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support nvidia hosted vision models
these models are not hosted on the common https://integrate.api.nvidia.com/v1. they are hosted on their own individual urls.
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1 changed files with 43 additions and 10 deletions
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@ -6,6 +6,7 @@
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import logging
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import warnings
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from functools import lru_cache
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from typing import AsyncIterator, List, Optional, Union
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from openai import APIConnectionError, AsyncOpenAI
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@ -82,12 +83,42 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
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# )
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self._config = config
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# make sure the client lives longer than any async calls
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self._client = AsyncOpenAI(
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base_url=f"{self._config.url}/v1",
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api_key=(self._config.api_key.get_secret_value() if self._config.api_key else "NO KEY"),
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timeout=self._config.timeout,
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)
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@lru_cache # noqa: B019
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def _get_client(self, provider_model_id: str) -> AsyncOpenAI:
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"""
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For hosted models, https://integrate.api.nvidia.com/v1 is the primary base_url. However,
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some models are hosted on different URLs. This function returns the appropriate client
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for the given provider_model_id.
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This relies on lru_cache and self._default_client to avoid creating a new client for each request
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or for each model that is hosted on https://integrate.api.nvidia.com/v1.
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:param provider_model_id: The provider model ID
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:return: An OpenAI client
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"""
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@lru_cache # noqa: B019
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def _get_client_for_base_url(base_url: str) -> AsyncOpenAI:
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"""
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Maintain a single OpenAI client per base_url.
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"""
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return AsyncOpenAI(
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base_url=base_url,
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api_key=(self._config.api_key.get_secret_value() if self._config.api_key else "NO KEY"),
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timeout=self._config.timeout,
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)
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special_model_urls = {
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"meta/llama-3.2-11b-vision-instruct": "https://ai.api.nvidia.com/v1/gr/meta/llama-3.2-11b-vision-instruct",
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"meta/llama-3.2-90b-vision-instruct": "https://ai.api.nvidia.com/v1/gr/meta/llama-3.2-90b-vision-instruct",
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}
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base_url = f"{self._config.url}/v1"
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if _is_nvidia_hosted(self._config) and provider_model_id in special_model_urls:
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base_url = special_model_urls[provider_model_id]
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return _get_client_for_base_url(base_url)
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async def completion(
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self,
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@ -103,9 +134,10 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
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await check_health(self._config) # this raises errors
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provider_model_id = self.get_provider_model_id(model_id)
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request = convert_completion_request(
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request=CompletionRequest(
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model=self.get_provider_model_id(model_id),
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model=provider_model_id,
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content=content,
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sampling_params=sampling_params,
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response_format=response_format,
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@ -116,7 +148,7 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
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)
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try:
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response = await self._client.completions.create(**request)
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response = await self._get_client(provider_model_id).completions.create(**request)
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except APIConnectionError as e:
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raise ConnectionError(f"Failed to connect to NVIDIA NIM at {self._config.url}: {e}") from e
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@ -152,7 +184,7 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
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input = [content.text if isinstance(content, TextContentItem) else content for content in flat_contents]
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model = self.get_provider_model_id(model_id)
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response = await self._client.embeddings.create(
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response = await self._get_client(model).embeddings.create(
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model=model,
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input=input,
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# extra_body={"input_type": "passage"|"query"}, # TODO(mf): how to tell caller's intent?
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@ -183,6 +215,7 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
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await check_health(self._config) # this raises errors
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provider_model_id = self.get_provider_model_id(model_id)
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request = await convert_chat_completion_request(
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request=ChatCompletionRequest(
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model=self.get_provider_model_id(model_id),
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@ -198,7 +231,7 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
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)
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try:
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response = await self._client.chat.completions.create(**request)
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response = await self._get_client(provider_model_id).chat.completions.create(**request)
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except APIConnectionError as e:
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raise ConnectionError(f"Failed to connect to NVIDIA NIM at {self._config.url}: {e}") from e
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