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RestMLRemoteInferenceIT.java
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/*
* Copyright OpenSearch Contributors
* SPDX-License-Identifier: Apache-2.0
*/
package org.opensearch.ml.rest;
import java.io.IOException;
import java.util.List;
import java.util.Map;
import java.util.concurrent.TimeUnit;
import java.util.function.Consumer;
import org.apache.commons.lang3.exception.ExceptionUtils;
import org.apache.hc.core5.http.HttpHeaders;
import org.apache.hc.core5.http.message.BasicHeader;
import org.junit.Before;
import org.junit.Ignore;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.ExpectedException;
import org.opensearch.client.Response;
import org.opensearch.client.ResponseException;
import org.opensearch.ml.common.MLTaskState;
import org.opensearch.ml.utils.TestHelper;
import com.google.common.collect.ImmutableList;
public class RestMLRemoteInferenceIT extends MLCommonsRestTestCase {
final String OPENAI_KEY = System.getenv("OPENAI_KEY");
final String COHERE_KEY = System.getenv("COHERE_KEY");
final String completionModelConnectorEntity = "{\n"
+ "\"name\": \"OpenAI Connector\",\n"
+ "\"description\": \"The connector to public OpenAI model service for GPT 3.5\",\n"
+ "\"version\": 1,\n"
+ "\"client_config\": {\n"
+ " \"max_connection\": 20,\n"
+ " \"connection_timeout\": 50000,\n"
+ " \"read_timeout\": 50000\n"
+ " },\n"
+ "\"protocol\": \"http\",\n"
+ "\"parameters\": {\n"
+ " \"endpoint\": \"api.openai.com\",\n"
+ " \"auth\": \"API_Key\",\n"
+ " \"content_type\": \"application/json\",\n"
+ " \"max_tokens\": 7,\n"
+ " \"temperature\": 0,\n"
+ " \"model\": \"gpt-3.5-turbo-instruct\"\n"
+ " },\n"
+ " \"credential\": {\n"
+ " \"openAI_key\": \""
+ OPENAI_KEY
+ "\"\n"
+ " },\n"
+ " \"actions\": [\n"
+ " {"
+ " \"action_type\": \"predict\",\n"
+ " \"method\": \"POST\",\n"
+ " \"url\": \"https://${parameters.endpoint}/v1/completions\",\n"
+ " \"headers\": {\n"
+ " \"Authorization\": \"Bearer ${credential.openAI_key}\"\n"
+ " },\n"
+ " \"request_body\": \"{ \\\"model\\\": \\\"${parameters.model}\\\", \\\"prompt\\\": \\\"${parameters.prompt}\\\", \\\"max_tokens\\\": ${parameters.max_tokens}, \\\"temperature\\\": ${parameters.temperature} }\"\n"
+ " }\n"
+ " ]\n"
+ "}";
@Rule
public ExpectedException exceptionRule = ExpectedException.none();
@Before
public void setup() throws IOException, InterruptedException {
disableClusterConnectorAccessControl();
// TODO Do we really need to wait this long? This adds 20s to every test case run.
// Can we instead check the cluster state and move on?
Thread.sleep(20000);
}
public void testCreate_Get_DeleteConnector() throws IOException {
Response response = createConnector(completionModelConnectorEntity);
Map responseMap = parseResponseToMap(response);
String connectorId = (String) responseMap.get("connector_id");
assertNotNull(connectorId); // Testing create connector
// Testing Get connector
response = TestHelper.makeRequest(client(), "GET", "/_plugins/_ml/connectors/" + connectorId, null, "", null);
responseMap = parseResponseToMap(response);
assertEquals("OpenAI Connector", responseMap.get("name"));
assertEquals("1", responseMap.get("version"));
assertEquals("The connector to public OpenAI model service for GPT 3.5", responseMap.get("description"));
assertEquals("http", responseMap.get("protocol"));
// Testing delete connector
response = TestHelper.makeRequest(client(), "DELETE", "/_plugins/_ml/connectors/" + connectorId, null, "", null);
responseMap = parseResponseToMap(response);
assertEquals("deleted", responseMap.get("result"));
}
private static String maskSensitiveInfo(String input) {
// Regex to remove the whole credential object and replace it with "***"
String regex = "\"credential\":\\{.*?}";
return input.replaceAll(regex, "\"credential\": \"***\"");
}
@Test
public void testMaskSensitiveInfo_withCredential() {
String input = "{\"credential\":{\"username\":\"admin\",\"password\":\"secret\"}}";
String expectedOutput = "{\"credential\": \"***\"}";
String actualOutput = maskSensitiveInfo(input);
assertEquals(expectedOutput, actualOutput);
}
@Test
public void testMaskSensitiveInfo_noCredential() {
String input = "{\"otherInfo\":\"someValue\"}";
String expectedOutput = "{\"otherInfo\":\"someValue\"}";
String actualOutput = maskSensitiveInfo(input);
assertEquals(expectedOutput, actualOutput);
}
@Test
public void testMaskSensitiveInfo_emptyInput() {
String input = "";
String expectedOutput = "";
String actualOutput = maskSensitiveInfo(input);
assertEquals(expectedOutput, actualOutput);
}
public void testSearchConnectors_beforeCreation() throws IOException {
String searchEntity = "{\n" + " \"query\": {\n" + " \"match_all\": {}\n" + " },\n" + " \"size\": 1000\n" + "}";
Response response = TestHelper
.makeRequest(client(), "GET", "/_plugins/_ml/connectors/_search", null, TestHelper.toHttpEntity(searchEntity), null);
Map responseMap = parseResponseToMap(response);
assertEquals(0.0, ((Map) ((Map) responseMap.get("hits")).get("total")).get("value"));
}
public void testSearchConnectors_afterCreation() throws IOException {
createConnector(completionModelConnectorEntity);
String searchEntity = "{\n" + " \"query\": {\n" + " \"match_all\": {}\n" + " },\n" + " \"size\": 1000\n" + "}";
Response response = TestHelper
.makeRequest(client(), "GET", "/_plugins/_ml/connectors/_search", null, TestHelper.toHttpEntity(searchEntity), null);
Map responseMap = parseResponseToMap(response);
assertEquals((Double) 1.0, (Double) ((Map) ((Map) responseMap.get("hits")).get("total")).get("value"));
}
public void testSearchRemoteModels_beforeCreation() throws IOException {
String searchEntity = "{\n" + " \"query\": {\n" + " \"match_all\": {}\n" + " },\n" + " \"size\": 1000\n" + "}";
Response response = TestHelper
.makeRequest(client(), "GET", "/_plugins/_ml/models/_search", null, TestHelper.toHttpEntity(searchEntity), null);
Map responseMap = parseResponseToMap(response);
assertEquals(0.0, ((Map) ((Map) responseMap.get("hits")).get("total")).get("value"));
}
public void testSearchRemoteModels_afterCreation() throws IOException {
registerRemoteModel();
String searchEntity = "{\n" + " \"query\": {\n" + " \"match_all\": {}\n" + " },\n" + " \"size\": 1000\n" + "}";
Response response = TestHelper
.makeRequest(client(), "GET", "/_plugins/_ml/models/_search", null, TestHelper.toHttpEntity(searchEntity), null);
Map responseMap = parseResponseToMap(response);
assertEquals(1.0, ((Map) ((Map) responseMap.get("hits")).get("total")).get("value"));
}
public void testSearchModelGroups_beforeCreation() throws IOException {
String searchEntity = "{\n" + " \"query\": {\n" + " \"match_all\": {}\n" + " },\n" + " \"size\": 1000\n" + "}";
Response response = TestHelper
.makeRequest(client(), "GET", "/_plugins/_ml/model_groups/_search", null, TestHelper.toHttpEntity(searchEntity), null);
Map responseMap = parseResponseToMap(response);
assertEquals(0.0, ((Map) ((Map) responseMap.get("hits")).get("total")).get("value"));
}
public void testSearchModelGroups_afterCreation() throws IOException {
registerRemoteModel();
String searchEntity = "{\n" + " \"query\": {\n" + " \"match_all\": {}\n" + " },\n" + " \"size\": 1000\n" + "}";
Response response = TestHelper
.makeRequest(client(), "GET", "/_plugins/_ml/model_groups/_search", null, TestHelper.toHttpEntity(searchEntity), null);
Map responseMap = parseResponseToMap(response);
assertEquals(1.0, ((Map) ((Map) responseMap.get("hits")).get("total")).get("value"));
}
public void testSearchMLTasks_beforeCreation() throws IOException {
String searchEntity = "{\n" + " \"query\": {\n" + " \"match_all\": {}\n" + " },\n" + " \"size\": 1000\n" + "}";
Response response = TestHelper
.makeRequest(client(), "GET", "/_plugins/_ml/tasks/_search", null, TestHelper.toHttpEntity(searchEntity), null);
Map responseMap = parseResponseToMap(response);
assertEquals(0.0, ((Map) ((Map) responseMap.get("hits")).get("total")).get("value"));
}
public void testSearchMLTasks_afterCreation() throws IOException {
registerRemoteModel();
String searchEntity = "{\n" + " \"query\": {\n" + " \"match_all\": {}\n" + " },\n" + " \"size\": 1000\n" + "}";
Response response = TestHelper
.makeRequest(client(), "GET", "/_plugins/_ml/tasks/_search", null, TestHelper.toHttpEntity(searchEntity), null);
Map responseMap = parseResponseToMap(response);
assertEquals(1.0, ((Map) ((Map) responseMap.get("hits")).get("total")).get("value"));
}
public void testDeployRemoteModel() throws IOException, InterruptedException {
Response response = createConnector(completionModelConnectorEntity);
Map responseMap = parseResponseToMap(response);
String connectorId = (String) responseMap.get("connector_id");
response = registerRemoteModel("openAI-GPT-3.5 completions", connectorId);
responseMap = parseResponseToMap(response);
String taskId = (String) responseMap.get("task_id");
waitForTask(taskId, MLTaskState.COMPLETED);
response = getTask(taskId);
responseMap = parseResponseToMap(response);
String modelId = (String) responseMap.get("model_id");
response = deployRemoteModel(modelId);
responseMap = parseResponseToMap(response);
assertEquals("COMPLETED", responseMap.get("status"));
taskId = (String) responseMap.get("task_id");
waitForTask(taskId, MLTaskState.COMPLETED);
}
public void testPredictWithAutoDeployAndTTL_RemoteModel() throws IOException, InterruptedException {
// Skip test if key is null
if (OPENAI_KEY == null) {
System.out.println("OPENAI_KEY is null");
return;
}
Response updateCBSettingResponse = TestHelper
.makeRequest(
client(),
"PUT",
"_cluster/settings",
null,
"{\"persistent\":{\"plugins.ml_commons.jvm_heap_memory_threshold\":100}}",
ImmutableList.of(new BasicHeader(HttpHeaders.USER_AGENT, ""))
);
assertEquals(200, updateCBSettingResponse.getStatusLine().getStatusCode());
Response response = createConnector(completionModelConnectorEntity);
Map responseMap = parseResponseToMap(response);
String connectorId = (String) responseMap.get("connector_id");
response = registerRemoteModelWithTTLAndSkipHeapMemCheck("openAI-GPT-3.5 completions", connectorId, 1);
responseMap = parseResponseToMap(response);
String modelId = (String) responseMap.get("model_id");
String predictInput = "{\n" + " \"parameters\": {\n" + " \"prompt\": \"Say this is a test\"\n" + " }\n" + "}";
response = predictRemoteModel(modelId, predictInput);
responseMap = parseResponseToMap(response);
List responseList = (List) responseMap.get("inference_results");
responseMap = (Map) responseList.get(0);
responseList = (List) responseMap.get("output");
responseMap = (Map) responseList.get(0);
responseMap = (Map) responseMap.get("dataAsMap");
responseList = (List) responseMap.get("choices");
if (responseList == null) {
assertTrue(checkThrottlingOpenAI(responseMap));
return;
}
responseMap = (Map) responseList.get(0);
assertFalse(((String) responseMap.get("text")).isEmpty());
getModelProfile(modelId, verifyRemoteModelDeployed());
TimeUnit.SECONDS.sleep(71);
assertTrue(getModelProfile(modelId, verifyRemoteModelDeployed()).isEmpty());
}
public void testPredictRemoteModelWithInterface(String testCase, Consumer<Map> verifyResponse, Consumer<Exception> verifyException)
throws IOException,
InterruptedException {
// Skip test if key is null
if (OPENAI_KEY == null) {
return;
}
Response response = createConnector(completionModelConnectorEntity);
Map responseMap = parseResponseToMap(response);
String connectorId = (String) responseMap.get("connector_id");
response = registerRemoteModelWithInterface("openAI-GPT-3.5 completions", connectorId, testCase);
responseMap = parseResponseToMap(response);
String taskId = (String) responseMap.get("task_id");
waitForTask(taskId, MLTaskState.COMPLETED);
response = getTask(taskId);
responseMap = parseResponseToMap(response);
String modelId = (String) responseMap.get("model_id");
response = deployRemoteModel(modelId);
responseMap = parseResponseToMap(response);
taskId = (String) responseMap.get("task_id");
waitForTask(taskId, MLTaskState.COMPLETED);
String predictInput = "{\n" + " \"parameters\": {\n" + " \"prompt\": \"Say this is a test\"\n" + " }\n" + "}";
try {
response = predictRemoteModel(modelId, predictInput);
responseMap = parseResponseToMap(response);
verifyResponse.accept(responseMap);
} catch (Exception e) {
verifyException.accept(e);
}
}
public void testPredictRemoteModelWithCorrectInterface() throws IOException, InterruptedException {
testPredictRemoteModelWithInterface("correctInterface", (responseMap) -> {
List responseList = (List) responseMap.get("inference_results");
responseMap = (Map) responseList.get(0);
responseList = (List) responseMap.get("output");
responseMap = (Map) responseList.get(0);
responseMap = (Map) responseMap.get("dataAsMap");
responseList = (List) responseMap.get("choices");
if (responseList == null) {
assertTrue(checkThrottlingOpenAI(responseMap));
return;
}
responseMap = (Map) responseList.get(0);
assertFalse(((String) responseMap.get("text")).isEmpty());
}, null);
}
public void testPredictRemoteModelWithWrongInputInterface() throws IOException, InterruptedException {
testPredictRemoteModelWithInterface("wrongInputInterface", null, (exception) -> {
assertTrue(exception instanceof org.opensearch.client.ResponseException);
String stackTrace = ExceptionUtils.getStackTrace(exception);
assertTrue(stackTrace.contains("Error validating input schema"));
});
}
public void testPredictRemoteModelWithWrongOutputInterface() throws IOException, InterruptedException {
testPredictRemoteModelWithInterface("wrongOutputInterface", null, (exception) -> {
assertTrue(exception instanceof org.opensearch.client.ResponseException);
String stackTrace = ExceptionUtils.getStackTrace(exception);
assertTrue(stackTrace.contains("Error validating output schema"));
});
}
public void testPredictRemoteModelWithSkipValidatingMissingParameter(
String testCase,
Consumer<Map> verifyResponse,
Consumer<Exception> verifyException
) throws IOException,
InterruptedException {
// Skip test if key is null
if (OPENAI_KEY == null) {
return;
}
Response response = createConnector(this.getConnectorBodyBySkipValidatingMissingParameter(testCase));
Map responseMap = parseResponseToMap(response);
String connectorId = (String) responseMap.get("connector_id");
response = registerRemoteModelWithInterface("openAI-GPT-3.5 completions", connectorId, "correctInterface");
responseMap = parseResponseToMap(response);
String taskId = (String) responseMap.get("task_id");
waitForTask(taskId, MLTaskState.COMPLETED);
response = getTask(taskId);
responseMap = parseResponseToMap(response);
String modelId = (String) responseMap.get("model_id");
response = deployRemoteModel(modelId);
responseMap = parseResponseToMap(response);
taskId = (String) responseMap.get("task_id");
waitForTask(taskId, MLTaskState.COMPLETED);
String predictInput = "{\n" + " \"parameters\": {\n" + " \"prompt\": \"Say this is a ${parameters.test}\"\n" + " }\n" + "}";
try {
response = predictRemoteModel(modelId, predictInput);
responseMap = parseResponseToMap(response);
verifyResponse.accept(responseMap);
} catch (Exception e) {
verifyException.accept(e);
}
}
public void testPredictRemoteModelWithSkipValidatingMissingParameterMissing() throws IOException, InterruptedException {
testPredictRemoteModelWithSkipValidatingMissingParameter("missing", null, (exception) -> {
assertTrue(exception.getMessage().contains("Some parameter placeholder not filled in payload: test"));
});
}
public void testPredictRemoteModelWithSkipValidatingMissingParameterEnabled() throws IOException, InterruptedException {
testPredictRemoteModelWithSkipValidatingMissingParameter("enabled", (responseMap) -> {
List responseList = (List) responseMap.get("inference_results");
responseMap = (Map) responseList.get(0);
responseList = (List) responseMap.get("output");
responseMap = (Map) responseList.get(0);
responseMap = (Map) responseMap.get("dataAsMap");
responseList = (List) responseMap.get("choices");
if (responseList == null) {
assertTrue(checkThrottlingOpenAI(responseMap));
return;
}
responseMap = (Map) responseList.get(0);
assertFalse(((String) responseMap.get("text")).isEmpty());
}, null);
}
public void testPredictRemoteModelWithSkipValidatingMissingParameterDisabled() throws IOException, InterruptedException {
testPredictRemoteModelWithSkipValidatingMissingParameter("disabled", null, (exception) -> {
assertTrue(exception.getMessage().contains("Some parameter placeholder not filled in payload: test"));
});
}
public void testOpenAIChatCompletionModel() throws IOException, InterruptedException {
// Skip test if key is null
if (OPENAI_KEY == null) {
return;
}
String entity = "{\n"
+ " \"name\": \"OpenAI chat model Connector\",\n"
+ " \"description\": \"The connector to public OpenAI model service for GPT 3.5\",\n"
+ " \"version\": 1,\n"
+ "\"client_config\": {\n"
+ " \"max_connection\": 20,\n"
+ " \"connection_timeout\": 50000,\n"
+ " \"read_timeout\": 50000\n"
+ " },\n"
+ " \"protocol\": \"http\",\n"
+ " \"parameters\": {\n"
+ " \"endpoint\": \"api.openai.com\",\n"
+ " \"auth\": \"API_Key\",\n"
+ " \"content_type\": \"application/json\",\n"
+ " \"max_tokens\": 7,\n"
+ " \"temperature\": 0,\n"
+ " \"model\": \"gpt-3.5-turbo\"\n"
+ " },\n"
+ " \"credential\": {\n"
+ " \"openAI_key\": \""
+ OPENAI_KEY
+ "\"\n"
+ " },\n"
+ " \"actions\": [\n"
+ " {\n"
+ " \"action_type\": \"predict\",\n"
+ " \"method\": \"POST\",\n"
+ " \"url\": \"https://api.openai.com/v1/chat/completions\",\n"
+ " \"headers\": { \n"
+ " \"Authorization\": \"Bearer ${credential.openAI_key}\"\n"
+ " },\n"
+ " \"request_body\": \"{ \\\"model\\\": \\\"${parameters.model}\\\", \\\"messages\\\": ${parameters.messages} }\"\n"
+ " }\n"
+ " ]\n"
+ "}";
Response response = createConnector(entity);
Map responseMap = parseResponseToMap(response);
String connectorId = (String) responseMap.get("connector_id");
response = registerRemoteModel("openAI-GPT-3.5 chat model", connectorId);
responseMap = parseResponseToMap(response);
String taskId = (String) responseMap.get("task_id");
waitForTask(taskId, MLTaskState.COMPLETED);
response = getTask(taskId);
responseMap = parseResponseToMap(response);
String modelId = (String) responseMap.get("model_id");
response = deployRemoteModel(modelId);
responseMap = parseResponseToMap(response);
taskId = (String) responseMap.get("task_id");
waitForTask(taskId, MLTaskState.COMPLETED);
String predictInput = "{\n"
+ " \"parameters\": {\n"
+ " \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]\n"
+ " }\n"
+ "}";
response = predictRemoteModel(modelId, predictInput);
responseMap = parseResponseToMap(response);
// TODO handle throttling error
assertNotNull(responseMap);
response = undeployRemoteModel(modelId);
responseMap = parseResponseToMap(response);
assertTrue(responseMap.toString().contains("undeployed"));
}
@Ignore
public void testOpenAIEditsModel() throws IOException, InterruptedException {
// Skip test if key is null
if (OPENAI_KEY == null) {
return;
}
String entity = "{\n"
+ " \"name\": \"OpenAI Edit model Connector\",\n"
+ " \"description\": \"The connector to public OpenAI edit model service\",\n"
+ " \"version\": 1,\n"
+ " \"protocol\": \"http\",\n"
+ "\"client_config\": {\n"
+ " \"max_connection\": 20,\n"
+ " \"connection_timeout\": 50000,\n"
+ " \"read_timeout\": 50000\n"
+ " },\n"
+ " \"parameters\": {\n"
+ " \"endpoint\": \"api.openai.com\",\n"
+ " \"auth\": \"API_Key\",\n"
+ " \"content_type\": \"application/json\",\n"
+ " \"model\": \"gpt-4\"\n"
+ " },\n"
+ " \"credential\": {\n"
+ " \"openAI_key\": \""
+ OPENAI_KEY
+ "\"\n"
+ " },\n"
+ " \"actions\": [\n"
+ " {\n"
+ " \"action_type\": \"predict\",\n"
+ " \"method\": \"POST\",\n"
+ " \"url\": \"https://api.openai.com/v1/chat/completions\",\n"
+ " \"headers\": { \n"
+ " \"Authorization\": \"Bearer ${credential.openAI_key}\"\n"
+ " },\n"
+ " \"request_body\": \"{ \\\"model\\\": \\\"${parameters.model}\\\", \\\"messages\\\": [{\\\"role\\\": \\\"user\\\", \\\"content\\\": \\\"${parameters.input}\\\"}]}\"\n"
+ " }\n"
+ " ]\n"
+ "}";
Response response = createConnector(entity);
Map responseMap = parseResponseToMap(response);
String connectorId = (String) responseMap.get("connector_id");
response = registerRemoteModel("openAI-GPT-4 edit model", connectorId);
responseMap = parseResponseToMap(response);
String taskId = (String) responseMap.get("task_id");
waitForTask(taskId, MLTaskState.COMPLETED);
response = getTask(taskId);
responseMap = parseResponseToMap(response);
String modelId = (String) responseMap.get("model_id");
response = deployRemoteModel(modelId);
responseMap = parseResponseToMap(response);
taskId = (String) responseMap.get("task_id");
waitForTask(taskId, MLTaskState.COMPLETED);
String predictInput = "{\"parameters\":{\"input\":\"What day of the wek is it?\"}}";
response = predictRemoteModel(modelId, predictInput);
responseMap = parseResponseToMap(response);
List responseList = (List) responseMap.get("inference_results");
responseMap = (Map) responseList.get(0);
responseList = (List) responseMap.get("output");
responseMap = (Map) responseList.get(0);
responseMap = (Map) responseMap.get("dataAsMap");
responseList = (List) responseMap.get("choices");
if (responseList == null) {
assertTrue(checkThrottlingOpenAI(responseMap));
return;
}
responseMap = (Map) responseList.get(0);
responseMap = (Map) responseMap.get("message");
assertFalse(((String) responseMap.get("content")).isEmpty());
}
@Ignore
public void testOpenAIModerationsModel() throws IOException, InterruptedException {
// Skip test if key is null
if (OPENAI_KEY == null) {
return;
}
String entity = "{\n"
+ " \"name\": \"OpenAI moderations model Connector\",\n"
+ " \"description\": \"The connector to public OpenAI moderations model service\",\n"
+ " \"version\": 1,\n"
+ "\"client_config\": {\n"
+ " \"max_connection\": 20,\n"
+ " \"connection_timeout\": 50000,\n"
+ " \"read_timeout\": 50000\n"
+ " },\n"
+ " \"protocol\": \"http\",\n"
+ " \"parameters\": {\n"
+ " \"endpoint\": \"api.openai.com\",\n"
+ " \"auth\": \"API_Key\",\n"
+ " \"content_type\": \"application/json\",\n"
+ " \"model\": \"moderations\"\n"
+ " },\n"
+ " \"credential\": {\n"
+ " \"openAI_key\": \""
+ OPENAI_KEY
+ "\"\n"
+ " },\n"
+ " \"actions\": [\n"
+ " {\n"
+ " \"action_type\": \"predict\",\n"
+ " \"method\": \"POST\",\n"
+ " \"url\": \"https://api.openai.com/v1/moderations\",\n"
+ " \"headers\": { \n"
+ " \"Authorization\": \"Bearer ${credential.openAI_key}\"\n"
+ " },\n"
+ " \"request_body\": \"{ \\\"input\\\": \\\"${parameters.input}\\\" }\"\n"
+ " }\n"
+ " ]\n"
+ "}";
Response response = createConnector(entity);
Map responseMap = parseResponseToMap(response);
String connectorId = (String) responseMap.get("connector_id");
response = registerRemoteModel("openAI-GPT-3.5 moderations model", connectorId);
responseMap = parseResponseToMap(response);
String taskId = (String) responseMap.get("task_id");
waitForTask(taskId, MLTaskState.COMPLETED);
response = getTask(taskId);
responseMap = parseResponseToMap(response);
String modelId = (String) responseMap.get("model_id");
response = deployRemoteModel(modelId);
responseMap = parseResponseToMap(response);
taskId = (String) responseMap.get("task_id");
waitForTask(taskId, MLTaskState.COMPLETED);
String predictInput = "{\n" + " \"parameters\": {\n" + " \"input\": \"I want to kill them.\"\n" + " }\n" + "}";
response = predictRemoteModel(modelId, predictInput);
responseMap = parseResponseToMap(response);
List responseList = (List) responseMap.get("inference_results");
responseMap = (Map) responseList.get(0);
responseList = (List) responseMap.get("output");
responseMap = (Map) responseList.get(0);
responseMap = (Map) responseMap.get("dataAsMap");
responseList = (List) responseMap.get("results");
if (responseList == null) {
assertTrue(checkThrottlingOpenAI(responseMap));
return;
}
responseMap = (Map) responseList.get(0);
assertTrue((Boolean) responseMap.get("flagged"));
responseMap = (Map) responseMap.get("categories");
assertTrue((Boolean) responseMap.get("violence"));
}
public void testOpenAITextEmbeddingModel_UTF8() throws IOException, InterruptedException {
testOpenAITextEmbeddingModel("UTF-8", (responseMap) -> {
List responseList = (List) responseMap.get("inference_results");
responseMap = (Map) responseList.get(0);
responseList = (List) responseMap.get("output");
responseMap = (Map) responseList.get(0);
responseList = (List) responseMap.get("data");
assertFalse(responseList.isEmpty());
}, null);
}
private void testOpenAITextEmbeddingModel(String charset, Consumer<Map> verifyResponse, Consumer<Exception> verifyException)
throws IOException,
InterruptedException {
// Skip test if key is null
if (OPENAI_KEY == null) {
return;
}
String entity = "{\n"
+ " \"name\": \"OpenAI text embedding model Connector\",\n"
+ " \"description\": \"The connector to public OpenAI text embedding model service\",\n"
+ " \"version\": 1,\n"
+ "\"client_config\": {\n"
+ " \"max_connection\": 20,\n"
+ " \"connection_timeout\": 50000,\n"
+ " \"read_timeout\": 50000\n"
+ " },\n"
+ " \"protocol\": \"http\",\n"
+ " \"parameters\": {\n"
+ " \"model\": \"text-embedding-ada-002\"\n"
+ " },\n"
+ " \"credential\": {\n"
+ " \"openAI_key\": \""
+ OPENAI_KEY
+ "\"\n"
+ " },\n"
+ " \"actions\": [\n"
+ " {\n"
+ " \"action_type\": \"predict\",\n"
+ " \"method\": \"POST\",\n"
+ " \"url\": \"https://api.openai.com/v1/embeddings\",\n"
+ " \"headers\": { \n"
+ " \"Authorization\": \"Bearer ${credential.openAI_key}\"\n"
+ " },\n"
+ " \"request_body\": \"{ \\\"input\\\": ${parameters.input}, \\\"model\\\": \\\"${parameters.model}\\\" }\",\n"
+ " \"pre_process_function\": \"connector.pre_process.openai.embedding\",\n"
+ " \"post_process_function\": \"connector.post_process.openai.embedding\"\n"
+ " }\n"
+ " ]\n"
+ "}";
Response response = createConnector(entity);
Map responseMap = parseResponseToMap(response);
String connectorId = (String) responseMap.get("connector_id");
response = registerRemoteModel("openAI text embedding model", connectorId);
responseMap = parseResponseToMap(response);
String taskId = (String) responseMap.get("task_id");
waitForTask(taskId, MLTaskState.COMPLETED);
response = getTask(taskId);
responseMap = parseResponseToMap(response);
String modelId = (String) responseMap.get("model_id");
response = deployRemoteModel(modelId);
responseMap = parseResponseToMap(response);
taskId = (String) responseMap.get("task_id");
waitForTask(taskId, MLTaskState.COMPLETED);
String predictInput = "{\n"
+ " \"parameters\": {\n"
+ " \"input\": [\"This is a string containing Moët Hennessy\"],\n"
+ " \"charset\": \""
+ charset
+ "\"\n"
+ " }\n"
+ "}";
try {
response = predictRemoteModel(modelId, predictInput);
responseMap = parseResponseToMap(response);
verifyResponse.accept(responseMap);
} catch (Exception e) {
verifyException.accept(e);
}
}
public void testCohereGenerateTextModel() throws IOException, InterruptedException {
// Skip test if key is null
if (COHERE_KEY == null) {
return;
}
String entity = "{\n"
+ " \"name\": \"Cohere generate text model Connector\",\n"
+ " \"description\": \"The connector to public Cohere generate text model service\",\n"
+ " \"version\": 1,\n"
+ "\"client_config\": {\n"
+ " \"max_connection\": 20,\n"
+ " \"connection_timeout\": 50000,\n"
+ " \"read_timeout\": 50000\n"
+ " },\n"
+ " \"protocol\": \"http\",\n"
+ " \"parameters\": {\n"
+ " \"endpoint\": \"api.cohere.ai\",\n"
+ " \"auth\": \"API_Key\",\n"
+ " \"content_type\": \"application/json\",\n"
+ " \"max_tokens\": \"20\"\n"
+ " },\n"
+ " \"credential\": {\n"
+ " \"cohere_key\": \""
+ COHERE_KEY
+ "\"\n"
+ " },\n"
+ " \"actions\": [\n"
+ " {\n"
+ " \"action_type\": \"predict\",\n"
+ " \"method\": \"POST\",\n"
+ " \"url\": \"https://${parameters.endpoint}/v1/generate\",\n"
+ " \"headers\": { \n"
+ " \"Authorization\": \"Bearer ${credential.cohere_key}\"\n"
+ " },\n"
+ " \"request_body\": \"{ \\\"max_tokens\\\": ${parameters.max_tokens}, \\\"return_likelihoods\\\": \\\"NONE\\\", \\\"truncate\\\": \\\"END\\\", \\\"prompt\\\": \\\"${parameters.prompt}\\\" }\"\n"
+ " }\n"
+ " ]\n"
+ "}";
Response response = createConnector(entity);
Map responseMap = parseResponseToMap(response);
String connectorId = (String) responseMap.get("connector_id");
response = registerRemoteModel("cohere generate text model", connectorId);
responseMap = parseResponseToMap(response);
String taskId = (String) responseMap.get("task_id");
waitForTask(taskId, MLTaskState.COMPLETED);
response = getTask(taskId);
responseMap = parseResponseToMap(response);
String modelId = (String) responseMap.get("model_id");
response = deployRemoteModel(modelId);
responseMap = parseResponseToMap(response);
taskId = (String) responseMap.get("task_id");
waitForTask(taskId, MLTaskState.COMPLETED);
String predictInput = "{\n"
+ " \"parameters\": {\n"
+ " \"prompt\": \"Once upon a time in a magical land called\",\n"
+ " \"max_tokens\": 40\n"
+ " }\n"
+ "}";
response = predictRemoteModel(modelId, predictInput);
responseMap = parseResponseToMap(response);
List responseList = (List) responseMap.get("inference_results");
responseMap = (Map) responseList.get(0);
responseList = (List) responseMap.get("output");
responseMap = (Map) responseList.get(0);
responseMap = (Map) responseMap.get("dataAsMap");
responseList = (List) responseMap.get("generations");
responseMap = (Map) responseList.get(0);
assertFalse(((String) responseMap.get("text")).isEmpty());
}
public static Response createConnector(String input) throws IOException {
try {
return TestHelper.makeRequest(client(), "POST", "/_plugins/_ml/connectors/_create", null, TestHelper.toHttpEntity(input), null);
} catch (ResponseException e) {
String sanitizedMessage = maskSensitiveInfo(e.getMessage());// Log sanitized message
throw new RuntimeException("Request failed: " + sanitizedMessage); // Re-throw sanitized exception
}
}
public static Response registerRemoteModel(String name, String connectorId) throws IOException {
return registerRemoteModel("remote_model_group", name, connectorId);
}
public static Response registerRemoteModel(String modelGroupName, String name, String connectorId) throws IOException {
String registerModelGroupEntity = "{\n"
+ " \"name\": \""
+ modelGroupName
+ "\",\n"
+ " \"description\": \"This is an example description\"\n"
+ "}";
Response response = TestHelper
.makeRequest(
client(),
"POST",
"/_plugins/_ml/model_groups/_register",
null,
TestHelper.toHttpEntity(registerModelGroupEntity),
null
);
Map responseMap = parseResponseToMap(response);
assertEquals((String) responseMap.get("status"), "CREATED");
String modelGroupId = (String) responseMap.get("model_group_id");
String registerModelEntity = "{\n"
+ " \"name\": \""
+ name
+ "\",\n"
+ " \"function_name\": \"remote\",\n"
+ " \"model_group_id\": \""
+ modelGroupId
+ "\",\n"
+ " \"version\": \"1.0.0\",\n"
+ " \"description\": \"test model\",\n"
+ " \"connector_id\": \""
+ connectorId
+ "\",\n"
+ " \"interface\": {\n"
+ " \"input\": {},\n"
+ " \"output\": {}\n"
+ " }\n"
+ "}";
return TestHelper
.makeRequest(client(), "POST", "/_plugins/_ml/models/_register", null, TestHelper.toHttpEntity(registerModelEntity), null);
}
public static Response registerRemoteModelWithTTLAndSkipHeapMemCheck(String name, String connectorId, int ttl) throws IOException {
String registerModelGroupEntity = "{\n"
+ " \"name\": \"remote_model_group\",\n"
+ " \"description\": \"This is an example description\"\n"
+ "}";
String updateJVMHeapThreshold = "{\"persistent\":{\"plugins.ml_commons.jvm_heap_memory_threshold\":0}}";
TestHelper.makeRequest(client(), "PUT", "/_cluster/settings", null, TestHelper.toHttpEntity(updateJVMHeapThreshold), null);
Response response = TestHelper
.makeRequest(
client(),
"POST",
"/_plugins/_ml/model_groups/_register",
null,
TestHelper.toHttpEntity(registerModelGroupEntity),
null
);
Map responseMap = parseResponseToMap(response);
assertEquals((String) responseMap.get("status"), "CREATED");
String modelGroupId = (String) responseMap.get("model_group_id");
String registerModelEntity = "{\n"
+ " \"name\": \""
+ name
+ "\",\n"
+ " \"function_name\": \"remote\",\n"
+ " \"model_group_id\": \""
+ modelGroupId
+ "\",\n"
+ " \"version\": \"1.0.0\",\n"
+ " \"description\": \"test model\",\n"
+ " \"connector_id\": \""
+ connectorId
+ "\",\n"
+ " \"deploy_setting\": "
+ " { \"model_ttl_minutes\": "
+ ttl
+ "},\n"
+ " \"interface\": {\n"
+ " \"input\": {},\n"
+ " \"output\": {}\n"
+ " }\n"
+ "}";
return TestHelper
.makeRequest(client(), "POST", "/_plugins/_ml/models/_register", null, TestHelper.toHttpEntity(registerModelEntity), null);
}
private String getConnectorBodyBySkipValidatingMissingParameter(String testCase) {
switch (testCase) {
case "missing":
return completionModelConnectorEntity;
case "enabled":
return "{\n"
+ "\"name\": \"OpenAI Connector\",\n"
+ "\"description\": \"The connector to public OpenAI model service for GPT 3.5\",\n"
+ "\"version\": 1,\n"
+ "\"client_config\": {\n"
+ " \"max_connection\": 20,\n"
+ " \"connection_timeout\": 50000,\n"
+ " \"read_timeout\": 50000\n"
+ " },\n"
+ "\"protocol\": \"http\",\n"
+ "\"parameters\": {\n"
+ " \"endpoint\": \"api.openai.com\",\n"
+ " \"auth\": \"API_Key\",\n"
+ " \"content_type\": \"application/json\",\n"
+ " \"max_tokens\": 7,\n"
+ " \"temperature\": 0,\n"
+ " \"model\": \"gpt-3.5-turbo-instruct\",\n"
+ " \"skip_validating_missing_parameters\": \"true\"\n"
+ " },\n"
+ " \"credential\": {\n"
+ " \"openAI_key\": \""
+ this.OPENAI_KEY
+ "\"\n"
+ " },\n"
+ " \"actions\": [\n"
+ " {"
+ " \"action_type\": \"predict\",\n"
+ " \"method\": \"POST\",\n"
+ " \"url\": \"https://${parameters.endpoint}/v1/completions\",\n"
+ " \"headers\": {\n"
+ " \"Authorization\": \"Bearer ${credential.openAI_key}\"\n"
+ " },\n"
+ " \"request_body\": \"{ \\\"model\\\": \\\"${parameters.model}\\\", \\\"prompt\\\": \\\"${parameters.prompt}\\\", \\\"max_tokens\\\": ${parameters.max_tokens}, \\\"temperature\\\": ${parameters.temperature} }\"\n"
+ " }\n"
+ " ]\n"
+ "}";
case "disabled":
return "{\n"
+ "\"name\": \"OpenAI Connector\",\n"
+ "\"description\": \"The connector to public OpenAI model service for GPT 3.5\",\n"
+ "\"version\": 1,\n"
+ "\"client_config\": {\n"
+ " \"max_connection\": 20,\n"
+ " \"connection_timeout\": 50000,\n"
+ " \"read_timeout\": 50000\n"
+ " },\n"
+ "\"protocol\": \"http\",\n"
+ "\"parameters\": {\n"
+ " \"endpoint\": \"api.openai.com\",\n"
+ " \"auth\": \"API_Key\",\n"
+ " \"content_type\": \"application/json\",\n"
+ " \"max_tokens\": 7,\n"
+ " \"temperature\": 0,\n"
+ " \"model\": \"gpt-3.5-turbo-instruct\",\n"
+ " \"skip_validating_missing_parameters\": \"false\"\n"
+ " },\n"
+ " \"credential\": {\n"
+ " \"openAI_key\": \""
+ this.OPENAI_KEY
+ "\"\n"
+ " },\n"
+ " \"actions\": [\n"
+ " {"
+ " \"action_type\": \"predict\",\n"
+ " \"method\": \"POST\",\n"
+ " \"url\": \"https://${parameters.endpoint}/v1/completions\",\n"
+ " \"headers\": {\n"
+ " \"Authorization\": \"Bearer ${credential.openAI_key}\"\n"
+ " },\n"
+ " \"request_body\": \"{ \\\"model\\\": \\\"${parameters.model}\\\", \\\"prompt\\\": \\\"${parameters.prompt}\\\", \\\"max_tokens\\\": ${parameters.max_tokens}, \\\"temperature\\\": ${parameters.temperature} }\"\n"
+ " }\n"
+ " ]\n"
+ "}";
default:
throw new IllegalArgumentException("Invalid test case");
}
}
public static Response registerRemoteModelWithInterface(String name, String connectorId, String testCase) throws IOException {
String registerModelGroupEntity = "{\n"
+ " \"name\": \"remote_model_group\",\n"
+ " \"description\": \"This is an example description\"\n"
+ "}";
Response response = TestHelper
.makeRequest(
client(),
"POST",
"/_plugins/_ml/model_groups/_register",
null,
TestHelper.toHttpEntity(registerModelGroupEntity),
null
);
Map responseMap = parseResponseToMap(response);
assertEquals((String) responseMap.get("status"), "CREATED");
String modelGroupId = (String) responseMap.get("model_group_id");
final String openaiConnectorEntityWithCorrectInterface = "{\n"
+ " \"name\": \""
+ name
+ "\",\n"
+ " \"model_group_id\": \""
+ modelGroupId
+ "\",\n"
+ " \"function_name\": \"remote\",\n"
+ " \"connector_id\": \""
+ connectorId
+ "\",\n"
+ " \"interface\": {\n"
+ " \"input\": {\n"
+ " \"properties\": {\n"
+ " \"parameters\": {\n"
+ " \"properties\": {\n"
+ " \"prompt\": {\n"
+ " \"type\": \"string\",\n"
+ " \"description\": \"This is a test description field\"\n"
+ " }\n"
+ " }\n"
+ " }\n"
+ " }\n"
+ " },\n"
+ " \"output\": {\n"
+ " \"properties\": {\n"
+ " \"inference_results\": {\n"
+ " \"type\": \"array\",\n"
+ " \"items\": {\n"
+ " \"type\": \"object\",\n"
+ " \"properties\": {\n"
+ " \"output\": {\n"
+ " \"type\": \"array\",\n"
+ " \"items\": {\n"
+ " \"properties\": {\n"
+ " \"name\": {\n"
+ " \"type\": \"string\",\n"
+ " \"description\": \"This is a test description field\"\n"
+ " },\n"
+ " \"dataAsMap\": {\n"
+ " \"type\": \"object\",\n"
+ " \"description\": \"This is a test description field\"\n"
+ " }\n"
+ " }\n"
+ " },\n"