The Anatomy of an Attanix Cluster: How We Detect What Matters

Understanding how Attanix processes and scores information is crucial for building effective memory systems. This article provides a detailed look at the internal workings of an Attanix cluster, explaining how we detect and prioritize what matters in your data.

Cluster Architecture Overview

An Attanix cluster consists of several key components working together to process and score information:

1. Ingestion Layer: Handles incoming data streams
2. Processing Layer: Analyzes and structures information
3. Storage Layer: Manages persistent storage
4. Query Layer: Handles information retrieval
5. Scoring Layer: Calculates salience scores

The Salience Scoring Process

Here's a detailed look at how Attanix calculates salience scores:

from attanix import Cluster

# Initialize a cluster with custom scoring configuration
cluster = Cluster(
    config={
        "scoring": {
            "context_weight": 0.6,
            "temporal_weight": 0.3,
            "relationship_weight": 0.4
        },
        "thresholds": {
            "minimum_salience": 0.4,
            "high_salience": 0.8
        }
    }
)

# Process a document through the scoring pipeline
async def score_document(document):
    # Initial processing
    processed = await cluster.process(document)
    
    # Calculate individual scores
    context_score = await cluster.score_context(processed)
    temporal_score = await cluster.score_temporal(processed)
    relationship_score = await cluster.score_relationships(processed)
    
    # Combine scores with weights
    final_score = (
        context_score * cluster.config["scoring"]["context_weight"] +
        temporal_score * cluster.config["scoring"]["temporal_weight"] +
        relationship_score * cluster.config["scoring"]["relationship_weight"]
    )
    
    return {
        "document": processed,
        "scores": {
            "context": context_score,
            "temporal": temporal_score,
            "relationships": relationship_score,
            "final": final_score
        }
    }

Key Scoring Components

1. Context Scoring

async def score_context(document):
    # Analyze document context
    context_analysis = await cluster.analyze_context(
        document=document,
        factors=[
            "relevance",
            "completeness",
            "accuracy"
        ]
    )
    
    # Calculate context score
    return sum(context_analysis.values()) / len(context_analysis)

2. Temporal Scoring

async def score_temporal(document):
    # Get temporal context
    temporal_context = await cluster.get_temporal_context(
        document=document,
        window="24h"
    )
    
    # Calculate temporal relevance
    return cluster.calculate_temporal_relevance(
        document=document,
        context=temporal_context
    )

3. Relationship Scoring

async def score_relationships(document):
    # Find related documents
    related = await cluster.find_related(
        document=document,
        max_results=10
    )
    
    # Calculate relationship strength
    return cluster.calculate_relationship_strength(
        document=document,
        related=related
    )

Implementation Examples

1. Document Processing

# Process and score a document
async def process_document(document):
    # Initial processing
    processed = await cluster.preprocess(document)
    
    # Extract features
    features = await cluster.extract_features(processed)
    
    # Calculate scores
    scores = await score_document({
        "content": processed,
        "features": features,
        "metadata": document.metadata
    })
    
    # Store results
    await cluster.store(
        document=processed,
        scores=scores,
        metadata=document.metadata
    )

2. Batch Processing

# Process multiple documents
async def process_batch(documents):
    # Create processing tasks
    tasks = [
        process_document(doc) for doc in documents
    ]
    
    # Process in parallel
    results = await asyncio.gather(*tasks)
    
    # Aggregate scores
    aggregate = await cluster.aggregate_scores(results)
    
    return aggregate

3. Real-time Scoring

# Score streaming data
async def score_stream(stream):
    async for document in stream:
        # Process immediately
        processed = await process_document(document)
        
        # Update cluster state
        await cluster.update_state(processed)
        
        # Emit results
        yield processed

Best Practices for Cluster Configuration

1. Scoring Weights

   • Balance context and temporal factors
   • Adjust for specific use cases
   • Monitor and tune regularly

2. Thresholds

   • Set appropriate minimum scores
   • Define high-salience boundaries
   • Consider false positive rates

3. Resource Management

   • Monitor processing load
   • Scale resources as needed
   • Optimize for latency

Real-World Applications

Attanix clusters are being used in various scenarios:

• Content Moderation: Identifying important patterns in user content
• Research Analysis: Processing and scoring academic papers
• Customer Support: Prioritizing and routing support tickets
• Security Monitoring: Detecting significant security events

The Future of Cluster Processing

As data volumes grow and use cases evolve, Attanix's cluster architecture continues to adapt. Future developments include:

• Enhanced parallel processing
• Improved scoring algorithms
• Better resource optimization
• More sophisticated relationship detection

Ready to explore Attanix's cluster architecture? Check out our documentation or try our cluster configuration guide.