Getting Started with Attanix: A Practical Guide

Attanix is designed to be simple to integrate while providing powerful memory capabilities for your AI applications. This guide will walk you through the process of setting up and using Attanix in your projects.

Installation

Getting started with Attanix is straightforward:

pip install attanix

Basic Usage

Here's a simple example of how to use Attanix in your application:

from attanix import Memory

# Initialize the memory system
memory = Memory("./data")

# Store some information
memory.store(
    content="The customer reported an issue with the login system",
    context={
        "priority": "high",
        "category": "authentication",
        "timestamp": "2024-04-10T10:30:00Z"
    }
)

# Query the memory
results = memory.query(
    "Recent authentication problems",
    context={"timeframe": "last 24 hours"}
)

Key Features in Action

1. Contextual Recall

Attanix's contextual recall allows you to find information based on relevance, not just similarity:

# Store a technical document
memory.store(
    content="The API requires authentication using JWT tokens",
    context={
        "document_type": "technical",
        "section": "authentication",
        "version": "2.0"
    }
)

# Query with context
results = memory.query(
    "How do I authenticate API requests?",
    context={"document_type": "technical"}
)

2. Deterministic + Semantic Search

Combine structured filters with semantic understanding:

# Store user feedback
memory.store(
    content="The dashboard is too slow to load",
    context={
        "feedback_type": "performance",
        "component": "dashboard",
        "severity": "high"
    }
)

# Query with both semantic and structured filters
results = memory.query(
    "Performance issues in the dashboard",
    filters={
        "component": "dashboard",
        "severity": "high"
    }
)

3. No Hallucinations

Every result is grounded in your actual data:

# Store product information
memory.store(
    content="Product X supports up to 1000 concurrent users",
    context={
        "source": "documentation",
        "version": "1.2",
        "verified": True
    }
)

# Query with source verification
results = memory.query(
    "What are the limits of Product X?",
    context={"require_verification": True}
)

Advanced Usage

Custom Salience Scoring

You can customize how Attanix determines what's important:

from attanix import Memory, SalienceConfig

# Configure custom salience
config = SalienceConfig(
    time_decay=0.95,  # How quickly importance decays over time
    context_weight=0.7,  # How much context matters
    content_weight=0.3   # How much content matters
)

memory = Memory("./data", config=config)

Real-time Updates

Attanix handles streaming data efficiently:

# Stream data to Attanix
for event in event_stream:
    memory.store(
        content=event.data,
        context={
            "timestamp": event.timestamp,
            "source": event.source
        }
    )
    
    # Query in real-time
    results = memory.query(
        "Current system status",
        context={"timeframe": "realtime"}
    )

Best Practices

1. Context Matters: Always provide relevant context when storing information
2. Structured Metadata: Use consistent metadata fields for better filtering
3. Regular Updates: Keep your memory system current with regular updates
4. Verification: Use the verification features for critical information

Next Steps

Ready to integrate Attanix into your application? Check out our documentation for more detailed information and advanced features. You can also try the interactive demo to see Attanix in action.

Need help? Our team is available to assist with integration and optimization. Contact us for personalized support.