Spring AI Vector Store

Since Camel 4.17

Only producer is supported

The Spring AI Vector Store component provides support for storing and retrieving documents in vector databases using Spring AI vector store capabilities.

Vector stores enable:

  • Storage of documents with embeddings

  • Similarity search based on vector distance

  • Metadata-based filtering

  • Document management (add, delete)

URI format

spring-ai-vector-store:storeId[?options]

Where storeId can be any string to uniquely identify the endpoint

Configuring Options

Camel components are configured on two separate levels:

  • component level

  • endpoint level

Configuring Component Options

At the component level, you set general and shared configurations that are, then, inherited by the endpoints. It is the highest configuration level.

For example, a component may have security settings, credentials for authentication, urls for network connection and so forth.

Some components only have a few options, and others may have many. Because components typically have pre-configured defaults that are commonly used, then you may often only need to configure a few options on a component; or none at all.

You can configure components using:

  • the Component DSL.

  • in a configuration file (application.properties, *.yaml files, etc).

  • directly in the Java code.

Configuring Endpoint Options

You usually spend more time setting up endpoints because they have many options. These options help you customize what you want the endpoint to do. The options are also categorized into whether the endpoint is used as a consumer (from), as a producer (to), or both.

Configuring endpoints is most often done directly in the endpoint URI as path and query parameters. You can also use the Endpoint DSL and DataFormat DSL as a type safe way of configuring endpoints and data formats in Java.

A good practice when configuring options is to use Property Placeholders.

Property placeholders provide a few benefits:

  • They help prevent using hardcoded urls, port numbers, sensitive information, and other settings.

  • They allow externalizing the configuration from the code.

  • They help the code to become more flexible and reusable.

The following two sections list all the options, firstly for the component followed by the endpoint.

Component Options

The Spring AI Vector Store component supports 8 options, which are listed below.

Name Description Default Type

configuration (producer)

The configuration.

SpringAiVectorStoreConfiguration

filterExpression (producer)

Filter expression for metadata-based filtering in searches.

String

lazyStartProducer (producer)

Whether the producer should be started lazy (on the first message). By starting lazy you can use this to allow CamelContext and routes to startup in situations where a producer may otherwise fail during starting and cause the route to fail being started. By deferring this startup to be lazy then the startup failure can be handled during routing messages via Camel’s routing error handlers. Beware that when the first message is processed then creating and starting the producer may take a little time and prolong the total processing time of the processing.

false

boolean

operation (producer)

The operation to perform on the vector store (ADD, DELETE, SIMILARITY_SEARCH).

Enum values:

  • ADD

  • DELETE

  • SIMILARITY_SEARCH

ADD

SpringAiVectorStoreOperation

similarityThreshold (producer)

The minimum similarity score threshold (0-1) for similarity search.

0

double

topK (producer)

The maximum number of similar documents to return for similarity search.

5

int

vectorStore (producer)

Autowired Required The VectorStore to use for vector operations.

VectorStore

autowiredEnabled (advanced)

Whether autowiring is enabled. This is used for automatic autowiring options (the option must be marked as autowired) by looking up in the registry to find if there is a single instance of matching type, which then gets configured on the component. This can be used for automatic configuring JDBC data sources, JMS connection factories, AWS Clients, etc.

true

boolean

Endpoint Options

The Spring AI Vector Store endpoint is configured using URI syntax:

spring-ai-vector-store:storeId

With the following path and query parameters:

Path Parameters (1 parameters)

Name Description Default Type

storeId (producer)

Required The id.

String

Query Parameters (6 parameters)

Name Description Default Type

filterExpression (producer)

Filter expression for metadata-based filtering in searches.

String

operation (producer)

The operation to perform on the vector store (ADD, DELETE, SIMILARITY_SEARCH).

Enum values:

  • ADD

  • DELETE

  • SIMILARITY_SEARCH

ADD

SpringAiVectorStoreOperation

similarityThreshold (producer)

The minimum similarity score threshold (0-1) for similarity search.

0

double

topK (producer)

The maximum number of similar documents to return for similarity search.

5

int

vectorStore (producer)

Autowired Required The VectorStore to use for vector operations.

VectorStore

lazyStartProducer (producer (advanced))

Whether the producer should be started lazy (on the first message). By starting lazy you can use this to allow CamelContext and routes to startup in situations where a producer may otherwise fail during starting and cause the route to fail being started. By deferring this startup to be lazy then the startup failure can be handled during routing messages via Camel’s routing error handlers. Beware that when the first message is processed then creating and starting the producer may take a little time and prolong the total processing time of the processing.

false

boolean

Message Headers

The Spring AI Vector Store component supports 8 message header(s), which is/are listed below:

Name Description Default Type

CamelSpringAiVectorStoreOperation (producer)

Constant: OPERATION

The operation to perform (ADD, DELETE, SIMILARITY_SEARCH).

Enum values:

  • ADD

  • DELETE

  • SIMILARITY_SEARCH

SpringAiVectorStoreOperation

CamelSpringAiVectorStoreTopK (producer)

Constant: TOP_K

The maximum number of similar documents to return (topK).

Integer

CamelSpringAiVectorStoreSimilarityThreshold (producer)

Constant: SIMILARITY_THRESHOLD

The similarity threshold (0-1).

Double

CamelSpringAiVectorStoreFilterExpression (producer)

Constant: FILTER_EXPRESSION

Filter expression for metadata-based filtering.

String

CamelSpringAiVectorStoreDocumentIds (producer)

Constant: DOCUMENT_IDS

List of document IDs (input for DELETE, output for ADD and SIMILARITY_SEARCH).

List

CamelSpringAiVectorStoreSimilarDocuments (producer)

Constant: SIMILAR_DOCUMENTS

List of similar documents found.

List

CamelSpringAiVectorStoreDocumentsAdded (producer)

Constant: DOCUMENTS_ADDED

Number of documents added.

Integer

CamelSpringAiVectorStoreDocumentsDeleted (producer)

Constant: DOCUMENTS_DELETED

Number of documents deleted.

Integer

Operations

The component supports three main operations:

ADD Operation

Stores documents in the vector store. Can accept:

  • String text (embeddings will be generated automatically)

  • Document objects

  • List of Documents

  • Output from the spring-ai-embeddings component (semi-automatic integration, this depends on the underlying Spring AI Vectore Store implementation)

After documents are added, the component sets the CamelSpringAiVectorStoreDocumentIds header containing a list of the IDs of the added documents. This allows you to reference or track the stored documents in subsequent processing steps.

Best Practice: Use the ADD operation directly with the vector store component when working with text. The component will automatically handle embedding generation internally if the vector store is configured with an embedding model. This is the recommended approach as it simplifies your routes and reduces the number of steps.

DELETE Operation

Removes documents from the vector store by their IDs.

SIMILARITY_SEARCH Operation

Searches for documents similar to a query text.

The component returns matching documents in the message body and sets the following headers:

  • CamelSpringAiVectorStoreSimilarDocuments - List of Document objects found

  • CamelSpringAiVectorStoreDocumentIds - List of IDs of the found documents

Usage Examples

Add Documents from Text (Recommended)

This is the recommended best practice for adding documents. The vector store component handles embedding generation internally:

from("direct:store")
    .to("spring-ai-vector-store:myStore?operation=ADD&vectorStore=#vectorStore");

When you send plain text to this route, the component automatically: 1. Generates embeddings using the embedding model configured in the vector store 2. Creates a Document object 3. Stores it in the vector store

This single-step approach is simpler and more efficient than manually chaining the embeddings component.

Add from Embeddings Component (Alternative)

If you need explicit control over the embedding process or want to manipulate embeddings before storing, you can chain the components:

from("direct:embedAndStore")
    .to("spring-ai-embeddings:test?embeddingModel=#embeddingModel")
    .to("spring-ai-vector-store:test?vectorStore=#vectorStore");
This approach is only necessary when you need to access or modify the embeddings between generation and storage. For most use cases, the direct ADD operation (shown above) is preferred.

Similarity Search

from("direct:search")
    .setBody(constant("What is AI?"))
    .to("spring-ai-vector-store:myStore?operation=SIMILARITY_SEARCH&topK=5&similarityThreshold=0.7")
    .log("Found ${header.CamelSpringAiVectorStoreSimilarDocuments.size()} similar documents")
    .log("Document IDs: ${header.CamelSpringAiVectorStoreDocumentIds}");

Delete Documents

Delete documents by specifying their IDs:

from("direct:delete")
    .setHeader(SpringAiVectorStoreHeaders.DOCUMENT_IDS, constant(Arrays.asList("doc1", "doc2")))
    .to("spring-ai-vector-store:myStore?operation=DELETE");

You can also chain operations to search for documents and then delete them using the returned IDs:

from("direct:searchAndDelete")
    .setBody(constant("outdated content"))
    .to("spring-ai-vector-store:myStore?operation=SIMILARITY_SEARCH&topK=10")
    .log("Found ${header.CamelSpringAiVectorStoreDocumentIds.size()} documents to delete")
    // The DOCUMENT_IDS header from SIMILARITY_SEARCH is automatically used by DELETE
    .setHeader(SpringAiVectorStoreHeaders.OPERATION, constant(SpringAiVectorStoreOperation.DELETE))
    .to("spring-ai-vector-store:myStore");

Dynamic Operation

from("direct:dynamic")
    .setHeader(SpringAiVectorStoreHeaders.OPERATION, constant(SpringAiVectorStoreOperation.ADD))
    .to("spring-ai-vector-store:myStore");

Configuration

Configuring the Vector Store

To use the ADD operation with automatic embedding generation (recommended), configure your vector store with an embedding model:

// Create a Qdrant Client
QdrantClient qdrantClient
                = new QdrantClient(QdrantGrpcClient.newBuilder(QDRANT.getGrpcHost(), QDRANT.getGrpcPort(), false).build());

// Create an embedding model
EmbeddingModel embeddingModel = new OllamaEmbeddingModel(ollamaApi, options);

// Create a vector store with the embedding model
// Example with Qdrant
VectorStore vectorStore = QdrantVectorStore.builder(qdrantClient, embeddingModel)
    .collectionName("my_collection")
    .initializeSchema(true)
    .build();

// Or with Simple Vector Store
VectorStore vectorStore = new SimpleVectorStore(embeddingModel);

// Register in Camel context
context.getRegistry().bind("vectorStore", vectorStore);

// Use in route - the component will use the configured embedding model
from("direct:start")
    .to("spring-ai-vector-store:test?vectorStore=#vectorStore&operation=ADD");
Make sure your vector store is configured with an embedding model to enable automatic embedding generation when using the ADD operation with text input.

Search Parameters

  • topK - Maximum number of results (default: 5)

  • similarityThreshold - Minimum similarity score 0-1 (default: 0.0)

  • filterExpression - Metadata-based filter expression

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