NexusMind

# Main Application Settings for NexusMind app: name: "NexusMind" version: "0.1.0" # Should ideally match pyproject.toml host: "0.0.0.0" port: 8000 # Default port for Uvicorn log_level: "INFO" # Default log level (DEBUG, INFO, WARNING, ERROR, CRITICAL) # debug: false # Set to true for development if needed, enables more verbose errors # NexusMind Core Parameters asr_got: default_parameters: # Parameters for Stage 1: Initialization initial_confidence: [0.9, 0.9, 0.9, 0.9] # P1.5 via P1.1 initial_layer: "root_layer" # P1.1 # Parameters for Stage 2: Decomposition default_decomposition_dimensions: # P1.2 - label: "Scope and Delimitations" description: "Clearly define the boundaries and specific focus of the research question." - label: "Core Objectives" description: "Identify the primary goals and desired outcomes of the analysis." - label: "Methodological Approach" description: "Outline the research methods and analytical techniques to be employed." - label: "Data Requirements and Availability" description: "Specify the types of data needed and assess their accessibility and quality." - label: "Key Assumptions" description: "List any underlying assumptions made at the outset of the investigation." - label: "Potential Challenges and Limitations" description: "Anticipate obstacles, constraints, or limitations that might affect the research." - label: "Expected Impact and Applications" description: "Consider the potential significance of the findings and their practical use cases." - label: "Ethical Considerations" description: "Address any ethical implications related to the research topic or methodology." - label: "Identification of Knowledge Gaps" # P1.15 via P1.2 description: "Pinpoint areas of uncertainty or missing information relevant to the query." - label: "Assessment of Potential Biases" # P1.17 via P1.2 description: "Identify possible cognitive, methodological, or data-related biases." dimension_confidence: [0.8, 0.8, 0.8, 0.8] # P1.2 # Parameters for Stage 3: Hypothesis/Planning hypotheses_per_dimension: # k from P1.3 min: 2 max: 4 hypothesis_confidence: [0.5, 0.5, 0.5, 0.5] # P1.3 default_disciplinary_tags: # P1.8 (initial tagging) - "general_science" - "interdisciplinary_studies" # Default plan types for hypotheses (can be expanded) default_plan_types: ["literature_review", "data_analysis", "simulation", "expert_consultation"] # Parameters for Stage 4: Evidence Integration evidence_max_iterations: 5 # P1.4 # Bayesian update parameters can be more detailed here if needed (P1.14) # Parameters for Stage 5: Pruning/Merging pruning_confidence_threshold: 0.2 # min(E[C]) from P1.5 pruning_impact_threshold: 0.3 # P1.5 considering P1.28 merging_semantic_overlap_threshold: 0.8 # P1.5 # Parameters for Stage 6: Subgraph Extraction (P1.6) subgraph_min_confidence_threshold: 0.6 subgraph_min_impact_threshold: 0.5 # temporal_recency_days: 365 # Example: only consider nodes/evidence from the last year # Parameters for Stage 8: Reflection (P1.7) # Thresholds for audit checks, e.g., high_confidence_coverage_min: 0.3 # Multi-layer network configuration (P1.23) # Define layers that hypotheses or other elements can belong to. # This is a global definition; specific node assignments happen during graph construction. layers: root_layer: description: "Core foundational layer for initial task understanding and decomposition." evidence_layer: description: "Layer primarily containing evidence nodes and their direct connections." hypothesis_analysis_layer: description: "Layer focused on hypothesis development, competition, and refinement." integration_synthesis_layer: description: "Layer for integrating diverse findings and synthesizing overall conclusions." # Add more domain-specific layers as needed, e.g.: # "immunology_perspective": # description: "Nodes and analyses related to immunological aspects." # "molecular_perspective": # description: "Nodes and analyses related to molecular biology aspects." # Configuration for Model Context Protocol (MCP) Server behavior mcp_settings: protocol_version: "2024-11-05" # As per original claude_desktop_config.json server_name: "NexusMind MCP Server" server_version: "0.1.0" # Match app.version vendor_name: "Your Organization" # Change this # display_name: "NexusMind" # If needed by MCP client # description: "NexusMind provides advanced scientific reasoning capabilities." # If needed # Optional: Direct Claude API integration settings (if the app needs to call Claude API itself) # claude_api: # api_key: "env_var:CLAUDE_API_KEY" # Example: Load from environment variable CLAUDE_API_KEY # default_model: "claude-3-opus-20240229" # timeout_seconds: 120 # max_retries: 2 # Example of how to structure a list of predefined knowledge domains or disciplines # These could be used for tagging, IBN detection, etc. (Ref P1.8) knowledge_domains: - name: "Skin Immunology" keywords: ["skin", "immune", "immunology", "dermatitis", "psoriasis"] description: "Focuses on the immune responses and mechanisms within the skin." - name: "Dermatology" keywords: ["dermatology", "skin diseases", "cutaneous"] description: "Branch of medicine dealing with the skin, hair, nails, and their diseases." - name: "Cutaneous Malignancies" keywords: ["skin cancer", "melanoma", "lymphoma", "carcinoma"] description: "Cancers arising from the skin." - name: "CTCL (Cutaneous T-Cell Lymphoma)" keywords: ["ctcl", "mycosis fungoides", "sezary syndrome"] description: "A rare type of non-Hodgkin lymphoma that affects the skin." - name: "Genomics" keywords: ["genomics", "gene expression", "dna", "rna", "sequencing"] description: "Study of genomes, their structure, function, evolution, and mapping." - name: "Microbiome" keywords: ["microbiome", "microbiota", "bacteria", "fungi", "virome"] description: "The community of microorganisms living together in a particular habitat, including the human body." - name: "Molecular Biology" keywords: ["molecular biology", "proteins", "enzymes", "pathways"] description: "Branch of biology that seeks to understand the molecular basis of biological activity." - name: "Machine Learning" keywords: ["machine learning", "ai", "neural networks", "deep learning", "prediction"] description: "Field of artificial intelligence that uses statistical techniques to give computer systems the ability to 'learn'." - name: "Biomedical LLMs" keywords: ["biomedical llm", "medical ai", "nlp in medicine"] description: "Large language models specialized for biomedical applications." # Add more domains based on Dr. Dey's profile (K3.3, K3.4) or general needs