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VectorAnalyzer - AI-Powered Semantic Search for News and Content Analysis

Vector search revolutionizes how you find and analyze content by understanding meaning rather than just matching keywords. The VectorAnalyzer worker uses advanced AI to convert text into mathematical vectors, then finds the most semantically similar content to your search query. This is perfect for market analysis, sentiment tracking, and intelligent content filtering.

This comprehensive guide shows you how to configure VectorAnalyzer, combine it with other workers, and build powerful semantic search workflows for news and content analysis.

How VectorAnalyzer Works​

AI-Powered Processing Pipeline​

Text Vectorization: Converts articles into 384-dimensional vectors using SentenceTransformers Similarity Calculation: Uses cosine similarity (0.0-1.0) to measure semantic relatedness Dynamic Filtering: Automatically filters results based on quality thresholds Sentiment Analysis: Classifies emotional tone (positive/negative) using DistilBERT Intelligent Ranking: Sorts by relevance, similarity, or date

Key Capabilities​

  • Semantic Understanding: Finds content about "renewable energy investments" even if articles use different terminology
  • Sentiment Scoring: Analyzes emotional context of each result
  • Flexible Sorting: Rank by semantic relevance, similarity scores, or publication date
  • Batch Processing: Handles large document collections efficiently
  • Dynamic Thresholds: Automatically adjusts result quality based on your top_percentage setting

Step-by-Step Usage Guide​

Basic VectorAnalyzer Configuration​

Step 1: Add to Canvas

  • Drag VectorAnalyzer worker onto your workflow canvas
  • Connect it to a data source (News worker, database query, etc.)

Step 2: Configure Data Input

  • data: Connect from previous worker's output (e.g., {{workers[0].result.results}})
  • query: Your semantic search terms (e.g., "market volatility trends")

Step 3: Set Quality Parameters

  • top_percentage: Quality filter (40 = top 40% most relevant)
  • sort_by: Ranking method (relevance/similarity/date)

Step 4: Optional Features

  • skip_sentiment: Enable for 2x faster processing (disables sentiment analysis)

Configuration:

{
  "data": "{{workers[0].result.results}}",
  "query": "artificial intelligence in finance",
  "top_percentage": 35,
  "sort_by": "relevance"
}

Input Data Structure:

[
  {
    "title": "AI Transforms Banking Operations",
    "body": "Artificial intelligence is revolutionizing financial services...",
    "date": "2025-11-15T10:00:00Z",
    "source": "TechNews"
  }
]

Output Structure:

{
  "found": true,
  "count": 12,
  "results": [
    {
      "title": "AI Transforms Banking Operations",
      "similarity": 0.87,
      "sentiment": "positive",
      "sentiment_score": 0.92,
      "rank": 1,
      "date": "2025-11-15T10:00:00Z",
      "source": "TechNews"
    }
  ],
  "sentiment_summary": {
    "total": 12,
    "positive": 8,
    "negative": 4,
    "average_score": 0.73
  }
}

Example: Sentiment-Focused Analysis​

Configuration:

{
  "data": "{{workers[0].result.results}}",
  "query": "economic growth indicators",
  "top_percentage": 50,
  "sort_by": "date",
  "skip_sentiment": false
}

Use Case: Monitor recent economic sentiment in financial news.

Example: Fast Processing Mode​

Configuration:

{
  "data": "{{workers[0].result.results}}",
  "query": "breaking news",
  "top_percentage": 20,
  "sort_by": "similarity",
  "skip_sentiment": true
}

Use Case: Quick filtering of breaking news without sentiment overhead.

Building Complete Workflows​

News Analysis Pipeline​

What You Will Build: A complete news monitoring system that fetches articles, filters by semantic relevance, and analyzes sentiment.

Workers Needed:

  1. Trigger - Starts the workflow
  2. Fetch NewsAPI - Retrieves news articles
  3. VectorAnalyzer - Filters and ranks by semantic similarity
  4. Table Widget - Displays results

Step 1: Add Trigger Worker

  • Drag Trigger onto canvas
  • Configure: Manual run or scheduled (every 15 minutes)
  • This provides the initial signal

Step 2: Fetch News with Fetch NewsAPI

  • Drag Fetch NewsAPI worker
  • Connect to Trigger
  • Configure:
    • categories: ["dmoz/Business/Investing/Stocks_and_Bonds"]
    • sources: ["bloomberg.com", "reuters.com", "cnbc.com"]
    • limit: 100
  • Outputs: Array of news articles

Step 3: Apply Semantic Filtering

  • Drag VectorAnalyzer worker
  • Connect to Fetch NewsAPI
  • Configure:
    • data: {{workers[1].result.results}}
    • query: "market volatility and economic indicators"
    • top_percentage: 40
    • sort_by: relevance

Step 4: Display Results

  • Add Table widget
  • Connect to VectorAnalyzer
  • Configure columns:
    • Title
    • Similarity score
    • Sentiment
    • Publication date
    • Source

Market Sentiment Dashboard​

What You Will Build: Real-time sentiment analysis for specific market themes.

Enhanced Workflow:

  1. Trigger (scheduled every 30 minutes)
  2. Fetch NewsAPI (multiple categories)
  3. VectorAnalyzer (sentiment analysis enabled)
  4. Sentiment Aggregator (custom worker for trend analysis)
  5. Dashboard Widget (visual sentiment trends)

Configuration Focus:

{
  "query": "interest rate decisions",
  "top_percentage": 30,
  "sort_by": "date"
}

Content Recommendation System​

What You Will Build: Personalized content discovery based on semantic similarity.

Workflow:

  1. User Input (search query)
  2. Database Query (fetch content library)
  3. VectorAnalyzer (find similar content)
  4. Recommendation Engine (rank and filter)
  5. Content Display (show recommendations)

Advanced Configuration Techniques​

Query Optimization Strategies​

Natural Language Queries:

  • ✅ "renewable energy investment opportunities"
  • ✅ "artificial intelligence applications in healthcare"
  • ❌ "green energy stocks" (too keyword-focused)

Context-Rich Queries:

  • Include domain context: "cryptocurrency market trends and adoption"
  • Add specificity: "federal reserve monetary policy decisions"

Quality Control Parameters​

Precision vs Recall:

  • top_percentage: 20 - High precision, fewer results
  • top_percentage: 60 - Balanced approach
  • top_percentage: 100 - Maximum recall, all results

Similarity Score Interpretation:

  • 0.8-1.0: Very strong semantic match
  • 0.6-0.8: Good relevance
  • 0.3-0.6: Moderate relevance
  • < 0.3: Weak or tangential relationship

Performance Optimization​

Speed Settings:

  • Enable skip_sentiment for 2x faster processing
  • Reduce top_percentage for quicker results
  • Limit input data size (100-200 articles recommended)

Batch Processing:

  • Process large datasets in chunks
  • Use parallel workflows for multiple queries
  • Cache embeddings for repeated searches

Practical Trading and Analysis Applications​

Market Theme Detection​

Query Examples:

  • "gold price movement and market volatility"
  • "interest rate policy changes"
  • "corporate earnings surprises"
  • "geopolitical trade tensions"

Analysis Approach:

  1. Set up scheduled workflow (every 15 minutes)
  2. Monitor similarity score distributions
  3. Alert when scores cluster above thresholds
  4. Correlate with price movements

Sentiment-Based Signals​

Workflow Enhancement:

  1. Fetch news articles
  2. Apply VectorAnalyzer with sentiment
  3. Aggregate sentiment by time periods
  4. Generate trading signals based on sentiment shifts

Signal Examples:

  • Bullish: Positive sentiment + high similarity scores
  • Bearish: Negative sentiment clustering
  • Neutral: Mixed sentiment, low similarity variance

News Flow Analysis​

Volume + Quality Monitoring:

  • Track article frequency on specific topics
  • Monitor similarity score changes over time
  • Identify "news spikes" indicating important events
  • Filter signal from noise using semantic clustering

Integration Patterns​

With Fetch NewsAPI​

Best Practices:

  • Use Fetch NewsAPI for data collection
  • Apply VectorAnalyzer for intelligent filtering
  • Chain multiple VectorAnalyzer workers for different queries
  • Combine results for comprehensive analysis

Example Multi-Query Setup:

// Worker 1: Broad market news
{
  "query": "market conditions",
  "top_percentage": 50
}

// Worker 2: Specific sector
{
  "query": "technology sector performance",
  "top_percentage": 30
}

With Database Workers​

Content Management Integration:

  • Query document databases
  • Apply semantic search across content libraries
  • Build recommendation systems
  • Enable intelligent content discovery

With Alert Systems​

Automated Monitoring:

  • Set up threshold-based alerts
  • Monitor sentiment changes
  • Track topic frequency spikes
  • Generate notifications for important developments

Troubleshooting and Best Practices​

Common Issues​

Low Similarity Scores:

  • Try more descriptive queries
  • Check if articles contain relevant text fields
  • Adjust top_percentage upward for more results

Slow Processing:

  • Enable skip_sentiment for faster results
  • Reduce input data size
  • Use shorter text fields (truncate long articles)

Unexpected Results:

  • Review query wording (use natural language)
  • Check text field availability in source data
  • Validate date sorting preferences

Performance Tips​

Query Crafting:

  • Use complete phrases rather than single words
  • Include context terms for better matching
  • Test queries on small datasets first

Workflow Design:

  • Process in batches for large datasets
  • Use parallel branches for multiple analyses
  • Cache frequently used embeddings

Result Validation:

  • Always review similarity scores
  • Check sentiment distribution reasonableness
  • Validate against known relevant articles

Advanced Use Cases​

Multi-Topic Analysis​

Parallel Processing:

// Multiple VectorAnalyzer workers
{
  "query": "commodity prices",
  "top_percentage": 40
},
{
  "query": "currency fluctuations",
  "top_percentage": 40
},
{
  "query": "bond yields",
  "top_percentage": 40
}

Combined Dashboard:

  • Aggregate results from multiple topics
  • Identify cross-market correlations
  • Monitor overall market sentiment

Trend Detection​

Time-Series Analysis:

  • Run workflows at regular intervals
  • Track similarity score changes over time
  • Identify emerging topics through score clustering
  • Generate trend alerts based on score thresholds

Content Clustering​

Semantic Grouping:

  • Use high similarity thresholds to find clusters
  • Group related articles automatically
  • Identify main themes and subtopics
  • Build topic hierarchies

Conclusion​

VectorAnalyzer transforms how you search and analyze content by understanding meaning rather than just keywords. By leveraging AI-powered vector embeddings and semantic similarity, you can build intelligent workflows that surface the most relevant information for your analysis.

The key to success lies in crafting natural language queries, understanding similarity scores, and combining VectorAnalyzer with complementary workers like Fetch NewsAPI. Whether you're monitoring market sentiment, building recommendation systems, or analyzing news trends, VectorAnalyzer provides the semantic search capabilities you need.

Start with simple queries and gradually refine your approach based on result quality. Remember that semantic search works best with descriptive, context-rich queries that capture the meaning you want to find. Experiment with different configurations and use the similarity scores to guide your optimization efforts.