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new 47453758 [Doc] Add documentation for Amazon Bedrock, Amazon
OpenSearch, and Amazon S3 Vectors integrations (#680)
47453758 is described below
commit 474537589d08443ab703297e52616894a745e1ab
Author: Avichay Marciano <[email protected]>
AuthorDate: Mon May 25 16:53:00 2026 +0300
[Doc] Add documentation for Amazon Bedrock, Amazon OpenSearch, and Amazon
S3 Vectors integrations (#680)
---
docs/content/docs/development/chat_models.md | 97 +++++++++++
docs/content/docs/development/embedding_models.md | 94 +++++++++-
docs/content/docs/development/vector_stores.md | 198 ++++++++++++++++++++++
3 files changed, 388 insertions(+), 1 deletion(-)
diff --git a/docs/content/docs/development/chat_models.md
b/docs/content/docs/development/chat_models.md
index 99ac9d7e..5e619ac4 100644
--- a/docs/content/docs/development/chat_models.md
+++ b/docs/content/docs/development/chat_models.md
@@ -146,6 +146,103 @@ public class MyAgent extends Agent {
## Built-in Providers
+### Amazon Bedrock
+
+Amazon Bedrock provides access to a wide range of foundation models from
leading AI providers through a unified API. The Flink Agents Bedrock
integration uses the [Converse
API](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html),
which provides a consistent interface across all supported models with native
tool calling support. Authentication is handled via SigV4 using the AWS default
credentials chain. No API keys are required.
+
+{{< hint info >}}
+Amazon Bedrock is only supported in Java currently. To use Amazon Bedrock from
Python agents, see [Using Cross-Language
Providers](#using-cross-language-providers).
+{{< /hint >}}
+
+#### Prerequisites
+
+1. An AWS account with [Amazon Bedrock model
access](https://docs.aws.amazon.com/bedrock/latest/userguide/model-access.html)
enabled for the models you plan to use
+2. IAM credentials configured via any method supported by the [AWS Default
Credentials
Provider](https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/credentials-chain.html)
(environment variables, `~/.aws/credentials`, IAM role, etc.)
+
+#### BedrockChatModelConnection Parameters
+
+{{< tabs "BedrockChatModelConnection Parameters" >}}
+
+{{< tab "Java" >}}
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `region` | String | `"us-east-1"` | AWS region for the Bedrock service |
+| `model` | String | None | Default model ID (can be overridden per setup) |
+| `max_retries` | int | `5` | Maximum number of API retry attempts (retries on
throttling, 429, 503) |
+
+{{< /tab >}}
+
+{{< /tabs >}}
+
+#### BedrockChatModelSetup Parameters
+
+{{< tabs "BedrockChatModelSetup Parameters" >}}
+
+{{< tab "Java" >}}
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `connection` | String | Required | Reference to connection method name |
+| `model` | String | Required | Bedrock model ID (e.g.
`"us.anthropic.claude-sonnet-4-20250514-v1:0"`) |
+| `prompt` | Prompt \| String | None | Prompt template or reference to prompt
resource |
+| `tools` | List<String> | None | List of tool names available to the model |
+| `temperature` | double | `0.1` | Sampling temperature (0.0 to 1.0) |
+| `max_tokens` | int | None | Maximum number of tokens to generate |
+
+{{< /tab >}}
+
+{{< /tabs >}}
+
+#### Usage Example
+
+{{< tabs "Amazon Bedrock Usage Example" >}}
+
+{{< tab "Java" >}}
+```java
+public class MyAgent extends Agent {
+ @ChatModelConnection
+ public static ResourceDescriptor bedrockConnection() {
+ return
ResourceDescriptor.Builder.newBuilder(ResourceName.ChatModel.BEDROCK_CONNECTION)
+ .addInitialArgument("region", "us-east-1")
+ .build();
+ }
+
+ @ChatModelSetup
+ public static ResourceDescriptor bedrockChatModel() {
+ return
ResourceDescriptor.Builder.newBuilder(ResourceName.ChatModel.BEDROCK_SETUP)
+ .addInitialArgument("connection", "bedrockConnection")
+ .addInitialArgument("model",
"us.anthropic.claude-sonnet-4-20250514-v1:0")
+ .addInitialArgument("temperature", 0.1d)
+ .addInitialArgument("max_tokens", 4096)
+ .build();
+ }
+
+ ...
+}
+```
+{{< /tab >}}
+
+{{< /tabs >}}
+
+#### Available Models
+
+Amazon Bedrock supports models from multiple providers through a single API.
Visit the [Amazon Bedrock Model IDs
documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html)
for the complete and up-to-date list of available models.
+
+Some popular options include:
+- **Claude** (Anthropic): `us.anthropic.claude-sonnet-4-6`,
`us.anthropic.claude-opus-4-7`, `us.anthropic.claude-opus-4-6-v1`
+- **Llama** (Meta): `us.meta.llama4-scout-17b-16e-instruct-v1:0`
+- **Mistral**: `mistral.mistral-large-2402-v1:0`
+- **Amazon Nova**: `us.amazon.nova-pro-v1:0`, `us.amazon.nova-lite-v1:0`
+
+{{< hint warning >}}
+Model availability varies by AWS region and requires explicit model access
enablement in the Bedrock console. Always check the [Amazon Bedrock
documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/models-regions.html)
for regional availability before implementing in production.
+{{< /hint >}}
+
+{{< hint warning >}}
+**Current limitations:** The integration uses text content blocks only.
Extended thinking / reasoning content blocks (e.g. Claude extended thinking),
citation blocks, and image / document content blocks are not yet supported.
+{{< /hint >}}
+
### Anthropic
Anthropic provides cloud-based chat models featuring the Claude family, known
for their strong reasoning, coding, and safety capabilities.
diff --git a/docs/content/docs/development/embedding_models.md
b/docs/content/docs/development/embedding_models.md
index c23b796d..5546ee9c 100644
--- a/docs/content/docs/development/embedding_models.md
+++ b/docs/content/docs/development/embedding_models.md
@@ -189,6 +189,99 @@ public class MyAgent extends Agent {
## Built-in Providers
+### Amazon Bedrock
+
+Amazon Bedrock provides embedding capabilities through the Amazon Titan Text
Embeddings V2 model via the [InvokeModel
API](https://docs.aws.amazon.com/bedrock/latest/userguide/titan-embedding-models.html).
The integration supports configurable output dimensions (256, 512, or 1024)
and parallelizes batch embedding via a configurable thread pool, since the
Titan V2 model processes one text per API call. Authentication is handled via
SigV4 using the AWS default credentials chain.
+
+{{< hint info >}}
+Amazon Bedrock embedding models are only supported in Java currently. To use
Amazon Bedrock embeddings from Python agents, see [Using Cross-Language
Providers](#using-cross-language-providers).
+{{< /hint >}}
+
+#### Prerequisites
+
+1. An AWS account with [Amazon Bedrock model
access](https://docs.aws.amazon.com/bedrock/latest/userguide/model-access.html)
enabled for Amazon Titan Text Embeddings V2
+2. IAM credentials configured via any method supported by the [AWS Default
Credentials
Provider](https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/credentials-chain.html)
+
+#### BedrockEmbeddingModelConnection Parameters
+
+{{< tabs "BedrockEmbeddingModelConnection Parameters" >}}
+
+{{< tab "Java" >}}
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `region` | String | `"us-east-1"` | AWS region for the Bedrock service |
+| `model` | String | `"amazon.titan-embed-text-v2:0"` | Default embedding
model ID |
+| `embed_concurrency` | int | `4` | Thread pool size for parallel batch
embedding |
+| `max_retries` | int | `5` | Maximum number of API retry attempts (retries on
throttling, 429, 503) |
+
+{{< /tab >}}
+
+{{< /tabs >}}
+
+#### BedrockEmbeddingModelSetup Parameters
+
+{{< tabs "BedrockEmbeddingModelSetup Parameters" >}}
+
+{{< tab "Java" >}}
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `connection` | String | Required | Reference to connection method name |
+| `model` | String | None | Override the default embedding model from the
connection |
+| `dimensions` | int | None | Output embedding dimensions: 256, 512, or 1024 |
+
+{{< /tab >}}
+
+{{< /tabs >}}
+
+#### Usage Example
+
+{{< tabs "Amazon Bedrock Embedding Usage Example" >}}
+
+{{< tab "Java" >}}
+```java
+public class MyAgent extends Agent {
+
+ @EmbeddingModelConnection
+ public static ResourceDescriptor bedrockEmbeddingConnection() {
+ return
ResourceDescriptor.Builder.newBuilder(ResourceName.EmbeddingModel.BEDROCK_CONNECTION)
+ .addInitialArgument("region", "us-east-1")
+ .addInitialArgument("embed_concurrency", 8)
+ .build();
+ }
+
+ @EmbeddingModelSetup
+ public static ResourceDescriptor bedrockEmbedding() {
+ return
ResourceDescriptor.Builder.newBuilder(ResourceName.EmbeddingModel.BEDROCK_SETUP)
+ .addInitialArgument("connection", "bedrockEmbeddingConnection")
+ .addInitialArgument("model", "amazon.titan-embed-text-v2:0")
+ .addInitialArgument("dimensions", 1024)
+ .build();
+ }
+
+ ...
+}
+```
+{{< /tab >}}
+
+{{< /tabs >}}
+
+#### Available Models
+
+The Bedrock embedding integration currently supports:
+- **Amazon Titan Text Embeddings V2** (`amazon.titan-embed-text-v2:0`):
supports 256, 512, or 1024 dimensions
+
+{{< hint info >}}
+The integration always requests **normalized** embeddings (unit vectors),
which makes cosine similarity equivalent to dot product. If you need raw,
un-normalized vectors, use a custom provider.
+{{< /hint >}}
+
+Visit the [Amazon Bedrock Embedding Models
documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/titan-embedding-models.html)
for the latest information.
+
+{{< hint warning >}}
+Model availability varies by AWS region and requires explicit model access
enablement in the Bedrock console. Always check the [Amazon Bedrock
documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/models-regions.html)
for regional availability before implementing in production.
+{{< /hint >}}
+
### Ollama
Ollama provides local embedding models that run on your machine, offering
privacy and control over your data.
@@ -473,7 +566,6 @@ Flink Agents supports cross-language embedding model
integration, allowing you t
- Cross-language resources are currently supported only when [running in
Flink]({{< ref "docs/operations/deployment#run-in-flink" >}}), not in local
development mode
- Complex object serialization between languages may have limitations
{{< /hint >}}
-
### How To Use
To leverage embedding model supports provided in a different language, you
need to declare the resource within a built-in cross-language wrapper, and
specify the target provider as an argument:
diff --git a/docs/content/docs/development/vector_stores.md
b/docs/content/docs/development/vector_stores.md
index 4efcbe0a..3ef3fa0a 100644
--- a/docs/content/docs/development/vector_stores.md
+++ b/docs/content/docs/development/vector_stores.md
@@ -444,6 +444,204 @@ public class MyAgent extends Agent {
## Built-in Providers
+### Amazon OpenSearch
+
+[Amazon
OpenSearch](https://docs.aws.amazon.com/opensearch-service/latest/developerguide/)
is a managed vector search service available in two flavors: OpenSearch
Service (provisioned domains) and OpenSearch Serverless (AOSS). The Flink
Agents integration supports both via a single `service_type` parameter, with
IAM (SigV4) or basic authentication.
+
+{{< hint info >}}
+Amazon OpenSearch is only supported in Java currently. To use Amazon
OpenSearch from Python agents, see [Using Cross-Language
Providers](#using-cross-language-providers).
+{{< /hint >}}
+
+{{< hint info >}}
+Amazon OpenSearch implements `CollectionManageableVectorStore`, enabling
[Long-Term Memory]({{< ref "docs/development/memory/long_term_memory" >}})
support. Collections map to OpenSearch indices. OpenSearch indices do not
natively support attaching arbitrary metadata, so any `metadata` passed to
`createCollectionIfNotExists` is ignored. Callers needing per-document
attributes should put them on the documents themselves.
+{{< /hint >}}
+
+#### Prerequisites
+
+1. Either an [OpenSearch
Service](https://docs.aws.amazon.com/opensearch-service/latest/developerguide/)
provisioned domain with KNN enabled (version 2.x+), or an [OpenSearch
Serverless](https://docs.aws.amazon.com/opensearch-service/latest/developerguide/serverless.html)
collection of type `VECTORSEARCH`
+2. For IAM auth: IAM credentials configured via the [AWS Default Credentials
Provider](https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/credentials-chain.html)
with appropriate access policies (or a Serverless data-access policy)
+3. For basic auth (Service domains only): username and password for the
OpenSearch domain
+
+#### OpenSearchVectorStore Parameters
+
+{{< tabs "OpenSearchVectorStore Parameters" >}}
+
+{{< tab "Java" >}}
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `embedding_model` | String | Required | Reference to embedding model
resource name |
+| `endpoint` | String | Required | OpenSearch endpoint URL (e.g.
`https://my-domain.us-east-1.es.amazonaws.com` for a domain, or the
`*.aoss.amazonaws.com` endpoint for Serverless) |
+| `index` | String | Required | Default index name for document operations |
+| `service_type` | String | `"serverless"` | OpenSearch flavor: `"serverless"`
(AOSS) or `"domain"` (OpenSearch Service) |
+| `auth` | String | `"iam"` | Authentication method: `"iam"` (SigV4) or
`"basic"`. Basic auth is supported on Service domains only |
+| `username` | String | None | Username for basic authentication (required if
`auth=basic`) |
+| `password` | String | None | Password for basic authentication (required if
`auth=basic`) |
+| `vector_field` | String | `"embedding"` | Name of the KNN vector field in
the index |
+| `content_field` | String | `"content"` | Name of the text content field in
the index |
+| `region` | String | `"us-east-1"` | AWS region |
+| `dims` | int | `1024` | Vector dimensionality used when this integration
creates an index |
+| `max_bulk_mb` | int | `5` | Maximum bulk payload size in MB |
+
+{{< /tab >}}
+
+{{< /tabs >}}
+
+#### Usage Example
+
+{{< tabs "Amazon OpenSearch Usage Example" >}}
+
+{{< tab "Java" >}}
+
+For an OpenSearch Serverless (AOSS) collection with IAM auth (the default):
+
+```java
+public class MyAgent extends Agent {
+
+ @EmbeddingModelConnection
+ public static ResourceDescriptor bedrockEmbeddingConnection() {
+ return
ResourceDescriptor.Builder.newBuilder(ResourceName.EmbeddingModel.BEDROCK_CONNECTION)
+ .addInitialArgument("region", "us-east-1")
+ .build();
+ }
+
+ @EmbeddingModelSetup
+ public static ResourceDescriptor bedrockEmbedding() {
+ return
ResourceDescriptor.Builder.newBuilder(ResourceName.EmbeddingModel.BEDROCK_SETUP)
+ .addInitialArgument("connection", "bedrockEmbeddingConnection")
+ .addInitialArgument("dimensions", 1024)
+ .build();
+ }
+
+ @VectorStore
+ public static ResourceDescriptor opensearchStore() {
+ return
ResourceDescriptor.Builder.newBuilder(ResourceName.VectorStore.OPENSEARCH_VECTOR_STORE)
+ .addInitialArgument("embedding_model", "bedrockEmbedding")
+ .addInitialArgument("endpoint",
"https://abc123.us-east-1.aoss.amazonaws.com";)
+ .addInitialArgument("index", "my-vectors")
+ // service_type defaults to "serverless"; auth defaults to
"iam"
+ .addInitialArgument("dims", 1024)
+ .build();
+ }
+
+ ...
+}
+```
+
+For an OpenSearch Service provisioned domain with IAM auth:
+
+```java
+@VectorStore
+public static ResourceDescriptor opensearchDomainStore() {
+ return
ResourceDescriptor.Builder.newBuilder(ResourceName.VectorStore.OPENSEARCH_VECTOR_STORE)
+ .addInitialArgument("embedding_model", "bedrockEmbedding")
+ .addInitialArgument("endpoint",
"https://my-domain.us-east-1.es.amazonaws.com";)
+ .addInitialArgument("index", "my-vectors")
+ .addInitialArgument("service_type", "domain")
+ .addInitialArgument("auth", "iam")
+ .addInitialArgument("dims", 1024)
+ .build();
+}
+```
+
+For an OpenSearch Service domain with basic auth:
+
+```java
+@VectorStore
+public static ResourceDescriptor opensearchDomainBasicAuth() {
+ return
ResourceDescriptor.Builder.newBuilder(ResourceName.VectorStore.OPENSEARCH_VECTOR_STORE)
+ .addInitialArgument("embedding_model", "bedrockEmbedding")
+ .addInitialArgument("endpoint",
"https://my-domain.us-east-1.es.amazonaws.com";)
+ .addInitialArgument("index", "my-vectors")
+ .addInitialArgument("service_type", "domain")
+ .addInitialArgument("auth", "basic")
+ .addInitialArgument("username", "admin")
+ .addInitialArgument("password", "your-password")
+ .addInitialArgument("dims", 1024)
+ .build();
+}
+```
+
+{{< /tab >}}
+
+{{< /tabs >}}
+
+### Amazon S3 Vectors
+
+[Amazon S3
Vectors](https://docs.aws.amazon.com/AmazonS3/latest/userguide/s3-vectors.html)
is a purpose-built vector storage service from Amazon S3 that provides native
support for storing and querying vector embeddings with sub-second query
performance. It uses the S3 Vectors SDK for PutVectors, QueryVectors,
GetVectors, and DeleteVectors operations.
+
+{{< hint info >}}
+Amazon S3 Vectors is only supported in Java currently. To use Amazon S3
Vectors from Python agents, see [Using Cross-Language
Providers](#using-cross-language-providers).
+{{< /hint >}}
+
+{{< hint warning >}}
+Amazon S3 Vectors does **not** implement `CollectionManageableVectorStore`, so
it does not support [Long-Term Memory]({{< ref
"docs/development/memory/long_term_memory" >}}) features. It also does not
support `size()` or get-all operations: explicit document IDs are required for
`get()` and `delete()`.
+{{< /hint >}}
+
+#### Prerequisites
+
+1. An [S3 Vectors vector
bucket](https://docs.aws.amazon.com/AmazonS3/latest/userguide/s3-vectors-buckets.html)
and vector index created in your AWS account
+2. IAM credentials configured via the [AWS Default Credentials
Provider](https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/credentials-chain.html)
with appropriate S3 Vectors permissions
+
+#### S3VectorsVectorStore Parameters
+
+{{< tabs "S3VectorsVectorStore Parameters" >}}
+
+{{< tab "Java" >}}
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `embedding_model` | String | Required | Reference to embedding model
resource name |
+| `vector_bucket` | String | Required | S3 Vectors bucket name |
+| `vector_index` | String | Required | S3 Vectors index name within the bucket
|
+| `region` | String | `"us-east-1"` | AWS region |
+
+{{< /tab >}}
+
+{{< /tabs >}}
+
+#### Usage Example
+
+{{< tabs "Amazon S3 Vectors Usage Example" >}}
+
+{{< tab "Java" >}}
+
+```java
+public class MyAgent extends Agent {
+
+ @EmbeddingModelConnection
+ public static ResourceDescriptor bedrockEmbeddingConnection() {
+ return
ResourceDescriptor.Builder.newBuilder(ResourceName.EmbeddingModel.BEDROCK_CONNECTION)
+ .addInitialArgument("region", "us-east-1")
+ .build();
+ }
+
+ @EmbeddingModelSetup
+ public static ResourceDescriptor bedrockEmbedding() {
+ return
ResourceDescriptor.Builder.newBuilder(ResourceName.EmbeddingModel.BEDROCK_SETUP)
+ .addInitialArgument("connection", "bedrockEmbeddingConnection")
+ .addInitialArgument("dimensions", 1024)
+ .build();
+ }
+
+ @VectorStore
+ public static ResourceDescriptor s3VectorsStore() {
+ return
ResourceDescriptor.Builder.newBuilder(ResourceName.VectorStore.S3_VECTORS_VECTOR_STORE)
+ .addInitialArgument("embedding_model", "bedrockEmbedding")
+ .addInitialArgument("vector_bucket", "my-vector-bucket")
+ .addInitialArgument("vector_index", "my-index")
+ .addInitialArgument("region", "us-east-1")
+ .build();
+ }
+
+ ...
+}
+```
+
+{{< /tab >}}
+
+{{< /tabs >}}
+
### Chroma
[Chroma](https://www.trychroma.com/home) is an open-source vector database
that provides efficient storage and querying of embeddings with support for
multiple deployment modes.
