Deploy pipelines

Neum AI provides a managed cloud enviornment to run and manage your pipelines. It offers capabilities like pipeline management, run scheduling, automatic synchronization as well as a high scale distributed architecture to run large jobs. Learn more about the difference between Local and Cloud enviornments for Neum AI.

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Test pipeline locally

Before we deploy the pipeline to Neum AI Cloud, lets run a quick local test to make sure things are working as expected.

print("Pipeline validation successful? " + pipeline.validate())

result = pipeline.run()
info = pipeline.sink.info()
print("Vectors written in pipeline run: " + result) 
print(info)

results = pipeline.search(query="What are the challenges with scaling RAG?", number_of_results=3)

for result in results:
  print(result.metadata)

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Deploy pipeline to Neum AI Cloud

To deploy the pipeline, we will leverage the built-in functions.

from neumai.Client.NeumClient import NeumClient

neumClient = NeumClient(api_key = '<INSERT NEUMAI API KEY>')
neumClient.create_pipeline(pipeline=pipeline)

Once deployed, you can go to Neum AI Cloud dashboard to see the status and pipeline information. Alternatively, you can programmatically query:

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Get pipeline information

from neumai.Client.NeumClient import NeumClient

neumClient = NeumClient(api_key = '<INSERT NEUMAI API KEY>')
neumClient.get_pipeline(pipeline_id='<pipeline_id from above>')

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Search pipeline

from neumai.Client.NeumClient import NeumClient

neumClient = NeumClient(api_key = '<INSERT NEUMAI API KEY>')
neumClient.search_pipeline(pipeline_id='<pipeline_id from above>', query=<search query>)