Spring AI Embeddings

Since Camel 4.17

Only producer is supported

The Spring AI Embeddings component provides support for computing embeddings using Spring AI embedding models.

Embeddings are numerical vector representations of text that capture semantic relationships between inputs. They are useful for:

  • Semantic similarity search

  • Text clustering and classification

  • Retrieval-Augmented Generation (RAG)

  • Recommendation systems

This is a low-level component for direct embedding computation. For most use cases, you can use the camel-spring-ai-chat component with appropriate configuration for RAG scenarios, or the camel-spring-ai-vector-store component which can handle embeddings automatically without requiring this component directly.

URI format

spring-ai-embeddings:embeddingId[?options]

Where embeddingId 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 Embeddings component supports 4 options, which are listed below.

Name Description Default Type

configuration (producer)

The configuration.

SpringAiEmbeddingsConfiguration

embeddingModel (producer)

Autowired Required The EmbeddingModel to use for generating embeddings.

EmbeddingModel

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

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 Embeddings endpoint is configured using URI syntax:

spring-ai-embeddings:embeddingId

With the following path and query parameters:

Path Parameters (1 parameters)

Name Description Default Type

embeddingId (producer)

Required The id.

String

Query Parameters (2 parameters)

Name Description Default Type

embeddingModel (producer)

Autowired Required The EmbeddingModel to use for generating embeddings.

EmbeddingModel

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 Embeddings component supports 6 message header(s), which is/are listed below:

Name Description Default Type

CamelSpringAiEmbeddingMetadata (producer)

Constant: EMBEDDING_METADATA

The embedding response metadata.

EmbeddingResponseMetadata

CamelSpringAiEmbeddingIndex (producer)

Constant: EMBEDDING_INDEX

The index of the embedding in the response.

Integer

CamelSpringAiEmbedding (producer)

Constant: EMBEDDING

The Embedding object.

Embedding

CamelSpringAiEmbeddingInputText (producer)

Constant: INPUT_TEXT

The input text that was embedded.

String

CamelSpringAiEmbeddings (producer)

Constant: EMBEDDINGS

List of Embedding objects.

List

CamelSpringAiEmbeddingInputTexts (producer)

Constant: INPUT_TEXTS

List of input texts that were embedded.

List

Usage

The component accepts two types of input:

  • Single text: A String containing the text to embed

  • Batch processing: A List<String> containing multiple texts to embed at once

The embedding vectors are returned in the message body, with additional metadata available in headers.

Single Text Example

from("direct:start")
    .to("spring-ai-embeddings:myEmbedding")
    .log("Embedding vector: ${body}");

When processing a single String, the component returns:

  • Message Body: The embedding vector as float[]

  • Headers:

    • CamelSpringAiEmbedding - The Embedding object

    • CamelSpringAiEmbeddingIndex - The index (0 for single embeddings)

    • CamelSpringAiEmbeddingInputText - The original input text

    • CamelSpringAiEmbeddingMetadata - Response metadata

Batch Processing Example

from("direct:batch")
    .setBody(constant(List.of("text 1", "text 2", "text 3")))
    .to("spring-ai-embeddings:myEmbedding")
    .log("Number of embeddings: ${body.size()}");

When processing a List<String>, the component returns:

  • Message Body: List of embedding vectors as List<float[]>

  • Headers:

    • CamelSpringAiEmbeddings - List of Embedding objects

    • CamelSpringAiEmbeddingInputTexts - The original input texts as List<String>

    • CamelSpringAiEmbeddingMetadata - Response metadata

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