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from $0.60 / 1,000 text chunkeds

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Text Splitter for RAG | LLM Chunking API

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Recursive text splitter and RAG chunker for embeddings, vector databases and LLM context. Split on paragraph, line, sentence, word or hard boundaries, add overlap, stable chunk IDs and previous/next chunk links. From $0.75 per 1,000 texts, platform usage included.

Pricing

from $0.60 / 1,000 text chunkeds

Rating

0.0

(0)

Developer

Rosario Vitale

Actor stats

Bookmarked

Total users

Monthly active users

2 hours ago

Last modified

Categories

Developer tools

Automation

You can access the Text Splitter for RAG | LLM Chunking API 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.

Python

JavaScript

CLI

OpenAPI

HTTP

MCP

# Set API token
$API_TOKEN=<YOUR_API_TOKEN>

# Prepare Actor input
$cat > input.json << 'EOF'
<{
<  "text": "Retrieval-Augmented Generation (RAG) combines a language model with an external knowledge base. Instead of relying only on what the model memorized during training, RAG retrieves relevant chunks of text and feeds them to the model as context.\n\nTo build a RAG system you first split your documents into chunks, create embeddings for each chunk, and store them in a vector database. At query time you embed the user's question, find the most similar chunks, and pass them to the model alongside the prompt.\n\nChunking matters a lot. Chunks that are too large dilute relevance and waste tokens, while chunks that are too small lose context. A common starting point is around 1000 characters per chunk with a small overlap, so that ideas spanning a boundary are not lost between neighbouring chunks."
<}
<EOF

# Run the Actor using an HTTP API
# See the full API reference at https://docs.apify.com/api/v2
$curl "https://api.apify.com/v2/actors/zenomastro~text-splitter-for-llm/runs?token=$API_TOKEN" \
<  -X POST \
<  -d @input.json \
<  -H 'Content-Type: application/json'

Text Splitter for RAG — Embedding Chunks API

Below, you can find a list of relevant HTTP API endpoints for calling the Text Splitter for RAG | LLM Chunking API Actor. For this, you’ll need an Apify account. Replace <YOUR_API_TOKEN> in the URLs with your Apify API token, which you can find under API & Integrations in Apify Console. For details, see the API reference.

Run Actor

POST
https://api.apify.com/v2/actors/zenomastro~text-splitter-for-llm/runs?token=<YOUR_API_TOKEN>

Note: By adding the method=POST query parameter, this API endpoint can be called using a GET request and thus used in third-party webhooks. Please refer to our Run Actor API documentation.

Run Actor synchronously and get dataset items

POST
https://api.apify.com/v2/actors/zenomastro~text-splitter-for-llm/run-sync-get-dataset-items?token=<YOUR_API_TOKEN>

Note: This endpoint supports both POST and GET request methods. However, only the POST method allows you to pass input data. For more information, please refer to our Run Actor synchronously and get dataset items API documentation.

Get Actor

GET
https://api.apify.com/v2/actors/zenomastro~text-splitter-for-llm?token=<YOUR_API_TOKEN>

For more information, please refer to our Get Actor API documentation.

Actors can be used to scrape web pages, extract data, or automate browser tasks. Use the Text Splitter for RAG | LLM Chunking API API programmatically via the Apify API.

You can choose from:

Text Splitter for RAG | LLM Chunking API API in Python

Text Splitter for RAG | LLM Chunking API API in JavaScript

Text Splitter for RAG | LLM Chunking API API through CLI

Text Splitter for RAG | LLM Chunking API OpenAPI definition

You can start Text Splitter for RAG | LLM Chunking API with the Apify API by sending an HTTP POST request to the Run Actorendpoint. An Actor’s input and its content type can be passed as a payload of the POST request, and additional options can be specified using URL query parameters. The Text Splitter for RAG | LLM Chunking API is identified within the API by its ID, which is the creator’s username and the name of the Actor.

When the Text Splitter for RAG | LLM Chunking API run finishes you can list the data from its default dataset(storage) via the API or you can preview the data directly on Apify Console.

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Readable text from 73.1% of 52 live pages over plain HTTP, including 10 of 10 documentation sites. Website to LLM text and a website to markdown converter in one run: clean Markdown, chunks with overlap for a vector database, 46 flat fields, and what changed since the last run.

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