Search through document pages using semantic similarity.

This endpoint performs a semantic search at the page level, which is best for:

• Finding broad context about a topic
• Getting an overview of document sections
• When you need more context around the matches

Performance Considerations:

• Fastest search performance of all levels
• Most efficient memory usage
• Best for initial broad searches
• Recommended for large document collections

Best Practices:

• Use for initial exploratory searches
• Combine with metadata filters for better performance
• Start with this before using more granular searches
• Ideal for queries needing broader context
• Use min_relevance to filter out low-quality matches

The search uses embeddings to find the most semantically similar pages to your query, regardless of exact keyword matches.

You can provide either:

• A text query (which will be converted to embeddings)
• A pre-computed embedding vector (1536 dimensions)

Filter options:

• min_relevance: Set a threshold (0-1) to only return results above a certain relevance score