Business Background
As enterprises embrace digital transformation, information retrieval is evolving from simple keyword matching to more advanced semantic understanding. Traditional search engines that rely on inverted indexes struggle to grasp the real meaning behind user queries.
This creates a bottleneck in user experience across search, recommendations, customer support, and Q&A systems. For instance, in an e-commerce platform, when a user searches for “white dress suitable for summer,” a keyword-based system might match only product titles or categories. However, it would miss the multidimensional semantics in “suitable for summer,” such as fabric, style, and breathability.
This problem also manifests in financial document search, smart customer support, and knowledge graph querying.
To address this, we aim to build a semantic search system based on vector retrieval. The core idea is to transform text fields in business data into semantic vectors in real time, store them in a vector-supporting database, and enable retrieval based on semantic similarity.
Key challenges include:
- High-performance access and synchronization of heterogeneous data sources
- Online generation of text embeddings via AI models
- Structured storage of embeddings and vector index construction
- A highly available and low-latency storage system for vector search
- End-to-end observability and scalability
Technology Stack & Core Architecture
To meet the system requirements, we chose a modern data engineering tech stack and implemented an end-to-end solution.
Apache SeaTunnel: Centralized Data Integration and Sync Engine
Apache SeaTunnel is an open-source, high-performance distributed data integration platform designed for both real-time and batch use cases. Key features include:
- Extensive connector plugin ecosystem: Supports 100+ data sources such as databases, message queues, file systems, object storage, and NoSQL systems
- Unified batch and stream processing: Suitable for full ingestion, incremental sync, and real-time CDC scenarios.
- Multi-engine support: Compatible with the native SeaTunnel Zeta engine, Flink, and Spark for flexible resource allocation.
- Pluggable transform layer: Custom Transform plugins can handle intermediate logic such as text preprocessing and external API calls for embeddings.
- Robust monitoring & ops: SeaTunnel Web offers graphical job orchestration and real-time monitoring.
In this solution, SeaTunnel serves as the data backbone. We use the Source module to extract raw data from databases or object storage, the Transform module to call the Amazon Bedrock API for text embeddings, and the Sink module to write the results into Amazon OpenSearch for vector-based semantic indexing.
Amazon Bedrock: Enterprise-Grade Embedding Service
Amazon Bedrock is a fully managed service from AWS that provides API access to popular foundation models (FMs) such as Claude (Anthropic), Cohere, Stability AI, Mistral, and Amazon’s own Titan models — without needing to build or manage any infrastructure.
For the text embedding task, we evaluated two models:
- Cohere Embed v3 Offers multimodal embedding for both text and image, supports 100+ languages, and excels in cross-lingual and semantic-rich retrieval systems. It’s especially effective for complex multi-domain matching tasks.
- Amazon Titan Embeddings v2 A native AWS offering, it provides compact, high-quality embeddings with 256/512/1024 dimensions. Titan strikes a good balance between compression and retrieval accuracy, ideal for low-latency, high-concurrency, and storage-sensitive use cases.
Using Bedrock’s API, we invoke the embedding models during the Transform stage in SeaTunnel, converting raw text fields into high-dimensional dense vectors while retaining metadata (ID, tags, etc.) for downstream processing.
Amazon OpenSearch: Cloud-Native Vector Search Storage
Amazon OpenSearch supports native knn_vector fields for indexing and retrieving vectorized data. It offers integration with popular ANN libraries like Faiss and NMSLIB and provides:
- High concurrency for vector insertions and searches
- Hybrid search: combine structured queries with semantic vector retrieval
- Customizable vector fields (dimension, distance metric, indexing parameters)
- KNN plugin support for HNSW and other ANN algorithms
- Seamless integration with OpenSearch query syntax for compound searches like “price range + similar description”
Using SeaTunnel’s OpenSearch Sink plugin, we can write both vectors and related structured fields (ID, title, tags) into OpenSearch — enabling low-code construction of a hybrid semantic+structured search engine.
System Architecture & Implementation Steps
Architecture Overview
Data Ingestion Example
Let’s walk through a real-world example using customer review data from Amazon’s e-commerce platform. The data is in JSON format:
{
"Item": {
"review_id": {"S": "AEEZL8Z5691IJ"},
"date": {"S": "1215475200"},
"customer": {"S": "Amazon Customer"},
"asin": {"S": "B000Q6R4MK"},
"review": {"S": "I can hear the caller just great -- but I get frequent \\"what?\\" \\"I can't hear you\\" etc."},
"rating": {"S": "4.0"}
}
}
In many e-commerce scenarios, we need to search based on the review field. Here, we aim to vectorize this field and write the results to OpenSearch.
Below is the SeaTunnel config file to process the JSON:
env {
\# Set the execution engine to SeaTunnel Zeta Engine
execution.engine = "seatunnel"
\# Set job mode to BATCH for processing the JSON file
job.mode = "BATCH"
}
source {
S3File {
path = "/data/3vk7gdzq6myxhn2kwexoiywjh4.json"
bucket = "s3a://opensearch"
file\_format\_type = "json"
\# AWS region configuration
fs.s3a.endpoint = "s3.us-east-1.amazonaws.com"
\# Use SimpleAWSCredentialsProvider instead of InstanceProfileCredentialsProvider
fs.s3a.aws.credentials.provider = "org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider"
\# Provide explicit AWS credentials -
access_key = ""
secret_key = ""
\# Additional S3A configuration options
hadoop\_s3\_properties {
"fs.s3a.impl" = "org.apache.hadoop.fs.s3a.S3AFileSystem"
"fs.s3a.connection.ssl.enabled" = "true"
}
\# Required schema for JSON file format
schema = {
fields {
Item = {
review_id = {
S = string
}
date = {
S = string
}
customer = {
S = string
}
asin = {
S = string
}
review = {
S = string
}
rating = {
S = string
}
}
}
}
\# Register output table
plugin_output = "s3_data"
}
}
transform {
\# First transform to extract the actual review text from the nested S field
Sql {
plugin_input = "s3_data"
plugin_output = "extracted_data"
query = "SELECT Item.review\_id.S as review\_id, Item.date.S as date, Item.customer.S as customer, Item.asin.S as asin, Item.review.S as review, Item.rating.S as rating FROM s3_data"
}
\# Use Amazon Bedrock to generate embeddings for the review field
Embedding {
plugin_input = "extracted_data"
plugin_output = "embedded_data"
\# Specify the model provider as AMAZON for Bedrock
model_provider = "AMAZON"
\# Specify the model ID for Amazon Titan Embeddings
model = "amazon.titan-embed-text-v2:0"
\# AWS region for Bedrock service
region = "us-east-1"
\# AWS credentials for Bedrock service -
api_key = ""
secret_key = ""
\# Define which fields to vectorize and their target fields
vectorization_fields {
review_embedding = review
}
\# Batch size for processing
single\_vectorized\_input_number = 10
dimension = 1024
}
}
sink {
Console {
plugin_input = "embedded_data"\# 使用 Embedding 转换后的数据
limit = 10
}
Elasticsearch {
plugin_input = "embedded_data"
\# OpenSearch endpoint
hosts = \["https://xxxxxx.us-east-1.es.amazonaws.com"\]
tls\_verify\_certificate = false
\# Index configuration
index = "reviews"
username = ""
password = ""
vectorization_fields = \["review_embedding"\]
vector_dimensions = 1024
}
}
In this stage, we run the job in SeaTunnel. For verification purposes, we direct the output to the console using a logging sink. As shown in the log, we can see that a new field review_embedding has been added to each entry:
Until the task is completed, see the statistics
Writing Embeddings to Amazon OpenSearch
Once the embedding is generated, we need to persist the results into a searchable system. In this case, we use the OpenSearch sink provided by SeaTunnel to write both the original fields and the generated semantic vector review_embedding into an OpenSearch index.
The corresponding configuration in SeaTunnel is as follows:
GET reviews/_search
{
"size": 5,
"query": {
"neural": {
"review_embedding": {
"query_text": "Installed and connected pretty well. Works good and keeps my eyes on the road. Took a few weeks to see the on and off button, but overall I like this and would suggest it.",
"model_id": "8xwrFJYB5648rVcWvwIU",
"k": 10
}
}
}
}
Summary and Outlook
Building a Scalable and Loosely Coupled Semantic Search Pipeline with Apache SeaTunnel, Amazon Bedrock, and Amazon OpenSearch
This article demonstrated how to build a scalable, loosely coupled semantic search pipeline by integrating Apache SeaTunnel, Amazon Bedrock, and Amazon OpenSearch. This solution integrates full-process from structured/unstructured text data ingestion to semantic vector-based retrieval.
Key Advantages of the Architecture
- Loosely Coupled Design: With SeaTunnel’s plugin-based architecture and modular support for
TransformandSinkThe logic for embedding model invocation and vector persistence is decoupled. This allows for seamless model replacement or downstream database switching in future iterations. - Cloud-Native AI Integration: By leveraging Amazon Bedrock’s API gateway and IAM-based access control, embedding capabilities can be integrated without the need to self-host model inference services — drastically reducing the operational barrier to adopting large-scale AI.
- Hybrid Search with Semantic and Structured Filtering: OpenSearch supports hybrid query execution, enabling both structured filtering (e.g., price ranges, categories) and semantic similarity ranking. This empowers a wide range of use cases — from e-commerce product search to enterprise knowledge retrieval.
Practical Tips for Enterprise-Scale Deployment
To successfully adopt this architecture in a production-grade enterprise environment, we recommend focusing on the following optimization areas:
1. Embedding Caching and Batch Inference Optimization
To reduce redundant model calls and lower embedding costs, implement deduplication and caching mechanisms (e.g., MD5 hashing or LRU cache) during the text embedding phase. Additionally, enable batch inference to process multiple texts in a single request — improving throughput and lowering API invocation costs for Bedrock.
2. Dimension Planning and Embedding Compression
Choose embedding dimensions that balance retrieval precision, query latency, and storage cost for each business scenario. For example, Titan Embedding 512 is suitable for mid-sized use cases. For large-scale applications, consider applying dimensionality reduction techniques like PCA to reduce vector size and storage footprint.
3. Vector Index Management and Lifecycle Control
Optimize your OpenSearch index by tuning parameters such as refresh_interval, segment merge policy, and HNSW-specific settings like M and EF. Establish a regular index rebuild strategy to maintain retrieval accuracy without sacrificing write performance.
4. Retrieval Quality Evaluation and Continuous Optimization
Construct offline evaluation datasets with labeled queries and ground truth results. Use standard metrics like Recall@K, MRR, and nDCG to measure embedding effectiveness. Combine these metrics with A/B testing to continuously iterate on model versions and index configurations.
Final Thoughts: From Data Pipelines to Semantic Understanding Platforms
This article outlines a practical path for transforming traditional data integration pipelines into intelligent, semantics-aware platforms. By integrating vector-based AI capabilities, organizations can unlock new potential in:
- Semantic search engines
- Personalized recommendation systems
- Knowledge retrieval platforms
- Enterprise-level Q&A assistants
Looking ahead, the fusion of multimodal embedding models and Retrieval-Augmented Generation (RAG) architectures will drive even deeper innovation. The synergy between Apache SeaTunnel and Amazon Bedrock positions them as a powerful pair for the future of AI-native data engineering.
