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marcel-dempersmarcel-dempers
committedOct 1, 2023
add openai files
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‎ai/openai/introduction/README.md

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# Introduction to Open AI
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## Overview
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What is [Open AI](https://openai.com/) ?
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* Research company on AI development
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* Builds and provides models
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* Builds and provides a standard protocol for using AI
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What is a model ?
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I see a model as a language super database. </br>
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Instead of writing a query, that is slow to query a traditional database like SQL, you can throw a question at a model and it gives you an answer really fast </br>
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Model examples:
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* GPT 3.5
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* GPT 4
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## Getting started
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The best way to get started and to understand OpenAI, is to learn hands on
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* Create an OpenAI account [here](https://openai.com/)
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## Chat GPT
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Here you can find the link to [ChatGPT](https://chat.openai.com/)
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## Open AI Playground
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Here you can find the link to the [OpenAI Playground](https://platform.openai.com/playground)
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## Build an AI powered app
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We can start with a `main.py` that reads a message
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```
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import sys
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message = sys.argv[0]
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```
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Then we will need the code from the Open AI playground and add it to our `main.py`. </br>
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Move the `import` statements to the top </br>
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Once you have tidied up everything, you can get the response message from the AI:
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```
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responseMessage = response.choices[0].message.content
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```
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Let's build our app
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```
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cd ai\openai\introduction
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docker build . -t ai-app
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```
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Set my OpenAI API key
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```
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$ENV:OPENAI_API_KEY=""
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```
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Run our AI App:
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```
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docker run -it -e OPENAI_API_KEY=$ENV:OPENAI_API_KEY ai-app
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```
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When we run the app, notice it has no concept of memory. </br>
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The playground works because it keeps track of all the user and AI messages and keeps appending new messages to it </br>
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So it can track the conversation.
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Let's keep track of messages, by writing it to a local file </br>
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We will also take the system message out and keep it as a constant in our code </br>
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Full example:
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```
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import sys
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import os
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import json
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import openai
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openai.api_key = os.getenv("OPENAI_API_KEY")
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#read the incoming message
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message = sys.argv[1]
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user_message = {
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"role" : "user",
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"content" : message
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}
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systemMessage = {
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"role": "system",
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"content": "You are a kubernetes exper that can assist developers with troubleshooting deployments\n\nTo help the developer you will need to know the namespaces as well as the pod name. Ask for missing information\n\nGenerate a command to help the developer surface logs or information\n"
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}
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# read the cached user messages if there are any
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userMessages = []
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if os.path.isfile("messages.json"):
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with open('messages.json', newline='') as messagesFile:
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data = messagesFile.read()
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userMessages = json.loads(data)
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# add the new message to it and update the cached messages
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userMessages.append(user_message)
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with open('messages.json', 'w', newline='') as messagesFile:
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msgJSON = json.dumps(userMessages)
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messagesFile.write(msgJSON)
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print(msgJSON)
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messages = []
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messages.append(systemMessage)
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messages.extend(userMessages)
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response = openai.ChatCompletion.create(
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model="gpt-3.5-turbo",
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messages=messages,
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temperature=1,
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max_tokens=256,
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top_p=1,
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frequency_penalty=0,
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presence_penalty=0
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)
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responseMessage = response.choices[0].message.content
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print(responseMessage)
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```
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Now we can mount our volume so we persist the cache of messages
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```
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docker run -it -e OPENAI_API_KEY=$ENV:OPENAI_API_KEY -v ${PWD}:/app ai-app "can you help me with my deployment?"
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Of course! I'd be happy to help with your deployment. Could you please provide me with the namespace and the name of the pod you're encountering issues with?
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docker run -it -e OPENAI_API_KEY=$ENV:OPENAI_API_KEY -v ${PWD}:/app ai-app "my pod is pod-123"
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Sure, I can help you with your deployment. Can you please provide me with the namespace in which the pod is running?
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docker run -it -e OPENAI_API_KEY=$ENV:OPENAI_API_KEY -v ${PWD}:/app ai-app "its in the products namespace"
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Great! To surface the logs for the pod "pod-123" in the "products" namespace, you can use the following command:
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```shell
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kubectl logs -n products pod-123
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```
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This command will retrieve the logs for the specified pod in the given namespace. Make sure you have the necessary permissions to access the namespace.
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```

‎ai/openai/introduction/dockerfile

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FROM python:3.11-alpine
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RUN mkdir /app
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WORKDIR /app
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COPY requirements.txt /app/requirements.txt
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RUN pip install -r requirements.txt
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COPY main.py /app/
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ENTRYPOINT ["python3", "main.py"]

‎ai/openai/introduction/main.py

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import sys
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import os
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import json
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import openai
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openai.api_key = os.getenv("OPENAI_API_KEY")
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#read the incoming message
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message = sys.argv[1]
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user_message = {
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"role" : "user",
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"content" : message
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}
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systemMessage = {
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"role": "system",
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"content": "You are a kubernetes exper that can assist developers with troubleshooting deployments\n\nTo help the developer you will need to know the namespaces as well as the pod name. Ask for missing information\n\nGenerate a command to help the developer surface logs or information\n"
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}
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# read the cached user messages if there are any
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userMessages = []
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if os.path.isfile("messages.json"):
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with open('messages.json', newline='') as messagesFile:
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data = messagesFile.read()
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userMessages = json.loads(data)
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# add the new message to it and update the cached messages
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userMessages.append(user_message)
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with open('messages.json', 'w', newline='') as messagesFile:
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msgJSON = json.dumps(userMessages)
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messagesFile.write(msgJSON)
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print(msgJSON)
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messages = []
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messages.append(systemMessage)
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messages.extend(userMessages)
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response = openai.ChatCompletion.create(
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model="gpt-3.5-turbo",
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messages=messages,
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temperature=1,
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max_tokens=256,
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top_p=1,
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frequency_penalty=0,
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presence_penalty=0
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)
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responseMessage = response.choices[0].message.content
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print(responseMessage)

‎ai/openai/introduction/requirements.txt

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openai==0.28.0

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