Semantic Chunking

Semantic chunking is a method of splitting documents into smaller chunks by analyzing semantic similarity between text segments using embeddings. It uses the chonkie library to identify natural breakpoints where the semantic meaning changes significantly, based on a configurable similarity threshold. This helps preserve context and meaning better than fixed-size chunking by ensuring semantically related content stays together in the same chunk, while splitting occurs at meaningful topic transitions.

Parameter	Type	Default	Description
embedder	Union[str, Embedder, BaseEmbeddings]	OpenAIEmbedder	The embedder configuration. Can be an Agno Embedder (e.g., OpenAIEmbedder, GeminiEmbedder), a Chonkie BaseEmbeddings instance (e.g., OpenAIEmbeddings), or a string model identifier (e.g., "text-embedding-3-small") for Chonkie's AutoEmbeddings.
chunk_size	int	5000	Maximum tokens allowed per chunk.
similarity_threshold	float	0.5	Similarity threshold for grouping sentences (0-1). Lower values create larger groups (fewer chunks).
similarity_window	int	3	Number of sentences to consider for similarity calculation.
min_sentences_per_chunk	int	1	Minimum number of sentences per chunk.
min_characters_per_sentence	int	24	Minimum number of characters per sentence.
delimiters	List[str]	[". ", "! ", "? ", "\n"]	Delimiters to split sentences on.
include_delimiters	Literal["prev", "next", None]	"prev"	Include delimiters in the chunk text. Specify whether to include with the previous or next sentence.
skip_window	int	0	Number of groups to skip when looking for similar content to merge. 0 (default) uses standard semantic grouping; higher values enable merging of non-consecutive semantically similar groups.
filter_window	int	5	Window length for the Savitzky-Golay filter used in boundary detection.
filter_polyorder	int	3	Polynomial order for the Savitzky-Golay filter.
filter_tolerance	float	0.2	Tolerance for the Savitzky-Golay filter boundary detection.
chunker_params	Dict[str, Any]	None	Additional parameters to pass directly to Chonkie's SemanticChunker.