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Veiledning: Lag en tilpasset søkemotor og et spørsmålssvarsystem

I denne veiledningen laster du fakturaer inn i Spark, henter strukturerte data med Azure Document Intelligence, oversetter tekst, beriker med Azure OpenAI, og skriver resultater til en Azure AI Search-indeks du kan søke i. Opplæringen tar omtrent 30 minutter å fullføre.

Forutsetninger

Sett opp avhengigheter

Importer pakker og koble til Azure-ressursene som brukes i denne arbeidsflyten.

import os
from pyspark.sql import SparkSession
from synapse.ml.core.platform import running_on_synapse, find_secret

# Bootstrap Spark Session
spark = SparkSession.builder.getOrCreate()

cognitive_key = find_secret("Foundry-resource-key") # replace with your Azure AI services key
cognitive_location = "eastus"

translator_key = find_secret("translator-key") # replace with your Azure Translator resource key
translator_location = "eastus"

search_key = find_secret("azure-search-key") # replace with your Azure AI Search key
search_service = "mmlspark-azure-search"
search_index = "form-demo-index-5"

openai_key = find_secret("openai-api-key") # replace with your Azure OpenAI key
openai_service_name = "synapseml-openai"
openai_deployment_name = "gpt-4o-mini"
openai_url = f"https://{openai_service_name}.openai.azure.com/"

Last inn data i Spark

Denne koden laster inn noen eksterne filer fra en Azure-lagringskonto som brukes til demoformål. Filene er ulike fakturaer, og koden leser dem inn i en dataramme.

from pyspark.sql.functions import udf
from pyspark.sql.types import StringType


def blob_to_url(blob):
    [prefix, postfix] = blob.split("@")
    container = prefix.split("/")[-1]
    split_postfix = postfix.split("/")
    account = split_postfix[0]
    filepath = "/".join(split_postfix[1:])
    return "https://{}/{}/{}".format(account, container, filepath)


df2 = (
    spark.read.format("binaryFile")
    .load("wasbs://ignite2021@mmlsparkdemo.blob.core.windows.net/form_subset/*")
    .select("path")
    .limit(10)
    .select(udf(blob_to_url, StringType())("path").alias("url"))
    .cache()
)

display(df2)

Bruk dokumentintelligens

Denne koden laster inn AnalyzeInvoices-transformatoren og sender en referanse til datarammen som inneholder fakturaene. Den kaller den forhåndsbygde fakturamodellen Azure Document Intelligence.

from synapse.ml.cognitive import AnalyzeInvoices

analyzed_df = (
    AnalyzeInvoices()
    .setSubscriptionKey(cognitive_key)
    .setLocation(cognitive_location)
    .setImageUrlCol("url")
    .setOutputCol("invoices")
    .setErrorCol("errors")
    .setConcurrency(5)
    .transform(df2)
    .cache()
)

display(analyzed_df)

Forenkle utskriften av dokumentintelligens

FormOntologyLearner-transformatoren utleder en tabellstruktur fra den dynamiske AnalyzeInvoices outputen, og organiserer den i kolonner og rader for enklere analyse nedstrøms.

from synapse.ml.cognitive import FormOntologyLearner

organized_df = (
    FormOntologyLearner()
    .setInputCol("invoices")
    .setOutputCol("extracted")
    .fit(analyzed_df)
    .transform(analyzed_df)
    .select("url", "extracted.*")
    .cache()
)

display(organized_df)

Ved å bruke en tabellbasert dataramme kan du flate ut de nestede tabellene som finnes i skjemaene ved å bruke SparkSQL.

from pyspark.sql.functions import explode, col

itemized_df = (
    organized_df.select("*", explode(col("Items")).alias("Item"))
    .drop("Items")
    .select("Item.*", "*")
    .drop("Item")
)

display(itemized_df)

Legg til oversettelser

Denne koden laster Translate, en transformator som kaller Azure Translator i Foundry Tools-tjenesten. Den opprinnelige teksten, som er på engelsk i kolonnen "Description", er maskinoversatt til flere språk. All output samles i arrayet "output.translations".

from synapse.ml.cognitive import Translate

translated_df = (
    Translate()
    .setSubscriptionKey(translator_key)
    .setLocation(translator_location)
    .setTextCol("Description")
    .setErrorCol("TranslationError")
    .setOutputCol("output")
    .setToLanguage(["zh-Hans", "fr", "ru", "cy"])
    .setConcurrency(5)
    .transform(itemized_df)
    .withColumn("Translations", col("output.translations")[0])
    .drop("output", "TranslationError")
    .cache()
)

display(translated_df)

Oversett produkter til emojis med OpenAI

from synapse.ml.cognitive.openai import OpenAIPrompt
from pyspark.sql.functions import trim, split

emoji_template = """ 
  Your job is to translate item names into emoji. Do not add anything but the emoji and end the translation with a comma
  
  Two Ducks: 🦆🦆,
  Light Bulb: 💡,
  Three Peaches: 🍑🍑🍑,
  Two kitchen stoves: ♨️♨️,
  A red car: 🚗,
  A person and a cat: 🧍🐈,
  A {Description}: """

prompter = (
    OpenAIPrompt()
    .setSubscriptionKey(openai_key)
    .setDeploymentName(openai_deployment_name)
    .setUrl(openai_url)
    .setMaxTokens(5)
    .setPromptTemplate(emoji_template)
    .setErrorCol("error")
    .setOutputCol("Emoji")
)

emoji_df = (
    prompter.transform(translated_df)
    .withColumn("Emoji", trim(split(col("Emoji"), ",").getItem(0)))
    .drop("error", "prompt")
    .cache()
)

display(emoji_df.select("Description", "Emoji"))

Utleder leverandøradresse kontinent med OpenAI

continent_template = """
Which continent does the following address belong to? 

Pick one value from Europe, Australia, North America, South America, Asia, Africa, Antarctica. 

Dont respond with anything but one of the above. If you don't know the answer or cannot figure it out from the text, return None. End your answer with a comma.

Address: "6693 Ryan Rd, North Whales",
Continent: Europe,
Address: "6693 Ryan Rd",
Continent: None,
Address: "{VendorAddress}",
Continent:"""

continent_df = (
    prompter.setOutputCol("Continent")
    .setPromptTemplate(continent_template)
    .transform(emoji_df)
    .withColumn("Continent", trim(split(col("Continent"), ",").getItem(0)))
    .drop("error", "prompt")
    .cache()
)

display(continent_df.select("VendorAddress", "Continent"))

Lag en Azure AI Search-indeks for skjemaene

from synapse.ml.cognitive import *
from pyspark.sql.functions import monotonically_increasing_id, lit

(
    continent_df.withColumn("DocID", monotonically_increasing_id().cast("string"))
    .withColumn("SearchAction", lit("upload"))
    .writeToAzureSearch(
        subscriptionKey=search_key,
        actionCol="SearchAction",
        serviceName=search_service,
        indexName=search_index,
        keyCol="DocID",
    )
)

Prøv et søkesøk

import requests

search_url = "https://{}.search.windows.net/indexes/{}/docs/search?api-version=2024-07-01".format(
    search_service, search_index
)
requests.post(
    search_url, json={"search": "door"}, headers={"api-key": search_key}
).json()

Bygg en chatbot som kan bruke Azure AI Search som et verktøy

import json
from openai import AzureOpenAI

client = AzureOpenAI(
    api_key=openai_key,
    api_version="2024-10-21",
    azure_endpoint=openai_url,
)

chat_context_prompt = f"""
You are a chatbot designed to answer questions with the help of a search engine that has the following information:

{continent_df.columns}

If you dont know the answer to a question say "I dont know". Do not lie or hallucinate information. Be brief. If you need to use the search engine to solve the please output a json in the form of {{"query": "example_query"}}
"""


def search_query_prompt(question):
    return f"""
Given the search engine above, what would you search for to answer the following question?

Question: "{question}"

Please output a json in the form of {{"query": "example_query"}}
"""


def search_result_prompt(query):
    search_results = requests.post(
        search_url, json={"search": query}, headers={"api-key": search_key}
    ).json()
    return f"""

You previously ran a search for "{query}" which returned the following results:

{search_results}

You should use the results to help you answer questions. If you dont know the answer to a question say "I dont know". Do not lie or hallucinate information. Be Brief and mention which query you used to solve the problem. 
"""


def prompt_gpt(messages):
    response = client.chat.completions.create(
        model=openai_deployment_name, messages=messages, max_tokens=None, top_p=0.95
    )
    return response.choices[0].message.content


def custom_chatbot(question):
    while True:
        try:
            query = json.loads(
                prompt_gpt(
                    [
                        {"role": "system", "content": chat_context_prompt},
                        {"role": "user", "content": search_query_prompt(question)},
                    ]
                )
            )["query"]

            return prompt_gpt(
                [
                    {"role": "system", "content": chat_context_prompt},
                    {"role": "system", "content": search_result_prompt(query)},
                    {"role": "user", "content": question},
                ]
            )
        except Exception as e:
            raise e

Still chatboten et spørsmål

custom_chatbot("What did Luke Diaz buy?")

Bekreft resultatene

display(
    continent_df.where(col("CustomerName") == "Luke Diaz")
    .select("Description")
    .distinct()
)

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