Memories Search (/v4/search)

Memories search (POST /v4/search) provides minimal-latency search optimized for real-time interactions. This endpoint prioritizes speed over extensive control, making it perfect for chatbots, Q&A systems, and any application where users expect immediate responses.

Basic Search

<Tabs> <Tab title="TypeScript"> ```typescript import Supermemory from 'supermemory';

const client = new Supermemory({
  apiKey: process.env.SUPERMEMORY_API_KEY!
});

const results = await client.search.memories({
  q: "machine learning applications",
  limit: 5
});

console.log(results)
```

</Tab> <Tab title="Python"> ```python from supermemory import Supermemory import os

client = Supermemory(api_key=os.environ.get("SUPERMEMORY_API_KEY"))

results = client.search.memories(
    q="machine learning applications",
    limit=5
)

console.log(results)
```

Sample Output:

json

{
  "results": [
    {
      "id": "mem_ml_apps_2024",
      "memory": "Machine learning applications span numerous industries including healthcare (diagnostic imaging, drug discovery), finance (fraud detection, algorithmic trading), autonomous vehicles (computer vision, path planning), and natural language processing (chatbots, translation services).",
      "similarity": 0.92,
      "title": "Machine Learning Industry Applications",
      "type": "text",
      "metadata": {
        "topic": "machine-learning",
        "industry": "technology",
        "created": "2024-01-10"
      }
    },
    {
      "id": "mem_ml_healthcare",
      "memory": "In healthcare, machine learning enables early disease detection through medical imaging analysis, personalized treatment recommendations, and drug discovery acceleration by predicting molecular behavior.",
      "similarity": 0.89,
      "title": "ML in Healthcare",
      "type": "text"
    }
  ],
  "total": 8,
  "timing": 87
}

Container Tag Filtering

Filter by user, project, or organization:

<Tabs> <Tab title="TypeScript"> ```typescript const results = await client.search.memories({ q: "project updates", containerTag: "user_123", // Note: singular, not plural limit: 10 }); ``` </Tab> <Tab title="Python"> ```python results = client.search.memories( q="project updates", container_tag="user_123", # Note: singular, not plural limit=10 ) ``` </Tab> <Tab title="cURL"> ```bash curl -X POST "https://api.supermemory.ai/v4/search" \ -H "Authorization: Bearer $SUPERMEMORY_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "q": "project updates", "containerTag": "user_123", "limit": 10 }' ``` </Tab> </Tabs>

Threshold Control

Control result quality with similarity threshold:

<Tabs> <Tab title="TypeScript"> ```typescript const results = await client.search.memories({ q: "artificial intelligence research", threshold: 0.7, // Higher = fewer, more similar results limit: 10 }); ``` </Tab> <Tab title="Python"> ```python results = client.search.memories( q="artificial intelligence research", threshold=0.7, # Higher = fewer, more similar results limit=10 ) ``` </Tab> <Tab title="cURL"> ```bash curl -X POST "https://api.supermemory.ai/v4/search" \ -H "Authorization: Bearer $SUPERMEMORY_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "q": "artificial intelligence research", "threshold": 0.7, "limit": 10 }' ``` </Tab> </Tabs>

Reranking

Improve result quality with secondary ranking:

<Tabs> <Tab title="TypeScript"> ```typescript const results = await client.search.memories({ q: "quantum computing breakthrough", rerank: true, // Better relevance, slight latency increase limit: 5 }); ``` </Tab> <Tab title="Python"> ```python results = client.search.memories( q="quantum computing breakthrough", rerank=True, # Better relevance, slight latency increase limit=5 ) ``` </Tab> <Tab title="cURL"> ```bash curl -X POST "https://api.supermemory.ai/v4/search" \ -H "Authorization: Bearer $SUPERMEMORY_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "q": "quantum computing breakthrough", "rerank": true, "limit": 5 }' ``` </Tab> </Tabs>

Query Rewriting

Improve search accuracy with automatic query expansion:

<Tabs> <Tab title="TypeScript"> ```typescript const results = await client.search.memories({ q: "How do neural networks learn?", rewriteQuery: true, // +400ms latency but better results limit: 5 }); ``` </Tab> <Tab title="Python"> ```python results = client.search.memories( q="How do neural networks learn?", rewrite_query=True, # +400ms latency but better results limit=5 ) ``` </Tab> <Tab title="cURL"> ```bash curl -X POST "https://api.supermemory.ai/v4/search" \ -H "Authorization: Bearer $SUPERMEMORY_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "q": "How do neural networks learn?", "rewriteQuery": true, "limit": 5 }' ``` </Tab> </Tabs>

Include documents, related memories, and summaries:

<Tabs> <Tab title="TypeScript"> ```typescript const results = await client.search.memories({ q: "machine learning trends", include: { documents: true, // Include source documents relatedMemories: true, // Include related memory entries summaries: true // Include memory summaries }, limit: 5 }); ``` </Tab> <Tab title="Python"> ```python results = client.search.memories( q="machine learning trends", include={ "documents": True, # Include source documents "relatedMemories": True, # Include related memory entries "summaries": True # Include memory summaries }, limit=5 ) ``` </Tab> <Tab title="cURL"> ```bash curl -X POST "https://api.supermemory.ai/v4/search" \ -H "Authorization: Bearer $SUPERMEMORY_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "q": "machine learning trends", "include": { "documents": true, "relatedMemories": true, "summaries": true }, "limit": 5 }' ``` </Tab> </Tabs>

Metadata Filtering

Simple metadata filtering for Memories search:

<Tabs> <Tab title="TypeScript"> ```typescript const results = await client.search.memories({ q: "research findings", filters: { AND: [ { key: "category", value: "science", negate: false }, { key: "status", value: "published", negate: false } ] }, limit: 10 }); ``` </Tab> <Tab title="Python"> ```python results = client.search.memories( q="research findings", filters={ "AND": [ {"key": "category", "value": "science", "negate": False}, {"key": "status", "value": "published", "negate": False} ] }, limit=10 ) ``` </Tab> <Tab title="cURL"> ```bash curl -X POST "https://api.supermemory.ai/v4/search" \ -H "Authorization: Bearer $SUPERMEMORY_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "q": "research findings", "filters": { "AND": [ {"key": "category", "value": "science", "negate": false}, {"key": "status", "value": "published", "negate": false} ] }, "limit": 10 }' ``` </Tab> </Tabs>

Chatbot Example

Optimal configuration for conversational AI:

<Tabs> <Tab title="TypeScript"> ```typescript // Optimized for chatbot responses const results = await client.search.memories({ q: userMessage, containerTag: userId, threshold: 0.6, // Balanced relevance rerank: false, // Skip for speed rewriteQuery: false, // Skip for speed limit: 3 // Few, relevant results });

// Quick response for chat
const context = results.results
  .map(r => r.memory)
  .join('\n\n');
```

</Tab> <Tab title="Python"> ```python # Optimized for chatbot responses results = client.search.memories( q=user_message, container_tag=user_id, threshold=0.6, # Balanced relevance rerank=False, # Skip for speed rewrite_query=False, # Skip for speed limit=3 # Few, relevant results )

# Quick response for chat
context = '\n\n'.join([r.memory for r in results.results])
```

</Tab> <Tab title="cURL"> ```bash # Optimized for chatbot responses curl -X POST "https://api.supermemory.ai/v4/search" \ -H "Authorization: Bearer $SUPERMEMORY_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "q": "user question here", "containerTag": "user_123", "threshold": 0.6, "rerank": false, "rewriteQuery": false, "limit": 3 }' ``` </Tab> </Tabs>

Complete Memories Search Example

Combining features for comprehensive results:

<Tabs> <Tab title="TypeScript"> ```typescript const results = await client.search.memories({ q: "machine learning model performance", containerTag: "research_team", filters: { AND: [ { key: "topic", value: "ai", negate: false } ] }, threshold: 0.7, rerank: true, rewriteQuery: false, // Skip for speed include: { documents: true, relatedMemories: false, summaries: true }, limit: 5 }); ``` </Tab> <Tab title="Python"> ```python results = client.search.memories( q="machine learning model performance", container_tag="research_team", filters={ "AND": [ {"key": "topic", "value": "ai", "negate": False} ] }, threshold=0.7, rerank=True, rewrite_query=False, # Skip for speed include={ "documents": True, "relatedMemories": False, "summaries": True }, limit=5 ) ``` </Tab> <Tab title="cURL"> ```bash curl -X POST "https://api.supermemory.ai/v4/search" \ -H "Authorization: Bearer $SUPERMEMORY_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "q": "machine learning model performance", "containerTag": "research_team", "filters": { "AND": [ {"key": "topic", "value": "ai", "negate": false} ] }, "threshold": 0.7, "rerank": true, "rewriteQuery": false, "include": { "documents": true, "relatedMemories": false, "summaries": true }, "limit": 5 }' ``` </Tab> </Tabs>

Hybrid Search Mode

Hybrid search mode allows you to search both memories and document chunks in a single request. When searchMode="hybrid", results contain objects with either a memory key (for memory results) or a chunk key (for chunk results).

Basic Hybrid Search

<Tabs> <Tab title="TypeScript"> ```typescript const results = await client.search.memories({ q: "machine learning best practices", searchMode: "hybrid", // Search memories + chunks limit: 10 });

// Handle mixed results
results.results.forEach(result => {
  if ('memory' in result) {
    console.log('Memory:', result.memory);
  } else if ('chunk' in result) {
    console.log('Chunk:', result.chunk);
    console.log('From document:', result.documents?.[0]?.title);
  }
});
```

</Tab> <Tab title="Python"> ```python results = client.search.memories( q="machine learning best practices", search_mode="hybrid", # Search memories + chunks limit=10 )

# Handle mixed results
for result in results.results:
    if 'memory' in result:
        print('Memory:', result['memory'])
    elif 'chunk' in result:
        print('Chunk:', result['chunk'])
        print('From document:', result.get('documents', [{}])[0].get('title'))
```

When to Use Hybrid Mode

Use hybrid mode when:

You want comprehensive search across both memories and documents
Memories might not exist for certain queries but document content is available
You need flexibility to get either memory or document chunk results
You want a single search endpoint that covers all content types

Use memories-only mode (searchMode="memories") when:

You only need user memories and preferences
You want faster, more focused results
You're building a personalized chatbot that relies on user context

Handling Mixed Results

When using hybrid mode, you'll receive mixed results. Here's how to process them:

<Tabs> <Tab title="TypeScript"> ```typescript const results = await client.search.memories({ q: "quantum computing applications", searchMode: "hybrid", limit: 10 });

// Separate memory and chunk results
const memoryResults = results.results.filter(r => 'memory' in r);
const chunkResults = results.results.filter(r => 'chunk' in r);

console.log(`Found ${memoryResults.length} memories and ${chunkResults.length} chunks`);

// Process memories
memoryResults.forEach(mem => {
  console.log('Memory:', mem.memory);
  console.log('Similarity:', mem.similarity);
});

// Process chunks
chunkResults.forEach(chunk => {
  console.log('Chunk:', chunk.chunk);
  console.log('Document:', chunk.documents?.[0]?.title);
  console.log('Similarity:', chunk.similarity);
});
```

</Tab> <Tab title="Python"> ```python results = client.search.memories( q="quantum computing applications", search_mode="hybrid", limit=10 )

# Separate memory and chunk results
memory_results = [r for r in results.results if 'memory' in r]
chunk_results = [r for r in results.results if 'chunk' in r]

print(f"Found {len(memory_results)} memories and {len(chunk_results)} chunks")

# Process memories
for mem in memory_results:
    print('Memory:', mem['memory'])
    print('Similarity:', mem['similarity'])

# Process chunks
for chunk in chunk_results:
    print('Chunk:', chunk['chunk'])
    print('Document:', chunk.get('documents', [{}])[0].get('title'))
    print('Similarity:', chunk['similarity'])
```

</Tab> </Tabs>

Hybrid Search with All Features

Combining hybrid mode with other features:

<Tabs> <Tab title="TypeScript"> ```typescript const results = await client.search.memories({ q: "research findings on AI", searchMode: "hybrid", containerTag: "research_team", threshold: 0.7, rerank: true, include: { documents: true, relatedMemories: true, summaries: true }, limit: 10 });

// Results are automatically sorted by similarity
// Memory results have 'memory' field, chunk results have 'chunk' field
results.results.forEach(result => {
  if ('memory' in result) {
    // Memory result
    console.log('Memory:', result.memory);
    console.log('Context:', result.context);
  } else {
    // Chunk result
    console.log('Chunk:', result.chunk);
    console.log('Document:', result.documents?.[0]);
  }
});
```

</Tab> <Tab title="Python"> ```python results = client.search.memories( q="research findings on AI", search_mode="hybrid", container_tag="research_team", threshold=0.7, rerank=True, include={ "documents": True, "relatedMemories": True, "summaries": True }, limit=10 )

# Results are automatically sorted by similarity
# Memory results have 'memory' field, chunk results have 'chunk' field
for result in results.results:
    if 'memory' in result:
        # Memory result
        print('Memory:', result['memory'])
        print('Context:', result.get('context'))
    else:
        # Chunk result
        print('Chunk:', result['chunk'])
        print('Document:', result.get('documents', [{}])[0])
```

</Tab> <Tab title="cURL"> ```bash curl -X POST "https://api.supermemory.ai/v4/search" \ -H "Authorization: Bearer $SUPERMEMORY_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "q": "research findings on AI", "searchMode": "hybrid", "containerTag": "research_team", "threshold": 0.7, "rerank": true, "include": { "documents": true, "relatedMemories": true, "summaries": true }, "limit": 10 }' ``` </Tab> </Tabs> <Note> **Important**: In hybrid mode, results are automatically merged and sorted by similarity score. Memory results and chunk results are deduplicated - if a chunk is already associated with a memory result, it won't appear as a separate chunk result. </Note>

Common Use Cases

Chatbots: Basic search with container tag and low threshold
Q&A Systems: Add reranking for better relevance
Knowledge Retrieval: Include documents and summaries
Real-time Search: Skip rewriting and reranking for maximum speed
Hybrid Search: Use searchMode="hybrid" when you need comprehensive search across both memories and documents