LLM Data Pipeline Pro

Crawl any website, extract clean text, split into chunks with quality scoring, and export to JSON, Pinecone, or Qdrant. Built for RAG pipelines and AI training data. Includes language detection, content type classification, and token counting.

Ai Training Data Enricher

fiery_dream/ai-training-data-enricher

Production-grade data enrichment and validation for LLM training datasets. Automatically clean, enrich, deduplicate, and validate your AI training data before fine-tuning.

Website Content to Markdown for LLM Training

easyapi/website-content-to-markdown-for-llm-training

🚀 Transform web content into clean, LLM-ready Markdown! 📘 Scrape multiple pages, extract main content, and convert to Markdown format. Perfect for AI researchers, data scientists, and LLM developers. Fast, efficient, and customizable. Supercharge your AI training data today! 🌐📝🧠

LLM Web Scraper

incredible_moment/llm-scraper

Turn any website into structured JSON using AI. Supports OpenAI GPT-4 and Anthropic Claude. Built in Rust to minimize compute costs while waiting for LLM responses. Extract data without selectors.

Ai Training Data Curator

digital_troubadour/ai-training-data-curator

Crawl websites and curate high-quality training data for LLM fine-tuning. Automatic deduplication, quality scoring, and language detection. Export to JSONL, Parquet, or CSV formats ready for OpenAI, Claude, or Llama training.

RAG Pipeline

labrat011/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.

Qdrant Integration

apify/qdrant-integration

Transfer data from Apify Actors to a Qdrant vector database.

AI Training Data Scraper - LLM and RAG-Ready

george.the.developer/ai-training-data-scraper

Extract web content formatted for LLM fine-tuning and RAG pipelines. Output in OpenAI JSONL, Claude JSONL, Markdown, or raw text.

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.