Toplu çıkarım işlem hatlarını dağıtın

Bu sayfa, AI Fonksiyonları kullanarak yapılandırılmamış verileri diğer Databricks verileri ve AI ürünlerine entegre ederek tam toplu çıkarım boru hatları oluşturmayı nasıl sağlayabileceğinizi gösteriyor. Göreve özgü fonksiyonlar ve genel amaçlı fonksiyonlar dahil olmak üzere yapay zeka fonksiyonları hakkında bilgi edinmek için bkz. Yapay Zeka Fonksiyonları kullanılarak yapılandırılmamış verileri dönüştür.

Bu işlem hatları alım, ön işleme, çıkarım ve işlem sonrası gibi uçtan uca iş akışları gerçekleştirebilir. İşlem hatları SQL veya Python'da oluşturulabilir ve şu şekilde dağıtılabilir:

  • Lakeflow işlem hatları
  • Databricks iş akışlarını kullanarak zamanlanmış iş akışları
  • Yapılandırılmış Akış kullanarak akış tahmin iş akışları

Gereksinimler

Lakeflow işlem hatlarında artımlı toplu çıkarım gerçekleştirme

Aşağıdaki örnek, verilerin sürekli güncelleştirilme zamanı için Lakeflow işlem hatlarını kullanarak artımlı toplu çıkarım gerçekleştirir.

1. Adım: Bir birimden ham haber verilerini alma

SQL

CREATE OR REFRESH STREAMING TABLE news_raw
COMMENT "Raw news articles ingested from volume."
AS SELECT *
FROM STREAM(read_files(
  '/Volumes/databricks_news_summarization_benchmarking_data/v01/csv',
  format => 'csv',
  header => true,
  mode => 'PERMISSIVE',
  multiLine => 'true'
));

Python

Paketleri içeri aktarın ve LLM yanıtının JSON şemasını Python değişkeni olarak tanımlayın

from pyspark import pipelines as dp
from pyspark.sql.functions import expr, get_json_object, concat

news_extraction_schema = (
    '{"type": "json_schema", "json_schema": {"name": "news_extraction", '
    '"schema": {"type": "object", "properties": {"title": {"type": "string"}, '
    '"category": {"type": "string", "enum": ["Politics", "Sports", "Technology", '
    '"Health", "Entertainment", "Business"]}}}, "strict": true}}'
)

Bir Unity Catalog biriminden verilerinizi içe aktarın.

@dp.table(
  comment="Raw news articles ingested from volume."
)
def news_raw():
  return (
    spark.readStream
      .format("cloudFiles")
      .option("cloudFiles.format", "csv")
      .option("header", True)
      .option("mode", "PERMISSIVE")
      .option("multiLine", "true")
      .load("/Volumes/databricks_news_summarization_benchmarking_data/v01/csv")
  )

2. Adım: Başlık ve kategori ayıklamak için LLM çıkarımı uygulama

SQL


CREATE OR REFRESH MATERIALIZED VIEW news_categorized
COMMENT "Extract category and title from news articles using LLM inference."
AS
SELECT
  inputs,
  ai_query(
    "system.ai.meta-llama-3-3-70b-instruct",
    "Extract the category of the following news article: " || inputs,
    responseFormat => '{
      "type": "json_schema",
      "json_schema": {
        "name": "news_extraction",
        "schema": {
          "type": "object",
          "properties": {
            "title": { "type": "string" },
            "category": {
              "type": "string",
              "enum": ["Politics", "Sports", "Technology", "Health", "Entertainment", "Business"]
            }
          }
        },
        "strict": true
      }
    }'
  ) AS meta_data
FROM news_raw
LIMIT 2;

Python

@dp.materialized_view(
  comment="Extract category and title from news articles using LLM inference."
)
def news_categorized():
  # Limit the number of rows to 2 as in the SQL version
  df_raw = spark.read.table("news_raw").limit(2)
  # Inject the JSON schema variable into the ai_query call using an f-string.
  return df_raw.withColumn(
    "meta_data",
    expr(
      f"ai_query('system.ai.meta-llama-3-3-70b-instruct', "
      f"concat('Extract the category of the following news article: ', inputs), "
      f"responseFormat => '{news_extraction_schema}')"
    )
  )

3. Adım: Özetlemeden önce LLM çıkarımı çıkışını doğrulama

SQL

CREATE OR REFRESH MATERIALIZED VIEW news_validated (
  CONSTRAINT valid_title EXPECT (size(split(get_json_object(meta_data, '$.title'), ' ')) >= 3),
  CONSTRAINT valid_category EXPECT (get_json_object(meta_data, '$.category') IN ('Politics', 'Sports', 'Technology', 'Health', 'Entertainment', 'Business'))
)
COMMENT "Validated news articles ensuring the title has at least 3 words and the category is valid."
AS
SELECT *
FROM news_categorized;

Python

@dp.materialized_view(
  comment="Validated news articles ensuring the title has at least 3 words and the category is valid."
)
@dp.expect("valid_title", "size(split(get_json_object(meta_data, '$.title'), ' ')) >= 3")
@dp.expect_or_fail("valid_category", "get_json_object(meta_data, '$.category') IN ('Politics', 'Sports', 'Technology', 'Health', 'Entertainment', 'Business')")
def news_validated():
  return spark.read.table("news_categorized")

4. Adım: Doğrulanmış verilerden haber makalelerini özetleme

SQL

CREATE OR REFRESH MATERIALIZED VIEW news_summarized
COMMENT "Summarized political news articles after validation."
AS
SELECT
  get_json_object(meta_data, '$.category') as category,
  get_json_object(meta_data, '$.title') as title,
  ai_query(
    "system.ai.meta-llama-3-3-70b-instruct",
    "Summarize the following political news article in 2-3 sentences: " || inputs
  ) AS summary
FROM news_validated;

Python

@dp.materialized_view(
  comment="Summarized political news articles after validation."
)
def news_summarized():
  df = spark.read.table("news_validated")
  return df.select(
    get_json_object("meta_data", "$.category").alias("category"),
    get_json_object("meta_data", "$.title").alias("title"),
    expr(
      "ai_query('system.ai.meta-llama-3-3-70b-instruct', "
      "concat('Summarize the following political news article in 2-3 sentences: ', inputs))"
    ).alias("summary")
  )

Databricks iş akışlarını kullanarak toplu çıkarım işlerini otomatikleştirme

Toplu çıkarım işlerini zamanlayın ve yapay zeka işlem hatlarını otomatikleştirin.

SQL

SELECT
   *,
   ai_query('system.ai.meta-llama-3-3-70b-instruct', request => concat("You are an opinion mining service. Given a piece of text, output an array of json results that extracts key user opinions, a classification, and a Positive, Negative, Neutral, or Mixed sentiment about that subject.
AVAILABLE CLASSIFICATIONS
Quality, Service, Design, Safety, Efficiency, Usability, Price
Examples below:
DOCUMENT
I got soup. It really did take only 20 minutes to make some pretty good soup. The noises it makes when it's blending are somewhat terrifying, but it gives a little beep to warn you before it does that. It made three or four large servings of soup. It's a single layer of steel, so the outside gets pretty hot. It can be hard to unplug the lid without knocking the blender against the side, which is not a nice sound. The soup was good and the recipes it comes with look delicious, but I'm not sure I'll use it often. 20 minutes of scary noises from the kitchen when I already need comfort food is not ideal for me. But if you aren't sensitive to loud sounds it does exactly what it says it does..
RESULT
[
 {'Classification': 'Efficiency', 'Comment': 'only 20 minutes','Sentiment': 'Positive'},
 {'Classification': 'Quality','Comment': 'pretty good soup','Sentiment': 'Positive'},
 {'Classification': 'Usability', 'Comment': 'noises it makes when it's blending are somewhat terrifying', 'Sentiment': 'Negative'},
 {'Classification': 'Safety','Comment': 'outside gets pretty hot','Sentiment': 'Negative'},
 {'Classification': 'Design','Comment': 'Hard to unplug the lid without knocking the blender against the side, which is not a nice sound', 'Sentiment': 'Negative'}
]
DOCUMENT
", REVIEW_TEXT, '\n\nRESULT\n')) as result
FROM catalog.schema.product_reviews
LIMIT 10

Python


import json
from pyspark.sql.functions import expr

# Define the opinion mining prompt as a multi-line string.
opinion_prompt = """You are an opinion mining service. Given a piece of text, output an array of json results that extracts key user opinions, a classification, and a Positive, Negative, Neutral, or Mixed sentiment about that subject.
AVAILABLE CLASSIFICATIONS
Quality, Service, Design, Safety, Efficiency, Usability, Price
Examples below:
DOCUMENT
I got soup. It really did take only 20 minutes to make some pretty good soup.The noises it makes when it's blending are somewhat terrifying, but it gives a little beep to warn you before it does that.It made three or four large servings of soup.It's a single layer of steel, so the outside gets pretty hot. It can be hard to unplug the lid without knocking the blender against the side, which is not a nice sound.The soup was good and the recipes it comes with look delicious, but I'm not sure I'll use it often. 20 minutes of scary noises from the kitchen when I already need comfort food is not ideal for me. But if you aren't sensitive to loud sounds it does exactly what it says it does.
RESULT
[
 {'Classification': 'Efficiency', 'Comment': 'only 20 minutes','Sentiment': 'Positive'},
 {'Classification': 'Quality','Comment': 'pretty good soup','Sentiment': 'Positive'},
 {'Classification': 'Usability', 'Comment': 'noises it makes when it's blending are somewhat terrifying', 'Sentiment': 'Negative'},
 {'Classification': 'Safety','Comment': 'outside gets pretty hot','Sentiment': 'Negative'},
 {'Classification': 'Design','Comment': 'Hard to unplug the lid without knocking the blender against the side, which is not a nice sound', 'Sentiment': 'Negative'}
]
DOCUMENT
"""

# Escape the prompt so it can be safely embedded in the SQL expression.
escaped_prompt = json.dumps(opinion_prompt)

# Read the source table and limit to 10 rows.
df = spark.table("catalog.schema.product_reviews").limit(10)

# Apply the LLM inference to each row, concatenating the prompt, the review text, and the tail string.
result_df = df.withColumn(
    "result",
    expr(f"ai_query('system.ai.meta-llama-3-3-70b-instruct', request => concat({escaped_prompt}, REVIEW_TEXT, '\\n\\nRESULT\\n'))")
)

# Display the result DataFrame.
display(result_df)

Yapılandırılmış Akış Kullanan Yapay Zeka İşlevleri

Yapılandırılmış Akış kullanarak ai_query neredeyse gerçek zamanlı veya mikro işleme senaryolarında yapay zeka çıkarımı uygulayın.

Adım 1. Statik Delta tablonuzu okuma

Statik Delta tablonuzu bir akışmış gibi okuyun.


from pyspark.sql import SparkSession
import pyspark.sql.functions as F

spark = SparkSession.builder.getOrCreate()

# Spark processes all existing rows exactly once in the first micro-batch.
df = spark.table("enterprise.docs")  # Replace with your table name containing enterprise documents
df.repartition(50).write.format("delta").mode("overwrite").saveAsTable("enterprise.docs")
df_stream = spark.readStream.format("delta").option("maxBytesPerTrigger", "50K").table("enterprise.docs")

# Define the prompt outside the SQL expression.
prompt = (
    "You are provided with an enterprise document. Summarize the key points in a concise paragraph. "
    "Do not include extra commentary or suggestions. Document: "
)

Adım 2. Uygula ai_query

Spark, tabloya yeni satırlar gelmediği sürece bunu statik veriler için yalnızca bir kez işler.


df_transformed = df_stream.select(
    "document_text",
    F.expr(f"""
      ai_query(
        'system.ai.meta-llama-3-1-8b-instruct',
        CONCAT('{prompt}', document_text)
      )
    """).alias("summary")
)

3. Adım: Özetlenmiş çıkışı yazma

Özetlenmiş çıkışı başka bir Delta tablosuna yazma


# Time-based triggers apply, but only the first trigger processes all existing static data.
query = df_transformed.writeStream \
    .format("delta") \
    .option("checkpointLocation", "/tmp/checkpoints/_docs_summary") \
    .outputMode("append") \
    .toTable("enterprise.docs_summary")

query.awaitTermination()
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