From 3dfb3e7823bfee89d9799dd6ee57776bd19644e0 Mon Sep 17 00:00:00 2001
From: "mintlify[bot]" <109931778+mintlify[bot]@users.noreply.github.com>
Date: Fri, 19 Dec 2025 00:09:30 +0000
Subject: [PATCH] Update apps/docs/search/examples/hybrid-search.mdx

Co-Authored-By: mintlify[bot] <109931778+mintlify[bot]@users.noreply.github.com>
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+---
+title: Hybrid search examples
+description: Examples of using hybrid search mode to search memories and document chunks
+---
+
+Hybrid search mode searches memories first, then falls back to document chunks when needed. This provides comprehensive results from both structured memories and raw document content.
+
+## Basic hybrid search
+
+Search for information across both memories and document chunks:
+
+<CodeGroup>
+
+```bash cURL
+curl -X POST https://api.supermemory.ai/v4/search \
+  -H "Authorization: Bearer YOUR_API_KEY" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "q": "What are the key features of machine learning?",
+    "searchMode": "hybrid",
+    "limit": 10,
+    "threshold": 0.7
+  }'
+```
+
+```javascript JavaScript
+const response = await fetch('https://api.supermemory.ai/v4/search', {
+  method: 'POST',
+  headers: {
+    'Authorization': 'Bearer YOUR_API_KEY',
+    'Content-Type': 'application/json'
+  },
+  body: JSON.stringify({
+    q: 'What are the key features of machine learning?',
+    searchMode: 'hybrid',
+    limit: 10,
+    threshold: 0.7
+  })
+});
+
+const data = await response.json();
+console.log(data);
+```
+
+```python Python
+import requests
+
+response = requests.post(
+    'https://api.supermemory.ai/v4/search',
+    headers={
+        'Authorization': 'Bearer YOUR_API_KEY',
+        'Content-Type': 'application/json'
+    },
+    json={
+        'q': 'What are the key features of machine learning?',
+        'searchMode': 'hybrid',
+        'limit': 10,
+        'threshold': 0.7
+    }
+)
+
+data = response.json()
+print(data)
+```
+
+</CodeGroup>
+
+### Response
+
+```json
+{
+  "results": [
+    {
+      "id": "mem_abc123",
+      "memory": "Machine learning key features: pattern recognition, automated learning, predictive modeling",
+      "metadata": {
+        "category": "ml_concepts",
+        "source": "training_notes"
+      },
+      "updatedAt": "2024-01-15T10:30:00Z",
+      "version": 1,
+      "rootMemoryId": null,
+      "similarity": 0.94,
+      "context": {
+        "parents": [],
+        "children": []
+      },
+      "documents": [],
+      "chunks": []
+    },
+    {
+      "id": "chunk_xyz789",
+      "chunk": "Machine learning algorithms can identify patterns in data without being explicitly programmed. Key features include supervised learning, unsupervised learning, and reinforcement learning approaches.",
+      "metadata": {
+        "source": "ml_textbook.pdf",
+        "page": 12
+      },
+      "updatedAt": "2024-01-14T09:00:00Z",
+      "similarity": 0.89,
+      "version": 1,
+      "context": {
+        "parents": [],
+        "children": []
+      },
+      "documents": [
+        {
+          "id": "doc_123",
+          "title": "Introduction to Machine Learning",
+          "type": "pdf",
+          "createdAt": "2024-01-10T08:00:00Z",
+          "updatedAt": "2024-01-14T09:00:00Z"
+        }
+      ],
+      "chunks": []
+    }
+  ],
+  "timing": 245,
+  "total": 2
+}
+```
+
+## Hybrid search with document metadata
+
+Include document information to understand the source of chunk results:
+
+<CodeGroup>
+
+```bash cURL
+curl -X POST https://api.supermemory.ai/v4/search \
+  -H "Authorization: Bearer YOUR_API_KEY" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "q": "neural network architectures",
+    "searchMode": "hybrid",
+    "limit": 5,
+    "threshold": 0.75,
+    "include": {
+      "documents": true,
+      "summaries": true
+    }
+  }'
+```
+
+```javascript JavaScript
+const response = await fetch('https://api.supermemory.ai/v4/search', {
+  method: 'POST',
+  headers: {
+    'Authorization': 'Bearer YOUR_API_KEY',
+    'Content-Type': 'application/json'
+  },
+  body: JSON.stringify({
+    q: 'neural network architectures',
+    searchMode: 'hybrid',
+    limit: 5,
+    threshold: 0.75,
+    include: {
+      documents: true,
+      summaries: true
+    }
+  })
+});
+
+const data = await response.json();
+```
+
+```python Python
+import requests
+
+response = requests.post(
+    'https://api.supermemory.ai/v4/search',
+    headers={
+        'Authorization': 'Bearer YOUR_API_KEY',
+        'Content-Type': 'application/json'
+    },
+    json={
+        'q': 'neural network architectures',
+        'searchMode': 'hybrid',
+        'limit': 5,
+        'threshold': 0.75,
+        'include': {
+            'documents': True,
+            'summaries': True
+        }
+    }
+)
+
+data = response.json()
+```
+
+</CodeGroup>
+
+### Response
+
+```json
+{
+  "results": [
+    {
+      "id": "chunk_def456",
+      "chunk": "Common neural network architectures include feedforward networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). Each architecture is optimized for different types of tasks.",
+      "metadata": {
+        "source": "deep_learning_guide.pdf",
+        "chapter": "3"
+      },
+      "updatedAt": "2024-01-16T14:20:00Z",
+      "similarity": 0.91,
+      "version": 1,
+      "context": {
+        "parents": [],
+        "children": []
+      },
+      "documents": [
+        {
+          "id": "doc_456",
+          "title": "Deep Learning Architectures",
+          "type": "pdf",
+          "metadata": {
+            "author": "Dr. Jane Smith",
+            "year": 2024
+          },
+          "summary": "A comprehensive guide to modern deep learning architectures, covering CNNs, RNNs, transformers, and more.",
+          "createdAt": "2024-01-15T10:00:00Z",
+          "updatedAt": "2024-01-16T14:20:00Z"
+        }
+      ],
+      "chunks": []
+    }
+  ],
+  "timing": 198,
+  "total": 1
+}
+```
+
+## Hybrid search with filtering
+
+Filter results by metadata while using hybrid search:
+
+<CodeGroup>
+
+```bash cURL
+curl -X POST https://api.supermemory.ai/v4/search \
+  -H "Authorization: Bearer YOUR_API_KEY" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "q": "optimization techniques",
+    "searchMode": "hybrid",
+    "limit": 10,
+    "threshold": 0.7,
+    "filters": {
+      "and": [
+        {
+          "key": "category",
+          "operator": "equals",
+          "value": "deep_learning"
+        },
+        {
+          "key": "difficulty",
+          "operator": "less_than_or_equal",
+          "value": 3
+        }
+      ]
+    }
+  }'
+```
+
+```javascript JavaScript
+const response = await fetch('https://api.supermemory.ai/v4/search', {
+  method: 'POST',
+  headers: {
+    'Authorization': 'Bearer YOUR_API_KEY',
+    'Content-Type': 'application/json'
+  },
+  body: JSON.stringify({
+    q: 'optimization techniques',
+    searchMode: 'hybrid',
+    limit: 10,
+    threshold: 0.7,
+    filters: {
+      and: [
+        {
+          key: 'category',
+          operator: 'equals',
+          value: 'deep_learning'
+        },
+        {
+          key: 'difficulty',
+          operator: 'less_than_or_equal',
+          value: 3
+        }
+      ]
+    }
+  })
+});
+
+const data = await response.json();
+```
+
+```python Python
+import requests
+
+response = requests.post(
+    'https://api.supermemory.ai/v4/search',
+    headers={
+        'Authorization': 'Bearer YOUR_API_KEY',
+        'Content-Type': 'application/json'
+    },
+    json={
+        'q': 'optimization techniques',
+        'searchMode': 'hybrid',
+        'limit': 10,
+        'threshold': 0.7,
+        'filters': {
+            'and': [
+                {
+                    'key': 'category',
+                    'operator': 'equals',
+                    'value': 'deep_learning'
+                },
+                {
+                    'key': 'difficulty',
+                    'operator': 'less_than_or_equal',
+                    'value': 3
+                }
+            ]
+        }
+    }
+)
+
+data = response.json()
+```
+
+</CodeGroup>
+
+## Hybrid search with reranking
+
+Use reranking to improve result relevance in hybrid search:
+
+<CodeGroup>
+
+```bash cURL
+curl -X POST https://api.supermemory.ai/v4/search \
+  -H "Authorization: Bearer YOUR_API_KEY" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "q": "best practices for model training",
+    "searchMode": "hybrid",
+    "limit": 10,
+    "threshold": 0.7,
+    "rerank": true,
+    "include": {
+      "documents": true,
+      "chunks": true
+    }
+  }'
+```
+
+```javascript JavaScript
+const response = await fetch('https://api.supermemory.ai/v4/search', {
+  method: 'POST',
+  headers: {
+    'Authorization': 'Bearer YOUR_API_KEY',
+    'Content-Type': 'application/json'
+  },
+  body: JSON.stringify({
+    q: 'best practices for model training',
+    searchMode: 'hybrid',
+    limit: 10,
+    threshold: 0.7,
+    rerank: true,
+    include: {
+      documents: true,
+      chunks: true
+    }
+  })
+});
+
+const data = await response.json();
+```
+
+```python Python
+import requests
+
+response = requests.post(
+    'https://api.supermemory.ai/v4/search',
+    headers={
+        'Authorization': 'Bearer YOUR_API_KEY',
+        'Content-Type': 'application/json'
+    },
+    json={
+        'q': 'best practices for model training',
+        'searchMode': 'hybrid',
+        'limit': 10,
+        'threshold': 0.7,
+        'rerank': True,
+        'include': {
+            'documents': True,
+            'chunks': True
+        }
+    }
+)
+
+data = response.json()
+```
+
+</CodeGroup>
+
+## Processing hybrid search results
+
+Here's how to handle both memory and chunk results:
+
+```javascript
+const response = await fetch('https://api.supermemory.ai/v4/search', {
+  method: 'POST',
+  headers: {
+    'Authorization': 'Bearer YOUR_API_KEY',
+    'Content-Type': 'application/json'
+  },
+  body: JSON.stringify({
+    q: 'machine learning concepts',
+    searchMode: 'hybrid',
+    limit: 10
+  })
+});
+
+const data = await response.json();
+
+// Process results based on type
+data.results.forEach(result => {
+  if (result.memory) {
+    // This is a memory result
+    console.log('Memory:', {
+      id: result.id,
+      content: result.memory,
+      similarity: result.similarity,
+      metadata: result.metadata
+    });
+  } else if (result.chunk) {
+    // This is a chunk result
+    console.log('Chunk:', {
+      id: result.id,
+      content: result.chunk,
+      similarity: result.similarity,
+      document: result.documents[0]?.title || 'Unknown'
+    });
+  }
+});
+```
+
+## When to use hybrid search
+
+Use hybrid search when:
+
+- **Incomplete memories**: Your memories might not cover all relevant information
+- **Document-heavy content**: You have large documents that haven't been fully processed into memories
+- **Exploratory search**: You want to discover information across all available sources
+- **Fallback mechanism**: You want to ensure results even when memories are sparse
+
+Use memories-only search when:
+
+- **Structured data**: You only want curated, structured memory entries
+- **Performance**: You need the fastest possible search (hybrid adds ~50-100ms)
+- **Memory-first approach**: Your application relies primarily on memories
+
+## Best practices
+
+1. **Set appropriate thresholds**: Use higher thresholds (0.75-0.85) for hybrid search to ensure quality
+2. **Include document metadata**: Always include documents to understand chunk sources
+3. **Handle both result types**: Check for `memory` or `chunk` fields in your code
+4. **Use reranking for quality**: Enable reranking when result quality is critical
+5. **Filter appropriately**: Use filters to narrow down results from both sources
+6. **Monitor performance**: Hybrid search is slightly slower due to parallel chunk search
+
+## Related
+
+<CardGroup cols={2}>
+  <Card title="Memory search" icon="brain" href="/search/examples/memory-search">
+    Examples of memories-only search
+  </Card>
+  <Card title="Parameters" icon="sliders" href="/search/parameters">
+    All search parameters
+  </Card>
+  <Card title="Response schema" icon="brackets-curly" href="/search/response-schema">
+    Understanding responses
+  </Card>
+  <Card title="Filtering" icon="filter" href="/search/filtering">
+    Advanced filtering
+  </Card>
+</CardGroup>