Vector databases (DBs) are used to compare NDAs or contracts by converting text into numerical representations (vectors) and measuring their similarity. This process starts by embedding the text of each document using machine learning models like BERT or sentence transformers, which capture semantic meaning. These embeddings are stored in a vector DB, which organizes them for efficient retrieval. When comparing documents, the DB calculates the distance between vectors—closer vectors indicate more similar content. For example, a confidentiality clause in one NDA might be compared to similar clauses in other contracts by querying the DB for nearest neighbors in the vector space.
A practical implementation involves chunking documents into sections (e.g., clauses, paragraphs) before generating embeddings. This allows granular comparisons, such as identifying whether a termination clause in one contract aligns with others. Developers might use cosine similarity or Euclidean distance to quantify similarity. For instance, a query could retrieve the top five most similar non-disclosure agreements to a target document, highlighting sections like indemnification or intellectual property rights. Tools like FAISS, Pinecone, or Chroma handle the storage and search efficiently, scaling to thousands of documents. Preprocessing steps, such as removing boilerplate or standardizing terms (e.g., replacing “Party A” with “Company”), improve accuracy by reducing noise in the embeddings.
Key considerations include selecting embedding models trained on legal text for better domain relevance and tuning the chunking strategy to balance context and performance. For example, splitting a contract into 200-word chunks might preserve clause-specific context without overwhelming the model. Developers should also validate results with domain experts, as semantic similarity doesn’t guarantee legal equivalence. A real-world workflow might involve ingesting a repository of NDAs into a vector DB, then building an API to compare new contracts against existing ones, flagging high-similarity clauses for review. This approach reduces manual effort and helps identify patterns, such as uncommon liability terms across agreements.