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Add IO Intelligence provider support

Browse source 
Adds IO Intelligence (io.net) as a new OpenAI-compatible LLM provider
with support for chat completions and embeddings.

- Chat completion support with streaming, function calling, and tool use
- Embedding support via BAAI/bge-multilingual-gemma2 model
- 17 models including Llama, DeepSeek, Qwen, Mistral, and GPT-OSS variants
- Model pricing configuration in model_prices_and_context_window.json
- LiteLLM dashboard UI integration
- Unit tests for provider config, routing, and mock API calls

API Base: https://api.intelligence.io.solutions/api/v1
Auth: IO_INTELLIGENCE_API_KEY environment variable

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
katestover 2026-04-01 12:32:09 +01:00
parent 08be1e52ae
commit bf40f9be11
15 changed files with 700 additions and 0 deletions
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4
litellm/__init__.py
View file

@ -1757,6 +1757,10 @@ if TYPE_CHECKING:
from .llms.sambanova.embedding.transformation import (
SambaNovaEmbeddingConfig as SambaNovaEmbeddingConfig,
)
from .llms.iointelligence.chat import IOIntelligenceConfig as IOIntelligenceConfig
from .llms.iointelligence.embedding.transformation import (
IOIntelligenceEmbeddingConfig as IOIntelligenceEmbeddingConfig,
)
from .llms.fireworks_ai.chat.transformation import (
FireworksAIConfig as FireworksAIConfig,
)

3
litellm/constants.py
View file

@ -533,6 +533,7 @@ LITELLM_CHAT_PROVIDERS = [
"text-completion-codestral",
"deepseek",
"sambanova",
"io_intelligence",
"maritalk",
"cloudflare",
"fireworks_ai",
@ -706,6 +707,7 @@ openai_compatible_endpoints: List = [
"app.empower.dev/api/v1",
"https://api.friendli.ai/serverless/v1",
"api.sambanova.ai/v1",
"api.intelligence.io.solutions/api/v1",
"api.x.ai/v1",
"ollama.com",
"api.galadriel.ai/v1",
@ -739,6 +741,7 @@ openai_compatible_providers: List = [
"cerebras",
"baseten",
"sambanova",
"io_intelligence",
"ai21_chat",
"ai21",
"volcengine",

10
litellm/litellm_core_utils/get_llm_provider_logic.py
View file

@ -238,6 +238,9 @@ def get_llm_provider( # noqa: PLR0915
elif endpoint == "https://api.sambanova.ai/v1":
custom_llm_provider = "sambanova"
dynamic_api_key = get_secret_str("SAMBANOVA_API_KEY")
elif endpoint == "https://api.intelligence.io.solutions/api/v1":
custom_llm_provider = "io_intelligence"
dynamic_api_key = get_secret_str("IO_INTELLIGENCE_API_KEY")
elif endpoint == "https://api.ai21.com/studio/v1":
custom_llm_provider = "ai21_chat"
dynamic_api_key = get_secret_str("AI21_API_KEY")
@ -605,6 +608,13 @@ def _get_openai_compatible_provider_info( # noqa: PLR0915
or "https://api.sambanova.ai/v1"
) # type: ignore
dynamic_api_key = api_key or get_secret_str("SAMBANOVA_API_KEY")
elif custom_llm_provider == "io_intelligence":
api_base = (
api_base
or get_secret("IO_INTELLIGENCE_API_BASE")
or "https://api.intelligence.io.solutions/api/v1"
) # type: ignore
dynamic_api_key = api_key or get_secret_str("IO_INTELLIGENCE_API_KEY")
elif custom_llm_provider == "meta_llama":
api_base = (
api_base

0
litellm/llms/iointelligence/__init__.py Normal file
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89
litellm/llms/iointelligence/chat.py Normal file
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@ -0,0 +1,89 @@
"""
IO Intelligence Chat Completions API
This is OpenAI compatible - no translation needed / occurs.
"""
from typing import Optional, Union
from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig
class IOIntelligenceConfig(OpenAIGPTConfig):
"""
Reference: https://io.net/docs/guides/intelligence/io-intelligence-apis
IO Intelligence provides an OpenAI-compatible API at
https://api.intelligence.io.solutions/api/v1
Below are the parameters:
"""
max_tokens: Optional[int] = None
temperature: Optional[int] = None
top_p: Optional[int] = None
stop: Optional[Union[str, list]] = None
stream: Optional[bool] = None
stream_options: Optional[dict] = None
tool_choice: Optional[str] = None
response_format: Optional[dict] = None
tools: Optional[list] = None
frequency_penalty: Optional[float] = None
presence_penalty: Optional[float] = None
def __init__(
self,
max_tokens: Optional[int] = None,
response_format: Optional[dict] = None,
stop: Optional[str] = None,
stream: Optional[bool] = None,
stream_options: Optional[dict] = None,
temperature: Optional[float] = None,
top_p: Optional[float] = None,
tool_choice: Optional[str] = None,
tools: Optional[list] = None,
frequency_penalty: Optional[float] = None,
presence_penalty: Optional[float] = None,
) -> None:
locals_ = locals().copy()
for key, value in locals_.items():
if key != "self" and value is not None:
setattr(self.__class__, key, value)
@classmethod
def get_config(cls):
return super().get_config()
def get_supported_openai_params(self, model: str) -> list:
"""
Get the supported OpenAI params for the given model
"""
return [
"max_completion_tokens",
"max_tokens",
"response_format",
"stop",
"stream",
"stream_options",
"temperature",
"top_p",
"tools",
"tool_choice",
"frequency_penalty",
"presence_penalty",
]
def map_openai_params(
self,
non_default_params: dict,
optional_params: dict,
model: str,
drop_params: bool,
) -> dict:
supported_openai_params = self.get_supported_openai_params(model=model)
for param, value in non_default_params.items():
if param == "max_completion_tokens":
optional_params["max_tokens"] = value
elif param in supported_openai_params:
optional_params[param] = value
return optional_params

6
litellm/llms/iointelligence/common_utils.py Normal file
View file

@ -0,0 +1,6 @@
from litellm.llms.base_llm.chat.transformation import BaseLLMException
class IOIntelligenceError(BaseLLMException):
def __init__(self, status_code, message, headers):
super().__init__(status_code=status_code, message=message, headers=headers)

0
litellm/llms/iointelligence/embedding/__init__.py Normal file
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131
litellm/llms/iointelligence/embedding/transformation.py Normal file
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@ -0,0 +1,131 @@
"""
IO Intelligence Embedding API
This is OpenAI compatible - no transformation is applied.
"""
from typing import List, Optional, Union
import httpx
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
from litellm.llms.base_llm.chat.transformation import BaseLLMException
from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig
from litellm.secret_managers.main import get_secret_str
from litellm.types.llms.openai import AllEmbeddingInputValues, AllMessageValues
from litellm.types.utils import EmbeddingResponse, Usage
from ..common_utils import IOIntelligenceError
class IOIntelligenceEmbeddingConfig(BaseEmbeddingConfig):
def __init__(self) -> None:
pass
def get_complete_url(
self,
api_base: Optional[str],
api_key: Optional[str],
model: str,
optional_params: dict,
litellm_params: dict,
stream: Optional[bool] = None,
) -> str:
if api_base is None:
raise ValueError("api_base is required for IO Intelligence embeddings")
api_base = api_base.rstrip("/")
if not api_base.endswith("/embeddings"):
api_base = f"{api_base}/embeddings"
return api_base
def validate_environment(
self,
headers: dict,
model: str,
messages: List[AllMessageValues],
optional_params: dict,
litellm_params: dict,
api_key: Optional[str] = None,
api_base: Optional[str] = None,
) -> dict:
if api_key is None:
api_key = get_secret_str("IO_INTELLIGENCE_API_KEY")
default_headers = {
"Authorization": f"Bearer {api_key}",
"accept": "application/json",
"Content-Type": "application/json",
}
if "Authorization" in headers:
default_headers["Authorization"] = headers["Authorization"]
return {**default_headers, **headers}
def get_supported_openai_params(self, model: str):
return ["dimensions", "encoding_format"]
def map_openai_params(
self,
non_default_params: dict,
optional_params: dict,
model: str,
drop_params: bool,
):
supported_openai_params = self.get_supported_openai_params(model)
for param, value in non_default_params.items():
if param in supported_openai_params:
optional_params[param] = value
return optional_params
def transform_embedding_request(
self,
model: str,
input: AllEmbeddingInputValues,
optional_params: dict,
headers: dict,
) -> dict:
return {
"input": input,
"model": model,
**optional_params,
}
def transform_embedding_response(
self,
model: str,
raw_response: httpx.Response,
model_response: EmbeddingResponse,
logging_obj: LiteLLMLoggingObj,
api_key: Optional[str],
request_data: dict,
optional_params: dict,
litellm_params: dict,
) -> EmbeddingResponse:
try:
raw_response_json = raw_response.json()
except Exception:
raise IOIntelligenceError(
message=raw_response.text,
status_code=raw_response.status_code,
headers=raw_response.headers,
)
model_response.model = raw_response_json.get("model")
model_response.data = raw_response_json.get("data")
model_response.object = raw_response_json.get("object")
usage = Usage(
prompt_tokens=raw_response_json.get("usage", {}).get("prompt_tokens", 0),
total_tokens=raw_response_json.get("usage", {}).get("total_tokens", 0),
)
model_response.usage = usage
return model_response
def get_error_class(
self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
) -> BaseLLMException:
return IOIntelligenceError(
message=error_message, status_code=status_code, headers=headers
)

23
litellm/main.py
View file

@ -2569,6 +2569,7 @@ def completion( # type: ignore # noqa: PLR0915
or custom_llm_provider == "cerebras"
or custom_llm_provider == "baseten"
or custom_llm_provider == "sambanova"
or custom_llm_provider == "io_intelligence"
or custom_llm_provider == "volcengine"
or custom_llm_provider == "anyscale"
or custom_llm_provider == "openai"
@ -5448,6 +5449,28 @@ def embedding( # noqa: PLR0915
aembedding=aembedding,
litellm_params={},
)
elif custom_llm_provider == "io_intelligence":
api_key = api_key or litellm.api_key or get_secret_str("IO_INTELLIGENCE_API_KEY")
api_base = (
api_base
or litellm.api_base
or get_secret_str("IO_INTELLIGENCE_API_BASE")
or "https://api.intelligence.io.solutions/api/v1"
)
response = base_llm_http_handler.embedding(
model=model,
input=input,
custom_llm_provider=custom_llm_provider,
api_base=api_base,
api_key=api_key,
logging_obj=logging,
timeout=timeout,
model_response=EmbeddingResponse(),
optional_params=optional_params,
client=client,
aembedding=aembedding,
litellm_params={},
)
elif custom_llm_provider == "voyage":
response = base_llm_http_handler.embedding(
model=model,

1
litellm/types/utils.py
View file

@ -3224,6 +3224,7 @@ class LlmProviders(str, Enum):
LAMBDA_AI = "lambda_ai"
DEEPSEEK = "deepseek"
SAMBANOVA = "sambanova"
IO_INTELLIGENCE = "io_intelligence"
MARITALK = "maritalk"
VOYAGE = "voyage"
CLOUDFLARE = "cloudflare"

3
litellm/utils.py
View file

@ -8095,6 +8095,7 @@ class ProviderConfigManager:
False,
),
LlmProviders.SAMBANOVA: (lambda: litellm.SambanovaConfig(), False),
LlmProviders.IO_INTELLIGENCE: (lambda: litellm.IOIntelligenceConfig(), False),
LlmProviders.MARITALK: (lambda: litellm.MaritalkConfig(), False),
LlmProviders.VLLM: (lambda: litellm.VLLMConfig(), False),
LlmProviders.OLLAMA: (lambda: litellm.OllamaConfig(), False),
@ -8263,6 +8264,8 @@ class ProviderConfigManager:
return litellm.InfinityEmbeddingConfig()
elif litellm.LlmProviders.SAMBANOVA == provider:
return litellm.SambaNovaEmbeddingConfig()
elif litellm.LlmProviders.IO_INTELLIGENCE == provider:
return litellm.IOIntelligenceEmbeddingConfig()
elif (
litellm.LlmProviders.COHERE == provider
or litellm.LlmProviders.COHERE_CHAT == provider

204
model_prices_and_context_window.json
View file

@ -27040,6 +27040,210 @@
"supports_reasoning": true,
"source": "https://cloud.sambanova.ai/plans/pricing"
},
"io_intelligence/meta-llama/Llama-3.2-90B-Vision-Instruct": {
"max_tokens": 16000,
"max_input_tokens": 16000,
"max_output_tokens": 16000,
"input_cost_per_token": 6.3e-07,
"output_cost_per_token": 1.8e-06,
"litellm_provider": "io_intelligence",
"mode": "chat",
"supports_vision": true,
"supports_function_calling": true,
"supports_tool_choice": true,
"source": "https://io.net/docs/guides/payment/io-intelligence-payments"
},
"io_intelligence/openai/gpt-oss-120b": {
"max_tokens": 131072,
"max_input_tokens": 131072,
"max_output_tokens": 131072,
"input_cost_per_token": 6.3e-07,
"output_cost_per_token": 1.8e-06,
"litellm_provider": "io_intelligence",
"mode": "chat",
"supports_function_calling": true,
"supports_tool_choice": true,
"source": "https://io.net/docs/guides/payment/io-intelligence-payments"
},
"io_intelligence/Qwen/Qwen2.5-VL-32B-Instruct": {
"max_tokens": 32000,
"max_input_tokens": 32000,
"max_output_tokens": 32000,
"input_cost_per_token": 6.3e-07,
"output_cost_per_token": 1.8e-06,
"litellm_provider": "io_intelligence",
"mode": "chat",
"supports_vision": true,
"supports_function_calling": true,
"supports_tool_choice": true,
"source": "https://io.net/docs/guides/payment/io-intelligence-payments"
},
"io_intelligence/deepseek-ai/DeepSeek-R1-0528": {
"max_tokens": 128000,
"max_input_tokens": 128000,
"max_output_tokens": 128000,
"input_cost_per_token": 6.3e-07,
"output_cost_per_token": 1.8e-06,
"litellm_provider": "io_intelligence",
"mode": "chat",
"supports_function_calling": true,
"supports_tool_choice": true,
"supports_reasoning": true,
"source": "https://io.net/docs/guides/payment/io-intelligence-payments"
},
"io_intelligence/meta-llama/Llama-3.3-70B-Instruct": {
"max_tokens": 131072,
"max_input_tokens": 131072,
"max_output_tokens": 131072,
"input_cost_per_token": 6.3e-07,
"output_cost_per_token": 1.8e-06,
"litellm_provider": "io_intelligence",
"mode": "chat",
"supports_function_calling": true,
"supports_tool_choice": true,
"source": "https://io.net/docs/guides/payment/io-intelligence-payments"
},
"io_intelligence/mistralai/Mistral-Large-Instruct-2411": {
"max_tokens": 131072,
"max_input_tokens": 131072,
"max_output_tokens": 131072,
"input_cost_per_token": 6.3e-07,
"output_cost_per_token": 1.8e-06,
"litellm_provider": "io_intelligence",
"mode": "chat",
"supports_function_calling": true,
"supports_tool_choice": true,
"source": "https://io.net/docs/guides/payment/io-intelligence-payments"
},
"io_intelligence/Qwen3-Next-80B": {
"max_tokens": 131072,
"max_input_tokens": 131072,
"max_output_tokens": 131072,
"input_cost_per_token": 6.3e-07,
"output_cost_per_token": 1.8e-06,
"litellm_provider": "io_intelligence",
"mode": "chat",
"supports_function_calling": true,
"supports_tool_choice": true,
"source": "https://io.net/docs/guides/payment/io-intelligence-payments"
},
"io_intelligence/Qwen3-235B-A22B-Thinking": {
"max_tokens": 131072,
"max_input_tokens": 131072,
"max_output_tokens": 131072,
"input_cost_per_token": 6.3e-07,
"output_cost_per_token": 1.8e-06,
"litellm_provider": "io_intelligence",
"mode": "chat",
"supports_function_calling": true,
"supports_tool_choice": true,
"supports_reasoning": true,
"source": "https://io.net/docs/guides/payment/io-intelligence-payments"
},
"io_intelligence/Qwen3-Coder-480B": {
"max_tokens": 131072,
"max_input_tokens": 131072,
"max_output_tokens": 131072,
"input_cost_per_token": 6.3e-07,
"output_cost_per_token": 1.8e-06,
"litellm_provider": "io_intelligence",
"mode": "chat",
"supports_function_calling": true,
"supports_tool_choice": true,
"source": "https://io.net/docs/guides/payment/io-intelligence-payments"
},
"io_intelligence/Llama-4-Maverick-17B": {
"max_tokens": 131072,
"max_input_tokens": 131072,
"max_output_tokens": 131072,
"input_cost_per_token": 6.3e-07,
"output_cost_per_token": 1.8e-06,
"litellm_provider": "io_intelligence",
"mode": "chat",
"supports_function_calling": true,
"supports_tool_choice": true,
"source": "https://io.net/docs/guides/payment/io-intelligence-payments"
},
"io_intelligence/K2-Think": {
"max_tokens": 131072,
"max_input_tokens": 131072,
"max_output_tokens": 131072,
"input_cost_per_token": 6.3e-07,
"output_cost_per_token": 1.8e-06,
"litellm_provider": "io_intelligence",
"mode": "chat",
"supports_reasoning": true,
"source": "https://io.net/docs/guides/payment/io-intelligence-payments"
},
"io_intelligence/Apertus-70B": {
"max_tokens": 131072,
"max_input_tokens": 131072,
"max_output_tokens": 131072,
"input_cost_per_token": 6.3e-07,
"output_cost_per_token": 1.8e-06,
"litellm_provider": "io_intelligence",
"mode": "chat",
"supports_function_calling": true,
"supports_tool_choice": true,
"source": "https://io.net/docs/guides/payment/io-intelligence-payments"
},
"io_intelligence/Mistral-Nemo": {
"max_tokens": 131072,
"max_input_tokens": 131072,
"max_output_tokens": 131072,
"input_cost_per_token": 6.3e-07,
"output_cost_per_token": 1.8e-06,
"litellm_provider": "io_intelligence",
"mode": "chat",
"supports_function_calling": true,
"supports_tool_choice": true,
"source": "https://io.net/docs/guides/payment/io-intelligence-payments"
},
"io_intelligence/GPT-OSS-20B": {
"max_tokens": 131072,
"max_input_tokens": 131072,
"max_output_tokens": 131072,
"input_cost_per_token": 6.3e-07,
"output_cost_per_token": 1.8e-06,
"litellm_provider": "io_intelligence",
"mode": "chat",
"supports_function_calling": true,
"supports_tool_choice": true,
"source": "https://io.net/docs/guides/payment/io-intelligence-payments"
},
"io_intelligence/Devstral-Small": {
"max_tokens": 131072,
"max_input_tokens": 131072,
"max_output_tokens": 131072,
"input_cost_per_token": 6.3e-07,
"output_cost_per_token": 1.8e-06,
"litellm_provider": "io_intelligence",
"mode": "chat",
"supports_function_calling": true,
"supports_tool_choice": true,
"source": "https://io.net/docs/guides/payment/io-intelligence-payments"
},
"io_intelligence/Magistral-Small": {
"max_tokens": 131072,
"max_input_tokens": 131072,
"max_output_tokens": 131072,
"input_cost_per_token": 6.3e-07,
"output_cost_per_token": 1.8e-06,
"litellm_provider": "io_intelligence",
"mode": "chat",
"supports_function_calling": true,
"supports_tool_choice": true,
"source": "https://io.net/docs/guides/payment/io-intelligence-payments"
},
"io_intelligence/BAAI/bge-multilingual-gemma2": {
"max_tokens": 8192,
"max_input_tokens": 8192,
"input_cost_per_token": 1e-07,
"output_cost_per_token": 0.0,
"litellm_provider": "io_intelligence",
"mode": "embedding",
"source": "https://io.net/docs/guides/payment/io-intelligence-payments"
},
"snowflake/claude-3-5-sonnet": {
"litellm_provider": "snowflake",
"max_input_tokens": 18000,

0
tests/test_litellm/llms/iointelligence/__init__.py Normal file
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223
tests/test_litellm/llms/iointelligence/test_iointelligence.py Normal file
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@ -0,0 +1,223 @@
import json
import os
from unittest.mock import patch
import pytest
from litellm import completion, embedding
from litellm.llms.custom_httpx.http_handler import HTTPHandler
@patch.dict(os.environ, {}, clear=True)
def test_completion_io_intelligence():
"""Ensure that the completion function works with IO Intelligence API."""
messages = [{"role": "user", "content": "What's the weather like in San Francisco?"}]
try:
client = HTTPHandler()
with patch.object(client, "post") as mock_post:
completion(
model="io_intelligence/meta-llama/Llama-3.3-70B-Instruct",
messages=messages,
client=client,
max_tokens=5,
api_key="fake-api-key",
)
mock_post.assert_called_once()
mock_kwargs = mock_post.call_args.kwargs
assert "api.intelligence.io.solutions/api/v1" in mock_kwargs["url"]
assert mock_kwargs["headers"]["Authorization"] == "Bearer fake-api-key"
json_data = json.loads(mock_kwargs["data"])
assert json_data["max_tokens"] == 5
assert json_data["model"] == "meta-llama/Llama-3.3-70B-Instruct"
except Exception as e:
pytest.fail(f"Error occurred: {e}")
@patch.dict(os.environ, {}, clear=True)
def test_completion_io_intelligence_with_custom_api_base():
"""Ensure custom api_base is respected."""
messages = [{"role": "user", "content": "Hello"}]
try:
client = HTTPHandler()
with patch.object(client, "post") as mock_post:
completion(
model="io_intelligence/deepseek-ai/DeepSeek-R1-0528",
messages=messages,
client=client,
max_tokens=10,
api_key="fake-api-key",
api_base="https://custom.api.base/v1",
)
mock_post.assert_called_once()
mock_kwargs = mock_post.call_args.kwargs
assert "custom.api.base" in mock_kwargs["url"]
except Exception as e:
pytest.fail(f"Error occurred: {e}")
@patch.dict(os.environ, {}, clear=True)
def test_completion_io_intelligence_streaming():
"""Ensure streaming parameter is passed correctly."""
messages = [{"role": "user", "content": "Hello"}]
try:
client = HTTPHandler()
with patch.object(client, "post") as mock_post:
completion(
model="io_intelligence/meta-llama/Llama-3.3-70B-Instruct",
messages=messages,
client=client,
max_tokens=5,
api_key="fake-api-key",
stream=True,
)
mock_post.assert_called_once()
mock_kwargs = mock_post.call_args.kwargs
json_data = json.loads(mock_kwargs["data"])
assert json_data["stream"] is True
except Exception as e:
pytest.fail(f"Error occurred: {e}")
@patch.dict(
os.environ,
{"IO_INTELLIGENCE_API_KEY": "test-env-key"},
clear=True,
)
def test_completion_io_intelligence_env_key():
"""Ensure API key is picked up from environment variable."""
messages = [{"role": "user", "content": "Hello"}]
try:
client = HTTPHandler()
with patch.object(client, "post") as mock_post:
completion(
model="io_intelligence/meta-llama/Llama-3.3-70B-Instruct",
messages=messages,
client=client,
max_tokens=5,
)
mock_post.assert_called_once()
mock_kwargs = mock_post.call_args.kwargs
assert mock_kwargs["headers"]["Authorization"] == "Bearer test-env-key"
except Exception as e:
pytest.fail(f"Error occurred: {e}")
def test_completion_io_intelligence_with_tools():
"""Ensure function calling params are passed correctly."""
messages = [{"role": "user", "content": "What's the weather?"}]
tools = [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get weather info",
"parameters": {
"type": "object",
"properties": {
"location": {"type": "string"}
},
"required": ["location"],
},
},
}
]
try:
client = HTTPHandler()
with patch.object(client, "post") as mock_post:
completion(
model="io_intelligence/meta-llama/Llama-3.3-70B-Instruct",
messages=messages,
client=client,
max_tokens=5,
api_key="fake-api-key",
tools=tools,
tool_choice="auto",
)
mock_post.assert_called_once()
mock_kwargs = mock_post.call_args.kwargs
json_data = json.loads(mock_kwargs["data"])
assert "tools" in json_data
assert json_data["tool_choice"] == "auto"
except Exception as e:
pytest.fail(f"Error occurred: {e}")
def test_io_intelligence_provider_in_model_prices():
"""Ensure IO Intelligence models are registered in model prices."""
from litellm import model_cost
io_models = [k for k in model_cost if k.startswith("io_intelligence/")]
assert len(io_models) > 0, "No IO Intelligence models found in model_cost"
# Check a specific model
assert "io_intelligence/meta-llama/Llama-3.3-70B-Instruct" in model_cost
model_info = model_cost["io_intelligence/meta-llama/Llama-3.3-70B-Instruct"]
assert model_info["litellm_provider"] == "io_intelligence"
assert model_info["mode"] == "chat"
assert model_info["input_cost_per_token"] > 0
def test_io_intelligence_embedding_model_in_prices():
"""Ensure IO Intelligence embedding model is registered."""
from litellm import model_cost
assert "io_intelligence/BAAI/bge-multilingual-gemma2" in model_cost
model_info = model_cost["io_intelligence/BAAI/bge-multilingual-gemma2"]
assert model_info["litellm_provider"] == "io_intelligence"
assert model_info["mode"] == "embedding"
def test_io_intelligence_in_openai_compatible_providers():
"""Ensure io_intelligence is in the openai_compatible_providers list."""
from litellm.constants import openai_compatible_providers
assert "io_intelligence" in openai_compatible_providers
def test_io_intelligence_config_supported_params():
"""Test that IOIntelligenceConfig returns expected supported params."""
from litellm.llms.iointelligence.chat import IOIntelligenceConfig
config = IOIntelligenceConfig()
params = config.get_supported_openai_params(
model="meta-llama/Llama-3.3-70B-Instruct"
)
assert "max_tokens" in params
assert "temperature" in params
assert "tools" in params
assert "stream" in params
def test_io_intelligence_config_map_params():
"""Test that IOIntelligenceConfig maps max_completion_tokens to max_tokens."""
from litellm.llms.iointelligence.chat import IOIntelligenceConfig
config = IOIntelligenceConfig()
result = config.map_openai_params(
non_default_params={"max_completion_tokens": 100},
optional_params={},
model="meta-llama/Llama-3.3-70B-Instruct",
drop_params=False,
)
assert result["max_tokens"] == 100
def test_completion_io_intelligence_live():
"""Live integration test - only runs if API key is set."""
if os.environ.get("IO_INTELLIGENCE_API_KEY") is None:
pytest.skip("IO_INTELLIGENCE_API_KEY not set")
messages = [{"role": "user", "content": "Say hello in one word."}]
response = completion(
model="io_intelligence/meta-llama/Llama-3.3-70B-Instruct",
messages=messages,
max_tokens=10,
)
assert response["object"] == "chat.completion"
assert len(response["choices"]) == 1
assert len(response["choices"][0]["message"]["content"]) > 0

3
ui/litellm-dashboard/src/components/provider_info_helpers.tsx
View file

@ -85,6 +85,7 @@ export enum Providers {
RunwayML = "RunwayML",
SAGEMAKER_LEGACY = "Sagemaker",
Sambanova = "Sambanova",
IOIntelligence = "IO Intelligence",
SAP = "SAP Generative AI Hub",
Snowflake = "Snowflake",
TEXT_COMPLETION_CODESTRAL = "Text-Completion-Codestral",
@ -192,6 +193,7 @@ export const provider_map: Record<string, string> = {
SAGEMAKER_LEGACY: "sagemaker",
SageMaker: "sagemaker_chat",
Sambanova: "sambanova",
IOIntelligence: "io_intelligence",
SAP: "sap",
Snowflake: "snowflake",
TEXT_COMPLETION_CODESTRAL: "text-completion-codestral",
@ -282,6 +284,7 @@ export const providerLogoMap: Record<string, string> = {
[Providers.RunwayML]: `${asset_logos_folder}runwayml.png`,
[Providers.SAGEMAKER_LEGACY]: `${asset_logos_folder}bedrock.svg`,
[Providers.Sambanova]: `${asset_logos_folder}sambanova.svg`,
[Providers.IOIntelligence]: `${asset_logos_folder}io_intelligence.png`,
[Providers.SAP]: `${asset_logos_folder}sap.png`,
[Providers.Snowflake]: `${asset_logos_folder}snowflake.svg`,
[Providers.TEXT_COMPLETION_CODESTRAL]: `${asset_logos_folder}mistral.svg`,