Trayan Azarov tazarov

Simplifying the process of utilizing OpenAI models' function calling capabilities, a new approach involves creating a meticulously structured JSON list. Consider the following example:

{
        "name": "get_current_weather",
        "description": "Get the current weather",
        "parameters": {
            "type": "object",
 "properties": {

This is a one-liner command to list and add a user as a reviewer to all pull requests in a github repo.

for pr in $(gh pr list --json number -q '.[].number'); do gh pr edit $pr --add-reviewer <user_id>; done

Note: The above works only in bash

kubectl get pods --all-namespaces -o jsonpath="{.items[*].spec.containers[*].image}" |\
tr -s '[[:space:]]' '\n' |\
sort |\
uniq -c

Installation

Check release in https://github.com/WireGuard/wireguard-vyatta-ubnt/releases

#https://github.com/WireGuard/wireguard-vyatta-ubnt/releases/download/1.0.20220627-1/ugw4-v1-v1.0.20220627-v1.0.20210914.deb

curl -OL https://github.com/WireGuard/wireguard-vyatta-ubnt/releases/download/1.0.20220627-1/ugw4-v1-v1.0.20220627-v1.0.20210914.deb

https://stackoverflow.com/questions/48993286/is-it-possible-to-route-traffic-to-a-specific-pod?rq=1

You can guarantee session affinity with services, but not as you are describing. So, your customers 1-1000 won't use pod-1, but they will use all the pods (as a service makes a simple load balancing), but each customer, when gets back to hit your service, will be redirected to the same pod.

Note: always within time specified in (default 10800):

	import uuid

	from chromadb.utils import embedding_functions
	import chromadb
	ef = embedding_functions.DefaultEmbeddingFunction()

	docs = ["Article by john", "Article by Jack", "Article by Jill"]
	client = chromadb.Client()
	embeddings = ef(docs)
	collection = client.get_or_create_collection("test-where-list")

	import uuid

	from chromadb.utils import embedding_functions
	from langchain.schema import Document
	from langchain.text_splitter import RecursiveCharacterTextSplitter

	long_text = """
	The Downsides of LLMs (Logical Language Models)
	In the age of artificial intelligence, Logical Language Models (LLMs) represent a significant leap forward in the field of natural language processing. These models are capable of comprehending, generating, and reasoning about human languages in a way that mimics human-like understanding. While the benefits of LLMs are numerous, it's essential to also recognize the downsides that accompany these advancements. This essay will explore the negative aspects of LLMs in terms of ethics, job displacement, security, and potential biases.

	openapi: 3.0.2
	info:
	title: FastAPI
	version: 0.1.0
	paths:
	/api/v1:
	get:
	summary: Root
	operationId: root
	responses:

	// https://stackoverflow.com/questions/52518298/how-to-create-a-script-mapper-in-keycloak
	// https://stackoverflow.com/questions/48199539/best-way-to-test-debug-javascript-mappers-policies-in-keycloak
	// https://docs.oracle.com/javase/10/nashorn/nashorn-java-api.htm#JSNUG119

	var client = keycloakSession.getContext().getClient();
	var forEach = Array.prototype.forEach;

	// print(user.getRealmRoleMappings());

	var isAdmin = false;

	import smtplib
	from email.mime.multipart import MIMEMultipart
	from email.mime.text import MIMEText


	def test_send_mail():

	# Replace [email protected] with your "From" address.
	# This address must be verified with Amazon SES.
	SENDER = "admin@org"