Vector databases (DBs) can capture procedural and substantive legal differences to a degree, but their effectiveness depends on how legal texts are processed into embeddings and the design of the database. Procedural law governs the rules and processes used to enforce rights (e.g., filing deadlines, court procedures), while substantive law defines the rights and duties themselves (e.g., contract terms, criminal statutes). Vector DBs store data as numerical vectors, enabling similarity searches. If legal documents are converted into embeddings that reflect these distinctions, the DB can identify patterns separating procedural from substantive content. However, this relies heavily on the quality of the embeddings and the legal context used to train the underlying model.
For example, consider two legal documents: one explaining the steps to file a motion (procedural) and another outlining the elements required to prove negligence (substantive). If an embedding model like BERT is fine-tuned on legal texts, it might encode procedural terms (“service of process,” “jurisdiction”) into vectors that cluster separately from substantive terms (“breach of duty,” “damages”). A vector DB could then group similar documents based on these embeddings. Developers could query the DB for “motion to dismiss procedures” and retrieve procedurally focused texts, even if the exact keywords aren’t present. This works because the embeddings capture contextual relationships, not just literal terms.
However, limitations exist. Legal language often mixes procedural and substantive elements (e.g., a contract clause specifying arbitration steps). If the embedding model isn’t trained to disentangle these aspects, the vectors might not cleanly separate the concepts. Additionally, vector DBs depend on the training data’s scope—if the model hasn’t seen enough examples of procedural vs. substantive texts, the embeddings may lack nuance. Developers can mitigate this by using domain-specific models or supplementing embeddings with metadata (e.g., labeling documents as “procedural” during ingestion). While vector DBs aren’t a perfect solution, they offer a scalable way to surface legal distinctions when paired with well-structured embeddings.