Building a RAG pipeline with Kreuzberg and LangChain

Most discussions about retrieval-augmented generation (RAG) focus on choosing the right model, tuning prompts, or experimenting with vector databases. In practice, these are rarely the hardest parts. The real bottleneck appears much earlier: getting clean, reliable text out of messy documents. There is a real challenge in ingestion, chunking, and embeddings. PDFs preserve visual layout rather than logical structure, Office files rely on completely different internal formats, and scanned documents require OCR before any text exists at all. Metadata is often incomplete or inconsistent, and small problems at this stage propagate downstream. If the extraction quality is poor, retrieval becomes unreliable, and the language model begins to produce weak or misleading answers. This is where Kreuzberg plays a central role, covering the entire early-stage data flow: document ingestion, text chunking, and embedding generation. A typical RAG pipeline can combine Kreuzberg for ingestion, chunking,