> ## Documentation Index
> Fetch the complete documentation index at: https://docs.upsonic.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Semantic Splitter

> Split text based on semantic topic shifts using embeddings

## Overview

Semantic splitter identifies boundaries by embedding sentences and finding points of high cosine distance between adjacent sentences, indicating topic changes. Uses statistical methods to determine breakpoint thresholds. Requires an embedding provider.

**Splitter Class:** `SemanticChunker`

**Config Class:** `SemanticChunkingConfig`

## Dependencies

```bash theme={null}
uv pip install numpy
```

Also requires an embedding provider (e.g., OpenAIEmbedding).

## Examples

```python theme={null}
from upsonic import Agent, Task, KnowledgeBase
from upsonic.loaders.text import TextLoader
from upsonic.loaders.config import TextLoaderConfig
from upsonic.embeddings import OpenAIEmbedding, OpenAIEmbeddingConfig
from upsonic.text_splitter.semantic import SemanticChunker, SemanticChunkingConfig, BreakpointThresholdType
from upsonic.vectordb import ChromaProvider, ChromaConfig, ConnectionConfig, Mode

# Configure splitter with embedding provider
embedding = OpenAIEmbedding(OpenAIEmbeddingConfig())
splitter_config = SemanticChunkingConfig(
    chunk_size=512,
    chunk_overlap=50,
    embedding_provider=embedding,
    breakpoint_threshold_type=BreakpointThresholdType.PERCENTILE,
    breakpoint_threshold_amount=95.0
)
splitter = SemanticChunker(splitter_config)

# Setup KnowledgeBase
loader = TextLoader(TextLoaderConfig())
vectordb = ChromaProvider(ChromaConfig(
    collection_name="semantic_docs",
    vector_size=1536,
    connection=ConnectionConfig(mode=Mode.IN_MEMORY)
))

kb = KnowledgeBase(
    sources=["document.txt"],
    embedding_provider=embedding,
    vectordb=vectordb,
    loaders=[loader],
    splitters=[splitter]
)

# Query with Agent
agent = Agent("anthropic/claude-sonnet-4-5")
task = Task("Identify different topics", context=[kb])
result = agent.do(task)
print(result)
```

## Parameters

| Parameter                     | Type                         | Description                           | Default       | Source   |
| ----------------------------- | ---------------------------- | ------------------------------------- | ------------- | -------- |
| `chunk_size`                  | `int`                        | Target size of each chunk             | 1024          | Base     |
| `chunk_overlap`               | `int`                        | Overlapping units between chunks      | 200           | Base     |
| `min_chunk_size`              | `int \| None`                | Minimum size for a chunk              | None          | Base     |
| `length_function`             | `Callable[[str], int]`       | Function to measure text length       | `len`         | Base     |
| `strip_whitespace`            | `bool`                       | Strip leading/trailing whitespace     | False         | Base     |
| `embedding_provider`          | `EmbeddingProvider`          | Required embedding provider instance  | Required      | Specific |
| `breakpoint_threshold_type`   | `BreakpointThresholdType`    | Statistical method for breakpoints    | `PERCENTILE`  | Specific |
| `breakpoint_threshold_amount` | `float`                      | Numeric value for threshold type      | 95.0          | Specific |
| `sentence_splitter`           | `Callable[[str], list[str]]` | Function to split text into sentences | Default regex | Specific |