Legal Case Recommender - USA

Using data from the United States Reports, the official reporter of the Supreme Court of the United States, this AI embedding identifies cases with similar legal arguments, facts, or outcomes. It aids legal research and strategy formulation by recommending pertinent cases.

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Rating: 4.9/5

Data Amount

1 TB

Data Freshness

Updated July 2023

Use Cases




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Example output

Could you recommend similar cases to Roe v. Wade?

Sure, here are some cases similar to Roe v. Wade that you might find interesting:

1. Planned Parenthood v. Casey: "This case reaffirmed the constitutional right to access abortion, and altered the standard for analyzing restrictions on that right."
View here

2. Griswold v. Connecticut: "This case established the right to privacy in the context of contraceptive use, setting a legal precedent for Roe v. Wade."
View here

Legal Case Recommender - USA

How do we keep the data updated?

Our system is linked directly with the United States Reports. We have an ongoing process that checks for newly published cases or updates to existing ones every week. New and updated data is preprocessed, indexed, and embeddings are generated before being stored in our vector database. This approach ensures our AI embeddings always have the latest legal case data.

How to use

Python Code

You can access the embedding using EmbedElite python package or curl request.

# This returns a list of embeddings
from embedelite import load_embedding

embeddings = load_embedding("legal-case-recommender-usa")

# This returns an object which can be directly inserted into Qdrant
result = load_embedding("legal-case-recommender-usa", embed_for="qdrant")
# result is {"embeddings": [], "documents": [] "ids": []}



curl -X POST -H "Content-Type: application/json" -d '{
"doc_id": "legal-case-recommender-usa"


    "mappings": {
        "properties": {
        "doc_source": {"type": "keyword"},
        "sentence": { "type": "text" },
        "embeddings": { "type": "dense_vector", "dims": 1536, "index": False },
        "doc_source": {"type": "keyword"}

Data credit to the United States Reports, without which this embedding wouldn’t be possible.