Show HN: RAG Large Data Pipeline with 1000 Models

cocoon-rag.github.io

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Hi, I'm a PhD in data + LLMs. I'm building an LLM chatbot for dbt.

The challenge isn't LLMs but dbt pipelines, which are too large (e.g., >1K models) to fit in the context window.

Traditional vector RAG only works well for texts but poorly for SQLs.

To solve this, we built a novel RAG using lineage.

I tested it on dbt projects with 1000+ models, and it works very well.

Some cool use cases the such chatbot does well: - Models discovery (I have a high-level question, which tables to use?)

- Safe model edits (I want to edit the models, what downstream models are affected?)

- Model debugging (This column looks wrong, how is it computed upstream?)

- New pipelines prototyping (I want to add a new metric, how are similar metrics computed?)

- Natural language querying (I want to understand customer better, recommend some queries)

- Pipelines optimizations (This model is slow, any inefficiency in the pipeline?) - etc.

Live demo on RAG for the Shopify dbt project (built by Fivetran): https://cocoon-data-transformation.github.io/page/pipeline

Enter your question, and it will generate a response live (refresh the page for the latest messages).

You'll need to provide LLM APIs (Claude 3.5 strongly recommended) and a dbt project (just need target/manifest.json).