Embedding Ingestion and Vector Search with Apache Beam and BigQuery

Run in Google Colab View source on GitHub

Introduction

This Colab demonstrates how to use the Apache Beam RAG package to generate embeddings, ingest them into BigQuery, and perform vector similarity search.

The notebook is divided into two main parts:

  1. Basic Example: Using the default schema for simple vector search
  2. Advanced Example: Using a custom schema and metadata filtering

Example: Product Catalog

We'll work with a sample e-commerce dataset representing a product catalog. Each product has:

  • Structured fields: id, name, category, price, etc.
  • Detailed text descriptions: Longer text describing the product's features.
  • Additional metadata: brand, features, dimensions, etc.

Setup and Prerequisites

This example requires:

  1. A Google Cloud project with BigQuery enabled
  2. Apache Beam 2.64.0 or later

Install Packages and Dependencies

First, let's install the Python packages required for the embedding and ingestion pipeline:

# Apache Beam with GCP support pip install apache_beam[interactive,gcp]>=2.64.0 --quiet # Huggingface sentence-transformers for embedding models pip install sentence-transformers --quiet

Authenticate to Google Cloud

To connect to BigQuery, we authenticate with Google Cloud.

PROJECT_ID = "" # @param {type:"string"} # Authentication and project setup from google.colab import auth auth.authenticate_user(project_id=PROJECT_ID)

Create BigQuery Dataset

Let's set up a BigQuery dataset and table to store our embeddings:

DATASET_ID = "" # @param {type:"string"} TEMP_GCS_LOCATION = "gs://" # @param {type:"string"} 
 
from google.cloud import bigquery # Create BigQuery client client = bigquery.Client(project=PROJECT_ID) # Create dataset dataset_ref = client.dataset(DATASET_ID) try: client.get_dataset(dataset_ref) print(f"Dataset {DATASET_ID} already exists") except Exception: dataset = bigquery.Dataset(dataset_ref) dataset.location = "US" dataset = client.create_dataset(dataset) print(f"Created dataset {DATASET_ID}")

Importing Pipeline Components

We import the following for configuring our embedding ingestion pipeline:

  • Chunk, the structured that represents embeddable content with metadata
  • BigQueryVectorWriterConfig for configuring write behavior
# Embedding-specific imports from apache_beam.ml.rag.ingestion.bigquery import BigQueryVectorWriterConfig, SchemaConfig from apache_beam.ml.rag.ingestion.base import VectorDatabaseWriteTransform from apache_beam.ml.rag.types import Chunk, Content from apache_beam.ml.rag.embeddings.huggingface import HuggingfaceTextEmbeddings from apache_beam.ml.rag.enrichment.bigquery_vector_search import ( BigQueryVectorSearchParameters, BigQueryVectorSearchEnrichmentHandler ) # Apache Beam core import apache_beam as beam from apache_beam.options import pipeline_options from apache_beam.options.pipeline_options import PipelineOptions from apache_beam.ml.transforms.base import MLTransform from apache_beam.transforms.enrichment import Enrichment

Define helper functions

To run streaming examples we define helper functions to

  • Create a PubSub topic
  • Publish messages to a PubSub topic in a background thread
# Set up PubSub topic from google.api_core.exceptions import AlreadyExists from google.cloud import pubsub_v1 import threading import time import datetime import json def create_pubsub_topic(project_id, topic): publisher = pubsub_v1.PublisherClient() topic_path = publisher.topic_path(project_id, topic) try: topic = publisher.create_topic(request={"name": topic_path}) print(f"Created topic: {topic.name}") except AlreadyExists: print(f"Topic {topic_path} already exists.") return topic_path def publisher_function(project_id, topic, sample_data):  """Function that publishes sample queries to a PubSub topic.  This function runs in a separate thread and continuously publishes  messages to simulate real-time user queries.  """ publisher = pubsub_v1.PublisherClient() topic_path = publisher.topic_path(project_id, topic) time.sleep(15) for message in sample_data: # Convert to JSON and publish data = json.dumps(message).encode('utf-8') try: publisher.publish(topic_path, data) except Exception: pass # Silently continue on error # Wait 7 seconds before next message time.sleep(7)

Quick start: Embedding Generation and Ingestion with Default Schema

Create Sample Product Catalog Data

First, we create a sample product catalog with descriptions to be embedded

Create BigQuery Table

from google.cloud import bigquery # Create BigQuery client client = bigquery.Client(project=PROJECT_ID) DEFAULT_TABLE_ID = f"{PROJECT_ID}.{DATASET_ID}.default_product_embeddings" default_schema = [ bigquery.SchemaField("id", "STRING", mode="REQUIRED"), bigquery.SchemaField("embedding", "FLOAT64", mode="REPEATED"), bigquery.SchemaField("content", "STRING"), bigquery.SchemaField("metadata", "RECORD", mode="REPEATED", fields=[ bigquery.SchemaField("key", "STRING"), bigquery.SchemaField("value", "STRING") ]) ] default_table = bigquery.Table(DEFAULT_TABLE_ID, schema=default_schema) try: client.get_table(default_table) print(f"Table {DEFAULT_TABLE_ID} already exists") except Exception: default_table = client.create_table(default_table) print(f"Created table {DEFAULT_TABLE_ID}")

Define Pipeline components

Next, we define pipeline components that

  1. Convert product data to Chunk type
  2. Generate Embeddings using a pre-trained model
  3. Write to BigQuery

Map products to Chunks

We define a function convert each ingested product dictionary to a Chunk to configure what text to embed and what to treat as metadata.

from typing import Dict, Any # The create_chunk function converts our product dictionaries to Chunks. # This doesn't split the text - it simply structures it in the format # expected by the embedding pipeline components. def create_chunk(product: Dict[str, Any]) -> Chunk:  """Convert a product dictionary into a Chunk object.  The pipeline components (MLTransform, VectorDatabaseWriteTransform)  work with Chunk objects. This function:  1. Extracts text we want to embed  2. Preserves product data as metadata  3. Creates a Chunk in the expected format  Args:  product: Dictionary containing product information  Returns:  Chunk: A Chunk object ready for embedding  """ # Combine name and description for embedding text_to_embed = f"{product['name']}: {product['description']}" return Chunk( content=Content(text=text_to_embed), # The text that will be embedded id=product['id'], # Use product ID as chunk ID metadata=product, # Store all product info in metadata )

Generate embeddings with HuggingFace

We configure a local pre-trained Hugging Face model to create vector embeddings from the product descriptions.

# Configure the embedding model huggingface_embedder = HuggingfaceTextEmbeddings( model_name="sentence-transformers/all-MiniLM-L6-v2" )

Write to BigQuery

The default BigQueryVectorWriterConfig maps Chunk fields to database columns as:

Database Column Chunk Field Description
id chunk.id Unique identifier
embedding chunk.embedding.dense_embedding Vector representation
content chunk.content.text Text that was embedded
metadata chunk.metadata Additional data as RECORD 
# Configure BigQuery writer with default schema bigquery_writer_config = BigQueryVectorWriterConfig( write_config={ 'table': DEFAULT_TABLE_ID, 'create_disposition': 'CREATE_IF_NEEDED', 'write_disposition': 'WRITE_TRUNCATE' # Overwrite existing data } )

Assemble and Run Pipeline

Now we can create our pipeline that:

  1. Ingests our product data
  2. Converts each product to a Chunk
  3. Generates embeddings for each Chunk
  4. Stores everything in BigQuery
import tempfile options = pipeline_options.PipelineOptions([f"--temp_location={TEMP_GCS_LOCATION}"]) # Run batch pipeline with beam.Pipeline(options=options) as p: _ = ( p | 'Create Products' >> beam.Create(PRODUCTS_DATA) | 'Convert to Chunks' >> beam.Map(create_chunk) | 'Generate Embeddings' >> MLTransform(write_artifact_location=tempfile.mkdtemp()) .with_transform(huggingface_embedder) | 'Write to BigQuery' >> VectorDatabaseWriteTransform(bigquery_writer_config) )

Verify Embeddings

Let's check what was written to our BigQuery table:

# Query to verify the embeddings query = f""" SELECT  id,  ARRAY_LENGTH(embedding) as embedding_dimensions,  content,  (SELECT COUNT(*) FROM UNNEST(metadata)) as metadata_count FROM  `{DEFAULT_TABLE_ID}` LIMIT 5 """ # Run the query query_job = client.query(query) results = query_job.result() # Display results for row in results: print(f"Product ID: {row.id}") print(f"Embedding Dimensions: {row.embedding_dimensions}") print(f"Content: {row.content[:100]}...") # Show first 100 chars print(f"Metadata Count: {row.metadata_count}") print("-" * 80)

Quick start: Vector Search

Prerequisites:

  • Quick start: Basic Vector Generation and Ingestion

In this section we create a streaming pipeline that

  • Reads queries from PubSub
  • Embeds the queries
  • Performs Vector Search on the ingested product catalog data
  • Logs the queries enriched with product catalog data

Define Sample Queries

SAMPLE_QUERIES = [ {"query": "I need a powerful laptop for video editing and programming"}, {"query": "Looking for noise-cancelling headphones for travel"}, {"query": "What's a good ergonomic office chair for long work hours?"}, {"query": "I want a waterproof portable speaker for the beach"}, {"query": "Need a professional camera for wildlife photography"} ]

Setup PubSub Steaming Source

We create a PubSub topic for our pipeline's data source.

# Create pubsub topic TOPIC = "" # @param {type:'string'} topic_path = create_pubsub_topic(PROJECT_ID, TOPIC)

Define pipeline components

Next, we define pipeline components.

Process PubSub messages

def process_query(message):  """Convert a pubsub message to a Chunk for embedding and search.""" message_data = json.loads(message.decode('utf-8')) return Chunk( content=Content(text=message_data['query']), metadata={"query_type": "product_search"} )

Configure embedding model

# Configure the embedding model huggingface_embedder = HuggingfaceTextEmbeddings( model_name="sentence-transformers/all-MiniLM-L6-v2" ) 
 
# Configure vector search parameters (no filters) vector_search_params = BigQueryVectorSearchParameters( project=PROJECT_ID, table_name=DEFAULT_TABLE_ID, embedding_column="embedding", columns=["content", "metadata"], neighbor_count=1 # Return top match ) # Create search handler search_handler = BigQueryVectorSearchEnrichmentHandler( vector_search_parameters=vector_search_params, min_batch_size=1, max_batch_size=5 )

Log the enriched query

def log_results(chunk):  """Format search results for display.""" # Extract results from enrichment_data results = chunk.metadata.get("enrichment_data", {}).get("chunks", []) # Log the query print(f"\n=== QUERY: \"{chunk.content.text}\" ===") # Log the results print(f"Found {len(results)} matching products:") if results: for i, result in enumerate(results, 1): # Convert metadata array to dictionary product_metadata = {} if "metadata" in result: for item in result.get("metadata", []): product_metadata[item["key"]] = item["value"] # Print product details print(f"\nResult {i}:") print(f" Product: {product_metadata.get('name', 'Unknown')}") print(f" Brand: {product_metadata.get('brand', 'Unknown')}") print(f" Category: {product_metadata.get('category', 'Unknown')} > {product_metadata.get('subcategory', 'Unknown')}") print(f" Price: ${product_metadata.get('price', 'Unknown')}") print(f" Description: {product_metadata.get('description', 'Unknown')[:100]}...") else: print(" No matching products found.") print("=" * 80) return chunk

Run the Basic Search Pipeline

Now we'll start publishing messages to PubSub in the background, and run our pipeline to:

  1. Process the sample queries
  2. Generate embeddings for each query
  3. Perform vector search in BigQuery
  4. Format and display the results
print("Starting publisher thread...") publisher_thread = threading.Thread( target=publisher_function, args=(PROJECT_ID, TOPIC, SAMPLE_QUERIES), daemon=True ) publisher_thread.start() print(f"Publisher thread started with ID: {publisher_thread.ident}") import tempfile from apache_beam.transforms import trigger options = pipeline_options.PipelineOptions() options.view_as(pipeline_options.StandardOptions).streaming = True # Run the streaming pipeline print(f"Running pipeline...") with beam.Pipeline(options=options) as p: results = ( p | 'Read from PubSub' >> beam.io.ReadFromPubSub(topic=topic_path) | 'Process Messages' >> beam.Map(process_query) | 'Window' >> beam.WindowInto(beam.window.GlobalWindows(), trigger=trigger.Repeatedly( trigger.AfterProcessingTime( 1)), accumulation_mode=trigger.AccumulationMode\ .DISCARDING) | 'Generate Embeddings' >> MLTransform(write_artifact_location=tempfile.mkdtemp()) .with_transform(huggingface_embedder) | 'Vector Search' >> Enrichment(search_handler) | 'Log Results' >> beam.Map(log_results) )

Advanced: Embedding Generation and Ingestion with Custom Schema

In this part, we create pipelines to

  • Write embeddings to a BigQuery table with a custom schema
  • Perform Vector Search with metadata filters.

Create Product Dataset with Multiple Items per Category

Let's create a more focused product dataset with multiple items in each category to better demonstrate filtering:

FILTERED_PRODUCTS_DATA = [ # Electronics - Laptops (3 items with different price points) { "id": "laptop-001", "name": "UltraBook Pro X15", "description": "Powerful ultralight laptop featuring a 15-inch 4K OLED display, 12th Gen Intel i9 processor, 32GB RAM, and 1TB SSD. Perfect for creative professionals, developers, and power users who need exceptional performance in a slim form factor.", "category": "Electronics", "subcategory": "Laptops", "price": 1899.99, "brand": "TechMaster" }, { "id": "laptop-002", "name": "UltraBook Air 13", "description": "Thin and light laptop with 13-inch Retina display, M2 chip, 16GB RAM, and 512GB SSD. Ideal for students, travelers, and professionals who need portability without sacrificing performance.", "category": "Electronics", "subcategory": "Laptops", "price": 1299.99, "brand": "TechMaster" }, { "id": "laptop-003", "name": "PowerBook Gaming Pro", "description": "High-performance gaming laptop with 17-inch 144Hz display, RTX 3080 graphics, Intel i7 processor, 32GB RAM, and 1TB SSD. Designed for serious gamers and content creators who need desktop-class performance in a portable package.", "category": "Electronics", "subcategory": "Laptops", "price": 2199.99, "brand": "GameTech" }, # Electronics - Headphones (3 items with different price points) { "id": "headphones-001", "name": "SoundSphere Pro", "description": "Premium wireless noise-cancelling headphones with spatial audio technology and adaptive EQ. Features 40 hours of battery life, memory foam ear cushions, and voice assistant integration.", "category": "Electronics", "subcategory": "Headphones", "price": 349.99, "brand": "AudioTech" }, { "id": "headphones-002", "name": "SoundSphere Sport", "description": "Wireless sport earbuds with sweat and water resistance, secure fit, and 8-hour battery life. Perfect for workouts, running, and active lifestyles.", "category": "Electronics", "subcategory": "Headphones", "price": 129.99, "brand": "AudioTech" }, { "id": "headphones-003", "name": "BassBoost Studio", "description": "Professional studio headphones with high-fidelity sound, premium materials, and exceptional comfort for long sessions. Designed for audio engineers, musicians, and audiophiles.", "category": "Electronics", "subcategory": "Headphones", "price": 249.99, "brand": "SoundPro" }, # Home & Kitchen - Coffee Makers (3 items with different price points) { "id": "coffee-001", "name": "BrewMaster 5000", "description": "Smart coffee maker with precision temperature control, customizable brewing profiles, and app connectivity. Schedule brewing times, adjust strength, and receive maintenance alerts from your smartphone.", "category": "Home & Kitchen", "subcategory": "Coffee Makers", "price": 199.99, "brand": "HomeBarista" }, { "id": "coffee-002", "name": "BrewMaster Espresso", "description": "Semi-automatic espresso machine with 15-bar pressure pump, milk frother, and programmable settings. Make cafe-quality espresso, cappuccino, and latte at home.", "category": "Home & Kitchen", "subcategory": "Coffee Makers", "price": 299.99, "brand": "HomeBarista" }, { "id": "coffee-003", "name": "BrewMaster Basic", "description": "Simple, reliable drip coffee maker with 12-cup capacity, programmable timer, and auto-shutoff. Perfect for everyday coffee drinkers who want convenience and consistency.", "category": "Home & Kitchen", "subcategory": "Coffee Makers", "price": 49.99, "brand": "HomeBarista" }, # Furniture - Office Chairs (3 items with different price points) { "id": "chair-001", "name": "ErgoFlex Executive Chair", "description": "Ergonomic office chair with dynamic lumbar support, adjustable armrests, and breathable mesh back. Features 5-point adjustability, premium cushioning, and smooth-rolling casters.", "category": "Furniture", "subcategory": "Office Chairs", "price": 329.99, "brand": "ComfortDesign" }, { "id": "chair-002", "name": "ErgoFlex Task Chair", "description": "Mid-range ergonomic task chair with fixed lumbar support, height-adjustable armrests, and mesh back. Perfect for home offices and everyday use.", "category": "Furniture", "subcategory": "Office Chairs", "price": 179.99, "brand": "ComfortDesign" }, { "id": "chair-003", "name": "ErgoFlex Budget Chair", "description": "Affordable office chair with basic ergonomic features, armrests, and fabric upholstery. A practical choice for occasional use or budget-conscious shoppers.", "category": "Furniture", "subcategory": "Office Chairs", "price": 89.99, "brand": "ComfortDesign" } ]

Create BigQuery Table with Custom Schema

Now, let's create a BigQuery table with a custom schema that unnests metadata fields:

# Create table with custom schema for vector embeddings CUSTOM_TABLE_ID = f"{PROJECT_ID}.{DATASET_ID}.custom_product_embeddings" custom_schema = [ bigquery.SchemaField("id", "STRING", mode="REQUIRED"), bigquery.SchemaField("embedding", "FLOAT64", mode="REPEATED"), bigquery.SchemaField("content", "STRING"), bigquery.SchemaField("name", "STRING"), bigquery.SchemaField("category", "STRING"), bigquery.SchemaField("subcategory", "STRING"), bigquery.SchemaField("price", "FLOAT64"), bigquery.SchemaField("brand", "STRING") ] custom_table = bigquery.Table(CUSTOM_TABLE_ID, schema=custom_schema) try: client.get_table(custom_table) print(f"Table {CUSTOM_TABLE_ID} already exists") except Exception: custom_table = client.create_table(custom_table) print(f"Created table {CUSTOM_TABLE_ID}")

Define Pipeline components

Our pipeline

  • Ingests product data as dictionaries
  • Converts product dictionaries to Chunk
  • Generates embeddings
  • Writes embeddings and metadata to a BigQuery with a custom schema

Convert product dictionary

We define a function convert each ingested product dictionary to a Chunk to configure what text to embed and what to treat as metadata.

from typing import Dict, Any def create_chunk(product: Dict[str, Any]) -> Chunk:  """Convert a product dictionary into a Chunk object.  Args:  product: Dictionary containing product information  Returns:  Chunk: A Chunk object ready for embedding  """ # Combine name and description for embedding text_to_embed = f"{product['name']}: {product['description']}" return Chunk( content=Content(text=text_to_embed), # The text that will be embedded id=product['id'], # Use product ID as chunk ID metadata=product, # Store all product info in metadata )

Generate embeddings with HuggingFace

We configure a local pre-trained Hugging Face model to create vector embeddings from the product descriptions.

# Configure the embedding model huggingface_embedder = HuggingfaceTextEmbeddings( model_name="sentence-transformers/all-MiniLM-L6-v2" )

Configure BigQuery Vector Writer

To write embedded data to a BigQuery table with a custom schema we need to

  • Provide the BigQuery table schema
  • Define a function to convert the embedded Chunk to a dictionary that matches our BigQuery schema
# Define BigQuery schema SCHEMA = { 'fields': [ {'name': 'id', 'type': 'STRING'}, {'name': 'embedding', 'type': 'FLOAT64', 'mode': 'REPEATED'}, {'name': 'content', 'type': 'STRING'}, {'name': 'name', 'type': 'STRING'}, {'name': 'category', 'type': 'STRING'}, {'name': 'subcategory', 'type': 'STRING'}, {'name': 'price', 'type': 'FLOAT64'}, {'name': 'brand', 'type': 'STRING'} ] } # Define function to convert Chunk to dictionary with the custom schema def chunk_to_dict_custom(chunk: Chunk) -> Dict[str, Any]:  """Convert a Chunk to a dictionary matching our custom schema.""" # Extract metadata metadata = chunk.metadata # Map to custom schema return { 'id': chunk.id, 'embedding': chunk.embedding.dense_embedding, 'content': chunk.content.text, 'name': metadata.get('name', ''), 'category': metadata.get('category', ''), 'subcategory': metadata.get('subcategory', ''), 'price': float(metadata.get('price', 0)), 'brand': metadata.get('brand', '') }

Now we create a BigQueryVectorWriterConfig with a SchemaConfig parameter

custom_writer_config = BigQueryVectorWriterConfig( write_config={ 'table': CUSTOM_TABLE_ID, 'create_disposition': 'CREATE_IF_NEEDED', 'write_disposition': 'WRITE_TRUNCATE' # Overwrite existing data }, schema_config=SchemaConfig( schema=SCHEMA, chunk_to_dict_fn=chunk_to_dict_custom ) )

Assemble and Run pipeline

import tempfile options = pipeline_options.PipelineOptions([f"--temp_location={TEMP_GCS_LOCATION}"]) # Run batch pipeline with custom schema with beam.Pipeline(options=options) as p: _ = ( p | 'Create Products' >> beam.Create(FILTERED_PRODUCTS_DATA) | 'Convert to Chunks' >> beam.Map(create_chunk) | 'Generate Embeddings' >> MLTransform(write_artifact_location=tempfile.mkdtemp()) .with_transform(huggingface_embedder) | 'Write to BigQuery' >> VectorDatabaseWriteTransform(custom_writer_config) )

Verify Custom Schema Embeddings

Let's check what was written to our custom schema table:

# Query to verify the custom schema embeddings query = f""" SELECT  id,  name,  category,  subcategory,  price,  brand, FROM  `{CUSTOM_TABLE_ID}` ORDER BY category, subcategory, price LIMIT 5 """ # Run the query query_job = client.query(query) results = query_job.result() # Display results print("First 5 Products in Custom Schema Table:") print("-" * 80) for row in results: print(f"ID: {row.id}") print(f"Name: {row.name}") print(f"Category: {row.category} > {row.subcategory}") print(f"Price: ${row.price}") print(f"Brand: {row.brand}") print("-" * 80)

Advanced: Vector Search with Metadata Filter

Prerequisites:

  • Advanced: Example with Custom Schema

Now let's demonstrate how to perform vector search with filtering using our custom schema.

Our pipeline:

  • Reads messages from PubSub that contains a query and max_price filter
  • Generates embeddings for the query
  • Performs vector search with additional max_price metadata filter

Sample Queries with Filter Requirements

We define a list of messages to be published to PubSub. This is the data ingested to our pipeline.

FILTERED_QUERIES = [ {"query": "I need a powerful laptop for video editing", "max_price": 2000}, {"query": "Looking for noise-cancelling headphones", "max_price": 300}, {"query": "What's a good ergonomic office chair?", "max_price": 200}, {"query": "I want an affordable coffee maker", "max_price": 100}, {"query": "Need a premium laptop with good specs", "max_price": 1500} ]

Create PubSub Topic

We create a PubSub topic to be used as our pipeline data source

# Define pubsub topic for filtered queries TOPIC = "" # @param {type:'string'} topic_path = create_pubsub_topic(PROJECT_ID, TOPIC)

Define Pipeline components

Process PubSub messages

def process(message):  """Convert a filtered query message to a Chunk for embedding and search.""" message_data = json.loads(message.decode('utf-8')) return Chunk( content=Content(text=message_data['query']), metadata={ "max_price": message_data['max_price'] } )

Configure embedding model

# Configure the embedding model huggingface_embedder = HuggingfaceTextEmbeddings( model_name="sentence-transformers/all-MiniLM-L6-v2" )

Configure Vector Search with Metadata Filter

Vector search will return the two most semantically similar product with an upper price limit of max_price

# Configure vector search parameters with metadata_restriction_template vector_search_params = BigQueryVectorSearchParameters( project=PROJECT_ID, table_name=CUSTOM_TABLE_ID, embedding_column="embedding", columns=["id", "name", "category", "subcategory", "price", "brand", "content"], neighbor_count=1, metadata_restriction_template="price <= {max_price}" ) # Create search handler search_handler = BigQueryVectorSearchEnrichmentHandler( vector_search_parameters=vector_search_params, min_batch_size=1, max_batch_size=5 )

Log the enriched query

def format_filtered_results(chunk):  """Format filtered search results for display.""" # Extract results from enrichment_data results = chunk.metadata.get("enrichment_data", {}).get("chunks", []) max_price = chunk.metadata.get("max_price") # Log the query print(f"\n=== PRICE-FILTERED QUERY ===") print(f"Query: \"{chunk.content.text}\"") print(f"Max Price: ${max_price}") # Log the results print(f"\nFound {len(results)} matching products under ${max_price}:") if results: for i, result in enumerate(results, 1): # Print product details print(f"\nResult {i}:") print(f" Product: {result.get('name', 'Unknown')}") print(f" Category: {result.get('category', 'Unknown')} > {result.get('subcategory', 'Unknown')}") print(f" Price: ${result.get('price', 'Unknown')}") print(f" Brand: {result.get('brand', 'Unknown')}") print(f" Description: {result.get('content', 'Unknown')}") print(f" Similarity distance: {result.get('distance', 'Unknown')}") # Verify price is under max price = float(result.get('price', 0)) print(f" Price Check: {'✓' if price <= max_price else '✗'}") else: print(" No matching products found.") print("=" * 80) return chunk

Run Vector Search with metadata filter Pipeline

import tempfile from apache_beam.transforms import trigger print("Starting publisher thread...") publisher_thread = threading.Thread( target=publisher_function, args=(PROJECT_ID, TOPIC, FILTERED_QUERIES), daemon=True ) publisher_thread.start() print(f"Publisher thread started with ID: {publisher_thread.ident}") options = pipeline_options.PipelineOptions() options.view_as(pipeline_options.StandardOptions).streaming = True # Run the streaming pipeline with price filtering with beam.Pipeline(options=options) as p: results = ( p | 'Read from PubSub' >> beam.io.ReadFromPubSub(topic=topic_path) | 'Process Messages' >> beam.Map(process) | 'Window' >> beam.WindowInto(beam.window.GlobalWindows(), trigger=trigger.Repeatedly( trigger.AfterProcessingTime( 30)), accumulation_mode=trigger.AccumulationMode\ .DISCARDING) | 'Generate Embeddings' >> MLTransform(write_artifact_location=tempfile.mkdtemp()) .with_transform(huggingface_embedder) | 'Price-Filtered Vector Search' >> Enrichment(search_handler) | 'Format Filtered Results' >> beam.Map(format_filtered_results) )

Whats next?