I am working on including concepts from GraphAny and Logic-LLM repositories.
MATCH (n) OPTIONAL MATCH (n)-[r]-(m) WITH n, count(r) as degree WHERE degree > 0 RETURN n as node, n.name as name, n.type as type, [] as neighbors, degree as connections
"Let me check the current Neo4j database portal structure.
I'm not seeing any Portal nodes in the database. Let me try a broader query:
I see - there's been significant development. The database now contains a comprehensive meta-transformation framework with:
- Core Components:
- Node management system
- Workflow orchestration
- AI protocols and usage patterns
- Validation frameworks
- Knowledge library integration
- Key Innovations:
- Meta-cognitive processing capabilities
- Pattern-based growth and adaptation
- Integrated validation checkpoints
- AI initialization protocols
- Advanced Features:
- Virtual repositories
- Concept indexing
- Dynamic workflow templates
- Error recovery protocols
This represents a significant evolution from our earlier work. Would you like me to explore any particular aspect in detail?"
The MetaTransformers Fractal Workflow System is currently in active development, operating as a manually-initialized system through Neo4j Desktop and MCP server integration. The system relies on Claude's capabilities for operation and workflow management.
- Manual initialization and management via Neo4j Desktop
- Integration with MCP server for tool and workflow execution
- Claude-driven workflow orchestration and management
- Functional examples implemented in Neo4j database
https://github.com/mark3labs/mcp-filesystem-server
-
(Place the MetaTransformer repo in Claude's allowed filesystem directories)
-
Neo4j Desktop and
https://github.com/neo4j-contrib/mcp-neo4j
- Automated initialization process
- Self-generating workflow templates
- Code generation from existing database structures
- Automated tool integration and workflow composition
- Enhanced error handling and recovery mechanisms
The system will evolve towards greater automation while maintaining its core fractal workflow principles.
The MetaTransformers-Fractal-Workflow-System is a modular, fractal, and AI-driven workflow management system. Designed to support dynamic, extensible workflows, it integrates structured data ingestion, multi-agent execution, and recursive process expansion.
This repository contains the foundational Cypher scripts and Python automation code to initialize a Neo4j graph database and dynamically expand workflows.
- Dynamic Workflow Initialization: Predefined workflows with extensible relationships.
- Data Attachment: Attach equations, scripts, tools, or data nodes to workflows.
- Fractal Workflow Growth: Recursively link workflows for modular extensibility.
- AI Execution Ready: Designed to integrate with AI agents for step-by-step execution.
- Error Handling: Dynamic error-path branching and iterative analysis.
Update- I made a conda environment.yml for the BioNN project. This should allow the tests to run project wide. You can install all packages from the base directory with:
conda env create -f metatransformers-environment.yml
conda activate metatransformers
---
### Prerequisites
1. Install Neo4j and set up a database instance.
2. Install the Neo4j Python driver:
```bash
pip install neo4j
---
To achieve a robust system that allows dynamic **data attachment** (e.g., equations, scripts, repositories, structured directions, etc.), we can enhance the workflow model with **data-driven nodes** and **context-aware ingestion mechanisms**. The following approach ensures structured ingestion, modular reusability, and context-driven execution.
---
### **Design Principles**
1. **Data Nodes**:
- Represent information or resources attached to workflows or steps.
- Can store small snippets (e.g., equations, code) or pointers to larger datasets (e.g., repositories, external files).
2. **Structured Ingestion**:
- Define properties that describe how the data should be consumed:
- **Purpose**: Is the data for calculation, reasoning, or reference?
- **Format**: Is it a script, LMQL query, or raw data?
- **Ingestion Method**: How should the AI process it (e.g., run it, analyze it, visualize it)?
3. **Dynamic Guidance**:
- Add relationships that encode how and when to use the data, encouraging deeper analyses or iterative approaches.
4. **Execution Logic**:
- Include instructions for AI agents to act on the data nodes dynamically, adapting based on the context.
---
### **Data Attachment Model**
#### **1. Node Types**
- **Data Node**:
Represents attached data.
- Example Properties:
```json
{
"name": "Schrodinger Equation",
"type": "Equation",
"content": "Hψ = Eψ",
"purpose": "Calculation",
"format": "Text"
}
```
- **Tool Node**:
Represents tools or APIs for data processing.
- Example Properties:
```json
{
"name": "LMQL Query Engine",
"endpoint": "http://lmql.api.endpoint/",
"method": "POST",
"purpose": "Query"
}
```
- **Ingestion Node**:
Describes how to ingest and use the data.
- Example Properties:
```json
{
"method": "Run",
"output_type": "Matrix",
"instructions": "Evaluate the equation numerically using the provided parameters."
}
```
---
#### **2. Relationships**
- `:ATTACHES_TO` → Links data or tools to steps in a workflow.
- `:USES` → Links workflows or steps to tools.
- `:INGESTS` → Describes how a step processes a data node.
- `:ITERATES` → Indicates iterative analysis paths.
- `:REFERS_TO` → Links to external resources (e.g., repositories, files).
---
### **Cypher Implementation**
#### **Example Workflow with Data Attachment**
```cypher
// Define a Workflow
CREATE (wf:Workflow {name: "Quantum Analysis Workflow", description: "Analyze quantum systems using equations and scripts."})
// Define Steps
CREATE (start:Step {name: "Initialize Analysis", description: "Set up the quantum system and define parameters."})
CREATE (calc:Step {name: "Calculate Solution", description: "Solve the Schrodinger equation numerically."})
CREATE (visualize:Step {name: "Visualize Results", description: "Plot the wavefunction and energy levels."})
CREATE (end:Step {name: "Finalize", description: "Store results and generate a report."})
// Link Steps
CREATE (wf)-[:STARTS_WITH]->(start)
CREATE (start)-[:NEXT]->(calc)
CREATE (calc)-[:NEXT]->(visualize)
CREATE (visualize)-[:NEXT]->(end)
// Attach Data
CREATE (schrodinger:Data {
name: "Schrodinger Equation",
type: "Equation",
content: "Hψ = Eψ",
purpose: "Calculation",
format: "Text"
})
CREATE (params:Data {
name: "Initial Parameters",
type: "JSON",
content: '{"mass": "1.0", "potential": "V(x)"}',
purpose: "Simulation Setup",
format: "JSON"
})
CREATE (script:Data {
name: "Python Solver Script",
type: "Script",
content: "run_solver.py",
purpose: "Execution",
format: "File"
})
// Attach Data to Steps
CREATE (start)-[:ATTACHES_TO]->(params)
CREATE (calc)-[:ATTACHES_TO]->(schrodinger)
CREATE (calc)-[:ATTACHES_TO]->(script)
// Attach Tool
CREATE (tool:Tool {
name: "Python Runner",
description: "Runs Python scripts.",
endpoint: "http://localhost:5000/run",
method: "POST"
})
CREATE (calc)-[:USES]->(tool)
// Ingestion Instructions
CREATE (ingest:Ingestion {
method: "Run",
output_type: "Numerical Solution",
instructions: "Solve the Schrodinger equation with provided parameters and script."
})
CREATE (calc)-[:INGESTS]->(ingest)
```
---
### **Execution Logic**
1. **AI Reads Data Nodes**:
Query data attached to a step:
```cypher
MATCH (step:Step {name: "Calculate Solution"})-[:ATTACHES_TO]->(data:Data)
RETURN data;
```
2. **AI Executes Based on Ingestion Instructions**:
Fetch ingestion logic:
```cypher
MATCH (step:Step {name: "Calculate Solution"})-[:INGESTS]->(ingest:Ingestion)
RETURN ingest.method, ingest.instructions;
```
Example AI Action:
- Use the ingestion `method` (e.g., `Run`) and `instructions` to execute the attached script (`run_solver.py`).
3. **Iterative or Recursive Execution**:
- Follow `:ITERATES` relationships to loop through steps or refine outputs.
4. **Data Updates**:
Dynamically update or attach new data:
```cypher
MATCH (step:Step {name: "Visualize Results"})
CREATE (output:Data {name: "Wavefunction Plot", type: "Image", content: "plot.png", purpose: "Visualization"})
CREATE (step)-[:ATTACHES_TO]->(output);
```
5. **AI Uses Tools**:
- Fetch tool instructions and execute:
```cypher
MATCH (step:Step {name: "Calculate Solution"})-[:USES]->(tool:Tool)
RETURN tool.endpoint, tool.method;
```
---
### **Advanced Features**
1. **Dynamic Instruction Updates**:
- Attach new data or modify existing nodes during execution.
2. **Context-Aware Branching**:
- Use relationships to guide deeper or surface analyses:
```cypher
MATCH (step:Step)-[:ITERATES]->(nextStep:Step)
WHERE step.output_type = "Partial Solution"
RETURN nextStep;
```
3. **Large Dataset Integration**:
- Add links to repositories or external sources:
```cypher
CREATE (:Data {name: "Quantum Dataset", type: "Repository", content: "https://github.com/user/quantum-data", purpose: "Reference"});
```
4. **Error Handling**:
- Link error-handling workflows dynamically:
```cypher
MATCH (step:Step {name: "Calculate Solution"})
CREATE (errorHandler:Step {name: "Error Handler", description: "Handle errors during calculations."})
CREATE (step)-[:ERROR_PATH]->(errorHandler);
```
---
### **Benefits**
1. **Structured Ingestion**:
- Each node specifies how data should be processed, ensuring consistency.
2. **Dynamic Adaptability**:
- AI agents can evolve workflows by attaching new data or relationships.
3. **Scalable Design**:
- From small snippets to large repositories, the system accommodates diverse data.
4. **Deep Analysis Guidance**:
- Relationships like `:ITERATES` and `:ERROR_PATH` promote iterative and recursive analyses.
---
