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.

<Tabs> <Tab title="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 </Tab> <Tab title="How it Works"> ```python Python # Get the most relevant travel plans first results = client.search( query="What are my upcoming travel plans?", rerank=True, 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"

</Tab>
<Tab title="Performance">
- **Latency**: 150-200ms additional
- **Accuracy**: Significantly improved
- **Ordering**: Much more relevant
- **Best for**: Top-N precision, user-facing results
</Tab>
</Tabs>

## Real-World Use Cases

<Tabs>
<Tab title="Personal AI Assistant">
```python 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
    user_id="user123"
)

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

</Tab> <Tab title="Customer Support"> ```python Python # Find specific product issues with high precision results = client.search( query="Problems with premium subscription billing", user_id="customer456" )

Returns only relevant billing problems, not general questions

</Tab>
<Tab title="Healthcare AI">
```python Python
# Critical medical information needs perfect accuracy
results = client.search(
    query="Patient allergies and contraindications",
    rerank=True,            # Most important info first
    user_id="patient789"
)

# Ensures critical allergy info appears first

</Tab> <Tab title="Learning Platform"> ```python Python # Find learning progress for specific topics results = client.search( query="Python programming progress and difficulties", rerank=True, # Recent progress first user_id="student123" )

Gets comprehensive view of Python learning journey

</Tab>
</Tabs>

## Choosing the Right Configuration

### Recommended Configurations

<CodeGroup>
```python Python
# Basic search - good for exploration
def quick_search(query, user_id):
    return client.search(
        query=query,
        user_id=user_id
    )

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

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

javascript

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

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

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

</CodeGroup>

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
    user_id="user123"
)
latency = time.time() - start_time
print(f"Search completed in {latency:.2f}s")

Optimization Tips

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