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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:
-
-
-
-```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)
-```
-
-
-
-### 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:
-
-
-
-```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()
-```
-
-
-
-### 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:
-
-
-
-```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()
-```
-
-
-
-## Hybrid search with reranking
-
-Use reranking to improve result relevance in hybrid search:
-
-
-
-```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()
-```
-
-
-
-## 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
-
-
-
- Examples of memories-only search
-
-
- All search parameters
-
-
- Understanding responses
-
-
- Advanced filtering
-
-