mirror of
https://github.com/tiennm99/litellm.git
synced 2026-08-16 08:23:06 +00:00
Merge branch 'main' into litellm_refactor_01
This commit is contained in:
Alexsander Hamir
@@ -475,6 +475,8 @@ router_settings:
|
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| DEFAULT_ALLOWED_FAILS | Maximum failures allowed before cooling down a model. Default is 3
|
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| DEFAULT_ANTHROPIC_CHAT_MAX_TOKENS | Default maximum tokens for Anthropic chat completions. Default is 4096
|
||||
| DEFAULT_BATCH_SIZE | Default batch size for operations. Default is 512
|
||||
| DEFAULT_CHUNK_OVERLAP | Default chunk overlap for RAG text splitters. Default is 200
|
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| DEFAULT_CHUNK_SIZE | Default chunk size for RAG text splitters. Default is 1000
|
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| DEFAULT_CLIENT_DISCONNECT_CHECK_TIMEOUT_SECONDS | Timeout in seconds for checking client disconnection. Default is 1
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| DEFAULT_COOLDOWN_TIME_SECONDS | Duration in seconds to cooldown a model after failures. Default is 5
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| DEFAULT_CRON_JOB_LOCK_TTL_SECONDS | Time-to-live for cron job locks in seconds. Default is 60 (1 minute)
|
||||
|
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Generated
+2
-2
@@ -1,7 +1,7 @@
|
||||
# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
|
||||
# This file is automatically @generated by Poetry 2.2.0 and should not be changed by hand.
|
||||
package = []
|
||||
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
lock-version = "2.1"
|
||||
python-versions = ">=3.8.1,<4.0, !=3.9.7"
|
||||
content-hash = "2cf39473e67ff0615f0a61c9d2ac9f02b38cc08cbb1bdb893d89bee002646623"
|
||||
|
||||
@@ -1,6 +1,6 @@
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||||
[tool.poetry]
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||||
name = "litellm-proxy-extras"
|
||||
version = "0.4.6"
|
||||
version = "0.4.7"
|
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description = "Additional files for the LiteLLM Proxy. Reduces the size of the main litellm package."
|
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authors = ["BerriAI"]
|
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readme = "README.md"
|
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@@ -22,7 +22,7 @@ requires = ["poetry-core"]
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||||
build-backend = "poetry.core.masonry.api"
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||||
|
||||
[tool.commitizen]
|
||||
version = "0.4.6"
|
||||
version = "0.4.7"
|
||||
version_files = [
|
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"pyproject.toml:version",
|
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"../requirements.txt:litellm-proxy-extras==",
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|
||||
@@ -533,6 +533,7 @@ featherless_ai_models: Set = set()
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palm_models: Set = set()
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groq_models: Set = set()
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azure_models: Set = set()
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azure_anthropic_models: Set = set()
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azure_text_models: Set = set()
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anyscale_models: Set = set()
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cerebras_models: Set = set()
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@@ -737,6 +738,8 @@ def add_known_models():
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groq_models.add(key)
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elif value.get("litellm_provider") == "azure":
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azure_models.add(key)
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elif value.get("litellm_provider") == "azure_anthropic":
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azure_anthropic_models.add(key)
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elif value.get("litellm_provider") == "anyscale":
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anyscale_models.add(key)
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elif value.get("litellm_provider") == "cerebras":
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@@ -876,6 +879,7 @@ model_list = list(
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| palm_models
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| groq_models
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| azure_models
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| azure_anthropic_models
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| anyscale_models
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| cerebras_models
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| galadriel_models
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@@ -965,6 +969,7 @@ models_by_provider: dict = {
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"palm": palm_models,
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"groq": groq_models,
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"azure": azure_models | azure_text_models,
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"azure_anthropic": azure_anthropic_models,
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"azure_text": azure_text_models,
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"anyscale": anyscale_models,
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"cerebras": cerebras_models,
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||||
@@ -163,7 +163,7 @@ class VertexAIVideoConfig(BaseVideoConfig, VertexBase):
|
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headers: dict,
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model: str,
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||||
api_key: Optional[str] = None,
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||||
litellm_params: Optional[GenericLiteLLMParams] = None,
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||||
litellm_params: Optional[Union[GenericLiteLLMParams, dict]] = None,
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) -> dict:
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"""
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Validate environment and return headers for Vertex AI OCR.
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|
||||
@@ -10140,6 +10140,19 @@
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"supports_response_schema": true,
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||||
"supports_tool_choice": true
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},
|
||||
"fireworks_ai/accounts/fireworks/models/glm-4p6": {
|
||||
"input_cost_per_token": 0.55e-06,
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||||
"output_cost_per_token": 2.19e-06,
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"litellm_provider": "fireworks_ai",
|
||||
"max_input_tokens": 202800,
|
||||
"max_output_tokens": 202800,
|
||||
"max_tokens": 202800,
|
||||
"mode": "chat",
|
||||
"source": "https://fireworks.ai/pricing",
|
||||
"supports_function_calling": true,
|
||||
"supports_response_schema": true,
|
||||
"supports_tool_choice": true
|
||||
},
|
||||
"fireworks_ai/accounts/fireworks/models/gpt-oss-120b": {
|
||||
"input_cost_per_token": 1.5e-07,
|
||||
"litellm_provider": "fireworks_ai",
|
||||
|
||||
+90
-75
@@ -89,82 +89,12 @@ class VertexPassthroughLoggingHandler:
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}
|
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|
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elif "predict" in url_route:
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from litellm.llms.vertex_ai.image_generation.image_generation_handler import (
|
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VertexImageGeneration,
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return VertexPassthroughLoggingHandler._handle_predict_response(
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httpx_response=httpx_response,
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logging_obj=logging_obj,
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url_route=url_route,
|
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kwargs=kwargs,
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)
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from litellm.llms.vertex_ai.multimodal_embeddings.transformation import (
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VertexAIMultimodalEmbeddingConfig,
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)
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from litellm.types.utils import PassthroughCallTypes
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|
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vertex_image_generation_class = VertexImageGeneration()
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model = VertexPassthroughLoggingHandler.extract_model_from_url(url_route)
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_json_response = httpx_response.json()
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litellm_prediction_response: Union[
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ModelResponse, EmbeddingResponse, ImageResponse
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] = ModelResponse()
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if vertex_image_generation_class.is_image_generation_response(
|
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_json_response
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):
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litellm_prediction_response = (
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vertex_image_generation_class.process_image_generation_response(
|
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_json_response,
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model_response=litellm.ImageResponse(),
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model=model,
|
||||
)
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)
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|
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logging_obj.call_type = (
|
||||
PassthroughCallTypes.passthrough_image_generation.value
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||||
)
|
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elif VertexPassthroughLoggingHandler._is_multimodal_embedding_response(
|
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json_response=_json_response,
|
||||
):
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# Use multimodal embedding transformation
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vertex_multimodal_config = VertexAIMultimodalEmbeddingConfig()
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litellm_prediction_response = (
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vertex_multimodal_config.transform_embedding_response(
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model=model,
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raw_response=httpx_response,
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model_response=litellm.EmbeddingResponse(),
|
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logging_obj=logging_obj,
|
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api_key="",
|
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request_data={},
|
||||
optional_params={},
|
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litellm_params={},
|
||||
)
|
||||
)
|
||||
else:
|
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litellm_prediction_response = litellm.vertexAITextEmbeddingConfig.transform_vertex_response_to_openai(
|
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response=_json_response,
|
||||
model=model,
|
||||
model_response=litellm.EmbeddingResponse(),
|
||||
)
|
||||
if isinstance(litellm_prediction_response, litellm.EmbeddingResponse):
|
||||
litellm_prediction_response.model = model
|
||||
|
||||
logging_obj.model = model
|
||||
logging_obj.model_call_details["model"] = logging_obj.model
|
||||
logging_obj.model_call_details["custom_llm_provider"] = "vertex_ai"
|
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logging_obj.custom_llm_provider = "vertex_ai"
|
||||
response_cost = litellm.completion_cost(
|
||||
completion_response=litellm_prediction_response,
|
||||
model=model,
|
||||
custom_llm_provider="vertex_ai",
|
||||
)
|
||||
|
||||
kwargs["response_cost"] = response_cost
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||||
kwargs["model"] = model
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||||
kwargs["custom_llm_provider"] = "vertex_ai"
|
||||
logging_obj.model_call_details["response_cost"] = response_cost
|
||||
|
||||
return {
|
||||
"result": litellm_prediction_response,
|
||||
"kwargs": kwargs,
|
||||
}
|
||||
elif "rawPredict" in url_route or "streamRawPredict" in url_route:
|
||||
from litellm.llms.vertex_ai.vertex_ai_partner_models import (
|
||||
get_vertex_ai_partner_model_config,
|
||||
@@ -266,6 +196,91 @@ class VertexPassthroughLoggingHandler:
|
||||
"kwargs": kwargs,
|
||||
}
|
||||
|
||||
@staticmethod
|
||||
def _handle_predict_response(
|
||||
httpx_response: httpx.Response,
|
||||
logging_obj: LiteLLMLoggingObj,
|
||||
url_route: str,
|
||||
kwargs: dict,
|
||||
) -> PassThroughEndpointLoggingTypedDict:
|
||||
"""Handle predict endpoint responses (embeddings, image generation)."""
|
||||
from litellm.llms.vertex_ai.image_generation.image_generation_handler import (
|
||||
VertexImageGeneration,
|
||||
)
|
||||
from litellm.llms.vertex_ai.multimodal_embeddings.transformation import (
|
||||
VertexAIMultimodalEmbeddingConfig,
|
||||
)
|
||||
from litellm.types.utils import PassthroughCallTypes
|
||||
|
||||
vertex_image_generation_class = VertexImageGeneration()
|
||||
|
||||
model = VertexPassthroughLoggingHandler.extract_model_from_url(url_route)
|
||||
|
||||
_json_response = httpx_response.json()
|
||||
|
||||
litellm_prediction_response: Union[
|
||||
ModelResponse, EmbeddingResponse, ImageResponse
|
||||
] = ModelResponse()
|
||||
if vertex_image_generation_class.is_image_generation_response(
|
||||
_json_response
|
||||
):
|
||||
litellm_prediction_response = (
|
||||
vertex_image_generation_class.process_image_generation_response(
|
||||
_json_response,
|
||||
model_response=litellm.ImageResponse(),
|
||||
model=model,
|
||||
)
|
||||
)
|
||||
|
||||
logging_obj.call_type = (
|
||||
PassthroughCallTypes.passthrough_image_generation.value
|
||||
)
|
||||
elif VertexPassthroughLoggingHandler._is_multimodal_embedding_response(
|
||||
json_response=_json_response,
|
||||
):
|
||||
# Use multimodal embedding transformation
|
||||
vertex_multimodal_config = VertexAIMultimodalEmbeddingConfig()
|
||||
litellm_prediction_response = (
|
||||
vertex_multimodal_config.transform_embedding_response(
|
||||
model=model,
|
||||
raw_response=httpx_response,
|
||||
model_response=litellm.EmbeddingResponse(),
|
||||
logging_obj=logging_obj,
|
||||
api_key="",
|
||||
request_data={},
|
||||
optional_params={},
|
||||
litellm_params={},
|
||||
)
|
||||
)
|
||||
else:
|
||||
litellm_prediction_response = litellm.vertexAITextEmbeddingConfig.transform_vertex_response_to_openai(
|
||||
response=_json_response,
|
||||
model=model,
|
||||
model_response=litellm.EmbeddingResponse(),
|
||||
)
|
||||
if isinstance(litellm_prediction_response, litellm.EmbeddingResponse):
|
||||
litellm_prediction_response.model = model
|
||||
|
||||
logging_obj.model = model
|
||||
logging_obj.model_call_details["model"] = logging_obj.model
|
||||
logging_obj.model_call_details["custom_llm_provider"] = "vertex_ai"
|
||||
logging_obj.custom_llm_provider = "vertex_ai"
|
||||
response_cost = litellm.completion_cost(
|
||||
completion_response=litellm_prediction_response,
|
||||
model=model,
|
||||
custom_llm_provider="vertex_ai",
|
||||
)
|
||||
|
||||
kwargs["response_cost"] = response_cost
|
||||
kwargs["model"] = model
|
||||
kwargs["custom_llm_provider"] = "vertex_ai"
|
||||
logging_obj.model_call_details["response_cost"] = response_cost
|
||||
|
||||
return {
|
||||
"result": litellm_prediction_response,
|
||||
"kwargs": kwargs,
|
||||
}
|
||||
|
||||
@staticmethod
|
||||
def _handle_logging_vertex_collected_chunks(
|
||||
litellm_logging_obj: LiteLLMLoggingObj,
|
||||
|
||||
@@ -126,6 +126,16 @@
|
||||
"options": null,
|
||||
"default_value": null
|
||||
},
|
||||
{
|
||||
"key": "api_key",
|
||||
"label": "AWS Bedrock API Key",
|
||||
"placeholder": null,
|
||||
"tooltip": "You can provide the API Key for accessing the Bedrock API.",
|
||||
"required": false,
|
||||
"field_type": "password",
|
||||
"options": null,
|
||||
"default_value": null
|
||||
},
|
||||
{
|
||||
"key": "aws_session_token",
|
||||
"label": "AWS Session Token",
|
||||
|
||||
@@ -10140,6 +10140,19 @@
|
||||
"supports_response_schema": true,
|
||||
"supports_tool_choice": true
|
||||
},
|
||||
"fireworks_ai/accounts/fireworks/models/glm-4p6": {
|
||||
"input_cost_per_token": 0.55e-06,
|
||||
"output_cost_per_token": 2.19e-06,
|
||||
"litellm_provider": "fireworks_ai",
|
||||
"max_input_tokens": 202800,
|
||||
"max_output_tokens": 202800,
|
||||
"max_tokens": 202800,
|
||||
"mode": "chat",
|
||||
"source": "https://fireworks.ai/pricing",
|
||||
"supports_function_calling": true,
|
||||
"supports_response_schema": true,
|
||||
"supports_tool_choice": true
|
||||
},
|
||||
"fireworks_ai/accounts/fireworks/models/gpt-oss-120b": {
|
||||
"input_cost_per_token": 1.5e-07,
|
||||
"litellm_provider": "fireworks_ai",
|
||||
|
||||
Generated
+98
-99
@@ -1,4 +1,4 @@
|
||||
# This file is automatically @generated by Poetry 2.1.4 and should not be changed by hand.
|
||||
# This file is automatically @generated by Poetry 2.2.0 and should not be changed by hand.
|
||||
|
||||
[[package]]
|
||||
name = "aiofiles"
|
||||
@@ -2055,6 +2055,26 @@ requests = ["requests (>=2.20.0,<3.0.0)"]
|
||||
testing = ["aiohttp (<3.10.0)", "aiohttp (>=3.6.2,<4.0.0)", "aioresponses", "cryptography (<39.0.0) ; python_version < \"3.8\"", "cryptography (<39.0.0) ; python_version < \"3.8\"", "cryptography (>=38.0.3)", "cryptography (>=38.0.3)", "flask", "freezegun", "grpcio", "mock", "oauth2client", "packaging", "pyjwt (>=2.0)", "pyopenssl (<24.3.0)", "pyopenssl (>=20.0.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-localserver", "pyu2f (>=0.1.5)", "requests (>=2.20.0,<3.0.0)", "responses", "urllib3"]
|
||||
urllib3 = ["packaging", "urllib3"]
|
||||
|
||||
[[package]]
|
||||
name = "google-cloud-iam"
|
||||
version = "2.19.1"
|
||||
description = "Google Cloud Iam API client library"
|
||||
optional = true
|
||||
python-versions = ">=3.7"
|
||||
groups = ["main"]
|
||||
markers = "python_version >= \"3.14\" and extra == \"extra-proxy\""
|
||||
files = [
|
||||
{file = "google_cloud_iam-2.19.1-py3-none-any.whl", hash = "sha256:11b08b86d82510021f9dd9f0beb5a08219e070deab09e28d4c0ce49f8c70997d"},
|
||||
{file = "google_cloud_iam-2.19.1.tar.gz", hash = "sha256:f059c369ad98af6be3401f0f5d087775d775fb96833be1e9ab8048c422fb1bf4"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0", extras = ["grpc"]}
|
||||
google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0"
|
||||
grpc-google-iam-v1 = ">=0.12.4,<1.0.0"
|
||||
proto-plus = {version = ">=1.25.0,<2.0.0", markers = "python_version >= \"3.13\""}
|
||||
protobuf = ">=3.20.2,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<7.0.0"
|
||||
|
||||
[[package]]
|
||||
name = "google-cloud-iam"
|
||||
version = "2.20.0"
|
||||
@@ -2062,7 +2082,7 @@ description = "Google Cloud Iam API client library"
|
||||
optional = true
|
||||
python-versions = ">=3.7"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"extra-proxy\""
|
||||
markers = "extra == \"extra-proxy\" and python_version < \"3.14\""
|
||||
files = [
|
||||
{file = "google_cloud_iam-2.20.0-py3-none-any.whl", hash = "sha256:643fcf6db3100772f222c7173bc1af15541a05ec1c43785191e835146ed150b8"},
|
||||
{file = "google_cloud_iam-2.20.0.tar.gz", hash = "sha256:06568ed8313f59fac46d21a5aae4c54eb1dda9f6bcecf2736c58ab1065dc9173"},
|
||||
@@ -2072,10 +2092,7 @@ files = [
|
||||
google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0", extras = ["grpc"]}
|
||||
google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0"
|
||||
grpc-google-iam-v1 = ">=0.12.4,<1.0.0"
|
||||
grpcio = [
|
||||
{version = ">=1.33.2,<2.0.0"},
|
||||
{version = ">=1.75.1,<2.0.0", markers = "python_version >= \"3.14\""},
|
||||
]
|
||||
grpcio = ">=1.33.2,<2.0.0"
|
||||
proto-plus = [
|
||||
{version = ">=1.22.3,<2.0.0"},
|
||||
{version = ">=1.25.0,<2.0.0", markers = "python_version >= \"3.13\""},
|
||||
@@ -2191,6 +2208,8 @@ files = [
|
||||
{file = "greenlet-3.2.4-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c2ca18a03a8cfb5b25bc1cbe20f3d9a4c80d8c3b13ba3df49ac3961af0b1018d"},
|
||||
{file = "greenlet-3.2.4-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:9fe0a28a7b952a21e2c062cd5756d34354117796c6d9215a87f55e38d15402c5"},
|
||||
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@@ -2218,6 +2241,8 @@ files = [
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@@ -2234,6 +2261,8 @@ files = [
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||||
[[package]]
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||||
name = "grpcio"
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||||
version = "1.76.0"
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||||
version = "1.67.1"
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||||
description = "HTTP/2-based RPC framework"
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optional = false
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||||
python-versions = ">=3.9"
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||||
python-versions = ">=3.8"
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groups = ["main", "dev", "proxy-dev"]
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files = [
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{file = "grpcio-1.67.1-cp39-cp39-win_amd64.whl", hash = "sha256:5db70d32d6703b89912af16d6d45d78406374a8b8ef0d28140351dd0ec610e98"},
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{file = "grpcio-1.67.1.tar.gz", hash = "sha256:3dc2ed4cabea4dc14d5e708c2b426205956077cc5de419b4d4079315017e9732"},
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||||
]
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||||
markers = {main = "extra == \"extra-proxy\""}
|
||||
|
||||
[package.dependencies]
|
||||
typing-extensions = ">=4.12,<5.0"
|
||||
|
||||
[package.extras]
|
||||
protobuf = ["grpcio-tools (>=1.76.0)"]
|
||||
protobuf = ["grpcio-tools (>=1.67.1)"]
|
||||
|
||||
[[package]]
|
||||
name = "grpcio-status"
|
||||
@@ -3070,15 +3089,15 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "litellm-proxy-extras"
|
||||
version = "0.4.6"
|
||||
version = "0.4.7"
|
||||
description = "Additional files for the LiteLLM Proxy. Reduces the size of the main litellm package."
|
||||
optional = true
|
||||
python-versions = "!=2.7.*,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,!=3.7.*,>=3.8"
|
||||
groups = ["main"]
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||||
markers = "extra == \"proxy\""
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||||
files = [
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||||
{file = "litellm_proxy_extras-0.4.6-py3-none-any.whl", hash = "sha256:665ad022fdfd06f3fe97071e49f3b240554a931e57d27850fb815a19d3ea901d"},
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||||
]
|
||||
|
||||
[[package]]
|
||||
@@ -6949,26 +6968,6 @@ examples = ["aiosqlite (>=0.21.0)", "fastapi (>=0.115.12)", "sqlalchemy[asyncio]
|
||||
granian = ["granian (>=2.3.1)"]
|
||||
uvicorn = ["uvicorn (>=0.34.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "starlette"
|
||||
version = "0.49.3"
|
||||
description = "The little ASGI library that shines."
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
groups = ["main", "dev"]
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||||
files = [
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||||
{file = "starlette-0.49.3-py3-none-any.whl", hash = "sha256:b579b99715fdc2980cf88c8ec96d3bf1ce16f5a8051a7c2b84ef9b1cdecaea2f"},
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{file = "starlette-0.49.3.tar.gz", hash = "sha256:1c14546f299b5901a1ea0e34410575bc33bbd741377a10484a54445588d00284"},
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||||
]
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||||
markers = {main = "python_version == \"3.9\" and extra == \"proxy\"", dev = "python_version == \"3.9\""}
|
||||
|
||||
[package.dependencies]
|
||||
anyio = ">=3.6.2,<5"
|
||||
typing-extensions = {version = ">=4.10.0", markers = "python_version < \"3.13\""}
|
||||
|
||||
[package.extras]
|
||||
full = ["httpx (>=0.27.0,<0.29.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.18)", "pyyaml"]
|
||||
|
||||
[[package]]
|
||||
name = "starlette"
|
||||
version = "0.50.0"
|
||||
@@ -6980,7 +6979,7 @@ files = [
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||||
{file = "starlette-0.50.0-py3-none-any.whl", hash = "sha256:9e5391843ec9b6e472eed1365a78c8098cfceb7a74bfd4d6b1c0c0095efb3bca"},
|
||||
{file = "starlette-0.50.0.tar.gz", hash = "sha256:a2a17b22203254bcbc2e1f926d2d55f3f9497f769416b3190768befe598fa3ca"},
|
||||
]
|
||||
markers = {main = "python_version >= \"3.10\" and (extra == \"mlflow\" or extra == \"proxy\")", dev = "python_version >= \"3.10\""}
|
||||
markers = {main = "python_version >= \"3.10\" and (extra == \"mlflow\" or extra == \"proxy\") or extra == \"proxy\""}
|
||||
|
||||
[package.dependencies]
|
||||
anyio = ">=3.6.2,<5"
|
||||
@@ -8007,4 +8006,4 @@ utils = ["numpydoc"]
|
||||
[metadata]
|
||||
lock-version = "2.1"
|
||||
python-versions = ">=3.9,<4.0"
|
||||
content-hash = "1f482f89088b4a28edb7aa58459d17bfd7df36b30c0d53e88e4f7a9b4a65b369"
|
||||
content-hash = "3b6e1c0e9697842545d175e3c9ba5b562635d271fe636b95158de874a1bbb1d5"
|
||||
|
||||
+1
-1
@@ -59,7 +59,7 @@ websockets = {version = "^13.1.0", optional = true}
|
||||
boto3 = {version = "1.36.0", optional = true}
|
||||
redisvl = {version = "^0.4.1", optional = true, markers = "python_version >= '3.9' and python_version < '3.14'"}
|
||||
mcp = {version = "^1.21.2", optional = true, python = ">=3.10"}
|
||||
litellm-proxy-extras = {version = "0.4.6", optional = true}
|
||||
litellm-proxy-extras = {version = "0.4.7", optional = true}
|
||||
rich = {version = "13.7.1", optional = true}
|
||||
litellm-enterprise = {version = "0.1.22", optional = true}
|
||||
diskcache = {version = "^5.6.1", optional = true}
|
||||
|
||||
+1
-1
@@ -44,7 +44,7 @@ sentry_sdk==2.21.0 # for sentry error handling
|
||||
detect-secrets==1.5.0 # Enterprise - secret detection / masking in LLM requests
|
||||
cryptography==44.0.1
|
||||
tzdata==2025.1 # IANA time zone database
|
||||
litellm-proxy-extras==0.4.6 # for proxy extras - e.g. prisma migrations
|
||||
litellm-proxy-extras==0.4.7 # for proxy extras - e.g. prisma migrations
|
||||
### LITELLM PACKAGE DEPENDENCIES
|
||||
python-dotenv==1.0.1 # for env
|
||||
tiktoken==0.8.0 # for calculating usage
|
||||
|
||||
@@ -2,7 +2,6 @@ import os
|
||||
import sys
|
||||
import pytest
|
||||
import asyncio
|
||||
import json
|
||||
from typing import Optional
|
||||
from unittest.mock import AsyncMock, patch
|
||||
|
||||
@@ -106,7 +105,7 @@ async def test_mcp_cost_tracking():
|
||||
await asyncio.sleep(2)
|
||||
|
||||
logged_standard_logging_payload = test_logger.standard_logging_payload
|
||||
print("logged_standard_logging_payload", json.dumps(logged_standard_logging_payload, indent=4))
|
||||
print("logged_standard_logging_payload", logged_standard_logging_payload)
|
||||
|
||||
# Add assertions
|
||||
assert response is not None
|
||||
@@ -208,7 +207,7 @@ async def test_mcp_cost_tracking_per_tool():
|
||||
await asyncio.sleep(2)
|
||||
|
||||
logged_standard_logging_payload_1 = test_logger.standard_logging_payload
|
||||
print("logged_standard_logging_payload_1", json.dumps(logged_standard_logging_payload_1, indent=4))
|
||||
print("logged_standard_logging_payload_1", logged_standard_logging_payload_1)
|
||||
|
||||
# Verify expensive tool cost
|
||||
assert logged_standard_logging_payload_1 is not None, "Standard logging payload 1 should not be None"
|
||||
@@ -229,7 +228,7 @@ async def test_mcp_cost_tracking_per_tool():
|
||||
await asyncio.sleep(2)
|
||||
|
||||
logged_standard_logging_payload_2 = test_logger.standard_logging_payload
|
||||
print("logged_standard_logging_payload_2", json.dumps(logged_standard_logging_payload_2, indent=4))
|
||||
print("logged_standard_logging_payload_2", logged_standard_logging_payload_2)
|
||||
|
||||
# Verify cheap tool cost
|
||||
assert logged_standard_logging_payload_2 is not None, "Standard logging payload 2 should not be None"
|
||||
@@ -340,6 +339,6 @@ async def test_mcp_tool_call_hook():
|
||||
|
||||
# check logged standard logging payload
|
||||
logged_standard_logging_payload = test_logger.standard_logging_payload
|
||||
print("logged_standard_logging_payload", json.dumps(logged_standard_logging_payload, indent=4))
|
||||
print("logged_standard_logging_payload", logged_standard_logging_payload)
|
||||
assert logged_standard_logging_payload is not None, "Standard logging payload should not be None"
|
||||
assert logged_standard_logging_payload["response_cost"] == 1.42
|
||||
|
||||
@@ -821,18 +821,22 @@ async def test_vertex_ai_anthropic_token_counting():
|
||||
assert response.original_response["input_tokens"] == 15
|
||||
|
||||
@pytest.mark.parametrize("vertex_location", ["global", "us-central1"])
|
||||
def test_vertex_ai_gemini_token_counting_endpoint(vertex_location):
|
||||
def test_vertex_ai_partner_models_token_counting_endpoint(vertex_location):
|
||||
"""
|
||||
Test that the VertexAIPartnerModelsTokenCounter builds the correct endpoint URL
|
||||
for different vertex locations, including the special 'global' location.
|
||||
"""
|
||||
from litellm.llms.vertex_ai.vertex_ai_partner_models.count_tokens.handler import (
|
||||
VertexAIPartnerModelsTokenCounter,
|
||||
)
|
||||
|
||||
endpoint = VertexAIPartnerModelsTokenCounter()._build_count_tokens_endpoint(
|
||||
model="gemini-2.5-pro",
|
||||
model="claude-3-5-sonnet-20241022",
|
||||
project_id="test-project",
|
||||
vertex_location=vertex_location,
|
||||
api_base=None,
|
||||
)
|
||||
if vertex_location == "global":
|
||||
assert endpoint == "https://aiplatform.googleapis.com"
|
||||
assert endpoint.startswith("https://aiplatform.googleapis.com")
|
||||
else:
|
||||
assert endpoint == f"https://{vertex_location}-aiplatform.googleapis.com"
|
||||
assert endpoint.startswith(f"https://{vertex_location}-aiplatform.googleapis.com")
|
||||
@@ -139,7 +139,7 @@ async def test_create_mcp_server_direct():
|
||||
mock_get_prisma.return_value = mock_prisma
|
||||
|
||||
# Mock server manager
|
||||
mock_manager.add_update_server = mock.Mock()
|
||||
mock_manager.add_update_server = mock.AsyncMock()
|
||||
mock_manager.reload_servers_from_database = mock.AsyncMock()
|
||||
|
||||
# Set up test data
|
||||
|
||||
@@ -0,0 +1,35 @@
|
||||
// Query keys factory
|
||||
|
||||
type ListParams = {
|
||||
page?: number;
|
||||
limit?: number;
|
||||
filters?: Record<string, string | number>;
|
||||
};
|
||||
|
||||
/**
|
||||
* Generates a query keys factory for a given resource.
|
||||
*
|
||||
* @param resource - The name of the resource (e.g., "books", "users", "keys")
|
||||
* @returns An object with query key generators following the standard pattern
|
||||
*
|
||||
* @example
|
||||
* ```ts
|
||||
* const bookKeys = createQueryKeys("books");
|
||||
* // bookKeys.all -> ["books"]
|
||||
* // bookKeys.lists() -> ["books", "list"]
|
||||
* // bookKeys.list({ page: 1 }) -> ["books", "list", { params: { page: 1 } }]
|
||||
* // bookKeys.details() -> ["books", "detail"]
|
||||
* // bookKeys.detail("123") -> ["books", "detail", "123"]
|
||||
* ```
|
||||
*/
|
||||
export function createQueryKeys<T extends string>(resource: T) {
|
||||
const all = [resource] as const;
|
||||
|
||||
return {
|
||||
all,
|
||||
lists: () => [...all, "list"] as const,
|
||||
list: (params?: ListParams) => [...all, "list", { params }] as const,
|
||||
details: () => [...all, "detail"] as const,
|
||||
detail: (uid: string) => [...all, "detail", uid] as const,
|
||||
};
|
||||
}
|
||||
@@ -0,0 +1,27 @@
|
||||
import { useQuery } from "@tanstack/react-query";
|
||||
import { createQueryKeys } from "../common/queryKeysFactory";
|
||||
import { modelInfoCall, modelHubCall } from "@/components/networking";
|
||||
|
||||
const modelKeys = createQueryKeys("models");
|
||||
const modelHubKeys = createQueryKeys("modelHub");
|
||||
|
||||
export const useModelsInfo = (accessToken: string | null, userID: string | null, userRole: string | null) => {
|
||||
return useQuery({
|
||||
queryKey: modelKeys.list({
|
||||
filters: {
|
||||
...(userID && { userID }),
|
||||
...(userRole && { userRole }),
|
||||
},
|
||||
}),
|
||||
queryFn: async () => await modelInfoCall(accessToken!, userID!, userRole!),
|
||||
enabled: Boolean(accessToken && userID && userRole),
|
||||
});
|
||||
};
|
||||
|
||||
export const useModelHub = (accessToken: string | null) => {
|
||||
return useQuery({
|
||||
queryKey: modelHubKeys.list({}),
|
||||
queryFn: async () => await modelHubCall(accessToken!),
|
||||
enabled: Boolean(accessToken),
|
||||
});
|
||||
};
|
||||
@@ -67,7 +67,7 @@ const useAuthorized = () => {
|
||||
userRole: formatUserRole(decoded?.user_role ?? null),
|
||||
premiumUser: decoded?.premium_user ?? null,
|
||||
disabledPersonalKeyCreation: decoded?.disabled_non_admin_personal_key_creation ?? null,
|
||||
showSSOBanner: decoded?.login_method === "username_password" ?? false,
|
||||
showSSOBanner: decoded?.login_method === "username_password",
|
||||
};
|
||||
};
|
||||
|
||||
|
||||
+23
-18
@@ -1,6 +1,7 @@
|
||||
/* @vitest-environment jsdom */
|
||||
import { render } from "@testing-library/react";
|
||||
import { describe, it, expect, vi } from "vitest";
|
||||
import { QueryClient, QueryClientProvider } from "@tanstack/react-query";
|
||||
import ModelsAndEndpointsView from "./ModelsAndEndpointsView";
|
||||
|
||||
// Minimal stubs to avoid Next.js router and network usage during render
|
||||
@@ -56,19 +57,24 @@ vi.mock("@/app/(dashboard)/hooks/useTeams", () => ({
|
||||
}),
|
||||
}));
|
||||
|
||||
const createQueryClient = () =>
|
||||
new QueryClient({
|
||||
defaultOptions: { queries: { retry: false, gcTime: 0 } },
|
||||
});
|
||||
|
||||
describe("ModelsAndEndpointsView", () => {
|
||||
it(
|
||||
"should render the models and endpoints view",
|
||||
async () => {
|
||||
// JSDOM polyfill for libraries expecting ResizeObserver (e.g., recharts)
|
||||
// Note: ResizeObserver is now globally mocked in setupTests.ts, but keeping this for backwards compatibility
|
||||
// eslint-disable-next-line @typescript-eslint/no-explicit-any
|
||||
(global as any).ResizeObserver = class {
|
||||
observe() {}
|
||||
unobserve() {}
|
||||
disconnect() {}
|
||||
};
|
||||
const { findByText } = render(
|
||||
it("should render the models and endpoints view", async () => {
|
||||
// JSDOM polyfill for libraries expecting ResizeObserver (e.g., recharts)
|
||||
// Note: ResizeObserver is now globally mocked in setupTests.ts, but keeping this for backwards compatibility
|
||||
// eslint-disable-next-line @typescript-eslint/no-explicit-any
|
||||
(global as any).ResizeObserver = class {
|
||||
observe() {}
|
||||
unobserve() {}
|
||||
disconnect() {}
|
||||
};
|
||||
const queryClient = createQueryClient();
|
||||
const { findByText } = render(
|
||||
<QueryClientProvider client={queryClient}>
|
||||
<ModelsAndEndpointsView
|
||||
accessToken="123"
|
||||
token="123"
|
||||
@@ -79,10 +85,9 @@ describe("ModelsAndEndpointsView", () => {
|
||||
setModelData={() => {}}
|
||||
premiumUser={false}
|
||||
teams={[]}
|
||||
/>,
|
||||
);
|
||||
expect(await findByText("Model Management", {}, { timeout: 10000 })).toBeInTheDocument();
|
||||
},
|
||||
15000,
|
||||
);
|
||||
/>
|
||||
</QueryClientProvider>,
|
||||
);
|
||||
expect(await findByText("Model Management", {}, { timeout: 10000 })).toBeInTheDocument();
|
||||
}, 15000);
|
||||
});
|
||||
|
||||
+37
-18
@@ -1,5 +1,6 @@
|
||||
import React, { useState, useEffect, useRef } from "react";
|
||||
import { Text, Grid, Col } from "@tremor/react";
|
||||
import { useQueryClient } from "@tanstack/react-query";
|
||||
import { CredentialItem, credentialListCall, CredentialsResponse } from "@/components/networking";
|
||||
|
||||
import { handleAddModelSubmit } from "@/components/add_model/handle_add_model_submit";
|
||||
@@ -9,7 +10,6 @@ import { getDisplayModelName } from "@/components/view_model/model_name_display"
|
||||
import { TabPanel, TabPanels, TabGroup, TabList, Tab, Icon } from "@tremor/react";
|
||||
import { DateRangePickerValue } from "@tremor/react";
|
||||
import {
|
||||
modelInfoCall,
|
||||
modelCostMap,
|
||||
modelMetricsCall,
|
||||
streamingModelMetricsCall,
|
||||
@@ -22,6 +22,7 @@ import {
|
||||
adminGlobalActivityExceptionsPerDeployment,
|
||||
allEndUsersCall,
|
||||
} from "@/components/networking";
|
||||
import { useModelsInfo } from "@/app/(dashboard)/hooks/models/useModels";
|
||||
import { Form } from "antd";
|
||||
import { Typography } from "antd";
|
||||
import { RefreshIcon } from "@heroicons/react/outline";
|
||||
@@ -152,6 +153,14 @@ const ModelsAndEndpointsView: React.FC<ModelDashboardProps> = ({
|
||||
const dropdownRef = useRef<HTMLDivElement>(null);
|
||||
|
||||
const [selectedTabIndex, setSelectedTabIndex] = useState(0);
|
||||
|
||||
const queryClient = useQueryClient();
|
||||
const {
|
||||
data: modelDataResponse,
|
||||
isLoading: isLoadingModels,
|
||||
refetch: refetchModels,
|
||||
} = useModelsInfo(accessToken, userID, userRole);
|
||||
|
||||
const setProviderModelsFn = (provider: Providers) => {
|
||||
const _providerModels = getProviderModels(provider, modelMap);
|
||||
setProviderModels(_providerModels);
|
||||
@@ -180,6 +189,7 @@ const ModelsAndEndpointsView: React.FC<ModelDashboardProps> = ({
|
||||
const uploadProps: UploadProps = {
|
||||
name: "file",
|
||||
accept: ".json",
|
||||
pastable: false,
|
||||
beforeUpload: (file) => {
|
||||
if (file.type === "application/json") {
|
||||
const reader = new FileReader();
|
||||
@@ -207,6 +217,9 @@ const ModelsAndEndpointsView: React.FC<ModelDashboardProps> = ({
|
||||
// Update the 'lastRefreshed' state to the current date and time
|
||||
const currentDate = new Date();
|
||||
setLastRefreshed(currentDate.toLocaleString());
|
||||
// Invalidate and refetch models data using React Query
|
||||
queryClient.invalidateQueries({ queryKey: ["models", "list"] });
|
||||
refetchModels();
|
||||
};
|
||||
|
||||
const handleSaveRetrySettings = async () => {
|
||||
@@ -239,13 +252,11 @@ const ModelsAndEndpointsView: React.FC<ModelDashboardProps> = ({
|
||||
};
|
||||
|
||||
useEffect(() => {
|
||||
if (!accessToken || !token || !userRole || !userID) {
|
||||
if (!accessToken || !token || !userRole || !userID || !modelDataResponse) {
|
||||
return;
|
||||
}
|
||||
const fetchData = async () => {
|
||||
try {
|
||||
// Replace with your actual API call for model data
|
||||
const modelDataResponse = await modelInfoCall(accessToken, userID, userRole);
|
||||
setModelData(modelDataResponse);
|
||||
const _providerSettings = await modelSettingsCall(accessToken);
|
||||
if (_providerSettings) {
|
||||
@@ -372,7 +383,7 @@ const ModelsAndEndpointsView: React.FC<ModelDashboardProps> = ({
|
||||
}
|
||||
};
|
||||
|
||||
if (accessToken && token && userRole && userID) {
|
||||
if (accessToken && token && userRole && userID && modelDataResponse) {
|
||||
fetchData();
|
||||
}
|
||||
|
||||
@@ -383,11 +394,9 @@ const ModelsAndEndpointsView: React.FC<ModelDashboardProps> = ({
|
||||
if (modelMap == null) {
|
||||
fetchModelMap();
|
||||
}
|
||||
}, [accessToken, token, userRole, userID, modelDataResponse]);
|
||||
|
||||
handleRefreshClick();
|
||||
}, [accessToken, token, userRole, userID, modelMap, lastRefreshed, selectedTeam]);
|
||||
|
||||
if (!modelData) {
|
||||
if (!modelData || isLoadingModels) {
|
||||
return <div>Loading...</div>;
|
||||
}
|
||||
|
||||
@@ -597,15 +606,25 @@ const ModelsAndEndpointsView: React.FC<ModelDashboardProps> = ({
|
||||
setEditModalVisible={setEditModalVisible}
|
||||
setSelectedModel={setSelectedModel}
|
||||
onModelUpdate={(updatedModel) => {
|
||||
// Update the model in the modelData.data array
|
||||
const updatedModelData = {
|
||||
...modelData,
|
||||
data: modelData.data.map((model: any) =>
|
||||
model.model_info.id === updatedModel.model_info.id ? updatedModel : model,
|
||||
),
|
||||
};
|
||||
setModelData(updatedModelData);
|
||||
// Trigger a refresh to update UI
|
||||
// Handle model deletion
|
||||
if (updatedModel.deleted) {
|
||||
const updatedModelData = {
|
||||
...modelData,
|
||||
data: modelData.data.filter((model: any) => model.model_info.id !== updatedModel.model_info.id),
|
||||
};
|
||||
setModelData(updatedModelData);
|
||||
} else {
|
||||
// Update the model in the modelData.data array
|
||||
const updatedModelData = {
|
||||
...modelData,
|
||||
data: modelData.data.map((model: any) =>
|
||||
model.model_info.id === updatedModel.model_info.id ? updatedModel : model,
|
||||
),
|
||||
};
|
||||
setModelData(updatedModelData);
|
||||
}
|
||||
// Invalidate cache and trigger a refresh to update UI
|
||||
queryClient.invalidateQueries({ queryKey: ["models", "list"] });
|
||||
handleRefreshClick();
|
||||
}}
|
||||
modelAccessGroups={availableModelAccessGroups}
|
||||
|
||||
@@ -0,0 +1,39 @@
|
||||
import React from "react";
|
||||
import { describe, it, expect, vi, beforeEach } from "vitest";
|
||||
import { screen, render, fireEvent, act } from "@testing-library/react";
|
||||
import { ProviderLogo } from "./ProviderLogo";
|
||||
import * as providerInfoHelpers from "../../provider_info_helpers";
|
||||
|
||||
vi.mock("../../provider_info_helpers");
|
||||
|
||||
describe("ProviderLogo", () => {
|
||||
beforeEach(() => {
|
||||
vi.resetAllMocks();
|
||||
});
|
||||
|
||||
it("should render the component", () => {
|
||||
vi.mocked(providerInfoHelpers.getProviderLogoAndName).mockReturnValue({
|
||||
logo: "",
|
||||
displayName: "Test Provider",
|
||||
});
|
||||
render(<ProviderLogo provider="test" />);
|
||||
expect(screen.getByText("t")).toBeInTheDocument();
|
||||
});
|
||||
|
||||
it("should show fallback when image fails to load", () => {
|
||||
vi.mocked(providerInfoHelpers.getProviderLogoAndName).mockReturnValue({
|
||||
logo: "/path/to/logo.png",
|
||||
displayName: "Test Provider",
|
||||
});
|
||||
render(<ProviderLogo provider="test" />);
|
||||
const img = screen.getByRole("img", { name: "test logo" });
|
||||
expect(img).toBeInTheDocument();
|
||||
|
||||
act(() => {
|
||||
fireEvent.error(img);
|
||||
});
|
||||
|
||||
expect(screen.getByText("t")).toBeInTheDocument();
|
||||
expect(screen.queryByRole("img")).not.toBeInTheDocument();
|
||||
});
|
||||
});
|
||||
@@ -0,0 +1,24 @@
|
||||
import React, { useState } from "react";
|
||||
import { getProviderLogoAndName } from "../../provider_info_helpers";
|
||||
|
||||
interface ProviderLogoProps {
|
||||
provider: string;
|
||||
className?: string;
|
||||
}
|
||||
|
||||
export const ProviderLogo: React.FC<ProviderLogoProps> = ({ provider, className = "w-4 h-4" }) => {
|
||||
const [hasError, setHasError] = useState(false);
|
||||
const { logo } = getProviderLogoAndName(provider);
|
||||
|
||||
const showFallback = hasError || !logo;
|
||||
|
||||
if (showFallback) {
|
||||
return (
|
||||
<div className={`${className} rounded-full bg-gray-200 flex items-center justify-center text-xs`}>
|
||||
{provider?.charAt(0) || "-"}
|
||||
</div>
|
||||
);
|
||||
}
|
||||
|
||||
return <img src={logo} alt={`${provider} logo`} className={className} onError={() => setHasError(true)} />;
|
||||
};
|
||||
@@ -3,7 +3,7 @@ import { ColumnDef } from "@tanstack/react-table";
|
||||
import { Badge, Button, Icon } from "@tremor/react";
|
||||
import { Tooltip } from "antd";
|
||||
import { ModelData } from "../../model_dashboard/types";
|
||||
import { getProviderLogoAndName } from "../../provider_info_helpers";
|
||||
import { ProviderLogo } from "./ProviderLogo";
|
||||
|
||||
export const columns = (
|
||||
userRole: string,
|
||||
@@ -62,28 +62,7 @@ export const columns = (
|
||||
{/* Provider Icon */}
|
||||
<div className="flex-shrink-0 mt-0.5">
|
||||
{model.provider ? (
|
||||
<img
|
||||
src={getProviderLogoAndName(model.provider).logo}
|
||||
alt={`${model.provider} logo`}
|
||||
className="w-4 h-4"
|
||||
onError={(e) => {
|
||||
const target = e.currentTarget as HTMLImageElement;
|
||||
const parent = target.parentElement;
|
||||
if (!parent || !parent.contains(target)) {
|
||||
return;
|
||||
}
|
||||
|
||||
try {
|
||||
const fallbackDiv = document.createElement("div");
|
||||
fallbackDiv.className =
|
||||
"w-4 h-4 rounded-full bg-gray-200 flex items-center justify-center text-xs";
|
||||
fallbackDiv.textContent = model.provider?.charAt(0) || "-";
|
||||
parent.replaceChild(fallbackDiv, target);
|
||||
} catch (error) {
|
||||
console.error("Failed to replace provider logo fallback:", error);
|
||||
}
|
||||
}}
|
||||
/>
|
||||
<ProviderLogo provider={model.provider} />
|
||||
) : (
|
||||
<div className="w-4 h-4 rounded-full bg-gray-200 flex items-center justify-center text-xs">-</div>
|
||||
)}
|
||||
|
||||
Reference in New Issue
Block a user