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import json
import time
from typing import Any, AsyncIterator
import httpx
from app.models.providers.base import (
AuthenticationError,
BaseProvider,
CompletionResponse,
ModelInfo,
ModelNotFoundError,
ProviderError,
RateLimitError,
TokenUsage,
)
class NVIDIAProvider(BaseProvider):
"""NVIDIA AI API provider supporting reasoning and code models."""
PROVIDER_NAME = "nvidia"
DEFAULT_BASE_URL = "https://integrate.api.nvidia.com/v1"
# Model definitions with configurations
MODELS = {
# Reasoning models
"step-3.5-flash": ModelInfo(
id="stepfun-ai/step-3.5-flash",
name="Step 3.5 Flash (Reasoning)",
provider="nvidia",
context_window=16384,
max_output_tokens=16384,
supports_functions=False,
supports_vision=False,
supports_streaming=True,
cost_per_1k_input=0.0, # Free tier
cost_per_1k_output=0.0,
),
"glm4.7": ModelInfo(
id="z-ai/glm4.7",
name="GLM 4.7 (Reasoning)",
provider="nvidia",
context_window=16384,
max_output_tokens=16384,
supports_functions=False,
supports_vision=False,
supports_streaming=True,
cost_per_1k_input=0.0,
cost_per_1k_output=0.0,
),
"deepseek-v3.2": ModelInfo(
id="deepseek-ai/deepseek-v3.2",
name="DeepSeek V3.2 (Reasoning)",
provider="nvidia",
context_window=8192,
max_output_tokens=8192,
supports_functions=False,
supports_vision=False,
supports_streaming=True,
cost_per_1k_input=0.0,
cost_per_1k_output=0.0,
),
"deepseek-r1": ModelInfo(
id="deepseek-ai/deepseek-r1",
name="DeepSeek R1 (Reasoning)",
provider="nvidia",
context_window=16384,
max_output_tokens=16384,
supports_functions=False,
supports_vision=False,
supports_streaming=True,
cost_per_1k_input=0.0,
cost_per_1k_output=0.0,
),
# Code models
"devstral-2-123b": ModelInfo(
id="mistralai/devstral-2-123b-instruct-2512",
name="Devstral 2 123B (Code)",
provider="nvidia",
context_window=8192,
max_output_tokens=8192,
supports_functions=False,
supports_vision=False,
supports_streaming=True,
cost_per_1k_input=0.0,
cost_per_1k_output=0.0,
),
# General models
"llama-3.3-70b": ModelInfo(
id="meta/llama-3.3-70b-instruct",
name="Llama 3.3 70B",
provider="nvidia",
context_window=8192,
max_output_tokens=8192,
supports_functions=False,
supports_vision=False,
supports_streaming=True,
cost_per_1k_input=0.0,
cost_per_1k_output=0.0,
),
"nemotron-70b": ModelInfo(
id="nvidia/llama-3.1-nemotron-70b-instruct",
name="Nemotron 70B",
provider="nvidia",
context_window=4096,
max_output_tokens=4096,
supports_functions=False,
supports_vision=False,
supports_streaming=True,
cost_per_1k_input=0.0,
cost_per_1k_output=0.0,
),
}
# Reasoning model configs
REASONING_CONFIGS = {
"step-3.5-flash": {
"temperature": 1.0,
"top_p": 0.9,
},
"glm4.7": {
"temperature": 1.0,
"top_p": 1.0,
"extra_body": {"chat_template_kwargs": {"enable_thinking": True, "clear_thinking": False}},
},
"deepseek-v3.2": {
"temperature": 1.0,
"top_p": 0.95,
"extra_body": {"chat_template_kwargs": {"thinking": True}},
},
"deepseek-r1": {
"temperature": 0.6,
"top_p": 0.95,
},
}
def __init__(
self,
api_key: str | None = None,
base_url: str | None = None,
timeout: float = 60.0,
max_retries: int = 2,
):
"""
Initialize NVIDIA provider.
Args:
api_key: NVIDIA API key
base_url: Base URL for NVIDIA API (defaults to integrate.api.nvidia.com)
timeout: Request timeout in seconds
max_retries: Maximum number of retries for failed requests
"""
super().__init__(api_key, base_url or self.DEFAULT_BASE_URL, timeout, max_retries)
self._last_request_time = 0.0
def _get_headers(self) -> dict[str, str]:
"""Get headers for NVIDIA API requests."""
return {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
}
async def _apply_rate_limit(self) -> None:
"""Apply rate limiting between requests."""
elapsed = time.time() - self._last_request_time
min_interval = 0.3 # 300ms between requests
if elapsed < min_interval:
import asyncio
await asyncio.sleep(min_interval - elapsed)
self._last_request_time = time.time()
async def complete(
self,
messages: list[dict[str, str]],
model: str = "devstral-2-123b",
temperature: float = 0.7,
max_tokens: int | None = None,
**kwargs: Any,
) -> CompletionResponse:
"""
Create a chat completion using NVIDIA models.
Args:
messages: List of message dictionaries with 'role' and 'content'
model: Model key (e.g., 'devstral-2-123b', 'llama-3.3-70b')
temperature: Sampling temperature
max_tokens: Maximum tokens to generate
**kwargs: Additional model-specific parameters
Returns:
CompletionResponse with generated text and metadata
Raises:
ModelNotFoundError: If model is not supported
AuthenticationError: If API key is invalid
RateLimitError: If rate limit is exceeded
ProviderError: For other API errors
"""
# Validate model
if model not in self.MODELS:
raise ModelNotFoundError(self.PROVIDER_NAME, model)
model_info = self.MODELS[model]
model_id = model_info.id
# Apply rate limiting
await self._apply_rate_limit()
# Build request payload
payload: dict[str, Any] = {
"model": model_id,
"messages": messages,
"temperature": temperature,
"max_tokens": max_tokens or model_info.max_output_tokens,
}
# Add reasoning model configs if applicable
if model in self.REASONING_CONFIGS:
config = self.REASONING_CONFIGS[model]
if "extra_body" in config:
payload["extra_body"] = config["extra_body"]
if "top_p" in config:
payload["top_p"] = config["top_p"]
# Add any additional kwargs
payload.update(kwargs)
try:
async with httpx.AsyncClient(timeout=self.timeout) as client:
response = await client.post(
f"{self.base_url}/chat/completions",
headers=self._get_headers(),
json=payload,
)
if response.status_code == 401:
raise AuthenticationError(self.PROVIDER_NAME, "Invalid NVIDIA API key")
elif response.status_code == 429:
raise RateLimitError(self.PROVIDER_NAME)
elif response.status_code >= 400:
error_detail = response.text
raise ProviderError(f"NVIDIA API error ({response.status_code}): {error_detail}", self.PROVIDER_NAME)
data = response.json()
# Extract response
choice = data["choices"][0]
content = choice["message"]["content"]
# Extract usage
usage_data = data.get("usage", {})
usage = TokenUsage(
prompt_tokens=usage_data.get("prompt_tokens", 0),
completion_tokens=usage_data.get("completion_tokens", 0),
total_tokens=usage_data.get("total_tokens", 0),
)
return CompletionResponse(
content=content,
model=model,
provider=self.PROVIDER_NAME,
usage=usage,
finish_reason=choice.get("finish_reason", "stop"),
raw_response=data,
)
except (AuthenticationError, RateLimitError, ProviderError, ModelNotFoundError):
raise
except Exception as e:
raise ProviderError(f"NVIDIA request failed: {str(e)}", self.PROVIDER_NAME) from e
async def complete_stream(
self,
messages: list[dict[str, str]],
model: str = "devstral-2-123b",
temperature: float = 0.7,
max_tokens: int | None = None,
**kwargs: Any,
) -> AsyncIterator[str]:
"""
Create a streaming chat completion.
Args:
messages: List of message dictionaries
model: Model key
temperature: Sampling temperature
max_tokens: Maximum tokens to generate
**kwargs: Additional parameters
Yields:
Content chunks as they arrive
Raises:
Same as complete()
"""
if model not in self.MODELS:
raise ModelNotFoundError(self.PROVIDER_NAME, model)
model_info = self.MODELS[model]
model_id = model_info.id
await self._apply_rate_limit()
payload: dict[str, Any] = {
"model": model_id,
"messages": messages,
"temperature": temperature,
"max_tokens": max_tokens or model_info.max_output_tokens,
"stream": True,
}
if model in self.REASONING_CONFIGS:
config = self.REASONING_CONFIGS[model]
if "extra_body" in config:
payload["extra_body"] = config["extra_body"]
if "top_p" in config:
payload["top_p"] = config["top_p"]
payload.update(kwargs)
try:
async with httpx.AsyncClient(timeout=self.timeout) as client:
async with client.stream(
"POST",
f"{self.base_url}/chat/completions",
headers=self._get_headers(),
json=payload,
) as response:
if response.status_code == 401:
raise AuthenticationError(self.PROVIDER_NAME, "Invalid NVIDIA API key")
elif response.status_code == 429:
raise RateLimitError(self.PROVIDER_NAME)
elif response.status_code >= 400:
error_detail = await response.aread()
raise ProviderError(f"NVIDIA API error: {error_detail.decode()}", self.PROVIDER_NAME)
async for line in response.aiter_lines():
if not line.strip() or not line.startswith("data: "):
continue
data_str = line[6:] # Remove 'data: ' prefix
if data_str == "[DONE]":
break
try:
data = json.loads(data_str)
if "choices" in data and data["choices"]:
delta = data["choices"][0].get("delta", {})
content = delta.get("content")
if content:
yield content
except json.JSONDecodeError:
continue
except (AuthenticationError, RateLimitError, ProviderError, ModelNotFoundError):
raise
except Exception as e:
raise ProviderError(f"NVIDIA streaming failed: {str(e)}", self.PROVIDER_NAME) from e
def list_models(self) -> list[ModelInfo]:
"""List all available NVIDIA models."""
return list(self.MODELS.values())
def get_models(self) -> list[ModelInfo]:
"""Get list of available models (required by abstract base)."""
return self.list_models()
async def stream(
self,
messages: list[dict[str, Any]],
model: str,
temperature: float = 0.7,
max_tokens: int | None = None,
**kwargs: Any,
) -> AsyncIterator[str]:
"""Stream a completion (delegates to complete_stream)."""
async for chunk in self.complete_stream(messages, model, temperature, max_tokens, **kwargs):
yield chunk
def get_model_info(self, model: str) -> ModelInfo:
"""Get information about a specific model."""
if model not in self.MODELS:
raise ModelNotFoundError(self.PROVIDER_NAME, model)
return self.MODELS[model]
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