chunking-embeddings

// **Text splitting strategies, embedding generation with FastEmbed, RAG pipeline integration**

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updated:March 4, 2026

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namechunking-embeddings

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Chunking & Embeddings

Text splitting strategies, embedding generation with FastEmbed, RAG pipeline integration

Chunking Architecture Overview

Location: crates/kreuzberg/src/chunking/, crates/kreuzberg/src/embeddings.rs

Extracted Text
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[1. Normalization] -> Clean whitespace, remove control chars
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[2. Chunk Strategy Selection] -> Fixed-size, semantic, syntax-aware, recursive
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[3. Overlap Management] -> Control context window overlap
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[4. Optional Embedding] -> Generate vectors with FastEmbed
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Output: Vec<Chunk> with text, vectors, metadata

Chunking Strategies

Location: crates/kreuzberg/src/chunking/mod.rs

Strategy	Pattern	Best For
Fixed-Size	Sliding window with configurable overlap	Uniform chunks for embedding models with fixed token limits
Semantic	Split by sentences, merge/split by similarity threshold	Smart context preservation for LLM consumption and semantic search
Syntax-Aware	Split by paragraph/section/heading/code-block structure	Preserving document structure (sections, code blocks) in RAG
Recursive (LangChain pattern)	Try separators in order: `\n\n`, `\n`, , ``	Best general-purpose chunking; auto-finds optimal split points

Key config fields per strategy (see struct definitions in chunking/mod.rs):

Fixed-Size: chunk_size, overlap, trim_whitespace
Semantic: target_chunk_size, min/max_chunk_size, semantic_threshold, use_sentence_boundaries
Syntax-Aware: chunk_by (Paragraph/Section/Heading/Sentence/CodeBlock), max_chunk_size, respect_code_blocks
Recursive: separators[], chunk_size, overlap

Chunking Configuration Presets

Location: crates/kreuzberg/src/chunking/mod.rs

Preset	Chunk Size	Overlap	Strategy	Use Case
Balanced	512 tokens	50	Semantic	RAG sweet spot
Compact	256 tokens	32	Fixed-Size	Dense vectors
Extended	1024 tokens	100	Recursive	Full context
Minimal	128 tokens	16	(default)	Lightweight embeddings

Usage: set config.chunking.preset = Some("balanced") in ExtractionConfig.

Embedding Generation with FastEmbed

Location: crates/kreuzberg/src/embeddings.rs

Model Selection

Model	Dimensions	Notes
`BAAI/bge-small-en-v1.5` (default)	384	Fast, excellent for RAG
`BAAI/bge-small-zh-v1.5`	384	Chinese optimized
`BAAI/bge-base-en-v1.5`	768	Better quality, slower
`jinaai/jina-embeddings-v2-base-en`	768	Long context (up to 8192 tokens)
`Custom(path)`	varies	Custom ONNX model path

Embedding Pattern

TextEmbeddingManager provides singleton-cached models per config. Pattern:

get_or_init_model() -- lazy-loads ONNX model (downloads if needed), caches in Arc<RwLock<HashMap>>
embed_chunks() -- collects chunk texts, calls model.embed(texts, batch_size), zips results back to ChunkWithEmbedding

Default config: batch_size=256, device=CPU, parallel_requests=4.

ONNX Runtime Requirement

Embeddings require ONNX Runtime. Feature-gated via:

[features]
embeddings = ["dep:fastembed", "dep:ort"]

Install: brew install onnxruntime (macOS) / apt install libonnxruntime libonnxruntime-dev (Linux). Verify: echo $ORT_DYLIB_PATH.

RAG Integration Pattern

The full extraction-to-RAG pipeline:

Extract: extract_file(path, config) -> ExtractionResult
Chunk: Apply preset strategy to result.content -> Vec<Chunk>
Embed: If embedding config present, TextEmbeddingManager::embed_chunks() -> Vec<ChunkWithEmbedding>
Output: RagDocument { file_path, metadata, chunks } ready for vector DB ingestion

See ChunkWithEmbedding struct in types.rs: contains text, embedding: Vec<f32>, dimensions, norm, metadata.

Critical Rules

Chunking is preprocessing - Always apply before embedding to ensure consistent vector sizes
Overlap prevents information loss - Set overlap to 15-20% of chunk size
Embedding models are stateful - Lazy load and cache to avoid repeated initialization
ONNX Runtime is required - Gracefully degrade if not available (skip embeddings)
Batch embedding for performance - Never embed single chunks; batch 50-1000 chunks
Normalize embeddings for search - Use L2 norm for cosine similarity
Cache embedding results - Don't re-embed identical text chunks
Model selection impacts quality - bge-small (384) for speed, bge-base (768) for quality

Related Skills

extraction-pipeline-patterns - Text extraction preceding chunking
api-server-mcp - Endpoint for chunking + embedding operations
ocr-backend-management - OCR text quality affects chunking success