Converting legal documents into embeddings involves transforming text into numerical vectors that capture semantic meaning. This process typically uses language models to analyze the text and generate dense vector representations. Legal documents pose unique challenges due to their length, complex terminology, and structural elements like clauses and references. Here’s how to approach it in three key steps.
First, preprocess the documents to prepare the text for the model. Legal texts often contain boilerplate language, cross-references, and section headers that need handling. Split the document into manageable chunks (e.g., paragraphs or sections) to avoid exceeding the token limit of models like BERT (512 tokens). Clean the text by removing extraneous formatting, tables, or footnotes, but retain critical structural markers like "Section 2(a)" that might influence meaning. For example, a contract could be split into individual clauses, each treated as a separate input chunk. Tokenization libraries like spaCy or Hugging Face’s Tokenizers can help segment text while preserving legal terms of art like “force majeure.”
Next, select an appropriate model and generate embeddings. General-purpose models like BERT or RoBERTa work but may struggle with legal jargon. Domain-specific models like Legal-BERT—pretrained on court opinions and contracts—better capture nuances. Use the model’s API to convert tokenized text: for a clause like “Parties shall arbitrate disputes in Delaware,” the model outputs a 768-dimensional vector. For multi-chunk documents, aggregate embeddings using methods like averaging or max-pooling. Some workflows use sentence-transformers (e.g., all-mpnet-base-v2) optimized for semantic similarity, which helps compare clauses across documents. Always test the model’s output with legal text samples to verify it captures distinctions like “warrant” (verb) vs. “warrant” (noun).
Finally, store and index the embeddings for practical use. Normalize vectors using techniques like L2 normalization to ensure consistent scaling. Use vector databases like FAISS, Pinecone, or Elasticsearch’s dense_vector type for efficient similarity searches. For instance, a compliance system could index employment contract clauses to quickly find all documents with non-compete clauses resembling a target vector. When updating embeddings, consider versioning to track changes in model outputs if you retrain or switch models. Always validate results—manually check if a query for “indemnification” embeddings correctly retrieves related clauses from insurance agreements.