fix(sdk): removing deprecating param from our sdk and docs changes with it (#4740)
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Kartik
@@ -1,6 +1,6 @@
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---
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title: Advanced Retrieval
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description: "Advanced memory search with keyword expansion, intelligent reranking, and precision filtering"
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description: "Advanced memory search with intelligent reranking for precise results"
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---
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## What is Advanced Retrieval?
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@@ -9,40 +9,6 @@ Advanced Retrieval gives you precise control over how memories are found and ran
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## Search Enhancement Options
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### Keyword Search
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Expands results to include memories with specific terms, names, and technical keywords.
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<Tabs>
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<Tab title="When to Use">
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- Searching for specific entities, names, or technical terms
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- Need comprehensive coverage of a topic
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- Want broader recall even if some results are less relevant
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- Working with domain-specific terminology
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</Tab>
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<Tab title="How it Works">
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```python Python
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# Find memories containing specific food-related terms
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results = client.search(
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query="What foods should I avoid?",
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keyword_search=True,
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user_id="user123"
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)
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# Results might include:
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# ✓ "Allergic to peanuts and shellfish"
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# ✓ "Lactose intolerant - avoid dairy"
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# ✓ "Mentioned avoiding gluten last week"
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```
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</Tab>
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<Tab title="Performance">
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- **Latency**: ~10ms additional
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- **Recall**: Significantly increased
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- **Precision**: Slightly decreased
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- **Best for**: Entity search, comprehensive coverage
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</Tab>
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</Tabs>
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### Reranking
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Reorders results using deep semantic understanding to put the most relevant memories first.
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@@ -77,41 +43,6 @@ results = client.search(
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</Tab>
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</Tabs>
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### Memory Filtering
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Filters results to keep only the most precisely relevant memories.
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<Tabs>
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<Tab title="When to Use">
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- Need highly specific, focused results
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- Working with large datasets where noise is problematic
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- Quality over quantity is essential
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- Building production or safety-critical applications
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</Tab>
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<Tab title="How it Works">
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```python Python
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# Get only the most relevant dietary restrictions
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results = client.search(
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query="What are my dietary restrictions?",
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filter_memories=True,
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user_id="user123"
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)
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# Before filtering: After filtering:
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# • "Allergic to nuts" → • "Allergic to nuts"
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# • "Likes Italian food" → • "Vegetarian diet"
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# • "Vegetarian diet" →
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# • "Eats dinner at 7pm" →
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```
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</Tab>
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<Tab title="Performance">
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- **Latency**: 200-300ms additional
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- **Precision**: Maximized
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- **Recall**: May be reduced
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- **Best for**: Focused queries, production systems
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</Tab>
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</Tabs>
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## Real-World Use Cases
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<Tabs>
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@@ -120,7 +51,6 @@ results = client.search(
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# Smart home assistant finding device preferences
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results = client.search(
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query="How do I like my bedroom temperature?",
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keyword_search=True, # Find specific temperature mentions
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rerank=True, # Get most recent preferences first
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user_id="user123"
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)
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@@ -133,8 +63,6 @@ results = client.search(
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# Find specific product issues with high precision
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results = client.search(
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query="Problems with premium subscription billing",
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keyword_search=True, # Find "premium", "billing", "subscription"
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filter_memories=True, # Only billing-related issues
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user_id="customer456"
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)
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@@ -147,11 +75,10 @@ results = client.search(
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results = client.search(
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query="Patient allergies and contraindications",
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rerank=True, # Most important info first
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filter_memories=True, # Only medical restrictions
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user_id="patient789"
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)
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# Ensures critical allergy info appears first and filters out non-medical data
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# Ensures critical allergy info appears first
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```
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</Tab>
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<Tab title="Learning Platform">
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@@ -159,7 +86,6 @@ results = client.search(
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# Find learning progress for specific topics
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results = client.search(
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query="Python programming progress and difficulties",
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keyword_search=True, # Find "Python", "programming", specific concepts
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rerank=True, # Recent progress first
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user_id="student123"
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)
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@@ -169,63 +95,57 @@ results = client.search(
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</Tab>
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</Tabs>
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## Choosing the Right Combination
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## Choosing the Right Configuration
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### Recommended Configurations
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<CodeGroup>
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```python Python
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# Fast and broad - good for exploration
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# Basic search - good for exploration
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def quick_search(query, user_id):
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return client.search(
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query=query,
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keyword_search=True,
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user_id=user_id
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)
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# Balanced - good for most applications
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# Reranked search - good for most applications
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def standard_search(query, user_id):
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return client.search(
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query=query,
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keyword_search=True,
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rerank=True,
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user_id=user_id
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)
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# High precision - good for critical applications
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# Reranked search - good for critical applications
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def precise_search(query, user_id):
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return client.search(
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query=query,
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rerank=True,
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filter_memories=True,
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user_id=user_id
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)
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```
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```javascript JavaScript
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// Fast and broad - good for exploration
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// Basic search - good for exploration
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function quickSearch(query, userId) {
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return client.search(query, {
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user_id: userId,
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keyword_search: true
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user_id: userId
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});
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}
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// Balanced - good for most applications
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// Reranked search - good for most applications
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function standardSearch(query, userId) {
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return client.search(query, {
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user_id: userId,
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keyword_search: true,
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rerank: true
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});
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}
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// High precision - good for critical applications
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// Reranked search - good for critical applications
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function preciseSearch(query, userId) {
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return client.search(query, {
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user_id: userId,
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rerank: true,
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filter_memories: true
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rerank: true
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});
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}
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```
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@@ -235,19 +155,15 @@ function preciseSearch(query, userId) {
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### Do
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- Start simple with just one enhancement and measure impact
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- Use keyword search for entity-heavy queries (names, places, technical terms)
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- Start simple with basic search and measure impact before enabling reranking
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- Use reranking when the top result quality matters most
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- Use filtering for production systems where precision is critical
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- Handle empty results gracefully when filtering is too aggressive
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- Monitor latency and adjust based on your application's needs
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- Handle empty results gracefully
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### Don't
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- Enable all options by default without measuring necessity
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- Use filtering for broad exploratory queries
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- Enable reranking by default without measuring necessity
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- Ignore latency impact in real-time applications
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- Forget to handle cases where filtering returns no results
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- Use advanced retrieval for simple, fast lookup scenarios
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## Performance Guidelines
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@@ -261,20 +177,18 @@ import time
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start_time = time.time()
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results = client.search(
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query="user preferences",
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keyword_search=True, # +10ms
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rerank=True, # +150ms
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filter_memories=True, # +250ms
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user_id="user123"
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)
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latency = time.time() - start_time
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print(f"Search completed in {latency:.2f}s") # ~0.41s expected
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print(f"Search completed in {latency:.2f}s")
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```
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### Optimization Tips
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1. **Cache frequent queries** to avoid repeated advanced processing
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2. **Use session-specific search** with `run_id` to reduce search space
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3. **Implement fallback logic** when filtering returns empty results
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3. **Implement fallback logic** when search returns empty results
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4. **Monitor and alert** on search latency patterns
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<Snippet file="get-help.mdx" />
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