Huge news: We just launched as a LangChain Core Provider—and we’re here to kill the #1 pain point of RAG: garbage document parsing.
Let’s cut to it: Building reliable AI used to feel like rolling the dice. Existing loaders mangle tables, drop critical data, and give zero way to verify outputs. You’d blindy feed messy text into embeddings, waste compute on garbage, and wonder why your app failed. I started Undatasio because this frustration broke more of my projects than I can count.
Our fix? Two non-negotiables: absolute parsing precision and total transparency—wrapped in a model no one else offers: pay only for the parses you accept. Bad output? It’s free. No excuses, no gotchas.
This isn’t "another loader" for LangChain. As a Core Provider, `UndatasioLoader` bakes quality control into the start of your chain: - Programmatically check parsed JSON before it hits embeddings - Reject docs that miss key fields (e.g., no `invoice_total`, wrong table columns) - See exactly where data came from with positional `bbox` coordinates (build your own validation UI in minutes)
No more wasting time or money on downstream garbage. Data prep should be the reliable part of your stack—not the scary one.
We’ve been grinding to make this integration feel native to LangChain, and partnering with their team to push it live has been a blast.
If you’re tired of RAG failing because your inputs are broken, give it a spin. We’re here all day to answer questions, and we need your feedback to make this even better.
Links to get started are in the comments—fire away!
Here’s how to get started:
1. Install the Package: pip install langchain-undatasio (PyPI Link: https://pypi.org/project/langchain-undatasio/)
2. Check out the Official Docs: (LangChain Provider Page: https://docs.langchain.com/oss/python/integrations/providers...)
3. Try the Live Demo: We've set up a Colab notebook with examples. (Google Colab Notebook: https://colab.research.google.com/drive/1k_UhPjNoiUXC7mkMOEI...)
I'll be here all day to answer any questions. Let me know what you think.
