dnielsen/DevOpsGAIWorkshop

DevOps for GenAI Workshop

This workshop explores the integration and impact of Generative AI (GAI) tools in DevOps and DevSecOps. We aim to comprehensively understand RAGs, Orchestraion, and Observilbility solutions realted (LLMs)that can be effectively utilized in modern software delivery and security practices.

In this workshop we will focus on what we are calling the LAM stack in order to learn the basics of data operations and engenering realted to Large Language Models (LLMs.)

LAM Stack for this workshop

Langchain or LlamaIndex (Orchestration)
Arize Pheonix (Observability)
MongoDB Atlas Vector Serach (RAG)

Code of Conduct

https://devopsdays.org/conduct/

GenAI Workshop Preparation Sheet Overview

Overview
Signup for a free OpenAI account
Create a Free MongoDB Atlas Account
Run the first example
Setup another Atlas Vector Database (Langchain Example)
Setup Amazon Sagemaker environment id using AWS
Setup Google Colab environment id using GCP
Chunking and Embedding Labs
Evaluation Labs
Optional Labs
Setting S3 Buckets for large files (optional)

1) Signup for a free OpenAI account

Create an OpenAI account
Select API keys in the left panel
Verify by phone
Create a secret key, save and copy it.

2) Create a Free MongoDB Atlas Account

Create an account

Select Product from top down menu
Select Try for Free Signup for a free account
Great, now verify your email
Fill in the little questionnaire
Takes you into the Deploy Database

Deploy Database Cluster

Select the free m0
Use the defaultname Cluster0
Default to N.Virginia (US-East-1)
Keep Automate security setup enabled
Keep Add sample dataset enabled
Provide AWS
Now create the deployment (button below/right)

Configure the database cluster

There is an automatic user created.
(if copy and paste doesn't work, consider reloading the page)
Fill out user and password & create the user
Change the IP address that have access: (by clicking IP Access List)
by default it will fill in your current IP address
but for the hackathon we don't exactly what that IP will be
Therefore we allow all IPs
Use 0.0.0.0/0 for IP
do this for hackathon only , for production restrict this
Now Create the Database and Cluster.

Get database cluster connection string

After the Database is created select the Connect button for Cluster0
Select Drivers
Select Python (3.12 or later is fine)
Copy the connect string: it Should look like this:

 mongodb+srv://<userid>:<password>@cluster0.ozciyn7.mongodb.net/?retryWrites=true&w=majority

Replace the < and > characters with the user and password you created (don't include the < and > characters)
Congratulations, you're all done and the mongodb database setup is completed

3) Setup and run the first example code

In this example we are going to load a HuggingFace dataset provided by MongoDB.

For more details see:

https://huggingface.co/datasets/AIatMongoDB/embedded_movies

Here are some examples applications by MongoDB

https://huggingface.co/AIatMongoDB

Setup the Database Collection

select Database button on the left
select the Browse Collections tab
select create databases
select add my own data
- Database sample_mflix
- Collection embedded_movies

Note: this same code can be used to load another MongoDB supplied HF dataset called "whatscooking"

Code example from the AIatMongoDB/embedded_movies (loading the collection) Use:

MDB_emedded_datasets.ipynb

Note: You can choose your own notebook interface to run your code. For the purposes of this workshop we have setup examples fro both AWS Sagemaker and Google Colab ( Section #5 for Sagemaker and Section #6 for Colab)

!pip install pymongo
!pip install dataset

import os
os.environ['MONGODB_ATLAS_URI'] = <your atlas connection string>

from pymongo import MongoClient
import datasets
from datasets import load_dataset
from bson import json_util

uri = os.environ.get('MONGODB_ATLAS_URI')
client = MongoClient(uri)
db_name = 'sample_mflix'
collection_name = 'embedded_movies'

embedded_movies_collection = client[db_name][collection_name]

dataset = load_dataset("AIatMongoDB/embedded_movies")

insert_data = []

for movie in dataset['train']:
    doc_movie = json_util.loads(json_util.dumps(movie))
    insert_data.append(doc_movie)

    if len(insert_data) == 1000:
        embedded_movies_collection.insert_many(insert_data)
        print("1000 records ingested")
        insert_data = []

if len(insert_data) > 0:
    embedded_movies_collection.insert_many(insert_data)
    insert_data = []

print("Data Ingested")

See Using Using Amazon Sagemaker from Google Colab - MDB_embedded_movies.ipynb

or

See Using Google Colab from Amazon Sagemaker - MDB_embedded_movies.ipynb

Browse the embedded_movies collection

Add a filter to see only one movie

{ "title": "Scarface" }

Deleting a Collection (Optional)

If you need to reload a collection.

Select Database
Select Brose Collections
In the left window highlight the collection name
Select the trash can icon and delete

4) Setup another Atlas Vector Database (Langchain Example)

This example is based on the following code:

https://python.langchain.com/docs/integrations/vectorstores/mongodb_atlas

set up Atlas database:
select Deployments > Database on the left side
Select Browse Collections tab
Select Create Database button on the left
database langchain_db
collection_name test

Setup Atlas search vector index

After the Database is created and set up, we'll set up an Atlas search vector index.
select Atlas Search on the left navigation side
select your database source: Cluster0
Click on “Create Search Index”
Select Atlas Vector Search (Json editor)
Select Database langchain_db and collection_name test
IndexName vector_index
Use the following json to configure:

{
  "fields": [
    {
      "numDimensions": 1536,
      "path": "embedding",
      "similarity": "cosine",
      "type": "vector"
    }
  ]
}

Here’s what it should look like when you're done: Figure 1

For more details see:

https://www.mongodb.com/docs/atlas/atlas-vector-search/create-index/

5) Using Amazon Sagemaker

From the AWS Console navigate the Amazon Sagemaker (note you can use the search bar.)

Setup a Notebook Instance

After the Atlas Vector Database (Cluster0) is created and set up, we'll set up a Notebook Instance:
select Notebook > Notebook Instances on the left side
Select the “Create notebook Instance” button on top right
In Notebook Instance Settings
Add a Notebook Instance name (e.g., MDB-test1)
Use the defaults for the other setting fields
See Figure 2.
In Permissions and encryption
Select “Create a new role” from dropdown (take the defaults)
See Figure 3

Configure a Git Repository

Select Git repository from the left window
Select the Default repository arrow
Select Clone a public Git repository to this notebook instance only
Paste the repo
Create notebook

Use this link to clone...

https://github.com/OperationalizingAI/DevOpsGAIWorkshop.git

(See Figure 4 for an example)

Figure 2

Figure 3

Figure 4

Note: Use github.com/OperationalizingAI/DevOpsGAIWorkshop.git for the cloned repo.

Create the Notebook

After the instance is running (inService) create the Python notebook.
Select the Open JupyterLabs action for the instance
Under Notebook select the conda_python3 box
Rename your notebook (e.g., MDB-example1.ipynb)
Import the sample notebook provided with the workshop
Load the same note books for Colab
For the Langchain Example
- MDBLoad-SM.ipynb
- MDBRetrieve-SM.ipynb
Run your code.

Note: The examples use a secrets manager. For Sagemaker we used Amazon Secrets Manager. Setup the following:

OPENAPI_API_KEY

MONGODB_ATLAS_CLUSTER_URI

Alternativly you can setup your keys in the notebook "getpass" or some other tool.

Using Amazon Secrets Manager

Setup your secrets in Amazon Secrets Manager
Remove all of the Google code from the norebook (installs and secrets management code)
Add the following code to your notebook

Note:

I had to comment out llama-cpp-python in requirements.txt (figure out later)

import os
import json
import boto3
from botocore.exceptions import ClientError

def get_secret():

    secret_name = "hackathon"
    region_name = "us-east-1"

    # Create a Secrets Manager client
    session = boto3.session.Session()
    client = session.client(
        service_name='secretsmanager',
        region_name=region_name
    )

    try:
        get_secret_value_response = client.get_secret_value(
            SecretId=secret_name
        )
    except ClientError as e:
        # For a list of exceptions thrown, see
        # https://docs.aws.amazon.com/secretsmanager/latest/apireference/API_GetSecretValue.html
        raise e

    secret = json.loads(get_secret_value_response['SecretString'])
    #print(secret)
    return secret

secret = get_secret()

openai_api_key = secret["OPENAI_API_KEY"]
os.environ['OPENAI_API_KEY'] = openai_api_key

MONGODB_ATLAS_CLUSTER_URI = secret["MONGODB_ATLAS_CLUSTER_URI"]
os.environ['MONGODB_ATLAS_CLUSTER_URI'] = MONGODB_ATLAS_CLUSTER_URI

langsmith_api_key = secret["LANGSMITH_API_KEY"]
os.environ['LANGSMITH_API_KEY'] = langsmith_api_key

6) Using Google Colab

Create a free account on Google Colab
Goto ( https://colab.research.google.com/ )
Clone the workshop repo
- open up a terminal window
- git clone https://github.com/OperationalizingAI/DevOpsGAIWorkshop.git
Load the same note books for Colab
- MDBLoad-GC.ipynb
- MDBRetrieve-GC.ipynb
Setup Google Secrets Manager (secrets)
For certain labs you may need to copy the requirements,txt, OpenAICLient.py, AltasClient.py from the repo directory to the root directory.
Run your code.

7) Chnunking and Embeddings Labs

Use chunking and embeddings examples that were usied in the demos

GenAI-Workshop-Demo1.ipynb - Using Gradio to demonstrate chunking/ebeddings for Deming’s Journey to Profound Knowledge
GenAI-Workshop-Demo2.ipynb - Using Embeddings to test Cosine Similarities
GenAI-Workshop-Demo3.ipynb - Chunking, Embedding, and Search (OpenContext Markdown)
- For Sagemaker remove all google installs
- You need to use AWS Secrets Manager for Sagemaker
- unzip catalog-yaml-format-20240309T155111Z-001.zip
- You need to change directory_path = '/home/ec2-user/SageMaker/DevOpsGAIWorkshop/catalog-yaml-format'
GenAI-Workshop-Demo4.ipynb - Open Context Documentaion Loader
- You need to use AWS Secrets Manager for Sagemaker
- Change all directory paths (each example) to '/home/ec2-user/SageMaker/DevOpsGAIWorkshop/catalog-yaml-format'

Optional Lab

GenAI-Workshop-Demo5.ipynb - Cosine Similarity from Atlas Vector Search
- For Sagemaker remove all google installs
- You need to use AWS Secrets Manager for Sagemaker
- Need to upate the AtlasClient.py code (see instructor)

8) Evaluation Labs

Using Pheonix Arize andLangsmith, we will look at different thinking strategies to analyze optimal relevance and similarity queries.

Arize Phoenix is an opensource tool for evaluating, troubleshoot, and fine tune your LLMs.

https://github.com/Arize-ai/phoenix

Lab Demo Example:

GenAI-Workshop-Demo6.ipynb - Evaluation of OpenContext Documentaion with Arize
For Sagemaker change install - pip install arize-phoenix>=3.14.0

This is what we are calling the LAM Stack Example...

https://colab.research.google.com/drive/1LXJW0YS-N8_5f6E6p1hGGLsZ2k4ZD5P3?usp=sharing

The following is a code that will work with Langhcain.

Install Arize-Phoenix

!pip install arize-phoenix

Import and setup the enviornment

from urllib.request import urlopen

import nest_asyncio
import numpy as np
import pandas as pd
import phoenix as px
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
from langchain.embeddings import OpenAIEmbeddings
from langchain.retrievers import KNNRetriever
from phoenix.experimental.evals import (
    HallucinationEvaluator,
    OpenAIModel,
    QAEvaluator,
    RelevanceEvaluator,
    run_evals,
)
from phoenix.session.evaluation import get_qa_with_reference, get_retrieved_documents
from phoenix.trace import DocumentEvaluations, SpanEvaluations
from phoenix.trace.langchain import LangChainInstrumentor
from tqdm import tqdm

nest_asyncio.apply()  # needed for concurrent evals in notebook environments

Before you run your quey

session = px.launch_app()
LangChainInstrumentor().instrument(

If you want to run evaluations for hallucinations, correctness, and relevance

queries_df = get_qa_with_reference(px.Client())
retrieved_documents_df = get_retrieved_documents(px.Client()

eval_model = OpenAIModel(
    model_name="gpt-4-turbo-preview",
)
hallucination_evaluator = HallucinationEvaluator(eval_model)
qa_correctness_evaluator = QAEvaluator(eval_model)
relevance_evaluator = RelevanceEvaluator(eval_model)

hallucination_eval_df, qa_correctness_eval_df = run_evals(
    dataframe=queries_df,
    evaluators=[hallucination_evaluator, qa_correctness_evaluator],
    provide_explanation=True,
)
relevance_eval_df = run_evals(
    dataframe=retrieved_documents_df,
    evaluators=[relevance_evaluator],
    provide_explanation=True,
)[0]

px.Client().log_evaluations(
    SpanEvaluations(eval_name="Hallucination", dataframe=hallucination_eval_df),
    SpanEvaluations(eval_name="QA Correctness", dataframe=qa_correctness_eval_df),
    DocumentEvaluations(eval_name="Relevance", dataframe=relevance_eval_df),
)

9) Optional Labs

We created a number of lab exerciese based on the work of Sujee Maniyam. He did an amazing job researching and putting together a number of examples. However, they are all designed to run locally on your machine. We have created a number of his labs in this repository under the directory "labs/" that work with Google Colab and Amazon's Sagemaker. You can find his orignal work here:

https://github.com/sujee/mongodb-atlas-vector-search]

There also some great examples in Prakul Argarwal's (from MongoDB) repository here:

https://github.com/prakul/MongoDB-AI-Resources

Here's a list of the labs:

MDB-lab1 - Connects to the client connection to the Atlas database. Assumes the sample_mflix databse exists.
MDB-Lab1-2 - Simple program to generate images using OpenAI Dall-e-3.
MDB-Lab2-1 - Simple OpenAI client connection test.
MDB-Lab2-2 - Run simple quries against the sample_mflux (Netflix Movies) dataset. Setup a vector search index to quest embeddings.
MDB-Lab2-3 - Run cosine similarity against chunked PDF files.
MDB-Lab2-4 - Query embeddings from text-embedding-ada-002 to use to search Atlas vector database. The langchain_db must exist already.
MDB-Lab3-1 - Querying with different embedding models.
MDB-Lab3-2 - Adding multiple embeddings to a collection.
MDB-Lab3-3 - Compare performance of different embeddings.
MDB-Lab4-1 - Load Uber and Lyft 10k PDFs into Atlas Vector Search.
MDB-Lab4-2 - DAL-E-3 Image generator.

10) Setting S3 Buckets for large files (optional)

Additional Notes for Processing Large Files
Setup a shared S3 blob
Bucket
Large-blobs
Upload the file(s)
Uncheck - Block all public access
Take all the default

Create Bucket
	Configure Bucket policy
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "Statement1",
            "Effect": "Allow",
            "Principal": "*",
            "Action": "s3:GetObject",
            "Resource": "arn:aws:s3:::large-blobs/*"
        }
    ]
}

From the Notebook console open the termial of the Instance…

	aws s3 cp s3://large-blobs/void.tar.gz ~/void.tar.gz

dnielsen / DevOpsGAIWorkshop