You can access the Tender RAG Chunker — Text Chunks for LLM 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 Integrations settings in Apify Console.

Python

JavaScript

CLI

OpenAPI

HTTP

MCP

1from apify_client import ApifyClient
2
3# Initialize the ApifyClient with your Apify API token
4# Replace '<YOUR_API_TOKEN>' with your token.
5client = ApifyClient("<YOUR_API_TOKEN>")
6
7# Prepare the Actor input
8run_input = {}
9
10# Run the Actor and wait for it to finish
11run = client.actor("adobeflex/tender-rag-chunker").call(run_input=run_input)
12
13# Fetch and print Actor results from the run's dataset (if there are any)
14print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
15for item in client.dataset(run["defaultDatasetId"]).iterate_items():
16    print(item)
17
18# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

Tender RAG Chunker — Text Chunks for LLM API in Python

The Apify API client for Python is the official library that allows you to use Tender RAG Chunker — Text Chunks for LLM API in Python, providing convenience functions and automatic retries on errors.

Install the apify-client

$pip install apify-client

Other API clients include:

Tender RAG Chunker — Text Chunks for LLM API in JavaScript

Tender RAG Chunker — Text Chunks for LLM API through CLI

Tender RAG Chunker — Text Chunks for LLM OpenAPI definition

Tender RAG Chunker — Text Chunks for LLM API

Text Splitter & Chunker for RAG / LLMs

zenomastro/text-splitter-for-llm

Split text into clean, overlapping chunks ready for embeddings, vector databases, RAG and LLM context. Configurable size, overlap, and split strategy.

Rosario Vitale

Rag Content Chunker

labrat011/rag-content-chunker

Turn raw text, Markdown, or Apify datasets into token-perfect RAG chunks with deterministic IDs, source metadata, and a billing-ready summary—ready for embeddings or vector DBs without extra glue code.

mick_

RAG Post Processor - Text Cleaner & Chunker for LLM Pipelines

jalicia/rag-post-processor

Clean and chunk scraped text for RAG and LLM pipelines. Strips HTML, collapses whitespace, splits into overlapping chunks ready for embedding. Works standalone or chained after any scraper. Per-row billing.

Jordan Wagner

Text Cleaner For RAG

junipr/text-cleaner-for-rag

Clean web text for RAG by removing boilerplate, normalizing whitespace, and producing chunk-ready text.

junipr

AI / RAG Web Crawler

groupoject/ai-rag-web-crawler

Crawl any website and extract clean, LLM-ready Markdown chunks to feed AI agents, chatbots, and RAG pipelines. One row per embeddable chunk.

Group Oject

Rag Embedding Generator

labrat011/rag-embedding-generator

Generate vector embeddings from text or chunked datasets using OpenAI or Cohere. Chains with RAG Content Chunker for end-to-end RAG pipelines. Outputs raw vectors ready for any vector database.

mick_

RAG-Ready Markdown Converter & Chunker

foxpink/apify-rag-markdown-chunker

Convert raw HTML/text into clean Markdown and split into ready-to-ingest chunks for RAG pipelines, Vector DBs, and LLM fine-tuning workflows.

Nguyễn Anh Duy

4.7

PDF to RAG Markdown Chunks for Embeddings

awesome_highboy/docforge

Convert PDFs into token-bounded Markdown chunks for RAG, embeddings, and vector databases (Pinecone, Chroma, Weaviate, Qdrant). Set maxTokens + overlap; get clean chunks with page number, token count, and SHA-256 content hash for dedup. JSON dataset ready for any LLM pipeline.