Legal Precedent Finder - USA

Built using a comprehensive corpus of case law from various U.S. legal databases, this embedding employs AI to uncover key precedents related to a specific area of law. It reduces research time and aids in understanding how courts have ruled in similar situations.

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Rating: 4.7/5

Data Amount

2 TB (full-text)

Data Freshness

Updated July 2023

Use Cases

Legal Case Law



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Example output

What are the precedents for freedom of speech in the workplace?

Here are some key precedents related to freedom of speech in the workplace:

1. Pickering v. Board of Education: "This case held that public employees can't be fired for making statements on matters of public concern, unless the speech impedes their duties."
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2. Garcetti v. Ceballos: "This case determined that when public employees make statements pursuant to their official duties, they are not speaking as citizens for First Amendment purposes."
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Legal Precedent Finder - USA

How do we keep the data updated?

We maintain active connections with various U.S. legal databases to ensure that we have access to the most recent legal precedents. Our system continuously monitors changes in case laws, and any new rulings are collected, processed, and indexed. This ensures our embeddings are up-to-date, accurately reflecting the current legal landscape.

How to use

Python Code

You can access the legal precedent embeddings using the embedelite Python package or by making a CURL request.

# This returns a list of embeddings
from embedelite import load_embedding

embeddings = load_embedding("legal-precedents-us")

# This returns an object that can be directly inserted into the database
result = load_embedding("legal-precedents-us", embed_for="embedelite")
# The result is {"embeddings": [], "documents": [], "ids": []}



curl -X POST -H "Content-Type: application/json" -d '{
  "doc_id": "legal-precedents-us"


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