Yes, vector search can effectively work with transcripts from depositions or hearings. Vector search is a technique that converts text into numerical representations (vectors) and uses similarity metrics to find relevant content. Legal transcripts, which are often lengthy and dense with context-specific language, can benefit from this approach because it allows for semantic matching rather than relying solely on keyword-based searches. For example, a search for “contract dispute details” could return results mentioning “agreement disagreements” or “breach of terms,” even if the exact phrase isn’t present.
To implement this, transcripts are first processed into embeddings—numerical vectors generated by machine learning models like BERT or Sentence Transformers. These models capture the semantic meaning of phrases, sentences, or entire paragraphs. For instance, a deposition transcript discussing “failure to deliver goods by the agreed date” might be embedded as a vector that’s mathematically closer to “missed shipment deadline” than to unrelated topics. A vector database (e.g., FAISS, Pinecone, or Elasticsearch’s vector search capabilities) then indexes these embeddings. When a user queries for “delivery delays,” the system converts the query into a vector and retrieves transcript segments with similar vectors, regardless of exact terminology.
Practical challenges include handling domain-specific jargon and ensuring accuracy. Legal transcripts often contain specialized terms (e.g., “force majeure” or “tortious interference”) that generic embedding models might not represent well. One solution is fine-tuning a pre-trained model on legal corpora or using a domain-specific model like LegalBERT. Additionally, preprocessing steps like splitting transcripts into logical chunks (e.g., question-answer pairs) and filtering noise (e.g., timestamps or speaker labels) can improve relevance. For example, a developer might segment a 100-page deposition into individual exchanges between attorneys and witnesses, embed each segment, and use cosine similarity to rank results during searches. This approach balances precision with the scalability needed for large datasets.