Reports Processing & Insight Generation

Reports Processing Architecture: End-to-end GCP architecture: Data ingestion → Document processing (OCR, NLP, AutoML) → Knowledge representation → Query APIs → Visualization dashboards with governance and security layers.

# Supported Data Formats
formats:
  documents:
    - PDF
    - Word (.docx)
    - Excel (.xlsx)
  images:
    - PNG
    - TIFF
    - JPEG
  structured:
    - CSV
    - JSON
    - XML

# Cloud Storage Configuration
storage:
  bucket: reports-processing-bucket
  lifecycle: 30-day retention
  access: IAM-controlled

# Document Processing Pipeline
from google.cloud import documentai, vision, language_v1, automl

def process_document(file_path: str) -> dict:
    # 1. OCR for scanned documents
    vision_client = vision.ImageAnnotatorClient()
    text = extract_text_ocr(vision_client, file_path)
    
    # 2. Entity extraction
    language_client = language_v1.LanguageServiceClient()
    entities = extract_entities(language_client, text)
    
    # 3. Sentiment analysis
    sentiment = analyze_sentiment(language_client, text)
    
    # 4. Custom classification (AutoML)
    automl_client = automl.PredictionServiceClient()
    categories = classify_content(automl_client, text)
    
    # 5. Knowledge graph construction
    relationships = build_knowledge_graph(entities)
    
    return {
        'text': text,
        'entities': entities,
        'sentiment': sentiment,
        'categories': categories,
        'relationships': relationships
    }

# Knowledge Graph Construction
def build_knowledge_graph(entities: list) -> dict:
    graph = {
        'nodes': [],
        'edges': []
    }
    
    # Extract regions, challenges, opportunities
    regions = [e for e in entities if e['type'] == 'LOCATION']
    challenges = [e for e in entities if e['category'] == 'CHALLENGE']
    opportunities = [e for e in entities if e['category'] == 'OPPORTUNITY']
    
    # Create relationships
    for region in regions:
        for challenge in challenges:
            if co_occur_in_context(region, challenge):
                graph['edges'].append({
                    'source': region['name'],
                    'target': challenge['name'],
                    'type': 'HAS_CHALLENGE',
                    'confidence': calculate_confidence(region, challenge)
                })
    
    return graph

# Cloud Function for Natural Language Queries
import functions_framework
from google.cloud import bigquery

@functions_framework.http
def query_insights(request):
    query_text = request.json.get('query')
    
    # Parse natural language query
    parsed = parse_nl_query(query_text)
    
    # Convert to BigQuery SQL
    sql = build_sql_from_parsed(parsed)
    
    # Example: "What are top challenges in Region X?"
    if parsed['intent'] == 'top_challenges':
        sql = f"""
        SELECT challenge, COUNT(*) as frequency
        FROM processed_reports 
        WHERE region = '{parsed['region']}'
        AND category = 'CHALLENGE'
        GROUP BY challenge
        ORDER BY frequency DESC
        LIMIT 5
        """
    
    # Execute query
    client = bigquery.Client()
    results = client.query(sql).to_dataframe()
    
    return {
        'query': query_text,
        'results': results.to_dict('records'),
        'sql_generated': sql
    }

-- BigQuery Views for Data Studio
-- Regional Challenges Summary
CREATE VIEW regional_challenges AS
SELECT 
    region,
    challenge_category,
    COUNT(*) as frequency,
    AVG(sentiment_score) as avg_sentiment,
    EXTRACT(YEAR FROM report_date) as year
FROM processed_reports
WHERE category = 'CHALLENGE'
GROUP BY region, challenge_category, year;

-- Sentiment Trends Over Time
CREATE VIEW sentiment_trends AS
SELECT 
    DATE_TRUNC(report_date, MONTH) as month,
    region,
    AVG(sentiment_score) as avg_sentiment,
    COUNT(*) as report_count
FROM processed_reports
GROUP BY month, region
ORDER BY month DESC;

-- Top Opportunities by Region
CREATE VIEW top_opportunities AS
SELECT 
    region,
    opportunity_text,
    confidence_score,
    ROW_NUMBER() OVER (PARTITION BY region ORDER BY confidence_score DESC) as rank
FROM processed_reports
WHERE category = 'OPPORTUNITY'
QUALIFY rank <= 5;