azure-genai-utils 0.0.2.7

Utility functions for Azure GenAI

Azure GenAI Utils

This repository contains a set of utilities for working with Azure GenAI. The utilities are written in Python and are designed to be used for Hackathons, Workshops, and other events where you need to quickly get started with Azure GenAI.

Requirements

• Azure Subscription
• Azure AI Foundry
• Bing Search API Key
• Python 3.8 or later
• .env file: Please do not forget to modify the .env file to match your account. Rename .env.sample to .env or copy and use it

Installation

PyPI

• pip install azure-genai-utils

From Source

• python setup.py install

Usage

Azure OpenAI Test

from azure_genai_utils.aoai_test import AOAI
aoai = AOAI()
aoai.simple_test()

Bing Search

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from azure_genai_utils.tools import BingSearch
from dotenv import load_dotenv

# You need to add BING_SUBSCRIPTION_KEY=xxxx in .env file
load_dotenv()

# Basic usage
bing = BingSearch(max_results=2, locale="ko-KR")
results = bing.invoke("Microsoft AutoGen")
print(results)

## Include news search results and format output
bing = BingSearch(
    max_results=2,
    locale="ko-KR",
    include_news=True,
    include_entity=False,
    format_output=True,
)
results = bing.invoke("Microsoft AutoGen")
print(results)

LangGraph Example (Bing Search + Azure GenAI)

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import json
from typing import Annotated
from typing_extensions import TypedDict
from langchain_openai import AzureChatOpenAI
from langchain_core.messages import ToolMessage
from langgraph.graph.message import add_messages
from langgraph.graph import StateGraph
from langgraph.prebuilt import ToolNode
from langgraph.graph import START, END
from azure_genai_utils.tools import BingSearch
from dotenv import load_dotenv

load_dotenv()

class State(TypedDict):
    messages: Annotated[list, add_messages]

llm = AzureChatOpenAI(model="gpt-4o-mini")
tool = BingSearch(max_results=3, format_output=False)
tools = [tool]
llm_with_tools = llm.bind_tools(tools)

def chatbot(state: State):
    answer = llm_with_tools.invoke(state["messages"])
    return {"messages": [answer]}

def route_tools(
    state: State,
):
    if messages := state.get("messages", []):
        ai_message = messages[-1]
    else:
        raise ValueError(f"No messages found in input state to tool_edge: {state}")

    if hasattr(ai_message, "tool_calls") and len(ai_message.tool_calls) > 0:
        return "tools"

    return END

graph_builder = StateGraph(State)
graph_builder.add_node("chatbot", chatbot)
tool_node = ToolNode(tools=[tool])
graph_builder.add_node("tools", tool_node)

graph_builder.add_conditional_edges(
    source="chatbot",
    path=route_tools,
    path_map={"tools": "tools", END: END},
)

graph_builder.add_edge("tools", "chatbot")
graph_builder.add_edge(START, "chatbot")
graph = graph_builder.compile()

# Test
inputs = {"messages": "Microsoft AutoGen"}

for event in graph.stream(inputs, stream_mode="values"):
    for key, value in event.items():
        print(f"\n==============\nSTEP: {key}\n==============\n")
        print(value[-1])

Synthetic Data Generation

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from azure_genai_utils.synthetic import (
    QADataGenerator,
    CustomQADataGenerator,
    QAType,
    generate_qas,
)

input_batch = [
    "The quick brown fox jumps over the lazy dog.",
    "What is the capital of France?",
]

model_config = {
    "deployment": "gpt-4o-mini",
    "model": "gpt-4o-mini",
    "max_tokens": 256,
}

try:
    qa_generator = QADataGenerator(model_config=model_config)
    # qa_generator = CustomQADataGenerator(
    #     model_config=model_config, templates_dir=f"./azure_genai_utils/synthetic/prompt_templates/ko"
    # )
    task = generate_qas(
        input_texts=input_batch,
        qa_generator=qa_generator,
        qa_type=QAType.LONG_ANSWER,
        num_questions=2,
        concurrency=3,
    )
except Exception as e:
    print(f"Error generating QAs: {e}")

License Summary

This sample code is provided under the Apache 2.0 license. See the LICENSE file.