---
description: "Configure LLM clients for synthetic data generation with NVIDIA APIs or custom endpoints"
categories: ["how-to-guides"]
tags: ["llm-client", "openai", "nvidia-api", "configuration"]
personas: ["data-scientist-focused", "mle-focused"]
difficulty: "beginner"
content_type: "how-to"
modality: "text-only"
---

# LLM Client Configuration

NeMo Curator's synthetic data generation uses OpenAI-compatible clients to communicate with LLM inference servers. This guide covers client configuration, performance tuning, and integration with various endpoints.

## Overview

Two client types are available:

- **`AsyncOpenAIClient`**: Recommended for high-throughput batch processing with concurrent requests
- **`OpenAIClient`**: Synchronous client for simpler use cases or debugging

For most SDG workloads, use `AsyncOpenAIClient` to maximize throughput.

## Basic Configuration

### NVIDIA API Endpoints

```python
from nemo_curator.models.client.openai_client import AsyncOpenAIClient

client = AsyncOpenAIClient(
    api_key="your-nvidia-api-key",  # Or use NVIDIA_API_KEY env var
    base_url="https://integrate.api.nvidia.com/v1",
    max_concurrent_requests=5,
)
```

### Environment Variables

Set your API key as an environment variable to avoid hardcoding credentials:

```bash
export NVIDIA_API_KEY="nvapi-..."
```

The underlying OpenAI client automatically uses the `OPENAI_API_KEY` environment variable if no `api_key` is provided. For NVIDIA APIs, explicitly pass the key:

```python
import os

client = AsyncOpenAIClient(
    api_key=os.environ["NVIDIA_API_KEY"],
    base_url="https://integrate.api.nvidia.com/v1",
)
```

## Generation Parameters

Configure LLM generation behavior using `GenerationConfig`:

```python
from nemo_curator.models.client.llm_client import GenerationConfig

config = GenerationConfig(
    max_tokens=2048,
    temperature=0.7,
    top_p=0.95,
    seed=42,  # For reproducibility
)
```

| Parameter | Type | Default | Description |
| --- | --- | --- | --- |
| `max_tokens` | int | 2048 | Maximum tokens to generate per request |
| `temperature` | float | 0.0 | Sampling temperature (0.0-2.0). Higher values increase randomness |
| `top_p` | float | 0.95 | Nucleus sampling parameter (0.0-1.0) |
| `top_k` | int | None | Top-k sampling (if supported by the endpoint) |
| `seed` | int | 0 | Random seed for reproducibility |
| `stop` | str/list | None | Stop sequences to end generation |
| `stream` | bool | False | Enable streaming (not recommended for batch processing) |
| `n` | int | 1 | Number of completions to generate per request |
| `extra_kwargs` | dict | None | Additional keyword arguments passed through to the OpenAI `create()` call |

## Performance Tuning

### Concurrency vs. Parallelism

The `max_concurrent_requests` parameter controls how many API requests the client can have in-flight simultaneously. This interacts with Ray's distributed workers:

- **Client-level concurrency**: `max_concurrent_requests` limits concurrent API calls per worker
- **Worker-level parallelism**: Ray distributes tasks across multiple workers

```python
# For NVIDIA API endpoints with rate limits
client = AsyncOpenAIClient(
    base_url="https://integrate.api.nvidia.com/v1",
    max_concurrent_requests=3,  # Conservative for cloud APIs
)
```

### Retry Configuration

The client includes automatic retry with exponential backoff for transient errors:

```python
client = AsyncOpenAIClient(
    base_url="https://integrate.api.nvidia.com/v1",
    max_retries=3,        # Number of retry attempts
    base_delay=1.0,       # Base delay in seconds
    timeout=120,          # Request timeout
)
```

The retry logic handles:
- **Rate limit errors (429)**: Automatic backoff with jitter
- **Connection errors**: Retry with exponential delay
- **Transient failures**: Configurable retry attempts

## Using Other OpenAI-Compatible Endpoints

The `AsyncOpenAIClient` works with any OpenAI-compatible API endpoint. Simply configure the `base_url` and `api_key` parameters:

```python
# OpenAI API
client = AsyncOpenAIClient(
    base_url="https://api.openai.com/v1",
    api_key="sk-...",  # Or set OPENAI_API_KEY env var
    max_concurrent_requests=5,
)

# Any OpenAI-compatible endpoint
client = AsyncOpenAIClient(
    base_url="http://your-endpoint/v1",
    api_key="your-api-key",
    max_concurrent_requests=5,
)
```

### Local Inference with InferenceServer

To serve models locally and connect them to `AsyncOpenAIClient`, use NeMo Curator's built-in [Inference Server](/curate-text/synthetic/inference-server) (Ray Serve + vLLM):

```python
from nemo_curator.core.serve import InferenceModelConfig, InferenceServer
from nemo_curator.models.client.openai_client import AsyncOpenAIClient

config = InferenceModelConfig(
    model_identifier="meta-llama/Llama-3-8B-Instruct",
    engine_kwargs={"tensor_parallel_size": 2},
)

with InferenceServer(models=[config]) as server:
    client = AsyncOpenAIClient(
        base_url=server.endpoint,
        api_key="unused",
        max_concurrent_requests=10,
    )
    # Use client in pipeline stages
```

## Complete Example

```python
import os
from nemo_curator.models.client.openai_client import AsyncOpenAIClient
from nemo_curator.models.client.llm_client import GenerationConfig
from nemo_curator.pipeline import Pipeline
from nemo_curator.stages.synthetic.qa_multilingual_synthetic import QAMultilingualSyntheticStage

# Configure client
client = AsyncOpenAIClient(
    api_key=os.environ.get("NVIDIA_API_KEY"),
    base_url="https://integrate.api.nvidia.com/v1",
    max_concurrent_requests=5,
    max_retries=3,
    base_delay=1.0,
)

# Configure generation
config = GenerationConfig(
    temperature=0.9,
    top_p=0.95,
    max_tokens=2048,
)

# Use in a pipeline stage
pipeline = Pipeline(name="sdg_example")
pipeline.add_stage(
    QAMultilingualSyntheticStage(
        prompt="Generate a Q&A pair about science in {language}.",
        languages=["English", "French", "German"],
        client=client,
        model_name="meta/llama-3.3-70b-instruct",
        num_samples=100,
        generation_config=config,
    )
)
```

## Troubleshooting

### Rate Limit Errors

If you encounter frequent 429 errors:
1. Reduce `max_concurrent_requests`
2. Increase `base_delay` for longer backoff
3. Consider using a local deployment for high-volume workloads

### Connection Timeouts

For slow networks or high-latency endpoints:
```python
client = AsyncOpenAIClient(
    base_url="...",
    timeout=300,  # Increase from default 120 seconds
)
```

---

## Next Steps

- [Multilingual Q&A](/curate-text/synthetic/multilingual-qa): Generate multilingual Q&A pairs
- [Nemotron-CC](/curate-text/synthetic/nemotron-cc): Advanced text transformation pipelines