Bereitstellen von Batch-Inferenzpipelines

Von Bedeutung

Dieses Feature befindet sich in der Public Preview.

Auf dieser Seite wird gezeigt, wie Sie KI-Funktionen in andere Databricks-Daten und KI-Produkte integrieren können, um vollständige Batchableitungspipelines zu erstellen. Diese Pipelines können End-to-End-Workflows durchführen, die Aufnahme, Vorverarbeitung, Inferenz und Nachbearbeitung umfassen. Pipelines können in SQL oder Python erstellt und bereitgestellt werden als:

Lakeflow Spark Declarative Pipelines
Geplante Workflows mithilfe von Databricks-Workflows
Streaming-Ableitungsworkflows mit strukturiertem Streaming

Anforderungen

Ein Arbeitsbereich in einer unterstützten Region für Foundation-Model-APIs.
Databricks Runtime 15.4 LTS oder höher ist für Batch-Inference-Workloads mit AI-Funktionen erforderlich.
Abfrageberechtigung für die Delta-Tabelle im Unity-Katalog, die die daten enthält, die Sie verwenden möchten.
Setzen Sie in den Eigenschaften der Tabelle pipelines.channel als "Vorschau" fest, um ai_query() zu verwenden. Siehe Anforderungen für eine Beispielabfrage.

Durchführen der inkrementellen Batch-Inferenz für Lakeflow Spark Declarative Pipelines

Im folgenden Beispiel wird eine inkrementelle Batchableitung mithilfe von Lakeflow Spark Declarative Pipelines ausgeführt, wenn Daten kontinuierlich aktualisiert werden.

Schritt 1: Aufnehmen von Rohdaten von Nachrichten aus einem Datenvolumen

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

Importieren Sie die Pakete, und definieren Sie das JSON-Schema der LLM-Antwort als Python-Variable.

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}}'
)

Importieren Sie Ihre Daten aus einem Unity-Katalog-Volume.

@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")
  )

Schritt 2: Anwenden einer LLM-Ableitung zum Extrahieren von Titel und Kategorie

SQL


CREATE OR REFRESH MATERIALIZED VIEW news_categorized
COMMENT "Extract category and title from news articles using LLM inference."
AS
SELECT
  inputs,
  ai_query(
    "databricks-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('databricks-meta-llama-3-3-70b-instruct', "
      f"concat('Extract the category of the following news article: ', inputs), "
      f"responseFormat => '{news_extraction_schema}')"
    )
  )

Schritt 3: Überprüfen der LLM-Ableitungsausgabe vor der Zusammenfassung

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")

Schritt 4: Zusammenfassen von Newsartikeln aus den überprüften Daten

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(
    "databricks-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('databricks-meta-llama-3-3-70b-instruct', "
      "concat('Summarize the following political news article in 2-3 sentences: ', inputs))"
    ).alias("summary")
  )

Automatisieren von Batchableitungsaufträgen mithilfe von Databricks-Workflows

Planen Sie Batch-Ableitungsaufträge, und automatisieren Sie KI-Pipelines.

SQL

SELECT
   *,
   ai_query('databricks-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('databricks-meta-llama-3-3-70b-instruct', request => concat({escaped_prompt}, REVIEW_TEXT, '\\n\\nRESULT\\n'))")
)

# Display the result DataFrame.
display(result_df)

KI-Funktionen mit strukturiertem Streaming

Wenden Sie KI-Ableitungen in nahezu Echtzeit- oder Mikrobatchszenarien mit ai_query und strukturiertem Streaming an.

Schritt 1. Lesen Sie Ihre statische Delta-Tabelle

Lesen Sie Die statische Delta-Tabelle so, als wäre sie ein Datenstrom.


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: "
)

Schritt 2. Anwenden `ai_query`

Spark verarbeitet dies nur einmal für statische Daten, es sei denn, neue Zeilen kommen in der Tabelle an.


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

Schritt 3: Schreiben der zusammengefassten Ausgabe

Schreiben der zusammengefassten Ausgabe in eine andere Delta-Tabelle


# 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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Last updated on 2025-11-13

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Bereitstellen von Batch-Inferenzpipelines

Anforderungen

Durchführen der inkrementellen Batch-Inferenz für Lakeflow Spark Declarative Pipelines

Schritt 1: Aufnehmen von Rohdaten von Nachrichten aus einem Datenvolumen

SQL

Python

Schritt 2: Anwenden einer LLM-Ableitung zum Extrahieren von Titel und Kategorie

SQL

Python

Schritt 3: Überprüfen der LLM-Ableitungsausgabe vor der Zusammenfassung

SQL

Python

Schritt 4: Zusammenfassen von Newsartikeln aus den überprüften Daten

SQL

Python

Automatisieren von Batchableitungsaufträgen mithilfe von Databricks-Workflows

SQL

Python

KI-Funktionen mit strukturiertem Streaming

Schritt 1. Lesen Sie Ihre statische Delta-Tabelle

Schritt 2. Anwenden ai_query

Schritt 3: Schreiben der zusammengefassten Ausgabe

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Zusätzliche Ressourcen

Schritt 2. Anwenden `ai_query`