RAG Document Processing
Convert documents into embedding-ready chunks, structured metadata, and clean Markdown — built for retrieval-augmented generation pipelines.
What RAG Pipelines Need
Right-Sized Chunks
Chunks split by heading boundaries with overlap — preserves context at boundaries.
Rich Metadata
Word counts, chapter titles, and source structure attached to every chunk.
Entity Awareness
Named entities and a knowledge graph help improve retrieval relevance.
From Document to Vector Database
Upload Source Documents
PDF, DOCX, or Markdown — any format your knowledge base already uses.
Generate a Knowledge Pack
Get chunks.json with heading-aware, overlap-protected chunks ready for embedding.
Ingest into Your Vector DB
Load chunks.json directly — each entry includes chapter context and word count.
Chunks combine heading-boundary splitting with fixed-size fallback and overlap — the same hybrid approach recommended for production RAG systems.
Ideal For
- Teams building internal document Q&A systems
- Developers ingesting PDFs into Pinecone, Weaviate, or Chroma
- Startups building AI support bots from product documentation
- Researchers processing papers for semantic search
- Anyone building a custom knowledge base for an LLM application