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
• Use min_relevance to filter out low-quality matches

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.

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