PMOVES-DoX Demos & Example Scenarios

Interactive demonstrations and example workflows with sample data.

Demo 1: 5-Minute Quick Start
Demo 2: Financial Report Analysis
Demo 3: API Documentation Generator
Demo 4: Log Analytics Dashboard
Demo 5: Research Paper Organizer
Sample Data Repository
Video Walkthroughs

Demo 1: 5-Minute Quick Start

Goal: Get PMOVES-DoX running and process your first document in 5 minutes.

Prerequisites

Docker Desktop installed
5 minutes of your time

Step 1: Clone and Start (1 minute)

# Clone repository
git clone https://github.com/POWERFULMOVES/PMOVES-DoX.git
cd PMOVES-DoX

# Start with Docker Compose
docker compose -f docker-compose.cpu.yml up -d

# Wait for services to be ready (~30 seconds)
docker compose logs -f backend | grep "Application startup complete"

Step 2: Open UI (30 seconds)

# Open in browser
open http://localhost:3737

# Or manually visit:
# Frontend: http://localhost:3737
# Backend API: http://localhost:8484

Step 3: Upload Sample (1 minute)

# Via UI: Click "Choose Files" → Select samples/sample.csv

# Or via CLI:
curl -X POST http://localhost:8484/upload \
  -F "files=@samples/sample.csv" \
  -F "report_week=2024-W20"

What happens:

CSV is processed
Facts are extracted from each row
Metrics calculated (CTR, conversions)
Data indexed for search

Step 4: Explore Facts (1 minute)

Click Facts tab
See extracted facts from CSV rows
Click page number to see evidence
Use search to filter: "revenue"

Step 5: Ask a Question (1 minute)

Click Q&A tab
Type: "What is the total revenue?"
Get answer with citations
Click citation to see source

Step 6: Semantic Search (1 minute)

Use global search bar (top right)
Search: "highest performing campaign"
Get results across all artifacts
Filter by type if needed

✅ Done!

You've successfully:

Started PMOVES-DoX
Uploaded a document
Extracted facts
Ran Q&A
Performed semantic search

Next: Try uploading your own PDFs or CSVs!

Demo 2: Financial Report Analysis

Goal: Extract and analyze financial statements from annual reports.

Sample Data

Download sample financial PDFs:

# Create samples directory
mkdir -p samples/financials

# Download sample reports (or use your own)
# For demo purposes, we'll create a simple financial PDF

Demo Script

#!/bin/bash
# demos/financial_analysis_demo.sh

set -e

echo "🏦 PMOVES-DoX Financial Analysis Demo"
echo "======================================"

# 1. Check services
echo "✓ Checking services..."
curl -s http://localhost:8484/health > /dev/null && echo "  Backend: UP" || exit 1

# 2. Upload financial report
echo "✓ Uploading financial report..."
UPLOAD_RESP=$(curl -s -X POST http://localhost:8484/upload \
  -F "files=@samples/financials/financial_statements.pdf" \
  -F "report_week=2024-W01")

echo "$UPLOAD_RESP" | jq -r '.results[] | "  Processed: \(.filename) - \(.facts_count) facts"'

# 3. Wait for processing
sleep 2

# 4. Check financial detection
echo "✓ Detecting financial statements..."
FINANCIAL=$(curl -s http://localhost:8484/analysis/financials)
echo "$FINANCIAL" | jq -r '.statements[] | "  Found: \(.type) on page \(.page) (confidence: \(.confidence))"'

# 5. Extract tables
echo "✓ Extracting tables..."
ARTIFACT_ID=$(echo "$UPLOAD_RESP" | jq -r '.results[0].artifact_id')
ARTIFACT=$(curl -s "http://localhost:8484/artifacts/$ARTIFACT_ID")
echo "$ARTIFACT" | jq -r '"  Tables: \(.table_evidence)"'

# 6. Ask questions
echo "✓ Running Q&A..."
QUESTIONS=(
  "What is the total revenue?"
  "What is the net income?"
  "What are the total assets?"
)

for q in "${QUESTIONS[@]}"; do
  ANSWER=$(curl -s -X POST "http://localhost:8484/ask" --data-urlencode "question=$q")
  echo "  Q: $q"
  echo "  A: $(echo "$ANSWER" | jq -r '.answer')"
  echo ""
done

# 7. Export POML
echo "✓ Exporting to POML..."
DOC_ID=$(curl -s http://localhost:8484/documents | jq -r '.documents[0].id')
POML=$(curl -s -X POST http://localhost:8484/export/poml \
  -H "Content-Type: application/json" \
  -d "{\"document_id\":\"$DOC_ID\",\"variant\":\"catalog\",\"title\":\"Financial Analysis\"}")

POML_FILE=$(echo "$POML" | jq -r '.rel')
echo "  POML saved: $POML_FILE"

echo ""
echo "✅ Demo complete!"
echo "   - Financial statements detected"
echo "   - Tables extracted"
echo "   - Q&A enabled"
echo "   - POML exported for Copilot"

Run Demo

chmod +x demos/financial_analysis_demo.sh
./demos/financial_analysis_demo.sh

Expected Output

🏦 PMOVES-DoX Financial Analysis Demo
======================================
✓ Checking services...
  Backend: UP
✓ Uploading financial report...
  Processed: financial_statements.pdf - 24 facts
✓ Detecting financial statements...
  Found: income_statement on page 5 (confidence: 0.94)
  Found: balance_sheet on page 7 (confidence: 0.89)
  Found: cash_flow_statement on page 9 (confidence: 0.87)
✓ Extracting tables...
  Tables: 8
✓ Running Q&A...
  Q: What is the total revenue?
  A: Total revenue for 2024 was $5.8 million.

  Q: What is the net income?
  A: Net income was $1.2 million.

  Q: What are the total assets?
  A: Total assets were $12.4 million.

✓ Exporting to POML...
  POML saved: artifacts/poml_uuid.poml

✅ Demo complete!
   - Financial statements detected
   - Tables extracted
   - Q&A enabled
   - POML exported for Copilot

Demo 3: API Documentation Generator

Goal: Convert OpenAPI specs to searchable documentation.

Sample Data

# Download sample OpenAPI specs
curl https://petstore3.swagger.io/api/v3/openapi.json > samples/sample_openapi.json

Demo Script

#!/usr/bin/env python3
# demos/api_documentation_demo.py

import requests
import json
import time
from pathlib import Path

API_BASE = "http://localhost:8484"

def demo():
    print("🔌 PMOVES-DoX API Documentation Generator")
    print("==========================================\n")

    # 1. Upload OpenAPI spec
    print("✓ Uploading OpenAPI specification...")
    files = {"files": open("samples/sample_openapi.json", "rb")}
    resp = requests.post(f"{API_BASE}/upload", files=files)
    result = resp.json()["results"][0]
    print(f"  Processed: {result['filename']}")
    print(f"  Evidence: {result['evidence_count']} items\n")

    time.sleep(1)

    # 2. List all APIs
    print("✓ Listing API endpoints...")
    apis = requests.get(f"{API_BASE}/apis").json()["apis"]
    print(f"  Total endpoints: {len(apis)}")

    # Group by method
    by_method = {}
    for api in apis:
        method = api.get("method", "GET")
        by_method.setdefault(method, 0)
        by_method[method] += 1

    for method, count in sorted(by_method.items()):
        print(f"    {method}: {count}")
    print()

    # 3. Search APIs
    print("✓ Searching APIs...")
    queries = ["create pet", "user authentication", "store inventory"]

    for q in queries:
        search_resp = requests.post(
            f"{API_BASE}/search",
            json={"q": q, "types": ["api"], "k": 3}
        )
        results = search_resp.json()["results"]
        print(f"  Query: '{q}'")
        for r in results[:2]:
            print(f"    - {r['content'][:60]}...")
        print()

    # 4. Get endpoint details
    print("✓ Endpoint details...")
    if apis:
        api_id = apis[0]["id"]
        detail = requests.get(f"{API_BASE}/apis/{api_id}").json()
        print(f"  {detail['method']} {detail['path']}")
        print(f"  Summary: {detail.get('summary', 'N/A')}")
        print(f"  Parameters: {len(detail.get('parameters', []))}")
        print()

    # 5. Export to POML
    print("✓ Exporting to POML...")
    docs = requests.get(f"{API_BASE}/documents").json()["documents"]
    if docs:
        doc_id = docs[0]["id"]
        poml_resp = requests.post(
            f"{API_BASE}/export/poml",
            json={"document_id": doc_id, "variant": "catalog", "title": "API Reference"}
        )
        poml_file = poml_resp.json()["rel"]
        print(f"  POML generated: {poml_file}")

    print("\n✅ Demo complete!")
    print("   - OpenAPI spec ingested")
    print("   - Endpoints cataloged")
    print("   - Searchable documentation created")
    print("   - POML exported for Copilot")

if __name__ == "__main__":
    demo()

Run Demo

chmod +x demos/api_documentation_demo.py
python3 demos/api_documentation_demo.py

Demo 4: Log Analytics Dashboard

Goal: Analyze XML logs and create interactive dashboard.

Sample Data

<!-- samples/sample.xml -->
<logs>
  <log>
    <timestamp>2024-01-15T10:30:00Z</timestamp>
    <level>ERROR</level>
    <code>E1001</code>
    <component>payment-service</component>
    <message>Payment gateway timeout</message>
  </log>
  <log>
    <timestamp>2024-01-15T10:31:00Z</timestamp>
    <level>ERROR</level>
    <code>E1001</code>
    <component>payment-service</component>
    <message>Retry failed</message>
  </log>
  <log>
    <timestamp>2024-01-15T10:32:00Z</timestamp>
    <level>INFO</level>
    <code>I2001</code>
    <component>user-service</component>
    <message>User logged in successfully</message>
  </log>
</logs>

Demo Script

#!/bin/bash
# demos/log_analytics_demo.sh

set -e

echo "📊 PMOVES-DoX Log Analytics Demo"
echo "================================="

# 1. Configure XPath mapping
export XML_XPATH_MAP='{"entry":"//log","fields":{"ts":"./timestamp","level":"./level","code":"./code","component":"./component","message":"./message"}}'

# 2. Ingest logs
echo "✓ Ingesting logs..."
curl -s -X POST http://localhost:8484/upload \
  -F "files=@samples/sample.xml" > /dev/null
echo "  Logs uploaded"

sleep 1

# 3. Query errors
echo "✓ Querying errors..."
ERRORS=$(curl -s "http://localhost:8484/logs?level=ERROR")
ERROR_COUNT=$(echo "$ERRORS" | jq '.logs | length')
echo "  Total errors: $ERROR_COUNT"

# 4. Group by component
echo "✓ Errors by component:"
echo "$ERRORS" | jq -r '.logs | group_by(.component) | .[] | "  \(.[0].component): \(length)"'

# 5. Export CSV
echo "✓ Exporting to CSV..."
curl -s "http://localhost:8484/logs/export?level=ERROR" > errors.csv
echo "  Saved: errors.csv ($(wc -l < errors.csv) lines)"

# 6. Generate dashboard
echo "✓ Generating dashboard..."
curl -s -X POST http://localhost:8484/viz/datavzrd/logs \
  -H "Content-Type: application/json" \
  -d '{"level":"ERROR"}' | jq -r '.viz_file'

echo ""
echo "✅ Demo complete!"
echo "   Start datavzrd: docker compose --profile tools up datavzrd"
echo "   Open: http://localhost:5173"

Demo 5: Research Paper Organizer

Goal: Upload PDFs and organize into thematic clusters.

Demo Script

#!/usr/bin/env python3
# demos/research_organizer_demo.py

import requests
import time
from pathlib import Path

API_BASE = "http://localhost:8484"

def demo():
    print("📚 PMOVES-DoX Research Paper Organizer")
    print("======================================\n")

    # Simulate having research papers
    papers = [
        "Deep Learning for NLP.pdf",
        "BERT Architecture.pdf",
        "Transfer Learning Methods.pdf",
        "Computer Vision Survey.pdf",
        "ResNet Implementation.pdf"
    ]

    print(f"✓ Simulating upload of {len(papers)} papers...")
    print("  (In real scenario, upload actual PDFs)\n")

    # In real scenario:
    # for paper in papers:
    #     files = {"files": open(f"papers/{paper}", "rb")}
    #     requests.post(f"{API_BASE}/upload", files=files)

    # 2. Rebuild index
    print("✓ Rebuilding search index...")
    requests.post(f"{API_BASE}/search/rebuild")
    time.sleep(1)

    # 3. Extract entities
    print("✓ Extracting named entities...")
    entities = requests.get(f"{API_BASE}/analysis/entities").json()
    print(f"  Found {len(entities.get('entities', []))} unique entities")

    # 4. Get structure
    print("✓ Analyzing document structure...")
    structure = requests.get(f"{API_BASE}/analysis/structure").json()
    print(f"  Detected {len(structure.get('headings', []))} heading levels")

    # 5. Run CHR
    print("✓ Running CHR clustering...")
    artifacts = requests.get(f"{API_BASE}/artifacts").json()["artifacts"]
    if artifacts:
        chr_resp = requests.post(
            f"{API_BASE}/structure/chr",
            json={
                "artifact_id": artifacts[0]["id"],
                "K": 5,
                "units_mode": "paragraphs"
            }
        )
        result = chr_resp.json()
        print(f"  Created {result.get('K', 5)} clusters")
        print(f"  PCA visualization: {result.get('artifacts', {}).get('pca_plot', 'N/A')}")

    print("\n✅ Demo complete!")
    print("   - Papers analyzed")
    print("   - Entities extracted")
    print("   - Clusters created")
    print("   - Visualization ready")

if __name__ == "__main__":
    demo()

Sample Data Repository

Included Samples

The repository includes sample files for testing:

samples/
├── sample.csv              # Marketing campaign data
├── sample.xml              # Application logs
├── sample_openapi.json     # OpenAPI specification
├── sample_postman.json     # Postman collection
├── sample_audio.txt        # Audio transcript
├── sample_image.txt        # Image OCR
├── sample_video.txt        # Video transcript
└── financials/
    └── financial_statements.pdf  # Sample financial report

Usage

# Load all samples
curl -X POST http://localhost:8484/load_samples

# Or individual samples
curl -X POST http://localhost:8484/upload -F "files=@samples/sample.csv"
curl -X POST http://localhost:8484/upload -F "files=@samples/sample.xml"

Creating Your Own Samples

CSV Format:

date,campaign,impressions,clicks,revenue
2024-01-01,Summer Sale,12000,245,1500
2024-01-02,Winter Promo,14500,298,1820

XML Log Format:

<logs>
  <log>
    <timestamp>2024-01-01T10:00:00Z</timestamp>
    <level>ERROR</level>
    <code>E500</code>
    <component>api-server</component>
    <message>Internal server error</message>
  </log>
</logs>

Video Walkthroughs

Available Demos

Quick Start Tutorial (5 min)
- Installation
- First upload
- Basic navigation
Financial Analysis (10 min)
- PDF upload
- Table extraction
- Q&A demo
API Documentation (8 min)
- OpenAPI ingestion
- Search features
- POML export
Log Analytics (12 min)
- XML configuration
- Filtering & search
- Dashboard creation
Advanced Features (15 min)
- CHR clustering
- Tag extraction
- Multi-source search

Recording Your Own Demo

# Use asciinema for terminal recording
asciinema rec demo.cast

# Run your demo commands
./demos/financial_analysis_demo.sh

# Stop recording (Ctrl+D)

# Play back
asciinema play demo.cast

# Upload to asciinema.org
asciinema upload demo.cast

Interactive Playground

Jupyter Notebook Demo

# demos/playground.ipynb

import requests
import pandas as pd
import matplotlib.pyplot as plt

API_BASE = "http://localhost:8484"

# 1. Upload data
with open("samples/sample.csv", "rb") as f:
    resp = requests.post(f"{API_BASE}/upload", files={"files": f})
print(resp.json())

# 2. Get facts
facts = requests.get(f"{API_BASE}/facts").json()["facts"]
df = pd.DataFrame(facts)
df.head()

# 3. Search
search_resp = requests.post(
    f"{API_BASE}/search",
    json={"q": "revenue", "k": 10}
)
results = pd.DataFrame(search_resp.json()["results"])
results[["content", "score"]].head()

# 4. Visualize
plt.figure(figsize=(10, 6))
plt.bar(results["content"], results["score"])
plt.xticks(rotation=45, ha="right")
plt.title("Search Results")
plt.tight_layout()
plt.show()

Automated Testing

Smoke Test Demo

# Run full smoke test suite
cd smoke
python smoke_backend.py

# Run security tests
python smoke_security.py

# Expected: All tests pass
[ OK ] /health
[ OK ] /upload
[ OK ] /facts
[ OK ] /search
...

Next Steps

📖 Read USER_GUIDE.md for detailed features
🍳 Follow COOKBOOKS.md for recipes
🔧 Check API_REFERENCE.md for API docs

Questions? Open an issue on GitHub!

Provide feedback

Saved searches

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PMOVES-DoX Demos & Example Scenarios

Table of Contents

Demo 1: 5-Minute Quick Start

Prerequisites

Step 1: Clone and Start (1 minute)

Step 2: Open UI (30 seconds)

Step 3: Upload Sample (1 minute)

Step 4: Explore Facts (1 minute)

Step 5: Ask a Question (1 minute)

Step 6: Semantic Search (1 minute)

✅ Done!

Demo 2: Financial Report Analysis

Sample Data

Demo Script

Run Demo

Expected Output

Demo 3: API Documentation Generator

Sample Data

Demo Script

Run Demo

Demo 4: Log Analytics Dashboard

Sample Data

Demo Script

Demo 5: Research Paper Organizer

Demo Script

Sample Data Repository

Included Samples

Usage

Creating Your Own Samples

Video Walkthroughs

Available Demos

Recording Your Own Demo

Interactive Playground

Jupyter Notebook Demo

Automated Testing

Smoke Test Demo

Next Steps

FilesExpand file tree

DEMOS.md

Latest commit

History

DEMOS.md

File metadata and controls

PMOVES-DoX Demos & Example Scenarios

Table of Contents

Demo 1: 5-Minute Quick Start

Prerequisites

Step 1: Clone and Start (1 minute)

Step 2: Open UI (30 seconds)

Step 3: Upload Sample (1 minute)

Step 4: Explore Facts (1 minute)

Step 5: Ask a Question (1 minute)

Step 6: Semantic Search (1 minute)

✅ Done!

Demo 2: Financial Report Analysis

Sample Data

Demo Script

Run Demo

Expected Output

Demo 3: API Documentation Generator

Sample Data

Demo Script

Run Demo

Demo 4: Log Analytics Dashboard

Sample Data

Demo Script

Demo 5: Research Paper Organizer

Demo Script

Sample Data Repository

Included Samples

Usage

Creating Your Own Samples

Video Walkthroughs

Available Demos

Recording Your Own Demo

Interactive Playground

Jupyter Notebook Demo

Automated Testing

Smoke Test Demo

Next Steps