doc : add ElasticsearchVectorStore doc

api/src/main/java/org/apache/flink/agents/api/vectorstores/VectorStoreQueryMode.java

@@ -23,15 +23,13 @@
  *
  * <ul>
  *   <li>{@link #SEMANTIC}: Use dense vector embeddings and similarity search.
- *   <li>{@link #KEYWORD}: Use keyword or lexical search when supported by the store.
- *   <li>{@link #HYBRID}: Combine semantic and keyword search strategies.
  * </ul>
  */
 public enum VectorStoreQueryMode {
     /** Semantic similarity search using embeddings. */
     SEMANTIC,
-    /** Keyword/lexical search (store dependent). */
-    KEYWORD,
-    /** Hybrid search combining semantic and keyword results. */
-    HYBRID;
+    /** Keyword/lexical search (store dependent). TODO: term-based retrieval */
+    //    KEYWORD,
+    /** Hybrid search combining semantic and keyword results. TODO: semantic + keyword retrieval */
+    //    HYBRID;
 }

docs/content/docs/development/vector_stores.md

@@ -24,10 +24,6 @@ under the License.
 
 # Vector Stores
 
-{{< hint info >}}
-Vector stores are currently supported in the Python API only. Java API support is planned for future releases.
-{{< /hint >}}
-
 {{< hint info >}}
 This page covers semantic search using vector stores. Additional query modes (keyword, hybrid) are planned for future releases.
 {{< /hint >}}
@@ -50,43 +46,99 @@ To use vector stores in your agents, you need to configure both a vector store a
 
 Flink Agents provides decorators to simplify vector store setup within agents:
 
+{{< tabs "Resource Decorators" >}}
+
+{{< tab "Python" >}}
+
 #### @vector_store
 
 The `@vector_store` decorator marks a method that creates a vector store. Vector stores automatically integrate with embedding models for text-based search.
 
+{{< /tab >}}
+
+{{< tab "Java" >}}
+
+#### @VectorStore
+
+The `@VectorStore` annotation marks a method that creates a vector store.
+
+{{< /tab >}}
+
+{{< /tabs >}}
+
 ### Query Objects
 
 Vector stores use structured query objects for consistent interfaces:
 
+{{< tabs "Query Objects" >}}
+
+{{< tab "Python" >}}
+
 ```python
 # Create a semantic search query
 query = VectorStoreQuery(
-    mode=VectorStoreQueryMode.SEMANTIC,
     query_text="What is Apache Flink Agents?",
     limit=3
 )
 ```
 
-### Query Results
+{{< /tab >}}
 
-When you execute a query, you receive a `VectorStoreQueryResult` object that contains the search results:
+{{< tab "Java" >}}
 
-```python
-# Execute the query
-result = vector_store.query(query)
+```java
+// Create a semantic search query
+VectorStoreQuery query = new VectorStoreQuery(
+        "What is Apache Flink Agents?", // query text
+        3 // limit
+);
 ```
 
+{{< /tab >}}
+
+{{< /tabs >}}
+
+### Query Results
+
+When you execute a query, you receive a `VectorStoreQueryResult` object that contains the search results:
+
 The `VectorStoreQueryResult` contains:
 - **documents**: A list of `Document` objects representing the retrieved results
 - Each `Document` has:
   - **content**: The actual text content of the document
   - **metadata**: Associated metadata (source, category, timestamp, etc.)
   - **id**: Unique identifier of the document (if available)
 
+{{< tabs "Query Results" >}}
+
+{{< tab "Python" >}}
+
+```python
+# Execute the query
+result = vector_store.query(query)
+```
+
+{{< /tab >}}
+
+{{< tab "Java" >}}
+
+```java
+// Execute the query
+VectorStoreQueryResult result = vectorStore.query(query);
+```
+
+{{< /tab >}}
+
+{{< /tabs >}}
+
 ### Usage Example
 
 Here's how to define and use vector stores in your agent:
 
+{{< tabs "Usage Example" >}}
+
+{{< tab "Python" >}}
+
 ```python
 class MyAgent(Agent):
 
@@ -127,7 +179,6 @@ class MyAgent(Agent):
         # Create a semantic search query
         user_query = str(event.input)
         query = VectorStoreQuery(
-            mode=VectorStoreQueryMode.SEMANTIC,
             query_text=user_query,
             limit=3
         )
@@ -139,12 +190,73 @@ class MyAgent(Agent):
         # Process the retrieved context as needed for your use case
 ```
 
+{{< /tab >}}
+
+{{< tab "Java" >}}
+
+```java
+public class MyAgent extends Agent {
+
+    @EmbeddingModelConnection
+    public static ResourceDescriptor embeddingConnection() {
+        return ResourceDescriptor.Builder.newBuilder(OpenAIEmbeddingModelConnection.class.getName())
+                .addInitialArgument("api_key", "your-api-key-here")
+                .build();
+    }
+
+    @EmbeddingModelSetup
+    public static ResourceDescriptor embeddingModel() {
+        return ResourceDescriptor.Builder.newBuilder(OpenAIEmbeddingModelSetup.class.getName())
+                .addInitialArgument("connection", "embeddingConnection")
+                .addInitialArgument("model", "text-embedding-3-small")
+                .build();
+    }
+
+    @VectorStore
+    public static ResourceDescriptor vectorStore() {
+        return ResourceDescriptor.Builder.newBuilder(ElasticsearchVectorStore.class.getName())
+                .addInitialArgument("embedding_model", "embeddingModel")
+                .addInitialArgument("host", "http://localhost:9200")
+                .addInitialArgument("index", "my_documents")
+                .addInitialArgument("vector_field", "content_vector")
+                .addInitialArgument("dims", 1536)
+                .build();
+    }
+
+    @Action(listenEvents = InputEvent.class)
+    public static void searchDocuments(InputEvent event, RunnerContext ctx) {
+        // Option 1: Manual search via the vector store
+        VectorStore vectorStore = (VectorStore) ctx.getResource("vectorStore", ResourceType.VECTOR_STORE);
+        String queryText = (String) event.getInput();
+        VectorStoreQuery query = new VectorStoreQuery(queryText, 3);
+        VectorStoreQueryResult result = vectorStore.query(query);
+
+        // Option 2: Request context retrieval via built-in events
+        ctx.sendEvent(new ContextRetrievalRequestEvent(queryText, "vectorStore"));
+    }
+
+    @Action(listenEvents = ContextRetrievalResponseEvent.class)
+    public static void onSearchResponse(ContextRetrievalResponseEvent event, RunnerContext ctx) {
+        List<Document> documents = event.getDocuments();
+        // Process the retrieved documents...
+    }
+}
+```
+
+{{< /tab >}}
+
+{{< /tabs >}}
+
 ## Built-in Providers
 
 ### Chroma
 
 [Chroma](https://www.trychroma.com/home) is an open-source vector database that provides efficient storage and querying of embeddings with support for multiple deployment modes.
 
+{{< hint info >}}
+Chroma is currently supported in the Python API only.
+{{< /hint >}}
+
 #### Prerequisites
 
 1. Install ChromaDB: `pip install chromadb`
@@ -169,6 +281,10 @@ class MyAgent(Agent):
 
 #### Usage Example
 
+{{< tabs "Chroma Usage Example" >}}
+
+{{< tab "Python" >}}
+
 ```python
 class MyAgent(Agent):
 
@@ -208,6 +324,10 @@ class MyAgent(Agent):
     ...
 ```
 
+{{< /tab >}}
+
+{{< /tabs >}}
+
 #### Deployment Modes
 
 ChromaDB supports multiple deployment modes:
@@ -265,6 +385,66 @@ def chroma_store() -> ResourceDescriptor:
     )
 ```
 
+### Elasticsearch
+
+[Elasticsearch](https://www.elastic.co/elasticsearch/) is a distributed, RESTful search and analytics engine that supports vector search through dense vector fields and K-Nearest Neighbors (KNN).
+
+{{< hint info >}}
+Elasticsearch is currently supported in the Java API only.
+{{< /hint >}}
+
+#### Prerequisites
+
+1. An Elasticsearch cluster (version 8.0 or later for KNN support).
+2. An index with a `dense_vector` field.
+
+#### ElasticsearchVectorStore Parameters
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `embedding_model` | str | Required | Reference to embedding model resource name |
+| `index` | str | Required | Target Elasticsearch index name |
+| `vector_field` | str | Required | Name of the dense vector field used for KNN |
+| `dims` | int | `768` | Vector dimensionality |
+| `k` | int | None | Number of nearest neighbors to return; can be overridden per query |
+| `num_candidates` | int | None | Candidate set size for ANN search; can be overridden per query |
+| `filter_query` | str | None | Raw JSON Elasticsearch filter query (DSL) applied as a post-filter |
+| `host` | str | `"http://localhost:9200"` | Elasticsearch endpoint |
+| `hosts` | str | None | Comma-separated list of Elasticsearch endpoints |
+| `username` | str | None | Username for basic authentication |
+| `password` | str | None | Password for basic authentication |
+| `api_key_base64` | str | None | Base64-encoded API key for authentication |
+| `api_key_id` | str | None | API key ID for authentication |
+| `api_key_secret` | str | None | API key secret for authentication |
+
+#### Usage Example
+
+{{< tabs "Elasticsearch Usage Example" >}}
+
+{{< tab "Java" >}}
+
+Here's how to define an Elasticsearch vector store in your Java agent:
+
+```java
+@VectorStore
+public static ResourceDescriptor vectorStore() {
+    return ResourceDescriptor.Builder.newBuilder(ElasticsearchVectorStore.class.getName())
+            .addInitialArgument("embedding_model", "embeddingModel")
+            .addInitialArgument("host", "http://localhost:9200")
+            .addInitialArgument("index", "my_documents")
+            .addInitialArgument("vector_field", "content_vector")
+            .addInitialArgument("dims", 1536)
+            // Optional authentication
+            // .addInitialArgument("username", "elastic")
+            // .addInitialArgument("password", "secret")
+            .build();
+}
+```
+
+{{< /tab >}}
+
+{{< /tabs >}}
+
 ## Custom Providers
 
 {{< hint warning >}}
@@ -277,6 +457,10 @@ If you want to use vector stores not offered by the built-in providers, you can
 
 The base class handles text-to-vector conversion and provides the high-level query interface. You only need to implement the core vector search functionality.
 
+{{< tabs "Custom Vector Store" >}}
+
+{{< tab "Python" >}}
+
 ```python
 class MyVectorStore(BaseVectorStore):
     # Add your custom configuration fields here
@@ -294,4 +478,42 @@ class MyVectorStore(BaseVectorStore):
         # - kwargs: Vector store-specific parameters
         # - Returns: List of Document objects matching the search criteria
         pass
-```
+```
+
+{{< /tab >}}
+
+{{< tab "Java" >}}
+
+```java
+public class MyVectorStore extends BaseVectorStore {
+
+    public MyVectorStore(
+            ResourceDescriptor descriptor,
+            BiFunction<String, ResourceType, Resource> getResource) {
+        super(descriptor, getResource);
+    }
+
+    @Override
+    public Map<String, Object> getStoreKwargs() {
+        // Return vector store-specific configuration
+        // These parameters are merged with query-specific parameters
+        Map<String, Object> kwargs = new HashMap<>();
+        kwargs.put("index", "my_index");
+        return kwargs;
+    }
+
+    @Override
+    public List<Document> queryEmbedding(float[] embedding, int limit, Map<String, Object> args) {
+        // Core method: perform vector search using pre-computed embedding
+        // - embedding: Pre-computed embedding vector for semantic search
+        // - limit: Maximum number of results to return
+        // - args: Vector store-specific parameters
+        // - Returns: List of Document objects matching the search criteria
+        return null;
+    }
+}
+```
+
+{{< /tab >}}
+
+{{< /tabs >}}