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John Snow Labs

John Snow Labs NLP & LLM ecosystem includes software libraries for state-of-the-art AI at scale, Responsible AI, No-Code AI, and access to over 20,000 models for Healthcare, Legal, Finance, etc.

Models are loaded with nlp.load and spark session is started >with nlp.start() under the hood. For all 24.000+ models, see the John Snow Labs Model Models Hub

Setting upโ€‹

%pip install --upgrade --quiet  johnsnowlabs
# If you have a enterprise license, you can run this to install enterprise features
# from johnsnowlabs import nlp
# nlp.install()


from langchain_community.embeddings.johnsnowlabs import JohnSnowLabsEmbeddings

Initialize Johnsnowlabs Embeddings and Spark Session

embedder = JohnSnowLabsEmbeddings("en.embed_sentence.biobert.clinical_base_cased")

Define some example texts . These could be any documents that you want to analyze - for example, news articles, social media posts, or product reviews.

texts = ["Cancer is caused by smoking", "Antibiotics aren't painkiller"]

Generate and print embeddings for the texts . The JohnSnowLabsEmbeddings class generates an embedding for each document, which is a numerical representation of the document's content. These embeddings can be used for various natural language processing tasks, such as document similarity comparison or text classification.

embeddings = embedder.embed_documents(texts)
for i, embedding in enumerate(embeddings):
print(f"Embedding for document {i+1}: {embedding}")

Generate and print an embedding for a single piece of text. You can also generate an embedding for a single piece of text, such as a search query. This can be useful for tasks like information retrieval, where you want to find documents that are similar to a given query.

query = "Cancer is caused by smoking"
query_embedding = embedder.embed_query(query)
print(f"Embedding for query: {query_embedding}")

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