To search for legal arguments or concepts using vectors, you convert legal texts into numerical representations (vectors) and compare their similarity in a high-dimensional space. This approach relies on embedding models trained to capture semantic meaning, allowing you to find documents or passages that address similar legal ideas even if they don’t share exact keywords. For example, a search for “breach of contract” might return cases discussing “failure to fulfill obligations” if the model recognizes their contextual similarity. The process involves three main steps: preprocessing legal texts, generating embeddings, and querying a vector database.
First, legal documents (cases, statutes, or briefs) are preprocessed to extract clean text. This may involve removing formatting, segmenting text into paragraphs, or filtering irrelevant content. Next, an embedding model like BERT, SBERT, or a legal-specific variant (e.g., LegalBERT) converts the text into vectors. These models are trained to place semantically similar phrases closer in the vector space. For instance, the vector for “negligence per se” might align closely with “statutory duty violations” if the model understands their legal equivalence. Developers can fine-tune these models on legal corpora to improve domain-specific accuracy.
Once vectors are generated, they’re stored in a vector database such as FAISS, Elasticsearch, or Pinecone. When a user submits a query (e.g., “precedent for punitive damages”), the query is converted into a vector using the same model. The database then retrieves the nearest vectors using similarity metrics like cosine similarity. For example, a search might return a Supreme Court opinion discussing “exemplary damages” because its vector aligns closely with the query. Developers can optimize performance by adjusting parameters like the number of nearest neighbors or using approximate nearest neighbor (ANN) algorithms for faster searches. This method enables semantic search beyond literal keyword matching, which is critical in legal contexts where terminology varies across jurisdictions or historical periods.