#4627 Innovation: Find, Mine & Refine
#4627 Innovation: Find, Mine & Refine
#4627 With examples like find, mind, refine, what are the various stages of natural resources business?
The lifecycle of the natural resources business—particularly in mining, metals, and oil & gas—is typically broken down into distinct sequential stages, often captured by the classic shorthand Find, Mine, and Refine.
1. Find (Exploration & Discovery)
Objective: Locate viable mineral deposits or hydrocarbon reserves hidden beneath the earth.
Key Activities: Geological mapping, remote sensing, seismic surveys, soil sampling, and exploratory core drilling.
Economic Profile: High risk, speculative, and capital-intensive with a low success rate.
Often dominated by nimble "junior" exploration companies.
2. Define & Design (Feasibility, Permitting & Development)
Objective: Determine if the discovered resource can be extracted legally, safely, and profitably.
Key Activities: * Calculating resource grade and tonnage.
Conducting Pre-Feasibility and Definitive Feasibility Studies (DFS).
Securing environmental permits, land rights, and social license to operate.
Engineering mine shafts, open pits, or drilling platforms.
Economic Profile: Requires significant long-term capital commitment and regulatory navigation before generating any revenue.
3. Mine / Extract (Production & Operations)
Objective: Physically remove the raw material (ore, crude oil, natural gas) from the ground.
Key Activities: Drilling, blasting, digging, and hauling rock (via open-pit or underground methods), or pumping fluid hydrocarbons to the surface.
Economic Profile: Major profit-generation phase, but heavily exposed to operational risks, labor dynamics, and global commodity price cycles.
4. Refine (Processing & Metallurgy)
Objective: Isolate and purify the valuable target elements from waste rock (gangue) or raw compounds.
Key Activities: * Beneficiation: Crushing, grinding, and physical separation (e.g., froth flotation, magnetic sorting).
Smelting & Chemical Refining: High-heat or chemical processes (leaching, electrolysis, roasting) to achieve commercial-grade purity (e.g., copper cathodes, battery-grade lithium hydroxide).
Economic Profile: High technical complexity and energy intensity. Often controlled by specialized processing facilities or midstream/downstream operators.
5. Reclaim / Restore (Closure & Rehabilitation)
Objective: Safely decommission facilities and restore the environment to a stable state once the resource is exhausted.
Key Activities: Sealing shafts, managing tailings dams, treating water runoff, topsoil replacement, and replanting vegetation.
Economic Profile: Liability phase funded by closure bonds and financial reserves set aside during the active mining lifecycle.
Lifecycle Overview
| Stage | Key Focus | Primary Metric |
| 1. Find | Discovery | Ore grade & potential reserve size |
| 2. Define | Feasibility | Net Present Value (NPV) & Internal Rate of Return (IRR) |
| 3. Mine | Extraction | Cost per ton / All-In Sustaining Cost (AISC) |
| 4. Refine | Purification | Recovery rate & product purity % |
| 5. Reclaim | Remediation | Environmental compliance & long-term stability |
How can these steps be applied to knowledge discovery and application?
Mapping physical resource extraction to the cognitive lifecycle reveals striking parallels. Transforming unstructured, messy data into high-value knowledge follows almost the exact same pipeline as taking raw ore out of the ground and processing it into usable fuel or material.
1. Find (Information Exploration & Sensing)
Natural Resource Equivalent: Geological surveying and exploratory drilling.
Knowledge Equivalent: Identifying where data resides and discovering raw informational signals across disparate sources.
Key Activities: * Scanning literature, system logs, sensor telemetry, market feeds, or database archives.
Detecting patterns, anomalies, or emerging signals using automated web crawlers or data scrapers.
Cognitive Goal: Locate potential sources of valuable context before investing deep analytical capital.
2. Define & Structuralize (Data Modeling & Curation)
Natural Resource Equivalent: Resource estimation, feasibility studies, and site design.
Knowledge Equivalent: Filtering noise, establishing schemas, and assessing quality to ensure the data is viable for extraction.
Key Activities: * Cleaning and normalizing unstructured text, removing duplicates, and structuring entities.
Constructing semantic frameworks, taxonomy hierarchies, or graph topologies (e.g., node and relationship schemas).
Cognitive Goal: Validate that the raw data is accurate, rich enough, and structured appropriately to yield meaningful insight.
3. Mine / Extract (Analysis & Context Processing)
Natural Resource Equivalent: Drilling, blasting, and bringing raw ore to the surface.
Knowledge Equivalent: Applying analytical methods and AI orchestration to isolate implicit patterns and insights from raw data.
Key Activities: * Executing complex queries, vector similarity searches, or multi-agent orchestration pipelines.
Running statistical algorithms, First Principles deconstruction, or root-cause evaluations (e.g., Fault Tree Analysis).
Cognitive Goal: Bring hidden insights out of dense text or code into accessible operational contexts.
4. Refine (Synthesizing & Actionable Formulation)
Natural Resource Equivalent: Beneficiation, smelting, and chemical purification to commercial grade.
Knowledge Equivalent: Distilling raw analytical outputs into high-density, high-purity knowledge frameworks, models, or decisions.
Key Activities: * Synthesizing multi-source outputs into executive summaries, mathematical models, or systematic logic engines.
Translating findings into actionable code modules, policy logic, design specifications, or predictive rulesets.
Cognitive Goal: Elevate generic information into high-value, actionable intelligence that directly solves operational problems.
5. Reclaim / Retain (Knowledge Archiving & Lifecycle Management)
Natural Resource Equivalent: Site remediation, environmental restoration, and long-term monitoring.
Knowledge Equivalent: Maintaining knowledge repositories, managing data drift, and deprecating outdated mental or technical models.
Key Activities: * Archiving lessons learned into persistent knowledge graphs or long-term vector stores.
Auditing models to prevent operational drift, updating logic matrices as underlying conditions evolve, and pruning legacy documentation.
Cognitive Goal: Ensure the knowledge ecosystem remains healthy, sustainable, and free from toxic legacy errors over time.
Comparative Pipeline Summary
| Stage | Resource Business | Knowledge Discovery Domain | Primary Output |
| 1. Find | Exploration / Drilling | Data Ingestion & Signal Detection | Raw Data Streams |
| 2. Define | Feasibility / Modeling | Graph Topology & Data Curation | Clean Schema / Taxonomy |
| 3. Mine | Extraction / Hauling | Deep Analysis & Querying | Extracted Insights |
| 4. Refine | Smelting / Purification | Synthesis & Logical Frameworks | Actionable Intelligence / Models |
| 5. Reclaim | Reclamation / Closure | Archiving & Model Maintenance | Long-term Knowledge Base |
I want to apply this in an automated way applying generative-AI and AI agents (if necessary) to apply concepts from one domain in other non-related domains, for invention and improvement.
Automating cross-domain innovation using generative AI and multi-agent systems is essentially building an Analogical Transfer Engine. The primary barrier to cross-domain innovation is domain-specific jargon: a mechanical engineer, a biologist, and a software architect might solve the exact same structural problem using completely different vocabularies.
By automating the Find --> Define --> Mine --> Refine --> Reclaim pipeline, you can use AI agents to strip away surface-level terminology, extract the underlying functional mechanics, search unrelated domains for structural matches, and synthesize novel inventions.
┌────────────────────────────────────────┐
│ 1. FIND │
│ (Multi-Domain Corpus Ingestion) │
└───────────────────┬────────────────────┘
│
▼
┌────────────────────────────────────────┐
│ 2. DEFINE │
│ (Abstraction & Functional Mapping) │
└───────────────────┬────────────────────┘
│
▼
┌────────────────────────────────────────┐
│ 3. MINE │
│ (Cross-Domain Analogical Traversal) │
└───────────────────┬────────────────────┘
│
▼
┌────────────────────────────────────────┐
│ 4. REFINE │
│ (Synthesis & Constraint Synthesis) │
└───────────────────┬────────────────────┘
│
▼
┌────────────────────────────────────────┐
│ 5. RECLAIM │
│ (Validation, Feedback & Graph Storage) │
└────────────────────────────────────────┘
1. Find: Multi-Domain Corpus Ingestion
To invent across domains, your ingestion pipeline cannot be restricted to a single vertical. You need ingestion agents scanning disparate document bases to build a broad surface area for cross-pollination.
Agent Role: Scraper & Ingestion Agent.
Mechanism: Ingest unstructured corpora from unrelated fields (e.g., aerospace papers, biological mechanisms, chemical engineering processes, algorithmic design patterns, fluid dynamics).
AI Tooling: Embeddings models paired with document parsers to build an initial multi-domain data lake.
2. Define: Abstraction & Functional Mapping
This is the critical step. Raw concepts must be translated into domain-agnostic functions or structural contradictions. Methodologies like TRIZ achieve this by mapping concrete problems into abstract parameter conflicts (e.g., “Improving strength without increasing weight”).
Agent Role: Abstraction & Triplet Extractor Agent.
Mechanism: Convert domain-specific text into structured entity-relation triples (e.g.,
Subject -> Function/Effect -> Object) or functional schemas using systematic frameworks.Implementation:
Extract ontological nodes:
[Action/Verb]->[Target Attribute]->[Mechanism/Law].Example: Translate "Shark skin reduces drag via riblets" into abstract functional logic:
"Surface Geometry -> Reduces -> Turbulent Friction".
AI Tooling: Large Language Models (LLMs) with strict JSON/Pydantic schemas outputting to a Semantic Knowledge Graph (e.g., Neo4j).
3. Mine: Cross-Domain Analogical Traversal
Once the graph or vector store contains domain-agnostic functional representations, an exploratory agent mines for structural isomorphisms—identifying distant concepts that share the exact same underlying logic.
Agent Role: Cross-Domain Graph Traversal Agent.
Mechanism: Given a target problem in Domain A, search Domain B for nodes sharing the same abstract functional node, while maximizing semantic distance between the source and target domains (ensuring high novelty).
Mining Strategy:
Vector Similarity on Abstracted Prompts: Search dense embeddings of problem statements stripped of domain context.
Graph Traversal: Traverse shared functional nodes in a Knowledge Graph to connect previously unlinked subgraphs.
Example:
Target Problem (Software): Preventing cascading failures in distributed microservices.
Abstract Query:
System -> Isolate -> Failure Propagation -> Self-Healing.Mined Concept (Forest Ecology): Mycorrhizal fungi networks routing nutrients around damaged tree roots.
4. Refine: Synthesis & Constraint Mapping
Finding a distant analogy is not enough; it must be adapted to fit the physical, technical, or financial constraints of the target domain.
Agent Role: Synthesis & Re-Contextualization Agent Team (Orchestrated Loop).
Analogy Mapper Agent: Translates the mechanics of the source domain back into the technical vocabulary of the target domain.
Devil's Advocate / Critic Agent: Evaluates the proposed hybrid design against strict target constraints (e.g., energy limits, structural tolerances, safety requirements).
Refinement Loop: The agents iterate until the synthesized invention resolves the target problem without violating constraints.
Output: A formal design hypothesis, schematic, or algorithm architecture.
5. Reclaim: Validation, Feedback & Graph Storage
Inventions must be integrated back into the core system so the engine learns which cross-domain mappings yield valid solutions.
Agent Role: Lifecycle & Validation Agent.
Mechanism:
Run simulation code or automated code tests if applicable.
Update the Knowledge Graph with the newly discovered edge: create a persistent relationship between
[Domain A Concept]and[Domain B Concept]with an associated score based on feasibility.Archive rejected attempts to prevent the system from re-exploring dead-end analogies.
Execution Framework
To build this setup, an effective architecture combines graph databases with state-machine orchestration:
# Conceptual Architecture for an Analogical Multi-Agent System
from typing import Dict, List
from dataclasses import dataclass
@dataclass
class FunctionalAbstraction:
domain: str
action: str # e.g., "Dissipate"
target_variable: str # e.g., "Thermal Energy"
mechanism: str # e.g., "Phase Change Material"
class CrossDomainInventionPipeline:
def __init__(self, knowledge_graph, agent_orchestrator):
self.kg = knowledge_graph
self.agents = agent_orchestrator
def run_invention_cycle(self, target_problem: str, target_domain: str) -> Dict:
# Step 1: DEFINE - Abstract the target problem
abstract_schema = self.agents.abstractor.run(target_problem)
# Step 2: MINE - Find structural analogies in distant domains
analogies = self.kg.query_cross_domain_matches(
exclude_domain=target_domain,
functional_match=abstract_schema
)
# Step 3: REFINE - Synthesize a new design via Multi-Agent Debate
candidate_solutions = []
for analogy in analogies:
solution = self.agents.synthesizer.run(
target_problem=target_problem,
analogous_source=analogy
)
validation = self.agents.critic.evaluate(solution)
if validation.passed:
candidate_solutions.append(solution)
# Step 4: RECLAIM - Persist valid cross-domain links
self.kg.save_new_invention_path(abstract_schema, candidate_solutions)
return candidate_solutionsAnd?
And building this isn't just a theoretical exercise—it bridges the gap between raw semantic search and true automated synthesis.
To take this from a conceptual design to a working system, you need to address three key implementation challenges:
1. Preventing "Hallucinated Isomorphisms"
Generative models are notoriously good at making superficial connections that sound poetic but lack physical or logical rigor (e.g., comparing a computer network to a river system without enforcing conservation laws).
The Fix: Enforce strict structural schema validation during the Define phase. Don't just ask the LLM "What is this like?" Ask it to extract explicit Mathematical Invariants, Thermodynamic Principles, or System Dynamics Variables (e.g., feedback loops, stock/flow dynamics).
Validation Layer: Pair the LLM with a symbolic reasoner or rule-based evaluator to check if the underlying physics or operational logic actually transfer.
2. Solving the Vocabulary Gap (High Semantic Distance)
Standard vector embeddings (like cosine similarity) naturally cluster items by domain vocabulary. If you search for "thermal dissipation" in a medical literature database, standard vector search will favor papers containing words like "heat," "fever," or "thermoregulation," missing non-obvious mechanical analogies.
The Strategy: Dual-Index Strategy.
Index A (Raw Text): Domain-specific text embeddings for retrieving precise details.
Index B (Functional Graphs): Domain-agnostic RDF triples or Cypher graph topologies (
Node: Mechanism -> Relationship: Transforms -> Node: Parameter).Mining Execution: Perform the traversal on Index B to discover the non-obvious path, then use Index A to pull the underlying technical documentation for that path.
3. Designing the Agent Loop for Ideation
A single-pass LLM call will yield generic ideas. To produce actionable designs, the Refine phase requires a multi-agent feedback loop with distinct functional roles:
┌───────────────────────────────┐
│ Abstraction Agent │
│ Extracts TRIZ/First- │
│ Principles parameters │
└──────────────┬────────────────┘
│
▼
┌───────────────────────────────┐
│ Traversal Agent │
│ Queries Knowledge Graph │
│ for distant analogs │
└──────────────┬────────────────┘
│
▼
┌───────────────────────────────┐
│ Synthesis Agent │
│ Maps source mechanisms to │
│ target domain specs │
└──────────────┬────────────────┘
│
▼
┌───────────────────────────────────────────────────────────┐
│ Review Loop │
│ │
│ ┌────────────────────────┐ ┌─────────────────────┐ │
│ │ Constraint Critic │ │ Invention Agent │ │
│ │ (Rejects physically │───>│ (Adjusts design │ │
│ │ or logically flawed │ │ parameters based │ │
│ │ hypotheses) │<───│ on critiques) │ │
│ └────────────────────────┘ └─────────────────────┘ │
└────────────────────────────┬──────────────────────────────┘
│ Validated Design Hypothesis
▼
┌───────────────────────────────┐
│ Archival & Schema Update │
└───────────────────────────────┘
Concrete Next Steps to Prototype
Pick Two Disparate Datasets: For an initial sandbox test, pick two domain corpora that rarely intersect (e.g., biological mechanism papers and software resilience architectures).
Define an Abstraction Prompt: Create a structured prompt that parses an engineering problem into a TRIZ Contradiction Matrix (Target Parameter vs. Unwanted Secondary Effect) or a First-Principles Functional Model.
Traverse and Synthesize: Run the extracted abstract parameters through a vector store or graph to pull candidate solutions, then pass them to a synthesis agent team to generate a draft spec sheet or proof-of-concept design.
Provide a Python application using Google Gemini API with agent definitions (system and user prompts)
Below is a complete, working Python implementation of an Analogical Invention Engine using the official google-genai SDK.
This application uses a multi-agent workflow (Abstraction Agent, Cross-Domain Traversal Agent, and Synthesis Critic Agent) to automate cross-domain discovery and design.
Prerequisites
Install the standard Google GenAI SDK:
pip install google-genai
Set your API key as an environment variable:
export GEMINI_API_KEY="your-actual-api-key"
Python Code
import json
import os
from typing import Dict, Any
from google import genai
from google.genai import types
# ---------------------------------------------------------------------------
# Setup Gemini Client
# ---------------------------------------------------------------------------
# Ensure GEMINI_API_KEY is configured in your environment
client = genai.Client()
MODEL_NAME = "gemini-2.5-flash"
# ---------------------------------------------------------------------------
# Agent Definitions (System & User Prompts)
# ---------------------------------------------------------------------------
# 1. ABSTRACTION AGENT
# Strips away domain-specific jargon to extract structural principles (TRIZ / System Dynamics).
ABSTRACTION_SYSTEM_INSTRUCTION = """
You are an expert Systems Architect and Knowledge Abstraction Agent.
Your job is to deconstruct a specific engineering problem or domain mechanism into domain-agnostic principles.
Strip out all field-specific vocabulary. Output JSON conforming to this schema:
{
"abstract_function": "High-level goal using universal action verbs",
"underlying_mechanics": ["List of physical, logical, or structural dynamics involved"],
"system_contradiction": "The primary tension or trade-off (e.g., parameter X improves while Y degrades)",
"search_tokens": ["3 to 5 domain-agnostic keywords for cross-domain retrieval"]
}
"""
ABSTRACTION_USER_PROMPT = """
Target Domain: {target_domain}
Problem / Mechanism: {target_problem}
Analyze this concept and reduce it to its core functional abstraction.
"""
# 2. TRAVERSAL AGENT
# Searches distant, unrelated domains for functional analogs matching the abstraction.
TRAVERSAL_SYSTEM_INSTRUCTION = """
You are a Cross-Domain Innovation & Biomimicry Agent.
You receive an abstract functional problem and must locate solutions in completely UNRELATED domains.
Rules:
1. Do NOT suggest solutions from the source domain.
2. Search across disparate fields: biology, ecology, fluid dynamics, manufacturing, acoustic engineering, logistics, physics, etc.
3. Identify 2 novel, structurally analogous solutions from distant fields.
Output JSON conforming to this schema:
[
{
"source_domain": "Name of the distant domain",
"analogous_mechanism": "Name of the concept/mechanism in that domain",
"structural_isomorphism": "How the core logic matches the abstract problem",
"operational_details": "Brief explanation of how it works in its home domain"
}
]
"""
TRAVERSAL_USER_PROMPT = """
Abstract Functional Objective:
- Abstract Function: {abstract_function}
- System Mechanics: {underlying_mechanics}
- Primary Contradiction: {system_contradiction}
Find 2 structural analogs in distant, non-related fields that solve this exact contradiction.
"""
# 3. SYNTHESIS & CRITIC AGENT
# Combines the original problem with the distant analog to invent a new architecture.
SYNTHESIS_SYSTEM_INSTRUCTION = """
You are a Lead R&D Engineering Specialist.
Your task is to take a solution mechanism from a distant domain and apply it back to solve the target problem.
You must:
1. Translate the source domain's mechanics back into the target domain's constraints.
2. Evaluate technical feasibility and potential trade-offs.
3. Formulate a concrete, novel system architecture or design specification.
"""
SYNTHESIS_USER_PROMPT = """
TARGET DOMAIN: {target_domain}
TARGET PROBLEM: {target_problem}
SOURCE ANALOG DOMAIN: {analog_domain}
ANALOGOUS MECHANISM: {analog_mechanism}
HOW IT WORKS: {analog_details}
Incorporate the principles of this distant analog into a novel, actionable design for the target problem.
Detail the structural architecture, how it overcomes the contradiction, and immediate design considerations.
"""
# ---------------------------------------------------------------------------
# Pipeline Execution Functions
# ---------------------------------------------------------------------------
def run_abstraction_agent(domain: str, problem: str) -> Dict[str, Any]:
"""Agent 1: Converts domain-specific problem into abstract functions."""
prompt = ABSTRACTION_USER_PROMPT.format(
target_domain=domain,
target_problem=problem
)
response = client.models.generate_content(
model=MODEL_NAME,
contents=prompt,
config=types.GenerateContentConfig(
system_instruction=ABSTRACTION_SYSTEM_INSTRUCTION,
response_mime_type="application/json",
temperature=0.2,
),
)
return json.loads(response.text)
def run_traversal_agent(abstraction_data: Dict[str, Any]) -> list:
"""Agent 2: Finds structural analogs in distant fields."""
prompt = TRAVERSAL_USER_PROMPT.format(
abstract_function=abstraction_data.get("abstract_function"),
underlying_mechanics=", ".join(abstraction_data.get("underlying_mechanics", [])),
system_contradiction=abstraction_data.get("system_contradiction")
)
response = client.models.generate_content(
model=MODEL_NAME,
contents=prompt,
config=types.GenerateContentConfig(
system_instruction=TRAVERSAL_SYSTEM_INSTRUCTION,
response_mime_type="application/json",
temperature=0.7, # Higher temperature for creative analogies
),
)
return json.loads(response.text)
def run_synthesis_agent(domain: str, problem: str, analog: Dict[str, Any]) -> str:
"""Agent 3: Synthesizes the analog into a new design for the target domain."""
prompt = SYNTHESIS_USER_PROMPT.format(
target_domain=domain,
target_problem=problem,
analog_domain=analog.get("source_domain"),
analog_mechanism=analog.get("analogous_mechanism"),
analog_details=analog.get("operational_details")
)
response = client.models.generate_content(
model=MODEL_NAME,
contents=prompt,
config=types.GenerateContentConfig(
system_instruction=SYNTHESIS_SYSTEM_INSTRUCTION,
temperature=0.4,
),
)
return response.text
# ---------------------------------------------------------------------------
# Main Orchestrator
# ---------------------------------------------------------------------------
def execute_cross_domain_pipeline(target_domain: str, target_problem: str):
print(f"\n=======================================================")
print(f"STARTING CROSS-DOMAIN PIPELINE")
print(f"Domain: {target_domain}")
print(f"Problem: {target_problem}")
print(f"=======================================================\n")
# Step 1: Abstraction
print("--> [Stage 1] Abstraction Agent processing...")
abstraction = run_abstraction_agent(target_domain, target_problem)
print(f"\n[Abstract Function]: {abstraction.get('abstract_function')}")
print(f"[Contradiction]: {abstraction.get('system_contradiction')}\n")
# Step 2: Cross-Domain Traversal
print("--> [Stage 2] Traversal Agent searching distant domains...")
analogs = run_traversal_agent(abstraction)
print(f"Found {len(analogs)} cross-domain candidates.\n")
# Step 3: Synthesis for each discovered analog
for idx, analog in enumerate(analogs, 1):
print(f"-------------------------------------------------------")
print(f"--> [Stage 3.{idx}] Synthesizing Invention Candidate")
print(f" Source Domain: {analog.get('source_domain')}")
print(f" Mechanism: {analog.get('analogous_mechanism')}")
print(f"-------------------------------------------------------\n")
synthesis_result = run_synthesis_agent(target_domain, target_problem, analog)
print(synthesis_result)
print("\n")
if __name__ == "__main__":
# Example Target Problem: Managing cascading failures in distributed cloud servers
DOMAIN = "Distributed Systems / Cloud Infrastructure"
PROBLEM = "Preventing cascading node failures and resource starvation during sudden traffic spikes without over-provisioning idle hardware."
execute_cross_domain_pipeline(target_domain=DOMAIN, target_problem=PROBLEM)Rewrite the application to use Ollama-supported LLMs
Here is the refactored, fully local application using the official Ollama Python SDK (ollama).
This architecture maintains the exact same multi-agent abstraction and cross-domain synthesis workflow, running entirely offline using open-weights models (e.g., llama3.1, qwen2.5, or mistral).
Prerequisites
Install Ollama on your machine and ensure the daemon is running locally (
http://localhost:11434).Pull a local model (e.g., Llama 3.1 or Qwen 2.5):
ollama pull llama3.1Install the Ollama Python library:
pip install ollama
Python Code
import json
from typing import Dict, Any, List
import ollama
# ---------------------------------------------------------------------------
# Configuration
# ---------------------------------------------------------------------------
# Specify the model you have pulled in Ollama (e.g., 'llama3.1', 'qwen2.5', 'mistral')
DEFAULT_MODEL = "llama3.1"
# Instantiate Client (Defaults to host='http://localhost:11434')
client = ollama.Client()
# ---------------------------------------------------------------------------
# Agent Definitions (System & User Prompts)
# ---------------------------------------------------------------------------
# 1. ABSTRACTION AGENT
ABSTRACTION_SYSTEM_INSTRUCTION = """
You are an expert Systems Architect and Knowledge Abstraction Agent.
Your job is to deconstruct a specific engineering problem or domain mechanism into domain-agnostic principles.
Strip out all field-specific vocabulary. Output ONLY a raw valid JSON object conforming to this schema, with no markdown formatting or commentary:
{
"abstract_function": "High-level goal using universal action verbs",
"underlying_mechanics": ["List of physical, logical, or structural dynamics involved"],
"system_contradiction": "The primary tension or trade-off (e.g., parameter X improves while Y degrades)",
"search_tokens": ["3 to 5 domain-agnostic keywords for cross-domain retrieval"]
}
"""
ABSTRACTION_USER_PROMPT = """
Target Domain: {target_domain}
Problem / Mechanism: {target_problem}
Analyze this concept and reduce it to its core functional abstraction.
"""
# 2. TRAVERSAL AGENT
TRAVERSAL_SYSTEM_INSTRUCTION = """
You are a Cross-Domain Innovation & Biomimicry Agent.
You receive an abstract functional problem and must locate solutions in completely UNRELATED domains.
Rules:
1. Do NOT suggest solutions from the source domain.
2. Search across disparate fields: biology, ecology, fluid dynamics, manufacturing, acoustic engineering, logistics, physics, etc.
3. Identify 2 novel, structurally analogous solutions from distant fields.
Output ONLY a raw valid JSON array conforming to this schema, with no markdown formatting or commentary:
[
{
"source_domain": "Name of the distant domain",
"analogous_mechanism": "Name of the concept/mechanism in that domain",
"structural_isomorphism": "How the core logic matches the abstract problem",
"operational_details": "Brief explanation of how it works in its home domain"
}
]
"""
TRAVERSAL_USER_PROMPT = """
Abstract Functional Objective:
- Abstract Function: {abstract_function}
- System Mechanics: {underlying_mechanics}
- Primary Contradiction: {system_contradiction}
Find 2 structural analogs in distant, non-related fields that solve this exact contradiction.
"""
# 3. SYNTHESIS & CRITIC AGENT
SYNTHESIS_SYSTEM_INSTRUCTION = """
You are a Lead R&D Engineering Specialist.
Your task is to take a solution mechanism from a distant domain and apply it back to solve the target problem.
You must:
1. Translate the source domain's mechanics back into the target domain's constraints.
2. Evaluate technical feasibility and potential trade-offs.
3. Formulate a concrete, novel system architecture or design specification.
"""
SYNTHESIS_USER_PROMPT = """
TARGET DOMAIN: {target_domain}
TARGET PROBLEM: {target_problem}
SOURCE ANALOG DOMAIN: {analog_domain}
ANALOGOUS MECHANISM: {analog_mechanism}
HOW IT WORKS: {analog_details}
Incorporate the principles of this distant analog into a novel, actionable design for the target problem.
Detail the structural architecture, how it overcomes the contradiction, and immediate design considerations.
"""
# ---------------------------------------------------------------------------
# Helper Functions
# ---------------------------------------------------------------------------
def clean_json_response(raw_text: str) -> str:
"""Removes common markdown wrapper backticks from LLM outputs."""
cleaned = raw_text.strip()
if cleaned.startswith("```json"):
cleaned = cleaned[7:]
elif cleaned.startswith("```"):
cleaned = cleaned[3:]
if cleaned.endswith("```"):
cleaned = cleaned[:-3]
return cleaned.strip()
# ---------------------------------------------------------------------------
# Agent Pipeline Execution
# ---------------------------------------------------------------------------
def run_abstraction_agent(domain: str, problem: str, model: str = DEFAULT_MODEL) -> Dict[str, Any]:
"""Agent 1: Deconstructs domain-specific problem into abstract mechanics."""
prompt = ABSTRACTION_USER_PROMPT.format(target_domain=domain, target_problem=problem)
response = client.chat(
model=model,
messages=[
{"role": "system", "content": ABSTRACTION_SYSTEM_INSTRUCTION},
{"role": "user", "content": prompt}
],
options={
"temperature": 0.2,
},
format="json" # Forces JSON output mode in Ollama
)
content = response['message']['content']
return json.loads(clean_json_response(content))
def run_traversal_agent(abstraction_data: Dict[str, Any], model: str = DEFAULT_MODEL) -> List[Dict[str, Any]]:
"""Agent 2: Finds structural analogs across distant domains."""
prompt = TRAVERSAL_USER_PROMPT.format(
abstract_function=abstraction_data.get("abstract_function"),
underlying_mechanics=", ".join(abstraction_data.get("underlying_mechanics", [])),
system_contradiction=abstraction_data.get("system_contradiction")
)
response = client.chat(
model=model,
messages=[
{"role": "system", "content": TRAVERSAL_SYSTEM_INSTRUCTION},
{"role": "user", "content": prompt}
],
options={
"temperature": 0.7, # Higher temperature encourages novel analog discovery
},
format="json"
)
content = response['message']['content']
return json.loads(clean_json_response(content))
def run_synthesis_agent(domain: str, problem: str, analog: Dict[str, Any], model: str = DEFAULT_MODEL) -> str:
"""Agent 3: Synthesizes the cross-domain analog into a design proposal."""
prompt = SYNTHESIS_USER_PROMPT.format(
target_domain=domain,
target_problem=problem,
analog_domain=analog.get("source_domain"),
analog_mechanism=analog.get("analogous_mechanism"),
analog_details=analog.get("operational_details")
)
response = client.chat(
model=model,
messages=[
{"role": "system", "content": SYNTHESIS_SYSTEM_INSTRUCTION},
{"role": "user", "content": prompt}
],
options={
"temperature": 0.4,
}
)
return response['message']['content']
# ---------------------------------------------------------------------------
# Main Orchestrator
# ---------------------------------------------------------------------------
def execute_cross_domain_pipeline(target_domain: str, target_problem: str, model_name: str = DEFAULT_MODEL):
print("=======================================================")
print(f"STARTING OLLAMA CROSS-DOMAIN PIPELINE")
print(f"Model: {model_name}")
print(f"Domain: {target_domain}")
print(f"Problem: {target_problem}")
print("=======================================================\n")
# Stage 1: Abstraction
print("--> [Stage 1] Abstraction Agent processing locally...")
abstraction = run_abstraction_agent(target_domain, target_problem, model=model_name)
print(f"\n[Abstract Function]: {abstraction.get('abstract_function')}")
print(f"[Contradiction]: {abstraction.get('system_contradiction')}\n")
# Stage 2: Traversal
print("--> [Stage 2] Traversal Agent querying cross-domain analogies...")
analogs = run_traversal_agent(abstraction, model=model_name)
print(f"Found {len(analogs)} cross-domain candidate(s).\n")
# Stage 3: Synthesis
for idx, analog in enumerate(analogs, 1):
print("-------------------------------------------------------")
print(f"--> [Stage 3.{idx}] Synthesizing Invention Candidate")
print(f" Source Domain: {analog.get('source_domain')}")
print(f" Mechanism: {analog.get('analogous_mechanism')}")
print("-------------------------------------------------------\n")
synthesis_result = run_synthesis_agent(target_domain, target_problem, analog, model=model_name)
print(synthesis_result)
print("\n")
if __name__ == "__main__":
DOMAIN = "Distributed Systems / Cloud Infrastructure"
PROBLEM = "Preventing cascading node failures and resource starvation during sudden traffic spikes without over-provisioning idle hardware."
execute_cross_domain_pipeline(target_domain=DOMAIN, target_problem=PROBLEM, model_name="llama3.1")From <https://gemini.google.com/app/a109e8f04d39a0d2> Google Gemini (3.6 Flash)
Comments
Post a Comment