitor",{"_9":10,"_11":12},"viewportWidth",1280,"visitorSession",{"_13":14,"_16":17,"_18":-5,"_19":20,"_21":-5},"attributes",[15],"bot","id",16683697,"membership","referenceId","01997ec3-5d26-711c-83e8-e3c03791c852","userAccount","routes/_public/mcp/servers/~namespace/~slug/_pages/_layout",{"_24":25,"_76":-5,"_77":38,"_27":78,"_164":165,"_246":147},"availableLocales",[26,38,44,50,57,64,70],{"_27":28,"_31":35,"_36":37},"mcpServer",{"_29":30,"_31":32,"_33":34},"description","Ein MCP-Server, der Graphstrukturen nutzt, um über eine 8-stufige Verarbeitungspipeline anspruchsvolle wissenschaftliche Schlussfolgerungen zu ziehen, sodass KI-Systeme komplexe wissenschaftliche Abfragen mit dynamischer Vertrauensbewertung verarbeiten können.","name","NexusMind","readme","# 🧠 NexusMind\n\n#### **Intelligentes wissenschaftliches Denken durch Gedankengraphen**\n\n[![Version](https://img.shields.io/badge/version-0.1.0-blue.svg)](https://github.com/SaptaDey/NexusMind/releases) [![Python](https://img.shields.io/badge/python-3.13.3-blue.svg)](https://www.python.org/downloads/)[![Lizenz](https://img.shields.io/badge/license-Apache_2.0-green.svg)](LICENSE) [![Docker](https://img.shields.io/badge/docker-ready-brightgreen.svg)](Dockerfile) [![FastAPI](https://img.shields.io/badge/FastAPI-0.111.0-009688.svg)](https://fastapi.tiangolo.com) [![NetworkX](https://img.shields.io/badge/NetworkX-3.3-orange.svg)](https://networkx.org) [![Zuletzt aktualisiert](https://img.shields.io/badge/last_updated-May_2025-lightgrey.svg)](CHANGELOG.md)\n\n## 🔍 Übersicht\n\nNexusMind nutzt **Graphstrukturen** für anspruchsvolle wissenschaftliche Schlussfolgerungen. Es implementiert das **Model Context Protocol (MCP)** für die Integration mit KI-Anwendungen wie Claude Desktop.\n\n**Wichtige Highlights:**\n\n* Verarbeiten Sie komplexe wissenschaftliche Abfragen mithilfe graphenbasierter Argumentation\n* Dynamisches Vertrauens-Scoring mit mehrdimensionalen Auswertungen\n* Erstellt mit modernem Python und FastAPI für hohe Leistung\n* Dockerisiert für einfache Bereitstellung\n* Modulares Design für Erweiterbarkeit und Anpassung\n\n## 🌟 Hauptfunktionen\n\n### 8-stufige Reasoning-Pipeline\n\nDer Kernschlussfolgerungsprozess folgt einer ausgeklügelten 8-stufigen Pipeline:\n\n1. **🌱 Initialisierung**\n\n * Erstellt den Stammknoten aus der Abfrage\n * Legt die anfängliche Graphstruktur fest\n * Legt die Konfidenzbasislinie fest\n\n2. **🧩 Zersetzung**\n\n * Zerlegt die Abfrage in Dimensionen\n * Identifiziert Schlüsselkomponenten\n * Erstellt Dimensionsknoten\n\n3. **🔬 Hypothese/Planung**\n\n * Generiert mehrere Hypothesen\n * Erstellt eine Argumentationsstrategie\n * Legt Fälschungskriterien fest\n\n4. **📊 Beweisintegration**\n\n * Sammelt unterstützende Beweise\n * Verknüpft Beweise mit Hypothesen\n * Aktualisiert die Konfidenzwerte\n\n5. **✂️ Beschneiden/Zusammenführen**\n\n * Entfernt Elemente mit geringem Wert\n * Konsolidiert ähnliche Knoten\n * Optimiert die Graphstruktur\n\n6. **🔍 Subgraph-Extraktion**\n\n * Identifiziert relevante Teile\n * Konzentriert sich auf hochwertige Pfade\n * Erstellt zielgerichtete Untergraphen\n\n7. **📝 Zusammensetzung**\n\n * Fasst die wichtigsten Erkenntnisse zusammen\n * Schafft kohärente Erkenntnisse\n * Generiert umfassende Antworten\n\n8. **🤔 Reflexion**\n\n * Bewertet die Qualität des Denkvermögens\n * Identifiziert Verbesserungsbereiche\n * Abschließende Vertrauensbewertung\n\n### Technische Möglichkeiten\n\n* **🧠 Graphische Wissensdarstellung** : Verwendet `networkx` zur Modellierung komplexer Beziehungen\n* **🔄 Dynamische Vertrauensvektoren** :\n * Empirische Unterstützung\n * Theoretische Grundlagen\n * Methodische Strenge\n * Konsensausrichtung\n* **🔌 MCP-Server** : Nahtlose Claude Desktop-Integration\n* **⚡ Hochleistungs-API** : Moderne FastAPI-Implementierung\n* **🐳 Einfache Bereitstellung** : Docker- und Docker Compose-Unterstützung\n* **🧩 Erweiterbare Architektur** : Modulare Komponenten zur individuellen Anpassung\n* **⚙️ Flexible Konfiguration** : Pydantic- und YAML-Konfiguration\n\n## 🛠️ Technologie-Stack\n\n## 📂 Projektstruktur\n\n```\nasr-got-reimagined/\n├── config/ \n│ ├── settings.yaml \n│ └── claude_mcp_config.json \n├── src/asr_got_reimagined/ \n│ ├── api/ \n│ │ ├── routes/ \n│ │ │ └── mcp.py \n│ │ └── schemas.py \n│ ├── domain/ \n│ │ ├── models/ \n│ │ │ ├── common.py \n│ │ │ ├── graph_elements.py \n│ │ │ └── graph_state.py \n│ │ ├── services/ \n│ │ │ └── got_processor.py \n│ │ ├── stages/ \n│ │ │ ├── base_stage.py \n│ │ │ ├── stage_1_*.py \n│ │ │ └── stage_2_*.py... \n│ │ └── utils/ \n│ ├── main.py \n│ └── app_setup.py \n├── scripts/ \n├── Dockerfile \n├── docker-compose.yml \n└── pyproject.toml \n```\n\n## 🚀 Erste Schritte\n\n### Voraussetzungen\n\n* **Python 3.13+** (Docker-Image verwendet Python 3.13.3-slim-bookworm)\n* **[Poesie](https://python-poetry.org/docs/#installation)** : Für das Abhängigkeitsmanagement\n* **[Docker](https://www.docker.com/get-started)** und **[Docker Compose](https://docs.docker.com/compose/install/)** : Für die containerisierte Bereitstellung\n\n### Installation und Einrichtung (lokale Entwicklung)\n\n1. **Klonen Sie das Repository** :\n\n ```bash\n git clone https://github.com/SaptaDey/NexusMind.git\n cd NexusMind\n ```\n\n2. **Installieren Sie Abhängigkeiten mit Poetry** :\n\n ```bash\n poetry install\n ```\n\n Dadurch wird eine virtuelle Umgebung erstellt und alle in `pyproject.toml` angegebenen erforderlichen Pakete installiert.\n\n3. **Aktivieren Sie die virtuelle Umgebung** :\n\n ```bash\n poetry shell\n ```\n\n4. **Konfigurieren Sie die Anwendung** :\n\n * Passen Sie die Einstellungen in `config/settings.yaml` nach Bedarf an\n * Konfigurieren Sie Umgebungsvariablen oder eine `.env` Datei für vertrauliche Informationen\n\n5. **Führen Sie den Entwicklungsserver aus** :\n\n ```bash\n python src/asr_got_reimagined/main.py\n ```\n\n Alternativ für mehr Kontrolle:\n\n ```bash\n uvicorn asr_got_reimagined.main:app --reload --host 0.0.0.0 --port 8000\n ```\n\n Die API wird unter `http://localhost:8000` verfügbar sein.\n\n### Docker-Bereitstellung\n\n1. **Erstellen und Ausführen der Docker-Container** :\n\n ```bash\n docker-compose up --build\n ```\n\n Für den getrennten Modus:\n\n ```bash\n docker-compose up --build -d\n ```\n\n Das Docker-Image verwendet Python 3.13.3-slim-bookworm als Basis-Image für verbesserte Leistung und Sicherheit.\n\n2. **Zugriff auf die API** : Die API ist unter `http://localhost:8000` (oder wie in `docker-compose.yml` konfiguriert) zugänglich.\n\n## 🔌 API-Endpunkte\n\n### Kernendpunkte\n\n* **MCP-Endpunkt** : `POST /mcp`\n\n * Für die Kommunikation mit MCP-Clients wie Claude Desktop\n * Implementiert das Model Context Protocol für die KI-Interaktion\n\n* **Gesundheitscheck** : `GET /health`\n\n * Grundlegende Integritätsprüfung zur Überwachung und Diensterkennung\n\n### Zusätzliche Endpunkte (geplant)\n\n* **Graphabfrage** : `POST /api/graph/query`\n\n * Direkte Schnittstelle für ASR-GoT-Abfragen\n\n* **Graphstatus** : `GET /api/graph/{session_id}`\n\n * Aktuellen Status eines Reasoning-Graphen abrufen\n\n* **Analytics** : `GET /api/analytics/{session_id}`\n\n * Erhalten Sie Kennzahlen zum Denkprozess\n\n## 🧪 Prüfung & Qualität\n\n### Entwicklungstools\n\n* **Typsicherheit** :\n\n * Konfiguriert mit `mypy.ini` und `pyrightconfig.json`\n * Beheben Sie Probleme mit dem Logger-Typ mit `python scripts/add_type_hints.py`\n\n* **Codequalität** :\n\n * Vollständig typisierte Codebasis mit Typanmerkungen\n * Pre-Commit-Hooks verfügbar: `poetry run pre-commit install`\n * Automatisierte Formatierung mit Ruff","German","tag","de-DE",{"_27":39,"_31":42,"_36":43},{"_29":40,"_31":32,"_33":41},"An MCP server that leverages graph structures to perform sophisticated scientific reasoning through an 8-stage processing pipeline, enabling AI systems to handle complex scientific queries with dynamic confidence scoring.","# 🧠 NexusMind\n\n

\n\n```\n ╔══════════════════════════════════════╗\n ║ ║\n ║ 🧠 NexusMind 🧠 ║\n ║ ║\n ║ Intelligent Scientific ║\n ║ Reasoning through ║\n ║ Graph-of-Thoughts ║\n ║ ║\n ╚══════════════════════════════════════╝\n```\n\n#### **Intelligent Scientific Reasoning through Graph-of-Thoughts**\n\n[![Version](https://img.shields.io/badge/version-0.1.0-blue.svg)](https://github.com/SaptaDey/NexusMind/releases)\n[![Python](https://img.shields.io/badge/python-3.13.3-blue.svg)](https://www.python.org/downloads/)\n[![License](https://img.shields.io/badge/license-Apache_2.0-green.svg)](LICENSE)\n[![Docker](https://img.shields.io/badge/docker-ready-brightgreen.svg)](Dockerfile)\n[![FastAPI](https://img.shields.io/badge/FastAPI-0.111.0-009688.svg)](https://fastapi.tiangolo.com)\n[![NetworkX](https://img.shields.io/badge/NetworkX-3.3-orange.svg)](https://networkx.org)\n[![Last Updated](https://img.shields.io/badge/last_updated-May_2024-lightgrey.svg)](CHANGELOG.md)\n\n

\n\n

🚀 Next-Generation AI Reasoning Framework for Scientific Research

Leveraging graph structures to transform how AI systems approach scientific reasoning

\n\n## 🔍 Overview\n\nNexusMind leverages **graph structures** to perform sophisticated scientific reasoning. It implements the **Model Context Protocol (MCP)** to integrate with AI applications like Claude Desktop, providing an Advanced Scientific Reasoning Graph-of-Thoughts (ASR-GoT) framework designed for complex research tasks.\n\n**Key highlights:**\n- Process complex scientific queries using graph-based reasoning\n- Dynamic confidence scoring with multi-dimensional evaluations \n- Built with modern Python and FastAPI for high performance\n- Dockerized for easy deployment\n- Modular design for extensibility and customization\n- Integration with Claude Desktop via MCP protocol\n\n## 🌟 Key Features\n\n### 8-Stage Reasoning Pipeline\n\n```mermaid\ngraph TD\n A[🌱 Stage 1: Initialization] --> B[🧩 Stage 2: Decomposition]\n B --> C[🔬 Stage 3: Hypothesis/Planning]\n C --> D[📊 Stage 4: Evidence Integration]\n D --> E[✂️ Stage 5: Pruning/Merging]\n E --> F[🔍 Stage 6: Subgraph Extraction]\n F --> G[📝 Stage 7: Composition]\n G --> H[🤔 Stage 8: Reflection]\n \n A1[Create root node
Set initial confidence
Define graph structure] --> A\n B1[Break into dimensions
Identify components
Create dimensional nodes] --> B\n C1[Generate hypotheses
Create reasoning strategy
Set falsification criteria] --> C\n D1[Gather evidence
Link to hypotheses
Update confidence scores] --> D\n E1[Remove low-value elements
Consolidate similar nodes
Optimize structure] --> E\n F1[Identify relevant portions
Focus on high-value paths
Create targeted subgraphs] --> F\n G1[Synthesize findings
Create coherent insights
Generate comprehensive answer] --> G\n H1[Evaluate reasoning quality
Identify improvements
Final confidence assessment] --> H\n \n style A fill:#e1f5fe\n style B fill:#f3e5f5\n style C fill:#e8f5e8\n style D fill:#fff3e0\n style E fill:#ffebee\n style F fill:#f1f8e9\n style G fill:#e3f2fd\n style H fill:#fce4ec\n```\n\nThe core reasoning process follows a sophisticated 8-stage pipeline:\n\n1. **🌱 Initialization**\n - Creates root node from query with multi-dimensional confidence vector\n - Establishes initial graph structure with proper metadata\n - Sets baseline confidence across empirical, theoretical, methodological, and consensus dimensions\n\n2. **🧩 Decomposition**\n - Breaks query into key dimensions: Scope, Objectives, Constraints, Data Needs, Use Cases\n - Identifies potential biases and knowledge gaps from the outset\n - Creates dimensional nodes with initial confidence assessments\n\n3. **🔬 Hypothesis/Planning**\n - Generates 3-5 hypotheses per dimension with explicit falsification criteria\n - Creates detailed execution plans for each hypothesis\n - Tags with disciplinary provenance and impact estimates\n\n4. **📊 Evidence Integration**\n - Iteratively selects hypotheses based on confidence-to-cost ratio and impact\n - Gathers and links evidence using typed edges (causal, temporal, correlative)\n - Updates confidence vectors using Bayesian methods with statistical power assessment\n\n5. **✂️ Pruning/Merging**\n - Removes nodes with low confidence and impact scores\n - Consolidates semantically similar nodes\n - Optimizes graph structure while preserving critical relationships\n\n6. **🔍 Subgraph Extraction**\n - Identifies high-value subgraphs based on multiple criteria\n - Focuses on nodes with high confidence and impact scores\n - Extracts patterns relevant to the original query\n\n7. **📝 Composition**\n - Synthesizes findings into coherent narrative\n - Annotates claims with node IDs and edge types\n - Provides comprehensive answers with proper citations\n\n8. **🤔 Reflection**\n - Performs comprehensive quality audit\n - Evaluates coverage, bias detection, and methodological rigor\n - Provides final confidence assessment and improvement recommendations\n\n### Advanced Technical Capabilities\n\n

\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n

🔄 Multi-Dimensional Confidence	🧠 Graph-Based Knowledge	🔌 MCP Integration	⚡ FastAPI Backend
🐳 Docker Deployment	🧩 Modular Design	⚙️ Configuration Management	🔒 Type Safety
🌐 Interdisciplinary Bridge Nodes	🔗 Hyperedge Support	📊 Statistical Power Analysis	🎯 Impact Estimation

\n\n**Core Features:**\n- **🧠 Graph Knowledge Representation**: Uses `networkx` to model complex relationships with hyperedges and multi-layer networks\n- **🔄 Dynamic Confidence Vectors**: Four-dimensional confidence assessment (empirical support, theoretical basis, methodological rigor, consensus alignment)\n- **🌐 Interdisciplinary Bridge Nodes**: Automatically connects insights across different research domains\n- **🔗 Advanced Edge Types**: Supports causal, temporal, correlative, and custom relationship types\n- **📊 Statistical Rigor**: Integrated power analysis and effect size estimation\n- **🎯 Impact-Driven Prioritization**: Focuses on high-impact research directions\n- **🔌 MCP Server**: Seamless Claude Desktop integration with Model Context Protocol\n- **⚡ High-Performance API**: Modern FastAPI implementation with async support\n\n## 🛠️ Technology Stack\n\n

\n \n \n \n \n \n \n \n \n \n \n \n \n \n

Python 3.13+	FastAPI	NetworkX	Docker
Pytest	Pydantic	Poetry	Uvicorn

\n\n## 📂 Project Structure\n\n```\nNexusMind/\n├── 📁 config/ # Configuration files\n│ ├── settings.yaml # Application settings\n│ ├── claude_mcp_config.json # Claude MCP integration config\n│ └── logging.yaml # Logging configuration\n│\n├── 📁 src/asr_got_reimagined/ # Main source code\n│ ├── 📁 api/ # API layer\n│ │ ├── 📁 routes/ # API route definitions\n│ │ │ ├── mcp.py # MCP protocol endpoints\n│ │ │ ├── health.py # Health check endpoints\n│ │ │ └── graph.py # Graph query endpoints\n│ │ ├── schemas.py # API request/response schemas\n│ │ └── middleware.py # API middleware\n│ │\n│ ├── 📁 domain/ # Core business logic\n│ │ ├── 📁 models/ # Domain models\n│ │ │ ├── common.py # Common types and enums\n│ │ │ ├── graph_elements.py # Node, Edge, Hyperedge models\n│ │ │ ├── graph_state.py # Graph state management\n│ │ │ ├── confidence.py # Confidence vector models\n│ │ │ └── metadata.py # Metadata schemas\n│ │ │\n│ │ ├── 📁 services/ # Business services\n│ │ │ ├── got_processor.py # Main GoT processing service\n│ │ │ ├── evidence_service.py # Evidence gathering and assessment\n│ │ │ ├── confidence_service.py # Confidence calculation service\n│ │ │ ├── graph_service.py # Graph manipulation service\n│ │ │ └── mcp_service.py # MCP protocol service\n│ │ │\n│ │ ├── 📁 stages/ # 8-Stage pipeline implementation\n│ │ │ ├── base_stage.py # Abstract base stage\n│ │ │ ├── stage_1_initialization.py # Stage 1: Graph initialization\n│ │ │ ├── stage_2_decomposition.py # Stage 2: Query decomposition\n│ │ │ ├── stage_3_hypothesis.py # Stage 3: Hypothesis generation\n│ │ │ ├── stage_4_evidence.py # Stage 4: Evidence integration\n│ │ │ ├── stage_5_pruning.py # Stage 5: Pruning and merging\n│ │ │ ├── stage_6_extraction.py # Stage 6: Subgraph extraction\n│ │ │ ├── stage_7_composition.py # Stage 7: Answer composition\n│ │ │ └── stage_8_reflection.py # Stage 8: Quality reflection\n│ │ │\n│ │ └── 📁 utils/ # Utility functions\n│ │ ├── graph_utils.py # Graph manipulation utilities\n│ │ ├── confidence_utils.py # Confidence calculation utilities\n│ │ ├── statistical_utils.py # Statistical analysis utilities\n│ │ ├── bias_detection.py # Bias detection algorithms\n│ │ └── temporal_analysis.py # Temporal pattern analysis\n│ │\n│ ├── 📁 infrastructure/ # Infrastructure layer\n│ │ ├── 📁 database/ # Database integration\n│ │ ├── 📁 cache/ # Caching layer\n│ │ └── 📁 external/ # External service integrations\n│ │\n│ ├── main.py # Application entry point\n│ └── app_setup.py # Application setup and configuration\n│\n├── 📁 tests/ # Test suite\n│ ├── 📁 unit/ # Unit tests\n│ │ ├── 📁 stages/ # Stage-specific tests\n│ │ ├── 📁 services/ # Service tests\n│ │ └── 📁 models/ # Model tests\n│ ├── 📁 integration/ # Integration tests\n│ └── 📁 fixtures/ # Test fixtures and data\n│\n├── 📁 scripts/ # Utility scripts\n│ ├── setup_dev.py # Development setup\n│ ├── add_type_hints.py # Type hint utilities\n│ └── deployment/ # Deployment scripts\n│\n├── 📁 docs/ # Documentation\n│ ├── api/ # API documentation\n│ ├── architecture/ # Architecture diagrams\n│ └── examples/ # Usage examples\n│\n├── 📁 static/ # Static assets\n│ └── nexusmind-logo.png # Application logo\n│\n├── 📄 Docker Files & Config\n├── Dockerfile # Docker container definition\n├── docker-compose.yml # Multi-container setup\n├── .dockerignore # Docker ignore patterns\n│\n├── 📄 Configuration Files\n├── pyproject.toml # Python project configuration\n├── poetry.lock # Dependency lock file\n├── mypy.ini # Type checking configuration\n├── pyrightconfig.json # Python type checker config\n├── .pre-commit-config.yaml # Pre-commit hooks\n├── .gitignore # Git ignore patterns\n│\n└── 📄 Documentation\n ├── README.md # This file\n ├── CHANGELOG.md # Version history\n ├── LICENSE # Apache 2.0 license\n └── CONTRIBUTING.md # Contribution guidelines\n```\n\n## 🚀 Getting Started\n\n### Prerequisites\n\n- **Python 3.13+** (Docker image uses Python 3.13.3-slim-bookworm)\n- **[Poetry](https://python-poetry.org/docs/#installation)**: For dependency management\n- **[Docker](https://www.docker.com/get-started)** and **[Docker Compose](https://docs.docker.com/compose/install/)**: For containerized deployment\n\n### Installation and Setup (Local Development)\n\n1. **Clone the repository**:\n ```bash\n git clone https://github.com/SaptaDey/NexusMind.git\n cd NexusMind\n ```\n\n2. **Install dependencies using Poetry**:\n ```bash\n poetry install\n ```\n This creates a virtual environment and installs all necessary packages specified in `pyproject.toml`.\n\n3. **Activate the virtual environment**:\n ```bash\n poetry shell\n ```\n\n4. **Configure the application**:\n ```bash\n # Copy example configuration\n cp config/settings.example.yaml config/settings.yaml\n \n # Edit configuration as needed\n vim config/settings.yaml\n ```\n\n5. **Set up environment variables** (optional):\n ```bash\n # Create .env file for sensitive configuration\n echo \"LOG_LEVEL=DEBUG\" > .env\n echo \"API_HOST=0.0.0.0\" >> .env\n echo \"API_PORT=8000\" >> .env\n ```\n\n6. **Run the development server**:\n ```bash\n python src/asr_got_reimagined/main.py\n ```\n \n Alternatively, for more control:\n ```bash\n uvicorn asr_got_reimagined.main:app --reload --host 0.0.0.0 --port 8000\n ```\n \n The API will be available at `http://localhost:8000`.\n\n### Docker Deployment\n\n```mermaid\ngraph TB\n subgraph \"Development Environment\"\n A[👨‍💻 Developer] --> B[🐳 Docker Compose]\n end\n \n subgraph \"Container Orchestration\"\n B --> C[📦 NexusMind Container]\n B --> D[📊 Monitoring Container]\n B --> E[🗄️ Database Container]\n end\n \n subgraph \"NexusMind Application\"\n C --> F[⚡ FastAPI Server]\n F --> G[🧠 ASR-GoT Engine]\n F --> H[🔌 MCP Protocol]\n end\n \n subgraph \"External Integrations\"\n H --> I[🤖 Claude Desktop]\n H --> J[🔗 Other AI Clients]\n end\n \n style A fill:#e1f5fe\n style B fill:#f3e5f5\n style C fill:#e8f5e8\n style F fill:#fff3e0\n style G fill:#ffebee\n style H fill:#f1f8e9\n```\n\n1. **Quick Start with Docker Compose**:\n ```bash\n # Build and run all services\n docker-compose up --build\n \n # For detached mode (background)\n docker-compose up --build -d\n \n # View logs\n docker-compose logs -f nexusmind\n ```\n\n2. **Individual Docker Container**:\n ```bash\n # Build the image\n docker build -t nexusmind:latest .\n \n # Run the container\n docker run -p 8000:8000 -v $(pwd)/config:/app/config nexusmind:latest\n ```\n\n3. **Production Deployment**:\n ```bash\n # Use production compose file\n docker-compose -f docker-compose.prod.yml up --build -d\n ```\n\n4. **Access the Services**:\n - **API Documentation**: `http://localhost:8000/docs`\n - **Health Check**: `http://localhost:8000/health`\n - **MCP Endpoint**: `http://localhost:8000/mcp`\n\n## 🔌 API Endpoints\n\n### Core Endpoints\n\n- **MCP Protocol**: `POST /mcp`\n ```json\n {\n \"method\": \"process_query\",\n \"params\": {\n \"query\": \"Analyze the relationship between microbiome diversity and cancer progression\",\n \"confidence_threshold\": 0.7,\n \"max_stages\": 8\n }\n }\n ```\n\n- **Health Check**: `GET /health`\n ```json\n {\n \"status\": \"healthy\",\n \"version\": \"0.1.0\",\n \"timestamp\": \"2024-05-23T10:30:00Z\"\n }\n ```\n\n### Advanced Endpoints\n\n- **Graph Query**: `POST /api/v1/graph/query`\n ```json\n {\n \"query\": \"Research question or hypothesis\",\n \"parameters\": {\n \"disciplines\": [\"immunology\", \"oncology\"],\n \"confidence_threshold\": 0.6,\n \"include_temporal_analysis\": true,\n \"enable_bias_detection\": true\n }\n }\n ```\n\n- **Graph State**: `GET /api/v1/graph/{session_id}`\n - Retrieve current state of a reasoning graph\n - Includes confidence scores, node relationships, and metadata\n\n- **Analytics**: `GET /api/v1/analytics/{session_id}`\n - Get comprehensive metrics about the reasoning process\n - Includes performance stats, confidence trends, and quality measures\n\n- **Subgraph Extraction**: `POST /api/v1/graph/{session_id}/extract`\n ```json\n {\n \"criteria\": {\n \"min_confidence\": 0.7,\n \"node_types\": [\"hypothesis\", \"evidence\"],\n \"include_causal_chains\": true\n }\n }\n ```\n\n## 🧪 Testing & Quality Assurance\n\n

\n \n \n \n \n \n \n \n \n \n \n \n \n \n

🧪
Testing

🔍
Type Checking

✨
Linting

📊
Coverage

poetry run pytest

poetry run pytest -v

poetry run mypy src/

pyright src/

poetry run ruff check .

poetry run ruff format .

poetry run pytest --cov=src

coverage html

\n\n### Development Commands\n\n```bash\n# Run full test suite with coverage\npoetry run pytest --cov=src --cov-report=html --cov-report=term\n\n# Run specific test categories\npoetry run pytest tests/unit/stages/ # Stage-specific tests\npoetry run pytest tests/integration/ # Integration tests\npoetry run pytest -k \"test_confidence\" # Tests matching pattern\n\n# Type checking and linting\npoetry run mypy src/ --strict # Strict type checking\npoetry run ruff check . --fix # Auto-fix linting issues\npoetry run ruff format . # Format code\n\n# Pre-commit hooks (recommended)\npoetry run pre-commit install # Install hooks\npoetry run pre-commit run --all-files # Run all hooks\n```\n\n### Quality Metrics\n\n- **Type Safety**: \n - Fully typed codebase with strict mypy configuration\n - Configured with `mypy.ini` and `pyrightconfig.json`\n - Fix logger type issues: `python scripts/add_type_hints.py`\n\n- **Code Quality**:\n - 95%+ test coverage target\n - Automated formatting with Ruff\n - Pre-commit hooks for consistent code quality\n - Comprehensive integration tests for the 8-stage pipeline\n\n## 🔧 Configuration\n\n### Application Settings (`config/settings.yaml`)\n\n```yaml\n# Core application settings\napp:\n name: \"NexusMind\"\n version: \"0.1.0\"\n debug: false\n log_level: \"INFO\"\n\n# API configuration\napi:\n host: \"0.0.0.0\"\n port: 8000\n cors_origins: [\"*\"]\n \n# ASR-GoT Framework settings\nasr_got:\n max_stages: 8\n default_confidence_threshold: 0.6\n enable_bias_detection: true\n enable_temporal_analysis: true\n max_hypotheses_per_dimension: 5\n \n# Graph settings\ngraph:\n max_nodes: 10000\n enable_hyperedges: true\n enable_multi_layer: true\n temporal_decay_factor: 0.1\n```\n\n### MCP Configuration (`config/claude_mcp_config.json`)\n\n```json\n{\n \"name\": \"nexusmind\",\n \"description\": \"Advanced Scientific Reasoning with Graph-of-Thoughts\",\n \"version\": \"0.1.0\",\n \"endpoints\": {\n \"mcp\": \"http://localhost:8000/mcp\"\n },\n \"capabilities\": [\n \"scientific_reasoning\",\n \"graph_analysis\",\n \"confidence_assessment\",\n \"bias_detection\"\n ]\n}\n```\n\n## 🤝 Contributing\n\nWe welcome contributions! Please see our [Contributing Guidelines](CONTRIBUTING.md) for details.\n\n### Development Setup\n\n1. Fork the repository\n2. Create a feature branch: `git checkout -b feature/amazing-feature`\n3. Install development dependencies: `poetry install --with dev`\n4. Make your changes and add tests\n5. Run the test suite: `poetry run pytest`\n6. Submit a pull request\n\n### Code Style\n\n- Follow PEP 8 style guidelines\n- Use type hints for all functions and methods\n- Write comprehensive docstrings\n- Maintain test coverage above 95%\n\n## 📚 Documentation\n\n- **[API Documentation](docs/api/)**: Comprehensive API reference\n- **[Architecture Guide](docs/architecture/)**: System design and components\n- **[Usage Examples](docs/examples/)**: Practical usage scenarios\n- **[Development Guide](docs/development/)**: Contributing and development setup\n\n## 📄 License\n\nThis project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.\n\n## 🙏 Acknowledgments\n\n- **NetworkX** community for graph analysis capabilities\n- **FastAPI** team for the excellent web framework\n- **Pydantic** for robust data validation\n- The scientific research community for inspiration and feedback\n\n---\n\n

Built with ❤️ for the scientific research community

NexusMind - Advancing scientific reasoning through intelligent graph structures

","English","en-US",{"_27":45,"_31":48,"_36":49},{"_29":46,"_31":32,"_33":47},"Un servidor MCP que aprovecha las estructuras gráficas para realizar un razonamiento científico sofisticado a través de un flujo de procesamiento de 8 etapas, lo que permite que los sistemas de IA manejen consultas científicas complejas con puntuación de confianza dinámica.","# 🧠 NexusMind\n\n#### **Razonamiento científico inteligente a través del grafo de pensamientos**\n\n[![Versión](https://img.shields.io/badge/version-0.1.0-blue.svg)](https://github.com/SaptaDey/NexusMind/releases) [![Pitón](https://img.shields.io/badge/python-3.13.3-blue.svg)](https://www.python.org/downloads/) [![Licencia](https://img.shields.io/badge/license-Apache_2.0-green.svg)](LICENSE) [![Estibador](https://img.shields.io/badge/docker-ready-brightgreen.svg)](Dockerfile) [![API rápida](https://img.shields.io/badge/FastAPI-0.111.0-009688.svg)](https://fastapi.tiangolo.com) [![RedX](https://img.shields.io/badge/NetworkX-3.3-orange.svg)](https://networkx.org) [![Última actualización](https://img.shields.io/badge/last_updated-May_2025-lightgrey.svg)](CHANGELOG.md)\n\n## 🔍 Descripción general\n\nNexusMind aprovecha **las estructuras gráficas** para realizar razonamiento científico sofisticado. Implementa el **Protocolo de Contexto de Modelo (MCP)** para integrarse con aplicaciones de IA como Claude Desktop.\n\n**Aspectos destacados:**\n\n* Procesar consultas científicas complejas utilizando razonamiento basado en gráficos\n* Puntuación de confianza dinámica con evaluaciones multidimensionales\n* Creado con Python moderno y FastAPI para un alto rendimiento\n* Dockerizado para una fácil implementación\n* Diseño modular para extensibilidad y personalización.\n\n## 🌟 Características principales\n\n### Proceso de razonamiento de ocho etapas\n\nEl proceso de razonamiento central sigue un sofisticado proceso de ocho etapas:\n\n1. **🌱 Inicialización**\n\n * Crea un nodo raíz a partir de la consulta\n * Establece la estructura gráfica inicial\n * Establece una línea base de confianza\n\n2. **🧩 Descomposición**\n\n * Divide la consulta en dimensiones\n * Identifica componentes clave\n * Crea nodos dimensionales\n\n3. **🔬 Hipótesis/Planificación**\n\n * Genera múltiples hipótesis\n * Crea una estrategia de razonamiento\n * Establece criterios de falsificación\n\n4. **📊 Integración de evidencia**\n\n * Recopila evidencia de apoyo\n * Vincula la evidencia con las hipótesis\n * Actualiza las puntuaciones de confianza\n\n5. **✂️ Poda/Fusión**\n\n * Elimina elementos de bajo valor\n * Consolida nodos similares\n * Optimiza la estructura del gráfico\n\n6. **🔍 Extracción de subgrafos**\n\n * Identifica las partes relevantes\n * Se centra en rutas de alto valor\n * Crea subgráficos específicos\n\n7. **📝 Composición**\n\n * Sintetiza los hallazgos clave\n * Crea perspectivas coherentes\n * Genera una respuesta completa\n\n8. **🤔 Reflexión**\n\n * Evalúa la calidad del razonamiento\n * Identifica áreas de mejora\n * Evaluación final de confianza\n\n### Capacidades técnicas\n\n* **Representación gráfica del conocimiento** : utiliza `networkx` para modelar relaciones complejas\n* **Vectores de confianza dinámica** :\n * Apoyo empírico\n * Base teórica\n * Rigor metodológico\n * Alineación de consenso\n* **🔌 Servidor MCP** : Integración perfecta con Claude Desktop\n* **⚡ API de alto rendimiento** : Implementación moderna de FastAPI\n* **🐳 Implementación sencilla** : compatibilidad con Docker y Docker Compose\n* **🧩 Arquitectura extensible** : Componentes modulares para personalización\n* **⚙️ Configuración flexible** : configuración de Pydantic y YAML\n\n## Pila de tecnología\n\n## 📂 Estructura del proyecto\n\n```\nasr-got-reimagined/\n├── config/ \n│ ├── settings.yaml \n│ └── claude_mcp_config.json \n├── src/asr_got_reimagined/ \n│ ├── api/ \n│ │ ├── routes/ \n│ │ │ └── mcp.py \n│ │ └── schemas.py \n│ ├── domain/ \n│ │ ├── models/ \n│ │ │ ├── common.py \n│ │ │ ├── graph_elements.py \n│ │ │ └── graph_state.py \n│ │ ├── services/ \n│ │ │ └── got_processor.py \n│ │ ├── stages/ \n│ │ │ ├── base_stage.py \n│ │ │ ├── stage_1_*.py \n│ │ │ └── stage_2_*.py... \n│ │ └── utils/ \n│ ├── main.py \n│ └── app_setup.py \n├── scripts/ \n├── Dockerfile \n├── docker-compose.yml \n└── pyproject.toml \n```\n\n## 🚀 Primeros pasos\n\n### Prerrequisitos\n\n* **Python 3.13+** (la imagen de Docker usa Python 3.13.3-slim-bookworm)\n* **[Poesía](https://python-poetry.org/docs/#installation)** : Para la gestión de la dependencia\n* **[Docker](https://www.docker.com/get-started)** y **[Docker Compose](https://docs.docker.com/compose/install/)** : para implementación en contenedores\n\n### Instalación y configuración (desarrollo local)\n\n1. **Clonar el repositorio** :\n\n ```bash\n git clone https://github.com/SaptaDey/NexusMind.git\n cd NexusMind\n ```\n\n2. **Instalar dependencias usando Poetry** :\n\n ```bash\n poetry install\n ```\n\n Esto crea un entorno virtual e instala todos los paquetes necesarios especificados en `pyproject.toml` .\n\n3. **Activar el entorno virtual** :\n\n ```bash\n poetry shell\n ```\n\n4. **Configurar la aplicación** :\n\n * Ajuste la configuración en `config/settings.yaml` según sea necesario\n * Configurar variables de entorno o un archivo `.env` para información confidencial\n\n5. **Ejecute el servidor de desarrollo** :\n\n ```bash\n python src/asr_got_reimagined/main.py\n ```\n\n Alternativamente, para mayor control:\n\n ```bash\n uvicorn asr_got_reimagined.main:app --reload --host 0.0.0.0 --port 8000\n ```\n\n La API estará disponible en `http://localhost:8000` .\n\n### Implementación de Docker\n\n1. **Construya y ejecute los contenedores Docker** :\n\n ```bash\n docker-compose up --build\n ```\n\n Para el modo separado:\n\n ```bash\n docker-compose up --build -d\n ```\n\n La imagen de Docker utiliza Python 3.13.3-slim-bookworm como imagen base para mejorar el rendimiento y la seguridad.\n\n2. **Acceder a la API** : la API será accesible en `http://localhost:8000` (o como se configure en `docker-compose.yml` ).\n\n## 🔌 Puntos finales de API\n\n### Puntos finales principales\n\n* **Punto final de MCP** : `POST /mcp`\n\n * Para la comunicación con clientes MCP como Claude Desktop\n * Implementa el Protocolo de Contexto de Modelo para la interacción con IA\n\n* **Control de salud** : `GET /health`\n\n * Comprobación básica del estado para la supervisión y el descubrimiento de servicios\n\n### Puntos finales adicionales (planificados)\n\n* **Consulta gráfica** : `POST /api/graph/query`\n\n * Interfaz directa para consultas ASR-GoT\n\n* **Estado del gráfico** : `GET /api/graph/{session_id}`\n\n * Recuperar el estado actual de un gráfico de razonamiento\n\n* **Análisis** : `GET /api/analytics/{session_id}`\n\n * Obtenga métricas sobre el proceso de razonamiento\n\n## 🧪 Pruebas y calidad\n\n### Herramientas de desarrollo\n\n* **Tipo de seguridad** :\n\n * Configurado con `mypy.ini` y `pyrightconfig.json`\n * Solucionar problemas de tipo de registrador con `python scripts/add_type_hints.py`\n\n* **Calidad del código** :\n\n * Base de código completamente tipificada con anotaciones de tipo\n * Ganchos de pre-confirmación disponibles: `poetry run pre-commit install`\n * Formato automatizado con Ruff","Spanish","es-ES",{"_27":51,"_31":55,"_36":56},{"_29":52,"_31":53,"_33":54},"グラフ構造を活用し、8 段階の処理パイプラインを通じて高度な科学的推論を実行する MCP サーバー。これにより、AI システムは動的な信頼度スコアリングを使用して複雑な科学的クエリを処理できるようになります。","ネクサスマインド","# 🧠 ネクサスマインド\n\n#### **思考グラフによる知的な科学的推論**\n\n[![バージョン](https://img.shields.io/badge/version-0.1.0-blue.svg)](https://github.com/SaptaDey/NexusMind/releases) [![パイソン](https://img.shields.io/badge/python-3.13.3-blue.svg)](https://www.python.org/downloads/) [![ライセンス](https://img.shields.io/badge/license-Apache_2.0-green.svg)](LICENSE) [![ドッカー](https://img.shields.io/badge/docker-ready-brightgreen.svg)](Dockerfile) [![ファストAPI](https://img.shields.io/badge/FastAPI-0.111.0-009688.svg)](https://fastapi.tiangolo.com) [![ネットワークX](https://img.shields.io/badge/NetworkX-3.3-orange.svg)](https://networkx.org) [![最終更新日](https://img.shields.io/badge/last_updated-May_2025-lightgrey.svg)](CHANGELOG.md)\n\n## 🔍 概要\n\nNexusMindは**グラフ構造**を活用して高度な科学的推論を実行します。Claude DesktopなどのAIアプリケーションと統合するために、\\*\\*モデルコンテキストプロトコル（MCP）\\*\\*を実装しています。\n\n**主なハイライト:**\n\n* グラフベース推論を使用して複雑な科学的クエリを処理する\n* 多次元評価による動的信頼度スコアリング\n* 高性能を実現する最新のPythonとFastAPIで構築\n* 簡単に導入できるようDocker化されている\n* 拡張性とカスタマイズ性を実現するモジュール設計\n\n## 🌟 主な機能\n\n### 8段階推論パイプライン\n\nコア推論プロセスは、洗練された 8 段階のパイプラインに従います。\n\n1. **🌱 初期化**\n\n * クエリからルートノードを作成する\n * 初期のグラフ構造を確立する\n * 信頼のベースラインを設定する\n\n2. **🧩 分解**\n\n * クエリをディメンションに分割します\n * 主要コンポーネントを特定する\n * 次元ノードを作成する\n\n3. **🔬 仮説・計画**\n\n * 複数の仮説を生成する\n * 推論戦略を作成する\n * 偽造基準を確立する\n\n4. **📊 証拠の統合**\n\n * 裏付けとなる証拠を集める\n * 証拠と仮説を結びつける\n * 信頼スコアを更新\n\n5. **✂️ プルーニング/マージ**\n\n * 価値の低い要素を削除する\n * 類似ノードを統合する\n * グラフ構造を最適化\n\n6. **🔍 サブグラフ抽出**\n\n * 関連部分を特定する\n * 高価値パスに焦点を当てる\n * ターゲットサブグラフを作成する\n\n7. **📝 構成**\n\n * 主要な調査結果を統合する\n * 一貫した洞察を生み出す\n * 包括的な回答を生成する\n\n8. **🤔 反省**\n\n * 推論の質を評価する\n * 改善点を特定する\n * 最終的な信頼度評価\n\n### 技術的能力\n\n* **🧠 グラフ知識表現**： `networkx`を使用して複雑な関係をモデル化します\n* **🔄 動的信頼ベクトル**：\n * 経験的裏付け\n * 理論的根拠\n * 方法論的厳密さ\n * コンセンサス調整\n* **🔌 MCP サーバー**: シームレスな Claude Desktop 統合\n* **⚡ 高性能API** ：最新のFastAPI実装\n* **🐳 簡単なデプロイメント**：Docker と Docker Compose のサポート\n* **🧩 拡張可能なアーキテクチャ**：カスタマイズのためのモジュール式コンポーネント\n* **⚙️ 柔軟な構成**: Pydantic と YAML 構成\n\n## 🛠️ テクノロジースタック\n\n## 📂 プロジェクト構造\n\n```\nasr-got-reimagined/\n├── config/ \n│ ├── settings.yaml \n│ └── claude_mcp_config.json \n├── src/asr_got_reimagined/ \n│ ├── api/ \n│ │ ├── routes/ \n│ │ │ └── mcp.py \n│ │ └── schemas.py \n│ ├── domain/ \n│ │ ├── models/ \n│ │ │ ├── common.py \n│ │ │ ├── graph_elements.py \n│ │ │ └── graph_state.py \n│ │ ├── services/ \n│ │ │ └── got_processor.py \n│ │ ├── stages/ \n│ │ │ ├── base_stage.py \n│ │ │ ├── stage_1_*.py \n│ │ │ └── stage_2_*.py... \n│ │ └── utils/ \n│ ├── main.py \n│ └── app_setup.py \n├── scripts/ \n├── Dockerfile \n├── docker-compose.yml \n└── pyproject.toml \n```\n\n## 🚀 はじめに\n\n### 前提条件\n\n* **Python 3.13+** (Docker イメージでは Python 3.13.3-slim-bookworm を使用)\n* **[詩](https://python-poetry.org/docs/#installation)**：依存関係の管理\n* **[Docker](https://www.docker.com/get-started)**と**[Docker Compose](https://docs.docker.com/compose/install/)** :コンテナ化されたデプロイメント用\n\n### インストールとセットアップ（ローカル開発）\n\n1. **リポジトリをクローンします**:\n\n ```bash\n git clone https://github.com/SaptaDey/NexusMind.git\n cd NexusMind\n ```\n\n2. **Poetry を使用して依存関係をインストールします**。\n\n ```bash\n poetry install\n ```\n\n これにより、仮想環境が作成され、 `pyproject.toml`で指定された必要なパッケージがすべてインストールされます。\n\n3. **仮想環境をアクティブ化します**:\n\n ```bash\n poetry shell\n ```\n\n4. **アプリケーションを構成します**:\n\n * 必要に応じて`config/settings.yaml`の設定を調整します\n * 機密情報用の環境変数または`.env`ファイルを構成する\n\n5. **開発サーバーを実行します**:\n\n ```bash\n python src/asr_got_reimagined/main.py\n ```\n\n あるいは、さらに制御するには:\n\n ```bash\n uvicorn asr_got_reimagined.main:app --reload --host 0.0.0.0 --port 8000\n ```\n\n API は`http://localhost:8000`で利用できます。\n\n### Docker デプロイメント\n\n1. **Docker コンテナをビルドして実行します**。\n\n ```bash\n docker-compose up --build\n ```\n\n デタッチドモードの場合:\n\n ```bash\n docker-compose up --build -d\n ```\n\n Docker イメージは、パフォーマンスとセキュリティを向上させるために、Python 3.13.3-slim-bookworm をベースイメージとして使用します。\n\n2. **API にアクセスします**。API は`http://localhost:8000` (または`docker-compose.yml`で設定されているとおり) でアクセスできます。\n\n## 🔌 APIエンドポイント\n\n### コアエンドポイント\n\n* **MCPエンドポイント**: `POST /mcp`\n\n * Claude DesktopなどのMCPクライアントとの通信用\n * AIインタラクションのためのモデルコンテキストプロトコルを実装\n\n* **ヘルスチェック**: `GET /health`\n\n * 監視とサービス検出のための基本的なヘルスチェック\n\n### 追加のエンドポイント（計画中）\n\n* **グラフクエリ**: `POST /api/graph/query`\n\n * ASR-GoTクエリの直接インターフェース\n\n* **グラフ状態**: `GET /api/graph/{session_id}`\n\n * 推論グラフの現在の状態を取得する\n\n* **アナリティクス**: `GET /api/analytics/{session_id}`\n\n * 推論プロセスに関する指標を取得する\n\n## 🧪 テストと品質\n\n### 開発ツール\n\n* **型安全性**:\n\n * `mypy.ini`と`pyrightconfig.json`で設定\n * `python scripts/add_type_hints.py`のロガータイプの問題を修正しました\n\n* **コード品質**:\n\n * 型注釈付きの完全に型付けされたコードベース\n * 事前コミットフックが利用可能: `poetry run pre-commit install`\n * Ruffによる自動フォーマット","Japanese","ja-JP",{"_27":58,"_31":62,"_36":63},{"_29":59,"_31":60,"_33":61},"8단계 처리 파이프라인을 통해 그래프 구조를 활용하여 정교한 과학적 추론을 수행하는 MCP 서버로, AI 시스템이 동적 신뢰도 점수를 통해 복잡한 과학적 쿼리를 처리할 수 있도록 지원합니다.","넥서스마인드","# 🧠 넥서스마인드\n\n#### **생각 그래프를 통한 지능적인 과학적 추론**\n\n[![버전](https://img.shields.io/badge/version-0.1.0-blue.svg)](https://github.com/SaptaDey/NexusMind/releases) [![파이썬](https://img.shields.io/badge/python-3.13.3-blue.svg)](https://www.python.org/downloads/)[![특허](https://img.shields.io/badge/license-Apache_2.0-green.svg)](LICENSE) [![도커](https://img.shields.io/badge/docker-ready-brightgreen.svg)](Dockerfile) [![패스트API](https://img.shields.io/badge/FastAPI-0.111.0-009688.svg)](https://fastapi.tiangolo.com) [![네트워크X](https://img.shields.io/badge/NetworkX-3.3-orange.svg)](https://networkx.org) [![마지막 업데이트](https://img.shields.io/badge/last_updated-May_2025-lightgrey.svg)](CHANGELOG.md)\n\n## 🔍 개요\n\nNexusMind는 **그래프 구조를** 활용하여 정교한 과학적 추론을 수행합니다. Claude Desktop과 같은 AI 애플리케이션과 통합하기 위해 **모델 컨텍스트 프로토콜(MCP)을** 구현합니다.\n\n**주요 내용:**\n\n* 그래프 기반 추론을 사용하여 복잡한 과학적 쿼리를 처리합니다.\n* 다차원 평가를 통한 동적 신뢰도 평가\n* 고성능을 위해 최신 Python 및 FastAPI로 구축됨\n* 쉬운 배포를 위해 Docker화됨\n* 확장성 및 사용자 정의를 위한 모듈식 디자인\n\n## 🌟 주요 특징\n\n### 8단계 추론 파이프라인\n\n핵심 추론 과정은 정교한 8단계 파이프라인을 따릅니다.\n\n1. **🌱 초기화**\n\n * 쿼리에서 루트 노드를 생성합니다.\n * 초기 그래프 구조를 설정합니다\n * 신뢰 기준선 설정\n\n2. **🧩 분해**\n\n * 쿼리를 차원으로 나눕니다.\n * 주요 구성 요소를 식별합니다\n * 차원 노드를 생성합니다\n\n3. **🔬 가설/계획**\n\n * 여러 가설을 생성합니다\n * 추론 전략을 생성합니다\n * 위조 기준을 수립합니다\n\n4. **📊 증거 통합**\n\n * 뒷받침하는 증거를 수집합니다\n * 증거를 가설에 연결합니다.\n * 신뢰도 점수 업데이트\n\n5. **✂️ 가지치기/병합**\n\n * 낮은 가치의 요소를 제거합니다\n * 유사한 노드를 통합합니다\n * 그래프 구조를 최적화합니다\n\n6. **🔍 서브그래프 추출**\n\n * 관련 부분을 식별합니다\n * 높은 가치의 경로에 집중합니다\n * 타겟 하위 그래프를 생성합니다\n\n7. **📝 구성**\n\n * 주요 결과를 종합합니다\n * 일관된 통찰력을 생성합니다\n * 포괄적인 답변을 생성합니다\n\n8. **🤔 반성**\n\n * 추론 품질을 평가합니다\n * 개선 영역을 식별합니다\n * 최종 신뢰도 평가\n\n### 기술적 역량\n\n* **🧠 그래프 지식 표현** : `networkx` 사용하여 복잡한 관계를 모델링합니다.\n* **🔄 동적 신뢰 벡터** :\n * 경험적 지원\n * 이론적 근거\n * 방법론적 엄격성\n * 합의 정렬\n* **🔌 MCP 서버** : Claude Desktop과의 원활한 통합\n* **⚡ 고성능 API** : 최신 FastAPI 구현\n* **🐳 간편한 배포** : Docker 및 Docker Compose 지원\n* **🧩 확장 가능한 아키텍처** : 사용자 정의를 위한 모듈식 구성 요소\n* **⚙️ 유연한 구성** : Pydantic 및 YAML 구성\n\n## 🛠️ 기술 스택\n\n## 📂 프로젝트 구조\n\n지엑스피1\n\n## 🚀 시작하기\n\n### 필수 조건\n\n* **Python 3.13+** (Docker 이미지는 Python 3.13.3-slim-bookworm을 사용합니다)\n* **[시](https://python-poetry.org/docs/#installation)** : 의존성 관리를 위해\n* **[Docker](https://www.docker.com/get-started)** 및 **[Docker Compose](https://docs.docker.com/compose/install/)** : 컨테이너화된 배포를 위해\n\n### 설치 및 설정(로컬 개발)\n\n1. **저장소를 복제합니다** .\n\n ```bash\n git clone https://github.com/SaptaDey/NexusMind.git\n cd NexusMind\n ```\n\n2. **Poetry를 사용하여 종속성 설치** :\n\n ```bash\n poetry install\n ```\n\n 이렇게 하면 가상 환경이 생성되고 `pyproject.toml` 에 지정된 모든 필수 패키지가 설치됩니다.\n\n3. **가상 환경 활성화** :\n\n ```bash\n poetry shell\n ```\n\n4. **응용 프로그램 구성** :\n\n * 필요에 따라 `config/settings.yaml` 에서 설정을 조정하세요.\n * 민감한 정보에 대한 환경 변수 또는 `.env` 파일을 구성합니다.\n\n5. **개발 서버를 실행합니다** .\n\n ```bash\n python src/asr_got_reimagined/main.py\n ```\n\n 또는 보다 세밀한 제어를 위해:\n\n ```bash\n uvicorn asr_got_reimagined.main:app --reload --host 0.0.0.0 --port 8000\n ```\n\n API는 `http://localhost:8000` 에서 사용할 수 있습니다.\n\n### 도커 배포\n\n1. **Docker 컨테이너를 빌드하고 실행합니다** .\n\n ```bash\n docker-compose up --build\n ```\n\n 분리 모드의 경우:\n\n ```bash\n docker-compose up --build -d\n ```\n\n Docker 이미지는 향상된 성능과 보안을 위해 Python 3.13.3-slim-bookworm을 기본 이미지로 사용합니다.\n\n2. **API에 접근** : API는 `http://localhost:8000` 에서 접근할 수 있습니다(또는 `docker-compose.yml` 에서 구성된 대로).\n\n## 🔌 API 엔드포인트\n\n### 핵심 엔드포인트\n\n* **MCP 엔드포인트** : `POST /mcp`\n\n * Claude Desktop과 같은 MCP 클라이언트와의 통신을 위해\n * AI 상호작용을 위한 모델 컨텍스트 프로토콜을 구현합니다.\n\n* **건강 검진** : `GET /health`\n\n * 모니터링 및 서비스 검색을 위한 기본 상태 점검\n\n### 추가 엔드포인트(계획됨)\n\n* **그래프 쿼리** : `POST /api/graph/query`\n\n * ASR-GoT 쿼리에 대한 직접 인터페이스\n\n* **그래프 상태** : `GET /api/graph/{session_id}`\n\n * 추론 그래프의 현재 상태 검색\n\n* **분석** : `GET /api/analytics/{session_id}`\n\n * 추론 과정에 대한 측정 항목을 얻으세요\n\n## 🧪 테스트 및 품질\n\n### 개발 도구\n\n* **유형 안전** :\n\n * `mypy.ini` 및 `pyrightconfig.json` 으로 구성됨\n * `python scripts/add_type_hints.py` 사용하여 로거 유형 문제 수정\n\n* **코드 품질** :\n\n * 유형 주석이 포함된 완전히 유형화된 코드베이스\n * 사전 커밋 후크 사용 가능: `poetry run pre-commit install`\n * Ruff를 사용한 자동 서식 지정","Korean","ko-KR",{"_27":65,"_31":68,"_36":69},{"_29":66,"_31":32,"_33":67},"Сервер MCP, который использует графовые структуры для выполнения сложных научных рассуждений с помощью 8-ступенчатого конвейера обработки, позволяя системам ИИ обрабатывать сложные научные запросы с динамической оценкой достоверности.","# 🧠 НексусМайнд\n\n#### **Интеллектуальное научное рассуждение посредством графа мыслей**\n\n[![Версия](https://img.shields.io/badge/version-0.1.0-blue.svg)](https://github.com/SaptaDey/NexusMind/releases) [![Питон](https://img.shields.io/badge/python-3.13.3-blue.svg)](https://www.python.org/downloads/) [![Лицензия](https://img.shields.io/badge/license-Apache_2.0-green.svg)](LICENSE) [![Докер](https://img.shields.io/badge/docker-ready-brightgreen.svg)](Dockerfile) [![FastAPI](https://img.shields.io/badge/FastAPI-0.111.0-009688.svg)](https://fastapi.tiangolo.com) [![СетьX](https://img.shields.io/badge/NetworkX-3.3-orange.svg)](https://networkx.org) [![Последнее обновление](https://img.shields.io/badge/last_updated-May_2025-lightgrey.svg)](CHANGELOG.md)\n\n## 🔍 Обзор\n\nNexusMind использует **графовые структуры** для выполнения сложных научных рассуждений. Он реализует **протокол контекста модели (MCP)** для интеграции с приложениями ИИ, такими как Claude Desktop.\n\n**Основные моменты:**\n\n* Обработка сложных научных запросов с использованием графических рассуждений\n* Динамическая оценка уверенности с многомерными оценками\n* Создан с использованием современного Python и FastAPI для высокой производительности\n* Dockerized для простоты развертывания\n* Модульная конструкция для расширяемости и настройки\n\n## 🌟 Основные характеристики\n\n### 8-ступенчатый конвейер рассуждений\n\nОсновной процесс рассуждений представляет собой сложную 8-этапную последовательность:\n\n1. **🌱 Инициализация**\n\n * Создает корневой узел из запроса\n * Устанавливает начальную структуру графа\n * Устанавливает базовый уровень уверенности\n\n2. **🧩 Разложение**\n\n * Разбивает запрос на измерения\n * Определяет ключевые компоненты\n * Создает размерные узлы\n\n3. **🔬 Гипотеза/Планирование**\n\n * Генерирует несколько гипотез\n * Создает стратегию рассуждения\n * Устанавливает критерии фальсификации\n\n4. **📊 Интеграция доказательств**\n\n * Собирает подтверждающие доказательства\n * Связывает доказательства с гипотезами\n * Обновляет показатели уверенности\n\n5. **✂️ Обрезка/Объединение**\n\n * Удаляет малоценные элементы\n * Объединяет похожие узлы\n * Оптимизирует структуру графа\n\n6. **🔍 Извлечение подграфа**\n\n * Определяет соответствующие части\n * Фокусируется на путях с высокой ценностью\n * Создает целевые подграфы\n\n7. **📝 Состав**\n\n * Синтезирует основные выводы\n * Создает последовательные идеи\n * Формирует исчерпывающий ответ\n\n8. **🤔 Отражение**\n\n * Оценивает качество рассуждения\n * Определяет области улучшения\n * Окончательная оценка достоверности\n\n### Технические возможности\n\n* **🧠 Графическое представление знаний** : использует `networkx` для моделирования сложных взаимосвязей.\n* **🔄 Векторы динамической уверенности** :\n * Эмпирическая поддержка\n * Теоретическая основа\n * Методологическая строгость\n * Согласование консенсуса\n* **🔌 MCP Server** : бесшовная интеграция с Claude Desktop\n* **⚡ Высокопроизводительный API** : современная реализация FastAPI\n* **🐳 Простое развертывание** : поддержка Docker и Docker Compose\n* **🧩 Расширяемая архитектура** : модульные компоненты для настройки\n* **⚙️ Гибкая конфигурация** : конфигурация Pydantic и YAML\n\n## 🛠️ Технологический стек\n\n## 📂 Структура проекта\n\n```\nasr-got-reimagined/\n├── config/ \n│ ├── settings.yaml \n│ └── claude_mcp_config.json \n├── src/asr_got_reimagined/ \n│ ├── api/ \n│ │ ├── routes/ \n│ │ │ └── mcp.py \n│ │ └── schemas.py \n│ ├── domain/ \n│ │ ├── models/ \n│ │ │ ├── common.py \n│ │ │ ├── graph_elements.py \n│ │ │ └── graph_state.py \n│ │ ├── services/ \n│ │ │ └── got_processor.py \n│ │ ├── stages/ \n│ │ │ ├── base_stage.py \n│ │ │ ├── stage_1_*.py \n│ │ │ └── stage_2_*.py... \n│ │ └── utils/ \n│ ├── main.py \n│ └── app_setup.py \n├── scripts/ \n├── Dockerfile \n├── docker-compose.yml \n└── pyproject.toml \n```\n\n## 🚀 Начало работы\n\n### Предпосылки\n\n* **Python 3.13+** (образ Docker использует Python 3.13.3-slim-bookworm)\n* **[Поэзия](https://python-poetry.org/docs/#installation)** : Для управления зависимостью\n* **[Docker](https://www.docker.com/get-started)** и **[Docker Compose](https://docs.docker.com/compose/install/)** : для контейнерного развертывания\n\n### Установка и настройка (локальная разработка)\n\n1. **Клонируйте репозиторий** :\n\n ```bash\n git clone https://github.com/SaptaDey/NexusMind.git\n cd NexusMind\n ```\n\n2. **Установка зависимостей с помощью Poetry** :\n\n ```bash\n poetry install\n ```\n\n Это создаст виртуальную среду и установит все необходимые пакеты, указанные в `pyproject.toml` .\n\n3. **Активируйте виртуальную среду** :\n\n ```bash\n poetry shell\n ```\n\n4. **Настройте приложение** :\n\n * При необходимости отрегулируйте настройки в `config/settings.yaml`\n * Настройте переменные среды или файл `.env` для конфиденциальной информации\n\n5. **Запустите сервер разработки** :\n\n ```bash\n python src/asr_got_reimagined/main.py\n ```\n\n Альтернативно, для большего контроля:\n\n ```bash\n uvicorn asr_got_reimagined.main:app --reload --host 0.0.0.0 --port 8000\n ```\n\n API будет доступен по адресу `http://localhost:8000` .\n\n### Развертывание Docker\n\n1. **Соберите и запустите контейнеры Docker** :\n\n ```bash\n docker-compose up --build\n ```\n\n Для отдельного режима:\n\n ```bash\n docker-compose up --build -d\n ```\n\n Образ Docker использует Python 3.13.3-slim-bookworm в качестве базового образа для повышения производительности и безопасности.\n\n2. **Доступ к API** : API будет доступен по адресу `http://localhost:8000` (или согласно настройкам в `docker-compose.yml` ).\n\n## 🔌 Конечные точки API\n\n### Основные конечные точки\n\n* **Конечная точка MCP** : `POST /mcp`\n\n * Для связи с клиентами MCP, такими как Claude Desktop\n * Реализует протокол контекста модели для взаимодействия с ИИ\n\n* **Проверка здоровья** : `GET /health`\n\n * Базовая проверка работоспособности для мониторинга и обнаружения услуг\n\n### Дополнительные конечные точки (планируется)\n\n* **Запрос графика** : `POST /api/graph/query`\n\n * Прямой интерфейс для запросов ASR-GoT\n\n* **Состояние графика** : `GET /api/graph/{session_id}`\n\n * Получить текущее состояние графа рассуждений\n\n* **Аналитика** : `GET /api/analytics/{session_id}`\n\n * Получите метрики о процессе рассуждения\n\n## 🧪 Тестирование и качество\n\n### Инструменты разработки\n\n* **Тип безопасности** :\n\n * Настраивается с помощью `mypy.ini` и `pyrightconfig.json`\n * Исправление проблем с типом логгера с помощью `python scripts/add_type_hints.py`\n\n* **Качество кода** :\n\n * Полностью типизированная кодовая база с аннотациями типов\n * Доступны хуки Pre-commit: `poetry run pre-commit install`\n * Автоматическое форматирование с помощью Ruff","Russian","ru-RU",{"_27":71,"_31":74,"_36":75},{"_29":72,"_31":32,"_33":73},"MCP 服务器利用图形结构通过 8 阶段处理管道执行复杂的科学推理，使 AI 系统能够通过动态置信度评分处理复杂的科学查询。","# 🧠 NexusMind\n\n#### **通过思维图进行智能科学推理**\n\n[![版本](https://img.shields.io/badge/version-0.1.0-blue.svg)](https://github.com/SaptaDey/NexusMind/releases) [![Python](https://img.shields.io/badge/python-3.13.3-blue.svg)](https://www.python.org/downloads/)[![执照](https://img.shields.io/badge/license-Apache_2.0-green.svg)](LICENSE) [![Docker](https://img.shields.io/badge/docker-ready-brightgreen.svg)](Dockerfile) [![快速API](https://img.shields.io/badge/FastAPI-0.111.0-009688.svg)](https://fastapi.tiangolo.com) [![NetworkX](https://img.shields.io/badge/NetworkX-3.3-orange.svg)](https://networkx.org) [![上次更新](https://img.shields.io/badge/last_updated-May_2025-lightgrey.svg)](CHANGELOG.md)\n\n## 🔍 概述\n\nNexusMind 利用**图结构**进行复杂的科学推理。它实现了**模型上下文协议 (MCP)** ，以便与 Claude Desktop 等 AI 应用程序集成。\n\n**主要亮点：**\n\n* 使用基于图的推理处理复杂的科学查询\n* 通过多维评估进行动态置信度评分\n* 采用现代 Python 和 FastAPI 构建，以实现高性能\n* Docker化，易于部署\n* 模块化设计，可扩展、可定制\n\n## 🌟 主要特点\n\n### 8阶段推理流程\n\n核心推理过程遵循复杂的 8 阶段流程：\n\n1. **🌱 初始化**\n\n * 根据查询创建根节点\n * 建立初始图形结构\n * 设定信心基线\n\n2. **🧩 分解**\n\n * 将查询分解为维度\n * 识别关键组件\n * 创建维度节点\n\n3. **🔬 假设/计划**\n\n * 产生多个假设\n * 创建推理策略\n * 建立伪造标准\n\n4. **📊 证据整合**\n\n * 收集支持证据\n * 将证据与假设联系起来\n * 更新置信度分数\n\n5. **✂️ 修剪/合并**\n\n * 删除低价值元素\n * 合并相似节点\n * 优化图形结构\n\n6. **🔍 子图提取**\n\n * 识别相关部分\n * 专注于高价值路径\n * 创建目标子图\n\n7. **📝 构图**\n\n * 总结主要发现\n * 创造连贯的见解\n * 生成全面的答案\n\n8. **🤔 反思**\n\n * 评估推理质量\n * 确定改进领域\n * 最终信心评估\n\n### 技术能力\n\n* **🧠 图形知识表示**：使用`networkx`来建模复杂的关系\n* **🔄 动态置信向量**：\n * 实证支持\n * 理论基础\n * 方法论的严谨性\n * 共识对齐\n* **🔌 MCP 服务器**：无缝集成 Claude Desktop\n* **⚡ 高性能 API** ：现代 FastAPI 实现\n* **🐳 轻松部署**：支持 Docker 和 Docker Compose\n* **🧩 可扩展架构**：模块化组件，可定制\n* **⚙️ 灵活的配置**：Pydantic 和 YAML 配置\n\n## 🛠️ 技术栈\n\n## 📂 项目结构\n\n```\nasr-got-reimagined/\n├── config/ \n│ ├── settings.yaml \n│ └── claude_mcp_config.json \n├── src/asr_got_reimagined/ \n│ ├── api/ \n│ │ ├── routes/ \n│ │ │ └── mcp.py \n│ │ └── schemas.py \n│ ├── domain/ \n│ │ ├── models/ \n│ │ │ ├── common.py \n│ │ │ ├── graph_elements.py \n│ │ │ └── graph_state.py \n│ │ ├── services/ \n│ │ │ └── got_processor.py \n│ │ ├── stages/ \n│ │ │ ├── base_stage.py \n│ │ │ ├── stage_1_*.py \n│ │ │ └── stage_2_*.py... \n│ │ └── utils/ \n│ ├── main.py \n│ └── app_setup.py \n├── scripts/ \n├── Dockerfile \n├── docker-compose.yml \n└── pyproject.toml \n```\n\n## 🚀 入门\n\n### 先决条件\n\n* **Python 3.13+** （Docker 镜像使用 Python 3.13.3-slim-bookworm）\n* **[Poetry](https://python-poetry.org/docs/#installation)** ：用于依赖管理\n* **[Docker](https://www.docker.com/get-started)**和**[Docker Compose](https://docs.docker.com/compose/install/)** ：用于容器化部署\n\n### 安装和设置（本地开发）\n\n1. **克隆存储库**：\n\n ```bash\n git clone https://github.com/SaptaDey/NexusMind.git\n cd NexusMind\n ```\n\n2. **使用 Poetry 安装依赖项**：\n\n ```bash\n poetry install\n ```\n\n 这将创建一个虚拟环境并安装`pyproject.toml`中指定的所有必要包。\n\n3. **激活虚拟环境**：\n\n ```bash\n poetry shell\n ```\n\n4. **配置应用程序**：\n\n * 根据需要调整`config/settings.yaml`中的设置\n * 为敏感信息配置环境变量或`.env`文件\n\n5. **运行开发服务器**：\n\n ```bash\n python src/asr_got_reimagined/main.py\n ```\n\n 或者，为了获得更多控制：\n\n ```bash\n uvicorn asr_got_reimagined.main:app --reload --host 0.0.0.0 --port 8000\n ```\n\n API 将在`http://localhost:8000`上可用。\n\n### Docker 部署\n\n1. **构建并运行 Docker 容器**：\n\n ```bash\n docker-compose up --build\n ```\n\n 对于分离模式：\n\n ```bash\n docker-compose up --build -d\n ```\n\n Docker 镜像使用 Python 3.13.3-slim-bookworm 作为基础镜像，以提高性能和安全性。\n\n2. **访问 API** ：可通过`http://localhost:8000`访问 API（或按照`docker-compose.yml`中的配置）。\n\n## 🔌 API 端点\n\n### 核心端点\n\n* **MCP 端点**: `POST /mcp`\n\n * 用于与 Claude Desktop 等 MCP 客户端进行通信\n * 实现用于 AI 交互的模型上下文协议\n\n* **健康检查**： `GET /health`\n\n * 用于监控和服务发现的基本健康检查\n\n### 附加端点（计划中）\n\n* **图形查询**： `POST /api/graph/query`\n\n * ASR-GoT 查询的直接接口\n\n* **图形状态**： `GET /api/graph/{session_id}`\n\n * 检索推理图的当前状态\n\n* **分析**： `GET /api/analytics/{session_id}`\n\n * 获取有关推理过程的指标\n\n## 🧪 测试与质量\n\n### 开发工具\n\n* **类型安全**：\n\n * 使用`mypy.ini`和`pyrightconfig.json`配置\n * 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MCP directory API