Repository: camunda/camunda-docs
Pull request #5726: docs: added vector DB support
Author: login: igpetrov
Labels: name: deploy
description: Stand up a temporary docs site with this PR
name: target:8.8
description: Issues included in the 8.8 release
name: 8.8.0-alpha5
description: June 2025 alpha release
Created: 2025-05-06T13:00:53Z | Merged: 2025-05-19T09:53:59Z

## Description

Adds documentation to a new Connector: Vector DB Connector

## When should this change go live?

It's for next minor (alpha5, sorry there is no tag existing like that) / 8.8.

- [ ] This is a bug fix, security concern, or something that needs **urgent release support**. (add `bug` or `support` label)
- [ ] This is already available but undocumented and should be released within a week. (add `available & undocumented` label)
- [ ] This is on a **specific schedule** and the assignee will coordinate a release with the Documentation team. (create draft PR and/or add `hold` label)
- [x] This is part of a scheduled **alpha or minor**. (add alpha or minor label)
- [ ] There is **no urgency** with this change (add `low prio` label)

## PR Checklist

<!-- Camunda maintains 18 months of minor versions. Backporting your change to multiple versions is common. -->

- [x] My changes are for **an upcoming minor release** and are in the `/docs` directory (version 8.8).
- [ ] My changes are for an **already released minor** and are in a `/versioned_docs` directory.

<!-- UNCOMMENT THIS SECTION IF APPLICABLE. Adding or removing pages requires extra steps.
- [ ] I included my new page in the sidebar file(s).
- [ ] I added a redirect for a renamed or deleted page to the .htaccess file.
-->

- [x] I added a DRI, team, or delegate as a reviewer for technical accuracy and grammar/style:
  - [x] [Engineering team review](https://github.com/camunda/camunda-docs/blob/main/howtos/documentation-guidelines.md#review-process)
  - [x] [Technical writer review](https://github.com/camunda/camunda-docs/blob/main/howtos/documentation-guidelines.md#review-process) via `@camunda/tech-writers` unless working with an embedded writer.

<!-- UNCOMMENT THIS SECTION IF APPLICABLE. Changes to **docs infra**, including updates to workflows and adding new npm packages, must be first discussed via issue or #ask-c8-documentation and linked for context.
- [ ] My changes require a [docs infrastructure review](https://github.com/camunda/camunda-docs/blob/main/howtos/documentation-guidelines.md#review-process). (add `dx` label) -->



Diff:
diff --git a/docs/components/connectors/out-of-the-box-connectors/embeddings-vector-db.md b/docs/components/connectors/out-of-the-box-connectors/embeddings-vector-db.md
new file mode 100644
index 00000000000..7742e2e3ead
--- /dev/null
+++ b/docs/components/connectors/out-of-the-box-connectors/embeddings-vector-db.md
@@ -0,0 +1,178 @@
+---
+id: embeddings-vector-db
+title: Vector database connector
+sidebar_label: Vector database
+description: Embed, store, and retrieve LLM embeddings and store them in vector databases.
+---
+
+import Tabs from "@theme/Tabs";
+import TabItem from "@theme/TabItem";
+
+The **vector database connector** allows embedding, storing, and retrieving Large Language Model (LLM) embeddings. This enables building AI-based solutions for your organizations, such as context document search, long-term LLM memory, and agentic AI interaction.
+
+:::note
+The **vector database connector** uses the [LangChain4J](https://docs.langchain4j.dev/) library. Data models and possible implementations are limited
+to the latest stable released LangChain4j library.
+:::
+
+## Prerequisites
+
+Before using the **vector database connector**, ensure you understand the
+[concept of LLM embeddings](https://huggingface.co/spaces/hesamation/primer-llm-embedding).
+
+To start using the **vector database connector**, ensure you have access to a supported LLM embeddings API to convert document content into vectorized embedding form. You will also need to have write access to a supported database.
+
+## Create a connector task
+
+import ConnectorTask from '../../../components/react-components/connector-task.md'
+
+<ConnectorTask/>
+
+## Operations
+
+<Tabs groupId="operations" defaultValue="embed" queryString values={
+[
+{label: 'Embed document', value: 'embed' },
+{label: 'Retrieve document', value: 'retrieve' },
+]}>
+
+<TabItem value='embed'>
+
+The **embed document** operation performs the following steps:
+
+1. [Consume a document](#embedding-document-configuration).
+2. Parse the document depending on a file format (optionally [split into text chunks](#splitting)).
+3. Convert chunks into a vector form with LLM help.
+4. Store produced vectors in a vector database.
+
+To perform this operation, enter the following:
+
+- **Operation** dropdown: **Embed document**.
+- **Embedding model**: Refer to the [relevant section](#embedding-models).
+- **Vector store**: Refer to the [relevant section](#vector-stores).
+- **Document**: Refer to the [relevant section](#embedding-document-configuration).
+
+As a result of this operation, you will get an array of created embedding chunk IDs,
+for example `["d599ec62-fe51-4a91-bbf0-26e1241f9079", "a1fad021-5148-42b4-aa02-7de9d590e69c"]`.
+
+</TabItem>
+
+<TabItem value='retrieve'>
+
+The **retrieve document** operation performs the following steps:
+
+1. Consumes a query.
+2. Convert the query into a vector form with LLM help.
+3. Perform a vector similarity search on previously-stored LLM embeddings.
+4. Store results in Camunda document storage.
+
+To perform this operation, enter the following:
+
+- **Operation** dropdown: **Retrieve document**.
+- **Search query**: Enter your search query.
+- **Max results**: Enter maximum amount of returned results.
+- **Min score**: Enter the lowest score threshold similarity value; the value should be between 0 and 1, for example, 0.81.
+- **Embedding model**: Refer to the [relevant section](#embedding-models).
+- **Vector store**: Refer to the [relevant section](#vector-stores).
+
+As a result of this operation, you will get an array of relevant chunks, where each includes a [chunk ID](#splitting),
+Camunda document reference metadata, similarity score, and the actual text content.
+
+```json
+{
+  "chunks": [
+    {
+      "chunkId": "e30d570b-2a3a-4f4a-9a0c-78f0f1acd383",
+      "documentReference": {
+        "storeId": "local",
+        "documentId": "2b36ec67-a78f-4f99-9371-8c6e5b332838",
+        "contentHash": "1c232bc1e553c10d00c3327dcca9012b6b4b0758a1c2afaad8b77c80fa1bd36e",
+        "metadata": {
+          "size": 116,
+          "fileName": null,
+          "processDefinitionId": null,
+          "processInstanceKey": null,
+          "customProperties": {},
+          "expiresAt": null,
+          "contentType": "text/plain"
+        },
+        "camunda.document.type": "camunda"
+      },
+      "score": 0.6721556,
+      "content": "Camunda is a platform for orchestrating and automating business processes. It helps organizations design, execute, and manage workflows, enabling them to optimize processes and improve efficiency."
+    }
+  ]
+}
+```
+
+</TabItem>
+
+</Tabs>
+
+## Embedding models
+
+### Amazon Bedrock
+
+The **vector database connector** currently supports only [Amazon Titan V1/V2 models](https://docs.aws.amazon.com/bedrock/latest/userguide/titan-embedding-models.html).
+Review the [official Amazon documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-embed-text.html) to understand how to choose request parameters.
+
+The **vector database connector** uses [LangChain4j implementation](https://docs.langchain4j.dev/integrations/embedding-models/amazon-bedrock).
+
+## Vector stores
+
+<Tabs groupId="vector" defaultValue="elasticsearch" queryString values={
+[
+{label: 'Elasticsearch', value: 'elasticsearch' },
+{label: 'Amazon OpenSearch', value: 'amazon' },
+]}>
+
+<TabItem value='elasticsearch'>
+
+The **vector database connector** can use Elasticsearch as a vector store. The Elasticsearch version must be 8+.
+
+Enter the following parameters:
+
+- **Base URL**: The Elasticsearch base URL, including protocol, for example `https://host:port`.
+- **Username**: For the Elasticsearch user that has read/write access.
+- **Password**: For the Elasticsearch user that has read/write access.
+- **Index name**: Name of the index where you wish to store embeddings.
+- - When embedding: If index is not present, the connector will create a new one.
+- - When retrieving: If the index is absent, the connector will raise an error.
+
+</TabItem>
+
+<TabItem value='amazon'>
+
+- **Access key** and **Secret key**: Enter AWS IAM credentials for the user that has read/write access.
+- **Server URL**: An Amazon OpenSearch URL _without_ protocol, for example `my-opensearch.aws.com:port`.
+- **Region**: Region of the Amazon OpenSearch instance.
+- **Index name**: Name of the index where you wish to store embeddings.
+- - When embedding: If index is not present, the connector will create a new one.
+- - When retrieving: If the index is absent, the connector will raise an error.
+
+</TabItem>
+
+</Tabs>
+
+## Embedding document configuration
+
+### Document source
+
+The **Document source** can be either **Plain text** or a **Camunda document**.
+
+**Plain text** can be useful when you deal with small size data that can fit into a text field or a process instance variable. Input will be handled as a regular UTF-8 text.
+
+:::note
+A FEEL [string conversion function](/components/modeler/feel/builtin-functions/feel-built-in-functions-conversion.md#stringfrom) might be useful if you have JSON input.
+:::
+
+The **Camunda document** might be useful when you deal with larger document pipelines that come from
+[webhook or user tasks](/guides/document-handling.md). Input documents will be parsed with [Apache Tika](https://tika.apache.org/), so files
+can be of any Apache Tika-supported formats.
+
+### Splitting
+
+**Splitting** is an action of breaking large documents into smaller pieces. It can be either recursive or no splitting
+at all. Seek guidance from your local data scientist to determine if you require splitting.
+
+Learn more about splitting in the [LangChain4j documentation](https://docs.langchain4j.dev/tutorials/rag#document-splitter).
diff --git a/sidebars.js b/sidebars.js
index 4e00e47b369..26bba9e584f 100644
--- a/sidebars.js
+++ b/sidebars.js
@@ -447,6 +447,7 @@ module.exports = {
             "components/connectors/out-of-the-box-connectors/sql",
             "components/connectors/out-of-the-box-connectors/twilio",
             "components/connectors/out-of-the-box-connectors/uipath",
+            "components/connectors/out-of-the-box-connectors/embeddings-vector-db",
             "components/connectors/out-of-the-box-connectors/whatsapp",
           ],
         },


Reviews:
user: login: github-actions[bot]
state: COMMENTED
author_association: CONTRIBUTOR
submitted_at: 2025-05-06T13:02:20Z
user: login: maff
body: Contents LGTM (some small nitpicks) 👍 but we should most likely remove the package.json/lock changes.

Should we also tag @camunda/tech-writers as defined in the template?
state: DISMISSED
author_association: MEMBER
submitted_at: 2025-05-06T13:46:20Z
user: login: igpetrov
state: COMMENTED
author_association: CONTRIBUTOR
submitted_at: 2025-05-07T09:59:40Z
user: login: igpetrov
state: COMMENTED
author_association: CONTRIBUTOR
submitted_at: 2025-05-07T10:01:24Z
user: login: igpetrov
state: COMMENTED
author_association: CONTRIBUTOR
submitted_at: 2025-05-07T10:02:39Z
user: login: igpetrov
state: COMMENTED
author_association: CONTRIBUTOR
submitted_at: 2025-05-07T10:03:26Z
user: login: igpetrov
state: COMMENTED
author_association: CONTRIBUTOR
submitted_at: 2025-05-07T10:03:41Z
user: login: igpetrov
state: COMMENTED
author_association: CONTRIBUTOR
submitted_at: 2025-05-07T10:03:58Z
user: login: igpetrov
state: COMMENTED
author_association: CONTRIBUTOR
submitted_at: 2025-05-07T10:04:10Z
user: login: igpetrov
state: COMMENTED
author_association: CONTRIBUTOR
submitted_at: 2025-05-07T10:04:31Z
user: login: igpetrov
state: COMMENTED
author_association: CONTRIBUTOR
submitted_at: 2025-05-07T10:04:45Z
user: login: igpetrov
state: COMMENTED
author_association: CONTRIBUTOR
submitted_at: 2025-05-07T10:06:09Z
user: login: igpetrov
state: COMMENTED
author_association: CONTRIBUTOR
submitted_at: 2025-05-07T10:07:36Z
user: login: christinaausley
body: I've added tabs to break up the content -- WDYT?
state: DISMISSED
author_association: CONTRIBUTOR
submitted_at: 2025-05-14T20:39:00Z
user: login: christinaausley
state: APPROVED
author_association: CONTRIBUTOR
submitted_at: 2025-05-15T16:42:25Z

Review comments:
diff_hunk: @@ -0,0 +1,147 @@
+---
+id: embeddings-vector-db
+title: Vector Database Connector
+sidebar_label: Vector Database
+description: Embed, store and retrieve LLM embeddings and store them into vector databases.
+---
+
+The **Vector Database Connector** allows embedding, storing and retrieving Large Language Model (LLM) embeddings. This enables building
+AI-based solutions for your organizations, such as context document search, long-term LLM memory and/or agentic AI interaction.
+
+:::note
+The **Vector Database Connector** uses the [Langchain4J](https://docs.langchain4j.dev/) library. Data models and possible implementations are limited
+to the latest stable released Langchain4j library.
+:::
+
+## Prerequisites
+
+Before using the **Vector Database Connector**, it is highly recommended to understand the
+[concept of LLM embeddings](https://huggingface.co/spaces/hesamation/primer-llm-embedding).
+
+To start using the **Vector Database Connector**, you need to have access to supported LLM embeddings API to convert
+documents content into vectorized embeddings form. You will also need to have write access to supported databases.
+
+## Create a Connector task
+
+import ConnectorTask from '../../../components/react-components/connector-task.md'
+
+<ConnectorTask/>
+
+## Operations
+
+### Embed document
+
+The **Embed document** operation performs the following steps: [consumes a document](#embedding-document-configuration),
+parses it depending on a file format, optionally [splits into text chunks](#splitting), converts chunks into a vector form
+with an LLM help, and finally, stores produced vectors into a vector database.
+
+To perform this operation, enter the following:
+
+- **Operation** dropdown: **Embed document**.
+- **Embedding model** section: refer to the [relevant section](#embedding-models).
+- **Vector store** section: refer to the [relevant section](#vector-stores).
+- **Document** section: refer to the [relevant section](#embedding-document-configuration).
+
+As a result of this operation you will get an array of created embeddings,
+e.g. `["d599ec62-fe51-4a91-bbf0-26e1241f9079", "a1fad021-5148-42b4-aa02-7de9d590e69c"]`.
+
+### Retrieve document
+
+The **Retrieve document** operation perform the following steps: consumes a query, converts it into a vector form with
+an LLM help, performs a vector similarity search on previously stored LLM embeddings, and then stores results into
+Camunda document storage.
+
+To perform this operation, enter the following:
+
+- **Operation** dropdown: **Retrieve document**.
+- **Search query** input field: enter your search query.
+- **Max results** input field: enter maximum amount of returned results.
+- **Min score** input field: enter the lowest score threshold similarity value; the value should be between 0 and 1, e.g. 0.81.
+- **Embedding model** section: refer to the [relevant section](#embedding-models).
+- **Vector store** section: refer to the [relevant section](#vector-stores).
+
+As a result of this operation you will get an array of relevant chunk array, where each includes a [chunk ID](#splitting),
+Camunda document reference metadata, similarity score, and the actual text content.
+
+```json
+{
+  "chunks": [
+    {
+      "chunkId": "e30d570b-2a3a-4f4a-9a0c-78f0f1acd383",
+      "documentReference": {
+        "storeId": "local",
+        "documentId": "2b36ec67-a78f-4f99-9371-8c6e5b332838",
+        "contentHash": "1c232bc1e553c10d00c3327dcca9012b6b4b0758a1c2afaad8b77c80fa1bd36e",
+        "metadata": {
+          "size": 116,
+          "fileName": null,
+          "processDefinitionId": null,
+          "processInstanceKey": null,
+          "customProperties": {},
+          "expiresAt": null,
+          "contentType": "text/plain"
+        },
+        "camunda.document.type": "camunda"
+      },
+      "score": 0.6721556,
+      "content": "Camunda is a platform for orchestrating and automating business processes. It helps organizations design, execute, and manage workflows, enabling them to optimize processes and improve efficiency."
+    }
+  ]
+}
+```
+
+## Embedding models
+
+### Amazon Bedrock
+
+The **Vector Database Connector** currently supports only [Amazon Titan V1/V2 models](https://docs.aws.amazon.com/bedrock/latest/userguide/titan-embedding-models.html).
+Please see an [official Amazon documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-embed-text.html)
+that will explain what are and how to choose request parameters.
+
+The **Vector Database Connector** uses [Langchain4J implementation](https://docs.langchain4j.dev/integrations/embedding-models/amazon-bedrock).
+
+## Vector stores
+
+### Elasticsearch
+
+The **Vector Database Connector** can use Elasticsearch as a vector store. The Elasticsearch version must be 8+.
+
+Enter the following parameters:
+
+- **Base URL**: the Elasticsearch base URL, including protocol, i.e. `https://host:port`.
+- **Username**: for the Elasticsearch user that has read/write access.
+- **Password**: for the Elasticsearch user that has read/write access.
+- **Index name**: name of the index where you wish to store embeddings.
+- - When embedding: if index is not present, the Connector will create a new one.
+- - When retrieving: if the index is absent, the Connector will raise an error.
+
+### Amazon Managed Opensearch
path: docs/components/connectors/out-of-the-box-connectors/embeddings-vector-db.md
user: login: github-actions[bot]
body: 🚫 **[vale]** <sub>reported by [reviewdog](https://github.com/reviewdog/reviewdog) :dog:</sub><br>[all.glossary] Inconsistent spelling detected. Use `OpenSearch` instead of `Opensearch`. Review the WCoE glossary - https://confluence.camunda.com/x/b5RZBw .
created_at: 2025-05-06T13:02:20Z
author_association: CONTRIBUTOR
diff_hunk: @@ -0,0 +1,147 @@
+---
+id: embeddings-vector-db
+title: Vector Database Connector
+sidebar_label: Vector Database
+description: Embed, store and retrieve LLM embeddings and store them into vector databases.
+---
+
+The **Vector Database Connector** allows embedding, storing and retrieving Large Language Model (LLM) embeddings. This enables building
+AI-based solutions for your organizations, such as context document search, long-term LLM memory and/or agentic AI interaction.
+
+:::note
+The **Vector Database Connector** uses the [Langchain4J](https://docs.langchain4j.dev/) library. Data models and possible implementations are limited
+to the latest stable released Langchain4j library.
+:::
+
+## Prerequisites
+
+Before using the **Vector Database Connector**, it is highly recommended to understand the
+[concept of LLM embeddings](https://huggingface.co/spaces/hesamation/primer-llm-embedding).
+
+To start using the **Vector Database Connector**, you need to have access to supported LLM embeddings API to convert
+documents content into vectorized embeddings form. You will also need to have write access to supported databases.
+
+## Create a Connector task
+
+import ConnectorTask from '../../../components/react-components/connector-task.md'
+
+<ConnectorTask/>
+
+## Operations
+
+### Embed document
+
+The **Embed document** operation performs the following steps: [consumes a document](#embedding-document-configuration),
+parses it depending on a file format, optionally [splits into text chunks](#splitting), converts chunks into a vector form
+with an LLM help, and finally, stores produced vectors into a vector database.
+
+To perform this operation, enter the following:
+
+- **Operation** dropdown: **Embed document**.
+- **Embedding model** section: refer to the [relevant section](#embedding-models).
+- **Vector store** section: refer to the [relevant section](#vector-stores).
+- **Document** section: refer to the [relevant section](#embedding-document-configuration).
+
+As a result of this operation you will get an array of created embeddings,
+e.g. `["d599ec62-fe51-4a91-bbf0-26e1241f9079", "a1fad021-5148-42b4-aa02-7de9d590e69c"]`.
+
+### Retrieve document
+
+The **Retrieve document** operation perform the following steps: consumes a query, converts it into a vector form with
+an LLM help, performs a vector similarity search on previously stored LLM embeddings, and then stores results into
+Camunda document storage.
+
+To perform this operation, enter the following:
+
+- **Operation** dropdown: **Retrieve document**.
+- **Search query** input field: enter your search query.
+- **Max results** input field: enter maximum amount of returned results.
+- **Min score** input field: enter the lowest score threshold similarity value; the value should be between 0 and 1, e.g. 0.81.
+- **Embedding model** section: refer to the [relevant section](#embedding-models).
+- **Vector store** section: refer to the [relevant section](#vector-stores).
+
+As a result of this operation you will get an array of relevant chunk array, where each includes a [chunk ID](#splitting),
+Camunda document reference metadata, similarity score, and the actual text content.
+
+```json
+{
+  "chunks": [
+    {
+      "chunkId": "e30d570b-2a3a-4f4a-9a0c-78f0f1acd383",
+      "documentReference": {
+        "storeId": "local",
+        "documentId": "2b36ec67-a78f-4f99-9371-8c6e5b332838",
+        "contentHash": "1c232bc1e553c10d00c3327dcca9012b6b4b0758a1c2afaad8b77c80fa1bd36e",
+        "metadata": {
+          "size": 116,
+          "fileName": null,
+          "processDefinitionId": null,
+          "processInstanceKey": null,
+          "customProperties": {},
+          "expiresAt": null,
+          "contentType": "text/plain"
+        },
+        "camunda.document.type": "camunda"
+      },
+      "score": 0.6721556,
+      "content": "Camunda is a platform for orchestrating and automating business processes. It helps organizations design, execute, and manage workflows, enabling them to optimize processes and improve efficiency."
+    }
+  ]
+}
+```
+
+## Embedding models
+
+### Amazon Bedrock
+
+The **Vector Database Connector** currently supports only [Amazon Titan V1/V2 models](https://docs.aws.amazon.com/bedrock/latest/userguide/titan-embedding-models.html).
+Please see an [official Amazon documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-embed-text.html)
+that will explain what are and how to choose request parameters.
+
+The **Vector Database Connector** uses [Langchain4J implementation](https://docs.langchain4j.dev/integrations/embedding-models/amazon-bedrock).
+
+## Vector stores
+
+### Elasticsearch
+
+The **Vector Database Connector** can use Elasticsearch as a vector store. The Elasticsearch version must be 8+.
+
+Enter the following parameters:
+
+- **Base URL**: the Elasticsearch base URL, including protocol, i.e. `https://host:port`.
+- **Username**: for the Elasticsearch user that has read/write access.
+- **Password**: for the Elasticsearch user that has read/write access.
+- **Index name**: name of the index where you wish to store embeddings.
+- - When embedding: if index is not present, the Connector will create a new one.
+- - When retrieving: if the index is absent, the Connector will raise an error.
+
+### Amazon Managed Opensearch
+
+- **Access key** and **Secret key**: enter AWS IAM credential for the user that has read/write access.
+- **Server URL**: is an Amazon Managed Opensearch URL _without_ protocol, i.e. `my-opensearch.aws.com:port`.
path: docs/components/connectors/out-of-the-box-connectors/embeddings-vector-db.md
user: login: github-actions[bot]
body: 🚫 **[vale]** <sub>reported by [reviewdog](https://github.com/reviewdog/reviewdog) :dog:</sub><br>[all.glossary] Inconsistent spelling detected. Use `OpenSearch` instead of `Opensearch`. Review the WCoE glossary - https://confluence.camunda.com/x/b5RZBw .
created_at: 2025-05-06T13:02:20Z
author_association: CONTRIBUTOR
diff_hunk: @@ -0,0 +1,147 @@
+---
+id: embeddings-vector-db
+title: Vector Database Connector
+sidebar_label: Vector Database
+description: Embed, store and retrieve LLM embeddings and store them into vector databases.
+---
+
+The **Vector Database Connector** allows embedding, storing and retrieving Large Language Model (LLM) embeddings. This enables building
+AI-based solutions for your organizations, such as context document search, long-term LLM memory and/or agentic AI interaction.
+
+:::note
+The **Vector Database Connector** uses the [Langchain4J](https://docs.langchain4j.dev/) library. Data models and possible implementations are limited
+to the latest stable released Langchain4j library.
+:::
+
+## Prerequisites
+
+Before using the **Vector Database Connector**, it is highly recommended to understand the
+[concept of LLM embeddings](https://huggingface.co/spaces/hesamation/primer-llm-embedding).
+
+To start using the **Vector Database Connector**, you need to have access to supported LLM embeddings API to convert
+documents content into vectorized embeddings form. You will also need to have write access to supported databases.
+
+## Create a Connector task
+
+import ConnectorTask from '../../../components/react-components/connector-task.md'
+
+<ConnectorTask/>
+
+## Operations
+
+### Embed document
+
+The **Embed document** operation performs the following steps: [consumes a document](#embedding-document-configuration),
+parses it depending on a file format, optionally [splits into text chunks](#splitting), converts chunks into a vector form
+with an LLM help, and finally, stores produced vectors into a vector database.
+
+To perform this operation, enter the following:
+
+- **Operation** dropdown: **Embed document**.
+- **Embedding model** section: refer to the [relevant section](#embedding-models).
+- **Vector store** section: refer to the [relevant section](#vector-stores).
+- **Document** section: refer to the [relevant section](#embedding-document-configuration).
+
+As a result of this operation you will get an array of created embeddings,
+e.g. `["d599ec62-fe51-4a91-bbf0-26e1241f9079", "a1fad021-5148-42b4-aa02-7de9d590e69c"]`.
+
+### Retrieve document
+
+The **Retrieve document** operation perform the following steps: consumes a query, converts it into a vector form with
+an LLM help, performs a vector similarity search on previously stored LLM embeddings, and then stores results into
+Camunda document storage.
+
+To perform this operation, enter the following:
+
+- **Operation** dropdown: **Retrieve document**.
+- **Search query** input field: enter your search query.
+- **Max results** input field: enter maximum amount of returned results.
+- **Min score** input field: enter the lowest score threshold similarity value; the value should be between 0 and 1, e.g. 0.81.
+- **Embedding model** section: refer to the [relevant section](#embedding-models).
+- **Vector store** section: refer to the [relevant section](#vector-stores).
+
+As a result of this operation you will get an array of relevant chunk array, where each includes a [chunk ID](#splitting),
+Camunda document reference metadata, similarity score, and the actual text content.
+
+```json
+{
+  "chunks": [
+    {
+      "chunkId": "e30d570b-2a3a-4f4a-9a0c-78f0f1acd383",
+      "documentReference": {
+        "storeId": "local",
+        "documentId": "2b36ec67-a78f-4f99-9371-8c6e5b332838",
+        "contentHash": "1c232bc1e553c10d00c3327dcca9012b6b4b0758a1c2afaad8b77c80fa1bd36e",
+        "metadata": {
+          "size": 116,
+          "fileName": null,
+          "processDefinitionId": null,
+          "processInstanceKey": null,
+          "customProperties": {},
+          "expiresAt": null,
+          "contentType": "text/plain"
+        },
+        "camunda.document.type": "camunda"
+      },
+      "score": 0.6721556,
+      "content": "Camunda is a platform for orchestrating and automating business processes. It helps organizations design, execute, and manage workflows, enabling them to optimize processes and improve efficiency."
+    }
+  ]
+}
+```
+
+## Embedding models
+
+### Amazon Bedrock
+
+The **Vector Database Connector** currently supports only [Amazon Titan V1/V2 models](https://docs.aws.amazon.com/bedrock/latest/userguide/titan-embedding-models.html).
+Please see an [official Amazon documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-embed-text.html)
+that will explain what are and how to choose request parameters.
+
+The **Vector Database Connector** uses [Langchain4J implementation](https://docs.langchain4j.dev/integrations/embedding-models/amazon-bedrock).
+
+## Vector stores
+
+### Elasticsearch
+
+The **Vector Database Connector** can use Elasticsearch as a vector store. The Elasticsearch version must be 8+.
+
+Enter the following parameters:
+
+- **Base URL**: the Elasticsearch base URL, including protocol, i.e. `https://host:port`.
+- **Username**: for the Elasticsearch user that has read/write access.
+- **Password**: for the Elasticsearch user that has read/write access.
+- **Index name**: name of the index where you wish to store embeddings.
+- - When embedding: if index is not present, the Connector will create a new one.
+- - When retrieving: if the index is absent, the Connector will raise an error.
+
+### Amazon Managed Opensearch
+
+- **Access key** and **Secret key**: enter AWS IAM credential for the user that has read/write access.
+- **Server URL**: is an Amazon Managed Opensearch URL _without_ protocol, i.e. `my-opensearch.aws.com:port`.
+- **Region**: of the Amazon Managed Opensearch instance.
path: docs/components/connectors/out-of-the-box-connectors/embeddings-vector-db.md
user: login: github-actions[bot]
body: 🚫 **[vale]** <sub>reported by [reviewdog](https://github.com/reviewdog/reviewdog) :dog:</sub><br>[all.glossary] Inconsistent spelling detected. Use `OpenSearch` instead of `Opensearch`. Review the WCoE glossary - https://confluence.camunda.com/x/b5RZBw .
created_at: 2025-05-06T13:02:20Z
author_association: CONTRIBUTOR
diff_hunk: @@ -0,0 +1,147 @@
+---
+id: embeddings-vector-db
+title: Vector Database Connector
+sidebar_label: Vector Database
+description: Embed, store and retrieve LLM embeddings and store them into vector databases.
+---
+
+The **Vector Database Connector** allows embedding, storing and retrieving Large Language Model (LLM) embeddings. This enables building
+AI-based solutions for your organizations, such as context document search, long-term LLM memory and/or agentic AI interaction.
+
+:::note
+The **Vector Database Connector** uses the [Langchain4J](https://docs.langchain4j.dev/) library. Data models and possible implementations are limited
+to the latest stable released Langchain4j library.
+:::
+
+## Prerequisites
+
+Before using the **Vector Database Connector**, it is highly recommended to understand the
+[concept of LLM embeddings](https://huggingface.co/spaces/hesamation/primer-llm-embedding).
+
+To start using the **Vector Database Connector**, you need to have access to supported LLM embeddings API to convert
+documents content into vectorized embeddings form. You will also need to have write access to supported databases.
path: docs/components/connectors/out-of-the-box-connectors/embeddings-vector-db.md
user: login: maff
body: "to a supported LLM embeddings API to convert document contents" and maybe "to a supported database"?
created_at: 2025-05-06T13:33:57Z
author_association: MEMBER
diff_hunk: @@ -0,0 +1,147 @@
+---
+id: embeddings-vector-db
+title: Vector Database Connector
+sidebar_label: Vector Database
+description: Embed, store and retrieve LLM embeddings and store them into vector databases.
+---
+
+The **Vector Database Connector** allows embedding, storing and retrieving Large Language Model (LLM) embeddings. This enables building
+AI-based solutions for your organizations, such as context document search, long-term LLM memory and/or agentic AI interaction.
+
+:::note
+The **Vector Database Connector** uses the [Langchain4J](https://docs.langchain4j.dev/) library. Data models and possible implementations are limited
+to the latest stable released Langchain4j library.
+:::
+
+## Prerequisites
+
+Before using the **Vector Database Connector**, it is highly recommended to understand the
+[concept of LLM embeddings](https://huggingface.co/spaces/hesamation/primer-llm-embedding).
+
+To start using the **Vector Database Connector**, you need to have access to supported LLM embeddings API to convert
+documents content into vectorized embeddings form. You will also need to have write access to supported databases.
+
+## Create a Connector task
+
+import ConnectorTask from '../../../components/react-components/connector-task.md'
+
+<ConnectorTask/>
+
+## Operations
+
+### Embed document
+
+The **Embed document** operation performs the following steps: [consumes a document](#embedding-document-configuration),
+parses it depending on a file format, optionally [splits into text chunks](#splitting), converts chunks into a vector form
+with an LLM help, and finally, stores produced vectors into a vector database.
+
+To perform this operation, enter the following:
+
+- **Operation** dropdown: **Embed document**.
+- **Embedding model** section: refer to the [relevant section](#embedding-models).
+- **Vector store** section: refer to the [relevant section](#vector-stores).
+- **Document** section: refer to the [relevant section](#embedding-document-configuration).
+
+As a result of this operation you will get an array of created embeddings,
path: docs/components/connectors/out-of-the-box-connectors/embeddings-vector-db.md
user: login: maff
body: Maybe we can elaborate what is in this array - are these the chunk IDs?
created_at: 2025-05-06T13:37:15Z
author_association: MEMBER
diff_hunk: @@ -0,0 +1,147 @@
+---
+id: embeddings-vector-db
+title: Vector Database Connector
+sidebar_label: Vector Database
+description: Embed, store and retrieve LLM embeddings and store them into vector databases.
+---
+
+The **Vector Database Connector** allows embedding, storing and retrieving Large Language Model (LLM) embeddings. This enables building
+AI-based solutions for your organizations, such as context document search, long-term LLM memory and/or agentic AI interaction.
+
+:::note
+The **Vector Database Connector** uses the [Langchain4J](https://docs.langchain4j.dev/) library. Data models and possible implementations are limited
+to the latest stable released Langchain4j library.
+:::
+
+## Prerequisites
+
+Before using the **Vector Database Connector**, it is highly recommended to understand the
+[concept of LLM embeddings](https://huggingface.co/spaces/hesamation/primer-llm-embedding).
+
+To start using the **Vector Database Connector**, you need to have access to supported LLM embeddings API to convert
+documents content into vectorized embeddings form. You will also need to have write access to supported databases.
+
+## Create a Connector task
+
+import ConnectorTask from '../../../components/react-components/connector-task.md'
+
+<ConnectorTask/>
+
+## Operations
+
+### Embed document
+
+The **Embed document** operation performs the following steps: [consumes a document](#embedding-document-configuration),
+parses it depending on a file format, optionally [splits into text chunks](#splitting), converts chunks into a vector form
+with an LLM help, and finally, stores produced vectors into a vector database.
+
+To perform this operation, enter the following:
+
+- **Operation** dropdown: **Embed document**.
+- **Embedding model** section: refer to the [relevant section](#embedding-models).
+- **Vector store** section: refer to the [relevant section](#vector-stores).
+- **Document** section: refer to the [relevant section](#embedding-document-configuration).
+
+As a result of this operation you will get an array of created embeddings,
+e.g. `["d599ec62-fe51-4a91-bbf0-26e1241f9079", "a1fad021-5148-42b4-aa02-7de9d590e69c"]`.
+
+### Retrieve document
+
+The **Retrieve document** operation perform the following steps: consumes a query, converts it into a vector form with
+an LLM help, performs a vector similarity search on previously stored LLM embeddings, and then stores results into
+Camunda document storage.
+
+To perform this operation, enter the following:
+
+- **Operation** dropdown: **Retrieve document**.
+- **Search query** input field: enter your search query.
+- **Max results** input field: enter maximum amount of returned results.
+- **Min score** input field: enter the lowest score threshold similarity value; the value should be between 0 and 1, e.g. 0.81.
+- **Embedding model** section: refer to the [relevant section](#embedding-models).
+- **Vector store** section: refer to the [relevant section](#vector-stores).
+
+As a result of this operation you will get an array of relevant chunk array, where each includes a [chunk ID](#splitting),
path: docs/components/connectors/out-of-the-box-connectors/embeddings-vector-db.md
user: login: maff
body: "an array of relevant chunks"?
created_at: 2025-05-06T13:38:07Z
author_association: MEMBER
diff_hunk: @@ -0,0 +1,147 @@
+---
+id: embeddings-vector-db
+title: Vector Database Connector
+sidebar_label: Vector Database
+description: Embed, store and retrieve LLM embeddings and store them into vector databases.
+---
+
+The **Vector Database Connector** allows embedding, storing and retrieving Large Language Model (LLM) embeddings. This enables building
+AI-based solutions for your organizations, such as context document search, long-term LLM memory and/or agentic AI interaction.
+
+:::note
+The **Vector Database Connector** uses the [Langchain4J](https://docs.langchain4j.dev/) library. Data models and possible implementations are limited
+to the latest stable released Langchain4j library.
+:::
+
+## Prerequisites
+
+Before using the **Vector Database Connector**, it is highly recommended to understand the
+[concept of LLM embeddings](https://huggingface.co/spaces/hesamation/primer-llm-embedding).
+
+To start using the **Vector Database Connector**, you need to have access to supported LLM embeddings API to convert
+documents content into vectorized embeddings form. You will also need to have write access to supported databases.
+
+## Create a Connector task
+
+import ConnectorTask from '../../../components/react-components/connector-task.md'
+
+<ConnectorTask/>
+
+## Operations
+
+### Embed document
+
+The **Embed document** operation performs the following steps: [consumes a document](#embedding-document-configuration),
+parses it depending on a file format, optionally [splits into text chunks](#splitting), converts chunks into a vector form
+with an LLM help, and finally, stores produced vectors into a vector database.
+
+To perform this operation, enter the following:
+
+- **Operation** dropdown: **Embed document**.
+- **Embedding model** section: refer to the [relevant section](#embedding-models).
+- **Vector store** section: refer to the [relevant section](#vector-stores).
+- **Document** section: refer to the [relevant section](#embedding-document-configuration).
+
+As a result of this operation you will get an array of created embeddings,
+e.g. `["d599ec62-fe51-4a91-bbf0-26e1241f9079", "a1fad021-5148-42b4-aa02-7de9d590e69c"]`.
+
+### Retrieve document
+
+The **Retrieve document** operation perform the following steps: consumes a query, converts it into a vector form with
+an LLM help, performs a vector similarity search on previously stored LLM embeddings, and then stores results into
+Camunda document storage.
+
+To perform this operation, enter the following:
+
+- **Operation** dropdown: **Retrieve document**.
+- **Search query** input field: enter your search query.
+- **Max results** input field: enter maximum amount of returned results.
+- **Min score** input field: enter the lowest score threshold similarity value; the value should be between 0 and 1, e.g. 0.81.
+- **Embedding model** section: refer to the [relevant section](#embedding-models).
+- **Vector store** section: refer to the [relevant section](#vector-stores).
+
+As a result of this operation you will get an array of relevant chunk array, where each includes a [chunk ID](#splitting),
+Camunda document reference metadata, similarity score, and the actual text content.
+
+```json
+{
+  "chunks": [
+    {
+      "chunkId": "e30d570b-2a3a-4f4a-9a0c-78f0f1acd383",
+      "documentReference": {
+        "storeId": "local",
+        "documentId": "2b36ec67-a78f-4f99-9371-8c6e5b332838",
+        "contentHash": "1c232bc1e553c10d00c3327dcca9012b6b4b0758a1c2afaad8b77c80fa1bd36e",
+        "metadata": {
+          "size": 116,
+          "fileName": null,
+          "processDefinitionId": null,
+          "processInstanceKey": null,
+          "customProperties": {},
+          "expiresAt": null,
+          "contentType": "text/plain"
+        },
+        "camunda.document.type": "camunda"
+      },
+      "score": 0.6721556,
+      "content": "Camunda is a platform for orchestrating and automating business processes. It helps organizations design, execute, and manage workflows, enabling them to optimize processes and improve efficiency."
+    }
+  ]
+}
+```
+
+## Embedding models
+
+### Amazon Bedrock
+
+The **Vector Database Connector** currently supports only [Amazon Titan V1/V2 models](https://docs.aws.amazon.com/bedrock/latest/userguide/titan-embedding-models.html).
+Please see an [official Amazon documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-embed-text.html)
path: docs/components/connectors/out-of-the-box-connectors/embeddings-vector-db.md
user: login: maff
body: "see _the_ official Amazon documentation"?
created_at: 2025-05-06T13:38:52Z
author_association: MEMBER
diff_hunk: @@ -0,0 +1,147 @@
+---
+id: embeddings-vector-db
+title: Vector Database Connector
+sidebar_label: Vector Database
+description: Embed, store and retrieve LLM embeddings and store them into vector databases.
+---
+
+The **Vector Database Connector** allows embedding, storing and retrieving Large Language Model (LLM) embeddings. This enables building
+AI-based solutions for your organizations, such as context document search, long-term LLM memory and/or agentic AI interaction.
+
+:::note
+The **Vector Database Connector** uses the [Langchain4J](https://docs.langchain4j.dev/) library. Data models and possible implementations are limited
+to the latest stable released Langchain4j library.
+:::
+
+## Prerequisites
+
+Before using the **Vector Database Connector**, it is highly recommended to understand the
+[concept of LLM embeddings](https://huggingface.co/spaces/hesamation/primer-llm-embedding).
+
+To start using the **Vector Database Connector**, you need to have access to supported LLM embeddings API to convert
+documents content into vectorized embeddings form. You will also need to have write access to supported databases.
+
+## Create a Connector task
+
+import ConnectorTask from '../../../components/react-components/connector-task.md'
+
+<ConnectorTask/>
+
+## Operations
+
+### Embed document
+
+The **Embed document** operation performs the following steps: [consumes a document](#embedding-document-configuration),
+parses it depending on a file format, optionally [splits into text chunks](#splitting), converts chunks into a vector form
+with an LLM help, and finally, stores produced vectors into a vector database.
+
+To perform this operation, enter the following:
+
+- **Operation** dropdown: **Embed document**.
+- **Embedding model** section: refer to the [relevant section](#embedding-models).
+- **Vector store** section: refer to the [relevant section](#vector-stores).
+- **Document** section: refer to the [relevant section](#embedding-document-configuration).
+
+As a result of this operation you will get an array of created embeddings,
+e.g. `["d599ec62-fe51-4a91-bbf0-26e1241f9079", "a1fad021-5148-42b4-aa02-7de9d590e69c"]`.
+
+### Retrieve document
+
+The **Retrieve document** operation perform the following steps: consumes a query, converts it into a vector form with
+an LLM help, performs a vector similarity search on previously stored LLM embeddings, and then stores results into
+Camunda document storage.
+
+To perform this operation, enter the following:
+
+- **Operation** dropdown: **Retrieve document**.
+- **Search query** input field: enter your search query.
+- **Max results** input field: enter maximum amount of returned results.
+- **Min score** input field: enter the lowest score threshold similarity value; the value should be between 0 and 1, e.g. 0.81.
+- **Embedding model** section: refer to the [relevant section](#embedding-models).
+- **Vector store** section: refer to the [relevant section](#vector-stores).
+
+As a result of this operation you will get an array of relevant chunk array, where each includes a [chunk ID](#splitting),
+Camunda document reference metadata, similarity score, and the actual text content.
+
+```json
+{
+  "chunks": [
+    {
+      "chunkId": "e30d570b-2a3a-4f4a-9a0c-78f0f1acd383",
+      "documentReference": {
+        "storeId": "local",
+        "documentId": "2b36ec67-a78f-4f99-9371-8c6e5b332838",
+        "contentHash": "1c232bc1e553c10d00c3327dcca9012b6b4b0758a1c2afaad8b77c80fa1bd36e",
+        "metadata": {
+          "size": 116,
+          "fileName": null,
+          "processDefinitionId": null,
+          "processInstanceKey": null,
+          "customProperties": {},
+          "expiresAt": null,
+          "contentType": "text/plain"
+        },
+        "camunda.document.type": "camunda"
+      },
+      "score": 0.6721556,
+      "content": "Camunda is a platform for orchestrating and automating business processes. It helps organizations design, execute, and manage workflows, enabling them to optimize processes and improve efficiency."
+    }
+  ]
+}
+```
+
+## Embedding models
+
+### Amazon Bedrock
+
+The **Vector Database Connector** currently supports only [Amazon Titan V1/V2 models](https://docs.aws.amazon.com/bedrock/latest/userguide/titan-embedding-models.html).
+Please see an [official Amazon documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-embed-text.html)
+that will explain what are and how to choose request parameters.
+
+The **Vector Database Connector** uses [Langchain4J implementation](https://docs.langchain4j.dev/integrations/embedding-models/amazon-bedrock).
+
+## Vector stores
+
+### Elasticsearch
+
+The **Vector Database Connector** can use Elasticsearch as a vector store. The Elasticsearch version must be 8+.
+
+Enter the following parameters:
+
+- **Base URL**: the Elasticsearch base URL, including protocol, i.e. `https://host:port`.
+- **Username**: for the Elasticsearch user that has read/write access.
+- **Password**: for the Elasticsearch user that has read/write access.
+- **Index name**: name of the index where you wish to store embeddings.
+- - When embedding: if index is not present, the Connector will create a new one.
+- - When retrieving: if the index is absent, the Connector will raise an error.
+
+### Amazon Managed Opensearch
+
+- **Access key** and **Secret key**: enter AWS IAM credential for the user that has read/write access.
+- **Server URL**: is an Amazon Managed Opensearch URL _without_ protocol, i.e. `my-opensearch.aws.com:port`.
+- **Region**: of the Amazon Managed Opensearch instance.
+- **Index name**: name of the index where you wish to store embeddings.
+- - When embedding: if index is not present, the Connector will create a new one.
+- - When retrieving: if the index is absent, the Connector will raise an error.
+
+## Embedding document configuration
+
+### Document source
+
+The **Document source** can be either a **Plain text** or a **Camunda document**.
+
+The **Plain text** can be useful when you deal with small size data that can fit into a text field or a process instance variable.
+Input will be handled as a regular UTF-8 text.
+
+Hint: a FEEL [string conversion function](/components/modeler/feel/builtin-functions/feel-built-in-functions-conversion/#stringfrom) might be useful if you have JSON input.
+
+The **Camunda document** might be useful when you deal with larger documents pipelines that come either from
+[webhook or user tasks](/guides/document-handling). Input documents will be parsed with [Apache Tika](https://tika.apache.org/) parser, hence files
path: docs/components/connectors/out-of-the-box-connectors/embeddings-vector-db.md
user: login: maff
body: "will be parsed with Apache Tika, hence"?
created_at: 2025-05-06T13:44:09Z
author_association: MEMBER
diff_hunk: @@ -45,6 +45,7 @@
     "docusaurus-plugin-openapi-docs": "^4.3.7",
     "docusaurus-theme-openapi-docs": "^4.3.7",
     "mixpanel-browser": "^2.64.0",
+    "npx": "^10.2.2",
path: package.json
user: login: maff
body: I don't think adding the package should be part of this PR 🤔 
created_at: 2025-05-06T13:45:08Z
author_association: MEMBER
diff_hunk: @@ -0,0 +1,147 @@
+---
+id: embeddings-vector-db
+title: Vector Database Connector
+sidebar_label: Vector Database
+description: Embed, store and retrieve LLM embeddings and store them into vector databases.
+---
+
+The **Vector Database Connector** allows embedding, storing and retrieving Large Language Model (LLM) embeddings. This enables building
+AI-based solutions for your organizations, such as context document search, long-term LLM memory and/or agentic AI interaction.
+
+:::note
+The **Vector Database Connector** uses the [Langchain4J](https://docs.langchain4j.dev/) library. Data models and possible implementations are limited
+to the latest stable released Langchain4j library.
+:::
+
+## Prerequisites
+
+Before using the **Vector Database Connector**, it is highly recommended to understand the
+[concept of LLM embeddings](https://huggingface.co/spaces/hesamation/primer-llm-embedding).
+
+To start using the **Vector Database Connector**, you need to have access to supported LLM embeddings API to convert
+documents content into vectorized embeddings form. You will also need to have write access to supported databases.
path: docs/components/connectors/out-of-the-box-connectors/embeddings-vector-db.md
user: login: igpetrov
body: Sorry, don't get what you mean. I have both statements 🤔 
created_at: 2025-05-07T09:59:40Z
author_association: CONTRIBUTOR
diff_hunk: @@ -0,0 +1,147 @@
+---
+id: embeddings-vector-db
+title: Vector Database Connector
+sidebar_label: Vector Database
+description: Embed, store and retrieve LLM embeddings and store them into vector databases.
+---
+
+The **Vector Database Connector** allows embedding, storing and retrieving Large Language Model (LLM) embeddings. This enables building
+AI-based solutions for your organizations, such as context document search, long-term LLM memory and/or agentic AI interaction.
+
+:::note
+The **Vector Database Connector** uses the [Langchain4J](https://docs.langchain4j.dev/) library. Data models and possible implementations are limited
+to the latest stable released Langchain4j library.
+:::
+
+## Prerequisites
+
+Before using the **Vector Database Connector**, it is highly recommended to understand the
+[concept of LLM embeddings](https://huggingface.co/spaces/hesamation/primer-llm-embedding).
+
+To start using the **Vector Database Connector**, you need to have access to supported LLM embeddings API to convert
+documents content into vectorized embeddings form. You will also need to have write access to supported databases.
path: docs/components/connectors/out-of-the-box-connectors/embeddings-vector-db.md
user: login: igpetrov
body: ah, you mean articles. Yeah, got it, good point. Consider diff suggestions:

```suggestion
To start using the **Vector Database Connector**, you need to have access to a supported LLM embeddings API to convert
documents content into vectorized embeddings form. You will also need to have write access to a supported databases.
```
created_at: 2025-05-07T10:01:24Z
author_association: CONTRIBUTOR
diff_hunk: @@ -0,0 +1,147 @@
+---
+id: embeddings-vector-db
+title: Vector Database Connector
+sidebar_label: Vector Database
+description: Embed, store and retrieve LLM embeddings and store them into vector databases.
+---
+
+The **Vector Database Connector** allows embedding, storing and retrieving Large Language Model (LLM) embeddings. This enables building
+AI-based solutions for your organizations, such as context document search, long-term LLM memory and/or agentic AI interaction.
+
+:::note
+The **Vector Database Connector** uses the [Langchain4J](https://docs.langchain4j.dev/) library. Data models and possible implementations are limited
+to the latest stable released Langchain4j library.
+:::
+
+## Prerequisites
+
+Before using the **Vector Database Connector**, it is highly recommended to understand the
+[concept of LLM embeddings](https://huggingface.co/spaces/hesamation/primer-llm-embedding).
+
+To start using the **Vector Database Connector**, you need to have access to supported LLM embeddings API to convert
+documents content into vectorized embeddings form. You will also need to have write access to supported databases.
+
+## Create a Connector task
+
+import ConnectorTask from '../../../components/react-components/connector-task.md'
+
+<ConnectorTask/>
+
+## Operations
+
+### Embed document
+
+The **Embed document** operation performs the following steps: [consumes a document](#embedding-document-configuration),
+parses it depending on a file format, optionally [splits into text chunks](#splitting), converts chunks into a vector form
+with an LLM help, and finally, stores produced vectors into a vector database.
+
+To perform this operation, enter the following:
+
+- **Operation** dropdown: **Embed document**.
+- **Embedding model** section: refer to the [relevant section](#embedding-models).
+- **Vector store** section: refer to the [relevant section](#vector-stores).
+- **Document** section: refer to the [relevant section](#embedding-document-configuration).
+
+As a result of this operation you will get an array of created embeddings,
path: docs/components/connectors/out-of-the-box-connectors/embeddings-vector-db.md
user: login: igpetrov
body: Good point. How about:

```suggestion
As a result of this operation you will get an array of created embeddings chunks IDs,
```

?
created_at: 2025-05-07T10:02:39Z
author_association: CONTRIBUTOR
diff_hunk: @@ -0,0 +1,147 @@
+---
+id: embeddings-vector-db
+title: Vector Database Connector
+sidebar_label: Vector Database
+description: Embed, store and retrieve LLM embeddings and store them into vector databases.
+---
+
+The **Vector Database Connector** allows embedding, storing and retrieving Large Language Model (LLM) embeddings. This enables building
+AI-based solutions for your organizations, such as context document search, long-term LLM memory and/or agentic AI interaction.
+
+:::note
+The **Vector Database Connector** uses the [Langchain4J](https://docs.langchain4j.dev/) library. Data models and possible implementations are limited
+to the latest stable released Langchain4j library.
+:::
+
+## Prerequisites
+
+Before using the **Vector Database Connector**, it is highly recommended to understand the
+[concept of LLM embeddings](https://huggingface.co/spaces/hesamation/primer-llm-embedding).
+
+To start using the **Vector Database Connector**, you need to have access to supported LLM embeddings API to convert
+documents content into vectorized embeddings form. You will also need to have write access to supported databases.
+
+## Create a Connector task
+
+import ConnectorTask from '../../../components/react-components/connector-task.md'
+
+<ConnectorTask/>
+
+## Operations
+
+### Embed document
+
+The **Embed document** operation performs the following steps: [consumes a document](#embedding-document-configuration),
+parses it depending on a file format, optionally [splits into text chunks](#splitting), converts chunks into a vector form
+with an LLM help, and finally, stores produced vectors into a vector database.
+
+To perform this operation, enter the following:
+
+- **Operation** dropdown: **Embed document**.
+- **Embedding model** section: refer to the [relevant section](#embedding-models).
+- **Vector store** section: refer to the [relevant section](#vector-stores).
+- **Document** section: refer to the [relevant section](#embedding-document-configuration).
+
+As a result of this operation you will get an array of created embeddings,
+e.g. `["d599ec62-fe51-4a91-bbf0-26e1241f9079", "a1fad021-5148-42b4-aa02-7de9d590e69c"]`.
+
+### Retrieve document
+
+The **Retrieve document** operation perform the following steps: consumes a query, converts it into a vector form with
+an LLM help, performs a vector similarity search on previously stored LLM embeddings, and then stores results into
+Camunda document storage.
+
+To perform this operation, enter the following:
+
+- **Operation** dropdown: **Retrieve document**.
+- **Search query** input field: enter your search query.
+- **Max results** input field: enter maximum amount of returned results.
+- **Min score** input field: enter the lowest score threshold similarity value; the value should be between 0 and 1, e.g. 0.81.
+- **Embedding model** section: refer to the [relevant section](#embedding-models).
+- **Vector store** section: refer to the [relevant section](#vector-stores).
+
+As a result of this operation you will get an array of relevant chunk array, where each includes a [chunk ID](#splitting),
path: docs/components/connectors/out-of-the-box-connectors/embeddings-vector-db.md
user: login: igpetrov
body: ```suggestion
As a result of this operation you will get an array of relevant chunks, where each includes a [chunk ID](#splitting),
```
created_at: 2025-05-07T10:03:25Z
author_association: CONTRIBUTOR
diff_hunk: @@ -0,0 +1,147 @@
+---
+id: embeddings-vector-db
+title: Vector Database Connector
+sidebar_label: Vector Database
+description: Embed, store and retrieve LLM embeddings and store them into vector databases.
+---
+
+The **Vector Database Connector** allows embedding, storing and retrieving Large Language Model (LLM) embeddings. This enables building
+AI-based solutions for your organizations, such as context document search, long-term LLM memory and/or agentic AI interaction.
+
+:::note
+The **Vector Database Connector** uses the [Langchain4J](https://docs.langchain4j.dev/) library. Data models and possible implementations are limited
+to the latest stable released Langchain4j library.
+:::
+
+## Prerequisites
+
+Before using the **Vector Database Connector**, it is highly recommended to understand the
+[concept of LLM embeddings](https://huggingface.co/spaces/hesamation/primer-llm-embedding).
+
+To start using the **Vector Database Connector**, you need to have access to supported LLM embeddings API to convert
+documents content into vectorized embeddings form. You will also need to have write access to supported databases.
+
+## Create a Connector task
+
+import ConnectorTask from '../../../components/react-components/connector-task.md'
+
+<ConnectorTask/>
+
+## Operations
+
+### Embed document
+
+The **Embed document** operation performs the following steps: [consumes a document](#embedding-document-configuration),
+parses it depending on a file format, optionally [splits into text chunks](#splitting), converts chunks into a vector form
+with an LLM help, and finally, stores produced vectors into a vector database.
+
+To perform this operation, enter the following:
+
+- **Operation** dropdown: **Embed document**.
+- **Embedding model** section: refer to the [relevant section](#embedding-models).
+- **Vector store** section: refer to the [relevant section](#vector-stores).
+- **Document** section: refer to the [relevant section](#embedding-document-configuration).
+
+As a result of this operation you will get an array of created embeddings,
+e.g. `["d599ec62-fe51-4a91-bbf0-26e1241f9079", "a1fad021-5148-42b4-aa02-7de9d590e69c"]`.
+
+### Retrieve document
+
+The **Retrieve document** operation perform the following steps: consumes a query, converts it into a vector form with
+an LLM help, performs a vector similarity search on previously stored LLM embeddings, and then stores results into
+Camunda document storage.
+
+To perform this operation, enter the following:
+
+- **Operation** dropdown: **Retrieve document**.
+- **Search query** input field: enter your search query.
+- **Max results** input field: enter maximum amount of returned results.
+- **Min score** input field: enter the lowest score threshold similarity value; the value should be between 0 and 1, e.g. 0.81.
+- **Embedding model** section: refer to the [relevant section](#embedding-models).
+- **Vector store** section: refer to the [relevant section](#vector-stores).
+
+As a result of this operation you will get an array of relevant chunk array, where each includes a [chunk ID](#splitting),
+Camunda document reference metadata, similarity score, and the actual text content.
+
+```json
+{
+  "chunks": [
+    {
+      "chunkId": "e30d570b-2a3a-4f4a-9a0c-78f0f1acd383",
+      "documentReference": {
+        "storeId": "local",
+        "documentId": "2b36ec67-a78f-4f99-9371-8c6e5b332838",
+        "contentHash": "1c232bc1e553c10d00c3327dcca9012b6b4b0758a1c2afaad8b77c80fa1bd36e",
+        "metadata": {
+          "size": 116,
+          "fileName": null,
+          "processDefinitionId": null,
+          "processInstanceKey": null,
+          "customProperties": {},
+          "expiresAt": null,
+          "contentType": "text/plain"
+        },
+        "camunda.document.type": "camunda"
+      },
+      "score": 0.6721556,
+      "content": "Camunda is a platform for orchestrating and automating business processes. It helps organizations design, execute, and manage workflows, enabling them to optimize processes and improve efficiency."
+    }
+  ]
+}
+```
+
+## Embedding models
+
+### Amazon Bedrock
+
+The **Vector Database Connector** currently supports only [Amazon Titan V1/V2 models](https://docs.aws.amazon.com/bedrock/latest/userguide/titan-embedding-models.html).
+Please see an [official Amazon documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-embed-text.html)
path: docs/components/connectors/out-of-the-box-connectors/embeddings-vector-db.md
user: login: igpetrov
body: ```suggestion
Please see the [official Amazon documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-embed-text.html)
```
created_at: 2025-05-07T10:03:41Z
author_association: CONTRIBUTOR
diff_hunk: @@ -0,0 +1,147 @@
+---
+id: embeddings-vector-db
+title: Vector Database Connector
+sidebar_label: Vector Database
+description: Embed, store and retrieve LLM embeddings and store them into vector databases.
+---
+
+The **Vector Database Connector** allows embedding, storing and retrieving Large Language Model (LLM) embeddings. This enables building
+AI-based solutions for your organizations, such as context document search, long-term LLM memory and/or agentic AI interaction.
+
+:::note
+The **Vector Database Connector** uses the [Langchain4J](https://docs.langchain4j.dev/) library. Data models and possible implementations are limited
+to the latest stable released Langchain4j library.
+:::
+
+## Prerequisites
+
+Before using the **Vector Database Connector**, it is highly recommended to understand the
+[concept of LLM embeddings](https://huggingface.co/spaces/hesamation/primer-llm-embedding).
+
+To start using the **Vector Database Connector**, you need to have access to supported LLM embeddings API to convert
+documents content into vectorized embeddings form. You will also need to have write access to supported databases.
+
+## Create a Connector task
+
+import ConnectorTask from '../../../components/react-components/connector-task.md'
+
+<ConnectorTask/>
+
+## Operations
+
+### Embed document
+
+The **Embed document** operation performs the following steps: [consumes a document](#embedding-document-configuration),
+parses it depending on a file format, optionally [splits into text chunks](#splitting), converts chunks into a vector form
+with an LLM help, and finally, stores produced vectors into a vector database.
+
+To perform this operation, enter the following:
+
+- **Operation** dropdown: **Embed document**.
+- **Embedding model** section: refer to the [relevant section](#embedding-models).
+- **Vector store** section: refer to the [relevant section](#vector-stores).
+- **Document** section: refer to the [relevant section](#embedding-document-configuration).
+
+As a result of this operation you will get an array of created embeddings,
+e.g. `["d599ec62-fe51-4a91-bbf0-26e1241f9079", "a1fad021-5148-42b4-aa02-7de9d590e69c"]`.
+
+### Retrieve document
+
+The **Retrieve document** operation perform the following steps: consumes a query, converts it into a vector form with
+an LLM help, performs a vector similarity search on previously stored LLM embeddings, and then stores results into
+Camunda document storage.
+
+To perform this operation, enter the following:
+
+- **Operation** dropdown: **Retrieve document**.
+- **Search query** input field: enter your search query.
+- **Max results** input field: enter maximum amount of returned results.
+- **Min score** input field: enter the lowest score threshold similarity value; the value should be between 0 and 1, e.g. 0.81.
+- **Embedding model** section: refer to the [relevant section](#embedding-models).
+- **Vector store** section: refer to the [relevant section](#vector-stores).
+
+As a result of this operation you will get an array of relevant chunk array, where each includes a [chunk ID](#splitting),
+Camunda document reference metadata, similarity score, and the actual text content.
+
+```json
+{
+  "chunks": [
+    {
+      "chunkId": "e30d570b-2a3a-4f4a-9a0c-78f0f1acd383",
+      "documentReference": {
+        "storeId": "local",
+        "documentId": "2b36ec67-a78f-4f99-9371-8c6e5b332838",
+        "contentHash": "1c232bc1e553c10d00c3327dcca9012b6b4b0758a1c2afaad8b77c80fa1bd36e",
+        "metadata": {
+          "size": 116,
+          "fileName": null,
+          "processDefinitionId": null,
+          "processInstanceKey": null,
+          "customProperties": {},
+          "expiresAt": null,
+          "contentType": "text/plain"
+        },
+        "camunda.document.type": "camunda"
+      },
+      "score": 0.6721556,
+      "content": "Camunda is a platform for orchestrating and automating business processes. It helps organizations design, execute, and manage workflows, enabling them to optimize processes and improve efficiency."
+    }
+  ]
+}
+```
+
+## Embedding models
+
+### Amazon Bedrock
+
+The **Vector Database Connector** currently supports only [Amazon Titan V1/V2 models](https://docs.aws.amazon.com/bedrock/latest/userguide/titan-embedding-models.html).
+Please see an [official Amazon documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-embed-text.html)
+that will explain what are and how to choose request parameters.
+
+The **Vector Database Connector** uses [Langchain4J implementation](https://docs.langchain4j.dev/integrations/embedding-models/amazon-bedrock).
+
+## Vector stores
+
+### Elasticsearch
+
+The **Vector Database Connector** can use Elasticsearch as a vector store. The Elasticsearch version must be 8+.
+
+Enter the following parameters:
+
+- **Base URL**: the Elasticsearch base URL, including protocol, i.e. `https://host:port`.
+- **Username**: for the Elasticsearch user that has read/write access.
+- **Password**: for the Elasticsearch user that has read/write access.
+- **Index name**: name of the index where you wish to store embeddings.
+- - When embedding: if index is not present, the Connector will create a new one.
+- - When retrieving: if the index is absent, the Connector will raise an error.
+
+### Amazon Managed Opensearch
path: docs/components/connectors/out-of-the-box-connectors/embeddings-vector-db.md
user: login: igpetrov
body: Good bot
created_at: 2025-05-07T10:03:58Z
author_association: CONTRIBUTOR
diff_hunk: @@ -0,0 +1,147 @@
+---
+id: embeddings-vector-db
+title: Vector Database Connector
+sidebar_label: Vector Database
+description: Embed, store and retrieve LLM embeddings and store them into vector databases.
+---
+
+The **Vector Database Connector** allows embedding, storing and retrieving Large Language Model (LLM) embeddings. This enables building
+AI-based solutions for your organizations, such as context document search, long-term LLM memory and/or agentic AI interaction.
+
+:::note
+The **Vector Database Connector** uses the [Langchain4J](https://docs.langchain4j.dev/) library. Data models and possible implementations are limited
+to the latest stable released Langchain4j library.
+:::
+
+## Prerequisites
+
+Before using the **Vector Database Connector**, it is highly recommended to understand the
+[concept of LLM embeddings](https://huggingface.co/spaces/hesamation/primer-llm-embedding).
+
+To start using the **Vector Database Connector**, you need to have access to supported LLM embeddings API to convert
+documents content into vectorized embeddings form. You will also need to have write access to supported databases.
+
+## Create a Connector task
+
+import ConnectorTask from '../../../components/react-components/connector-task.md'
+
+<ConnectorTask/>
+
+## Operations
+
+### Embed document
+
+The **Embed document** operation performs the following steps: [consumes a document](#embedding-document-configuration),
+parses it depending on a file format, optionally [splits into text chunks](#splitting), converts chunks into a vector form
+with an LLM help, and finally, stores produced vectors into a vector database.
+
+To perform this operation, enter the following:
+
+- **Operation** dropdown: **Embed document**.
+- **Embedding model** section: refer to the [relevant section](#embedding-models).
+- **Vector store** section: refer to the [relevant section](#vector-stores).
+- **Document** section: refer to the [relevant section](#embedding-document-configuration).
+
+As a result of this operation you will get an array of created embeddings,
+e.g. `["d599ec62-fe51-4a91-bbf0-26e1241f9079", "a1fad021-5148-42b4-aa02-7de9d590e69c"]`.
+
+### Retrieve document
+
+The **Retrieve document** operation perform the following steps: consumes a query, converts it into a vector form with
+an LLM help, performs a vector similarity search on previously stored LLM embeddings, and then stores results into
+Camunda document storage.
+
+To perform this operation, enter the following:
+
+- **Operation** dropdown: **Retrieve document**.
+- **Search query** input field: enter your search query.
+- **Max results** input field: enter maximum amount of returned results.
+- **Min score** input field: enter the lowest score threshold similarity value; the value should be between 0 and 1, e.g. 0.81.
+- **Embedding model** section: refer to the [relevant section](#embedding-models).
+- **Vector store** section: refer to the [relevant section](#vector-stores).
+
+As a result of this operation you will get an array of relevant chunk array, where each includes a [chunk ID](#splitting),
+Camunda document reference metadata, similarity score, and the actual text content.
+
+```json
+{
+  "chunks": [
+    {
+      "chunkId": "e30d570b-2a3a-4f4a-9a0c-78f0f1acd383",
+      "documentReference": {
+        "storeId": "local",
+        "documentId": "2b36ec67-a78f-4f99-9371-8c6e5b332838",
+        "contentHash": "1c232bc1e553c10d00c3327dcca9012b6b4b0758a1c2afaad8b77c80fa1bd36e",
+        "metadata": {
+          "size": 116,
+          "fileName": null,
+          "processDefinitionId": null,
+          "processInstanceKey": null,
+          "customProperties": {},
+          "expiresAt": null,
+          "contentType": "text/plain"
+        },
+        "camunda.document.type": "camunda"
+      },
+      "score": 0.6721556,
+      "content": "Camunda is a platform for orchestrating and automating business processes. It helps organizations design, execute, and manage workflows, enabling them to optimize processes and improve efficiency."
+    }
+  ]
+}
+```
+
+## Embedding models
+
+### Amazon Bedrock
+
+The **Vector Database Connector** currently supports only [Amazon Titan V1/V2 models](https://docs.aws.amazon.com/bedrock/latest/userguide/titan-embedding-models.html).
+Please see an [official Amazon documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-embed-text.html)
+that will explain what are and how to choose request parameters.
+
+The **Vector Database Connector** uses [Langchain4J implementation](https://docs.langchain4j.dev/integrations/embedding-models/amazon-bedrock).
+
+## Vector stores
+
+### Elasticsearch
+
+The **Vector Database Connector** can use Elasticsearch as a vector store. The Elasticsearch version must be 8+.
+
+Enter the following parameters:
+
+- **Base URL**: the Elasticsearch base URL, including protocol, i.e. `https://host:port`.
+- **Username**: for the Elasticsearch user that has read/write access.
+- **Password**: for the Elasticsearch user that has read/write access.
+- **Index name**: name of the index where you wish to store embeddings.
+- - When embedding: if index is not present, the Connector will create a new one.
+- - When retrieving: if the index is absent, the Connector will raise an error.
+
+### Amazon Managed Opensearch
path: docs/components/connectors/out-of-the-box-connectors/embeddings-vector-db.md
user: login: igpetrov
body: ```suggestion
### Amazon Managed OpenSearch
```
created_at: 2025-05-07T10:04:10Z
author_association: CONTRIBUTOR
diff_hunk: @@ -0,0 +1,147 @@
+---
+id: embeddings-vector-db
+title: Vector Database Connector
+sidebar_label: Vector Database
+description: Embed, store and retrieve LLM embeddings and store them into vector databases.
+---
+
+The **Vector Database Connector** allows embedding, storing and retrieving Large Language Model (LLM) embeddings. This enables building
+AI-based solutions for your organizations, such as context document search, long-term LLM memory and/or agentic AI interaction.
+
+:::note
+The **Vector Database Connector** uses the [Langchain4J](https://docs.langchain4j.dev/) library. Data models and possible implementations are limited
+to the latest stable released Langchain4j library.
+:::
+
+## Prerequisites
+
+Before using the **Vector Database Connector**, it is highly recommended to understand the
+[concept of LLM embeddings](https://huggingface.co/spaces/hesamation/primer-llm-embedding).
+
+To start using the **Vector Database Connector**, you need to have access to supported LLM embeddings API to convert
+documents content into vectorized embeddings form. You will also need to have write access to supported databases.
+
+## Create a Connector task
+
+import ConnectorTask from '../../../components/react-components/connector-task.md'
+
+<ConnectorTask/>
+
+## Operations
+
+### Embed document
+
+The **Embed document** operation performs the following steps: [consumes a document](#embedding-document-configuration),
+parses it depending on a file format, optionally [splits into text chunks](#splitting), converts chunks into a vector form
+with an LLM help, and finally, stores produced vectors into a vector database.
+
+To perform this operation, enter the following:
+
+- **Operation** dropdown: **Embed document**.
+- **Embedding model** section: refer to the [relevant section](#embedding-models).
+- **Vector store** section: refer to the [relevant section](#vector-stores).
+- **Document** section: refer to the [relevant section](#embedding-document-configuration).
+
+As a result of this operation you will get an array of created embeddings,
+e.g. `["d599ec62-fe51-4a91-bbf0-26e1241f9079", "a1fad021-5148-42b4-aa02-7de9d590e69c"]`.
+
+### Retrieve document
+
+The **Retrieve document** operation perform the following steps: consumes a query, converts it into a vector form with
+an LLM help, performs a vector similarity search on previously stored LLM embeddings, and then stores results into
+Camunda document storage.
+
+To perform this operation, enter the following:
+
+- **Operation** dropdown: **Retrieve document**.
+- **Search query** input field: enter your search query.
+- **Max results** input field: enter maximum amount of returned results.
+- **Min score** input field: enter the lowest score threshold similarity value; the value should be between 0 and 1, e.g. 0.81.
+- **Embedding model** section: refer to the [relevant section](#embedding-models).
+- **Vector store** section: refer to the [relevant section](#vector-stores).
+
+As a result of this operation you will get an array of relevant chunk array, where each includes a [chunk ID](#splitting),
+Camunda document reference metadata, similarity score, and the actual text content.
+
+```json
+{
+  "chunks": [
+    {
+      "chunkId": "e30d570b-2a3a-4f4a-9a0c-78f0f1acd383",
+      "documentReference": {
+        "storeId": "local",
+        "documentId": "2b36ec67-a78f-4f99-9371-8c6e5b332838",
+        "contentHash": "1c232bc1e553c10d00c3327dcca9012b6b4b0758a1c2afaad8b77c80fa1bd36e",
+        "metadata": {
+          "size": 116,
+          "fileName": null,
+          "processDefinitionId": null,
+          "processInstanceKey": null,
+          "customProperties": {},
+          "expiresAt": null,
+          "contentType": "text/plain"
+        },
+        "camunda.document.type": "camunda"
+      },
+      "score": 0.6721556,
+      "content": "Camunda is a platform for orchestrating and automating business processes. It helps organizations design, execute, and manage workflows, enabling them to optimize processes and improve efficiency."
+    }
+  ]
+}
+```
+
+## Embedding models
+
+### Amazon Bedrock
+
+The **Vector Database Connector** currently supports only [Amazon Titan V1/V2 models](https://docs.aws.amazon.com/bedrock/latest/userguide/titan-embedding-models.html).
+Please see an [official Amazon documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-embed-text.html)
+that will explain what are and how to choose request parameters.
+
+The **Vector Database Connector** uses [Langchain4J implementation](https://docs.langchain4j.dev/integrations/embedding-models/amazon-bedrock).
+
+## Vector stores
+
+### Elasticsearch
+
+The **Vector Database Connector** can use Elasticsearch as a vector store. The Elasticsearch version must be 8+.
+
+Enter the following parameters:
+
+- **Base URL**: the Elasticsearch base URL, including protocol, i.e. `https://host:port`.
+- **Username**: for the Elasticsearch user that has read/write access.
+- **Password**: for the Elasticsearch user that has read/write access.
+- **Index name**: name of the index where you wish to store embeddings.
+- - When embedding: if index is not present, the Connector will create a new one.
+- - When retrieving: if the index is absent, the Connector will raise an error.
+
+### Amazon Managed Opensearch
+
+- **Access key** and **Secret key**: enter AWS IAM credential for the user that has read/write access.
+- **Server URL**: is an Amazon Managed Opensearch URL _without_ protocol, i.e. `my-opensearch.aws.com:port`.
path: docs/components/connectors/out-of-the-box-connectors/embeddings-vector-db.md
user: login: igpetrov
body: Good bot

```suggestion
- **Server URL**: is an Amazon Managed OpenSearch URL _without_ protocol, i.e. `my-opensearch.aws.com:port`.
```
created_at: 2025-05-07T10:04:31Z
author_association: CONTRIBUTOR
diff_hunk: @@ -0,0 +1,147 @@
+---
+id: embeddings-vector-db
+title: Vector Database Connector
+sidebar_label: Vector Database
+description: Embed, store and retrieve LLM embeddings and store them into vector databases.
+---
+
+The **Vector Database Connector** allows embedding, storing and retrieving Large Language Model (LLM) embeddings. This enables building
+AI-based solutions for your organizations, such as context document search, long-term LLM memory and/or agentic AI interaction.
+
+:::note
+The **Vector Database Connector** uses the [Langchain4J](https://docs.langchain4j.dev/) library. Data models and possible implementations are limited
+to the latest stable released Langchain4j library.
+:::
+
+## Prerequisites
+
+Before using the **Vector Database Connector**, it is highly recommended to understand the
+[concept of LLM embeddings](https://huggingface.co/spaces/hesamation/primer-llm-embedding).
+
+To start using the **Vector Database Connector**, you need to have access to supported LLM embeddings API to convert
+documents content into vectorized embeddings form. You will also need to have write access to supported databases.
+
+## Create a Connector task
+
+import ConnectorTask from '../../../components/react-components/connector-task.md'
+
+<ConnectorTask/>
+
+## Operations
+
+### Embed document
+
+The **Embed document** operation performs the following steps: [consumes a document](#embedding-document-configuration),
+parses it depending on a file format, optionally [splits into text chunks](#splitting), converts chunks into a vector form
+with an LLM help, and finally, stores produced vectors into a vector database.
+
+To perform this operation, enter the following:
+
+- **Operation** dropdown: **Embed document**.
+- **Embedding model** section: refer to the [relevant section](#embedding-models).
+- **Vector store** section: refer to the [relevant section](#vector-stores).
+- **Document** section: refer to the [relevant section](#embedding-document-configuration).
+
+As a result of this operation you will get an array of created embeddings,
+e.g. `["d599ec62-fe51-4a91-bbf0-26e1241f9079", "a1fad021-5148-42b4-aa02-7de9d590e69c"]`.
+
+### Retrieve document
+
+The **Retrieve document** operation perform the following steps: consumes a query, converts it into a vector form with
+an LLM help, performs a vector similarity search on previously stored LLM embeddings, and then stores results into
+Camunda document storage.
+
+To perform this operation, enter the following:
+
+- **Operation** dropdown: **Retrieve document**.
+- **Search query** input field: enter your search query.
+- **Max results** input field: enter maximum amount of returned results.
+- **Min score** input field: enter the lowest score threshold similarity value; the value should be between 0 and 1, e.g. 0.81.
+- **Embedding model** section: refer to the [relevant section](#embedding-models).
+- **Vector store** section: refer to the [relevant section](#vector-stores).
+
+As a result of this operation you will get an array of relevant chunk array, where each includes a [chunk ID](#splitting),
+Camunda document reference metadata, similarity score, and the actual text content.
+
+```json
+{
+  "chunks": [
+    {
+      "chunkId": "e30d570b-2a3a-4f4a-9a0c-78f0f1acd383",
+      "documentReference": {
+        "storeId": "local",
+        "documentId": "2b36ec67-a78f-4f99-9371-8c6e5b332838",
+        "contentHash": "1c232bc1e553c10d00c3327dcca9012b6b4b0758a1c2afaad8b77c80fa1bd36e",
+        "metadata": {
+          "size": 116,
+          "fileName": null,
+          "processDefinitionId": null,
+          "processInstanceKey": null,
+          "customProperties": {},
+          "expiresAt": null,
+          "contentType": "text/plain"
+        },
+        "camunda.document.type": "camunda"
+      },
+      "score": 0.6721556,
+      "content": "Camunda is a platform for orchestrating and automating business processes. It helps organizations design, execute, and manage workflows, enabling them to optimize processes and improve efficiency."
+    }
+  ]
+}
+```
+
+## Embedding models
+
+### Amazon Bedrock
+
+The **Vector Database Connector** currently supports only [Amazon Titan V1/V2 models](https://docs.aws.amazon.com/bedrock/latest/userguide/titan-embedding-models.html).
+Please see an [official Amazon documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-embed-text.html)
+that will explain what are and how to choose request parameters.
+
+The **Vector Database Connector** uses [Langchain4J implementation](https://docs.langchain4j.dev/integrations/embedding-models/amazon-bedrock).
+
+## Vector stores
+
+### Elasticsearch
+
+The **Vector Database Connector** can use Elasticsearch as a vector store. The Elasticsearch version must be 8+.
+
+Enter the following parameters:
+
+- **Base URL**: the Elasticsearch base URL, including protocol, i.e. `https://host:port`.
+- **Username**: for the Elasticsearch user that has read/write access.
+- **Password**: for the Elasticsearch user that has read/write access.
+- **Index name**: name of the index where you wish to store embeddings.
+- - When embedding: if index is not present, the Connector will create a new one.
+- - When retrieving: if the index is absent, the Connector will raise an error.
+
+### Amazon Managed Opensearch
+
+- **Access key** and **Secret key**: enter AWS IAM credential for the user that has read/write access.
+- **Server URL**: is an Amazon Managed Opensearch URL _without_ protocol, i.e. `my-opensearch.aws.com:port`.
+- **Region**: of the Amazon Managed Opensearch instance.
path: docs/components/connectors/out-of-the-box-connectors/embeddings-vector-db.md
user: login: igpetrov
body: Good bot
```suggestion
- **Region**: of the Amazon Managed OpenSearch instance.
```
created_at: 2025-05-07T10:04:45Z
author_association: CONTRIBUTOR
diff_hunk: @@ -0,0 +1,147 @@
+---
+id: embeddings-vector-db
+title: Vector Database Connector
+sidebar_label: Vector Database
+description: Embed, store and retrieve LLM embeddings and store them into vector databases.
+---
+
+The **Vector Database Connector** allows embedding, storing and retrieving Large Language Model (LLM) embeddings. This enables building
+AI-based solutions for your organizations, such as context document search, long-term LLM memory and/or agentic AI interaction.
+
+:::note
+The **Vector Database Connector** uses the [Langchain4J](https://docs.langchain4j.dev/) library. Data models and possible implementations are limited
+to the latest stable released Langchain4j library.
+:::
+
+## Prerequisites
+
+Before using the **Vector Database Connector**, it is highly recommended to understand the
+[concept of LLM embeddings](https://huggingface.co/spaces/hesamation/primer-llm-embedding).
+
+To start using the **Vector Database Connector**, you need to have access to supported LLM embeddings API to convert
+documents content into vectorized embeddings form. You will also need to have write access to supported databases.
+
+## Create a Connector task
+
+import ConnectorTask from '../../../components/react-components/connector-task.md'
+
+<ConnectorTask/>
+
+## Operations
+
+### Embed document
+
+The **Embed document** operation performs the following steps: [consumes a document](#embedding-document-configuration),
+parses it depending on a file format, optionally [splits into text chunks](#splitting), converts chunks into a vector form
+with an LLM help, and finally, stores produced vectors into a vector database.
+
+To perform this operation, enter the following:
+
+- **Operation** dropdown: **Embed document**.
+- **Embedding model** section: refer to the [relevant section](#embedding-models).
+- **Vector store** section: refer to the [relevant section](#vector-stores).
+- **Document** section: refer to the [relevant section](#embedding-document-configuration).
+
+As a result of this operation you will get an array of created embeddings,
+e.g. `["d599ec62-fe51-4a91-bbf0-26e1241f9079", "a1fad021-5148-42b4-aa02-7de9d590e69c"]`.
+
+### Retrieve document
+
+The **Retrieve document** operation perform the following steps: consumes a query, converts it into a vector form with
+an LLM help, performs a vector similarity search on previously stored LLM embeddings, and then stores results into
+Camunda document storage.
+
+To perform this operation, enter the following:
+
+- **Operation** dropdown: **Retrieve document**.
+- **Search query** input field: enter your search query.
+- **Max results** input field: enter maximum amount of returned results.
+- **Min score** input field: enter the lowest score threshold similarity value; the value should be between 0 and 1, e.g. 0.81.
+- **Embedding model** section: refer to the [relevant section](#embedding-models).
+- **Vector store** section: refer to the [relevant section](#vector-stores).
+
+As a result of this operation you will get an array of relevant chunk array, where each includes a [chunk ID](#splitting),
+Camunda document reference metadata, similarity score, and the actual text content.
+
+```json
+{
+  "chunks": [
+    {
+      "chunkId": "e30d570b-2a3a-4f4a-9a0c-78f0f1acd383",
+      "documentReference": {
+        "storeId": "local",
+        "documentId": "2b36ec67-a78f-4f99-9371-8c6e5b332838",
+        "contentHash": "1c232bc1e553c10d00c3327dcca9012b6b4b0758a1c2afaad8b77c80fa1bd36e",
+        "metadata": {
+          "size": 116,
+          "fileName": null,
+          "processDefinitionId": null,
+          "processInstanceKey": null,
+          "customProperties": {},
+          "expiresAt": null,
+          "contentType": "text/plain"
+        },
+        "camunda.document.type": "camunda"
+      },
+      "score": 0.6721556,
+      "content": "Camunda is a platform for orchestrating and automating business processes. It helps organizations design, execute, and manage workflows, enabling them to optimize processes and improve efficiency."
+    }
+  ]
+}
+```
+
+## Embedding models
+
+### Amazon Bedrock
+
+The **Vector Database Connector** currently supports only [Amazon Titan V1/V2 models](https://docs.aws.amazon.com/bedrock/latest/userguide/titan-embedding-models.html).
+Please see an [official Amazon documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-embed-text.html)
+that will explain what are and how to choose request parameters.
+
+The **Vector Database Connector** uses [Langchain4J implementation](https://docs.langchain4j.dev/integrations/embedding-models/amazon-bedrock).
+
+## Vector stores
+
+### Elasticsearch
+
+The **Vector Database Connector** can use Elasticsearch as a vector store. The Elasticsearch version must be 8+.
+
+Enter the following parameters:
+
+- **Base URL**: the Elasticsearch base URL, including protocol, i.e. `https://host:port`.
+- **Username**: for the Elasticsearch user that has read/write access.
+- **Password**: for the Elasticsearch user that has read/write access.
+- **Index name**: name of the index where you wish to store embeddings.
+- - When embedding: if index is not present, the Connector will create a new one.
+- - When retrieving: if the index is absent, the Connector will raise an error.
+
+### Amazon Managed Opensearch
+
+- **Access key** and **Secret key**: enter AWS IAM credential for the user that has read/write access.
+- **Server URL**: is an Amazon Managed Opensearch URL _without_ protocol, i.e. `my-opensearch.aws.com:port`.
+- **Region**: of the Amazon Managed Opensearch instance.
+- **Index name**: name of the index where you wish to store embeddings.
+- - When embedding: if index is not present, the Connector will create a new one.
+- - When retrieving: if the index is absent, the Connector will raise an error.
+
+## Embedding document configuration
+
+### Document source
+
+The **Document source** can be either a **Plain text** or a **Camunda document**.
+
+The **Plain text** can be useful when you deal with small size data that can fit into a text field or a process instance variable.
+Input will be handled as a regular UTF-8 text.
+
+Hint: a FEEL [string conversion function](/components/modeler/feel/builtin-functions/feel-built-in-functions-conversion/#stringfrom) might be useful if you have JSON input.
+
+The **Camunda document** might be useful when you deal with larger documents pipelines that come either from
+[webhook or user tasks](/guides/document-handling). Input documents will be parsed with [Apache Tika](https://tika.apache.org/) parser, hence files
path: docs/components/connectors/out-of-the-box-connectors/embeddings-vector-db.md
user: login: igpetrov
body: ```suggestion
[webhook or user tasks](/guides/document-handling). Input documents will be parsed with [Apache Tika](https://tika.apache.org/), hence files
```
created_at: 2025-05-07T10:06:09Z
author_association: CONTRIBUTOR
diff_hunk: @@ -45,6 +45,7 @@
     "docusaurus-plugin-openapi-docs": "^4.3.7",
     "docusaurus-theme-openapi-docs": "^4.3.7",
     "mixpanel-browser": "^2.64.0",
+    "npx": "^10.2.2",
path: package.json
user: login: igpetrov
body: Good point. That's somehow slipped my publish commit 
created_at: 2025-05-07T10:07:36Z
author_association: CONTRIBUTOR

PR comments:
user: login: github-actions[bot]
created_at: 2025-05-06T13:01:16Z
body: :wave: :robot: :thinking: Hello, @christinaausley! Did you make your changes in all the right places? 

These files were changed only in *docs/*. You might want to duplicate these changes in *versioned_docs/version-8.7/*.<br/><ul> <li>docs/components/connectors/out-of-the-box-connectors/embeddings-vector-db.md</li> </ul>

You may have done this intentionally, but we wanted to point it out in case you didn't. You can read more about the versioning within our docs in our [documentation guidelines](https://github.com/camunda/camunda-docs/blob/c666b7e5e35bfe31468ce4ac679ea7b62ab86d73/howtos/documentation-guidelines.md#versions).
<!-- thollander/actions-comment-pull-request "check-versions" -->
author_association: CONTRIBUTOR
user: login: igpetrov
created_at: 2025-05-07T10:14:59Z
body: Hi @maff, thank you for the review. I addressed the changes you mentioned. The @camunda/tech-writers will be added after we review the PR internally and no major concerns left.
author_association: CONTRIBUTOR
user: login: maff
created_at: 2025-05-07T11:35:50Z
body: Sounds good, I'll use diff suggestions next time 😉 
author_association: MEMBER
user: login: igpetrov
created_at: 2025-05-14T11:06:23Z
body: Hey @camunda/tech-writers could we please review this doc?
author_association: CONTRIBUTOR
user: login: christinaausley
created_at: 2025-05-14T15:41:35Z
body: @igpetrov I will review this today.
author_association: CONTRIBUTOR
user: login: igpetrov
created_at: 2025-05-15T08:11:48Z
body: > I've added tabs to break up the content -- WDYT?

Looks better. Thank you!
author_association: CONTRIBUTOR
user: login: github-actions[bot]
created_at: 2025-05-19T09:57:59Z
body: <!-- add-pr-comment:add-pr-comment -->

:broom: Preview environment for this PR has been torn down.
author_association: CONTRIBUTOR