RAG Ingestor: Multi-Source Chunks for Vector DBs

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

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.

Transform documentation websites into RAG-ready chunks with semantic understanding, quality scoring, and direct vector database integration.

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.

Transform any website into vector-store-ready knowledge chunks for Pinecone, Weaviate, LangChain, LlamaIndex, Supabase, n8n & more. AI-generated Q&A pairs, smart chunking, PII scrubbing. Build hallucination-free RAG chatbots in minutes.

Scrape any website into clean text & Markdown with RAG-ready chunks and token counts for LLMs, vector databases (Pinecone, Qdrant) and AI chatbots. Also extracts linked PDF/Word/Excel. Anti-block, robots.txt-aware. Website-to-text for beginners, full RAG pipeline for pros. CPU only, no API key.

Crawl documentation sites (ReadTheDocs, GitBook, Docusaurus, Mintlify) into RAG-ready Markdown/JSON chunks with stable chunk IDs, heading breadcrumbs, word counts, and token estimates.