Announcing the launch of our Semantic Search API in CourtListener – a major leap forward in how you search legal data. We’ve been working diligently to bring semantic search to our case law database for quite some time. Back in March, we shared a status update with extensive details on the machine learning techniques and models powering this feature. Since then, we’ve completed many rounds of testing and generated embeddings for all our case law data. The functionality is now live through our API, complementing the keyword search you already know and love. Try It Now
- Key Features – Unlike keyword search, semantic search lets you search using natural language. It looks beyond exact matches to understand the meaning and intent behind your query. This means it can surface relevant precedents even when they’re phrased differently—something keyword searches often miss.
- How It Works – An encoder machine learning model, fine-tuned for information retrieval, represents legal opinions as embeddings (high-dimensional numbers) that capture their semantic meaning. When you submit a query, the same model translates it into embeddings, and an approximate nearest neighbor algorithm identifies opinions with the most semantically similar meanings.
- For a detailed comparison of keyword versus semantic search, check out our help page on Citegeist.
- Semantic search can be frustrating when it ignores words that must be in the results. To address this, we’ve implemented hybrid search. This feature lets you combine keyword phrases with natural language, so you can query by both keywords and semantic meaning simultaneously. To ensure keywords are in the results, enclose your keywords in quotation marks.
- All your favorite filters—dates, courts, and other parameters—work seamlessly with semantic search, just like with keyword search. And like keyword search, results are intelligently boosted by our Citegeist relevancy algorithm.
