Advanced Retrieval

Advanced memory search with intelligent reranking for precise results

What is Advanced Retrieval?

Advanced Retrieval gives you precise control over how memories are found and ranked. While basic search uses semantic similarity, these advanced options help you find exactly what you need, when you need it.

Search Enhancement Options

Reranking

Reorders results using deep semantic understanding to put the most relevant memories first.

When to Use

• Need the most relevant result at the top
• Result order is critical for your application
• Want consistent quality across different queries
• Building user-facing features where accuracy matters

How it Works

Python

# Get the most relevant travel plans first
results = client.search(
    query="What are my upcoming travel plans?",
    rerank=True,
    filters={"user_id": "user123"},
)

# Before reranking:        After reranking:
# 1. "Went to Paris"   →   1. "Tokyo trip next month"
# 2. "Tokyo trip next" →   2. "Need to book hotel in Tokyo"  
# 3. "Need hotel"      →   3. "Went to Paris last year"

Performance

• Latency: 150-200ms additional
• Accuracy: Significantly improved
• Ordering: Much more relevant
• Best for: Top-N precision, user-facing results

Real-World Use Cases

Personal AI Assistant

Python

# Smart home assistant finding device preferences
results = client.search(
    query="How do I like my bedroom temperature?",
    rerank=True,           # Get most recent preferences first
    filters={"user_id": "user123"},
)

# Finds: "Keep bedroom at 68°F", "Too cold last night at 65°F", etc.

Customer Support

Python

# Find specific product issues with high precision
results = client.search(
    query="Problems with premium subscription billing",
    filters={"user_id": "customer456"},
)

# Returns only relevant billing problems, not general questions

Healthcare AI

Python

# Critical medical information needs perfect accuracy
results = client.search(
    query="Patient allergies and contraindications",
    rerank=True,            # Most important info first
    filters={"user_id": "patient789"},
)

# Ensures critical allergy info appears first

Learning Platform

Python

# Find learning progress for specific topics
results = client.search(
    query="Python programming progress and difficulties",
    rerank=True,           # Recent progress first
    filters={"user_id": "student123"},
)

# Gets comprehensive view of Python learning journey

Choosing the Right Configuration

Recommended Configurations

Python

# Basic search - good for exploration
def quick_search(query, user_id):
    return client.search(
        query=query,
        filters={"user_id": user_id},
    )

# Reranked search - good for most applications
def standard_search(query, user_id):
    return client.search(
        query=query,
        rerank=True,
        filters={"user_id": user_id},
    )

# Reranked search - good for critical applications
def precise_search(query, user_id):
    return client.search(
        query=query,
        rerank=True,
        filters={"user_id": user_id},
    )

// Basic search - good for exploration
function quickSearch(query, userId) {
    return client.search(query, {
        filters: { user_id: userId },
    });
}

// Reranked search - good for most applications
function standardSearch(query, userId) {
    return client.search(query, {
        filters: { user_id: userId },
        rerank: true,
    });
}

// Reranked search - good for critical applications
function preciseSearch(query, userId) {
    return client.search(query, {
        filters: { user_id: userId },
        rerank: true,
    });
}

Best Practices

Do

• Start simple with basic search and measure impact before enabling reranking
• Use reranking when the top result quality matters most
• Monitor latency and adjust based on your application’s needs
• Handle empty results gracefully

Don’t

• Enable reranking by default without measuring necessity
• Ignore latency impact in real-time applications
• Use advanced retrieval for simple, fast lookup scenarios

Performance Guidelines

Latency Expectations

Python

# Performance monitoring example
import time

start_time = time.time()
results = client.search(
    query="user preferences",
    rerank=True,         # +150ms
    filters={"user_id": "user123"},
)
latency = time.time() - start_time
print(f"Search completed in {latency:.2f}s")

Optimization Tips

1. Cache frequent queries to avoid repeated advanced processing
2. Use session-specific search with run_id to reduce search space
3. Implement fallback logic when search returns empty results
4. Monitor and alert on search latency patterns

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