Add embed endpoint

README.md

@@ -47,8 +47,11 @@ The `Pinecone` class is your main entry point into the Pinecone Java SDK. You ca
 your `apiKey`, either by passing it as an argument in your code or by setting it as an environment variable called 
 `PINECONE_API_KEY`.
 
-Note: for pod-based indexes, you will also need an `environment` variable. You can set pass this as an argument in 
-your code or set it as an environment variable called `PINECONE_ENVIRONMENT`.
+This internally instantiates a single shared OkHttpClient instance, which is used for both control plane and inference
+operations. Note that the OkHttpClient performs best when you create a single `OkHttpClient` instance and reuse it 
+for all of your HTTP calls. This is because each client holds its own connection pool and thread pools. Reusing 
+connections and threads reduces latency and saves memory. Conversely, creating a client for each request wastes 
+resources on idle pools. More details on the OkHttpClient can be found [here](https://github.com/square/okhttp/blob/f2771425cb714a5b0b27238bd081b2516b4d640f/okhttp/src/main/kotlin/okhttp3/OkHttpClient.kt#L54).
 
 ```java
 import io.pinecone.clients.Pinecone;
@@ -542,7 +545,44 @@ Pinecone pinecone = new Pinecone.Builder("PINECONE_API_KEY").build();
 pinecone.deleteCollection("example-collection");
 ```
 
+## Inference
+
+The Pinecone SDK now supports creating embeddings via the [Inference API](https://docs.pinecone.io/guides/inference/understanding-inference).
+
+```java
+import io.pinecone.clients.Pinecone;
+import org.openapitools.control.client.ApiException;
+import org.openapitools.control.client.model.Embedding;
+import org.openapitools.control.client.model.EmbeddingsList;
+
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+...
+
+Pinecone pinecone = new Pinecone.Builder("PINECONE_API_KEY").build();
+Inference inference = pinecone.getInferenceClient();
+
+// Prepare input sentences to be embedded
+List<String> inputs = new ArrayList<>();
+inputs.add("The quick brown fox jumps over the lazy dog.");
+inputs.add("Lorem ipsum");
+
+// Specify the embedding model and parameters
+String embeddingModel = "multilingual-e5-large";
+
+Map<String, Object> parameters = new HashMap<>();
+parameters.put("input_type", "query");
+parameters.put("truncate", "END");
+
+// Generate embeddings for the input data
+EmbeddingsList embeddings = inference.embed(embeddingModel, parameters, inputs);
+
+// Get embedded data
+List<Embedding> embeddedData = embeddings.getData();
+```
+
 ## Examples
 
-- The data and control plane operation examples can be found in `io/pinecone/integration` folder.
-- A full end-to-end Semantic Search example can be found in the [Java Examples](https://github.com/pinecone-io/java-examples/tree/main) repo on Github.
+- The data and control plane operation examples can be found in `io/pinecone/integration` folder.

src/integration/java/io/pinecone/integration/inference/EmbedTest.java

@@ -0,0 +1,56 @@
+package io.pinecone.integration.inference;
+
+import io.pinecone.clients.Inference;
+import io.pinecone.clients.Pinecone;
+import org.junit.jupiter.api.Assertions;
+import org.junit.jupiter.api.Test;
+import org.openapitools.control.client.ApiException;
+import org.openapitools.control.client.model.EmbeddingsList;
+
+import java.util.*;
+
+import static org.junit.Assert.*;
+import static org.junit.jupiter.api.Assertions.assertNotNull;
+
+public class EmbedTest {
+
+    private static final Pinecone pinecone = new Pinecone
+            .Builder(System.getenv("PINECONE_API_KEY"))
+            .withSourceTag("pinecone_test")
+            .build();
+    private static final Inference inference = pinecone.getInferenceClient();
+
+    @Test
+    public void testGenerateEmbeddings() throws ApiException {
+        List<String> inputs = new ArrayList<>(1);
+        inputs.add("The quick brown fox jumps over the lazy dog.");
+        inputs.add("Lorem ipsum");
+
+        String embeddingModel = "multilingual-e5-large";
+
+        Map<String, Object> parameters = new HashMap<>();
+        parameters.put("input_type", "query");
+        parameters.put("truncate", "END");
+        EmbeddingsList embeddings = inference.embed(embeddingModel, parameters, inputs);
+
+        assertNotNull(embeddings, "Expected embedding to be not null");
+        Assertions.assertEquals(embeddingModel, embeddings.getModel());
+        Assertions.assertEquals(1024, embeddings.getData().get(0).getValues().size());
+        Assertions.assertEquals(2, embeddings.getData().size());
+    }
+
+    @Test
+    public void testGenerateEmbeddingsInvalidInputs() throws ApiException {
+        String embeddingModel = "multilingual-e5-large";
+        List<String> inputs = new ArrayList<>();
+        Map<String, Object> parameters = new HashMap<>();
+        parameters.put("input_type", "query");
+        parameters.put("truncate", "END");
+
+        Exception exception = assertThrows(Exception.class, () -> {
+            inference.embed(embeddingModel, parameters, inputs);
+        });
+
+        Assertions.assertTrue(exception.getMessage().contains("Must specify at least one input"));
+    }
+}

src/main/java/io/pinecone/clients/Inference.java

@@ -0,0 +1,68 @@
+package io.pinecone.clients;
+
+import org.openapitools.control.client.ApiClient;
+import org.openapitools.control.client.ApiException;
+import org.openapitools.control.client.api.InferenceApi;
+import org.openapitools.control.client.model.EmbedRequest;
+import org.openapitools.control.client.model.EmbedRequestInputsInner;
+import org.openapitools.control.client.model.EmbedRequestParameters;
+import org.openapitools.control.client.model.EmbeddingsList;
+
+import java.util.List;
+import java.util.Map;
+import java.util.stream.Collectors;
+
+/**
+ * The Inference class provides methods to interact with Pinecone's inference API through the Java SDK. It allows users
+ * to send input data to generate embeddings using a specified model.
+ * <p>
+ * This class utilizes the {@link InferenceApi} to make API calls to the Pinecone inference service.
+ *
+ */
+
+public class Inference {
+
+    private final InferenceApi inferenceApi;
+
+    /**
+     * Constructs an instance of {@link Inference} class.
+     *
+     * @param apiClient The ApiClient object used to configure the API connection.
+     */
+    public Inference(ApiClient apiClient) {
+        inferenceApi = new InferenceApi(apiClient);
+    }
+
+    /**
+     * Sends input data and parameters to the embedding model and returns a list of embeddings.
+     *
+     * @param model      The embedding model to use.
+     * @param parameters A map containing model-specific parameters.
+     * @param inputs     A list of input strings to generate embeddings for.
+     * @return EmbeddingsList containing the embeddings for the provided inputs.
+     * @throws ApiException If the API call fails, an ApiException is thrown.
+     */
+    public EmbeddingsList embed(String model, Map<String, Object> parameters, List<String> inputs) throws ApiException {
+        EmbedRequestParameters embedRequestParameters = new EmbedRequestParameters();
+        parameters.forEach(embedRequestParameters::putAdditionalProperty);
+
+        EmbedRequest embedRequest = new EmbedRequest()
+                .model(model)
+                .parameters(embedRequestParameters)
+                .inputs(convertToEmbedInputs(inputs));
+
+        return inferenceApi.embed(embedRequest);
+    }
+
+    /**
+     * Converts a list of input strings to EmbedRequestInputsInner objects.
+     *
+     * @param inputs A list of input strings.
+     * @return A list of EmbedRequestInputsInner objects containing the input data.
+     */
+    private List<EmbedRequestInputsInner> convertToEmbedInputs(List<String> inputs) {
+        return inputs.stream()
+                .map(input -> new EmbedRequestInputsInner().text(input))
+                .collect(Collectors.toList());
+    }
+}

src/main/java/io/pinecone/clients/Pinecone.java

@@ -18,7 +18,12 @@
 
 /**
  * The Pinecone class is the main entry point for interacting with Pinecone via the Java SDK.
- * It is used to create, delete, and manage your indexes and collections.
+ * It is used to create, delete, and manage your indexes and collections, along with the inference api.
+ * Note that the Pinecone class instantiates a single shared {@link OkHttpClient} instance,
+ * which is used for both control plane and inference operations.The OkHttpClient performs best when you create a single
+ * `OkHttpClient` instance and reuse it for all of your HTTP calls. This is because each client holds its own connection
+ * pool and thread pools. Reusing connections and threads reduces latency and saves memory. Conversely, creating a
+ * client for each request wastes resources on idle pools.
  * <p>
  * To instantiate the Pinecone class, use the {@link Pinecone.Builder} class to pass
  * an API key and any other optional configuration.
@@ -871,6 +876,18 @@ public AsyncIndex getAsyncIndexConnection(String indexName) throws PineconeValid
         return new AsyncIndex(connection, indexName);
     }
 
+    /**
+     * A method to create and return a new instance of the {@link Inference} client.
+     * <p>
+     * This method initializes the Inference client using the current ApiClient
+     * from the {@link ManageIndexesApi}. The {@link Inference} client can then be used
+     * to interact with Pinecone's inference API.
+     * @return A new {@link Inference} client instance.
+     */
+    public Inference getInferenceClient() {
+        return new Inference(manageIndexesApi.getApiClient());
+    }
+
     PineconeConnection getConnection(String indexName) {
         return connectionsMap.computeIfAbsent(indexName, key -> new PineconeConnection(config));
     }