Client library for Enterprise h2oGPTe
Project description
h2oGPTe Python Client
Install
pip install h2ogpte
Usage
from h2ogpte import H2OGPTE
client = H2OGPTE(
address='https://h2ogpte.genai.h2o.ai',
api_key='sk-XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX',
)
# Create a new collection
collection_id = client.create_collection(
name='Contracts',
description='Paper clip supply contracts',
)
# Create documents
# Note: Done for demonstration purposes only (not usually needed)
with open('dunder_mifflin.txt', 'w') as f:
f.write('There were 55 paper clips shipped, 22 to Scranton and 33 to Filmer.')
with open('initech.txt', 'w') as f:
f.write('David Brent did not sign any contract with Initech.')
# Upload documents
# Many file types are supported: text/image/audio documents and archives
with open('dunder_mifflin.txt', 'rb') as f:
dunder_mifflin = client.upload('Dunder Mifflin.txt', f)
with open('initech.txt', 'rb') as f:
initech = client.upload('IniTech.txt', f)
# Ingest documents (Creates previews, chunks and embeddings)
client.ingest_uploads(collection_id, [dunder_mifflin, initech])
# Create a chat session
chat_session_id = client.create_chat_session(collection_id)
# Query the collection
with client.connect(chat_session_id) as session:
reply = session.query(
'How many paper clips were shipped to Scranton?',
timeout=60,
)
print(reply.content)
reply = session.query(
'Did David Brent co-sign the contract with Initech?',
timeout=60,
)
print(reply.content)
# Summarize each document
documents = client.list_documents_in_collection(collection_id, offset=0, limit=99)
for doc in documents:
summary = client.summarize_document(
document_id=doc.id,
timeout=60,
)
print(summary.content)
# Chat with LLM without a collection
chat_session_id = client.create_chat_session()
with client.connect(chat_session_id) as session:
reply = session.query(
'Why is drinking water good for you?',
timeout=60,
)
print(reply.content)
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