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----
-title: Response schema
-description: Understanding the search API response structure
----
-
-The search API returns different response structures depending on the search mode and result type.
-
-## Response structure
-
-```json
-{
- "results": [...],
- "timing": 245,
- "total": 10
-}
-```
-
-### Top-level fields
-
-
- Array of search results. Can contain memory results, chunk results, or both (in hybrid mode).
-
-
-
- Search execution time in milliseconds.
-
-
-
- Total number of results returned.
-
-
-## Result types
-
-In hybrid search mode, results can be either memory results or chunk results. Each type has a different structure.
-
-### Memory result
-
-Memory results contain the `memory` field and represent structured memory entries.
-
-```json
-{
- "id": "mem_abc123",
- "memory": "John prefers machine learning over traditional programming",
- "metadata": {
- "category": "preferences",
- "confidence": 0.95
- },
- "updatedAt": "2024-01-15T10:30:00Z",
- "version": 2,
- "rootMemoryId": "mem_root456",
- "similarity": 0.92,
- "context": {
- "parents": [...],
- "children": [...]
- },
- "documents": [...],
- "chunks": [...]
-}
-```
-
-
- Unique identifier for the memory entry.
-
-
-
- The memory content. **Only present in memory results.**
-
-
-
- Custom metadata associated with the memory.
-
-
-
- ISO 8601 timestamp of when the memory was last updated.
-
-
-
- Version number of the memory (increments with updates).
-
-
-
- ID of the root memory if this memory is part of a memory chain.
-
-
-
- Similarity score between 0 and 1 (higher is more similar).
-
-
-
- Related memories (parents and children) if `include.relatedMemories` is true.
-
- ```json
- {
- "parents": [
- {
- "id": "mem_parent123",
- "memory": "Parent memory content",
- "relation": "updates"
- }
- ],
- "children": [
- {
- "id": "mem_child456",
- "memory": "Child memory content",
- "relation": "extends"
- }
- ]
- }
- ```
-
-
-
- Associated documents if `include.documents` is true. See [Document schema](#document-schema).
-
-
-
- Top 5 relevant document chunks if `include.chunks` is true. See [Chunk schema](#chunk-schema).
-
-
-### Chunk result
-
-Chunk results contain the `chunk` field and represent raw document content. **Only returned in hybrid search mode.**
-
-```json
-{
- "id": "chunk_xyz789",
- "chunk": "This is a chunk of content from a document about machine learning preferences...",
- "metadata": {
- "source": "document_123.pdf",
- "page": 5
- },
- "updatedAt": "2024-01-15T10:30:00Z",
- "similarity": 0.88,
- "version": 1,
- "context": {
- "parents": [],
- "children": []
- },
- "documents": [
- {
- "id": "doc_123",
- "title": "Machine Learning Guide",
- "type": "pdf",
- "createdAt": "2024-01-10T08:00:00Z",
- "updatedAt": "2024-01-15T10:30:00Z"
- }
- ],
- "chunks": []
-}
-```
-
-
- Unique identifier for the chunk.
-
-
-
- The chunk content. **Only present in chunk results.**
-
-
-
- Metadata from the parent document.
-
-
-
- ISO 8601 timestamp of when the document was last updated.
-
-
-
- Similarity score between 0 and 1 (higher is more similar).
-
-
-
- Always 1 for chunk results.
-
-
-
- Always empty for chunk results (chunks don't have parent/child relationships).
-
-
-
- Array containing the parent document. Always includes exactly one document.
-
-
-
- Always empty for chunk results.
-
-
-## Nested schemas
-
-### Document schema
-
-```json
-{
- "id": "doc_123",
- "title": "Machine Learning Guide",
- "type": "pdf",
- "metadata": {
- "author": "John Doe",
- "tags": ["ml", "ai"]
- },
- "summary": "A comprehensive guide to machine learning...",
- "createdAt": "2024-01-10T08:00:00Z",
- "updatedAt": "2024-01-15T10:30:00Z"
-}
-```
-
-
- Document identifier (custom ID if provided, otherwise internal ID).
-
-
-
- Document title (if `include.documents` is true).
-
-
-
- Document type (e.g., "pdf", "txt", "html").
-
-
-
- Document metadata (if `include.documents` is true).
-
-
-
- Document summary (if `include.summaries` is true).
-
-
-
- ISO 8601 timestamp of document creation.
-
-
-
- ISO 8601 timestamp of last document update.
-
-
-### Chunk schema
-
-```json
-{
- "content": "This is a chunk of content from the document...",
- "score": 0.89,
- "position": 3,
- "documentId": "doc_123"
-}
-```
-
-
- The chunk content.
-
-
-
- Similarity score for this chunk (0-1).
-
-
-
- Position of the chunk within the document (0-indexed).
-
-
-
- ID of the parent document.
-
-
-## Distinguishing result types
-
-To determine if a result is a memory or chunk:
-
-```javascript
-if (result.memory) {
- // This is a memory result
- console.log("Memory:", result.memory);
-} else if (result.chunk) {
- // This is a chunk result
- console.log("Chunk:", result.chunk);
-}
-```
-
-## Example responses
-
-### Memories mode response
-
-```json
-{
- "results": [
- {
- "id": "mem_abc123",
- "memory": "John prefers machine learning over traditional programming",
- "metadata": {
- "category": "preferences"
- },
- "updatedAt": "2024-01-15T10:30:00Z",
- "version": 2,
- "rootMemoryId": null,
- "similarity": 0.92
- }
- ],
- "timing": 145,
- "total": 1
-}
-```
-
-### Hybrid mode response
-
-```json
-{
- "results": [
- {
- "id": "mem_abc123",
- "memory": "John prefers machine learning over traditional programming",
- "metadata": {
- "category": "preferences"
- },
- "updatedAt": "2024-01-15T10:30:00Z",
- "version": 2,
- "rootMemoryId": null,
- "similarity": 0.92,
- "context": {
- "parents": [],
- "children": []
- },
- "documents": [],
- "chunks": []
- },
- {
- "id": "chunk_xyz789",
- "chunk": "Machine learning is a subset of artificial intelligence...",
- "metadata": {
- "source": "ml_guide.pdf"
- },
- "updatedAt": "2024-01-14T09:00:00Z",
- "similarity": 0.88,
- "version": 1,
- "context": {
- "parents": [],
- "children": []
- },
- "documents": [
- {
- "id": "doc_123",
- "title": "Machine Learning Guide",
- "type": "pdf",
- "createdAt": "2024-01-10T08:00:00Z",
- "updatedAt": "2024-01-14T09:00:00Z"
- }
- ],
- "chunks": []
- }
- ],
- "timing": 245,
- "total": 2
-}
-```
-
-## Notes
-
-- In hybrid mode, results are sorted by similarity score regardless of type
-- Chunk results that are already associated with memory results are automatically deduplicated
-- The `context` field is always empty for chunk results
-- The `documents` array for chunk results always contains exactly one document (the parent)
-- Memory results can have multiple associated documents if the memory references multiple sources