AI Law LIbrarians Blog
Guest post from Andrew Dang, ASU Law Student and LLM Developer.
The language of law has many layers. Legal facts are more than objective truths; they tell the story and ultimately decide who wins or loses. A statute can have multiple interpretations, and those interpretations depend on factors like the judge, context, purpose, and history of the statute. Legal language has distinct features, including rare legal terms of art like “restrictive covenant,” “promissory estoppel,” “tort,” and “novation.” This complex legal terminology poses challenges for normal semantic search queries.
Vector databases represent an exciting new trend, and for good reason. Rather than relying on traditional Boolean logic, semantic search leverages word associations by creating embeddings and storing them in a vector database. In machine learning and natural language processing, embeddings depict words or sentences as dense vectors of real numbers in a continuous vector space. This numerical representation of text is typically generated by a model that tokenizes the text and learns embeddings from the data. Vectors capture the contextual and semantic meaning of each word. When a user makes a semantic query, the search system works to interpret their intent and context. The system then breaks the query into individual words or tokens, converts them into vector representations using embedding models, and returns ranked results based on their relevance. Unlike Boolean search which requires specific syntax, (“AND”, “OR”, etc.) semantic search allows for queries in natural language and opens up a whole new world of potential when searches are not constrained by the rules of exact matching of text.
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Unlocking the Power of Semantic Searches in the Legal Domain
