Pricing

from $5.00 / 1,000 results

Rag Pipeline

One-click RAG pipeline: chunks text, generates embeddings, and stores vectors in Pinecone or Qdrant. Provide your content and API keys -- the orchestrator handles the rest.

Pricing

from $5.00 / 1,000 results

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Developer

Mick

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4 days ago

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Automation

You can access the Rag Pipeline 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

1import { ApifyClient } from 'apify-client';
2
3// Initialize the ApifyClient with your Apify API token
4// Replace the '<YOUR_API_TOKEN>' with your token
5const client = new ApifyClient({
6    token: '<YOUR_API_TOKEN>',
7});
8
9// Prepare Actor input
10const input = {};
11
12// Run the Actor and wait for it to finish
13const run = await client.actor("labrat011/rag-pipeline").call(input);
14
15// Fetch and print Actor results from the run's dataset (if any)
16console.log('Results from dataset');
17console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
18const { items } = await client.dataset(run.defaultDatasetId).listItems();
19items.forEach((item) => {
20    console.dir(item);
21});
22
23// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

Rag Pipeline API in JavaScript

The Apify API client for JavaScript is the official library that allows you to use Rag Pipeline API in JavaScript or TypeScript, providing convenience functions and automatic retries on errors.

Install the apify-client

$npm install apify-client

Other API clients include:

Rag Pipeline API in Python

Rag Pipeline API through CLI

Rag Pipeline OpenAPI definition

Rag Pipeline API

Rag Vector Store Writer

labrat011/rag-vector-store-writer

Apify Actor that writes embedding vectors to Pinecone or Qdrant vector databases. Chains directly with RAG Embedding Generator output or accepts raw vectors with metadata. Handles batching, retries, collection creation, metadata mapping, and ID generation. Bring your own vector DB API key.

Mick

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 Pipeline Data Collector

scraper_guru/rag-pipeline-data-collector

AI-ready web content extraction for RAG systems, LLMs, and AI agents. Single-page or multi-page scraping with parallel processing.

LIAICHI MUSTAPHA

AI-Powered Smart Web Scraper

cloud9_ai/ai-web-scraper

Intelligent content extraction from any website using Crawlee + AI. Auto-detects structure, adapts to layout changes, handles JavaScript rendering. No custom code needed. Extract articles, products, listings from 1000s of pages.

cloud9

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

Docs to Markdown + AI Embeddings → Vector DB Crawler

badruddeen/docs-to-markdown-ai-embeddings---vector-db-crawler

Turn any documentation site into clean Markdown, intelligently chunked content with embeddings (Azure/OpenAI), and directly upsert into MongoDB Atlas, Pinecone, Weaviate, Qdrant, or Milvus — ready for RAG, AI assistants, and semantic search in minutes.

Badruddeen Naseem

5.0

LLM Data Pipeline Pro

sanztheo/llm-data-pipeline-pro

Transform websites into LLM training data. Scrape, validate, deduplicate, chunk for RAG, and export to OpenAI/Anthropic/Mistral formats. Built-in PII detection and GDPR compliance. Vector DB export to Pinecone & Qdrant.