Documents
Search Document Paragraphs
Search through document paragraphs using semantic similarity.
This endpoint performs a semantic search at the paragraph level, which is best for:
- Finding specific topics or concepts
- Getting more focused results than page-level search
- When you need a moderate amount of context
Performance Considerations:
- Moderate search performance
- Balanced memory usage
- Good for medium-sized document collections
- More vectors to search than pages
Best Practices:
- Use when page-level search is too broad
- Ideal for finding specific explanations
- Good for topic-focused research
- Consider using metadata filters to improve performance
Paragraphs provide a natural unit of text that maintains coherent thoughts and ideas, making this search level ideal for finding self-contained explanations or descriptions.
POST
Headers
Body
application/json
Request model for searching paragraphs.
The search query
Optional metadata filters using MongoDB-like query syntax
Number of results to return
Required range:
1 <= x <= 100
Whether to optimize metadata filter
Whether to optimize search query
Response
200
application/json
Successful Response
Response model for paragraph search.
Response data
Schema for API error responses.
Example:
{
"error_code": "RESOURCE_NOT_FOUND",
"message": "Resource not found"
}
Metadata for API responses