Distribuera batchinferenspipelines

2025-10-07

Viktigt!

Den här funktionen finns som allmänt tillgänglig förhandsversion.

Den här sidan visar hur du kan integrera AI Functions i andra Databricks-data och AI-produkter för att skapa fullständiga pipelines för batchinferens. Dessa pipelines kan utföra arbetsflöden från slutpunkt till slutpunkt som omfattar inmatning, förbearbetning, slutsatsdragning och efterbearbetning. Pipelines kan skapas i SQL eller Python och distribueras som:

Deklarativa pipelines för Lakeflow
Schemalagda arbetsflöden med Databricks-arbetsflöden
Arbetsflöden för strömmande inferens med strukturerad strömning

Krav

En arbetsyta i en region där Foundation Model-API:er stöds.
Databricks Runtime 15.4 LTS eller senare krävs för batchinferens med AI Functions.
Frågebehörighet i deltatabellen i Unity Catalog som innehåller de data som du vill använda.
Ställ in pipelines.channel i tabellegenskaperna som "förhandsversion" för att använda ai_query(). Se Krav för en exempelfråga.

Gör inkrementella batchinferenser på deklarativa Lakeflow-pipelines

I följande exempel utförs inkrementell batch-baserad inferens med Lakeflow Declarative Pipelines när data kontinuerligt uppdateras.

Steg 1: Importera rå nyhetsdata från en volym

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

Importera paketen och definiera JSON-schemat för LLM-svaret som en Python-variabel

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}}'
)

Mata in dina data från en Unity Catalog-volym.

@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")
  )

Steg 2: Tillämpa LLM-slutsatsdragning för att extrahera rubrik och kategori

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}')"
    )
  )

Steg 3: Verifiera LLM-inferensutdata före sammanfattning

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")

Steg 4: Sammanfatta nyhetsartiklar från verifierade data

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")
  )

Automatisera batchinferensjobb med databricks-arbetsflöden

Schemalägg batchinferensuppgifter och automatisera AI-flöden.

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)

AI Functions med strukturerad direktuppspelning

Tillämpa AI-slutsatsdragning i scenarier i nära realtid eller mikrobatch med hjälp av ai_query och strukturerad direktuppspelning.

Steg 1. Läs din statiska Delta-tabell

Läs din statiska Delta-tabell som om den vore en ström.


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: "
)

Steg 2. Använd `ai_query`

Spark bearbetar detta bara en gång för statiska data om inte nya rader tas emot i tabellen.


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")
)

Steg 3: Skriv sammanfattade utdata

Skriva sammanfattade utdata till en annan Delta-tabell


# 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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Dela via

Distribuera batchinferenspipelines

Krav

Gör inkrementella batchinferenser på deklarativa Lakeflow-pipelines

Steg 1: Importera rå nyhetsdata från en volym

SQL

python

Steg 2: Tillämpa LLM-slutsatsdragning för att extrahera rubrik och kategori

SQL

python

Steg 3: Verifiera LLM-inferensutdata före sammanfattning

SQL

python

Steg 4: Sammanfatta nyhetsartiklar från verifierade data

SQL

python

Automatisera batchinferensjobb med databricks-arbetsflöden

SQL

python

AI Functions med strukturerad direktuppspelning

Steg 1. Läs din statiska Delta-tabell

Steg 2. Använd ai_query

Steg 3: Skriv sammanfattade utdata

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Ytterligare resurser

Steg 2. Använd `ai_query`