Documentation Index
Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
Use this file to discover all available pages before exploring further.
Gain access to the johnsnowlabs ecosystem of enterprise NLP libraries
with over 21.000 enterprise NLP models in over 200 languages with the open source johnsnowlabs library.
For all 24.000+ models, see the John Snow Labs Model Models Hub
Installation and setup
To [install enterprise features](https://nlp.johnsnowlabs.com/docs/en/jsl/install_licensed_quick, run:
# for more details see https://nlp.johnsnowlabs.com/docs/en/jsl/install_licensed_quick
nlp.install()
gpu,cpu,apple_silicon,aarch based optimized binaries.
By default cpu binaries are used.
Once a session is started, you must restart your notebook to switch between GPU or CPU, or changes will not take effect.
Embed query with CPU:
document = "foo bar"
embedding = JohnSnowLabsEmbeddings('embed_sentence.bert')
output = embedding.embed_query(document)
Embed query with GPU:
document = "foo bar"
embedding = JohnSnowLabsEmbeddings('embed_sentence.bert','gpu')
output = embedding.embed_query(document)
Embed query with apple silicon (M1,M2,etc..):
documents = ["foo bar", 'bar foo']
embedding = JohnSnowLabsEmbeddings('embed_sentence.bert','apple_silicon')
output = embedding.embed_query(document)
Embed query with AARCH:
documents = ["foo bar", 'bar foo']
embedding = JohnSnowLabsEmbeddings('embed_sentence.bert','aarch')
output = embedding.embed_query(document)
Embed document with CPU:
documents = ["foo bar", 'bar foo']
embedding = JohnSnowLabsEmbeddings('embed_sentence.bert','gpu')
output = embedding.embed_documents(documents)
Embed document with GPU:
documents = ["foo bar", 'bar foo']
embedding = JohnSnowLabsEmbeddings('embed_sentence.bert','gpu')
output = embedding.embed_documents(documents)
Embed document with apple silicon (M1,M2,etc..):
```python
documents = ["foo bar", 'bar foo']
embedding = JohnSnowLabsEmbeddings('embed_sentence.bert','apple_silicon')
output = embedding.embed_documents(documents)
Embed Document with AARCH:
```python
documents = ["foo bar", 'bar foo']
embedding = JohnSnowLabsEmbeddings('embed_sentence.bert','aarch')
output = embedding.embed_documents(documents)
