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All the scripts and code for this tutorial are available in the vectorDB recipes repository.

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Use case: Financial services regulatory knowledge base

Imagine you’re a major investment bank. Your team is tasked with building a critical Retrieval-Augmented Generation (RAG) system. This system must provide instant, accurate answers to compliance officers about ever-changing financial regulations. A wrong or out-of-date answer isn’t just an inconvenience—it could lead to multi-million dollar fines, reputational damage, and regulatory audits. Your knowledge base is a living entity, constantly evolving with:
  • Daily regulatory updates from government bodies.
  • New internal policy documents and interpretations.
  • A/B testing of different embedding models and text chunking strategies to improve accuracy.
This dynamic environment creates a series of high-stakes challenges that traditional vector databases are ill-equipped to handle.

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Pain points solved by LanceDB

  1. “Our RAG gave different answers yesterday versus today. Which version was used in the official compliance report?” Without versioning, you can’t prove what the AI knew at a specific point in time, making audits impossible.
  2. “The new embedding model we deployed corrupted half the vectors. Can we instantly roll back our 10TB dataset?” With traditional systems, a rollback means a painful, hours-long (or days-long) process of re-indexing from a backup, leading to significant downtime.
  3. “Regulators want to audit an AI-assisted decision from three months ago. How can we prove what data the model had access to at that exact moment?” Reproducibility is key for compliance. You must be able to reconstruct the exact state of the knowledge base for any historical query.
  4. “We need to A/B test a new chunking strategy, but we can’t disrupt the production system or duplicate the entire dataset.” Experimentation is vital for improvement, but it can’t come at the cost of production stability or a massive infrastructure bill.
LanceDB’s zero-cost data evolution and time-travel capabilities directly address these critical enterprise pain points, providing the foundation for a reliable, auditable, and production-ready RAG system.

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Dataset: The U.S. Federal Register

To make this use case realistic, we’ll use a perfect real-world dataset: The U.S. Federal Register, the official daily journal of the United States Government. It contains all new rules, proposed rules, and notices from federal agencies. It is the canonical source for regulatory changes, and it’s updated every business day. It even has a public API, allowing us to simulate the real-time ingestion of new documents. An example output of the workflow defined in main.py is shown below.
main.py