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To build semantic search in Java, turn document passages and user queries into compatible embeddings, store the document vectors with useful metadata, then retrieve the closest matches for each query. Spring AI and LangChain4j provide Java integrations; PostgreSQL with PGVector, OpenSearch, and Elasticsearch are possible backends. The right setup depends on your existing stack and whether you need relational storage, dedicated semantic-search workflows, or combined keyword and vector retrieval.
How Java semantic search works
An embedding model converts text into a numerical vector that captures aspects of its meaning. A vector store persists those vectors—often alongside the original text and metadata—and finds stored vectors similar to a query vector. The model creates embeddings; the store indexes and retrieves them. Spring AI describes this split and exposes a VectorStore abstraction for application code. Spring AI’s vector database reference
A typical application has two paths:
- Ingestion: load source material, split long documents into passages, attach metadata, generate embeddings, and store the records.
- Query: embed the user’s query with a compatible model, retrieve a top-K set of similar passages, and use the results in the application or a RAG prompt.
Semantic similarity is not a replacement for every kind of search. A query for an exact identifier, product code, or uncommon name may need lexical matching as well as vector similarity.
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Choose the Java abstraction and search backend
Choose the Java integration and the backend as related but separate decisions. Framework abstractions can make it easier to switch stores, but they may not expose every backend-specific capability; use a native client when the required operation is not available through the abstraction.
| Option | When it fits | Checks and trade-offs |
|---|---|---|
| PostgreSQL with PGVector | Your application already uses PostgreSQL and you want vector retrieval alongside relational data. | Confirm the extension and schema setup, vector dimensions, metadata needs, index type, and performance on your workload. Spring AI documents exact and approximate search options. |
| OpenSearch | Your team operates OpenSearch and wants its semantic-search workflows or configurable ingestion and indexing. | Configure an embedding model and matching index dimensions. Automated setup can simplify the workflow; manual setup offers more control. |
| Elasticsearch | You want vector retrieval integrated with full-text search, filters, and other search features. | Choose between a managed semantic-text workflow and a more customized approach, then evaluate hybrid-search relevance and operational fit. |
For the Java layer, Spring AI provides a VectorStore abstraction and integrations including PGVector. LangChain4j offers embedding-store integrations, including PgVectorEmbeddingStore. Compare them against the surrounding application, integration coverage, release compatibility, and the backend operations you need. Spring AI vector database integrations · LangChain4j embedding-store tutorial
Ingest documents and preserve useful context
Prepare passages and metadata
Build records from source content and keep metadata that will help identify or filter results, such as a source ID, title, section, date, or access-control attributes. Split lengthy material into retrieval-sized passages before embedding; OpenSearch documents a workflow that applies text chunking before text embedding. There is no universal chunk size or overlap: tune those choices against your corpus and the questions users actually ask. OpenSearch semantic search
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Write through the store integration
In Spring AI’s general pattern, the application loads content into Document objects and adds them to a VectorStore. The store handles embedding and persistence. The PGVector example then uses similaritySearch with a query and a top-K value. For other integrations, follow the selected store’s configuration and ingestion API. Spring AI vector database reference · Spring AI PGVector reference
Configure Spring AI with PGVector
Spring AI’s PGVector reference lists the spring-ai-starter-vector-store-pgvector starter, a PostgreSQL data source, and an EmbeddingModel. It also documents configuration for dimensions, distance type, and index type. Verify the artifact version and dependency management against the current Spring AI release train before using a build file; the documentation’s sample settings are examples, not universal tuning recommendations. Spring AI PGVector setup and configuration
- Add the integration and configure PostgreSQL. Include the PGVector starter appropriate to your Spring AI release and configure a working PostgreSQL data source with the required PGVector support.
- Configure an embedding model. The application needs an
EmbeddingModelto create vectors for ingested documents and queries. - Match dimensions and choose index behavior. Configure the vector dimensions to match the model’s output, then choose the index and distance settings appropriate to the application.
- Decide who initializes the schema. Spring AI schema initialization is opt-in. If Spring AI should initialize it, enable that configuration explicitly; otherwise, create and manage the schema through your own database process.
- Ingest documents and query. Add prepared documents to the store, then call its similarity search with a query and a suitable top-K value.
LangChain4j also documents a PGVector integration through PgVectorEmbeddingStore. Its integration page displays dev.langchain4j:langchain4j-pgvector:1.21.0-beta31; that is a page-specific beta version, not a general stable-version recommendation. The guide also describes hybrid search using both an embedding and query text. LangChain4j PGVector integration
Match vector dimensions and distance behavior
The vector field or index must accept the number of dimensions produced by the embedding model. The stored document vectors and query vectors must use compatible dimensions and embedding behavior. OpenSearch’s semantic-search documentation calls out setting output_dimension when the model differs from the workflow template’s default; Elastic likewise explains that vector dimensions are fixed by the model and must match. OpenSearch semantic-search dimensions · Elastic vector search
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In PGVector, changing the configured dimension can require recreating the vector table. Treat an embedding-model change as a data and index migration, not just an application configuration edit: verify compatibility, plan how to regenerate stored vectors when needed, and account for the table or index changes required by the selected setup. Spring AI PGVector reference
Choose exact or approximate nearest-neighbor search
Spring AI documents three PGVector index choices:
NONE: exact nearest-neighbor search, without an approximate index.IVFFlat: documented as faster to build and lower-memory than HNSW.HNSW: documented as offering a better speed-recall trade-off than IVFFlat and not requiring a training step, at the cost of higher memory and a slower build.
These are qualitative comparisons from the documentation, not benchmark results for your data. Measure recall, latency, memory use, and index-build time on representative data and queries before selecting an approximate index. Spring AI’s PGVector example uses HNSW and cosine distance, but neither is the right choice for every workload. Spring AI PGVector index and distance options
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Query, filter, and improve relevance
Use a compatible embedding setup for queries and ingestion, retrieve a manageable top-K set, and apply metadata filters when the application needs to restrict results by source, date, section, or access. Spring AI exposes similarity thresholds and metadata filter expressions, but the documentation does not prescribe a universal top-K or threshold. Set them by evaluating real queries against expected relevant passages rather than copying a sample value. Spring AI search controls
Use hybrid retrieval when exact wording matters
Compare pure vector retrieval with a combination of lexical and vector search for queries that depend on exact identifiers, names, product codes, or rare terms. Elastic documents combining meaning-based vector retrieval with full-text matching, filters, and other search operations. LangChain4j’s PGVector guide also documents a hybrid-search mode that uses both an embedding and query text. Elastic vector search and hybrid retrieval · LangChain4j PGVector integration
Evaluate the system before relying on it
Semantic-search quality depends on the corpus, chunking, embedding model, index, query mix, and any lexical or metadata filters. There is no universal accuracy, latency, or cost figure established for Java vector-search implementations. Build an evaluation set from representative user questions and known relevant passages, then check whether the retrieved results are useful and whether the system returns important exact matches. Measure latency and resource use on the deployment setup you intend to operate; do not treat an index’s documented qualitative trade-offs as a substitute for those measurements.
Review the official integration and backend documentation before pinning versions or copying configuration: framework releases, artifact versions, and defaults can change. The key compatibility check remains concrete: the selected model’s vector dimensions must agree with the vector field or index, and your application must use compatible embedding behavior for stored content and queries.
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