Semantic RAG Document Chunker and Token Slicer
Pricing
from $1.00 / 1,000 document chunkeds
Deterministic document chunker that splits text, markdown, and tables without cutting sentences or code blocks in half.
You can access the Semantic RAG Document Chunker and Token Slicer programmatically from your own applications by using the Apify API. You can also choose the language preference from below. To use the Apify API, you’ll need an Apify account and your API token, found in API & Integrations in Apify Console.
1from apify_client import ApifyClient2
3# Initialize the ApifyClient with your Apify API token4# Replace '<YOUR_API_TOKEN>' with your token.5client = ApifyClient("<YOUR_API_TOKEN>")6
7# Prepare the Actor input8run_input = { "items": [{9 "id": "doc-001",10 "text": """# Introduction to Autonomous RAG11
12Retrieval Augmented Generation combines information retrieval with language modeling.13
14## Vector Embeddings15Embeddings convert semantic meaning into dense mathematical vectors.16
17```python18def get_embedding(text):19 return model.encode(text)20```21
22## Conclusion23Chunking preservation avoids truncating code blocks or tables.""",24 }] }25
26# Run the Actor and wait for it to finish27run = client.actor("n4d.h1dr/rag-smart-chunker").call(run_input=run_input)28
29# Fetch and print Actor results from the run's dataset (if there are any)30print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")31for item in client.dataset(run.default_dataset_id).iterate_items():32 print(item)33
34# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-startThe Apify API client for Python is the official library that allows you to use Semantic RAG Document Chunker and Token Slicer API in Python, providing convenience functions and automatic retries on errors.
Install the apify-client
$pip install apify-clientOther API clients include: