This section shows how to take the two SNOs we built and feed them into the synthesis engine to generate a new, candidate SNO.
1. Initial Critic Evaluation
Before synthesis, each parent SNO needs a TrustScore. This score, typically assigned by a separate CriticPipeline, represents the quality and credibility of the SNO. For this tutorial, we’ll assign them manually.
# In a real workflow, a Critic component would analyze and score each SNO.
# For this example, we'll set the scores directly.
# Let's say Geosyncline theory was plausible for its time, but Plate Tectonics is much stronger.
SNO_geosyncline.trust_score = 0.75
SNO_plate_tectonics.trust_score = 0.95
print(f"Geosyncline Trust Score: {SNO_geosyncline.trust_score}")
print(f"Plate Tectonics Trust Score: {SNO_plate_tectonics.trust_score}")
2. Identifying the Chiral Pair
The system first needs to confirm that the two SNOs are in a state of productive conflict. This is done by a ChiralPairDetector, which checks if the theories are semantically opposed.
from cns_tools.detectors import ChiralPairDetector
# Initialize the detector.
detector = ChiralPairDetector(cscore_threshold=0.8)
# The detector calculates a "Chirality Score" (CScore) for the pair.
c_score = detector.calculate_cscore(SNO_geosyncline, SNO_plate_tectonics)
print(f"Calculated CScore (Chirality): {c_score:.4f}")
# Check if the pair meets the criteria for synthesis.
is_synthesis_candidate = detector.is_candidate_pair(SNO_geosyncline, SNO_plate_tectonics)
if is_synthesis_candidate:
print("\nThis is a high-potential pair for synthesis!")
else:
print("\nThis pair does not meet the criteria for synthesis.")
# For the tutorial, we'll assume the CScore is high enough to proceed.
# A high CScore indicates the SNOs have opposing core ideas, making them
# perfect for synthesis.
3. Running the Generative Synthesis Engine
The GenerativeSynthesisEngine takes the conflicting pair and uses a Large Language Model (LLM) to generate a new, higher-order hypothesis that attempts to resolve the contradiction.
from cns_tools.synthesis import GenerativeSynthesisEngine
# Initialize the synthesis engine with a connection to an LLM.
synthesis_engine = GenerativeSynthesisEngine(llm_backend="gpt-4-turbo")
print("\nInvoking the Generative Synthesis Engine...")
# The engine takes the two parent SNOs as input.
SNO_synthesis_candidate = synthesis_engine.synthesize(
sno_a=SNO_geosyncline,
sno_b=SNO_plate_tectonics
)
print("Candidate Synthesis SNO generated successfully!")
print("\n--- Generated Hypothesis ---")
# The new hypothesis is extracted from the candidate SNO.
# (We're using a hypothetical function to convert the embedding back to text for this demo)
from cns_tools.utils import get_text_from_embedding
generated_hypothesis_text = get_text_from_embedding(SNO_synthesis_candidate.hypothesis_embedding)
print(generated_hypothesis_text)
# Mock output for the tutorial:
# --- Generated Hypothesis ---
# The Earth's lithosphere is a dynamic system of moving plates, not a static crust.
# While geosynclines represent real areas of significant sediment deposition, their formation
# and subsequent uplift into mountain ranges are best explained by the convergent boundaries
# of these moving plates, driven by mantle convection, rather than a simple vertical
# buckling mechanism on a cooling Earth.
The engine has produced a new SNO containing a hypothesis that integrates concepts from both parents. The next step is to analyze this result.