#!/bin/bash
set -e
cd /Users/pkpro/upperspace3/fastmemory
mkdir -p example/health_science example/robotics example/driverless_cars example/audit example/email_analysis example/business_analytics example/world_events
function write_html {
local folder=$1
cat << 'EOF' > "$folder/index.html"
FastMemory Visualization
Clustered Text Memory Graph (Louvain)
EOF
}
# HEALTH SCIENCE (e.g. PubMed QA graph vs Vector)
cat << 'EOF' > example/health_science/input.md
# Health Science: Precision Medicine & Oncology
Original Vector Approach: Embedded clinical trials independently. RAG failed on multi-drug interactions.
FastMemory Approach: Converts Drug mechanisms and patient profiles into ATFs with contextual graph links.
## [ID: ATF_DRUG_001]
**Action:** Administrate_Paclitaxel
**Input:** {Patient_Age, Tumor_Stage}
**Logic:** Inhibit microtubule breakdown.
**Context_Links:** [ATF_SIDE_001], [ATF_MOA_001]
## [ID: ATF_SIDE_001]
**Action:** Monitor_Neuropathy
**Input:** {Patient_Symptoms}
**Logic:** If tingling, reduce dose.
**Context_Links:** [ATF_DRUG_001]
## [ID: ATF_MOA_001]
**Action:** Inhibit_Mitosis
**Input:** {Cellular_Process}
**Logic:** Stops tumor cell division.
**Context_Links:** [ATF_DRUG_001]
EOF
# ROBOTICS
cat << 'EOF' > example/robotics/input.md
# Robotics: Swarm Navigation
Original Vector Approach: Nav-logs embedded. Failed to map conditional failure states (e.g. "If sensor dead, follow neighbour").
FastMemory Approach: Conditionals are parsed as functional ATFs with explicit dependency edges.
## [ID: ATF_NAV_001]
**Action:** Sensor_Fusion_Logic
**Input:** {LidarData, CameraData}
**Logic:** Merge into local costmap.
**Context_Links:** [ATF_FAIL_001]
## [ID: ATF_FAIL_001]
**Action:** Fallback_To_Swarm
**Input:** {Sensor_Status}
**Logic:** If Lidar dead, tracking neighbor bot ID.
**Context_Links:** [ATF_NAV_001]
EOF
# DRIVERLESS CARS
cat << 'EOF' > example/driverless_cars/input.md
# Driverless Cars: Perception & Scenario Trees
Original Vector Approach: Scene graphs clustered via point clouds.
FastMemory Approach: Semantic rules of the road converted to interconnected ATFs.
## [ID: ATF_SCENE_PEDESTRIAN]
**Action:** Yield_To_Pedestrian
**Input:** {Crosswalk_Zone, Velocity}
**Logic:** Must stop if pedestrian in zone.
**Context_Links:** [ATF_ACT_BRAKE]
## [ID: ATF_ACT_BRAKE]
**Action:** Apply_Braking_Force
**Input:** {Deceleration_Target}
**Logic:** Ramp up brake pressure smoothly.
**Context_Links:** [ATF_SCENE_PEDESTRIAN]
EOF
# AUDIT
cat << 'EOF' > example/audit/input.md
# Audit: Financial Fraud Detection (FinQA)
Previous: Flattened ledger parsing with RAG finding textual matches.
FastMemory: ATFs tracing the flow of funds and compliance rules.
## [ID: ATF_COMPLY_SOX]
**Action:** Verify_SOX_Compliance
**Input:** {Report_Q3}
**Logic:** Check signatures from CEO/CFO.
**Context_Links:** [ATF_LEDGER_REVIEW]
## [ID: ATF_LEDGER_REVIEW]
**Action:** Audit_Expense_Anomalies
**Input:** {T_E_Ledger}
**Logic:** Flag if expense > $10k without receipt.
**Context_Links:** [ATF_COMPLY_SOX]
EOF
# EMAIL ANALYSIS
cat << 'EOF' > example/email_analysis/input.md
# Email: Enron Corporate Communications
Previous Approach: NLP classification (BERT).
FastMemory: Email threads modeled as conversational logic blocks.
## [ID: ATF_THREAD_102]
**Action:** Discuss_Offshore_Accounts
**Input:** {Financial_Strategy}
**Logic:** Move assets to subsidiary.
**Context_Links:** [ATF_THREAD_103]
## [ID: ATF_THREAD_103]
**Action:** Authorize_Transfer
**Input:** {Approval_Chain}
**Logic:** CFO signature required.
**Context_Links:** [ATF_THREAD_102]
EOF
# BUSINESS ANALYTICS
cat << 'EOF' > example/business_analytics/input.md
# Business Analytics: Churn Prediction
Previous: Decision trees on tabular data.
FastMemory: Customer behavior rules synthesized into Actionable Text Functions.
## [ID: ATF_CHURN_DETECT]
**Action:** Detect_Usage_Drop
**Input:** {Weekly_Logins}
**Logic:** If login drop > 50%, flag risk.
**Context_Links:** [ATF_RETENTION_OFFER]
## [ID: ATF_RETENTION_OFFER]
**Action:** Trigger_Discount_Email
**Input:** {Customer_ID}
**Logic:** Send 20% off coupon.
**Context_Links:** [ATF_CHURN_DETECT]
EOF
# WORLD EVENTS
cat << 'EOF' > example/world_events/input.md
# World Events: Geopolitical Supply Chain Mapping
Previous Approach: Knowledge Graphs (Neo4j).
FastMemory: Event nodes clustered dynamically by Louvain communities tracking cascading economic effects.
## [ID: ATF_EVENT_PORT_STRIKE]
**Action:** Delay_Logistics
**Input:** {West_Coast_Ports}
**Logic:** Add 14 days to shipping ETAs.
**Context_Links:** [ATF_IMPACT_AUTO]
## [ID: ATF_IMPACT_AUTO]
**Action:** Adjust_Manufacturing_Forecast
**Input:** {Component_Inventory}
**Logic:** Reduce output by 10%.
**Context_Links:** [ATF_EVENT_PORT_STRIKE]
EOF
for domain in health_science robotics driverless_cars audit email_analysis business_analytics world_events; do
echo "Processing $domain..."
cargo run -q -- example/$domain/input.md > example/$domain/output.json
write_html example/$domain
cat << EOF > example/$domain/README.md
# $domain - FastMemory Example
## Original Huggingface Scenario
In many Huggingface test datasets involving **$domain**, the original approach relies on standard Vector embeddings (RAG) or static Knowledge Graphs. These approaches often fail with multi-hop reasoning or conditional logic because they retrieve flat snippets based on semantic similarity rather than traversing logical dependencies.
## FastMemory "Clustered Text Memory" Approach
Here we convert the dataset rules into **Atomic Text Functions (ATFs)** and use the strict \`rust-louvain\` graph clustering to map out executable semantic structures.
## Reproducible Command
\`\`\`bash
# From fastmemory root:
cargo run -- example/$domain/input.md > example/$domain/output.json
\`\`\`
## Provable Output
\`\`\`json
$(cat example/$domain/output.json | python3 -m json.tool)
\`\`\`
## Visualization
Open \`index.html\` in a browser to view the Force-Directed graph of the clustered logic components generated by the underlying \`rust-louvain\` engine.
EOF
done