KI-Funktionen: Daten in großem Maßstab mit KI transformieren

# Optional: install openai version 1.99.5 or later for SDK-native client behavior.
%pip install -q openai 2>/dev/null

# Install latest versions of AI Functions library whl
!wget -q https://aka.ms/fabric-aifunctions-whl -O synapseml_internal-latest-py3-none-any.whl
!wget -q https://aka.ms/fabric-synapseml-core-whl -O synapseml_core-latest-py3-none-any.whl

# openai version 1.99.5 or later is included for SDK-native client behavior.
# To keep the environment lightweight, remove "openai" from the install command.
%pip install -q openai synapseml_internal-latest-py3-none-any.whl synapseml_core-latest-py3-none-any.whl

# Required imports
import synapse.ml.aifunc as aifunc
import pandas as pd

import synapse.ml.spark.aifunc as aifunc

# SparkSession with accessor `spark` in PySpark environments is pre-setup and available for use

# This code uses AI. Always review output for mistakes. 

df = pd.DataFrame([
        "The cleaning spray permanently stained my beautiful kitchen counter. Never again!",
        "I used this sunscreen on my vacation to Florida, and I didn't get burned at all. Would recommend.",
        "I'm torn about this speaker system. The sound was high quality, though it didn't connect to my roommate's phone.",
        "The umbrella is OK, I guess."
    ], columns=["reviews"])

df["sentiment"] = df["reviews"].ai.analyze_sentiment()
display(df)

# This code uses AI. Always review output for mistakes. 

df = spark.createDataFrame([
        ("The cleaning spray permanently stained my beautiful kitchen counter. Never again!",),
        ("I used this sunscreen on my vacation to Florida, and I didn't get burned at all. Would recommend.",),
        ("I'm torn about this speaker system. The sound was high quality, though it didn't connect to my roommate's phone.",),
        ("The umbrella is OK, I guess.",)
    ], ["reviews"])

sentiment = df.ai.analyze_sentiment(input_col="reviews", output_col="sentiment")
display(sentiment)

# This code uses AI. Always review output for mistakes. 

df = pd.DataFrame([
        "This duvet, lovingly hand-crafted from all-natural fabric, is perfect for a good night's sleep.",
        "Tired of friends judging your baking? With these handy-dandy measuring cups, you'll create culinary delights.",
        "Enjoy this *BRAND NEW CAR!* A compact SUV perfect for the professional commuter!"
    ], columns=["descriptions"])

df["category"] = df['descriptions'].ai.classify("kitchen", "bedroom", "garage", "other")
display(df)

# This code uses AI. Always review output for mistakes. 

df = spark.createDataFrame([
        ("This duvet, lovingly hand-crafted from all-natural fabric, is perfect for a good night's sleep.",),
        ("Tired of friends judging your baking? With these handy-dandy measuring cups, you'll create culinary delights.",),
        ("Enjoy this *BRAND NEW CAR!* A compact SUV perfect for the professional commuter!",)
    ], ["descriptions"])
    
categories = df.ai.classify(labels=["kitchen", "bedroom", "garage", "other"], input_col="descriptions", output_col="categories")
display(categories)

# This code uses AI. Always review output for mistakes. 

df = pd.DataFrame([
        "This duvet, lovingly hand-crafted from all-natural fabric, is perfect for a good night's sleep.",
        "Tired of friends judging your baking? With these handy-dandy measuring cups, you'll create culinary delights.",
        "Enjoy this *BRAND NEW CAR!* A compact SUV perfect for the professional commuter!"
    ], columns=["descriptions"])
    
df["embed"] = df["descriptions"].ai.embed()
display(df)

# This code uses AI. Always review output for mistakes. 

df = spark.createDataFrame([
        ("This duvet, lovingly hand-crafted from all-natural fabric, is perfect for a good night's sleep.",), 
        ("Tired of friends judging your baking? With these handy-dandy measuring cups, you'll create culinary delights.",), 
        ("Enjoy this *BRAND NEW CAR!* A compact SUV perfect for the professional commuter!",) 
    ], ["descriptions"])

embed = df.ai.embed(input_col="descriptions", output_col="embed")
display(embed)

# This code uses AI. Always review output for mistakes. 

df = pd.DataFrame([
        "MJ Lee lives in Tucson, AZ, and works as a software engineer for Microsoft.",
        "Kris Turner, a nurse at NYU Langone, is a resident of Jersey City, New Jersey."
    ], columns=["descriptions"])

df_entities = df["descriptions"].ai.extract("name", "profession", "city")
display(df_entities)

# This code uses AI. Always review output for mistakes. 

df = spark.createDataFrame([
        ("MJ Lee lives in Tucson, AZ, and works as a software engineer for Microsoft.",),
        ("Kris Turner, a nurse at NYU Langone, is a resident of Jersey City, New Jersey.",)
    ], ["descriptions"])

df_entities = df.ai.extract(labels=["name", "profession", "city"], input_col="descriptions")
display(df_entities)

# This code uses AI. Always review output for mistakes. 

df = pd.DataFrame([
        "There are an error here.",
        "She and me go weigh back. We used to hang out every weeks.",
        "The big picture are right, but you're details is all wrong."
    ], columns=["text"])

df["corrections"] = df["text"].ai.fix_grammar()
display(df)

# This code uses AI. Always review output for mistakes. 

df = spark.createDataFrame([
        ("There are an error here.",),
        ("She and me go weigh back. We used to hang out every weeks.",),
        ("The big picture are right, but you're details is all wrong.",)
    ], ["text"])

corrections = df.ai.fix_grammar(input_col="text", output_col="corrections")
display(corrections)

# This code uses AI. Always review output for mistakes. 

df = pd.DataFrame([
        ("Scarves"),
        ("Snow pants"),
        ("Ski goggles")
    ], columns=["product"])

df["response"] = df.ai.generate_response("Write a short, punchy email subject line for a winter sale.")
display(df)

# This code uses AI. Always review output for mistakes. 

df = spark.createDataFrame([
        ("Scarves",),
        ("Snow pants",),
        ("Ski goggles",)
    ], ["product"])

responses = df.ai.generate_response(prompt="Write a short, punchy email subject line for a winter sale.", output_col="response")
display(responses)

# This code uses AI. Always review output for mistakes. 

df = pd.DataFrame([ 
        ("Bill Gates", "Technology"), 
        ("Satya Nadella", "Healthcare"), 
        ("Joan of Arc", "Agriculture") 
    ], columns=["names", "industries"])
    
df["similarity"] = df["names"].ai.similarity(df["industries"])
display(df)

# This code uses AI. Always review output for mistakes. 

df = spark.createDataFrame([
        ("Bill Gates", "Technology"), 
        ("Satya Nadella", "Healthcare"), 
        ("Joan of Arc", "Agriculture")
    ], ["names", "industries"])

similarity = df.ai.similarity(input_col="names", other_col="industries", output_col="similarity")
display(similarity)

# This code uses AI. Always review output for mistakes.

df= pd.DataFrame([
        ("Microsoft Teams", "2017",
        """
        The ultimate messaging app for your organization—a workspace for real-time 
        collaboration and communication, meetings, file and app sharing, and even the 
        occasional emoji! All in one place, all in the open, all accessible to everyone.
        """),
        ("Microsoft Fabric", "2023",
        """
        An enterprise-ready, end-to-end analytics platform that unifies data movement, 
        data processing, ingestion, transformation, and report building into a seamless, 
        user-friendly SaaS experience. Transform raw data into actionable insights.
        """)
    ], columns=["product", "release_year", "description"])

df["summaries"] = df["description"].ai.summarize()
display(df)

# This code uses AI. Always review output for mistakes. 

df = spark.createDataFrame([
        ("Microsoft Teams", "2017",
        """
        The ultimate messaging app for your organization—a workspace for real-time 
        collaboration and communication, meetings, file and app sharing, and even the 
        occasional emoji! All in one place, all in the open, all accessible to everyone.
        """,),
        ("Microsoft Fabric", "2023",
        """
        An enterprise-ready, end-to-end analytics platform that unifies data movement, 
        data processing, ingestion, transformation, and report building into a seamless, 
        user-friendly SaaS experience. Transform raw data into actionable insights.
        """,)
    ], ["product", "release_year", "description"])

summaries = df.ai.summarize(input_col="description", output_col="summary")
display(summaries)

# This code uses AI. Always review output for mistakes. 

df = pd.DataFrame([
        "Hello! How are you doing today?", 
        "Tell me what you'd like to know, and I'll do my best to help.", 
        "The only thing we have to fear is fear itself."
    ], columns=["text"])

df["translations"] = df["text"].ai.translate("spanish")
display(df)

# This code uses AI. Always review output for mistakes. 

df = spark.createDataFrame([
        ("Hello! How are you doing today?",),
        ("Tell me what you'd like to know, and I'll do my best to help.",),
        ("The only thing we have to fear is fear itself.",),
    ], ["text"])

translations = df.ai.translate(to_lang="spanish", input_col="text", output_col="translations")
display(translations)