pavlovmilen

      public async Task<ChromaDbResult> QueryByTextAsync(string queryText)
      {
          try
          {
              var payload = new { parameters = new { query_text = queryText, n_results = 2 } };
              string jsonPayload = JsonConvert.SerializeObject(payload);

              var requestContent = new StringContent(jsonPayload, System.Text.Encoding.UTF8, "application/json");

              var response = await _client.PostAsync("query", requestContent);

pavlovmilen

var url = _configuration.GetValue<string>("AzureOpenAiApi:Endpoint");
var key = _configuration.GetValue<string>("AzureOpenAiApi:SubscriptionKey");
var openAIClient = new OpenAIClient(new Uri(url), new AzureKeyCredential(key)); 

Response<ChatCompletions> responseWithoutStream =
                    await openAIClient.GetChatCompletionsAsync(
                        new ChatCompletionsOptions()
                        {
                            Messages =
                            {

pavlovmilen

//https://youropenaiinstance.openai.azure.com/
//c#
// Note: The Azure OpenAI client library for .NET is in preview.
// Install the .NET library via NuGet: dotnet add package Azure.AI.OpenAI --version 1.0.0-beta.5
using Azure;
using Azure.AI.OpenAI;

OpenAIClient client = new OpenAIClient(
  new Uri("https://youropenaiinstance.openai.azure.com/"),
  new AzureKeyCredential(Environment.GetEnvironmentVariable("AZURE_OPENAI_API_KEY")));

pavlovmilen

# Deployment
apiVersion: apps/v1
kind: Deployment
metadata:
  name: chromadb
spec:
  replicas: 1
  selector:
    matchLabels:
      app: chromadb

pavlovmilen

# Use the official chromadb image
FROM chromadb/chroma:0.4.21

# Set environment variables
ENV FLASK_APP=app.py
ENV FLASK_RUN_HOST=0.0.0.0
ENV FLASK_RUN_PORT=8000

# Set the working directory
WORKDIR /app

pavlovmilen

from flask import Flask, request
from flask_restful import Api, Resource
import requests
import os
import json
import traceback
import chromadb
from openai import AzureOpenAI
import chromadb.utils.embedding_functions as embedding_functions

pavlovmilen

1. Create IP address az network public-ip create --resource-group resources_ml-aks-uks_ml-aks-uks --name pip-ml-aks-uks --sku Standard --allocation-method static --query publicIp.ipAddress -o ts

2. Create a namespace for your ingress resources kubectl create namespace nginx-ingress

3. Add the official stable repository helm repo add ingress-nginx https://kubernetes.github.io/ingress-nginx helm repo add stable https://charts.helm.sh/stable
   helm repo update

pavlovmilen

1. Set default context
kubectl config set-context --current --namespace=my-namespace
2. Exec in to pod
kubectl exec -it my-pod -- /bin/sh (may have to exec on cloud cli)

pavlovmilen

Using the K8s Dashboard Locally

Actual repo is here: https://github.com/kubernetes/dashboard

1. Install the Dashboard

kubectl apply -f https://raw.githubusercontent.com/kubernetes/dashboard/v2.4.0/aio/deploy/recommended.yaml

2. Create a Sample User Account that can Access the Dashboard via Token

kubectl apply -f https://gist.githubusercontent.com/dahlsailrunner/bbd453f3bb6259b66c08a70d0908283f/raw/5727723217e2df4b65d8933adf04d009cfb0fe3f/local-dashboard-account.yml

pavlovmilen

/// filters out dependencies like polling queues that are not attached to any larger operation.
public class AzureDependencyFilterTelemetryProcessor : ITelemetryProcessor
{
    private readonly ITelemetryProcessor _inner;

    public AzureDependencyFilterTelemetryProcessor(ITelemetryProcessor inner)
    {
        _inner = inner;
    }