tazarov

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

tazarov

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

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": {

tazarov

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

tazarov

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.

tazarov

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")

tazarov

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")

tazarov

tazarov

tazarov

import chromadb
from chromadb.config import Settings

client = chromadb.PersistentClient(path="test", settings=Settings(allow_reset=True))

client.reset()
col = client.get_or_create_collection("test")

col.add(ids=["1", "2", "3"], documents=["He is a technology freak and he loves AI topics", "AI technology are advancing at a fast pace", "Innovation in LLMs is a hot topic"])
col.query(query_texts=["technology"], where_document={"$or":[{"$contains":"technology"}, {"$contains":"freak"}]})