cedricvidal

# uv add asyncio azure-identity dotenv-azd openai-agents

from openai import AsyncAzureOpenAI
from agents import Agent, Runner, set_default_openai_client, set_tracing_export_api_key
from azure.identity import DefaultAzureCredential, get_bearer_token_provider
from dotenv import load_dotenv
from dotenv_azd import load_azd_env
import asyncio
import os

cedricvidal

#!/usr/bin/env python

import rich_click as click
import json
from pathlib import Path
from dotenv import load_dotenv
from sys import stdout
from patchdiff import diff as patch_diff

load_dotenv()

cedricvidal

# print env vars
cat .azure/test/.env | xargs -i echo {}

# use for a command and merge into current env
env $(cat .azure/test/.env | xargs) env

# use as a docker run --env-file
docker run -it --env-file <(cat .azure/test/.env | xargs -i echo {}) ubuntu env

cedricvidal

#!/usr/local/bin/python3.12
# -*- coding: utf-8 -*-
import re
import sys

# LiteLLM doesn't support DefaultAzureCredential and get_bearer_token_provider
# There has been a discussion on the subject but no clear path forward:
# See https://github.com/BerriAI/litellm/issues/4417

# This monkey patches LiteLLM to force it to use the DefaultAzureCredential and get_bearer_token_provider

cedricvidal

#!/bin/bash

dedup_env() {
    local -A env_ary
    while IFS== read -r key value; do
        value=$(echo "$value" | sed 's/^ *"//' | sed 's/" *$//')
        env_ary[$key]=$value
    done <<EOM
$(cat $*)
EOM

cedricvidal

var deployments = [
    {
      name: 'text-embedding-ada-002'
      format: 'OpenAI'
    }
    {
      name: 'llama'
      format: 'serverless'
    }
  ]

cedricvidal

from typing import Any

def constant_hash(func): 
    """
    Wraps a function and changes its hashing behavior to a constant.
    This allows Hugginface to only use functiona parameters for hashing and nothing else in
    the function's captured context
    """
    return ConstantHash(func)

cedricvidal

# Those endpoints don't use the usual Azure OpenAI scheme, they use the OpenAI scheme.
# They also take the model field to route to the proper deployment, but I haven't verified this works

# Tested with openai 1.13.3

from openai import OpenAI
import logging

logging.basicConfig(level=logging.DEBUG,
                    format='%(asctime)s - %(levelname)s - %(filename)s:%(funcName)s:%(lineno)d - %(message)s',

cedricvidal

# Generate a UUID5 from a name using SHA-256
def uuid5_sha256(namespace, name):
    """Generate a UUID from the SHA-1 hash of a namespace UUID and a name."""
    if isinstance(name, str):
        name = bytes(name, "utf-8")
    from uuid import UUID
    import hashlib
    hash = hashlib.sha256(namespace.bytes + name).digest()
    return UUID(bytes=hash[:16], version=5)

cedricvidal

import os
import yaml
import logging.config
import logging
import coloredlogs

def setup_logging(default_path='logging.yaml', default_level=logging.INFO, env_key='LOG_CFG'):
    """
    | **@author:** Prathyush SP
    | Logging Setup