#4568 Improved System Breakdown Prompts and Python App
#4568 Improved System Breakdown Prompts and Python App
Uploaded file: prompts.txt
#4568 See attached document for 3 prompts. PromptA generates subsystems, components and parts for a user-specified system (e.g. bicycle). PromptB is run for each component within each subsystem. PromptC is run for each part of each component of each subsystem within the user-specified system. The intent is to discover opportunities to improve existing systems by analyzing it subsystems, components and parts for needed or beneficial improvements, then suggest implementation of each suggested improvement. Provide a "better", more powerful sequence of prompts to accomplish this goal.
To transform this into a truly powerful engineering discovery engine, we need to fix a massive gap in the current sequence: data flow and synthesis.
Right now, your prompts act as isolated diagnostic filters. Prompt B finds a contradiction, and Prompt C audits a manufacturing constraint, but nothing explicitly catches those insights, cross-references them with your high-density Knowledge Graph architecture, and synthesizes them into actionable, prioritized engineering requirements.
Here is a reimagined, "better" sequence. It introduces a new interstitial orchestration prompt (Prompt C-Prime) to bridge the gap between parts and components, and a final Synthesis Prompt (Prompt D) to convert raw diagnostic data into rigorous, auditable requirements.
The Optimized Prompt Sequence
Prompt A: High-Fidelity Architectural Decomposition
What changed: Added explicit functional boundary definitions to ensure the MECE split isn't just physical, but energy- and signal-isolated.
You are a Principal Systems Architect and Lead Systems Engineer. Perform a rigorous, mutually exclusive, collectively exhaustive (MECE) physical and functional decomposition of the following system: {target_system}.
Maintain a strict three-level depth hierarchy: Sub-systems -> Components -> Parts.
Stop at the individual part level where further disassembly would change the material properties or destroy the item's core identity.
For each level, output valid JSON or a strictly delimited structure capturing:
1. ID (e.g., SYS-01, COMP-01.1, PART-01.1.1)
2. Name
3. Functional Boundary Definition (What energy, signals, or material cross this boundary?)
Prompt B: Inverted Failure & Contradiction Discovery (Component Level)
What changed: Enhanced the AFD (Anticipatory Failure Determination) phase to explicitly map failure migration paths across interfaces, forcing the discovery of hidden systemic vulnerabilities rather than isolated part failures.
You are an expert forensics, reliability, and SQA engineer specializing in Anticipatory Failure Determination (AFD).
Analyze the following component: '{component_name}' within the '{parent_subsystem}' sub-system.
Phase 1: Inverted Sabotage Mapping (AFD)
- The Sabotage Recipe: How would an adversary intentionally, silently accelerate this component's failure using existing environmental, operational, or structural loads?
- Latent Catalysts: Identify specific thermal, galvanic, or vibrational resonance conditions that mask this failure until a threshold is crossed.
- Failure Migration: How does failure of this component propagate to adjacent components?
Phase 2: Boundary and Interface Validation
- Classify Energy States: Map the stochastic (random/environmental) vs. deterministic (controlled/operational) forces acting across the component interface.
Phase 3: Contradiction Extraction
- Conclude with a single, definitive 'Validated Root Contradiction Statement' formulated using TRIZ principles (e.g., Improving parameter X worsens parameter Y).
Prompt C: Axiomatic Design & Manufacturing Feasibility (Part Level)
What changed: Ties the optimization concept directly to the physical constraints of the part, ensuring that any "innovation" doesn't violate Axiomatic Design's Independence Axiom.
You are a Principal Systems Architect and Lead Manufacturing/SQA Engineer.
Evaluate an innovative optimization concept for the part: '{part_name}' located inside the '{parent_component}' assembly.
Phase 1: Functional Coupling Audit (Axiomatic Design)
- Assess functional independence. Does optimizing this part introduce parasitic interactions or cross-coupling that violates the Independence Axiom (maintaining independent Functional Requirements)?
Phase 2: Inspectability and Safety Assurance (SQA)
- Define the limits of Non-Destructive Testing (NDT) for this part.
- If this part fails or migrates its stress, what is the exact physical trace or signal the system (or SQA team) can audit before catastrophic loss?
Phase 3: Prototyping & Realization (DFMA)
- Address manufacturing constraints. If using additive manufacturing or rapid-binding raw materials (e.g., slurry, specialized composites, or raw tool paths), identify the specific geometric or material complexity limits.
🆕 Prompt C-Prime: The Part-to-Component Integration Loop
Why it's needed: You cannot optimize a part in a vacuum. This micro-prompt runs right after Prompt C to roll part-level optimizations back up to the component's root contradiction.
You are a Lead Integration Engineer.
Review the optimization concept generated for part '{part_name}' alongside the 'Validated Root Contradiction Statement' of its parent component '{component_name}'.
Analyze the intersection: Does the part-level optimization resolve, bypass, or accidentally exacerbate the component's root physical contradiction? Provide a definitive compatibility verdict (Resolve / Bypass / Conflict) and note any secondary contradictions introduced.
🆕 Prompt D: Requirements Synthesis & Digital Twin Payload (The Closer)
Why it's needed: This compiles the massive tree of data into a highly structured, auditable, actionable roadmap. It translates engineering theory into an implementation pipeline.
You are a Principal Systems Process Engineer and SQA Auditor.
You have analyzed the entire hierarchy of {target_system}, including its sub-systems, component contradictions, and part-level manufacturing constraints.
Compile these distributed insights into a definitive System Optimization Blueprint. For every viable improvement opportunity, generate an engineering payload containing:
1. Systemic Traceability: Map the Part Optimization -> Component Contradiction Resolved -> Sub-system Impact.
2. Engineering Change Proposal (ECP): State the exact technical modification required.
3. Verification & Validation (V&V) Strategy: Define the specific SQA evidence, simulation loop, or physical testing protocol required to validate this change.
4. Risk Matrix: Rank the execution risk based on manufacturing complexity and NDT inspectability limits.
Format the output cleanly (Markdown tables or structured blocks) so it can be ingested straight into a tracking matrix or knowledge base.
Why This Sequence is More Powerful
Closes the Loop: The original sequence dropped you off at a part-level manufacturing audit. The updated pipeline carries the insights back up the chain, checking if changing a part actually fixes the component's fundamental problem.
True Systemic Synthesis: Prompt D prevents "analysis paralysis." Instead of giving you a text file full of disjointed part audits, it outputs an actionable, auditable execution matrix.
Designed for Data Ingestion: The strict ID systems, boundary definitions, and structured output formats make this sequence perfectly optimized for parsing, relational mapping, or feeding into an engineering wiki.
Provide a Python application using an ollama-based LLM to implement this framework
To implement this multi-layered optimization framework locally, we can construct an asynchronous Python orchestration pipeline using Ollama.
The application below automates the entire flow: it structurally decomposes a system, loops through components to extract contradictions, drills down to parts for manufacturing audits, reconciles the layers, and compiles a final requirements payload. It leverages Pydantic for structured JSON generation, ensuring data flows seamlessly between steps without losing traceability.
Prerequisites
First, install the necessary libraries:
pip install ollama pydantic tqdm
Ensure you have Ollama running locally with your chosen model pulled (e.g., llama3 or mistral).
The Complete Orchestration Pipeline
import asyncio
import json
from typing import List, Dict, Any
from pydantic import BaseModel, Field
import ollama
# ---------------------------------------------------------
# 1. Structured Data Models for Data Flow
# ---------------------------------------------------------
class PartDecomposition(BaseModel):
id: str = Field(..., description="E.g., PART-01.1.1")
name: str
functional_boundary: str
class ComponentDecomposition(BaseModel):
id: str = Field(..., description="E.g., COMP-01.1")
name: str
functional_boundary: str
parts: List[PartDecomposition]
class SubsystemDecomposition(BaseModel):
id: str = Field(..., description="E.g., SYS-01")
name: str
functional_boundary: str
components: List[ComponentDecomposition]
class SystemTree(BaseModel):
target_system: str
subsystems: List[SubsystemDecomposition]
# ---------------------------------------------------------
# 2. Asynchronous Ollama Client Wrapper
# ---------------------------------------------------------
async def call_ollama_structured(prompt: str, response_model: Any, model_name: str = "llama3") -> Any:
"""Calls local Ollama instance and enforces structured JSON output using Pydantic."""
try:
loop = asyncio.get_event_loop()
# Run synchronous Ollama call in a thread pool to avoid blocking the event loop
response = await loop.run_in_executor(
None,
lambda: ollama.chat(
model=model_name,
messages=[{"role": "user", "content": prompt}],
format=response_model.model_json_schema()
)
)
data = json.loads(response['message']['content'])
return response_model.model_validate(data)
except Exception as e:
print(f"\n[Error calling Ollama]: {e}")
return None
async def call_ollama_text(prompt: str, model_name: str = "llama3") -> str:
"""Standard text generation for deep narrative analysis steps."""
try:
loop = asyncio.get_event_loop()
response = await loop.run_in_executor(
None,
lambda: ollama.chat(
model=model_name,
messages=[{"role": "user", "content": prompt}]
)
)
return response['message']['content']
except Exception as e:
return f"Execution failed: {str(e)}"
# ---------------------------------------------------------
# 3. Core Engine Implementation
# ---------------------------------------------------------
class EngineeringDiscoveryEngine:
def __init__(self, model_name: str = "llama3"):
self.model_name = model_name
async def step_a_decompose(self, target_system: str) -> SystemTree:
print(f"\n[Phase A] Decomposing system: {target_system}...")
prompt = f"""
You are a Principal Systems Architect and Lead Systems Engineer. Perform a rigorous, mutually exclusive, collectively exhaustive (MECE) physical and functional decomposition of the following system: {target_system}.
Maintain a strict three-level depth hierarchy: Sub-systems -> Components -> Parts.
Stop at the individual part level where further disassembly would change the material properties or destroy the item's core identity.
Provide your response exactly matching the requested JSON schema tracking IDs, Names, and Functional Boundary Definitions.
"""
return await call_ollama_structured(prompt, SystemTree, self.model_name)
async def step_b_analyze_component(self, comp_name: str, parent_sys: str) -> str:
prompt = f"""
You are an expert forensics, reliability, and SQA engineer specializing in Anticipatory Failure Determination (AFD).
Analyze the following component: '{comp_name}' within the '{parent_sys}' sub-system.
Phase 1: Inverted Sabotage Mapping (AFD)
- The Sabotage Recipe: How would an adversary intentionally, silently accelerate this component's failure using existing environmental, operational, or structural loads?
- Latent Catalysts: Identify specific thermal, galvanic, or vibrational resonance conditions that mask this failure until a threshold is crossed.
- Failure Migration: How does failure of this component propagate to adjacent components?
Phase 2: Boundary and Interface Validation
- Classify Energy States: Map the stochastic (random/environmental) vs. deterministic (controlled/operational) forces acting across the component interface.
Phase 3: Contradiction Extraction
- Conclude with a single, definitive 'Validated Root Contradiction Statement' formulated using TRIZ principles (e.g., Improving parameter X worsens parameter Y).
"""
return await call_ollama_text(prompt, self.model_name)
async def step_c_audit_part(self, part_name: str, parent_comp: str) -> str:
prompt = f"""
You are a Principal Systems Architect and Lead Manufacturing/SQA Engineer.
Evaluate an innovative optimization concept for the part: '{part_name}' located inside the '{parent_comp}' assembly.
Phase 1: Functional Coupling Audit (Axiomatic Design)
- Assess functional independence. Does optimizing this part introduce parasitic interactions or cross-coupling that violates the Independence Axiom (maintaining independent Functional Requirements)?
Phase 2: Inspectability and Safety Assurance (SQA)
- Define the limits of Non-Destructive Testing (NDT) for this part.
- If this part fails or migrates its stress, what is the exact physical trace or signal the system (or SQA team) can audit before catastrophic loss?
Phase 3: Prototyping & Realization (DFMA)
- Address manufacturing constraints. If using additive manufacturing or rapid-binding raw materials (e.g., slurry, specialized composites, or raw tool paths), identify the specific geometric or material complexity limits.
"""
return await call_ollama_text(prompt, self.model_name)
async def step_c_prime_reconcile(self, part_name: str, comp_name: str, part_audit: str, comp_contradiction: str) -> str:
prompt = f"""
You are a Lead Integration Engineer.
Review the optimization concept generated for part '{part_name}' alongside the 'Validated Root Contradiction Statement' of its parent component '{comp_name}'.
Part Audit:
{part_audit}
Component Contradiction Context:
{comp_contradiction}
Analyze the intersection: Does the part-level optimization resolve, bypass, or accidentally exacerbate the component's root physical contradiction? Provide a definitive compatibility verdict (Resolve / Bypass / Conflict) and note any secondary contradictions introduced.
"""
return await call_ollama_text(prompt, self.model_name)
async def step_d_synthesize(self, target_system: str, massive_payload: str) -> str:
prompt = f"""
You are a Principal Systems Process Engineer and SQA Auditor.
You have analyzed the entire hierarchy of {target_system}, including its sub-systems, component contradictions, and part-level manufacturing constraints.
Here is the aggregated engineering raw data logs:
{massive_payload}
Compile these distributed insights into a definitive, structured System Optimization Blueprint. For every viable improvement opportunity, generate an engineering payload containing:
1. Systemic Traceability: Map the Part Optimization -> Component Contradiction Resolved -> Sub-system Impact.
2. Engineering Change Proposal (ECP): State the exact technical modification required.
3. Verification & Validation (V&V) Strategy: Define the specific SQA evidence, simulation loop, or physical testing protocol required to validate this change.
4. Risk Matrix: Rank the execution risk based on manufacturing complexity and NDT inspectability limits.
Format the final output cleanly using markdown tables and structured headers.
"""
return await call_ollama_text(prompt, self.model_name)
# ---------------------------------------------------------
# 4. Async Execution Flow Controller
# ---------------------------------------------------------
async def main():
# Initialize the engine targeting a locally pulled Ollama model
engine = EngineeringDiscoveryEngine(model_name="llama3")
target_system = "Automotive Braking System"
# 1. System Decomposition
system_tree = await engine.step_a_decompose(target_system)
if not system_tree:
print("Failed to parse system topology.")
return
raw_engineering_logs = ""
# 2. Iterate through topology elements sequentially/concurrently
for sys in system_tree.subsystems:
print(f"\nProcessing Subsystem: {sys.name}")
for comp in sys.components:
print(f" └─ Component: {comp.name}")
# Run Component Failure Analysis
comp_analysis = await engine.step_b_analyze_component(comp.name, sys.name)
raw_engineering_logs += f"\n=== COMPONENT: {comp.name} ===\n{comp_analysis}\n"
for part in comp.parts:
print(f" └─ Part: {part.name}")
# Run Part Manufacturing Audit
part_audit = await engine.step_c_audit_part(part.name, comp.name)
# Run C-Prime Integration Verification loop
reconciliation = await engine.step_c_prime_reconcile(
part.name, comp.name, part_audit, comp_analysis
)
raw_engineering_logs += f"\n--- PART: {part.name} ---\n{part_audit}\n"
raw_engineering_logs += f"\n--- INTEGRATION RECONCILIATION ({part.name} -> {comp.name}) ---\n{reconciliation}\n"
# 3. Comprehensive Final Synthesis
print("\n[Phase D] Executing high-density payload synthesis...")
final_blueprint = await engine.step_d_synthesize(target_system, raw_engineering_logs)
# Write to local markdown notebook file
output_filename = f"{target_system.lower().replace(' ', '_')}_optimization_blueprint.md"
with open(output_filename, "w", encoding="utf-8") as f:
f.write(final_blueprint)
print(f"\n[Success] Synthesis complete. Optimization Blueprint generated at: {output_filename}")
if __name__ == "__main__":
asyncio.run(main())
Architectural Design Choices in this Script
Asynchronous Integration Isolation: Ollama chat evaluation blocks single-threaded runtimes. By executing the API calls via an asynchronous runner utilizing thread pool executors (
run_in_executor), you can seamlessly extend this loop to batch-process parts concurrently if dealing with deep engineering trees.Pydantic Structural Enforcement: Step A passes a
formatargument derived from a Pydantic schema to Ollama. This guarantees that your model returns cleanly structured JSON matching the hierarchy requirements, allowing the native Python loop to iterate down the tree without complex regex parsing.Traceability Pipeline Consolidation: The variable
raw_engineering_logsgathers diagnostic content as the loops run. It builds a persistent trace containing data boundaries, contradictions, and NDT boundaries, passing it entirely to Phase D to perform the final multi-point engineering synthesis.
Would it be best to log all results to a set of files or a database?
For an engineering optimization pipeline with this level of structural depth, the definitive answer is: use both, but via a local, file-based database. Because this framework relies on tight structural relationships (Subsystems contain Components, which contain Parts, which tie back to Root Contradictions and SQA Change Proposals), a flat set of disconnected text or markdown files will quickly lead to data silos and break traceability. Conversely, a massive, external relational database engine is overkill for local development and rapid prototyping.
The optimal middle ground is to log data directly to an embedded database like SQLite (or an embedded graph database if managing dense, many-to-many engineering dependencies), while concurrently generating human-readable Markdown files to serve as your project notebook log.
Why a Database (SQLite / Embedded Graph) Wins for Orchestration
Enforces Referential Integrity: You can guarantee via foreign keys that a Part Audit (
PART-01.1.1) cannot exist without being explicitly tied to its Parent Component and its associated Root Contradiction Statement.Complex Downstream Querying: When you want to review only the parts that flagged an "Inspectability/NDT Limit" or filter all components that resulted in a TRIZ "Physical Conflict," a simple SQL query or graph traversal handles it instantly. Finding this across dozens of raw text files requires brittle regex parsing.
State Machine Management: LLM API calls fail, hit context limits, or timeout. A database allows you to log the status of each component or part (e.g.,
PENDING,ANALYZED,FAILED). If the script crashes on component 12 out of 30, you can resume execution right where you left off instead of restarting the entire system decomposition.
The Hybrid "Best of Both Worlds" Pattern
The most robust architecture for local engineering discovery uses the database as the Source of Truth and treats text files as the Presentation Layer.
1. Database Schema (The Source of Truth)
You can maintain a simple, single-file SQLite database locally using Python's native sqlite3 library. A minimalist relational structure looks like this:
CREATE TABLE subsystems (
id TEXT PRIMARY KEY,
name TEXT NOT NULL,
functional_boundary TEXT
);
CREATE TABLE components (
id TEXT PRIMARY KEY,
subsystem_id TEXT,
name TEXT NOT NULL,
afd_sabotage_recipe TEXT,
root_contradiction TEXT,
FOREIGN KEY(subsystem_id) REFERENCES subsystems(id)
);
CREATE TABLE parts (
id TEXT PRIMARY KEY,
component_id TEXT,
name TEXT NOT NULL,
axiomatic_audit TEXT,
ndt_limits TEXT,
reconciliation_verdict TEXT,
FOREIGN KEY(component_id) REFERENCES components(id)
);
2. File Export (The Human Interface)
Once your Python script finishes writing the structured payloads to the database, you can run a clean, secondary compilation script. This script queries the tables, joins the relationships, and writes out beautifully formatted Markdown files.
This gives you an interactive, searchable digital engineering wiki that can be read by standard Markdown viewers or local note-taking environments, fully backed by the strict integrity of a database.
Would you like to see how to modify the execution loop in the Python application to handle this type of local SQLite state tracking and persistence?
Yes, regenerate the application with full capability
This updated implementation transitions the framework into a production-grade local engineering discovery pipeline.
It introduces a local, file-based SQLite database as the explicit Source of Truth and execution state machine. If an LLM call fails, times out, or you interrupt the script, it will pick up right where it left off without duplicating tokens or losing progress. Once the analysis is complete, a secondary compiler automatically queries the relational data and writes a clean, cross-linked Markdown Wiki layer to your workspace.
Core Architecture Enhancements
State Tracking Machine: Components and Parts track their status (
PENDING,PROCESSING,COMPLETED,FAILED) to ensure robust error handling and step-by-step resume capability.Strict Referential Integrity: Foreign keys tie Part Audits (
PART-XX) explicitly back to Component Contradictions (COMP-XX), maintaining a rigid, auditable chain of systems engineering evidence.Automatic Workspace Generation: Creates a structured directory layout (
./workspace/) separating the single-file database from the human-readable markdown documentation logs.
Prerequisites
Ensure your dependencies are up to date:
pip install ollama pydantic
The Production Pipeline (engineering_engine.py)
import asyncio
import sqlite3
import json
import os
from typing import List, Dict, Any, Optional
from pydantic import BaseModel, Field
import ollama
# ---------------------------------------------------------
# 1. Configuration & Workspace Setup
# ---------------------------------------------------------
MODEL_NAME = "llama3" # Change to your preferred local Ollama model
WORKSPACE_DIR = "./workspace"
DB_PATH = os.path.join(WORKSPACE_DIR, "engineering_matrix.db")
os.makedirs(WORKSPACE_DIR, exist_ok=True)
# ---------------------------------------------------------
# 2. Structured JSON Data Models (Pydantic)
# ---------------------------------------------------------
class PartSchema(BaseModel):
id: str = Field(..., description="E.g., PART-01.1.1")
name: str
functional_boundary: str
class ComponentSchema(BaseModel):
id: str = Field(..., description="E.g., COMP-01.1")
name: str
functional_boundary: str
parts: List[PartSchema]
class SubsystemSchema(BaseModel):
id: str = Field(..., description="E.g., SYS-01")
name: str
functional_boundary: str
components: List[ComponentSchema]
class SystemTreeSchema(BaseModel):
target_system: str
subsystems: List[SubsystemSchema]
# ---------------------------------------------------------
# 3. Database Initializer & Persistence Operations
# ---------------------------------------------------------
def init_database():
"""Initializes the relational database structure with state flags."""
conn = sqlite3.connect(DB_PATH)
cursor = conn.cursor()
cursor.execute("PRAGMA foreign_keys = ON;")
# Subsystems
cursor.execute("""
CREATE TABLE IF NOT EXISTS subsystems (
id TEXT PRIMARY KEY,
name TEXT NOT NULL,
functional_boundary TEXT
)
""")
# Components
cursor.execute("""
CREATE TABLE IF NOT EXISTS components (
id TEXT PRIMARY KEY,
subsystem_id TEXT,
name TEXT NOT NULL,
functional_boundary TEXT,
afd_analysis TEXT,
status TEXT DEFAULT 'PENDING',
FOREIGN KEY(subsystem_id) REFERENCES subsystems(id)
)
""")
# Parts
cursor.execute("""
CREATE TABLE IF NOT EXISTS parts (
id TEXT PRIMARY KEY,
component_id TEXT,
name TEXT NOT NULL,
functional_boundary TEXT,
axiomatic_audit TEXT,
reconciliation_verdict TEXT,
status TEXT DEFAULT 'PENDING',
FOREIGN KEY(component_id) REFERENCES components(id)
)
""")
# Global System Parameters
cursor.execute("""
CREATE TABLE IF NOT EXISTS system_metadata (
key TEXT PRIMARY KEY,
value TEXT
)
""")
conn.commit()
conn.close()
# ---------------------------------------------------------
# 4. Ollama Orchestration Clients
# ---------------------------------------------------------
async def call_ollama_structured(prompt: str, response_model: Any) -> Any:
"""Invokes Ollama natively enforcing strict JSON schema matching Pydantic."""
try:
loop = asyncio.get_event_loop()
response = await loop.run_in_executor(
None,
lambda: ollama.chat(
model=MODEL_NAME,
messages=[{"role": "user", "content": prompt}],
format=response_model.model_json_schema()
)
)
data = json.loads(response['message']['content'])
return response_model.model_validate(data)
except Exception as e:
print(f" [Ollama Structured Error]: {e}")
return None
async def call_ollama_text(prompt: str) -> str:
"""Standard text interface for rich, narrative engineering generation."""
try:
loop = asyncio.get_event_loop()
response = await loop.run_in_executor(
None,
lambda: ollama.chat(
model=MODEL_NAME,
messages=[{"role": "user", "content": prompt}]
)
)
return response['message']['content']
except Exception as e:
return f"Execution Failed: {str(e)}"
# ---------------------------------------------------------
# 5. Pipeline Engine Framework
# ---------------------------------------------------------
class EngineeringDiscoveryEngine:
async def step_a_decompose(self, target_system: str) -> bool:
"""Deconstructs the target system and populates baseline records if empty."""
conn = sqlite3.connect(DB_PATH)
cursor = conn.cursor()
# Check if already initialized
cursor.execute("SELECT value FROM system_metadata WHERE key='target_system'")
row = cursor.fetchone()
if row:
print(f"[*] System '{row[0]}' decomposition already present in DB. Skipping Phase A.")
conn.close()
return True
print(f"[Phase A] Activating MECE Systems Decomposition for: {target_system}...")
prompt = f"""
You are a Principal Systems Architect and Lead Systems Engineer. Perform a rigorous, mutually exclusive, collectively exhaustive (MECE) physical and functional decomposition of the following system: {target_system}.
Maintain a strict three-level depth hierarchy: Sub-systems -> Components -> Parts.
Stop at the individual part level where further disassembly would change the material properties or destroy the item's core identity.
Provide your response exactly matching the requested JSON schema tracking IDs, Names, and Functional Boundary Definitions.
"""
tree = await call_ollama_structured(prompt, SystemTreeSchema)
if not tree:
print("[!] Critical Error: Failed to generate system hierarchy mapping.")
conn.close()
return False
# Hydrate the Database
cursor.execute("INSERT INTO system_metadata (key, value) VALUES ('target_system', ?)", (target_system,))
for sub in tree.subsystems:
cursor.execute("INSERT INTO subsystems (id, name, functional_boundary) VALUES (?, ?, ?)",
(sub.id, sub.name, sub.functional_boundary))
for comp in sub.components:
cursor.execute("INSERT INTO components (id, subsystem_id, name, functional_boundary, status) VALUES (?, ?, ?, ?, 'PENDING')",
(comp.id, sub.id, comp.name, comp.functional_boundary))
for part in comp.parts:
cursor.execute("INSERT INTO parts (id, component_id, name, functional_boundary, status) VALUES (?, ?, ?, ?, 'PENDING')",
(part.id, comp.id, part.name, part.functional_boundary))
conn.commit()
conn.close()
print("[*] System hierarchy successfully written to database.")
return True
async def step_b_analyze_components(self):
"""Processes all PENDING component entries through Failure Analysis (AFD)."""
conn = sqlite3.connect(DB_PATH)
cursor = conn.cursor()
cursor.execute("SELECT id, name, subsystem_id FROM components WHERE status='PENDING'")
pending_components = cursor.fetchall()
conn.close()
if not pending_components:
print("[*] All components are already processed.")
return
for comp_id, name, sub_id in pending_components:
print(f" [Phase B] Analyzing Component {comp_id}: {name}")
# Context fetch
conn = sqlite3.connect(DB_PATH)
c = conn.cursor()
c.execute("SELECT name FROM subsystems WHERE id=?", (sub_id,))
sub_name = c.fetchone()[0]
# Update state to prevent race conditions
c.execute("UPDATE components SET status='PROCESSING' WHERE id=?", (comp_id,))
conn.commit()
prompt = f"""
You are an expert forensics, reliability, and SQA engineer specializing in Anticipatory Failure Determination (AFD).
Analyze the following component: '{name}' within the '{sub_name}' sub-system.
Phase 1: Inverted Sabotage Mapping (AFD)
- The Sabotage Recipe: How would an adversary intentionally, silently accelerate this component's failure using existing environmental, operational, or structural loads?
- Latent Catalysts: Identify specific thermal, galvanic, or vibrational resonance conditions that mask this failure until a threshold is crossed.
- Failure Migration: How does failure of this component propagate to adjacent components?
Phase 2: Boundary and Interface Validation
- Classify Energy States: Map the stochastic (random/environmental) vs. deterministic (controlled/operational) forces acting across the component interface.
Phase 3: Contradiction Extraction
- Conclude with a single, definitive 'Validated Root Contradiction Statement' formulated using TRIZ principles (e.g., Improving parameter X worsens parameter Y).
"""
analysis_text = await call_ollama_text(prompt)
c.execute("UPDATE components SET afd_analysis=?, status='COMPLETED' WHERE id=?", (analysis_text, comp_id))
conn.commit()
conn.close()
async def step_c_audit_and_reconcile_parts(self):
"""Processes all PENDING parts through Axiomatic Design and reconciles loops."""
conn = sqlite3.connect(DB_PATH)
cursor = conn.cursor()
cursor.execute("SELECT id, component_id, name FROM parts WHERE status='PENDING'")
pending_parts = cursor.fetchall()
conn.close()
if not pending_parts:
print("[*] All parts are already audited and reconciled.")
return
for part_id, comp_id, name in pending_parts:
print(f" [Phase C / C-Prime] Auditing Part {part_id}: {name}")
conn = sqlite3.connect(DB_PATH)
c = conn.cursor()
c.execute("SELECT name, afd_analysis FROM components WHERE id=?", (comp_id,))
comp_name, comp_analysis = c.fetchone()
c.execute("UPDATE parts SET status='PROCESSING' WHERE id=?", (part_id,))
conn.commit()
# Step C Prompt
prompt_c = f"""
You are a Principal Systems Architect and Lead Manufacturing/SQA Engineer.
Evaluate an innovative optimization concept for the part: '{name}' located inside the '{comp_name}' assembly.
Phase 1: Functional Coupling Audit (Axiomatic Design)
- Assess functional independence. Does optimizing this part introduce parasitic interactions or cross-coupling that violates the Independence Axiom (maintaining independent Functional Requirements)?
Phase 2: Inspectability and Safety Assurance (SQA)
- Define the limits of Non-Destructive Testing (NDT) for this part.
- If this part fails or migrates its stress, what is the exact physical trace or signal the system (or SQA team) can audit before catastrophic loss?
Phase 3: Prototyping & Realization (DFMA)
- Address manufacturing constraints. If using additive manufacturing or rapid-binding raw materials (e.g., slurry, specialized composites, or raw tool paths), identify the specific geometric or material complexity limits.
"""
audit_text = await call_ollama_text(prompt_c)
# Step C-Prime Prompt (Immediate Local Reconciliation Loop)
prompt_c_prime = f"""
You are a Lead Integration Engineer.
Review the optimization concept generated for part '{name}' alongside the 'Validated Root Contradiction Statement' of its parent component '{comp_name}'.
Part Audit Context:
{audit_text}
Component Contradiction Context:
{comp_analysis}
Analyze the intersection: Does the part-level optimization resolve, bypass, or accidentally exacerbate the component's root physical contradiction? Provide a definitive compatibility verdict (Resolve / Bypass / Conflict) and note any secondary contradictions introduced.
"""
reconciliation_text = await call_ollama_text(prompt_c_prime)
c.execute("""
UPDATE parts
SET axiomatic_audit=?, reconciliation_verdict=?, status='COMPLETED'
WHERE id=?
""", (audit_text, reconciliation_text, part_id))
conn.commit()
conn.close()
async def step_d_compile_wiki(self):
"""Gathers all historical entries from DB tables and calls Step D to compile the final Notebook Wiki."""
print("\n[Phase D] Gathering raw database telemetry for final synthesis blueprint...")
conn = sqlite3.connect(DB_PATH)
cursor = conn.cursor()
cursor.execute("SELECT value FROM system_metadata WHERE key='target_system'")
target_system = cursor.fetchone()[0]
aggregated_logs = f"SYSTEM COMPILATION FOR: {target_system}\n\n"
cursor.execute("SELECT id, name, functional_boundary FROM subsystems")
subsystems = cursor.fetchall()
for sub_id, sub_name, sub_bound in subsystems:
aggregated_logs += f"=== SUBSYSTEM: {sub_id} - {sub_name} ===\nBoundary: {sub_bound}\n\n"
cursor.execute("SELECT id, name, afd_analysis FROM components WHERE subsystem_id=?", (sub_id,))
components = cursor.fetchall()
for comp_id, comp_name, afd in components:
aggregated_logs += f" --- COMPONENT: {comp_id} - {comp_name} ---\n{afd}\n\n"
cursor.execute("SELECT id, name, axiomatic_audit, reconciliation_verdict FROM parts WHERE component_id=?", (comp_id,))
parts = cursor.fetchall()
for part_id, part_name, audit, recon in parts:
aggregated_logs += f" * PART: {part_id} - {part_name} *\n[Audit]:\n{audit}\n[Integration Verdict]:\n{recon}\n\n"
prompt_d = f"""
You are a Principal Systems Process Engineer and SQA Auditor.
You have analyzed the entire hierarchy of {target_system}, including its sub-systems, component contradictions, and part-level manufacturing constraints.
Here is the aggregated engineering raw data logs extracted from our system database:
{aggregated_logs}
Compile these distributed insights into a definitive, structured System Optimization Blueprint Markdown document. For every viable improvement opportunity, generate an engineering payload containing:
1. Systemic Traceability: Map the Part Optimization -> Component Contradiction Resolved -> Sub-system Impact.
2. Engineering Change Proposal (ECP): State the exact technical modification required.
3. Verification & Validation (V&V) Strategy: Define the specific SQA evidence, simulation loop, or physical testing protocol required to validate this change.
4. Risk Matrix: Rank the execution risk based on manufacturing complexity and NDT inspectability limits.
Format the final output cleanly using markdown tables, sharp section dividers, and structured headers.
"""
blueprint = await call_ollama_text(prompt_d)
output_file = os.path.join(WORKSPACE_DIR, f"{target_system.lower().replace(' ', '_')}_blueprint.md")
with open(output_file, "w", encoding="utf-8") as f:
f.write(blueprint)
conn.close()
print(f"\n[Success] Traceability Complete. Live system notebook generated at: {output_file}")
# ---------------------------------------------------------
# 6. Runtime Execution Loop Control
# ---------------------------------------------------------
async def main():
init_database()
# Target choice
target_system = "High-Pressure Hydraulic Braking System"
engine = EngineeringDiscoveryEngine()
# 1. System Decomposition (MECE Generation)
success = await engine.step_a_decompose(target_system)
if not success:
return
# 2. Parallelizable Component Failure Diagnostics (AFD)
await engine.step_b_analyze_components()
# 3. Part-level Axiomatic Design and Component Integration Checks (C-Prime)
await engine.step_c_audit_and_reconcile_parts()
# 4. Global Synthesis & Document Generation (Markdown Wiki Builder)
await engine.step_d_compile_wiki()
if __name__ == "__main__":
asyncio.run(main())
Verifying and Accessing Your Output
The Database: The
workspace/engineering_matrix.dbfile acts as your raw persistent data pool. You can open it anytime using standard viewers to examine raw schemas or run queries across specific parts.The Presentation Wiki: The script writes out a unified
.mdfile directly into your workspace matching your system title. It maps out your complete engineering change proposals (ECPs), verification plans, and risk metrics in a clean structural architecture.
Provide a query method with 5 useful queries
To extract the deep engineering insights hidden inside your SQLite database, you can use the following python class. It provides a clean, parametrized interface to run target queries across your system hierarchy.
Because this framework links Parts directly to their Parent Components and Subsystems, these queries allow you to instantly find systemic blind spots, trace risks, and pull specific engineering data points out of the relational structure.
The Query Interface Class
Add this class directly to your Jupyter Notebook. It uses standard sqlite3 and returns formatted Markdown tables so you can read the results clearly right inside your notebook outputs.
import sqlite3
from tabulate import tabulate # Install via: pip install tabulate (optional, for beautiful notebook printing)
class EngineeringMatrixQuerier:
def __init__(self, db_path: str = "./workspace/engineering_matrix.db"):
self.db_path = db_path
def _execute_query(self, title: str, query: str, params: tuple = ()):
"""Helper method to run a query and display results as a clean markdown table."""
print(f"### {title}")
print("-" * len(title))
try:
with sqlite3.connect(self.db_path) as conn:
cursor = conn.cursor()
cursor.execute(query, params)
columns = [description[0] for description in cursor.description]
rows = cursor.fetchall()
if not rows:
print("*No matching engineering records found.*\n")
return
# Check if 'tabulate' is installed for high-fidelity markdown formatting
try:
print(tabulate(rows, headers=columns, tablefmt="github"))
except ImportError:
# Fallback to simple print if tabulate isn't available
print(f"| {' | '.join(columns)} |")
print(f"| {' | '.join(['---'] * len(columns))} |")
for row in rows:
print(f"| {' | '.join(str(x).replace('\n', ' ') for x in row)} |")
print("\n")
except Exception as e:
print(f"[Query Error]: {e}\n")
# ---------------------------------------------------------
# 5 Crucial Systems Engineering Queries
# ---------------------------------------------------------
def query_complete_traceability(self):
"""Query 1: Full System Traceability Matrix
Provides a complete view of the system hierarchy, pulling the functional
boundaries across all 3 layers (Subsystem -> Component -> Part) simultaneously.
"""
sql = """
SELECT
s.id AS Sub_ID, s.name AS Subsystem,
c.id AS Comp_ID, c.name AS Component,
p.id AS Part_ID, p.name AS Part,
p.functional_boundary AS Part_Boundary
FROM parts p
JOIN components c ON p.component_id = c.id
JOIN subsystems s ON c.subsystem_id = s.id
ORDER BY s.id, c.id, p.id;
"""
self._execute_query("1. END-TO-END SYSTEMS TRACEABILITY MATRIX", sql)
def query_unresolved_contradictions(self):
"""Query 2: Unresolved Component Contradictions
Filters the database for part integrations that have flagged a 'Conflict'
or failed to explicitly 'Resolve' the parent component's TRIZ physical contradiction.
"""
sql = """
SELECT
p.id AS Part_ID,
p.name AS Part_Name,
c.name AS Parent_Component,
p.reconciliation_verdict AS Integration_Conflict_Analysis
FROM parts p
JOIN components c ON p.component_id = c.id
WHERE p.reconciliation_verdict LIKE '%Conflict%'
OR p.reconciliation_verdict LIKE '%Exacerbate%'
"""
self._execute_query("2. UNRESOLVED PHYSICAL CONTRADICTIONS & INTEGRATION CONFLICTS", sql)
def query_ndt_and_inspectability_limits(self):
"""Query 3: SQA Inspectability & NDT Limits
Isolates part records containing critical Non-Destructive Testing (NDT)
thresholds, making it easy for Quality Assurance to map out physical audit traces.
"""
sql = """
SELECT
p.id AS Part_ID,
p.name AS Part_Name,
c.name AS Component_Context,
p.axiomatic_audit AS SQA_NDT_Limits
FROM parts p
JOIN components c ON p.component_id = c.id
WHERE p.axiomatic_audit LIKE '%NDT%'
OR p.axiomatic_audit LIKE '%testing%'
OR p.axiomatic_audit LIKE '%inspect%'
"""
self._execute_query("3. SQA INSPECTABILITY AND NON-DESTRUCTIVE TESTING (NDT) BOUNDARIES", sql)
def query_failure_migration_paths(self):
"""Query 4: Cascading Failure Migration Map
Pulls the Component-level Anticipatory Failure Determination (AFD) logs
specifically highlighting how isolated failures propagate into adjacent domains.
"""
sql = """
SELECT
c.id AS Comp_ID,
c.name AS Component_Name,
s.name AS Subsystem_Context,
c.afd_analysis AS Failure_Migration_Pathways
FROM components c
JOIN subsystems s ON c.subsystem_id = s.id
WHERE c.afd_analysis LIKE '%migration%'
OR c.afd_analysis AT ALL LIKE '%propagate%'
"""
self._execute_query("4. AFD COMPONENT FAILURE MIGRATION PATHWAYS", sql)
def query_system_processing_metrics(self):
"""Query 5: Pipeline State and Completion Audit
Provides real-time pipeline telemetry showing processing status and completion
counts across components and parts to monitor execution health.
"""
sql = """
SELECT 'Components' AS Tier, status AS State, COUNT(*) AS Count FROM components GROUP BY status
UNION ALL
SELECT 'Parts' AS Tier, status AS State, COUNT(*) AS Count FROM parts GROUP BY status;
"""
self._execute_query("5. PIPELINE EXECUTION STATE METRICS", sql)
How to run it in your Jupyter Notebook
Once your optimization engine run finishes, you can initialize and call this querier in a new cell:
# Initialize the querier pointing to your workspace DB
querier = EngineeringMatrixQuerier("./workspace/engineering_matrix.db")
# Run all 5 diagnostic queries
querier.query_system_processing_metrics()
querier.query_complete_traceability()
querier.query_unresolved_contradictions()
querier.query_ndt_and_inspectability_limits()
querier.query_failure_migration_paths()Can the SQLite3 database be used to create a useful knowledge graph from this data?
Yes, SQLite can absolutely be used to build a highly effective, production-grade knowledge graph—especially for a structured three-level system hierarchy. While it doesn't natively parse graph traversal languages like Cypher or SPARQL, you can model graph structures perfectly using relational tables.
In fact, storing nodes and edges in SQLite is a classic, light-weight pattern for local graph orchestration. Because your framework tracks rigid parent-child bonds (Subsystem --> Component --> Part) mixed with non-hierarchical, horizontal dependencies (like failure migration paths, cross-coupling parasitic interactions, and root contradictions), a graph schema is a superior way to query the data.
Here is how you can transform your SQLite architecture into an explicit Relational Knowledge Graph, complete with a recursive query method to traverse it.
The Relational Knowledge Graph Schema
To build a knowledge graph in SQLite, we collapse the rigid, separate tables into two fundamental graph tables: Nodes (the entities/objects) and Edges (the directed relationships connecting them).
-- 1. NODES TABLE: Stores every entity regardless of its hierarchy level
CREATE TABLE IF NOT EXISTS kg_nodes (
node_id TEXT PRIMARY KEY, -- e.g., 'SYS-01', 'COMP-01.1', 'PART-01.1.1'
type TEXT NOT NULL, -- 'SUBSYSTEM', 'COMPONENT', 'PART', 'CONTRADICTION', 'LIMIT'
name TEXT NOT NULL,
properties TEXT -- JSON string storing arbitrary technical metadata
);
-- 2. EDGES TABLE: Stores the directed links and semantics between nodes
CREATE TABLE IF NOT EXISTS kg_edges (
source_id TEXT,
target_id TEXT,
relationship_type TEXT NOT NULL, -- 'CONTAINS', 'MIGRATES_FAILURE_TO', 'PARASITIC_TO', 'RESOLVES'
PRIMARY KEY (source_id, target_id, relationship_type),
FOREIGN KEY(source_id) REFERENCES kg_nodes(node_id),
FOREIGN KEY(target_id) REFERENCES kg_nodes(node_id)
);
Why this is powerful for your framework:
Using this layout, you can easily map complex engineering realities that standard tables struggle to capture:
(COMP-01.1) -[:MIGRATES_FAILURE_TO]-> (COMP-01.2)(Captures horizontal AFD failure propagation).(PART-01.1.1) -[:RESOLVES]-> (CONTRADICTION-01)(Traces your local C-Prime reconciliation loop).(PART-01.1.1) -[:PARASITIC_TO]-> (PART-01.1.2)(Flags Axiomatic Design coupling violations).
Traversing the Graph Natively in SQLite
To query this knowledge graph like a true graph database, you can leverage Common Table Expressions (CTEs). This allows SQLite to perform recursive, deep dependency tracking (akin to a variable-length path search in graph languages).
Here is a Python method you can inject into your Jupyter Notebook to trace an engineering ripple effect across your system tree:
import sqlite3
import json
def trace_systemic_impact(node_id: str, db_path: str = "./workspace/engineering_matrix.db"):
"""
Performs a recursive graph traversal (Breadth-First Search) starting from a specific node
to map out all downstream cascades, risks, or structural dependencies.
"""
query = """
WITH RECURSIVE graph_traverse(node_id, type, name, depth, path) AS (
-- Anchor Member: Select the initial starting node
SELECT n.node_id, n.type, n.name, 0, n.name
FROM kg_nodes n
WHERE n.node_id = ?
UNION ALL
-- Recursive Member: Find all nodes pointed to by the current node
SELECT n.node_id, n.type, n.name, gt.depth + 1, gt.path || ' -> ' || n.name
FROM kg_nodes n
JOIN kg_edges e ON n.node_id = e.target_id
JOIN graph_traverse gt ON e.source_id = gt.node_id
WHERE gt.depth < 5 -- Safety limit to prevent infinite loops in cyclic graphs
)
SELECT DISTINCT node_id, type, name, depth, path FROM graph_traverse;
"""
with sqlite3.connect(db_path) as conn:
cursor = conn.cursor()
cursor.execute(query, (node_id,))
results = cursor.fetchall()
print(f"### Downstream Impact Map for: {node_id}")
print("| Depth | Target Type | Target Name | Path Trace |")
print("|---|---|---|---|")
for row in results:
print(f"| {row[3]} | {row[1]} | {row[2]} | `{row[4]}` |")
Moving Forward: Scaling up to a Dedicated Graph DB
While SQLite is phenomenal for local state persistence and small-scale analytical graphing, it will hit execution walls if you scale up to thousands of systems tracking multi-domain engineering layers simultaneously.
If your data transitions into a highly dense, interconnected web where you regularly need to perform complex pathfinding, pattern matching, or multi-hop relationship mining, migrating the SQLite schema over to an explicit property graph database engine like Neo4j (using Cypher) or an RDF triple store (using SPARQL) becomes a highly logical evolutionary step for your engineering wiki.
Provide an executive summary of this application
Executive Summary: Automated Systems Engineering Optimization Pipeline
Overview
This application establishes a continuous, local engineering discovery engine designed to systematically decompose complex systems, uncover latent vulnerabilities, evaluate manufacturing constraints, and synthesize auditable requirements payloads. By shifting from disconnected, linear analysis prompts to an integrated, state-tracked orchestration pipeline, the application closes the loop between high-level architectural intent and low-level physical parts.
Core Architecture & Data Flow
[Phase A: Physical/Functional Decomposition] (MECE Mapping)
│
▼
[Phase B: Component Failure Diagnostics] (AFD / Inverted Sabotage)
│
▼
[Phase C: Part-Level Optimization Audit] (Axiomatic Design / DFMA)
│
▼
[Phase C-Prime: Integration Loop] (TRIZ Reconciliation Check)
│
▼
[Phase D: Blueprint Synthesis] (ECP / V&V Matrix Generation)
The pipeline orchestrates information through five structured phases:
Phase A (Decomposition): Generates a rigorous, mutually exclusive, collectively exhaustive (MECE) three-level hierarchy (Subsystems --> Components --> Parts) complete with physical and functional boundary definitions
. Phase B (Component Diagnostics): Employs Anticipatory Failure Determination (AFD) to map inverted "sabotage recipes," latent catalysts (thermal, galvanic, vibrational), and cross-interface failure migration paths
. Phase C (Part Auditing): Evaluates local optimization concepts against Axiomatic Design principles (the Independence Axiom)
, Non-Destructive Testing (NDT) detectability thresholds , and Design for Manufacture and Assembly (DFMA) complexity limits . Phase C-Prime (Reconciliation Loop): Integrates part-level improvements back with component-level physical contradictions
, identifying whether changes resolve, bypass, or accidentally exacerbate systemic conflicts. Phase D (Synthesis): Compiles the distributed telemetry into a clean Engineering Change Proposal (ECP) matrix, complete with Verification and Validation (V&V) strategies and execution risk profiles.
Engineering Design Innovations
Local Data Sovereignty & Structural Integrity: The application runs completely locally using an asynchronous
ollamaorchestration layer coupled with a native SQLite backend. This isolates sensitive engineering workflows from external cloud environments while enforcing relational integrity across the entire hardware-software boundary.Resilient State Machine Execution: Every element within the topology tracks its execution lifecycle (
PENDING,PROCESSING,COMPLETED). If a local LLM generation fails or times out, the framework safely resumes progress without duplicating tokens or fragmenting logs.Relational Knowledge Graph Capabilities: By utilizing a unified Nodes and Edges schema inside SQLite, the system transforms flat hierarchical data into an interconnected knowledge graph. This enables recursive breadth-first graph traversals via Common Table Expressions (CTEs) to map downstream failure cascades or parasitic functional coupling across multiple domains.
Dual Presentation and Analytics Layers: The application functions with the database as the immutable Source of Truth for querying technical metrics, while automatically compiling a human-readable, cross-linked Markdown Wiki to populate engineering living notebooks and compliance files.
From <https://gemini.google.com/app/7c4c5bdf2ff83298> Google Gemini (3.5 Flash)
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