Julius, We've good problems! (and expectation)

    Oiiii diretamente do Brasil =)

I’ve great expectation for the day when Julius can directly access my S3 buckets and even Redshift. Delivering the models directly to the endpoints would also be a dream. How can I do it today?

I bring a good problem. I am using Julius a lot, thank you very much to everyone in the community for that. Python codes, R and a lot of things that it helps me with in my day-to-day life as a manager.
However, I am having MANY CHATS, and they are getting lost. I would like to suggest that we could create folders, tags and search within the chats. I imagine this could be a nice feature for users like me who have over 200 chats and would like to catalog them with great care.


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That is an awesome recommendation! I think it would be helpful for organization of chats like you said.

As for access to S3 buckets and Redshift, have you tried this:

  1. Set Up AWS Access:
  • Create IAM Role: Create a role with AmazonS3FullAccess and AmazonRedshiftFullAccess policies.
  • Configure AWS Credentials: Use AWS CLI (aws configure) or set environment variables for AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, and AWS_DEFAULT_REGION.
  1. Access S3 Buckets:
  • Install boto3: pip install boto3
  • Python Code:
    import boto3
    s3 = boto3.client(‘s3’)
    s3.download_file(‘bucket-name’, ‘object-key’, ‘local-file-path’)
  1. Access Redshift:
  • Install psycopg2: pip install psycopg2-binary
    *Python code:
    import psycopg2
    conn = psycopg2.connect(dbname=‘db-name’, user=‘username’, password=‘password’, host=‘endpoint’, port=‘5439’)
    cur = conn.cursor()
    cur.execute(“SELECT * FROM your_table LIMIT 10;”)
    rows = cur.fetchall()
    for row in rows:
  1. Deploy Models to Endpoints:
  • Install SageMaker SDK: pip install sagemaker
  • Python code:
    import sagemaker
    from sagemaker import get_execution_role
    from sagemaker.model import Model
    model_data = ‘s3://your-bucket/model.tar.gz’
    role = get_execution_role()
    model = Model(model_data=model_data, role=role, image_uri=‘your-docker-image-uri’)
    predictor = model.deploy(initial_instance_count=1, instance_type=‘ml.m4.xlarge’)
    result = predictor.predict(data)
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Hey Chris! Thanks for join this XD

Yes, i acctualy use this kind of code at jupyter for while. What i’m looking for is that i could chat with my s3 bucket or redshift in julius chat.For example, I set the structure of my data warehouse and share for my c-level team. So they could chat with the data on the s3 or redshift direct from Julius.

It could be like a way to democratize the acess to data directly from the consumer without an analyst. Can you imagine this? You clean and scruture the data and people use as they need and permissions.

I think that my CEO could be so happy if he can ask anything to his company data with natural language at Julius.
Every data centralized on redshift and Julius chat with it.

Haha, thanks for having me :rofl:
You had an interesting idea! I wanted to see if I could have helped in any way. But, I guess I misunderstood the inquiry, whoops! Sorry about that!

Oh, that would be super neat to do! Yeah, I’m not entirely sure if Julius offers that or not, but I also have never attempted to try it so I don’t know. The concept seems very niche and specialized, which is super interesting and cool!
Thanks for sharing :slight_smile: