Overview

The Caselaw Access Project (CAP) provides a publicly available, digitized archive of U.S. court decisions spanning centuries. Although this dataset is a rich resource for legal research and analysis, it has traditionally been limited to text-based queries and manual examination. Our current initiative addresses this constraint by vectorizing the CAP dataset, making it more accessible and useful for modern AI-driven applications—specifically those leveraging Large Language Models (LLMs) through Retrieval-Augmented Generation (RAG) techniques.

What is the Caselaw Access Project?

The Caselaw Access Project, spearheaded by Harvard Law School Library and Ravel Law, offers over 6.7 million court decisions digitally preserved from approximately 40 million pages. The dataset encompasses a broad array of federal and state jurisdictions, providing an extensive landscape of American legal precedent.

For more information and direct access to the underlying dataset, visit: https://case.law/

Why Vectorize the Data?

Traditional Limitations

Text-based datasets require keyword searches and manual filtering, which can be time-consuming and imprecise. Researchers and AI models must sift through massive volumes of documents to find relevant information.

Benefits of Vectorization

By converting text into vector embeddings, we enable more nuanced, semantic search and retrieval. Instead of relying solely on keyword matches, LLMs can understand the context and relationships between cases. This approach allows for:

1. Semantic Search: Quickly identify documents with conceptually similar content—even if they don’t share the same keywords.
2. Efficient RAG Workflows: Large Language Models can reference vectorized data to provide accurate, contextually relevant answers, reducing the likelihood of “hallucinations.”
3. Scalability: As the dataset grows, vector-based retrieval scales more efficiently than manual or keyword-based methods.

End Goal

Our project aims to create a readily available vectorized version of the Caselaw Access Project dataset. This work lays the groundwork for:

• Research Tools: More advanced, semantic-driven research capabilities for legal scholars, data scientists, and historians.
• Developer Integration: Straightforward integration into next-generation AI applications that rely on semantic retrieval for legal analysis or decision support.
• Public Access: A step toward making the law more understandable and navigable for anyone, from legal professionals to curious citizens.

Next Steps

Once vectorization is complete, we plan to make the embedded dataset accessible to interested parties—be they researchers, legal technologists, or AI developers. By doing so, we hope to foster innovation, encourage new lines of scholarly inquiry, and simplify the complexity inherent in legal research. Also, if this project is completed on time, we hope to leverage it with the LegalEase project we are working on right now.

In short, our vectorization initiative is about enhancing the usability of an already invaluable resource, bringing centuries of legal history into the modern AI ecosystem.