> ## Documentation Index
> Fetch the complete documentation index at: https://support.lilt.com/llms.txt
> Use this file to discover all available pages before exploring further.

# LILT 5.3 Release Notes

> 2025 Q4 Release

# **On-Prem Features and Updates**

## **Multimodal OCR Service**

LILT 5.3 introduces a powerful new neural-based OCR service that delivers significantly improved accuracy for image and scanned PDF translation. The multimodal OCR service leverages advanced machine learning models to better extract text from complex documents, including those with mixed content types, tables, and challenging layouts.

**Key Benefits:**

* Enhanced accuracy for scanned PDFs and images
* Improved handling of complex document layouts
* Better recognition of tables and formatted content
* Seamless integration with existing translation workflows

**Configuration:**
To enable the multimodal OCR service, update your `values.yaml`:

```yaml theme={null}
ocr:
  enabled: true
  env:
    MQ_PWD: "<your-rabbitmq-password>"  # Add to secrets.yaml
```

## **Enhanced LLM Inference Architecture**

LILT 5.3 restructures the LLM inference system into dedicated, optimized services for different AI models. This architectural improvement provides better resource management, improved performance, and more granular control over AI capabilities.

### **New Specialized Inference Services:**

**Llama vLLM Inference**

* Optimized inference for Llama language models
* GPU-accelerated processing (requires 4 GPUs)
* High-performance vLLM backend
* Resource requirements: 100G memory, 5-14 CPUs

**Whisper Speech-to-Text**

* Real-time speech-to-text transcription
* GPU-accelerated for faster processing
* Supports multiple languages
* Ideal for audio/video translation workflows

**Gemma Model Inference**

* Dedicated inference for Gemma3 models
* Optimized for specific translation tasks
* GPU-accelerated processing

**Emma-500 Model Inference**

* Specialized inference for Emma-500 models
* Advanced language understanding capabilities

**Configuration:**
Enable specific inference services based on your needs:

```yaml theme={null}
llama-vllm-inference:
  enabled: true  # Enable Llama models

llm-inference-whisper:
  enabled: true  # Enable speech-to-text

gemma-vllm-inference:
  enabled: true  # Enable Gemma models

emma-500-vllm-inference:
  enabled: true  # Enable Emma-500 models
```

**GPU Requirements:** Ensure GPU nodes are available and labeled with `capability: gpu`.

## **Third-Party MT Batch Processing**

A new CPU-based batch worker specifically designed for third-party machine translation (3pMT) processing has been added. This dedicated worker improves throughput and reliability for integrations with external MT providers.

**Features:**

* Dedicated CPU-based processing for 3pMT
* Configurable worker pools
* 24G memory for large document handling
* Improved batch processing reliability

**Configuration:**

```yaml theme={null}
batch-worker-cpu-3pmt:
  enabled: true
  env:
    BATCH_WORKERS_MANAGER_3PMT_CPU_MAX_WORKERS: "1"
    BATCH_WORKERS_MANAGER_3PMT_CPU_STANDBY_WORKERS: "0"
```

## **Enhanced Document Conversion**

Document conversion timeouts have been made configurable, allowing better handling of large and complex documents. The new timeout settings apply to both the converter and file-translation services.

**Configuration:**

```yaml theme={null}
converter:
  env:
    APRYSE_TIMEOUT_MINUTES: "5"  # Adjust based on document complexity

file-translation:
  env:
    APRYSE_TIMEOUT_MINUTES: "5"  # Adjust based on document complexity
```

Increase timeout values for particularly large or complex documents to prevent conversion failures.

## **MySQL SSL Support**

LILT 5.3 adds comprehensive SSL/TLS support for MySQL connections, enhancing security for database communications. SSL is now enabled by default with flexible configuration options.

**Configuration:**

```yaml theme={null}
file-job:
  env:
    MYSQL_SSL_MODE: "PREFERRED"
    MYSQL_SSL_ENABLED: "true"

memory:
  env:
    MYSQL_SSL_MODE: "PREFERRED"
    MYSQL_SSL_ENABLED: "true"
```

**SSL Modes:**

* `REQUIRED`: Forces SSL connections (recommended for production)
* `PREFERRED`: Uses SSL if available, falls back to unencrypted
* `DISABLED`: Disables SSL (not recommended)

## **Core Platform Updates**

### **Internal Architecture Improvements**

**Service Naming Standardization**

* AV scanner service renamed to `lilt-av-scan` for consistency
* ConfigMap and Secret resources standardized with `lilt-` prefix
* Improved service discovery and management

***

# **Breaking Changes**

## **LLM Inference Service Restructuring**

The generic `llm-inference` service has been **removed** and replaced with specialized inference services. This change improves resource utilization and provides better control over AI model deployment.

**Migration Required:**
If you previously had `llm-inference` enabled, you must:

1. **Disable the old service:**
   ```yaml theme={null}
   llm-inference:
     enabled: false  # Remove this service
   ```

2. **Enable the appropriate replacement services:**
   ```yaml theme={null}
   # For Llama models
   llama-vllm-inference:
     enabled: true

   # For speech-to-text
   llm-inference-whisper:
     enabled: true

   # For Gemma models
   gemma-vllm-inference:
     enabled: true

   # For Emma-500 models
   emma-500-vllm-inference:
     enabled: true
   ```

3. **Update GPU node labels** if not already configured:
   ```bash theme={null}
   kubectl label nodes <node-name> capability=gpu
   ```

## **ConfigMap and Secret Name Changes**

Several services now reference standardized `lilt-configmap` and `lilt-secrets` instead of service-specific resources.

**Affected Services:**

* Translation Memory (tm)
* Lexicon

**Migration Steps:**

1. Ensure your deployment creates resources with the new names:
   * `lilt-configmap` (previously `front-configmap`)
   * `lilt-secrets` (previously `front-secrets`)

2. If using custom Helm templates, update references:
   ```yaml theme={null}
   # Old:
   configMapName: front-configmap
   secretsName: front-secrets

   # New:
   configMapName: lilt-configmap
   secretsName: lilt-secrets
   ```

## **Init Container File Path Changes**

Services using the two-stage init container pattern (lexicon, file-job) must update file path configurations.

**Old Format:**

```yaml theme={null}
lexicon:
  init:
    filePaths: "s3://lilt/lexicon-data/..."

file-job:
  init:
    filePaths: "s3://lilt/tesseract/..."
```

**New Format:**

```yaml theme={null}
lexicon:
  init:
    filePaths: "lexicon-data/..."  # Remove s3://lilt/ prefix

file-job:
  init:
    filePaths: "tesseract/..."  # Remove s3://lilt/ prefix
```

**Action Required:** If you have customized init container configurations, update file paths to remove the `s3://lilt/` prefix.

***

# **New Configuration Requirements**

## **Required Secrets**

### **Core-API S3 Credentials**

Add to `secrets.yaml`:

```yaml theme={null}
core-api:
  onpremValues:
    env:
      S3_ACCESS_KEY: "<your-minio-access-key>"
      S3_SECRET_KEY: "<your-minio-secret-key>"
```

Set these to match your MinIO or S3-compatible storage credentials.

### **Multimodal OCR (If Enabling)**

Add to `secrets.yaml`:

```yaml theme={null}
multimodal:
  env:
    MQ_PWD: "<your-rabbitmq-password>"
```

## **Optional Configuration Updates**

### **Backend Configuration Toggle**

A new toggle allows enabling/disabling backend configuration features:

```yaml theme={null}
front:
  onpremValues:
    backend_config:
      enabled: false  # Set to true if needed
```

### **BigQuery Integration**

For customers using BigQuery analytics:

```yaml theme={null}
core-api:
  env:
    BIG_QUERY_ENABLED: "false"  # Set to true if using BigQuery
```

### **Batch Worker Memory**

Batch worker memory limits have been increased for improved performance:

```yaml theme={null}
batchv4:
  resources:
    requests:
      memory: 2G  # Increased from 1G
    limits:
      memory: 2G
```

**Action Required:** Ensure worker nodes have sufficient memory capacity.

***

# **Infrastructure Requirements**

## **GPU Node Requirements**

If enabling LLM inference services, ensure GPU nodes are available:

| Service                 | GPU Count | Memory   | CPU      |
| ----------------------- | --------- | -------- | -------- |
| llama-vllm-inference    | 4         | 100G     | 5-14     |
| llm-inference-whisper   | Yes       | Variable | Variable |
| gemma-vllm-inference    | Yes       | Variable | Variable |
| emma-500-vllm-inference | Yes       | Variable | Variable |

**Required Node Label:** `capability: gpu`

**Setup:**

```bash theme={null}
kubectl label nodes <gpu-node-name> capability=gpu
```

## **Worker Node Requirements**

CPU-intensive services require worker nodes:

| Service               | Memory   | CPU      |
| --------------------- | -------- | -------- |
| batch-worker-cpu-3pmt | 24G      | 1        |
| Other batch workers   | Variable | Variable |

**Required Node Label:** `node-type: worker`

**Setup:**

```bash theme={null}
kubectl label nodes <worker-node-name> node-type=worker
```

***

# **Troubleshooting**

## **LLM Inference Services Not Starting**

**Symptom:** LLM inference pods stuck in `Pending` state

**Solution:**

1. Verify GPU nodes are available:
   ```bash theme={null}
   kubectl get nodes -l capability=gpu
   ```

2. Check GPU resources:
   ```bash theme={null}
   kubectl describe nodes -l capability=gpu | grep nvidia.com/gpu
   ```

3. Ensure proper node labels:
   ```bash theme={null}
   kubectl label nodes <node-name> capability=gpu
   ```

## **Multimodal OCR Connection Issues**

**Symptom:** Multimodal service cannot connect to RabbitMQ

**Solution:**

1. Verify RabbitMQ credentials in `secrets.yaml`:
   ```yaml theme={null}
   multimodal:
     env:
       MQ_PWD: "<correct-rabbitmq-password>"
   ```

2. Check RabbitMQ connectivity:
   ```bash theme={null}
   kubectl logs -n lilt deployment/multimodal-ocr
   ```

## **Batch Worker Memory Issues**

**Symptom:** Batch workers being OOMKilled

**Solution:**

1. Verify node capacity:
   ```bash theme={null}
   kubectl describe nodes -l node-type=worker | grep -A 5 Allocatable
   ```

2. Increase memory if needed:
   ```yaml theme={null}
   batch-worker-cpu-3pmt:
     resources:
       requests:
         memory: 32G  # Increase if needed
       limits:
         memory: 32G
   ```

## **ConfigMap/Secret Not Found Errors**

**Symptom:** Services failing with "configmap 'lilt-configmap' not found"

**Solution:**
Ensure the main Lilt chart creates these resources. Check your Helm templates include:

```yaml theme={null}
apiVersion: v1
kind: ConfigMap
metadata:
  name: lilt-configmap
  namespace: lilt
---
apiVersion: v1
kind: Secret
metadata:
  name: lilt-secrets
  namespace: lilt
```

## **S3/MinIO Connection Issues**

**Symptom:** Services cannot access S3/MinIO storage

**Solution:**

1. Verify S3 credentials in `secrets.yaml`:
   ```yaml theme={null}
   core-api:
     onpremValues:
       env:
         S3_ACCESS_KEY: "<access-key>"
         S3_SECRET_KEY: "<secret-key>"
   ```

2. Verify internal MinIO endpoint is accessible:
   ```bash theme={null}
   kubectl exec -n lilt deployment/core-api -- curl -k https://minio.lilt.svc.cluster.local:9000
   ```