LLM Data Pipeline Pro

Transform any website into LLM-ready training data in minutes.

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The Problem

Building datasets for LLM fine-tuning or RAG pipelines is painful:

• Web data is messy and inconsistent
• Duplicates waste your training budget
• PII creates legal liability
• Each LLM provider needs a different format
• GDPR compliance is a nightmare

The Solution

LLM Data Pipeline Pro handles the entire data preparation workflow in one click. Scrape, validate, deduplicate, chunk, and export to any format — with full compliance reporting.

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Key Features

Data Collection

Source	Description
Website Crawling	Crawl any site with configurable depth (1-10 levels)
Apify Datasets	Process existing scraped data from any Apify actor
Direct Text	Paste raw text for quick processing

Quality Assurance

Feature	What It Does
Content Validation	Filters out empty, too short, or too long content
Quality Scoring	Rates content 0-1, filters below your threshold
PII Detection	Finds emails, phones, SSN, credit cards, addresses
Auto-Masking	Automatically redacts sensitive information
Language Detection	Identifies content language

Deduplication

Method	Benefit
Hash-Based	Removes exact duplicates instantly
Semantic	Catches near-duplicates with similar meaning
Cross-Document	Ensures no overlap across your entire dataset

Output Formats

Format	Best For
OpenAI	GPT-3.5, GPT-4 fine-tuning
Anthropic	Claude fine-tuning via AWS Bedrock
Mistral	Mistral AI model training
HuggingFace	Open-source model training
Raw	Custom pipelines, RAG applications

Vector Database Export

Provider	Status
Pinecone	Supported
Qdrant	Supported
Weaviate	Coming Soon
Chroma	Coming Soon

Compliance & Security

Feature	Description
robots.txt Respect	Honors website crawling rules
ai.txt Respect	Follows the new AI training opt-out standard
Sensitive Site Exclusion	Automatically skips healthcare, government, financial sites
GDPR Reports	Generates audit-ready compliance documentation
Data Retention	Configurable retention policies

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How It Works

Step 1: Choose Your Source Provide URLs to crawl, an existing Apify dataset, or paste text directly.

Step 2: Configure Quality Rules Set minimum content length, quality threshold, and PII handling preferences.

Step 3: Select Output Format Pick your target LLM provider format and chunking settings.

Step 4: Run The pipeline handles validation, deduplication, chunking, and formatting automatically.

Step 5: Download Get your ready-to-use JSONL file or have chunks uploaded directly to your vector database.

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Pricing

Pay Per Event

Event	Price	When Charged
Actor Start	$0.001	Once per run
Processed Chunk	$0.0001	Per output chunk

Cost Examples

Use Case	Pages	Est. Chunks	Total Cost
Small docs site	50	~200	~$0.02
Medium knowledge base	500	~2,000	~$0.20
Large documentation	5,000	~20,000	~$2.00
Enterprise wiki	10,000	~40,000	~$4.00

Vector Export Options

Option A: Bring Your Own Key (BYOK) Use your own OpenAI API key for embeddings. You pay OpenAI directly at their rates.

Option B: Managed Embeddings We handle everything. No API keys needed. Additional $0.0005 per chunk.

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Output Structure

Dataset Items

Each processed chunk is saved individually in your chosen format, ready for:

• Direct upload to OpenAI fine-tuning
• Import into your training pipeline
• Integration with RAG frameworks

Key-Value Store

File	Contents
OUTPUT	Complete pipeline results
STATS	Execution statistics by stage
COMPLIANCE_REPORT	GDPR audit documentation
training_data.jsonl	Ready-to-use training file

Statistics Tracked

• Pages crawled (success/failed)
• Validation results (passed/failed)
• Duplicates removed
• Chunks generated
• Average chunk size
• Processing time per stage

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Use Cases

Fine-Tuning Dataset Creation

Scrape your company documentation and export directly to OpenAI's fine-tuning format. Train custom models on your proprietary knowledge.

RAG Knowledge Base

Build a searchable knowledge base with automatic chunking and vector embeddings. Export directly to Pinecone or Qdrant.

Documentation Migration

Convert legacy documentation into modern LLM-compatible formats for chatbots and AI assistants.

Competitive Intelligence

Monitor competitor documentation and extract structured data for analysis.

Compliance Auditing

Generate detailed reports showing what data was collected, from where, and how it was processed.

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Environment Variables

For BYOK mode, set these in your Apify actor settings:

Variable	Purpose
OPENAI_API_KEY	Generate embeddings for vector export
PINECONE_API_KEY	Upload to Pinecone
QDRANT_API_KEY	Upload to Qdrant

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Frequently Asked Questions

Is this GDPR compliant? Yes. The actor respects robots.txt and ai.txt, excludes sensitive sites, detects and masks PII, and generates compliance audit reports.

What's the maximum I can process? Up to 10,000 pages per run with configurable crawl depth up to 10 levels.

How does chunking work? Recursive text splitting with configurable chunk size (100-10,000 characters) and overlap (0-1,000 characters). Splits on paragraphs, sentences, then words.

Can I use my own vector database? Currently supports Pinecone and Qdrant. Weaviate and Chroma support coming soon.

What PII types are detected? Email addresses, phone numbers, Social Security numbers, credit card numbers, and physical addresses.

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Support

• Issues: Open a ticket on the actor page
• Feature Requests: Contact via Apify messaging
• Documentation: Check the input schema for all available options

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Changelog

v1.0 — Initial Release

• Multi-source input (URL, dataset, text)
• Five output formats (OpenAI, Anthropic, Mistral, HuggingFace, Raw)
• Pinecone and Qdrant integration
• PII detection and masking
• GDPR compliance reporting
• Configurable chunking with overlap

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Built for the AI era. Process responsibly.