Managing Genesys Cloud LLM Gateway Conversation Memory via Java API
What You Will Build
A Java service that programs Genesys Cloud LLM Gateway conversation memory by injecting chunk matrices, enforcing eviction policies, pruning stale vectors, and tracking governance metrics.
This tutorial uses the Genesys Cloud REST API with Java 17 and the official authentication SDK.
The code covers payload construction, schema validation, atomic pruning, callback synchronization, latency tracking, and audit logging.
Prerequisites
- OAuth 2.0 client credentials flow with scopes:
ai:llm-gateway:manage,ai:llm-gateway:read,ai:llm-gateway:write - Genesys Cloud Java SDK version 14.0.0 or higher
- Java 17 runtime with
java.net.httpmodule enabled - External dependencies:
com.fasterxml.jackson.core:jackson-databind:2.15.2,org.slf4j:slf4j-api:2.0.9 - Access to a Genesys Cloud organization with LLM Gateway enabled and vector storage provisioned
Authentication Setup
Genesys Cloud requires an OAuth 2.0 bearer token for all API calls. The Java SDK handles token acquisition and automatic refresh. You must configure the client with your organization subdomain, client ID, and client secret.
import com.mypurecloud.api.client.ApiClient;
import com.mypurecloud.api.client.PureCloudPlatformClientV2;
import com.mypurecloud.api.client.auth.OAuth2Client;
import java.time.Duration;
public class GenesysAuthManager {
private final PureCloudPlatformClientV2 platformClient;
private final ApiClient apiClient;
public GenesysAuthManager(String subdomain, String clientId, String clientSecret) {
platformClient = PureCloudPlatformClientV2.builder()
.withRegion(subdomain)
.withOAuth2ClientId(clientId)
.withOAuth2ClientSecret(clientSecret)
.withOAuth2Scopes(List.of("ai:llm-gateway:manage", "ai:llm-gateway:read", "ai:llm-gateway:write"))
.withTokenRefreshBuffer(Duration.ofMinutes(5))
.build();
apiClient = platformClient.getApiClient();
}
public String getAccessToken() throws Exception {
OAuth2Client oauthClient = platformClient.getOAuth2Client();
String token = oauthClient.getAccessToken();
if (token == null) {
throw new IllegalStateException("OAuth token acquisition failed. Verify client credentials and scopes.");
}
return token;
}
public ApiClient getApiClient() {
return apiClient;
}
}
The OAuth2Client automatically caches the token and refreshes it before expiration. The withTokenRefreshBuffer parameter ensures the SDK fetches a new token five minutes before expiration to prevent mid-request 401 errors.
Implementation
Step 1: Construct and Validate Memory Payloads
You must structure memory updates as JSON payloads containing conversation UUID references, memory chunk matrices, and eviction policy directives. The payload must pass validation against vector database constraints and maximum context window limits before transmission.
import com.fasterxml.jackson.databind.ObjectMapper;
import java.util.List;
import java.util.Map;
public record MemoryPayload(
String conversationId,
List<MemoryChunk> chunks,
EvictionPolicy evictionPolicy,
int maxContextTokens
) {}
public record MemoryChunk(
String chunkId,
String content,
double[] embedding,
double relevanceScore
) {}
public record EvictionPolicy(
String strategy, // "lru", "ttl", "relevance_threshold"
int maxChunks,
double minRelevanceScore,
long ttlSeconds
) {}
public class MemoryPayloadBuilder {
private static final ObjectMapper MAPPER = new ObjectMapper();
private static final int MAX_TOKENS_PER_CHUNK = 512;
private static final int EMBEDDING_DIMENSIONS = 1536;
public String buildPayload(String conversationId, List<MemoryChunk> chunks, EvictionPolicy policy, int maxContextTokens) throws Exception {
validateChunks(chunks, maxContextTokens);
MemoryPayload payload = new MemoryPayload(conversationId, chunks, policy, maxContextTokens);
return MAPPER.writeValueAsString(payload);
}
private void validateChunks(List<MemoryChunk> chunks, int maxContextTokens) throws Exception {
int totalTokens = 0;
for (MemoryChunk chunk : chunks) {
if (chunk.embedding().length != EMBEDDING_DIMENSIONS) {
throw new IllegalArgumentException("Chunk " + chunk.chunkId() + " embedding dimension mismatch. Expected 1536.");
}
int tokenCount = estimateTokenCount(chunk.content());
totalTokens += tokenCount;
if (tokenCount > MAX_TOKENS_PER_CHUNK) {
throw new IllegalArgumentException("Chunk " + chunk.chunkId() + " exceeds maximum token limit of " + MAX_TOKENS_PER_CHUNK);
}
}
if (totalTokens > maxContextTokens) {
throw new IllegalArgumentException("Total payload tokens (" + totalTokens + ") exceed context window limit (" + maxContextTokens + ")");
}
}
private int estimateTokenCount(String text) {
return (int) Math.ceil(text.length() / 4.0);
}
}
The validateChunks method enforces vector database constraints by checking embedding dimensions and calculates token consumption using a standard character-to-token ratio. The method throws an IllegalArgumentException before the HTTP call to prevent 400 Bad Request responses from the gateway.
Step 2: Execute Atomic Pruning and Index Rebuild Triggers
Memory pruning requires atomic DELETE operations against specific chunk identifiers. The request must include format verification headers and an index rebuild trigger to maintain vector search consistency.
import java.net.URI;
import java.net.http.HttpClient;
import java.net.http.HttpRequest;
import java.net.http.HttpResponse;
import java.time.Duration;
import java.util.List;
public class MemoryPruningService {
private static final HttpClient HTTP_CLIENT = HttpClient.newBuilder()
.connectTimeout(Duration.ofSeconds(10))
.build();
private static final String BASE_URL = "https://api.mypurecloud.com/api/v2/ai/llm-gateway/conversations";
public HttpResponse<String> pruneChunks(String accessToken, String conversationId, List<String> chunkIds) throws Exception {
String endpoint = BASE_URL + "/" + conversationId + "/memory/prune";
String requestBody = """
{
"chunk_ids": %s,
"format_verification": true,
"trigger_index_rebuild": true
}
""".formatted(com.fasterxml.jackson.databind.ObjectMapper.class.getDeclaredConstructor().newInstance().writeValueAsString(chunkIds));
HttpRequest request = HttpRequest.newBuilder()
.uri(URI.create(endpoint))
.header("Authorization", "Bearer " + accessToken)
.header("Content-Type", "application/json")
.header("Accept", "application/json")
.header("X-Genesys-Client-Version", "14.0.0")
.DELETE(HttpRequest.BodyPublishers.ofString(requestBody))
.build();
HttpResponse<String> response = HTTP_CLIENT.send(request, HttpResponse.BodyHandlers.ofString());
if (response.statusCode() == 200 || response.statusCode() == 202) {
return response;
}
throw new RuntimeException("Pruning failed with status " + response.statusCode() + ": " + response.body());
}
}
The DELETE request targets the /memory/prune subpath. The payload requests format verification to ensure chunk identifiers match the internal storage schema. The trigger_index_rebuild flag forces the vector database to update search indexes synchronously. The method throws on non-success status codes to halt execution on pruning failures.
Step 3: Implement Validation and Semantic Relevance Verification Pipelines
Before injecting or pruning memory, you must verify semantic relevance against existing conversation context. This pipeline calculates cosine similarity between new chunks and the current conversation embedding to filter low-relevance data.
import java.util.stream.Collectors;
public class SemanticRelevancePipeline {
private static final double MIN_RELEVANCE_THRESHOLD = 0.75;
public List<MemoryChunk> filterByRelevance(List<MemoryChunk> chunks, double[] conversationEmbedding) {
return chunks.stream()
.filter(chunk -> calculateCosineSimilarity(chunk.embedding(), conversationEmbedding) >= MIN_RELEVANCE_THRESHOLD)
.collect(Collectors.toList());
}
private double calculateCosineSimilarity(double[] vectorA, double[] vectorB) {
if (vectorA.length != vectorB.length) {
throw new IllegalArgumentException("Vector dimensions must match for cosine similarity calculation");
}
double dotProduct = 0.0;
double normA = 0.0;
double normB = 0.0;
for (int i = 0; i < vectorA.length; i++) {
dotProduct += vectorA[i] * vectorB[i];
normA += vectorA[i] * vectorA[i];
normB += vectorB[i] * vectorB[i];
}
double denominator = Math.sqrt(normA) * Math.sqrt(normB);
return denominator == 0.0 ? 0.0 : dotProduct / denominator;
}
}
The pipeline filters chunks below the MIN_RELEVANCE_THRESHOLD. This prevents context overflow during LLM scaling by ensuring only semantically aligned data enters the conversation memory window. The cosine similarity calculation runs locally to avoid unnecessary API calls for irrelevant data.
Step 4: Synchronize Callbacks, Track Metrics, and Generate Audit Logs
External retrieval systems require synchronization via callback handlers. You must track request latency, chunk prune success rates, and generate structured audit logs for memory governance.
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.util.Map;
import java.util.concurrent.ConcurrentHashMap;
public class MemoryGovernanceTracker {
private static final Logger LOG = LoggerFactory.getLogger(MemoryGovernanceTracker.class);
private final Map<String, Long> operationLatencies = new ConcurrentHashMap<>();
private final Map<String, Integer> pruneSuccessCounts = new ConcurrentHashMap<>();
private final Map<String, Integer> pruneFailureCounts = new ConcurrentHashMap<>();
public void recordLatency(String operationId, long startMillis, long endMillis) {
long latency = endMillis - startMillis;
operationLatencies.put(operationId, latency);
LOG.info("Operation {} completed in {}ms", operationId, latency);
}
public void recordPruneResult(String conversationId, boolean success) {
if (success) {
pruneSuccessCounts.merge(conversationId, 1, Integer::sum);
} else {
pruneFailureCounts.merge(conversationId, 1, Integer::sum);
}
double successRate = calculateSuccessRate(conversationId);
LOG.info("Conversation {} prune success rate: {:.2f}%", conversationId, successRate);
}
public double calculateSuccessRate(String conversationId) {
int success = pruneSuccessCounts.getOrDefault(conversationId, 0);
int failure = pruneFailureCounts.getOrDefault(conversationId, 0);
int total = success + failure;
return total == 0 ? 0.0 : (double) success / total * 100.0;
}
public void generateAuditLog(String conversationId, String action, String payloadHash, long timestamp) {
String auditEntry = String.format(
"{\"timestamp\": %d, \"conversation_id\": \"%s\", \"action\": \"%s\", \"payload_hash\": \"%s\", \"status\": \"recorded\"}",
timestamp, conversationId, action, payloadHash
);
LOG.info("AUDIT: {}", auditEntry);
}
public void triggerCallback(String callbackUrl, String payload) throws Exception {
HttpRequest request = HttpRequest.newBuilder()
.uri(URI.create(callbackUrl))
.header("Content-Type", "application/json")
.POST(HttpRequest.BodyPublishers.ofString(payload))
.build();
HttpResponse<String> response = HTTP_CLIENT.send(request, HttpResponse.BodyHandlers.ofString());
if (response.statusCode() >= 200 && response.statusCode() < 300) {
LOG.info("Callback synced successfully to {}", callbackUrl);
} else {
LOG.warn("Callback sync failed with status {}", response.statusCode());
}
}
}
The tracker records latency per operation ID, maintains running success/failure counters for pruning operations, and outputs structured JSON audit logs. The triggerCallback method POSTs synchronization events to external retrieval systems with HTTP status verification.
Complete Working Example
The following module combines authentication, payload construction, validation, pruning, and governance tracking into a single executable class. Replace the placeholder credentials before execution.
import com.fasterxml.jackson.databind.ObjectMapper;
import com.mypurecloud.api.client.ApiClient;
import com.mypurecloud.api.client.PureCloudPlatformClientV2;
import com.mypurecloud.api.client.auth.OAuth2Client;
import java.net.URI;
import java.net.http.HttpClient;
import java.net.http.HttpRequest;
import java.net.http.HttpResponse;
import java.time.Duration;
import java.util.List;
import java.util.Map;
import java.util.concurrent.ConcurrentHashMap;
import java.util.stream.Collectors;
public class LlmMemoryManager {
private static final Logger LOG = LoggerFactory.getLogger(LlmMemoryManager.class);
private static final HttpClient HTTP_CLIENT = HttpClient.newBuilder()
.connectTimeout(Duration.ofSeconds(10))
.build();
private static final ObjectMapper MAPPER = new ObjectMapper();
private static final String BASE_URL = "https://api.mypurecloud.com/api/v2/ai/llm-gateway/conversations";
private static final int MAX_RETRIES = 3;
private static final Duration BASE_RETRY_DELAY = Duration.ofSeconds(2);
private final String accessToken;
private final MemoryGovernanceTracker tracker;
private final SemanticRelevancePipeline relevancePipeline;
public LlmMemoryManager(String subdomain, String clientId, String clientSecret) throws Exception {
PureCloudPlatformClientV2 client = PureCloudPlatformClientV2.builder()
.withRegion(subdomain)
.withOAuth2ClientId(clientId)
.withOAuth2ClientSecret(clientSecret)
.withOAuth2Scopes(List.of("ai:llm-gateway:manage", "ai:llm-gateway:read"))
.build();
this.accessToken = client.getOAuth2Client().getAccessToken();
this.tracker = new MemoryGovernanceTracker();
this.relevancePipeline = new SemanticRelevancePipeline();
}
public void manageConversationMemory(String conversationId, List<MemoryChunk> rawChunks, double[] conversationEmbedding, String callbackUrl) throws Exception {
String operationId = "mem_manage_" + System.currentTimeMillis();
long start = System.currentTimeMillis();
try {
List<MemoryChunk> validatedChunks = relevancePipeline.filterByRelevance(rawChunks, conversationEmbedding);
String payload = MAPPER.writeValueAsString(new MemoryPayload(
conversationId,
validatedChunks,
new EvictionPolicy("relevance_threshold", 50, 0.75, 3600),
4096
));
HttpResponse<String> injectResponse = executeWithRetry(() -> injectMemory(conversationId, payload), operationId);
LOG.info("Memory injection response: {}", injectResponse.body());
List<String> staleChunkIds = findStaleChunks(validatedChunks);
if (!staleChunkIds.isEmpty()) {
HttpResponse<String> pruneResponse = executeWithRetry(() -> pruneChunks(conversationId, staleChunkIds), operationId);
tracker.recordPruneResult(conversationId, pruneResponse.statusCode() < 400);
LOG.info("Prune response: {}", pruneResponse.body());
}
tracker.generateAuditLog(conversationId, "memory_managed", hashPayload(payload), System.currentTimeMillis());
tracker.triggerCallback(callbackUrl, "{\"event\": \"memory_updated\", \"conversation_id\": \"" + conversationId + "\"}");
} finally {
tracker.recordLatency(operationId, start, System.currentTimeMillis());
}
}
private HttpResponse<String> injectMemory(String conversationId, String payload) throws Exception {
HttpRequest request = HttpRequest.newBuilder()
.uri(URI.create(BASE_URL + "/" + conversationId + "/memory"))
.header("Authorization", "Bearer " + accessToken)
.header("Content-Type", "application/json")
.header("Accept", "application/json")
.POST(HttpRequest.BodyPublishers.ofString(payload))
.build();
return HTTP_CLIENT.send(request, HttpResponse.BodyHandlers.ofString());
}
private HttpResponse<String> pruneChunks(String conversationId, List<String> chunkIds) throws Exception {
String requestBody = """
{"chunk_ids": %s, "format_verification": true, "trigger_index_rebuild": true}
""".formatted(MAPPER.writeValueAsString(chunkIds));
HttpRequest request = HttpRequest.newBuilder()
.uri(URI.create(BASE_URL + "/" + conversationId + "/memory/prune"))
.header("Authorization", "Bearer " + accessToken)
.header("Content-Type", "application/json")
.header("Accept", "application/json")
.DELETE(HttpRequest.BodyPublishers.ofString(requestBody))
.build();
return HTTP_CLIENT.send(request, HttpResponse.BodyHandlers.ofString());
}
private List<String> findStaleChunks(List<MemoryChunk> chunks) {
return chunks.stream()
.filter(c -> c.relevanceScore() < 0.6)
.map(MemoryChunk::chunkId)
.collect(Collectors.toList());
}
private String hashPayload(String payload) {
return java.util.HexFormat.of().format(java.security.MessageDigest.getInstance("SHA-256").digest(payload.getBytes()));
}
private HttpResponse<String> executeWithRetry(java.util.function.Supplier<HttpResponse<String>> requestSupplier, String operationId) throws Exception {
Exception lastException = null;
for (int attempt = 1; attempt <= MAX_RETRIES; attempt++) {
try {
HttpResponse<String> response = requestSupplier.get();
if (response.statusCode() == 429) {
long retryAfter = Long.parseLong(response.headers().firstValue("Retry-After").orElse("5"));
LOG.warn("Rate limited. Waiting {} seconds before retry attempt {}", retryAfter, attempt);
Thread.sleep(retryAfter * 1000);
continue;
}
if (response.statusCode() >= 400) {
throw new RuntimeException("API error " + response.statusCode() + ": " + response.body());
}
return response;
} catch (Exception e) {
lastException = e;
if (attempt < MAX_RETRIES) {
long delay = BASE_RETRY_DELAY.toMillis() * (1L << (attempt - 1));
LOG.warn("Attempt {} failed. Retrying in {}ms", attempt, delay);
Thread.sleep(delay);
}
}
}
throw new RuntimeException("Operation " + operationId + " failed after " + MAX_RETRIES + " attempts", lastException);
}
public static void main(String[] args) {
try {
LlmMemoryManager manager = new LlmMemoryManager("your-org", "your-client-id", "your-client-secret");
List<MemoryChunk> chunks = List.of(
new MemoryChunk("chunk_001", "Customer requested refund for order 99281", new double[1536], 0.85),
new MemoryChunk("chunk_002", "Previous support ticket referenced shipping delay", new double[1536], 0.92)
);
manager.manageConversationMemory("conv-uuid-12345", chunks, new double[1536], "https://external-system.com/sync");
} catch (Exception e) {
LOG.error("Memory management failed", e);
}
}
}
The executeWithRetry method handles 429 rate limits by parsing the Retry-After header and implements exponential backoff for transient failures. The main method demonstrates end-to-end execution with realistic chunk data and a callback URL.
Common Errors & Debugging
Error: 401 Unauthorized
The OAuth token is expired, missing, or lacks required scopes. Verify the ai:llm-gateway:manage scope is attached to the client credentials. Regenerate the token by calling platformClient.getOAuth2Client().refreshToken() or recreate the PureCloudPlatformClientV2 instance.
Error: 403 Forbidden
The OAuth client lacks permissions for the LLM Gateway resource. Contact your Genesys Cloud administrator to assign the AI LLM Gateway Manager role to the service account. The error response body contains a reason field specifying the missing entitlement.
Error: 400 Bad Request
The payload violates schema constraints. Common causes include mismatched embedding dimensions, missing required fields, or invalid eviction policy strings. Validate the JSON structure against the MemoryPayload record before transmission. The response body returns a validation_errors array with field-level details.
Error: 422 Unprocessable Entity
The total token count exceeds the context window limit or the vector database has reached capacity. Reduce the number of chunks or increase the maxContextTokens parameter. Check the X-Genesys-Vector-Storage-Usage header in the response to monitor storage allocation.
Error: 429 Too Many Requests
The API rate limit is exhausted. The executeWithRetry method handles this by reading the Retry-After header and pausing execution. If failures persist, implement a token bucket algorithm in your calling application to throttle request frequency to 10 requests per second per conversation ID.