keep moving

Dear companies, please stop using the term “Copilot” for everything. It is honestly confusing and doesn’t help anyone. Imagine a conversation like this:

Person A: Hey, have you tried Copilot? It's honestly amazing!
Person B: Yeah! I was using it to finish writing my Python assignment in half the time!
Person C: I didn't know you can do that? I was using Copilot to setup my AWS ECS services
Person D: Wait what? Isn't Copilot for summarizing emails and drafting Word documents?

¯\_(ツ)_/¯
How many “Copilots” are out there really? Let’s see.

Github Copilot

This should be familiar to developers.

GitHub Copilot is an AI coding assistant that helps you write code faster and with less effort, allowing you to focus more energy on problem solving and collaboration.

From the website

In short: Coding assistant

AWS Copilot

This one is less well-known, unless you are a AWS power user.

AWS Copilot is an open source command line interface that makes it easy for developers to build, release, and operate production ready containerized applications on AWS App Runner, Amazon ECS, and AWS Fargate.

From the website

To be fair, AWS Copilot was launched in 2020, before everyone started using the term to mean some form of AI assistant.

In short: Another AWS command line tool

It gets more confusing from here.

Microsoft Copilot (formerly known as Bing Chat, or Bing AI)

I can’t actually find an official definition for Microsoft Copilot. Lol.
As of Nov 2024, if you do a search and end up in the above page, all you see is a chat interface and page titled “Microsoft Copilot – Your AI companion”.

In short: ChatGPT variant

Note that if you sign in using your work Microsoft account, you will be unable to use Microsoft Copilot. You must either:

1. Sign in with a personal account, or
2. Sign out completely, or
3. Use Microsoft 365 Copilot (see next section)

In addition, the features you can use in (1) and (2) are slightly different. Sounds confusing? Let’s go on.

Microsoft 365 Copilot

The official page for Microsoft 365 Copilot is full of marketing-speak. It doesn’t help that different features are available depending on the region you are in. For the purpose of this article, we’ll go with the US version.

Based on info from the page, Microsoft 365 Copilot is an AI-powered virtual assistant that is integrated into Microsoft 365 apps like Word, Excel, PowerPoint, and Outlook. The gist of what is included is covered in the pricing section – a per-user license is required. When you meet others at work talking about “Copilot”, they are mostly likely referring to this.

In short: Clippy on steroids

Microsoft Dynamics 365 Copilot

There’s not a lot of content on this. Just a blog post from Microsoft. From what I can tell, Microsoft Dynamics 365 Copilot is an add-on to Microsoft Dynamics 365 range of products that deals with CRM and ERP.

In short: A chat interface for Dynamics 365

Microsoft Copilot in Azure

This newly launched feature is in preview, and the official website says: Simplify operations and management from cloud to edge with an AI companion.

In short: FAQ for Azure

Actually there are more, like this and that, and also not forgetting this. And I’m sure I’m missing quite a few others as well. Hopefully we will quickly move past this fad and avoid situations like the hypothetical conversion at the beginning of this article.

Or if you are still confused, you can always ask “Copilot”. 🙂

AWS is launching not one, but two new AI certifications, as demand for AI skills skyrockets.

For those who are unaware, AWS certification comes in 4 levels, roughly in terms of difficulty/professional experience required:

• Foundational
• Associate
• Professional
• Specialty

Previously the only AWS AI certification available is their specialty one: AWS Certified Machine Learning – Specialty, which is aimed at data scientists and ML engineers. To fill the intermediate gaps, AWS will be launching the following certifications:

The former is aimed at generalists (business analyst, product or project manager, sales professional) whereas the latter is targeting developers/engineers doing ML work but whom may not be full-time ML specialists.

These new certifications are marked as beta, so expect syllabus and content to change. They are currently priced at a discounted price of
USD75. The usual foundational certifications are USD100 and associate ones are USD150, so this is quite a good offer.

Registration for the new certification opens on 13 Aug 2024 and you can use Skill Builder resources listed in the certification page to prepare for it.

More vector database choices for Amazon Bedrock Knowledge Base. This might make sense if you are already using MongoDB Atlas.

MongoDB Atlas vector store in Knowledge Bases for Amazon Bedrock is available in the US East (N. Virginia) and US West (Oregon) Regions. Be sure to check the full Region list for future updates.

Since the last writeup, AWS has added support for Anthropic Claude 3 model to AWS Bedrock Knowledge Base (ABKB). It has also added the ability to add your own metadata to your source files in order to perform filtering when doing query. For example, you may want to add a metadata to a certain set of files to indicate that they are from year 2023. Then during your query, you can include a filter to indicate you only want to use data from year 2023. This provides another set of tools for developers to create more relevant and targeted query. Note that filtering is only supported for FAISS vector engine.

If you’re looking to integrate ABKB into your code, there are two primary methods: using one of the AWS SDK or interacting through HTTP API. In this article, we will be using Boto3, the AWS SDK for Python. Here is a simple example to do a retrieve and generate query using Boto3. This example uses the new Claude 3 Sonnet model.

import boto3
import json

AWS_ACCESS_KEY="_your_access_key_"
AWS_SECRET_KEY="_your_secret_key_"
REGION_NAME="_your_region_"

client = boto3.client('bedrock-agent-runtime',
                      aws_access_key_id=AWS_ACCESS_KEY,
                      aws_secret_access_key=AWS_SECRET_KEY,
                      region_name=REGION_NAME
)

# retrieval and generate
response = client.retrieve_and_generate(
    input={
        'text': 'how to apply for leave'
    },
    retrieveAndGenerateConfiguration={
        'knowledgeBaseConfiguration': {
            'knowledgeBaseId': 'LEBQPJQ9BY',
            'modelArn': 'arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-3-sonnet-20240229-v1:0',
            'retrievalConfiguration': {
                'vectorSearchConfiguration': {
                    'overrideSearchType': 'HYBRID'
                }
            }
        },
        'type': 'KNOWLEDGE_BASE'
    }
)

print(json.dumps(response))

Running the code produces the following output in JSON:

{
  "ResponseMetadata": {
		...trimmed...
  },
  "citations": [
    {
      "generatedResponsePart": {
        "textResponsePart": {
          "span": {
            "end": 705,
            "start": 364
          },
          "text": "...trimmed..."
        }
      },
      "retrievedReferences": [
        {
          "content": {
            "text": "...trimmed..."
          },
          "location": {
            "s3Location": {
              "uri": "s3://...trimmed..."
            },
            "type": "S3"
          }
        }
      ]
    }
  ],
  "output": {
    "text": "To apply for leave as an employee on the Workday mobile app:\n\n1. Navigate to your Workday Mobile Homepage and select 'View Applications' under 'Frequently Used'\n2. Select 'Time Off'\n3. Select the date(s) you want to apply for leave\n4. Select 'Next' and choose the leave type\n5. Select any required reasons or upload attachments if applicable\n6. Submit the request To apply for leave as an employee on the Workday desktop:\n\n1. Go to the Workday Homepage and select the 'Absence' worklet\n2. Under 'Request', select 'Request Absence'\n3. Select the date(s) for the leave and click 'Request Absence'\n4. Choose the leave type\n5. Select 'Next' and provide any required reasons or attachments\n6. Submit the request"
  },
  "sessionId": "c8332417-df3c-41e5-8516-ad38cc09de15"
}

For this simple task there is not much difference in output from the various Claude models. I expect the differences will be more pronounced for complex tasks or those involving much larger context window.

With this, we conclude the three-part series on Amazon Bedrock Knowledge Base. I have covered everything from creating the knowledge base, testing it in the playground, to executing queries via CLI and SDK. Hopefully this gives a good overview of the processes involved and capabilities of this new service.