Vector databases are one of the most important technologies behind modern AI applications.
When you build a Java AI application, you often need to search for information based on meaning, not just exact keywords.
For example, a user may ask:
How can I create an object from another Java class?
Your knowledge base might contain:
Java object creation using constructors
A traditional keyword search may not consider these phrases similar enough.
A vector database can perform similarity search using numerical vector representations called embeddings.
The basic architecture is:
User Question
↓
Embedding Model
↓
Query Vector
↓
Vector Database
↓
Similar Vectors
↓
Relevant Documents
↓
LLM
↓
Answer
Spring AI describes vector databases as specialized databases for similarity search and provides a common VectorStore abstraction across multiple implementations. LangChain4j uses the EmbeddingStore abstraction for the same general purpose.
1. What Is a Vector?
A vector is a list of numbers representing information in a mathematical space.
For example:
[0.12, -0.45, 0.78, 0.21, ...]
An embedding model converts text into such vectors.
For example:
"Java inheritance"
↓
[0.12, -0.45, 0.78, ...]
Another sentence:
"One Java class extends another class"
↓
[0.14, -0.43, 0.76, ...]
The actual numbers are produced by the embedding model.
The important idea is that semantically related content can be represented close together in vector space.
2. What Is a Vector Database?
A vector database is a system designed to store embeddings and efficiently search for vectors that are similar to a query vector.
Traditional database search might do:
WHERE name = 'Java'
Vector search does something conceptually closer to:
Find vectors most similar to this query vector
Spring AI describes this distinction directly: vector databases perform similarity searches rather than traditional exact-match queries.
A simplified structure is:
Document
↓
Embedding
↓
Vector Database
and later:
Question
↓
Embedding
↓
Vector Search
↓
Similar Documents
3. Why Do AI Applications Need Vector Databases?
Suppose you have:
100,000 documents
A user asks:
How do I configure Spring Boot database connection pooling?
You do not want to send all 100,000 documents to an LLM.
Instead:
Question
↓
Embedding
↓
Vector Search
↓
Top relevant documents
↓
LLM
That is one of the main foundations of RAG.
Spring AI documents this exact relationship: data is loaded into a vector database, similar documents are retrieved for a query, and those documents are supplied as context to the AI model.
4. Vector Database vs Normal Database
A normal relational database is excellent for structured application data.
For example:
Users
Orders
Products
Employees
Payments
A vector database is optimized for similarity search.
For example:
Document embeddings
Knowledge-base chunks
Semantic search
RAG retrieval
The two can also be used together.
Java Application
│
├── SQL Database
│ └── Users, Orders, Products
│
└── Vector Store
└── Document Embeddings
A modern AI application frequently needs both.
5. Embeddings Are the Bridge
The vector database itself does not decide the meaning of your text.
The embedding model converts the text into a vector.
The complete flow is:
Text
↓
Embedding Model
↓
Vector
↓
Vector Database
For example:
"Java Spring Boot"
↓
Embedding Model
↓
[0.17, -0.32, 0.91, ...]
Another question:
"How do I develop a Spring application?"
↓
Embedding Model
↓
[0.19, -0.35, 0.88, ...]
The vector database compares these representations.
6. The Embedding Dimension
Every embedding model produces vectors of a particular dimensionality.
For example, conceptually:
Model A → 384 dimensions
Model B → 768 dimensions
Model C → another dimension
The vector store must be configured consistently with the embedding model being used.
For example, LangChain4j's JVector integration explicitly requires the configured dimension to match the embedding model's output dimension.
So you should not arbitrarily define:
vector(384)
unless your chosen embedding model actually produces 384-dimensional embeddings.
7. Similarity Search
Once vectors are stored, the main operation is similarity search.
Suppose your database contains:
Document A → Vector A
Document B → Vector B
Document C → Vector C
Document D → Vector D
The user asks a question.
The question is converted into:
Query Vector
The vector database compares it with stored vectors.
The result might be:
Document B → most similar
Document D → second
Document A → third
Document C → fourth
The top results can then be passed to the LLM.
8. Similarity Metrics
Different vector systems can use different similarity or distance measures.
Common concepts include:
Cosine similarity
Dot product
Euclidean distance
L1 distance
For example, pgvector supports L2 distance, inner product, cosine distance, and L1 distance.
The correct metric depends on the embedding model and application.
You should not assume that one metric is universally best.
9. Cosine Similarity
Cosine similarity compares the angle between vectors.
Conceptually:
Vector A
↗
/
/
Query Vector
If the vectors point in similar directions, their semantic representations can be considered similar.
This is commonly used in semantic-search applications.
The exact search operator and index strategy depend on the vector database.
10. Top-K Search
A vector search often returns the top K results.
For example:
Top 3
Top 5
Top 10
Suppose:
Question
↓
Vector Search
↓
Top 5 chunks
The application can then send those five chunks to the LLM.
Choosing K is an application design decision.
Too few results:
Important information may be missed.
Too many:
The context may become noisy or unnecessarily large.
LangChain4j's retrieval components expose controls such as maximum results and minimum relevance score.
11. Metadata
A vector record normally contains more than just a vector.
You may store:
ID
Vector
Text
Metadata
Example:
{
"id": "doc-1001",
"text": "Spring Boot configuration...",
"metadata": {
"category": "spring",
"version": "3",
"source": "spring-guide.pdf"
}
}
Metadata is extremely useful for filtering.
For example:
category = spring
or:
version = 3
LangChain4j supports metadata storage and filtering across many embedding stores.
12. Metadata Filtering
Suppose your database contains:
Java Documents
Python Documents
SQL Documents
HR Documents
A user asks:
Explain Java interfaces.
You can retrieve only documents matching:
category = java
Conceptually:
Question
↓
Vector Search
+
Metadata Filter
↓
Relevant Java Documents
This can improve both accuracy and security.
13. User-Level Access Control
Metadata can also be used for access control.
Imagine:
User A
├── Document 1
└── Document 2
User B
├── Document 3
└── Document 4
Your Java application can store:
userId = 100
as metadata.
Then retrieval can be restricted accordingly.
Logged-in User
↓
Java
↓
Metadata Filter
↓
Allowed Documents
↓
Vector Search
This is important for multi-user RAG systems.
14. Popular Vector Database Options
Java developers can choose from many vector-store technologies.
Examples include:
PostgreSQL + PGVector
Qdrant
Pinecone
Milvus
Weaviate
Redis
Elasticsearch
OpenSearch
Chroma
MongoDB Atlas
Spring AI currently provides vector-store integrations for many of these, including PGVector, Qdrant, Pinecone, Redis, Weaviate, Elasticsearch, Milvus, MongoDB Atlas, and others.
LangChain4j also supports a large collection of embedding stores, including PGVector, Qdrant, Milvus, Pinecone, Weaviate, Redis, Elasticsearch and many others.
15. PostgreSQL + PGVector
For Java developers already familiar with relational databases, PostgreSQL plus pgvector is an especially interesting option.
pgvector is an open-source PostgreSQL extension for vector similarity search.
You enable it with:
CREATE EXTENSION vector;
Then you can create a vector column:
CREATE TABLE documents (
id BIGSERIAL PRIMARY KEY,
content TEXT,
embedding VECTOR(384)
);
The 384 here is only an example. It must match the actual embedding dimension.
16. Insert a Vector
pgvector allows vectors to be stored directly in PostgreSQL.
For example:
INSERT INTO documents (content, embedding)
VALUES (
'Java inheritance tutorial',
'[0.12, -0.45, 0.78, ...]'
);
In a real Java application, the embedding would be generated by an embedding model rather than manually typed.
17. Search with PGVector
pgvector supports nearest-neighbor queries.
For example:
SELECT *
FROM documents
ORDER BY embedding <-> '[0.11, -0.42, 0.75, ...]'
LIMIT 5;
The <-> operator is used for L2 distance.
pgvector also supports cosine distance and inner-product operators.
18. Why PGVector Is Interesting for Java
Suppose your application already uses:
Spring Boot
PostgreSQL
JPA
You can potentially use PostgreSQL for both normal relational data and vector data.
Architecture:
Spring Boot
│
├── PostgreSQL tables
│ ├── Users
│ ├── Products
│ └── Orders
│
└── PGVector
└── Document embeddings
This can simplify infrastructure for applications that do not need a separate specialized vector database.
Spring AI currently provides a PgVectorStore implementation for PostgreSQL/PGVector.
19. Spring AI VectorStore
Spring AI provides a common abstraction:
VectorStore
This allows application code to interact with vector stores without having to implement every provider from scratch.
Conceptually:
Java
↓
Spring AI VectorStore
↓
Vector Database
The same abstraction can be backed by different vector-store implementations.
Spring AI's current documentation lists VectorStore and VectorStoreRetriever as the main abstractions for storing and retrieving documents.
20. Spring AI Document
Spring AI's RAG/vector-store model revolves around documents.
Conceptually:
Document document =
new Document("Java Spring Boot tutorial");
A document can also contain metadata.
For example:
Map<String, Object> metadata =
Map.of(
"category", "java",
"source", "java-guide.pdf"
);
Then the document can be written to a vector store.
21. Writing Documents to VectorStore
Conceptually:
vectorStore.add(documents);
The Spring AI pipeline handles the required embedding operations through the configured embedding model and vector-store integration.
The conceptual flow is:
Java Document
↓
Embedding Model
↓
Vector
↓
VectorStore
This is exactly the kind of abstraction Spring AI is designed to provide.
22. Searching with Spring AI
A vector store can be queried using a natural-language question.
Conceptually:
List<Document> results =
vectorStore.similaritySearch(
SearchRequest.builder()
.query("How does Java inheritance work?")
.topK(5)
.build()
);
The vector store converts or uses the query embedding and returns similar documents through the Spring AI abstraction.
Spring AI documents similaritySearch(...) through VectorStore and VectorStoreRetriever.
23. Spring AI RAG Architecture
A simple Spring AI RAG application can be:
USER
│
▼
Spring Boot
│
▼
Question
│
▼
VectorStore
│
▼
Relevant Documents
│
▼
Chat Model
│
▼
Answer
Spring AI's QuestionAnswerAdvisor is specifically designed to add retrieved vector-store context to a user query before it is sent to the chat model.
24. Spring AI + Ollama
You can combine local AI with a vector database.
For example:
Spring Boot
↓
Spring AI
↓
Vector Database
↓
Relevant Context
↓
Ollama
↓
Local LLM
This creates a local RAG architecture.
A typical setup can therefore contain:
Embedding Model
↓
Vector Store
↓
Ollama LLM
The embedding model and generation model do not have to be the same model.
25. LangChain4j EmbeddingStore
LangChain4j calls its vector-store abstraction:
EmbeddingStore
The documentation describes EmbeddingStore as a store for embeddings and the associated embedded content.
A simplified architecture is:
EmbeddingModel
↓
Embedding
↓
EmbeddingStore
LangChain4j supports many embedding stores through this abstraction.
26. LangChain4j Example
A conceptual example is:
EmbeddingStore<TextSegment> store =
new InMemoryEmbeddingStore<>();
Embedding embedding =
embeddingModel.embed(
"Java inheritance tutorial"
).content();
store.add(
embedding,
TextSegment.from(
"Java inheritance tutorial"
)
);
Then:
Embedding query =
embeddingModel.embed(
"How does inheritance work in Java?"
).content();
A similarity search can then retrieve related segments.
The actual implementation depends on which LangChain4j embedding-store integration you choose.
27. In-Memory Vector Store
For learning, an in-memory store is very convenient.
Java Application
↓
Memory
↓
Vectors
There is no separate database server.
This is good for:
Learning
Testing
Small demos
Proofs of concept
But it is usually not the right choice for a serious persistent production system.
Spring AI explicitly describes SimpleVectorStore as suitable for testing/demonstration rather than production.
28. JVector
Java developers can also consider Java-native vector search technologies.
JVector is a pure-Java embedded vector search engine with approximate nearest-neighbor search. LangChain4j currently provides a JVector integration.
Its architecture can be:
Java
↓
JVector
↓
Local Vector Index
This can be attractive when you want vector search inside a Java process without a separate database service.
JVector supports configurable similarity functions, persistence, dynamic updates, and graph-based approximate nearest-neighbor search.
29. Vector Database vs Vector Library
These terms can sometimes be confusing.
A specialized vector database:
Qdrant
Milvus
Pinecone
is generally designed to act as a persistent service.
A Java embedded/vector-search library can instead operate within your application:
Java
↓
JVector
And a relational database extension can combine both approaches:
PostgreSQL
+
PGVector
The right choice depends on your scale, infrastructure, query requirements, and operational preferences.
30. Indexing Pipeline
Before a document can be searched semantically, it needs to be indexed.
The complete pipeline is:
PDF / DOC / HTML / TXT
↓
Text Extraction
↓
Chunking
↓
Embedding Model
↓
Vectors
↓
Vector Database
Suppose you have:
spring-boot-guide.pdf
The system might create:
Chunk 1
Chunk 2
Chunk 3
...
Chunk 500
Then every chunk receives an embedding.
31. Why Store Chunks Instead of Whole Documents?
Suppose a 300-page PDF is represented by only one vector.
A query about page 217 may retrieve the entire document.
That is not ideal.
Instead:
300-page PDF
↓
Many chunks
↓
Many embeddings
Now the vector search can retrieve smaller and more relevant pieces.
This is one of the main reasons vector databases are so useful for RAG.
32. Document IDs
Each vector record should normally have an identifiable ID.
For example:
doc-001-chunk-001
doc-001-chunk-002
doc-001-chunk-003
Or use a database-generated unique ID.
IDs help with:
Updating documents
Deleting documents
Tracking sources
Deduplication
Debugging
Access control
33. Source Information
A RAG application should ideally preserve source metadata.
For example:
{
"source": "spring-guide.pdf",
"page": 128,
"section": "Database Configuration"
}
Then the AI application can return:
Answer:
...
Sources:
spring-guide.pdf, page 128
This makes the result easier to verify.
34. Updating Documents
Suppose you have:
employee-policy-v1.pdf
Later it becomes:
employee-policy-v2.pdf
The old vectors should not continue to be retrieved as if they were current.
A document-indexing system should be able to:
Delete old chunks
Insert new chunks
Update metadata
Track version
Metadata makes this easier.
35. Deleting Vectors
A production vector store should support removal of old embeddings.
For example:
Delete document ID
or:
Delete where metadata.documentId = 123
LangChain4j's embedding-store ecosystem includes support for removing embeddings across many implementations.
36. Hybrid Search
Vector search is not always enough.
Suppose the user searches:
CVE-2026-12345
An exact keyword search can be very useful.
For natural-language questions:
How do I configure Spring Boot authentication?
semantic search can be useful.
A modern search system can combine:
Keyword Search
+
Vector Search
This is often called hybrid search.
Some vector/search systems support dense, sparse, or hybrid retrieval directly. For example, current LangChain4j documentation identifies Milvus's current v2 integration as supporting dense, sparse, and hybrid search, including BM25.
37. Vector Search + Reranking
A common architecture is:
Question
↓
Vector Search
↓
Top 20 results
↓
Reranker
↓
Best 5 results
↓
LLM
The vector database performs the initial broad retrieval.
A reranking stage then attempts to identify the strongest candidates.
This can improve retrieval quality for complex applications.
38. Vector Database + Chat Memory
A vector database and chat memory are not the same thing.
Chat memory answers:
What did the user say earlier in this conversation?
Vector search answers:
Which documents in my knowledge base are relevant?
You can combine both:
User Message
↓
Chat Memory
+
Vector Retrieval
↓
LLM
↓
Response
This produces a much more capable AI assistant.
39. Vector Database + SQL
You may also have:
SQL Database
containing structured information.
For example:
Customer
Order
Product
Price
Inventory
And a vector database containing:
Product descriptions
Documentation
Support articles
Manuals
Java can use both:
User
↓
Java
├── SQL Query
└── Vector Search
↓
Combined Context
↓
LLM
This is a very common enterprise architecture.
40. Vector Database + AI Tools
A vector database can provide knowledge.
Tools can provide actions.
For example:
RAG
→ Search company policy
Tool
→ Create leave request
Architecture:
AI
/ \
/ \
RAG Tools
│ │
▼ ▼
Knowledge Actions
This distinction is important:
Vector Search
= Find information
Tool Calling
= Perform an operation
41. Vector Database + AI Agents
An AI agent can use vector search as one of its tools.
For example:
User
↓
Agent
├── Search Knowledge Base
├── Query Database
├── Call REST API
└── Generate Answer
The vector database becomes one component of a larger agent architecture.
42. Security in Vector Databases
Security is important because vector stores can contain sensitive content.
Protect:
Database credentials
Embedding data
Document content
Metadata
User access
Network access
For multi-user systems, retrieval must respect document permissions.
The architecture should be:
Authenticated User
↓
Java Authorization
↓
Allowed Metadata Scope
↓
Vector Search
↓
Allowed Documents
Do not rely only on the LLM to enforce access control.
The Java application and data layer should enforce authorization.
43. Vector Database Performance
Important performance factors include:
Number of vectors
Vector dimensions
Index type
Search algorithm
Storage
CPU
RAM
Concurrency
Metadata filters
A small prototype:
10,000 vectors
is very different from:
100 million vectors
The architecture that works for one may not work for the other.
44. Exact Search vs Approximate Search
For a very small dataset, brute-force comparison may be perfectly acceptable.
For a huge dataset, comparing the query against every vector may become expensive.
Approximate nearest-neighbor algorithms are designed to search large vector spaces more efficiently.
JVector, for example, provides graph-based approximate nearest-neighbor search.
Different vector stores use different indexing techniques and implementations.
45. HNSW
One algorithm family you will often encounter is:
HNSW
It stands for:
Hierarchical Navigable Small World
It is a graph-based approximate nearest-neighbor search approach.
You do not need to understand its mathematics before building your first RAG application.
At the application level, remember:
Vectors
↓
Vector Index
↓
Fast Similarity Search
46. Java RAG with PGVector
A practical Java stack might be:
Java
↓
Spring Boot
↓
Spring AI
↓
Embedding Model
↓
PostgreSQL + PGVector
↓
LLM
For example:
User Question
↓
Spring Boot
↓
Embedding Model
↓
PGVector
↓
Relevant Chunks
↓
Ollama / Gemini / Claude / OpenAI
↓
Answer
Spring AI currently provides a dedicated PGVector integration.
47. Java Local RAG
A fully local architecture can be:
Java
↓
Spring AI
├── Local Embedding Model
├── Local Vector Store
└── Ollama
The data flow is:
Private Documents
↓
Local Embeddings
↓
Local Vector Database
↓
Ollama
↓
Local LLM
This can reduce dependence on external AI APIs, although the operational requirements move onto your own machine/server.
48. Cloud RAG
You can also use hosted services.
Java
↓
Cloud Vector Database
↓
Cloud LLM
Examples could include:
Java
↓
Pinecone / Qdrant Cloud / managed search
↓
Cloud LLM
The correct choice depends on:
Privacy
Cost
Scale
Latency
Operations
Hardware
49. Hybrid Architecture
A hybrid system can be:
Java
↓
Local / Private Vector Database
↓
Relevant Context
↓
Cloud LLM
↓
Answer
This can keep retrieval infrastructure under your control while using a hosted model for generation.
Another option is:
Java
↓
Local Vector DB
↓
Ollama
↓
Local LLM
50. Choosing a Vector Database
There is no universal vector database that is correct for every Java project.
Think about:
Dataset size
Search requirements
Metadata filtering
Hybrid search
Persistence
Cloud vs local
Existing infrastructure
Java integration
Operational complexity
Cost
For example:
Small demo
→ In-memory
Java-native embedded search
→ JVector
Already using PostgreSQL
→ PGVector
Dedicated vector infrastructure
→ Qdrant / Milvus / Weaviate
Managed cloud service
→ Managed vector/search provider
These are architectural options rather than a universal ranking.
Spring AI and LangChain4j both support a broad range of vector-store implementations, which makes experimentation easier.
51. Spring AI vs LangChain4j
Both frameworks provide Java abstractions.
Spring AI
Useful when your application is already based on:
Spring Boot
Spring configuration
Spring services
Spring ecosystem
Important concepts include:
VectorStore
Document
EmbeddingModel
ChatClient
Retrieval
RAG
Spring AI's current vector-store documentation lists numerous implementations behind the VectorStore abstraction.
LangChain4j
Useful when you want a Java-focused AI framework with:
EmbeddingModel
EmbeddingStore
ContentRetriever
AI Services
RAG
Tools
Agents
Chat Memory
LangChain4j states that its goal is to simplify integrating LLMs and vector stores into Java applications and provides unified APIs across many providers.
52. Vector Database Project Example
A very practical project is:
Java Technical Documentation Search
Store:
Java Documentation
Spring Boot Documentation
SQL Documentation
Docker Documentation
Indexing:
Documents
↓
Chunking
↓
Embeddings
↓
Vector Database
Search:
User Question
↓
Embedding
↓
Vector Search
↓
Relevant Documentation
Generation:
Relevant Documentation
+
User Question
↓
LLM
↓
Answer
53. Example Project Structure
A Spring Boot project could look like:
src/main/java/com/example/vectorai
│
├── controller
│ └── SearchController.java
│
├── service
│ ├── EmbeddingService.java
│ ├── VectorSearchService.java
│ └── RagService.java
│
├── ingestion
│ ├── DocumentLoader.java
│ └── DocumentIndexer.java
│
├── config
│ ├── AiConfig.java
│ └── VectorStoreConfig.java
│
├── dto
│ ├── SearchRequest.java
│ └── SearchResponse.java
│
└── Application.java
For larger applications, separate ingestion and query services can be useful.
54. Ingestion API
You could expose:
POST /api/documents
The Java service could:
Receive document
↓
Extract text
↓
Split into chunks
↓
Generate embeddings
↓
Store in vector database
55. Search API
Then expose:
POST /api/search
Request:
{
"query": "How does Java dependency injection work?"
}
Response:
{
"results": [
{
"text": "Dependency injection...",
"source": "spring-guide.pdf"
}
]
}
This is a useful first project before adding an LLM.
56. RAG API
Finally, add:
POST /api/rag/chat
The flow becomes:
Question
↓
Vector Search
↓
Context
↓
LLM
↓
Answer
Now the project has become a real RAG application.
57. Important Difference: Search vs RAG
A vector database can be useful even without an LLM.
For example:
User
↓
Semantic Search
↓
Search Results
That is semantic search.
RAG adds:
Search Results
↓
LLM
↓
Generated Answer
So:
Vector Search
≠
RAG
Instead:
Vector Search
+
LLM
+
Retrieved Context
=
RAG
58. Important Difference: Embedding Model vs LLM
Another common beginner confusion is assuming the LLM generates the vector.
There are usually separate roles:
Embedding Model
→ Converts text to vectors
LLM
→ Generates natural-language answers
The architecture is:
Document
↓
Embedding Model
↓
Vector DB
and later:
Question
↓
Embedding Model
↓
Vector DB
↓
Context
↓
LLM
↓
Answer
The embedding model and LLM can be completely different models.
59. Important Difference: Vector Database vs LLM
The vector database does not normally generate the final natural-language answer.
For example:
Vector Database:
"Here are the five most similar chunks."
Then:
LLM:
"Based on these chunks, here is the answer..."
So:
Vector Database
= Retrieval
LLM
= Generation
60. Final Architecture
A complete Java vector-search/RAG system can look like this:
DOCUMENTS
│
▼
Document Reader
│
▼
Chunking
│
▼
Embedding Model
│
▼
Vector Database
│
│
│
USER │
│ │
▼ │
QUESTION │
│ │
▼ │
Embedding Model │
│ │
└──────────────┬───────────┘
▼
Similarity Search
│
▼
Relevant Document Chunks
│
▼
Context Construction
│
▼
LLM
│
▼
Answer
│
▼
USER
The important responsibilities are:
Java
→ Application logic and orchestration
Embedding Model
→ Text → vectors
Vector Database
→ Store and search vectors
Retriever
→ Select relevant content
LLM
→ Generate the answer
RAG
→ Combines retrieval with generation
61. Java Vector Database Learning Path
A practical learning order is:
Java
↓
Spring Boot
↓
Basic LLM API
↓
Embeddings
↓
Vector Database
↓
Similarity Search
↓
Metadata
↓
Filtering
↓
RAG
↓
Reranking
↓
Hybrid Search
↓
Tool Calling
↓
AI Agents
Do not start by trying to build a large agent.
First understand this:
Text
↓
Embedding
↓
Vector
↓
Vector Database
↓
Similarity Search
Then add:
Retriever
↓
LLM
↓
RAG
Conclusion
Java + Vector Databases is a core topic for anyone building modern Java AI applications.
The key architecture is:
Documents
↓
Embeddings
↓
Vector Database
Question
↓
Embedding
↓
Similarity Search
↓
Relevant Context
↓
LLM
↓
Answer
The main concepts you should understand are:
Embedding
Vector
Dimension
Similarity
Top-K
Metadata
Filtering
Indexing
Vector Store
Retriever
RAG
Reranking
Hybrid Search
For Java, two important ecosystems are Spring AI and LangChain4j. Spring AI provides a common VectorStore abstraction and integrations for many vector databases, while LangChain4j provides EmbeddingStore, retrieval, RAG, and integrations with a large range of embedding stores.
A practical beginner setup is:
Java
↓
Spring Boot
↓
Spring AI
↓
Embedding Model
↓
PGVector / Qdrant / another Vector Store
↓
Ollama or Cloud LLM
↓
RAG
Once you understand vector databases, the next major Java AI topic is Java + Embeddings, where you can go deeper into how text becomes vectors, embedding dimensions, similarity calculations, local embedding models, and how Java generates and stores embeddings.
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