mirror of
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Merge branch 'main' into peteski22/bug/get_optional_params
This commit is contained in:
Peter Wilson
commit
a2b836b6a4
127 changed files with 3718 additions and 542 deletions
|
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@ -20,9 +20,9 @@ print(f"response: {response}")
|
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|
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```yaml
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model_list:
|
||||
- model_name: dall-e-2 ### RECEIVED MODEL NAME ###
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- model_name: gpt-image-1 ### RECEIVED MODEL NAME ###
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litellm_params: # all params accepted by litellm.image_generation()
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model: azure/dall-e-2 ### MODEL NAME sent to `litellm.image_generation()` ###
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model: azure/gpt-image-1 ### MODEL NAME sent to `litellm.image_generation()` ###
|
||||
api_base: https://my-endpoint-europe-berri-992.openai.azure.com/
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api_key: "os.environ/AZURE_API_KEY_EU" # does os.getenv("AZURE_API_KEY_EU")
|
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rpm: 6 # [OPTIONAL] Rate limit for this deployment: in requests per minute (rpm)
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|
|
@ -47,7 +47,7 @@ curl -X POST 'http://0.0.0.0:4000/v1/images/generations' \
|
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-H 'Content-Type: application/json' \
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-H 'Authorization: Bearer sk-1234' \
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-D '{
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"model": "dall-e-2",
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"model": "gpt-image-1",
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"prompt": "A cute baby sea otter",
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"n": 1,
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"size": "1024x1024"
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|
|
@ -104,7 +104,7 @@ Any non-openai params, will be treated as provider-specific params, and sent in
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litellm_logging_obj=None,
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custom_llm_provider=None,
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- `model`: *string (optional)* The model to use for image generation. Defaults to openai/dall-e-2
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- `model`: *string (optional)* The model to use for image generation. Defaults to openai/gpt-image-1
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- `n`: *int (optional)* The number of images to generate. Must be between 1 and 10. For dall-e-3, only n=1 is supported.
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|
||||
|
|
@ -112,7 +112,7 @@ Any non-openai params, will be treated as provider-specific params, and sent in
|
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|
||||
- `response_format`: *string (optional)* The format in which the generated images are returned. Must be one of url or b64_json.
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- `size`: *string (optional)* The size of the generated images. Must be one of 256x256, 512x512, or 1024x1024 for dall-e-2. Must be one of 1024x1024, 1792x1024, or 1024x1792 for dall-e-3 models.
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- `size`: *string (optional)* The size of the generated images. Must be one of 256x256, 512x512, or 1024x1024 for gpt-image-1. Must be one of 1024x1024, 1792x1024, or 1024x1792 for dall-e-3 models.
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|
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- `timeout`: *integer* - The maximum time, in seconds, to wait for the API to respond. Defaults to 600 seconds (10 minutes).
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|
||||
|
|
@ -148,13 +148,14 @@ Any non-openai params, will be treated as provider-specific params, and sent in
|
|||
from litellm import image_generation
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import os
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os.environ['OPENAI_API_KEY'] = ""
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response = image_generation(model='dall-e-2', prompt="cute baby otter")
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response = image_generation(model='gpt-image-1', prompt="cute baby otter")
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```
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|
||||
| Model Name | Function Call | Required OS Variables |
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||||
|----------------------|---------------------------------------------|--------------------------------------|
|
||||
| dall-e-2 | `image_generation(model='dall-e-2', prompt="cute baby otter")` | `os.environ['OPENAI_API_KEY']` |
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| gpt-image-1 | `image_generation(model='gpt-image-1', prompt="cute baby otter")` | `os.environ['OPENAI_API_KEY']` |
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| dall-e-3 | `image_generation(model='dall-e-3', prompt="cute baby otter")` | `os.environ['OPENAI_API_KEY']` |
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| dall-e-2 | `image_generation(model='dall-e-2', prompt="cute baby otter")` | `os.environ['OPENAI_API_KEY']` |
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|
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## Azure OpenAI Image Generation Models
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@ -182,8 +183,9 @@ print(response)
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|
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| Model Name | Function Call |
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|----------------------|---------------------------------------------|
|
||||
| dall-e-2 | `image_generation(model="azure/<your deployment name>", prompt="cute baby otter")` |
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| gpt-image-1 | `image_generation(model="azure/<your deployment name>", prompt="cute baby otter")` |
|
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| dall-e-3 | `image_generation(model="azure/<your deployment name>", prompt="cute baby otter")` |
|
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| dall-e-2 | `image_generation(model="azure/<your deployment name>", prompt="cute baby otter")` |
|
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|
||||
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## OpenAI Compatible Image Generation Models
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|
|
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|||
83
docs/my-website/docs/observability/agentops_integration.md
Normal file
83
docs/my-website/docs/observability/agentops_integration.md
Normal file
|
|
@ -0,0 +1,83 @@
|
|||
# 🖇️ AgentOps - LLM Observability Platform
|
||||
|
||||
:::tip
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||||
|
||||
This is community maintained. Please make an issue if you run into a bug:
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||||
https://github.com/BerriAI/litellm
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||||
|
||||
:::
|
||||
|
||||
[AgentOps](https://docs.agentops.ai) is an observability platform that enables tracing and monitoring of LLM calls, providing detailed insights into your AI operations.
|
||||
|
||||
## Using AgentOps with LiteLLM
|
||||
|
||||
LiteLLM provides `success_callbacks` and `failure_callbacks`, allowing you to easily integrate AgentOps for comprehensive tracing and monitoring of your LLM operations.
|
||||
|
||||
### Integration
|
||||
|
||||
Use just a few lines of code to instantly trace your responses **across all providers** with AgentOps:
|
||||
Get your AgentOps API Keys from https://app.agentops.ai/
|
||||
```python
|
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import litellm
|
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|
||||
# Configure LiteLLM to use AgentOps
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litellm.success_callback = ["agentops"]
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|
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# Make your LLM calls as usual
|
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response = litellm.completion(
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model="gpt-3.5-turbo",
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messages=[{"role": "user", "content": "Hello, how are you?"}],
|
||||
)
|
||||
```
|
||||
|
||||
Complete Code:
|
||||
|
||||
```python
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||||
import os
|
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from litellm import completion
|
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|
||||
# Set env variables
|
||||
os.environ["OPENAI_API_KEY"] = "your-openai-key"
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os.environ["AGENTOPS_API_KEY"] = "your-agentops-api-key"
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|
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# Configure LiteLLM to use AgentOps
|
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litellm.success_callback = ["agentops"]
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|
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# OpenAI call
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response = completion(
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model="gpt-4",
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messages=[{"role": "user", "content": "Hi 👋 - I'm OpenAI"}],
|
||||
)
|
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|
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print(response)
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```
|
||||
|
||||
### Configuration Options
|
||||
|
||||
The AgentOps integration can be configured through environment variables:
|
||||
|
||||
- `AGENTOPS_API_KEY` (str, optional): Your AgentOps API key
|
||||
- `AGENTOPS_ENVIRONMENT` (str, optional): Deployment environment (defaults to "production")
|
||||
- `AGENTOPS_SERVICE_NAME` (str, optional): Service name for tracing (defaults to "agentops")
|
||||
|
||||
### Advanced Usage
|
||||
|
||||
You can configure additional settings through environment variables:
|
||||
|
||||
```python
|
||||
import os
|
||||
|
||||
# Configure AgentOps settings
|
||||
os.environ["AGENTOPS_API_KEY"] = "your-agentops-api-key"
|
||||
os.environ["AGENTOPS_ENVIRONMENT"] = "staging"
|
||||
os.environ["AGENTOPS_SERVICE_NAME"] = "my-service"
|
||||
|
||||
# Enable AgentOps tracing
|
||||
litellm.success_callback = ["agentops"]
|
||||
```
|
||||
|
||||
### Support
|
||||
|
||||
For issues or questions, please refer to:
|
||||
- [AgentOps Documentation](https://docs.agentops.ai)
|
||||
- [LiteLLM Documentation](https://docs.litellm.ai)
|
||||
|
|
@ -299,6 +299,9 @@ router_settings:
|
|||
|------|-------------|
|
||||
| ACTIONS_ID_TOKEN_REQUEST_TOKEN | Token for requesting ID in GitHub Actions
|
||||
| ACTIONS_ID_TOKEN_REQUEST_URL | URL for requesting ID token in GitHub Actions
|
||||
| AGENTOPS_ENVIRONMENT | Environment for AgentOps logging integration
|
||||
| AGENTOPS_API_KEY | API Key for AgentOps logging integration
|
||||
| AGENTOPS_SERVICE_NAME | Service Name for AgentOps logging integration
|
||||
| AISPEND_ACCOUNT_ID | Account ID for AI Spend
|
||||
| AISPEND_API_KEY | API Key for AI Spend
|
||||
| ALLOWED_EMAIL_DOMAINS | List of email domains allowed for access
|
||||
|
|
|
|||
|
|
@ -411,6 +411,7 @@ const sidebars = {
|
|||
type: "category",
|
||||
label: "Logging & Observability",
|
||||
items: [
|
||||
"observability/agentops_integration",
|
||||
"observability/langfuse_integration",
|
||||
"observability/lunary_integration",
|
||||
"observability/mlflow",
|
||||
|
|
|
|||
|
|
@ -113,6 +113,7 @@ _custom_logger_compatible_callbacks_literal = Literal[
|
|||
"pagerduty",
|
||||
"humanloop",
|
||||
"gcs_pubsub",
|
||||
"agentops",
|
||||
"anthropic_cache_control_hook",
|
||||
]
|
||||
logged_real_time_event_types: Optional[Union[List[str], Literal["*"]]] = None
|
||||
|
|
|
|||
|
|
@ -45,6 +45,14 @@ class SpanAttributes:
|
|||
"""
|
||||
The name of the model being used.
|
||||
"""
|
||||
LLM_PROVIDER = "llm.provider"
|
||||
"""
|
||||
The provider of the model, such as OpenAI, Azure, Google, etc.
|
||||
"""
|
||||
LLM_SYSTEM = "llm.system"
|
||||
"""
|
||||
The AI product as identified by the client or server
|
||||
"""
|
||||
LLM_PROMPTS = "llm.prompts"
|
||||
"""
|
||||
Prompts provided to a completions API.
|
||||
|
|
@ -65,15 +73,40 @@ class SpanAttributes:
|
|||
"""
|
||||
Number of tokens in the prompt.
|
||||
"""
|
||||
LLM_TOKEN_COUNT_PROMPT_DETAILS_CACHE_WRITE = "llm.token_count.prompt_details.cache_write"
|
||||
"""
|
||||
Number of tokens in the prompt that were written to cache.
|
||||
"""
|
||||
LLM_TOKEN_COUNT_PROMPT_DETAILS_CACHE_READ = "llm.token_count.prompt_details.cache_read"
|
||||
"""
|
||||
Number of tokens in the prompt that were read from cache.
|
||||
"""
|
||||
LLM_TOKEN_COUNT_PROMPT_DETAILS_AUDIO = "llm.token_count.prompt_details.audio"
|
||||
"""
|
||||
The number of audio input tokens presented in the prompt
|
||||
"""
|
||||
LLM_TOKEN_COUNT_COMPLETION = "llm.token_count.completion"
|
||||
"""
|
||||
Number of tokens in the completion.
|
||||
"""
|
||||
LLM_TOKEN_COUNT_COMPLETION_DETAILS_REASONING = "llm.token_count.completion_details.reasoning"
|
||||
"""
|
||||
Number of tokens used for reasoning steps in the completion.
|
||||
"""
|
||||
LLM_TOKEN_COUNT_COMPLETION_DETAILS_AUDIO = "llm.token_count.completion_details.audio"
|
||||
"""
|
||||
The number of audio input tokens generated by the model
|
||||
"""
|
||||
LLM_TOKEN_COUNT_TOTAL = "llm.token_count.total"
|
||||
"""
|
||||
Total number of tokens, including both prompt and completion.
|
||||
"""
|
||||
|
||||
LLM_TOOLS = "llm.tools"
|
||||
"""
|
||||
List of tools that are advertised to the LLM to be able to call
|
||||
"""
|
||||
|
||||
TOOL_NAME = "tool.name"
|
||||
"""
|
||||
Name of the tool being used.
|
||||
|
|
@ -112,6 +145,19 @@ class SpanAttributes:
|
|||
The id of the user
|
||||
"""
|
||||
|
||||
PROMPT_VENDOR = "prompt.vendor"
|
||||
"""
|
||||
The vendor or origin of the prompt, e.g. a prompt library, a specialized service, etc.
|
||||
"""
|
||||
PROMPT_ID = "prompt.id"
|
||||
"""
|
||||
A vendor-specific id used to locate the prompt.
|
||||
"""
|
||||
PROMPT_URL = "prompt.url"
|
||||
"""
|
||||
A vendor-specific url used to locate the prompt.
|
||||
"""
|
||||
|
||||
|
||||
class MessageAttributes:
|
||||
"""
|
||||
|
|
@ -151,6 +197,10 @@ class MessageAttributes:
|
|||
The JSON string representing the arguments passed to the function
|
||||
during a function call.
|
||||
"""
|
||||
MESSAGE_TOOL_CALL_ID = "message.tool_call_id"
|
||||
"""
|
||||
The id of the tool call.
|
||||
"""
|
||||
|
||||
|
||||
class MessageContentAttributes:
|
||||
|
|
@ -186,6 +236,25 @@ class ImageAttributes:
|
|||
"""
|
||||
|
||||
|
||||
class AudioAttributes:
|
||||
"""
|
||||
Attributes for audio
|
||||
"""
|
||||
|
||||
AUDIO_URL = "audio.url"
|
||||
"""
|
||||
The url to an audio file
|
||||
"""
|
||||
AUDIO_MIME_TYPE = "audio.mime_type"
|
||||
"""
|
||||
The mime type of the audio file
|
||||
"""
|
||||
AUDIO_TRANSCRIPT = "audio.transcript"
|
||||
"""
|
||||
The transcript of the audio file
|
||||
"""
|
||||
|
||||
|
||||
class DocumentAttributes:
|
||||
"""
|
||||
Attributes for a document.
|
||||
|
|
@ -257,6 +326,10 @@ class ToolCallAttributes:
|
|||
Attributes for a tool call
|
||||
"""
|
||||
|
||||
TOOL_CALL_ID = "tool_call.id"
|
||||
"""
|
||||
The id of the tool call.
|
||||
"""
|
||||
TOOL_CALL_FUNCTION_NAME = "tool_call.function.name"
|
||||
"""
|
||||
The name of function that is being called during a tool call.
|
||||
|
|
@ -268,6 +341,18 @@ class ToolCallAttributes:
|
|||
"""
|
||||
|
||||
|
||||
class ToolAttributes:
|
||||
"""
|
||||
Attributes for a tools
|
||||
"""
|
||||
|
||||
TOOL_JSON_SCHEMA = "tool.json_schema"
|
||||
"""
|
||||
The json schema of a tool input, It is RECOMMENDED that this be in the
|
||||
OpenAI tool calling format: https://platform.openai.com/docs/assistants/tools
|
||||
"""
|
||||
|
||||
|
||||
class OpenInferenceSpanKindValues(Enum):
|
||||
TOOL = "TOOL"
|
||||
CHAIN = "CHAIN"
|
||||
|
|
@ -284,3 +369,21 @@ class OpenInferenceSpanKindValues(Enum):
|
|||
class OpenInferenceMimeTypeValues(Enum):
|
||||
TEXT = "text/plain"
|
||||
JSON = "application/json"
|
||||
|
||||
|
||||
class OpenInferenceLLMSystemValues(Enum):
|
||||
OPENAI = "openai"
|
||||
ANTHROPIC = "anthropic"
|
||||
COHERE = "cohere"
|
||||
MISTRALAI = "mistralai"
|
||||
VERTEXAI = "vertexai"
|
||||
|
||||
|
||||
class OpenInferenceLLMProviderValues(Enum):
|
||||
OPENAI = "openai"
|
||||
ANTHROPIC = "anthropic"
|
||||
COHERE = "cohere"
|
||||
MISTRALAI = "mistralai"
|
||||
GOOGLE = "google"
|
||||
AZURE = "azure"
|
||||
AWS = "aws"
|
||||
|
|
|
|||
3
litellm/integrations/agentops/__init__.py
Normal file
3
litellm/integrations/agentops/__init__.py
Normal file
|
|
@ -0,0 +1,3 @@
|
|||
from .agentops import AgentOps
|
||||
|
||||
__all__ = ["AgentOps"]
|
||||
118
litellm/integrations/agentops/agentops.py
Normal file
118
litellm/integrations/agentops/agentops.py
Normal file
|
|
@ -0,0 +1,118 @@
|
|||
"""
|
||||
AgentOps integration for LiteLLM - Provides OpenTelemetry tracing for LLM calls
|
||||
"""
|
||||
import os
|
||||
from dataclasses import dataclass
|
||||
from typing import Optional, Dict, Any
|
||||
from litellm.integrations.opentelemetry import OpenTelemetry, OpenTelemetryConfig
|
||||
from litellm.llms.custom_httpx.http_handler import _get_httpx_client
|
||||
|
||||
@dataclass
|
||||
class AgentOpsConfig:
|
||||
endpoint: str = "https://otlp.agentops.cloud/v1/traces"
|
||||
api_key: Optional[str] = None
|
||||
service_name: Optional[str] = None
|
||||
deployment_environment: Optional[str] = None
|
||||
auth_endpoint: str = "https://api.agentops.ai/v3/auth/token"
|
||||
|
||||
@classmethod
|
||||
def from_env(cls):
|
||||
return cls(
|
||||
endpoint="https://otlp.agentops.cloud/v1/traces",
|
||||
api_key=os.getenv("AGENTOPS_API_KEY"),
|
||||
service_name=os.getenv("AGENTOPS_SERVICE_NAME", "agentops"),
|
||||
deployment_environment=os.getenv("AGENTOPS_ENVIRONMENT", "production"),
|
||||
auth_endpoint="https://api.agentops.ai/v3/auth/token"
|
||||
)
|
||||
|
||||
class AgentOps(OpenTelemetry):
|
||||
"""
|
||||
AgentOps integration - built on top of OpenTelemetry
|
||||
|
||||
Example usage:
|
||||
```python
|
||||
import litellm
|
||||
|
||||
litellm.success_callback = ["agentops"]
|
||||
|
||||
response = litellm.completion(
|
||||
model="gpt-3.5-turbo",
|
||||
messages=[{"role": "user", "content": "Hello, how are you?"}],
|
||||
)
|
||||
```
|
||||
"""
|
||||
def __init__(
|
||||
self,
|
||||
config: Optional[AgentOpsConfig] = None,
|
||||
):
|
||||
if config is None:
|
||||
config = AgentOpsConfig.from_env()
|
||||
|
||||
# Prefetch JWT token for authentication
|
||||
jwt_token = None
|
||||
project_id = None
|
||||
if config.api_key:
|
||||
try:
|
||||
response = self._fetch_auth_token(config.api_key, config.auth_endpoint)
|
||||
jwt_token = response.get("token")
|
||||
project_id = response.get("project_id")
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
headers = f"Authorization=Bearer {jwt_token}" if jwt_token else None
|
||||
|
||||
otel_config = OpenTelemetryConfig(
|
||||
exporter="otlp_http",
|
||||
endpoint=config.endpoint,
|
||||
headers=headers
|
||||
)
|
||||
|
||||
# Initialize OpenTelemetry with our config
|
||||
super().__init__(
|
||||
config=otel_config,
|
||||
callback_name="agentops"
|
||||
)
|
||||
|
||||
# Set AgentOps-specific resource attributes
|
||||
resource_attrs = {
|
||||
"service.name": config.service_name or "litellm",
|
||||
"deployment.environment": config.deployment_environment or "production",
|
||||
"telemetry.sdk.name": "agentops",
|
||||
}
|
||||
|
||||
if project_id:
|
||||
resource_attrs["project.id"] = project_id
|
||||
|
||||
self.resource_attributes = resource_attrs
|
||||
|
||||
def _fetch_auth_token(self, api_key: str, auth_endpoint: str) -> Dict[str, Any]:
|
||||
"""
|
||||
Fetch JWT authentication token from AgentOps API
|
||||
|
||||
Args:
|
||||
api_key: AgentOps API key
|
||||
auth_endpoint: Authentication endpoint
|
||||
|
||||
Returns:
|
||||
Dict containing JWT token and project ID
|
||||
"""
|
||||
headers = {
|
||||
"Content-Type": "application/json",
|
||||
"Connection": "keep-alive",
|
||||
}
|
||||
|
||||
client = _get_httpx_client()
|
||||
try:
|
||||
response = client.post(
|
||||
url=auth_endpoint,
|
||||
headers=headers,
|
||||
json={"api_key": api_key},
|
||||
timeout=10
|
||||
)
|
||||
|
||||
if response.status_code != 200:
|
||||
raise Exception(f"Failed to fetch auth token: {response.text}")
|
||||
|
||||
return response.json()
|
||||
finally:
|
||||
client.close()
|
||||
|
|
@ -1,3 +1,4 @@
|
|||
import json
|
||||
from typing import TYPE_CHECKING, Any, Optional, Union
|
||||
|
||||
from litellm._logging import verbose_logger
|
||||
|
|
@ -12,36 +13,141 @@ else:
|
|||
Span = Any
|
||||
|
||||
|
||||
def set_attributes(span: Span, kwargs, response_obj):
|
||||
def cast_as_primitive_value_type(value) -> Union[str, bool, int, float]:
|
||||
"""
|
||||
Converts a value to an OTEL-supported primitive for Arize/Phoenix observability.
|
||||
"""
|
||||
if value is None:
|
||||
return ""
|
||||
if isinstance(value, (str, bool, int, float)):
|
||||
return value
|
||||
try:
|
||||
return str(value)
|
||||
except Exception:
|
||||
return ""
|
||||
|
||||
|
||||
def safe_set_attribute(span: Span, key: str, value: Any):
|
||||
"""
|
||||
Sets a span attribute safely with OTEL-compliant primitive typing for Arize/Phoenix.
|
||||
"""
|
||||
primitive_value = cast_as_primitive_value_type(value)
|
||||
span.set_attribute(key, primitive_value)
|
||||
|
||||
|
||||
def set_attributes(span: Span, kwargs, response_obj): # noqa: PLR0915
|
||||
"""
|
||||
Populates span with OpenInference-compliant LLM attributes for Arize and Phoenix tracing.
|
||||
"""
|
||||
from litellm.integrations._types.open_inference import (
|
||||
MessageAttributes,
|
||||
OpenInferenceSpanKindValues,
|
||||
SpanAttributes,
|
||||
ToolCallAttributes,
|
||||
)
|
||||
|
||||
try:
|
||||
optional_params = kwargs.get("optional_params", {})
|
||||
litellm_params = kwargs.get("litellm_params", {})
|
||||
standard_logging_payload: Optional[StandardLoggingPayload] = kwargs.get(
|
||||
"standard_logging_object"
|
||||
)
|
||||
if standard_logging_payload is None:
|
||||
raise ValueError("standard_logging_object not found in kwargs")
|
||||
|
||||
#############################################
|
||||
############ LLM CALL METADATA ##############
|
||||
#############################################
|
||||
|
||||
if standard_logging_payload and (
|
||||
metadata := standard_logging_payload["metadata"]
|
||||
):
|
||||
span.set_attribute(SpanAttributes.METADATA, safe_dumps(metadata))
|
||||
# Set custom metadata for observability and trace enrichment.
|
||||
metadata = (
|
||||
standard_logging_payload.get("metadata")
|
||||
if standard_logging_payload
|
||||
else None
|
||||
)
|
||||
if metadata is not None:
|
||||
safe_set_attribute(span, SpanAttributes.METADATA, safe_dumps(metadata))
|
||||
|
||||
#############################################
|
||||
########## LLM Request Attributes ###########
|
||||
#############################################
|
||||
|
||||
# The name of the LLM a request is being made to
|
||||
# The name of the LLM a request is being made to.
|
||||
if kwargs.get("model"):
|
||||
span.set_attribute(SpanAttributes.LLM_MODEL_NAME, kwargs.get("model"))
|
||||
safe_set_attribute(
|
||||
span,
|
||||
SpanAttributes.LLM_MODEL_NAME,
|
||||
kwargs.get("model"),
|
||||
)
|
||||
|
||||
span.set_attribute(
|
||||
# The LLM request type.
|
||||
safe_set_attribute(
|
||||
span,
|
||||
"llm.request.type",
|
||||
standard_logging_payload["call_type"],
|
||||
)
|
||||
|
||||
# The Generative AI Provider: Azure, OpenAI, etc.
|
||||
safe_set_attribute(
|
||||
span,
|
||||
SpanAttributes.LLM_PROVIDER,
|
||||
litellm_params.get("custom_llm_provider", "Unknown"),
|
||||
)
|
||||
|
||||
# The maximum number of tokens the LLM generates for a request.
|
||||
if optional_params.get("max_tokens"):
|
||||
safe_set_attribute(
|
||||
span,
|
||||
"llm.request.max_tokens",
|
||||
optional_params.get("max_tokens"),
|
||||
)
|
||||
|
||||
# The temperature setting for the LLM request.
|
||||
if optional_params.get("temperature"):
|
||||
safe_set_attribute(
|
||||
span,
|
||||
"llm.request.temperature",
|
||||
optional_params.get("temperature"),
|
||||
)
|
||||
|
||||
# The top_p sampling setting for the LLM request.
|
||||
if optional_params.get("top_p"):
|
||||
safe_set_attribute(
|
||||
span,
|
||||
"llm.request.top_p",
|
||||
optional_params.get("top_p"),
|
||||
)
|
||||
|
||||
# Indicates whether response is streamed.
|
||||
safe_set_attribute(
|
||||
span,
|
||||
"llm.is_streaming",
|
||||
str(optional_params.get("stream", False)),
|
||||
)
|
||||
|
||||
# Logs the user ID if present.
|
||||
if optional_params.get("user"):
|
||||
safe_set_attribute(
|
||||
span,
|
||||
"llm.user",
|
||||
optional_params.get("user"),
|
||||
)
|
||||
|
||||
# The unique identifier for the completion.
|
||||
if response_obj and response_obj.get("id"):
|
||||
safe_set_attribute(span, "llm.response.id", response_obj.get("id"))
|
||||
|
||||
# The model used to generate the response.
|
||||
if response_obj and response_obj.get("model"):
|
||||
safe_set_attribute(
|
||||
span,
|
||||
"llm.response.model",
|
||||
response_obj.get("model"),
|
||||
)
|
||||
|
||||
# Required by OpenInference to mark span as LLM kind.
|
||||
safe_set_attribute(
|
||||
span,
|
||||
SpanAttributes.OPENINFERENCE_SPAN_KIND,
|
||||
OpenInferenceSpanKindValues.LLM.value,
|
||||
)
|
||||
|
|
@ -50,77 +156,132 @@ def set_attributes(span: Span, kwargs, response_obj):
|
|||
# for /chat/completions
|
||||
# https://docs.arize.com/arize/large-language-models/tracing/semantic-conventions
|
||||
if messages:
|
||||
span.set_attribute(
|
||||
last_message = messages[-1]
|
||||
safe_set_attribute(
|
||||
span,
|
||||
SpanAttributes.INPUT_VALUE,
|
||||
messages[-1].get("content", ""), # get the last message for input
|
||||
last_message.get("content", ""),
|
||||
)
|
||||
|
||||
# LLM_INPUT_MESSAGES shows up under `input_messages` tab on the span page
|
||||
# LLM_INPUT_MESSAGES shows up under `input_messages` tab on the span page.
|
||||
for idx, msg in enumerate(messages):
|
||||
# Set the role per message
|
||||
span.set_attribute(
|
||||
f"{SpanAttributes.LLM_INPUT_MESSAGES}.{idx}.{MessageAttributes.MESSAGE_ROLE}",
|
||||
msg["role"],
|
||||
prefix = f"{SpanAttributes.LLM_INPUT_MESSAGES}.{idx}"
|
||||
# Set the role per message.
|
||||
safe_set_attribute(
|
||||
span, f"{prefix}.{MessageAttributes.MESSAGE_ROLE}", msg.get("role")
|
||||
)
|
||||
# Set the content per message
|
||||
span.set_attribute(
|
||||
f"{SpanAttributes.LLM_INPUT_MESSAGES}.{idx}.{MessageAttributes.MESSAGE_CONTENT}",
|
||||
# Set the content per message.
|
||||
safe_set_attribute(
|
||||
span,
|
||||
f"{prefix}.{MessageAttributes.MESSAGE_CONTENT}",
|
||||
msg.get("content", ""),
|
||||
)
|
||||
|
||||
if standard_logging_payload and (
|
||||
model_params := standard_logging_payload["model_parameters"]
|
||||
):
|
||||
# Capture tools (function definitions) used in the LLM call.
|
||||
tools = optional_params.get("tools")
|
||||
if tools:
|
||||
for idx, tool in enumerate(tools):
|
||||
function = tool.get("function")
|
||||
if not function:
|
||||
continue
|
||||
prefix = f"{SpanAttributes.LLM_TOOLS}.{idx}"
|
||||
safe_set_attribute(
|
||||
span, f"{prefix}.{SpanAttributes.TOOL_NAME}", function.get("name")
|
||||
)
|
||||
safe_set_attribute(
|
||||
span,
|
||||
f"{prefix}.{SpanAttributes.TOOL_DESCRIPTION}",
|
||||
function.get("description"),
|
||||
)
|
||||
safe_set_attribute(
|
||||
span,
|
||||
f"{prefix}.{SpanAttributes.TOOL_PARAMETERS}",
|
||||
json.dumps(function.get("parameters")),
|
||||
)
|
||||
|
||||
# Capture tool calls made during function-calling LLM flows.
|
||||
functions = optional_params.get("functions")
|
||||
if functions:
|
||||
for idx, function in enumerate(functions):
|
||||
prefix = f"{MessageAttributes.MESSAGE_TOOL_CALLS}.{idx}"
|
||||
safe_set_attribute(
|
||||
span,
|
||||
f"{prefix}.{ToolCallAttributes.TOOL_CALL_FUNCTION_NAME}",
|
||||
function.get("name"),
|
||||
)
|
||||
|
||||
# Capture invocation parameters and user ID if available.
|
||||
model_params = (
|
||||
standard_logging_payload.get("model_parameters")
|
||||
if standard_logging_payload
|
||||
else None
|
||||
)
|
||||
if model_params:
|
||||
# The Generative AI Provider: Azure, OpenAI, etc.
|
||||
span.set_attribute(
|
||||
SpanAttributes.LLM_INVOCATION_PARAMETERS, safe_dumps(model_params)
|
||||
safe_set_attribute(
|
||||
span,
|
||||
SpanAttributes.LLM_INVOCATION_PARAMETERS,
|
||||
safe_dumps(model_params),
|
||||
)
|
||||
|
||||
if model_params.get("user"):
|
||||
user_id = model_params.get("user")
|
||||
if user_id is not None:
|
||||
span.set_attribute(SpanAttributes.USER_ID, user_id)
|
||||
safe_set_attribute(span, SpanAttributes.USER_ID, user_id)
|
||||
|
||||
#############################################
|
||||
########## LLM Response Attributes ##########
|
||||
# https://docs.arize.com/arize/large-language-models/tracing/semantic-conventions
|
||||
#############################################
|
||||
if hasattr(response_obj, "get"):
|
||||
for choice in response_obj.get("choices", []):
|
||||
response_message = choice.get("message", {})
|
||||
span.set_attribute(
|
||||
SpanAttributes.OUTPUT_VALUE, response_message.get("content", "")
|
||||
)
|
||||
|
||||
# This shows up under `output_messages` tab on the span page
|
||||
# This code assumes a single response
|
||||
span.set_attribute(
|
||||
f"{SpanAttributes.LLM_OUTPUT_MESSAGES}.0.{MessageAttributes.MESSAGE_ROLE}",
|
||||
response_message.get("role"),
|
||||
)
|
||||
span.set_attribute(
|
||||
f"{SpanAttributes.LLM_OUTPUT_MESSAGES}.0.{MessageAttributes.MESSAGE_CONTENT}",
|
||||
# Captures response tokens, message, and content.
|
||||
if hasattr(response_obj, "get"):
|
||||
for idx, choice in enumerate(response_obj.get("choices", [])):
|
||||
response_message = choice.get("message", {})
|
||||
safe_set_attribute(
|
||||
span,
|
||||
SpanAttributes.OUTPUT_VALUE,
|
||||
response_message.get("content", ""),
|
||||
)
|
||||
|
||||
usage = response_obj.get("usage")
|
||||
# This shows up under `output_messages` tab on the span page.
|
||||
prefix = f"{SpanAttributes.LLM_OUTPUT_MESSAGES}.{idx}"
|
||||
safe_set_attribute(
|
||||
span,
|
||||
f"{prefix}.{MessageAttributes.MESSAGE_ROLE}",
|
||||
response_message.get("role"),
|
||||
)
|
||||
safe_set_attribute(
|
||||
span,
|
||||
f"{prefix}.{MessageAttributes.MESSAGE_CONTENT}",
|
||||
response_message.get("content", ""),
|
||||
)
|
||||
|
||||
# Token usage info.
|
||||
usage = response_obj and response_obj.get("usage")
|
||||
if usage:
|
||||
span.set_attribute(
|
||||
safe_set_attribute(
|
||||
span,
|
||||
SpanAttributes.LLM_TOKEN_COUNT_TOTAL,
|
||||
usage.get("total_tokens"),
|
||||
)
|
||||
|
||||
# The number of tokens used in the LLM response (completion).
|
||||
span.set_attribute(
|
||||
safe_set_attribute(
|
||||
span,
|
||||
SpanAttributes.LLM_TOKEN_COUNT_COMPLETION,
|
||||
usage.get("completion_tokens"),
|
||||
)
|
||||
|
||||
# The number of tokens used in the LLM prompt.
|
||||
span.set_attribute(
|
||||
safe_set_attribute(
|
||||
span,
|
||||
SpanAttributes.LLM_TOKEN_COUNT_PROMPT,
|
||||
usage.get("prompt_tokens"),
|
||||
)
|
||||
pass
|
||||
|
||||
except Exception as e:
|
||||
verbose_logger.error(f"Error setting arize attributes: {e}")
|
||||
verbose_logger.error(
|
||||
f"[Arize/Phoenix] Failed to set OpenInference span attributes: {e}"
|
||||
)
|
||||
if hasattr(span, "record_exception"):
|
||||
span.record_exception(e)
|
||||
|
|
|
|||
|
|
@ -13,10 +13,15 @@ import uuid
|
|||
from datetime import datetime
|
||||
from typing import Any, Dict, List, Optional, Union
|
||||
|
||||
import httpx
|
||||
|
||||
import litellm
|
||||
from litellm._logging import verbose_logger
|
||||
from litellm.integrations.custom_batch_logger import CustomBatchLogger
|
||||
from litellm.integrations.datadog.datadog import DataDogLogger
|
||||
from litellm.litellm_core_utils.prompt_templates.common_utils import (
|
||||
handle_any_messages_to_chat_completion_str_messages_conversion,
|
||||
)
|
||||
from litellm.llms.custom_httpx.http_handler import (
|
||||
get_async_httpx_client,
|
||||
httpxSpecialProvider,
|
||||
|
|
@ -106,7 +111,6 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger):
|
|||
},
|
||||
)
|
||||
|
||||
response.raise_for_status()
|
||||
if response.status_code != 202:
|
||||
raise Exception(
|
||||
f"DataDogLLMObs: Unexpected response - status_code: {response.status_code}, text: {response.text}"
|
||||
|
|
@ -116,6 +120,10 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger):
|
|||
f"DataDogLLMObs: Successfully sent batch - status_code: {response.status_code}"
|
||||
)
|
||||
self.log_queue.clear()
|
||||
except httpx.HTTPStatusError as e:
|
||||
verbose_logger.exception(
|
||||
f"DataDogLLMObs: Error sending batch - {e.response.text}"
|
||||
)
|
||||
except Exception as e:
|
||||
verbose_logger.exception(f"DataDogLLMObs: Error sending batch - {str(e)}")
|
||||
|
||||
|
|
@ -133,7 +141,11 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger):
|
|||
|
||||
metadata = kwargs.get("litellm_params", {}).get("metadata", {})
|
||||
|
||||
input_meta = InputMeta(messages=messages) # type: ignore
|
||||
input_meta = InputMeta(
|
||||
messages=handle_any_messages_to_chat_completion_str_messages_conversion(
|
||||
messages
|
||||
)
|
||||
)
|
||||
output_meta = OutputMeta(messages=self._get_response_messages(response_obj))
|
||||
|
||||
meta = Meta(
|
||||
|
|
|
|||
|
|
@ -28,6 +28,7 @@ from litellm._logging import _is_debugging_on, verbose_logger
|
|||
from litellm.batches.batch_utils import _handle_completed_batch
|
||||
from litellm.caching.caching import DualCache, InMemoryCache
|
||||
from litellm.caching.caching_handler import LLMCachingHandler
|
||||
|
||||
from litellm.constants import (
|
||||
DEFAULT_MOCK_RESPONSE_COMPLETION_TOKEN_COUNT,
|
||||
DEFAULT_MOCK_RESPONSE_PROMPT_TOKEN_COUNT,
|
||||
|
|
@ -36,6 +37,7 @@ from litellm.cost_calculator import (
|
|||
RealtimeAPITokenUsageProcessor,
|
||||
_select_model_name_for_cost_calc,
|
||||
)
|
||||
from litellm.integrations.agentops import AgentOps
|
||||
from litellm.integrations.anthropic_cache_control_hook import AnthropicCacheControlHook
|
||||
from litellm.integrations.arize.arize import ArizeLogger
|
||||
from litellm.integrations.custom_guardrail import CustomGuardrail
|
||||
|
|
@ -2685,7 +2687,15 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915
|
|||
"""
|
||||
try:
|
||||
custom_logger_init_args = custom_logger_init_args or {}
|
||||
if logging_integration == "lago":
|
||||
if logging_integration == "agentops": # Add AgentOps initialization
|
||||
for callback in _in_memory_loggers:
|
||||
if isinstance(callback, AgentOps):
|
||||
return callback # type: ignore
|
||||
|
||||
agentops_logger = AgentOps()
|
||||
_in_memory_loggers.append(agentops_logger)
|
||||
return agentops_logger # type: ignore
|
||||
elif logging_integration == "lago":
|
||||
for callback in _in_memory_loggers:
|
||||
if isinstance(callback, LagoLogger):
|
||||
return callback # type: ignore
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@ import io
|
|||
import mimetypes
|
||||
import re
|
||||
from os import PathLike
|
||||
from typing import Dict, List, Literal, Mapping, Optional, Union, cast
|
||||
from typing import Any, Dict, List, Literal, Mapping, Optional, Union, cast
|
||||
|
||||
from litellm.types.llms.openai import (
|
||||
AllMessageValues,
|
||||
|
|
@ -32,6 +32,35 @@ DEFAULT_ASSISTANT_CONTINUE_MESSAGE = ChatCompletionAssistantMessage(
|
|||
)
|
||||
|
||||
|
||||
def handle_any_messages_to_chat_completion_str_messages_conversion(
|
||||
messages: Any,
|
||||
) -> List[Dict[str, str]]:
|
||||
"""
|
||||
Handles any messages to chat completion str messages conversion
|
||||
|
||||
Relevant Issue: https://github.com/BerriAI/litellm/issues/9494
|
||||
"""
|
||||
import json
|
||||
|
||||
if isinstance(messages, list):
|
||||
try:
|
||||
return cast(
|
||||
List[Dict[str, str]],
|
||||
handle_messages_with_content_list_to_str_conversion(messages),
|
||||
)
|
||||
except Exception:
|
||||
return [{"input": json.dumps(message, default=str)} for message in messages]
|
||||
elif isinstance(messages, dict):
|
||||
try:
|
||||
return [{"input": json.dumps(messages, default=str)}]
|
||||
except Exception:
|
||||
return [{"input": str(messages)}]
|
||||
elif isinstance(messages, str):
|
||||
return [{"input": messages}]
|
||||
else:
|
||||
return [{"input": str(messages)}]
|
||||
|
||||
|
||||
def handle_messages_with_content_list_to_str_conversion(
|
||||
messages: List[AllMessageValues],
|
||||
) -> List[AllMessageValues]:
|
||||
|
|
|
|||
|
|
@ -1,11 +1,14 @@
|
|||
from typing import TYPE_CHECKING, Any, Optional, cast
|
||||
from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, cast
|
||||
|
||||
import httpx
|
||||
|
||||
import litellm
|
||||
from litellm._logging import verbose_logger
|
||||
from litellm.llms.openai.responses.transformation import OpenAIResponsesAPIConfig
|
||||
from litellm.secret_managers.main import get_secret_str
|
||||
from litellm.types.llms.openai import *
|
||||
from litellm.types.responses.main import *
|
||||
from litellm.types.router import GenericLiteLLMParams
|
||||
from litellm.utils import _add_path_to_api_base
|
||||
|
||||
if TYPE_CHECKING:
|
||||
|
|
@ -41,11 +44,7 @@ class AzureOpenAIResponsesAPIConfig(OpenAIResponsesAPIConfig):
|
|||
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:
|
||||
"""
|
||||
Constructs a complete URL for the API request.
|
||||
|
|
@ -92,3 +91,48 @@ class AzureOpenAIResponsesAPIConfig(OpenAIResponsesAPIConfig):
|
|||
final_url = httpx.URL(new_url).copy_with(params=query_params)
|
||||
|
||||
return str(final_url)
|
||||
|
||||
#########################################################
|
||||
########## DELETE RESPONSE API TRANSFORMATION ##############
|
||||
#########################################################
|
||||
def transform_delete_response_api_request(
|
||||
self,
|
||||
response_id: str,
|
||||
api_base: str,
|
||||
litellm_params: GenericLiteLLMParams,
|
||||
headers: dict,
|
||||
) -> Tuple[str, Dict]:
|
||||
"""
|
||||
Transform the delete response API request into a URL and data
|
||||
|
||||
Azure OpenAI API expects the following request:
|
||||
- DELETE /openai/responses/{response_id}?api-version=xxx
|
||||
|
||||
This function handles URLs with query parameters by inserting the response_id
|
||||
at the correct location (before any query parameters).
|
||||
"""
|
||||
from urllib.parse import urlparse, urlunparse
|
||||
|
||||
# Parse the URL to separate its components
|
||||
parsed_url = urlparse(api_base)
|
||||
|
||||
# Insert the response_id at the end of the path component
|
||||
# Remove trailing slash if present to avoid double slashes
|
||||
path = parsed_url.path.rstrip("/")
|
||||
new_path = f"{path}/{response_id}"
|
||||
|
||||
# Reconstruct the URL with all original components but with the modified path
|
||||
delete_url = urlunparse(
|
||||
(
|
||||
parsed_url.scheme, # http, https
|
||||
parsed_url.netloc, # domain name, port
|
||||
new_path, # path with response_id added
|
||||
parsed_url.params, # parameters
|
||||
parsed_url.query, # query string
|
||||
parsed_url.fragment, # fragment
|
||||
)
|
||||
)
|
||||
|
||||
data: Dict = {}
|
||||
verbose_logger.debug(f"delete response url={delete_url}")
|
||||
return delete_url, data
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
import types
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import TYPE_CHECKING, Any, Dict, Optional, Union
|
||||
from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Union
|
||||
|
||||
import httpx
|
||||
|
||||
|
|
@ -10,6 +10,7 @@ from litellm.types.llms.openai import (
|
|||
ResponsesAPIResponse,
|
||||
ResponsesAPIStreamingResponse,
|
||||
)
|
||||
from litellm.types.responses.main import *
|
||||
from litellm.types.router import GenericLiteLLMParams
|
||||
|
||||
if TYPE_CHECKING:
|
||||
|
|
@ -73,11 +74,7 @@ class BaseResponsesAPIConfig(ABC):
|
|||
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:
|
||||
"""
|
||||
OPTIONAL
|
||||
|
|
@ -122,6 +119,31 @@ class BaseResponsesAPIConfig(ABC):
|
|||
"""
|
||||
pass
|
||||
|
||||
#########################################################
|
||||
########## DELETE RESPONSE API TRANSFORMATION ##############
|
||||
#########################################################
|
||||
@abstractmethod
|
||||
def transform_delete_response_api_request(
|
||||
self,
|
||||
response_id: str,
|
||||
api_base: str,
|
||||
litellm_params: GenericLiteLLMParams,
|
||||
headers: dict,
|
||||
) -> Tuple[str, Dict]:
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def transform_delete_response_api_response(
|
||||
self,
|
||||
raw_response: httpx.Response,
|
||||
logging_obj: LiteLLMLoggingObj,
|
||||
) -> DeleteResponseResult:
|
||||
pass
|
||||
|
||||
#########################################################
|
||||
########## END DELETE RESPONSE API TRANSFORMATION ##########
|
||||
#########################################################
|
||||
|
||||
def get_error_class(
|
||||
self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
|
||||
) -> BaseLLMException:
|
||||
|
|
|
|||
|
|
@ -650,6 +650,49 @@ class HTTPHandler:
|
|||
except Exception as e:
|
||||
raise e
|
||||
|
||||
def delete(
|
||||
self,
|
||||
url: str,
|
||||
data: Optional[Union[dict, str]] = None, # type: ignore
|
||||
json: Optional[dict] = None,
|
||||
params: Optional[dict] = None,
|
||||
headers: Optional[dict] = None,
|
||||
timeout: Optional[Union[float, httpx.Timeout]] = None,
|
||||
stream: bool = False,
|
||||
):
|
||||
try:
|
||||
if timeout is not None:
|
||||
req = self.client.build_request(
|
||||
"DELETE", url, data=data, json=json, params=params, headers=headers, timeout=timeout # type: ignore
|
||||
)
|
||||
else:
|
||||
req = self.client.build_request(
|
||||
"DELETE", url, data=data, json=json, params=params, headers=headers # type: ignore
|
||||
)
|
||||
response = self.client.send(req, stream=stream)
|
||||
response.raise_for_status()
|
||||
return response
|
||||
except httpx.TimeoutException:
|
||||
raise litellm.Timeout(
|
||||
message=f"Connection timed out after {timeout} seconds.",
|
||||
model="default-model-name",
|
||||
llm_provider="litellm-httpx-handler",
|
||||
)
|
||||
except httpx.HTTPStatusError as e:
|
||||
if stream is True:
|
||||
setattr(e, "message", mask_sensitive_info(e.response.read()))
|
||||
setattr(e, "text", mask_sensitive_info(e.response.read()))
|
||||
else:
|
||||
error_text = mask_sensitive_info(e.response.text)
|
||||
setattr(e, "message", error_text)
|
||||
setattr(e, "text", error_text)
|
||||
|
||||
setattr(e, "status_code", e.response.status_code)
|
||||
|
||||
raise e
|
||||
except Exception as e:
|
||||
raise e
|
||||
|
||||
def __del__(self) -> None:
|
||||
try:
|
||||
self.close()
|
||||
|
|
|
|||
|
|
@ -36,6 +36,7 @@ from litellm.types.llms.openai import (
|
|||
ResponsesAPIResponse,
|
||||
)
|
||||
from litellm.types.rerank import OptionalRerankParams, RerankResponse
|
||||
from litellm.types.responses.main import DeleteResponseResult
|
||||
from litellm.types.router import GenericLiteLLMParams
|
||||
from litellm.types.utils import EmbeddingResponse, FileTypes, TranscriptionResponse
|
||||
from litellm.utils import CustomStreamWrapper, ModelResponse, ProviderConfigManager
|
||||
|
|
@ -1015,6 +1016,7 @@ class BaseLLMHTTPHandler:
|
|||
client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
|
||||
_is_async: bool = False,
|
||||
fake_stream: bool = False,
|
||||
litellm_metadata: Optional[Dict[str, Any]] = None,
|
||||
) -> Union[
|
||||
ResponsesAPIResponse,
|
||||
BaseResponsesAPIStreamingIterator,
|
||||
|
|
@ -1041,6 +1043,7 @@ class BaseLLMHTTPHandler:
|
|||
timeout=timeout,
|
||||
client=client if isinstance(client, AsyncHTTPHandler) else None,
|
||||
fake_stream=fake_stream,
|
||||
litellm_metadata=litellm_metadata,
|
||||
)
|
||||
|
||||
if client is None or not isinstance(client, HTTPHandler):
|
||||
|
|
@ -1064,11 +1067,7 @@ class BaseLLMHTTPHandler:
|
|||
|
||||
api_base = responses_api_provider_config.get_complete_url(
|
||||
api_base=litellm_params.api_base,
|
||||
api_key=litellm_params.api_key,
|
||||
model=model,
|
||||
optional_params=response_api_optional_request_params,
|
||||
litellm_params=dict(litellm_params),
|
||||
stream=stream,
|
||||
)
|
||||
|
||||
data = responses_api_provider_config.transform_responses_api_request(
|
||||
|
|
@ -1113,6 +1112,8 @@ class BaseLLMHTTPHandler:
|
|||
model=model,
|
||||
logging_obj=logging_obj,
|
||||
responses_api_provider_config=responses_api_provider_config,
|
||||
litellm_metadata=litellm_metadata,
|
||||
custom_llm_provider=custom_llm_provider,
|
||||
)
|
||||
|
||||
return SyncResponsesAPIStreamingIterator(
|
||||
|
|
@ -1120,6 +1121,8 @@ class BaseLLMHTTPHandler:
|
|||
model=model,
|
||||
logging_obj=logging_obj,
|
||||
responses_api_provider_config=responses_api_provider_config,
|
||||
litellm_metadata=litellm_metadata,
|
||||
custom_llm_provider=custom_llm_provider,
|
||||
)
|
||||
else:
|
||||
# For non-streaming requests
|
||||
|
|
@ -1156,6 +1159,7 @@ class BaseLLMHTTPHandler:
|
|||
timeout: Optional[Union[float, httpx.Timeout]] = None,
|
||||
client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
|
||||
fake_stream: bool = False,
|
||||
litellm_metadata: Optional[Dict[str, Any]] = None,
|
||||
) -> Union[ResponsesAPIResponse, BaseResponsesAPIStreamingIterator]:
|
||||
"""
|
||||
Async version of the responses API handler.
|
||||
|
|
@ -1183,11 +1187,7 @@ class BaseLLMHTTPHandler:
|
|||
|
||||
api_base = responses_api_provider_config.get_complete_url(
|
||||
api_base=litellm_params.api_base,
|
||||
api_key=litellm_params.api_key,
|
||||
model=model,
|
||||
optional_params=response_api_optional_request_params,
|
||||
litellm_params=dict(litellm_params),
|
||||
stream=stream,
|
||||
)
|
||||
|
||||
data = responses_api_provider_config.transform_responses_api_request(
|
||||
|
|
@ -1234,6 +1234,8 @@ class BaseLLMHTTPHandler:
|
|||
model=model,
|
||||
logging_obj=logging_obj,
|
||||
responses_api_provider_config=responses_api_provider_config,
|
||||
litellm_metadata=litellm_metadata,
|
||||
custom_llm_provider=custom_llm_provider,
|
||||
)
|
||||
|
||||
# Return the streaming iterator
|
||||
|
|
@ -1242,6 +1244,8 @@ class BaseLLMHTTPHandler:
|
|||
model=model,
|
||||
logging_obj=logging_obj,
|
||||
responses_api_provider_config=responses_api_provider_config,
|
||||
litellm_metadata=litellm_metadata,
|
||||
custom_llm_provider=custom_llm_provider,
|
||||
)
|
||||
else:
|
||||
# For non-streaming, proceed as before
|
||||
|
|
@ -1265,6 +1269,163 @@ class BaseLLMHTTPHandler:
|
|||
logging_obj=logging_obj,
|
||||
)
|
||||
|
||||
async def async_delete_response_api_handler(
|
||||
self,
|
||||
response_id: str,
|
||||
responses_api_provider_config: BaseResponsesAPIConfig,
|
||||
litellm_params: GenericLiteLLMParams,
|
||||
logging_obj: LiteLLMLoggingObj,
|
||||
custom_llm_provider: Optional[str],
|
||||
extra_headers: Optional[Dict[str, Any]] = None,
|
||||
extra_body: Optional[Dict[str, Any]] = None,
|
||||
timeout: Optional[Union[float, httpx.Timeout]] = None,
|
||||
client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
|
||||
_is_async: bool = False,
|
||||
) -> DeleteResponseResult:
|
||||
"""
|
||||
Async version of the delete response API handler.
|
||||
Uses async HTTP client to make requests.
|
||||
"""
|
||||
if client is None or not isinstance(client, AsyncHTTPHandler):
|
||||
async_httpx_client = get_async_httpx_client(
|
||||
llm_provider=litellm.LlmProviders(custom_llm_provider),
|
||||
params={"ssl_verify": litellm_params.get("ssl_verify", None)},
|
||||
)
|
||||
else:
|
||||
async_httpx_client = client
|
||||
|
||||
headers = responses_api_provider_config.validate_environment(
|
||||
api_key=litellm_params.api_key,
|
||||
headers=extra_headers or {},
|
||||
model="None",
|
||||
)
|
||||
|
||||
if extra_headers:
|
||||
headers.update(extra_headers)
|
||||
|
||||
api_base = responses_api_provider_config.get_complete_url(
|
||||
api_base=litellm_params.api_base,
|
||||
litellm_params=dict(litellm_params),
|
||||
)
|
||||
|
||||
url, data = responses_api_provider_config.transform_delete_response_api_request(
|
||||
response_id=response_id,
|
||||
api_base=api_base,
|
||||
litellm_params=litellm_params,
|
||||
headers=headers,
|
||||
)
|
||||
|
||||
## LOGGING
|
||||
logging_obj.pre_call(
|
||||
input=input,
|
||||
api_key="",
|
||||
additional_args={
|
||||
"complete_input_dict": data,
|
||||
"api_base": api_base,
|
||||
"headers": headers,
|
||||
},
|
||||
)
|
||||
|
||||
try:
|
||||
response = await async_httpx_client.delete(
|
||||
url=url, headers=headers, data=json.dumps(data), timeout=timeout
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
raise self._handle_error(
|
||||
e=e,
|
||||
provider_config=responses_api_provider_config,
|
||||
)
|
||||
|
||||
return responses_api_provider_config.transform_delete_response_api_response(
|
||||
raw_response=response,
|
||||
logging_obj=logging_obj,
|
||||
)
|
||||
|
||||
def delete_response_api_handler(
|
||||
self,
|
||||
response_id: str,
|
||||
responses_api_provider_config: BaseResponsesAPIConfig,
|
||||
litellm_params: GenericLiteLLMParams,
|
||||
logging_obj: LiteLLMLoggingObj,
|
||||
custom_llm_provider: Optional[str],
|
||||
extra_headers: Optional[Dict[str, Any]] = None,
|
||||
extra_body: Optional[Dict[str, Any]] = None,
|
||||
timeout: Optional[Union[float, httpx.Timeout]] = None,
|
||||
client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
|
||||
_is_async: bool = False,
|
||||
) -> Union[DeleteResponseResult, Coroutine[Any, Any, DeleteResponseResult]]:
|
||||
"""
|
||||
Async version of the responses API handler.
|
||||
Uses async HTTP client to make requests.
|
||||
"""
|
||||
if _is_async:
|
||||
return self.async_delete_response_api_handler(
|
||||
response_id=response_id,
|
||||
responses_api_provider_config=responses_api_provider_config,
|
||||
litellm_params=litellm_params,
|
||||
logging_obj=logging_obj,
|
||||
custom_llm_provider=custom_llm_provider,
|
||||
extra_headers=extra_headers,
|
||||
extra_body=extra_body,
|
||||
timeout=timeout,
|
||||
client=client,
|
||||
)
|
||||
if client is None or not isinstance(client, HTTPHandler):
|
||||
sync_httpx_client = _get_httpx_client(
|
||||
params={"ssl_verify": litellm_params.get("ssl_verify", None)}
|
||||
)
|
||||
else:
|
||||
sync_httpx_client = client
|
||||
|
||||
headers = responses_api_provider_config.validate_environment(
|
||||
api_key=litellm_params.api_key,
|
||||
headers=extra_headers or {},
|
||||
model="None",
|
||||
)
|
||||
|
||||
if extra_headers:
|
||||
headers.update(extra_headers)
|
||||
|
||||
api_base = responses_api_provider_config.get_complete_url(
|
||||
api_base=litellm_params.api_base,
|
||||
litellm_params=dict(litellm_params),
|
||||
)
|
||||
|
||||
url, data = responses_api_provider_config.transform_delete_response_api_request(
|
||||
response_id=response_id,
|
||||
api_base=api_base,
|
||||
litellm_params=litellm_params,
|
||||
headers=headers,
|
||||
)
|
||||
|
||||
## LOGGING
|
||||
logging_obj.pre_call(
|
||||
input=input,
|
||||
api_key="",
|
||||
additional_args={
|
||||
"complete_input_dict": data,
|
||||
"api_base": api_base,
|
||||
"headers": headers,
|
||||
},
|
||||
)
|
||||
|
||||
try:
|
||||
response = sync_httpx_client.delete(
|
||||
url=url, headers=headers, data=json.dumps(data), timeout=timeout
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
raise self._handle_error(
|
||||
e=e,
|
||||
provider_config=responses_api_provider_config,
|
||||
)
|
||||
|
||||
return responses_api_provider_config.transform_delete_response_api_response(
|
||||
raw_response=response,
|
||||
logging_obj=logging_obj,
|
||||
)
|
||||
|
||||
def create_file(
|
||||
self,
|
||||
create_file_data: CreateFileRequest,
|
||||
|
|
|
|||
|
|
@ -7,6 +7,7 @@ from litellm._logging import verbose_logger
|
|||
from litellm.llms.base_llm.responses.transformation import BaseResponsesAPIConfig
|
||||
from litellm.secret_managers.main import get_secret_str
|
||||
from litellm.types.llms.openai import *
|
||||
from litellm.types.responses.main import *
|
||||
from litellm.types.router import GenericLiteLLMParams
|
||||
|
||||
from ..common_utils import OpenAIError
|
||||
|
|
@ -110,11 +111,7 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig):
|
|||
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:
|
||||
"""
|
||||
Get the endpoint for OpenAI responses API
|
||||
|
|
@ -190,7 +187,7 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig):
|
|||
|
||||
model_class = event_models.get(cast(ResponsesAPIStreamEvents, event_type))
|
||||
if not model_class:
|
||||
raise ValueError(f"Unknown event type: {event_type}")
|
||||
return GenericEvent
|
||||
|
||||
return model_class
|
||||
|
||||
|
|
@ -217,3 +214,39 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig):
|
|||
f"Error getting model info in OpenAIResponsesAPIConfig: {e}"
|
||||
)
|
||||
return False
|
||||
|
||||
#########################################################
|
||||
########## DELETE RESPONSE API TRANSFORMATION ##############
|
||||
#########################################################
|
||||
def transform_delete_response_api_request(
|
||||
self,
|
||||
response_id: str,
|
||||
api_base: str,
|
||||
litellm_params: GenericLiteLLMParams,
|
||||
headers: dict,
|
||||
) -> Tuple[str, Dict]:
|
||||
"""
|
||||
Transform the delete response API request into a URL and data
|
||||
|
||||
OpenAI API expects the following request
|
||||
- DELETE /v1/responses/{response_id}
|
||||
"""
|
||||
url = f"{api_base}/{response_id}"
|
||||
data: Dict = {}
|
||||
return url, data
|
||||
|
||||
def transform_delete_response_api_response(
|
||||
self,
|
||||
raw_response: httpx.Response,
|
||||
logging_obj: LiteLLMLoggingObj,
|
||||
) -> DeleteResponseResult:
|
||||
"""
|
||||
Transform the delete response API response into a DeleteResponseResult
|
||||
"""
|
||||
try:
|
||||
raw_response_json = raw_response.json()
|
||||
except Exception:
|
||||
raise OpenAIError(
|
||||
message=raw_response.text, status_code=raw_response.status_code
|
||||
)
|
||||
return DeleteResponseResult(**raw_response_json)
|
||||
|
|
|
|||
|
|
@ -1437,6 +1437,76 @@
|
|||
"output_cost_per_pixel": 0.0,
|
||||
"litellm_provider": "openai"
|
||||
},
|
||||
"gpt-image-1": {
|
||||
"mode": "image_generation",
|
||||
"input_cost_per_pixel": 4.0054321e-8,
|
||||
"output_cost_per_pixel": 0.0,
|
||||
"litellm_provider": "openai",
|
||||
"supported_endpoints": ["/v1/images/generations"]
|
||||
},
|
||||
"low/1024-x-1024/gpt-image-1": {
|
||||
"mode": "image_generation",
|
||||
"input_cost_per_pixel": 1.0490417e-8,
|
||||
"output_cost_per_pixel": 0.0,
|
||||
"litellm_provider": "openai",
|
||||
"supported_endpoints": ["/v1/images/generations"]
|
||||
},
|
||||
"medium/1024-x-1024/gpt-image-1": {
|
||||
"mode": "image_generation",
|
||||
"input_cost_per_pixel": 4.0054321e-8,
|
||||
"output_cost_per_pixel": 0.0,
|
||||
"litellm_provider": "openai",
|
||||
"supported_endpoints": ["/v1/images/generations"]
|
||||
},
|
||||
"high/1024-x-1024/gpt-image-1": {
|
||||
"mode": "image_generation",
|
||||
"input_cost_per_pixel": 1.59263611e-7,
|
||||
"output_cost_per_pixel": 0.0,
|
||||
"litellm_provider": "openai",
|
||||
"supported_endpoints": ["/v1/images/generations"]
|
||||
},
|
||||
"low/1024-x-1536/gpt-image-1": {
|
||||
"mode": "image_generation",
|
||||
"input_cost_per_pixel": 1.0172526e-8,
|
||||
"output_cost_per_pixel": 0.0,
|
||||
"litellm_provider": "openai",
|
||||
"supported_endpoints": ["/v1/images/generations"]
|
||||
},
|
||||
"medium/1024-x-1536/gpt-image-1": {
|
||||
"mode": "image_generation",
|
||||
"input_cost_per_pixel": 4.0054321e-8,
|
||||
"output_cost_per_pixel": 0.0,
|
||||
"litellm_provider": "openai",
|
||||
"supported_endpoints": ["/v1/images/generations"]
|
||||
},
|
||||
"high/1024-x-1536/gpt-image-1": {
|
||||
"mode": "image_generation",
|
||||
"input_cost_per_pixel": 1.58945719e-7,
|
||||
"output_cost_per_pixel": 0.0,
|
||||
"litellm_provider": "openai",
|
||||
"supported_endpoints": ["/v1/images/generations"]
|
||||
},
|
||||
"low/1536-x-1024/gpt-image-1": {
|
||||
"mode": "image_generation",
|
||||
"input_cost_per_pixel": 1.0172526e-8,
|
||||
"output_cost_per_pixel": 0.0,
|
||||
"litellm_provider": "openai",
|
||||
"supported_endpoints": ["/v1/images/generations"]
|
||||
},
|
||||
"medium/1536-x-1024/gpt-image-1": {
|
||||
"mode": "image_generation",
|
||||
"input_cost_per_pixel": 4.0054321e-8,
|
||||
"output_cost_per_pixel": 0.0,
|
||||
"litellm_provider": "openai",
|
||||
"supported_endpoints": ["/v1/images/generations"]
|
||||
},
|
||||
"high/1536-x-1024/gpt-image-1": {
|
||||
"mode": "image_generation",
|
||||
"input_cost_per_pixel": 1.58945719e-7,
|
||||
"output_cost_per_pixel": 0.0,
|
||||
"litellm_provider": "openai",
|
||||
"supported_endpoints": ["/v1/images/generations"]
|
||||
},
|
||||
"gpt-4o-transcribe": {
|
||||
"mode": "audio_transcription",
|
||||
"input_cost_per_token": 0.0000025,
|
||||
|
|
@ -1490,7 +1560,6 @@
|
|||
"supports_prompt_caching": false,
|
||||
"supports_system_messages": true,
|
||||
"supports_tool_choice": true,
|
||||
"supports_native_streaming": false,
|
||||
"supports_reasoning": true
|
||||
},
|
||||
"azure/gpt-4o-audio-preview-2024-12-17": {
|
||||
|
|
|
|||
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|
|
@ -1 +1 @@
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3:I[12011,["665","static/chunks/3014691f-0b72c78cfebbd712.js","42","static/chunks/42-59f99bfbf676f282.js","899","static/chunks/899-9af4feaf6f21839c.js","250","static/chunks/250-a927a558002d8fb9.js","461","static/chunks/app/onboarding/page-4f4c436bd23d48a0.js"],"default",1]
|
||||
3:I[12011,["665","static/chunks/3014691f-b7b79b78e27792f3.js","42","static/chunks/42-69f5b4e6a9942a9f.js","899","static/chunks/899-57685cedd1dcbc78.js","250","static/chunks/250-7d480872c0e251dc.js","461","static/chunks/app/onboarding/page-9598003bc1e91371.js"],"default",1]
|
||||
4:I[4707,[],""]
|
||||
5:I[36423,[],""]
|
||||
0:["FPIQgzUY81b7nl8zNun4_",[[["",{"children":["onboarding",{"children":["__PAGE__",{}]}]},"$undefined","$undefined",true],["",{"children":["onboarding",{"children":["__PAGE__",{},[["$L1",["$","$L2",null,{"props":{"params":{},"searchParams":{}},"Component":"$3"}],null],null],null]},[null,["$","$L4",null,{"parallelRouterKey":"children","segmentPath":["children","onboarding","children"],"error":"$undefined","errorStyles":"$undefined","errorScripts":"$undefined","template":["$","$L5",null,{}],"templateStyles":"$undefined","templateScripts":"$undefined","notFound":"$undefined","notFoundStyles":"$undefined"}]],null]},[[[["$","link","0",{"rel":"stylesheet","href":"/ui/_next/static/css/86f6cc749f6b8493.css","precedence":"next","crossOrigin":"$undefined"}],["$","link","1",{"rel":"stylesheet","href":"/ui/_next/static/css/3da1b0cfa7d4e161.css","precedence":"next","crossOrigin":"$undefined"}]],["$","html",null,{"lang":"en","children":["$","body",null,{"className":"__className_cf7686","children":["$","$L4",null,{"parallelRouterKey":"children","segmentPath":["children"],"error":"$undefined","errorStyles":"$undefined","errorScripts":"$undefined","template":["$","$L5",null,{}],"templateStyles":"$undefined","templateScripts":"$undefined","notFound":[["$","title",null,{"children":"404: This page could not be found."}],["$","div",null,{"style":{"fontFamily":"system-ui,\"Segoe UI\",Roboto,Helvetica,Arial,sans-serif,\"Apple Color Emoji\",\"Segoe UI Emoji\"","height":"100vh","textAlign":"center","display":"flex","flexDirection":"column","alignItems":"center","justifyContent":"center"},"children":["$","div",null,{"children":[["$","style",null,{"dangerouslySetInnerHTML":{"__html":"body{color:#000;background:#fff;margin:0}.next-error-h1{border-right:1px solid rgba(0,0,0,.3)}@media (prefers-color-scheme:dark){body{color:#fff;background:#000}.next-error-h1{border-right:1px solid rgba(255,255,255,.3)}}"}}],["$","h1",null,{"className":"next-error-h1","style":{"display":"inline-block","margin":"0 20px 0 0","padding":"0 23px 0 0","fontSize":24,"fontWeight":500,"verticalAlign":"top","lineHeight":"49px"},"children":"404"}],["$","div",null,{"style":{"display":"inline-block"},"children":["$","h2",null,{"style":{"fontSize":14,"fontWeight":400,"lineHeight":"49px","margin":0},"children":"This page could not be found."}]}]]}]}]],"notFoundStyles":[]}]}]}]],null],null],["$L6",null]]]]
|
||||
0:["fzhvjOFL6KeNsWYrLD4ya",[[["",{"children":["onboarding",{"children":["__PAGE__",{}]}]},"$undefined","$undefined",true],["",{"children":["onboarding",{"children":["__PAGE__",{},[["$L1",["$","$L2",null,{"props":{"params":{},"searchParams":{}},"Component":"$3"}],null],null],null]},[null,["$","$L4",null,{"parallelRouterKey":"children","segmentPath":["children","onboarding","children"],"error":"$undefined","errorStyles":"$undefined","errorScripts":"$undefined","template":["$","$L5",null,{}],"templateStyles":"$undefined","templateScripts":"$undefined","notFound":"$undefined","notFoundStyles":"$undefined"}]],null]},[[[["$","link","0",{"rel":"stylesheet","href":"/ui/_next/static/css/86f6cc749f6b8493.css","precedence":"next","crossOrigin":"$undefined"}],["$","link","1",{"rel":"stylesheet","href":"/ui/_next/static/css/005c96178151b9fd.css","precedence":"next","crossOrigin":"$undefined"}]],["$","html",null,{"lang":"en","children":["$","body",null,{"className":"__className_cf7686","children":["$","$L4",null,{"parallelRouterKey":"children","segmentPath":["children"],"error":"$undefined","errorStyles":"$undefined","errorScripts":"$undefined","template":["$","$L5",null,{}],"templateStyles":"$undefined","templateScripts":"$undefined","notFound":[["$","title",null,{"children":"404: This page could not be found."}],["$","div",null,{"style":{"fontFamily":"system-ui,\"Segoe UI\",Roboto,Helvetica,Arial,sans-serif,\"Apple Color Emoji\",\"Segoe UI Emoji\"","height":"100vh","textAlign":"center","display":"flex","flexDirection":"column","alignItems":"center","justifyContent":"center"},"children":["$","div",null,{"children":[["$","style",null,{"dangerouslySetInnerHTML":{"__html":"body{color:#000;background:#fff;margin:0}.next-error-h1{border-right:1px solid rgba(0,0,0,.3)}@media (prefers-color-scheme:dark){body{color:#fff;background:#000}.next-error-h1{border-right:1px solid rgba(255,255,255,.3)}}"}}],["$","h1",null,{"className":"next-error-h1","style":{"display":"inline-block","margin":"0 20px 0 0","padding":"0 23px 0 0","fontSize":24,"fontWeight":500,"verticalAlign":"top","lineHeight":"49px"},"children":"404"}],["$","div",null,{"style":{"display":"inline-block"},"children":["$","h2",null,{"style":{"fontSize":14,"fontWeight":400,"lineHeight":"49px","margin":0},"children":"This page could not be found."}]}]]}]}]],"notFoundStyles":[]}]}]}]],null],null],["$L6",null]]]]
|
||||
6:[["$","meta","0",{"name":"viewport","content":"width=device-width, initial-scale=1"}],["$","meta","1",{"charSet":"utf-8"}],["$","title","2",{"children":"LiteLLM Dashboard"}],["$","meta","3",{"name":"description","content":"LiteLLM Proxy Admin UI"}],["$","link","4",{"rel":"icon","href":"/ui/favicon.ico","type":"image/x-icon","sizes":"16x16"}],["$","meta","5",{"name":"next-size-adjust"}]]
|
||||
1:null
|
||||
|
|
|
|||
|
|
@ -33,7 +33,7 @@ model_list:
|
|||
|
||||
litellm_settings:
|
||||
num_retries: 0
|
||||
callbacks: ["prometheus"]
|
||||
callbacks: ["datadog_llm_observability"]
|
||||
check_provider_endpoint: true
|
||||
|
||||
files_settings:
|
||||
|
|
|
|||
|
|
@ -687,6 +687,8 @@ class GenerateKeyResponse(KeyRequestBase):
|
|||
token: Optional[str] = None
|
||||
created_by: Optional[str] = None
|
||||
updated_by: Optional[str] = None
|
||||
created_at: Optional[datetime] = None
|
||||
updated_at: Optional[datetime] = None
|
||||
|
||||
@model_validator(mode="before")
|
||||
@classmethod
|
||||
|
|
|
|||
|
|
@ -43,6 +43,9 @@ from litellm.types.proxy.management_endpoints.common_daily_activity import (
|
|||
SpendAnalyticsPaginatedResponse,
|
||||
SpendMetrics,
|
||||
)
|
||||
from litellm.types.proxy.management_endpoints.internal_user_endpoints import (
|
||||
UserListResponse,
|
||||
)
|
||||
|
||||
router = APIRouter()
|
||||
|
||||
|
|
@ -899,15 +902,47 @@ async def get_user_key_counts(
|
|||
return result
|
||||
|
||||
|
||||
@router.get(
|
||||
"/user/get_users",
|
||||
tags=["Internal User management"],
|
||||
dependencies=[Depends(user_api_key_auth)],
|
||||
)
|
||||
def _validate_sort_params(
|
||||
sort_by: Optional[str], sort_order: str
|
||||
) -> Optional[Dict[str, str]]:
|
||||
order_by: Dict[str, str] = {}
|
||||
|
||||
if sort_by is None:
|
||||
return None
|
||||
# Validate sort_by is a valid column
|
||||
valid_columns = [
|
||||
"user_id",
|
||||
"user_email",
|
||||
"created_at",
|
||||
"spend",
|
||||
"user_alias",
|
||||
"user_role",
|
||||
]
|
||||
if sort_by not in valid_columns:
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail={
|
||||
"error": f"Invalid sort column. Must be one of: {', '.join(valid_columns)}"
|
||||
},
|
||||
)
|
||||
|
||||
# Validate sort_order
|
||||
if sort_order.lower() not in ["asc", "desc"]:
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail={"error": "Invalid sort order. Must be 'asc' or 'desc'"},
|
||||
)
|
||||
|
||||
order_by[sort_by] = sort_order.lower()
|
||||
|
||||
return order_by
|
||||
|
||||
|
||||
@router.get(
|
||||
"/user/list",
|
||||
tags=["Internal User management"],
|
||||
dependencies=[Depends(user_api_key_auth)],
|
||||
response_model=UserListResponse,
|
||||
)
|
||||
async def get_users(
|
||||
role: Optional[str] = fastapi.Query(
|
||||
|
|
@ -916,15 +951,29 @@ async def get_users(
|
|||
user_ids: Optional[str] = fastapi.Query(
|
||||
default=None, description="Get list of users by user_ids"
|
||||
),
|
||||
sso_user_ids: Optional[str] = fastapi.Query(
|
||||
default=None, description="Get list of users by sso_user_id"
|
||||
),
|
||||
user_email: Optional[str] = fastapi.Query(
|
||||
default=None, description="Filter users by partial email match"
|
||||
),
|
||||
team: Optional[str] = fastapi.Query(
|
||||
default=None, description="Filter users by team id"
|
||||
),
|
||||
page: int = fastapi.Query(default=1, ge=1, description="Page number"),
|
||||
page_size: int = fastapi.Query(
|
||||
default=25, ge=1, le=100, description="Number of items per page"
|
||||
),
|
||||
sort_by: Optional[str] = fastapi.Query(
|
||||
default=None,
|
||||
description="Column to sort by (e.g. 'user_id', 'user_email', 'created_at', 'spend')",
|
||||
),
|
||||
sort_order: str = fastapi.Query(
|
||||
default="asc", description="Sort order ('asc' or 'desc')"
|
||||
),
|
||||
):
|
||||
"""
|
||||
Get a paginated list of users, optionally filtered by role.
|
||||
|
||||
Used by the UI to populate the user lists.
|
||||
Get a paginated list of users with filtering and sorting options.
|
||||
|
||||
Parameters:
|
||||
role: Optional[str]
|
||||
|
|
@ -935,17 +984,20 @@ async def get_users(
|
|||
- internal_user_viewer
|
||||
user_ids: Optional[str]
|
||||
Get list of users by user_ids. Comma separated list of user_ids.
|
||||
sso_ids: Optional[str]
|
||||
Get list of users by sso_ids. Comma separated list of sso_ids.
|
||||
user_email: Optional[str]
|
||||
Filter users by partial email match
|
||||
team: Optional[str]
|
||||
Filter users by team id. Will match if user has this team in their teams array.
|
||||
page: int
|
||||
The page number to return
|
||||
page_size: int
|
||||
The number of items per page
|
||||
|
||||
Currently - admin-only endpoint.
|
||||
|
||||
Example curl:
|
||||
```
|
||||
http://0.0.0.0:4000/user/list?user_ids=default_user_id,693c1a4a-1cc0-4c7c-afe8-b5d2c8d52e17
|
||||
```
|
||||
sort_by: Optional[str]
|
||||
Column to sort by (e.g. 'user_id', 'user_email', 'created_at', 'spend')
|
||||
sort_order: Optional[str]
|
||||
Sort order ('asc' or 'desc')
|
||||
"""
|
||||
from litellm.proxy.proxy_server import prisma_client
|
||||
|
||||
|
|
@ -958,35 +1010,57 @@ async def get_users(
|
|||
# Calculate skip and take for pagination
|
||||
skip = (page - 1) * page_size
|
||||
|
||||
# Prepare the query conditions
|
||||
# Build where conditions based on provided parameters
|
||||
where_conditions: Dict[str, Any] = {}
|
||||
|
||||
if role:
|
||||
where_conditions["user_role"] = {
|
||||
"contains": role,
|
||||
"mode": "insensitive", # Case-insensitive search
|
||||
}
|
||||
where_conditions["user_role"] = role # Exact match instead of contains
|
||||
|
||||
if user_ids and isinstance(user_ids, str):
|
||||
user_id_list = [uid.strip() for uid in user_ids.split(",") if uid.strip()]
|
||||
where_conditions["user_id"] = {
|
||||
"in": user_id_list, # Now passing a list of strings as required by Prisma
|
||||
"in": user_id_list,
|
||||
}
|
||||
|
||||
users: Optional[
|
||||
List[LiteLLM_UserTable]
|
||||
] = await prisma_client.db.litellm_usertable.find_many(
|
||||
if user_email is not None and isinstance(user_email, str):
|
||||
where_conditions["user_email"] = {
|
||||
"contains": user_email,
|
||||
"mode": "insensitive", # Case-insensitive search
|
||||
}
|
||||
|
||||
if team is not None and isinstance(team, str):
|
||||
where_conditions["teams"] = {
|
||||
"has": team # Array contains for string arrays in Prisma
|
||||
}
|
||||
|
||||
if sso_user_ids is not None and isinstance(sso_user_ids, str):
|
||||
sso_id_list = [sid.strip() for sid in sso_user_ids.split(",") if sid.strip()]
|
||||
where_conditions["sso_user_id"] = {
|
||||
"in": sso_id_list,
|
||||
}
|
||||
|
||||
## Filter any none fastapi.Query params - e.g. where_conditions: {'user_email': {'contains': Query(None), 'mode': 'insensitive'}, 'teams': {'has': Query(None)}}
|
||||
where_conditions = {k: v for k, v in where_conditions.items() if v is not None}
|
||||
|
||||
# Build order_by conditions
|
||||
|
||||
order_by: Optional[Dict[str, str]] = (
|
||||
_validate_sort_params(sort_by, sort_order)
|
||||
if sort_by is not None and isinstance(sort_by, str)
|
||||
else None
|
||||
)
|
||||
|
||||
users = await prisma_client.db.litellm_usertable.find_many(
|
||||
where=where_conditions,
|
||||
skip=skip,
|
||||
take=page_size,
|
||||
order={"created_at": "desc"},
|
||||
order=order_by
|
||||
if order_by
|
||||
else {"created_at": "desc"}, # Default to created_at desc if no sort specified
|
||||
)
|
||||
|
||||
# Get total count of user rows
|
||||
total_count = await prisma_client.db.litellm_usertable.count(
|
||||
where=where_conditions # type: ignore
|
||||
)
|
||||
total_count = await prisma_client.db.litellm_usertable.count(where=where_conditions)
|
||||
|
||||
# Get key count for each user
|
||||
if users is not None:
|
||||
|
|
@ -1009,7 +1083,7 @@ async def get_users(
|
|||
LiteLLM_UserTableWithKeyCount(
|
||||
**user.model_dump(), key_count=user_key_counts.get(user.user_id, 0)
|
||||
)
|
||||
) # Return full key object
|
||||
)
|
||||
else:
|
||||
user_list = []
|
||||
|
||||
|
|
|
|||
|
|
@ -1347,10 +1347,13 @@ async def generate_key_helper_fn( # noqa: PLR0915
|
|||
create_key_response = await prisma_client.insert_data(
|
||||
data=key_data, table_name="key"
|
||||
)
|
||||
|
||||
key_data["token_id"] = getattr(create_key_response, "token", None)
|
||||
key_data["litellm_budget_table"] = getattr(
|
||||
create_key_response, "litellm_budget_table", None
|
||||
)
|
||||
key_data["created_at"] = getattr(create_key_response, "created_at", None)
|
||||
key_data["updated_at"] = getattr(create_key_response, "updated_at", None)
|
||||
except Exception as e:
|
||||
verbose_proxy_logger.error(
|
||||
"litellm.proxy.proxy_server.generate_key_helper_fn(): Exception occured - {}".format(
|
||||
|
|
|
|||
|
|
@ -1296,7 +1296,7 @@ class ProxyConfig:
|
|||
config=config, base_dir=os.path.dirname(os.path.abspath(file_path or ""))
|
||||
)
|
||||
|
||||
verbose_proxy_logger.debug(f"loaded config={json.dumps(config, indent=4)}")
|
||||
# verbose_proxy_logger.debug(f"loaded config={json.dumps(config, indent=4)}")
|
||||
return config
|
||||
|
||||
def _process_includes(self, config: dict, base_dir: str) -> dict:
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
import asyncio
|
||||
import contextvars
|
||||
from functools import partial
|
||||
from typing import Any, Dict, Iterable, List, Literal, Optional, Union
|
||||
from typing import Any, Coroutine, Dict, Iterable, List, Literal, Optional, Union
|
||||
|
||||
import httpx
|
||||
|
||||
|
|
@ -24,6 +24,7 @@ from litellm.types.llms.openai import (
|
|||
ToolChoice,
|
||||
ToolParam,
|
||||
)
|
||||
from litellm.types.responses.main import *
|
||||
from litellm.types.router import GenericLiteLLMParams
|
||||
from litellm.utils import ProviderConfigManager, client
|
||||
|
||||
|
|
@ -121,7 +122,8 @@ async def aresponses(
|
|||
if isinstance(response, ResponsesAPIResponse):
|
||||
response = ResponsesAPIRequestUtils._update_responses_api_response_id_with_model_id(
|
||||
responses_api_response=response,
|
||||
kwargs=kwargs,
|
||||
litellm_metadata=kwargs.get("litellm_metadata", {}),
|
||||
custom_llm_provider=custom_llm_provider,
|
||||
)
|
||||
return response
|
||||
except Exception as e:
|
||||
|
|
@ -253,13 +255,15 @@ def responses(
|
|||
fake_stream=responses_api_provider_config.should_fake_stream(
|
||||
model=model, stream=stream, custom_llm_provider=custom_llm_provider
|
||||
),
|
||||
litellm_metadata=kwargs.get("litellm_metadata", {}),
|
||||
)
|
||||
|
||||
# Update the responses_api_response_id with the model_id
|
||||
if isinstance(response, ResponsesAPIResponse):
|
||||
response = ResponsesAPIRequestUtils._update_responses_api_response_id_with_model_id(
|
||||
responses_api_response=response,
|
||||
kwargs=kwargs,
|
||||
litellm_metadata=kwargs.get("litellm_metadata", {}),
|
||||
custom_llm_provider=custom_llm_provider,
|
||||
)
|
||||
|
||||
return response
|
||||
|
|
@ -271,3 +275,162 @@ def responses(
|
|||
completion_kwargs=local_vars,
|
||||
extra_kwargs=kwargs,
|
||||
)
|
||||
|
||||
|
||||
@client
|
||||
async def adelete_responses(
|
||||
response_id: str,
|
||||
# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
|
||||
# The extra values given here take precedence over values defined on the client or passed to this method.
|
||||
extra_headers: Optional[Dict[str, Any]] = None,
|
||||
extra_query: Optional[Dict[str, Any]] = None,
|
||||
extra_body: Optional[Dict[str, Any]] = None,
|
||||
timeout: Optional[Union[float, httpx.Timeout]] = None,
|
||||
# LiteLLM specific params,
|
||||
custom_llm_provider: Optional[str] = None,
|
||||
**kwargs,
|
||||
) -> DeleteResponseResult:
|
||||
"""
|
||||
Async version of the DELETE Responses API
|
||||
|
||||
DELETE /v1/responses/{response_id} endpoint in the responses API
|
||||
|
||||
"""
|
||||
local_vars = locals()
|
||||
try:
|
||||
loop = asyncio.get_event_loop()
|
||||
kwargs["adelete_responses"] = True
|
||||
|
||||
# get custom llm provider from response_id
|
||||
decoded_response_id: DecodedResponseId = (
|
||||
ResponsesAPIRequestUtils._decode_responses_api_response_id(
|
||||
response_id=response_id,
|
||||
)
|
||||
)
|
||||
response_id = decoded_response_id.get("response_id") or response_id
|
||||
custom_llm_provider = (
|
||||
decoded_response_id.get("custom_llm_provider") or custom_llm_provider
|
||||
)
|
||||
|
||||
func = partial(
|
||||
delete_responses,
|
||||
response_id=response_id,
|
||||
custom_llm_provider=custom_llm_provider,
|
||||
extra_headers=extra_headers,
|
||||
extra_query=extra_query,
|
||||
extra_body=extra_body,
|
||||
timeout=timeout,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
ctx = contextvars.copy_context()
|
||||
func_with_context = partial(ctx.run, func)
|
||||
init_response = await loop.run_in_executor(None, func_with_context)
|
||||
|
||||
if asyncio.iscoroutine(init_response):
|
||||
response = await init_response
|
||||
else:
|
||||
response = init_response
|
||||
return response
|
||||
except Exception as e:
|
||||
raise litellm.exception_type(
|
||||
model=None,
|
||||
custom_llm_provider=custom_llm_provider,
|
||||
original_exception=e,
|
||||
completion_kwargs=local_vars,
|
||||
extra_kwargs=kwargs,
|
||||
)
|
||||
|
||||
|
||||
@client
|
||||
def delete_responses(
|
||||
response_id: str,
|
||||
# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
|
||||
# The extra values given here take precedence over values defined on the client or passed to this method.
|
||||
extra_headers: Optional[Dict[str, Any]] = None,
|
||||
extra_query: Optional[Dict[str, Any]] = None,
|
||||
extra_body: Optional[Dict[str, Any]] = None,
|
||||
timeout: Optional[Union[float, httpx.Timeout]] = None,
|
||||
# LiteLLM specific params,
|
||||
custom_llm_provider: Optional[str] = None,
|
||||
**kwargs,
|
||||
) -> Union[DeleteResponseResult, Coroutine[Any, Any, DeleteResponseResult]]:
|
||||
"""
|
||||
Synchronous version of the DELETE Responses API
|
||||
|
||||
DELETE /v1/responses/{response_id} endpoint in the responses API
|
||||
|
||||
"""
|
||||
local_vars = locals()
|
||||
try:
|
||||
litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj") # type: ignore
|
||||
litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None)
|
||||
_is_async = kwargs.pop("adelete_responses", False) is True
|
||||
|
||||
# get llm provider logic
|
||||
litellm_params = GenericLiteLLMParams(**kwargs)
|
||||
|
||||
# get custom llm provider from response_id
|
||||
decoded_response_id: DecodedResponseId = (
|
||||
ResponsesAPIRequestUtils._decode_responses_api_response_id(
|
||||
response_id=response_id,
|
||||
)
|
||||
)
|
||||
response_id = decoded_response_id.get("response_id") or response_id
|
||||
custom_llm_provider = (
|
||||
decoded_response_id.get("custom_llm_provider") or custom_llm_provider
|
||||
)
|
||||
|
||||
if custom_llm_provider is None:
|
||||
raise ValueError("custom_llm_provider is required but passed as None")
|
||||
|
||||
# get provider config
|
||||
responses_api_provider_config: Optional[BaseResponsesAPIConfig] = (
|
||||
ProviderConfigManager.get_provider_responses_api_config(
|
||||
model=None,
|
||||
provider=litellm.LlmProviders(custom_llm_provider),
|
||||
)
|
||||
)
|
||||
|
||||
if responses_api_provider_config is None:
|
||||
raise ValueError(
|
||||
f"DELETE responses is not supported for {custom_llm_provider}"
|
||||
)
|
||||
|
||||
local_vars.update(kwargs)
|
||||
|
||||
# Pre Call logging
|
||||
litellm_logging_obj.update_environment_variables(
|
||||
model=None,
|
||||
optional_params={
|
||||
"response_id": response_id,
|
||||
},
|
||||
litellm_params={
|
||||
"litellm_call_id": litellm_call_id,
|
||||
},
|
||||
custom_llm_provider=custom_llm_provider,
|
||||
)
|
||||
|
||||
# Call the handler with _is_async flag instead of directly calling the async handler
|
||||
response = base_llm_http_handler.delete_response_api_handler(
|
||||
response_id=response_id,
|
||||
custom_llm_provider=custom_llm_provider,
|
||||
responses_api_provider_config=responses_api_provider_config,
|
||||
litellm_params=litellm_params,
|
||||
logging_obj=litellm_logging_obj,
|
||||
extra_headers=extra_headers,
|
||||
extra_body=extra_body,
|
||||
timeout=timeout or request_timeout,
|
||||
_is_async=_is_async,
|
||||
client=kwargs.get("client"),
|
||||
)
|
||||
|
||||
return response
|
||||
except Exception as e:
|
||||
raise litellm.exception_type(
|
||||
model=None,
|
||||
custom_llm_provider=custom_llm_provider,
|
||||
original_exception=e,
|
||||
completion_kwargs=local_vars,
|
||||
extra_kwargs=kwargs,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
import asyncio
|
||||
import json
|
||||
from datetime import datetime
|
||||
from typing import Optional
|
||||
from typing import Any, Dict, Optional
|
||||
|
||||
import httpx
|
||||
|
||||
|
|
@ -10,6 +10,7 @@ from litellm.litellm_core_utils.asyncify import run_async_function
|
|||
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
|
||||
from litellm.litellm_core_utils.thread_pool_executor import executor
|
||||
from litellm.llms.base_llm.responses.transformation import BaseResponsesAPIConfig
|
||||
from litellm.responses.utils import ResponsesAPIRequestUtils
|
||||
from litellm.types.llms.openai import (
|
||||
OutputTextDeltaEvent,
|
||||
ResponseCompletedEvent,
|
||||
|
|
@ -33,6 +34,8 @@ class BaseResponsesAPIStreamingIterator:
|
|||
model: str,
|
||||
responses_api_provider_config: BaseResponsesAPIConfig,
|
||||
logging_obj: LiteLLMLoggingObj,
|
||||
litellm_metadata: Optional[Dict[str, Any]] = None,
|
||||
custom_llm_provider: Optional[str] = None,
|
||||
):
|
||||
self.response = response
|
||||
self.model = model
|
||||
|
|
@ -41,8 +44,12 @@ class BaseResponsesAPIStreamingIterator:
|
|||
self.responses_api_provider_config = responses_api_provider_config
|
||||
self.completed_response: Optional[ResponsesAPIStreamingResponse] = None
|
||||
self.start_time = datetime.now()
|
||||
|
||||
# set request kwargs
|
||||
self.litellm_metadata = litellm_metadata
|
||||
self.custom_llm_provider = custom_llm_provider
|
||||
|
||||
def _process_chunk(self, chunk):
|
||||
def _process_chunk(self, chunk) -> Optional[ResponsesAPIStreamingResponse]:
|
||||
"""Process a single chunk of data from the stream"""
|
||||
if not chunk:
|
||||
return None
|
||||
|
|
@ -70,6 +77,17 @@ class BaseResponsesAPIStreamingIterator:
|
|||
logging_obj=self.logging_obj,
|
||||
)
|
||||
)
|
||||
|
||||
# if "response" in parsed_chunk, then encode litellm specific information like custom_llm_provider
|
||||
response_object = getattr(openai_responses_api_chunk, "response", None)
|
||||
if response_object:
|
||||
response = ResponsesAPIRequestUtils._update_responses_api_response_id_with_model_id(
|
||||
responses_api_response=response_object,
|
||||
litellm_metadata=self.litellm_metadata,
|
||||
custom_llm_provider=self.custom_llm_provider,
|
||||
)
|
||||
setattr(openai_responses_api_chunk, "response", response)
|
||||
|
||||
# Store the completed response
|
||||
if (
|
||||
openai_responses_api_chunk
|
||||
|
|
@ -102,8 +120,17 @@ class ResponsesAPIStreamingIterator(BaseResponsesAPIStreamingIterator):
|
|||
model: str,
|
||||
responses_api_provider_config: BaseResponsesAPIConfig,
|
||||
logging_obj: LiteLLMLoggingObj,
|
||||
litellm_metadata: Optional[Dict[str, Any]] = None,
|
||||
custom_llm_provider: Optional[str] = None,
|
||||
):
|
||||
super().__init__(response, model, responses_api_provider_config, logging_obj)
|
||||
super().__init__(
|
||||
response,
|
||||
model,
|
||||
responses_api_provider_config,
|
||||
logging_obj,
|
||||
litellm_metadata,
|
||||
custom_llm_provider,
|
||||
)
|
||||
self.stream_iterator = response.aiter_lines()
|
||||
|
||||
def __aiter__(self):
|
||||
|
|
@ -163,8 +190,17 @@ class SyncResponsesAPIStreamingIterator(BaseResponsesAPIStreamingIterator):
|
|||
model: str,
|
||||
responses_api_provider_config: BaseResponsesAPIConfig,
|
||||
logging_obj: LiteLLMLoggingObj,
|
||||
litellm_metadata: Optional[Dict[str, Any]] = None,
|
||||
custom_llm_provider: Optional[str] = None,
|
||||
):
|
||||
super().__init__(response, model, responses_api_provider_config, logging_obj)
|
||||
super().__init__(
|
||||
response,
|
||||
model,
|
||||
responses_api_provider_config,
|
||||
logging_obj,
|
||||
litellm_metadata,
|
||||
custom_llm_provider,
|
||||
)
|
||||
self.stream_iterator = response.iter_lines()
|
||||
|
||||
def __iter__(self):
|
||||
|
|
@ -228,12 +264,16 @@ class MockResponsesAPIStreamingIterator(BaseResponsesAPIStreamingIterator):
|
|||
model: str,
|
||||
responses_api_provider_config: BaseResponsesAPIConfig,
|
||||
logging_obj: LiteLLMLoggingObj,
|
||||
litellm_metadata: Optional[Dict[str, Any]] = None,
|
||||
custom_llm_provider: Optional[str] = None,
|
||||
):
|
||||
super().__init__(
|
||||
response=response,
|
||||
model=model,
|
||||
responses_api_provider_config=responses_api_provider_config,
|
||||
logging_obj=logging_obj,
|
||||
litellm_metadata=litellm_metadata,
|
||||
custom_llm_provider=custom_llm_provider,
|
||||
)
|
||||
|
||||
# one-time transform
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
import base64
|
||||
from typing import Any, Dict, Optional, Tuple, Union, cast, get_type_hints
|
||||
from typing import Any, Dict, Optional, Union, cast, get_type_hints
|
||||
|
||||
import litellm
|
||||
from litellm._logging import verbose_logger
|
||||
|
|
@ -9,6 +9,7 @@ from litellm.types.llms.openai import (
|
|||
ResponsesAPIOptionalRequestParams,
|
||||
ResponsesAPIResponse,
|
||||
)
|
||||
from litellm.types.responses.main import DecodedResponseId
|
||||
from litellm.types.utils import SpecialEnums, Usage
|
||||
|
||||
|
||||
|
|
@ -83,30 +84,36 @@ class ResponsesAPIRequestUtils:
|
|||
@staticmethod
|
||||
def _update_responses_api_response_id_with_model_id(
|
||||
responses_api_response: ResponsesAPIResponse,
|
||||
kwargs: Dict[str, Any],
|
||||
custom_llm_provider: Optional[str],
|
||||
litellm_metadata: Optional[Dict[str, Any]] = None,
|
||||
) -> ResponsesAPIResponse:
|
||||
"""Update the responses_api_response_id with the model_id"""
|
||||
litellm_metadata: Dict[str, Any] = kwargs.get("litellm_metadata", {}) or {}
|
||||
"""
|
||||
Update the responses_api_response_id with model_id and custom_llm_provider
|
||||
|
||||
This builds a composite ID containing the custom LLM provider, model ID, and original response ID
|
||||
"""
|
||||
litellm_metadata = litellm_metadata or {}
|
||||
model_info: Dict[str, Any] = litellm_metadata.get("model_info", {}) or {}
|
||||
model_id = model_info.get("id")
|
||||
updated_id = ResponsesAPIRequestUtils._build_responses_api_response_id(
|
||||
model_id=model_id,
|
||||
custom_llm_provider=custom_llm_provider,
|
||||
response_id=responses_api_response.id,
|
||||
)
|
||||
|
||||
responses_api_response.id = updated_id
|
||||
return responses_api_response
|
||||
|
||||
@staticmethod
|
||||
def _build_responses_api_response_id(
|
||||
custom_llm_provider: Optional[str],
|
||||
model_id: Optional[str],
|
||||
response_id: str,
|
||||
) -> str:
|
||||
"""Build the responses_api_response_id"""
|
||||
if model_id is None:
|
||||
return response_id
|
||||
assembled_id: str = str(
|
||||
SpecialEnums.LITELLM_MANAGED_RESPONSE_COMPLETE_STR.value
|
||||
).format(model_id, response_id)
|
||||
).format(custom_llm_provider, model_id, response_id)
|
||||
base64_encoded_id: str = base64.b64encode(assembled_id.encode("utf-8")).decode(
|
||||
"utf-8"
|
||||
)
|
||||
|
|
@ -115,12 +122,12 @@ class ResponsesAPIRequestUtils:
|
|||
@staticmethod
|
||||
def _decode_responses_api_response_id(
|
||||
response_id: str,
|
||||
) -> Tuple[Optional[str], str]:
|
||||
) -> DecodedResponseId:
|
||||
"""
|
||||
Decode the responses_api_response_id
|
||||
|
||||
Returns:
|
||||
Tuple of model_id, response_id (from upstream provider)
|
||||
DecodedResponseId: Structured tuple with custom_llm_provider, model_id, and response_id
|
||||
"""
|
||||
try:
|
||||
# Remove prefix and decode
|
||||
|
|
@ -129,16 +136,45 @@ class ResponsesAPIRequestUtils:
|
|||
|
||||
# Parse components using known prefixes
|
||||
if ";" not in decoded_id:
|
||||
return None, response_id
|
||||
return DecodedResponseId(
|
||||
custom_llm_provider=None,
|
||||
model_id=None,
|
||||
response_id=response_id,
|
||||
)
|
||||
|
||||
model_part, response_part = decoded_id.split(";", 1)
|
||||
model_id = model_part.replace("litellm:model_id:", "")
|
||||
decoded_response_id = response_part.replace("response_id:", "")
|
||||
parts = decoded_id.split(";")
|
||||
|
||||
return model_id, decoded_response_id
|
||||
# Format: litellm:custom_llm_provider:{};model_id:{};response_id:{}
|
||||
custom_llm_provider = None
|
||||
model_id = None
|
||||
|
||||
if (
|
||||
len(parts) >= 3
|
||||
): # Full format with custom_llm_provider, model_id, and response_id
|
||||
custom_llm_provider_part = parts[0]
|
||||
model_id_part = parts[1]
|
||||
response_part = parts[2]
|
||||
|
||||
custom_llm_provider = custom_llm_provider_part.replace(
|
||||
"litellm:custom_llm_provider:", ""
|
||||
)
|
||||
model_id = model_id_part.replace("model_id:", "")
|
||||
decoded_response_id = response_part.replace("response_id:", "")
|
||||
else:
|
||||
decoded_response_id = response_id
|
||||
|
||||
return DecodedResponseId(
|
||||
custom_llm_provider=custom_llm_provider,
|
||||
model_id=model_id,
|
||||
response_id=decoded_response_id,
|
||||
)
|
||||
except Exception as e:
|
||||
verbose_logger.debug(f"Error decoding response_id '{response_id}': {e}")
|
||||
return None, response_id
|
||||
return DecodedResponseId(
|
||||
custom_llm_provider=None,
|
||||
model_id=None,
|
||||
response_id=response_id,
|
||||
)
|
||||
|
||||
|
||||
class ResponseAPILoggingUtils:
|
||||
|
|
|
|||
|
|
@ -31,11 +31,10 @@ class ResponsesApiDeploymentCheck(CustomLogger):
|
|||
if previous_response_id is None:
|
||||
return healthy_deployments
|
||||
|
||||
model_id, response_id = (
|
||||
ResponsesAPIRequestUtils._decode_responses_api_response_id(
|
||||
response_id=previous_response_id,
|
||||
)
|
||||
decoded_response = ResponsesAPIRequestUtils._decode_responses_api_response_id(
|
||||
response_id=previous_response_id,
|
||||
)
|
||||
model_id = decoded_response.get("model_id")
|
||||
if model_id is None:
|
||||
return healthy_deployments
|
||||
|
||||
|
|
|
|||
|
|
@ -8,7 +8,9 @@ from typing import Any, Dict, List, Literal, Optional, TypedDict
|
|||
|
||||
|
||||
class InputMeta(TypedDict):
|
||||
messages: List[Any]
|
||||
messages: List[
|
||||
Dict[str, str]
|
||||
] # Relevant Issue: https://github.com/BerriAI/litellm/issues/9494
|
||||
|
||||
|
||||
class OutputMeta(TypedDict):
|
||||
|
|
|
|||
|
|
@ -50,7 +50,7 @@ from openai.types.responses.response_create_params import (
|
|||
ToolParam,
|
||||
)
|
||||
from openai.types.responses.response_function_tool_call import ResponseFunctionToolCall
|
||||
from pydantic import BaseModel, Discriminator, Field, PrivateAttr
|
||||
from pydantic import BaseModel, ConfigDict, Discriminator, Field, PrivateAttr
|
||||
from typing_extensions import Annotated, Dict, Required, TypedDict, override
|
||||
|
||||
from litellm.types.llms.base import BaseLiteLLMOpenAIResponseObject
|
||||
|
|
@ -1013,6 +1013,9 @@ class ResponsesAPIStreamEvents(str, Enum):
|
|||
RESPONSE_FAILED = "response.failed"
|
||||
RESPONSE_INCOMPLETE = "response.incomplete"
|
||||
|
||||
# Part added
|
||||
RESPONSE_PART_ADDED = "response.reasoning_summary_part.added"
|
||||
|
||||
# Output item events
|
||||
OUTPUT_ITEM_ADDED = "response.output_item.added"
|
||||
OUTPUT_ITEM_DONE = "response.output_item.done"
|
||||
|
|
@ -1200,6 +1203,12 @@ class ErrorEvent(BaseLiteLLMOpenAIResponseObject):
|
|||
param: Optional[str]
|
||||
|
||||
|
||||
class GenericEvent(BaseLiteLLMOpenAIResponseObject):
|
||||
type: str
|
||||
|
||||
model_config = ConfigDict(extra="allow", protected_namespaces=())
|
||||
|
||||
|
||||
# Union type for all possible streaming responses
|
||||
ResponsesAPIStreamingResponse = Annotated[
|
||||
Union[
|
||||
|
|
@ -1226,6 +1235,7 @@ ResponsesAPIStreamingResponse = Annotated[
|
|||
WebSearchCallSearchingEvent,
|
||||
WebSearchCallCompletedEvent,
|
||||
ErrorEvent,
|
||||
GenericEvent,
|
||||
],
|
||||
Discriminator("type"),
|
||||
]
|
||||
|
|
|
|||
|
|
@ -0,0 +1,18 @@
|
|||
from typing import Any, Dict, List, Literal, Optional, Union
|
||||
|
||||
from fastapi import HTTPException
|
||||
from pydantic import BaseModel, EmailStr
|
||||
|
||||
from litellm.proxy._types import LiteLLM_UserTableWithKeyCount
|
||||
|
||||
|
||||
class UserListResponse(BaseModel):
|
||||
"""
|
||||
Response model for the user list endpoint
|
||||
"""
|
||||
|
||||
users: List[LiteLLM_UserTableWithKeyCount]
|
||||
total: int
|
||||
page: int
|
||||
page_size: int
|
||||
total_pages: int
|
||||
|
|
@ -1,5 +1,6 @@
|
|||
from typing import Literal
|
||||
|
||||
from pydantic import PrivateAttr
|
||||
from typing_extensions import Any, List, Optional, TypedDict
|
||||
|
||||
from litellm.types.llms.base import BaseLiteLLMOpenAIResponseObject
|
||||
|
|
@ -46,3 +47,30 @@ class GenericResponseOutputItem(BaseLiteLLMOpenAIResponseObject):
|
|||
status: str # "completed", "in_progress", etc.
|
||||
role: str # "assistant", "user", etc.
|
||||
content: List[OutputText]
|
||||
|
||||
|
||||
class DeleteResponseResult(BaseLiteLLMOpenAIResponseObject):
|
||||
"""
|
||||
Result of a delete response request
|
||||
|
||||
{
|
||||
"id": "resp_6786a1bec27481909a17d673315b29f6",
|
||||
"object": "response",
|
||||
"deleted": true
|
||||
}
|
||||
"""
|
||||
|
||||
id: Optional[str]
|
||||
object: Optional[str]
|
||||
deleted: Optional[bool]
|
||||
|
||||
# Define private attributes using PrivateAttr
|
||||
_hidden_params: dict = PrivateAttr(default_factory=dict)
|
||||
|
||||
|
||||
class DecodedResponseId(TypedDict, total=False):
|
||||
"""Structure representing a decoded response ID"""
|
||||
|
||||
custom_llm_provider: Optional[str]
|
||||
model_id: Optional[str]
|
||||
response_id: str
|
||||
|
|
|
|||
|
|
@ -2254,7 +2254,9 @@ class SpecialEnums(Enum):
|
|||
LITELM_MANAGED_FILE_ID_PREFIX = "litellm_proxy"
|
||||
LITELLM_MANAGED_FILE_COMPLETE_STR = "litellm_proxy:{};unified_id,{}"
|
||||
|
||||
LITELLM_MANAGED_RESPONSE_COMPLETE_STR = "litellm:model_id:{};response_id:{}"
|
||||
LITELLM_MANAGED_RESPONSE_COMPLETE_STR = (
|
||||
"litellm:custom_llm_provider:{};model_id:{};response_id:{}"
|
||||
)
|
||||
|
||||
|
||||
LLMResponseTypes = Union[
|
||||
|
|
|
|||
|
|
@ -180,10 +180,18 @@ from litellm.types.utils import (
|
|||
all_litellm_params,
|
||||
)
|
||||
|
||||
with resources.open_text(
|
||||
"litellm.litellm_core_utils.tokenizers", "anthropic_tokenizer.json"
|
||||
) as f:
|
||||
json_data = json.load(f)
|
||||
try:
|
||||
# Python 3.9+
|
||||
with resources.files("litellm.litellm_core_utils.tokenizers").joinpath(
|
||||
"anthropic_tokenizer.json"
|
||||
).open("r") as f:
|
||||
json_data = json.load(f)
|
||||
except (ImportError, AttributeError, TypeError):
|
||||
with resources.open_text(
|
||||
"litellm.litellm_core_utils.tokenizers", "anthropic_tokenizer.json"
|
||||
) as f:
|
||||
json_data = json.load(f)
|
||||
|
||||
# Convert to str (if necessary)
|
||||
claude_json_str = json.dumps(json_data)
|
||||
import importlib.metadata
|
||||
|
|
@ -516,9 +524,9 @@ def function_setup( # noqa: PLR0915
|
|||
function_id: Optional[str] = kwargs["id"] if "id" in kwargs else None
|
||||
|
||||
## DYNAMIC CALLBACKS ##
|
||||
dynamic_callbacks: Optional[
|
||||
List[Union[str, Callable, CustomLogger]]
|
||||
] = kwargs.pop("callbacks", None)
|
||||
dynamic_callbacks: Optional[List[Union[str, Callable, CustomLogger]]] = (
|
||||
kwargs.pop("callbacks", None)
|
||||
)
|
||||
all_callbacks = get_dynamic_callbacks(dynamic_callbacks=dynamic_callbacks)
|
||||
|
||||
if len(all_callbacks) > 0:
|
||||
|
|
@ -1202,9 +1210,9 @@ def client(original_function): # noqa: PLR0915
|
|||
exception=e,
|
||||
retry_policy=kwargs.get("retry_policy"),
|
||||
)
|
||||
kwargs[
|
||||
"retry_policy"
|
||||
] = reset_retry_policy() # prevent infinite loops
|
||||
kwargs["retry_policy"] = (
|
||||
reset_retry_policy()
|
||||
) # prevent infinite loops
|
||||
litellm.num_retries = (
|
||||
None # set retries to None to prevent infinite loops
|
||||
)
|
||||
|
|
@ -3031,10 +3039,10 @@ def get_optional_params( # noqa: PLR0915
|
|||
|
||||
if "response_format" in non_default_params:
|
||||
if provider_config is not None:
|
||||
non_default_params[
|
||||
"response_format"
|
||||
] = provider_config.get_json_schema_from_pydantic_object(
|
||||
response_format=non_default_params["response_format"]
|
||||
non_default_params["response_format"] = (
|
||||
provider_config.get_json_schema_from_pydantic_object(
|
||||
response_format=non_default_params["response_format"]
|
||||
)
|
||||
)
|
||||
else:
|
||||
non_default_params["response_format"] = type_to_response_format_param(
|
||||
|
|
@ -4050,9 +4058,9 @@ def _count_characters(text: str) -> int:
|
|||
|
||||
|
||||
def get_response_string(response_obj: Union[ModelResponse, ModelResponseStream]) -> str:
|
||||
_choices: Union[
|
||||
List[Union[Choices, StreamingChoices]], List[StreamingChoices]
|
||||
] = response_obj.choices
|
||||
_choices: Union[List[Union[Choices, StreamingChoices]], List[StreamingChoices]] = (
|
||||
response_obj.choices
|
||||
)
|
||||
|
||||
response_str = ""
|
||||
for choice in _choices:
|
||||
|
|
@ -6596,8 +6604,8 @@ class ProviderConfigManager:
|
|||
|
||||
@staticmethod
|
||||
def get_provider_responses_api_config(
|
||||
model: str,
|
||||
provider: LlmProviders,
|
||||
model: Optional[str] = None,
|
||||
) -> Optional[BaseResponsesAPIConfig]:
|
||||
if litellm.LlmProviders.OPENAI == provider:
|
||||
return litellm.OpenAIResponsesAPIConfig()
|
||||
|
|
|
|||
|
|
@ -1437,6 +1437,76 @@
|
|||
"output_cost_per_pixel": 0.0,
|
||||
"litellm_provider": "openai"
|
||||
},
|
||||
"gpt-image-1": {
|
||||
"mode": "image_generation",
|
||||
"input_cost_per_pixel": 4.0054321e-8,
|
||||
"output_cost_per_pixel": 0.0,
|
||||
"litellm_provider": "openai",
|
||||
"supported_endpoints": ["/v1/images/generations"]
|
||||
},
|
||||
"low/1024-x-1024/gpt-image-1": {
|
||||
"mode": "image_generation",
|
||||
"input_cost_per_pixel": 1.0490417e-8,
|
||||
"output_cost_per_pixel": 0.0,
|
||||
"litellm_provider": "openai",
|
||||
"supported_endpoints": ["/v1/images/generations"]
|
||||
},
|
||||
"medium/1024-x-1024/gpt-image-1": {
|
||||
"mode": "image_generation",
|
||||
"input_cost_per_pixel": 4.0054321e-8,
|
||||
"output_cost_per_pixel": 0.0,
|
||||
"litellm_provider": "openai",
|
||||
"supported_endpoints": ["/v1/images/generations"]
|
||||
},
|
||||
"high/1024-x-1024/gpt-image-1": {
|
||||
"mode": "image_generation",
|
||||
"input_cost_per_pixel": 1.59263611e-7,
|
||||
"output_cost_per_pixel": 0.0,
|
||||
"litellm_provider": "openai",
|
||||
"supported_endpoints": ["/v1/images/generations"]
|
||||
},
|
||||
"low/1024-x-1536/gpt-image-1": {
|
||||
"mode": "image_generation",
|
||||
"input_cost_per_pixel": 1.0172526e-8,
|
||||
"output_cost_per_pixel": 0.0,
|
||||
"litellm_provider": "openai",
|
||||
"supported_endpoints": ["/v1/images/generations"]
|
||||
},
|
||||
"medium/1024-x-1536/gpt-image-1": {
|
||||
"mode": "image_generation",
|
||||
"input_cost_per_pixel": 4.0054321e-8,
|
||||
"output_cost_per_pixel": 0.0,
|
||||
"litellm_provider": "openai",
|
||||
"supported_endpoints": ["/v1/images/generations"]
|
||||
},
|
||||
"high/1024-x-1536/gpt-image-1": {
|
||||
"mode": "image_generation",
|
||||
"input_cost_per_pixel": 1.58945719e-7,
|
||||
"output_cost_per_pixel": 0.0,
|
||||
"litellm_provider": "openai",
|
||||
"supported_endpoints": ["/v1/images/generations"]
|
||||
},
|
||||
"low/1536-x-1024/gpt-image-1": {
|
||||
"mode": "image_generation",
|
||||
"input_cost_per_pixel": 1.0172526e-8,
|
||||
"output_cost_per_pixel": 0.0,
|
||||
"litellm_provider": "openai",
|
||||
"supported_endpoints": ["/v1/images/generations"]
|
||||
},
|
||||
"medium/1536-x-1024/gpt-image-1": {
|
||||
"mode": "image_generation",
|
||||
"input_cost_per_pixel": 4.0054321e-8,
|
||||
"output_cost_per_pixel": 0.0,
|
||||
"litellm_provider": "openai",
|
||||
"supported_endpoints": ["/v1/images/generations"]
|
||||
},
|
||||
"high/1536-x-1024/gpt-image-1": {
|
||||
"mode": "image_generation",
|
||||
"input_cost_per_pixel": 1.58945719e-7,
|
||||
"output_cost_per_pixel": 0.0,
|
||||
"litellm_provider": "openai",
|
||||
"supported_endpoints": ["/v1/images/generations"]
|
||||
},
|
||||
"gpt-4o-transcribe": {
|
||||
"mode": "audio_transcription",
|
||||
"input_cost_per_token": 0.0000025,
|
||||
|
|
@ -1490,7 +1560,6 @@
|
|||
"supports_prompt_caching": false,
|
||||
"supports_system_messages": true,
|
||||
"supports_tool_choice": true,
|
||||
"supports_native_streaming": false,
|
||||
"supports_reasoning": true
|
||||
},
|
||||
"azure/gpt-4o-audio-preview-2024-12-17": {
|
||||
|
|
|
|||
353
poetry.lock
generated
353
poetry.lock
generated
|
|
@ -548,7 +548,7 @@ version = "3.4.1"
|
|||
description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
groups = ["main"]
|
||||
groups = ["main", "dev", "proxy-dev"]
|
||||
files = [
|
||||
{file = "charset_normalizer-3.4.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:91b36a978b5ae0ee86c394f5a54d6ef44db1de0815eb43de826d41d21e4af3de"},
|
||||
{file = "charset_normalizer-3.4.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7461baadb4dc00fd9e0acbe254e3d7d2112e7f92ced2adc96e54ef6501c5f176"},
|
||||
|
|
@ -742,6 +742,24 @@ ssh = ["bcrypt (>=3.1.5)"]
|
|||
test = ["certifi", "cryptography-vectors (==43.0.3)", "pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-xdist"]
|
||||
test-randomorder = ["pytest-randomly"]
|
||||
|
||||
[[package]]
|
||||
name = "deprecated"
|
||||
version = "1.2.18"
|
||||
description = "Python @deprecated decorator to deprecate old python classes, functions or methods."
|
||||
optional = false
|
||||
python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7"
|
||||
groups = ["dev", "proxy-dev"]
|
||||
files = [
|
||||
{file = "Deprecated-1.2.18-py2.py3-none-any.whl", hash = "sha256:bd5011788200372a32418f888e326a09ff80d0214bd961147cfed01b5c018eec"},
|
||||
{file = "deprecated-1.2.18.tar.gz", hash = "sha256:422b6f6d859da6f2ef57857761bfb392480502a64c3028ca9bbe86085d72115d"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
wrapt = ">=1.10,<2"
|
||||
|
||||
[package.extras]
|
||||
dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "setuptools ; python_version >= \"3.12\"", "tox"]
|
||||
|
||||
[[package]]
|
||||
name = "distro"
|
||||
version = "1.9.0"
|
||||
|
|
@ -1116,14 +1134,14 @@ protobuf = ">=3.20.2,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4
|
|||
name = "googleapis-common-protos"
|
||||
version = "1.70.0"
|
||||
description = "Common protobufs used in Google APIs"
|
||||
optional = true
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"extra-proxy\""
|
||||
groups = ["main", "dev", "proxy-dev"]
|
||||
files = [
|
||||
{file = "googleapis_common_protos-1.70.0-py3-none-any.whl", hash = "sha256:b8bfcca8c25a2bb253e0e0b0adaf8c00773e5e6af6fd92397576680b807e0fd8"},
|
||||
{file = "googleapis_common_protos-1.70.0.tar.gz", hash = "sha256:0e1b44e0ea153e6594f9f394fef15193a68aaaea2d843f83e2742717ca753257"},
|
||||
]
|
||||
markers = {main = "extra == \"extra-proxy\""}
|
||||
|
||||
[package.dependencies]
|
||||
grpcio = {version = ">=1.44.0,<2.0.0", optional = true, markers = "extra == \"grpc\""}
|
||||
|
|
@ -1154,10 +1172,9 @@ protobuf = ">=3.20.2,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4
|
|||
name = "grpcio"
|
||||
version = "1.70.0"
|
||||
description = "HTTP/2-based RPC framework"
|
||||
optional = true
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
groups = ["main"]
|
||||
markers = "python_version < \"3.10\" and extra == \"extra-proxy\""
|
||||
groups = ["main", "dev", "proxy-dev"]
|
||||
files = [
|
||||
{file = "grpcio-1.70.0-cp310-cp310-linux_armv7l.whl", hash = "sha256:95469d1977429f45fe7df441f586521361e235982a0b39e33841549143ae2851"},
|
||||
{file = "grpcio-1.70.0-cp310-cp310-macosx_12_0_universal2.whl", hash = "sha256:ed9718f17fbdb472e33b869c77a16d0b55e166b100ec57b016dc7de9c8d236bf"},
|
||||
|
|
@ -1215,6 +1232,7 @@ files = [
|
|||
{file = "grpcio-1.70.0-cp39-cp39-win_amd64.whl", hash = "sha256:a31d7e3b529c94e930a117b2175b2efd179d96eb3c7a21ccb0289a8ab05b645c"},
|
||||
{file = "grpcio-1.70.0.tar.gz", hash = "sha256:8d1584a68d5922330025881e63a6c1b54cc8117291d382e4fa69339b6d914c56"},
|
||||
]
|
||||
markers = {main = "python_version < \"3.10\" and extra == \"extra-proxy\"", dev = "python_version < \"3.10\"", proxy-dev = "python_version < \"3.10\""}
|
||||
|
||||
[package.extras]
|
||||
protobuf = ["grpcio-tools (>=1.70.0)"]
|
||||
|
|
@ -1223,10 +1241,9 @@ protobuf = ["grpcio-tools (>=1.70.0)"]
|
|||
name = "grpcio"
|
||||
version = "1.71.0"
|
||||
description = "HTTP/2-based RPC framework"
|
||||
optional = true
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
groups = ["main"]
|
||||
markers = "python_version >= \"3.10\" and extra == \"extra-proxy\""
|
||||
groups = ["main", "dev", "proxy-dev"]
|
||||
files = [
|
||||
{file = "grpcio-1.71.0-cp310-cp310-linux_armv7l.whl", hash = "sha256:c200cb6f2393468142eb50ab19613229dcc7829b5ccee8b658a36005f6669fdd"},
|
||||
{file = "grpcio-1.71.0-cp310-cp310-macosx_12_0_universal2.whl", hash = "sha256:b2266862c5ad664a380fbbcdbdb8289d71464c42a8c29053820ee78ba0119e5d"},
|
||||
|
|
@ -1280,45 +1297,28 @@ files = [
|
|||
{file = "grpcio-1.71.0-cp39-cp39-win_amd64.whl", hash = "sha256:63e41b91032f298b3e973b3fa4093cbbc620c875e2da7b93e249d4728b54559a"},
|
||||
{file = "grpcio-1.71.0.tar.gz", hash = "sha256:2b85f7820475ad3edec209d3d89a7909ada16caab05d3f2e08a7e8ae3200a55c"},
|
||||
]
|
||||
markers = {main = "python_version >= \"3.10\" and extra == \"extra-proxy\"", dev = "python_version >= \"3.10\"", proxy-dev = "python_version >= \"3.10\""}
|
||||
|
||||
[package.extras]
|
||||
protobuf = ["grpcio-tools (>=1.71.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "grpcio-status"
|
||||
version = "1.70.0"
|
||||
version = "1.62.3"
|
||||
description = "Status proto mapping for gRPC"
|
||||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
python-versions = ">=3.6"
|
||||
groups = ["main"]
|
||||
markers = "python_version < \"3.10\" and extra == \"extra-proxy\""
|
||||
markers = "extra == \"extra-proxy\""
|
||||
files = [
|
||||
{file = "grpcio_status-1.70.0-py3-none-any.whl", hash = "sha256:fc5a2ae2b9b1c1969cc49f3262676e6854aa2398ec69cb5bd6c47cd501904a85"},
|
||||
{file = "grpcio_status-1.70.0.tar.gz", hash = "sha256:0e7b42816512433b18b9d764285ff029bde059e9d41f8fe10a60631bd8348101"},
|
||||
{file = "grpcio-status-1.62.3.tar.gz", hash = "sha256:289bdd7b2459794a12cf95dc0cb727bd4a1742c37bd823f760236c937e53a485"},
|
||||
{file = "grpcio_status-1.62.3-py3-none-any.whl", hash = "sha256:f9049b762ba8de6b1086789d8315846e094edac2c50beaf462338b301a8fd4b8"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
googleapis-common-protos = ">=1.5.5"
|
||||
grpcio = ">=1.70.0"
|
||||
protobuf = ">=5.26.1,<6.0dev"
|
||||
|
||||
[[package]]
|
||||
name = "grpcio-status"
|
||||
version = "1.71.0"
|
||||
description = "Status proto mapping for gRPC"
|
||||
optional = true
|
||||
python-versions = ">=3.9"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"extra-proxy\" and python_version >= \"3.10\""
|
||||
files = [
|
||||
{file = "grpcio_status-1.71.0-py3-none-any.whl", hash = "sha256:843934ef8c09e3e858952887467f8256aac3910c55f077a359a65b2b3cde3e68"},
|
||||
{file = "grpcio_status-1.71.0.tar.gz", hash = "sha256:11405fed67b68f406b3f3c7c5ae5104a79d2d309666d10d61b152e91d28fb968"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
googleapis-common-protos = ">=1.5.5"
|
||||
grpcio = ">=1.71.0"
|
||||
protobuf = ">=5.26.1,<6.0dev"
|
||||
grpcio = ">=1.62.3"
|
||||
protobuf = ">=4.21.6"
|
||||
|
||||
[[package]]
|
||||
name = "gunicorn"
|
||||
|
|
@ -1550,27 +1550,23 @@ all = ["flake8 (>=7.1.1)", "mypy (>=1.11.2)", "pytest (>=8.3.2)", "ruff (>=0.6.2
|
|||
|
||||
[[package]]
|
||||
name = "importlib-metadata"
|
||||
version = "8.5.0"
|
||||
version = "7.1.0"
|
||||
description = "Read metadata from Python packages"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
groups = ["main"]
|
||||
groups = ["main", "dev", "proxy-dev"]
|
||||
files = [
|
||||
{file = "importlib_metadata-8.5.0-py3-none-any.whl", hash = "sha256:45e54197d28b7a7f1559e60b95e7c567032b602131fbd588f1497f47880aa68b"},
|
||||
{file = "importlib_metadata-8.5.0.tar.gz", hash = "sha256:71522656f0abace1d072b9e5481a48f07c138e00f079c38c8f883823f9c26bd7"},
|
||||
{file = "importlib_metadata-7.1.0-py3-none-any.whl", hash = "sha256:30962b96c0c223483ed6cc7280e7f0199feb01a0e40cfae4d4450fc6fab1f570"},
|
||||
{file = "importlib_metadata-7.1.0.tar.gz", hash = "sha256:b78938b926ee8d5f020fc4772d487045805a55ddbad2ecf21c6d60938dc7fcd2"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
zipp = ">=3.20"
|
||||
zipp = ">=0.5"
|
||||
|
||||
[package.extras]
|
||||
check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\""]
|
||||
cover = ["pytest-cov"]
|
||||
doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
|
||||
enabler = ["pytest-enabler (>=2.2)"]
|
||||
docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
|
||||
perf = ["ipython"]
|
||||
test = ["flufl.flake8", "importlib-resources (>=1.3) ; python_version < \"3.9\"", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-perf (>=0.9.2)"]
|
||||
type = ["pytest-mypy"]
|
||||
testing = ["flufl.flake8", "importlib-resources (>=1.3) ; python_version < \"3.9\"", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy ; platform_python_implementation != \"PyPy\"", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
|
||||
|
||||
[[package]]
|
||||
name = "importlib-resources"
|
||||
|
|
@ -2326,6 +2322,142 @@ datalib = ["numpy (>=1)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)"]
|
|||
realtime = ["websockets (>=13,<16)"]
|
||||
voice-helpers = ["numpy (>=2.0.2)", "sounddevice (>=0.5.1)"]
|
||||
|
||||
[[package]]
|
||||
name = "opentelemetry-api"
|
||||
version = "1.25.0"
|
||||
description = "OpenTelemetry Python API"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
groups = ["dev", "proxy-dev"]
|
||||
files = [
|
||||
{file = "opentelemetry_api-1.25.0-py3-none-any.whl", hash = "sha256:757fa1aa020a0f8fa139f8959e53dec2051cc26b832e76fa839a6d76ecefd737"},
|
||||
{file = "opentelemetry_api-1.25.0.tar.gz", hash = "sha256:77c4985f62f2614e42ce77ee4c9da5fa5f0bc1e1821085e9a47533a9323ae869"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
deprecated = ">=1.2.6"
|
||||
importlib-metadata = ">=6.0,<=7.1"
|
||||
|
||||
[[package]]
|
||||
name = "opentelemetry-exporter-otlp"
|
||||
version = "1.25.0"
|
||||
description = "OpenTelemetry Collector Exporters"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
groups = ["dev", "proxy-dev"]
|
||||
files = [
|
||||
{file = "opentelemetry_exporter_otlp-1.25.0-py3-none-any.whl", hash = "sha256:d67a831757014a3bc3174e4cd629ae1493b7ba8d189e8a007003cacb9f1a6b60"},
|
||||
{file = "opentelemetry_exporter_otlp-1.25.0.tar.gz", hash = "sha256:ce03199c1680a845f82e12c0a6a8f61036048c07ec7a0bd943142aca8fa6ced0"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
opentelemetry-exporter-otlp-proto-grpc = "1.25.0"
|
||||
opentelemetry-exporter-otlp-proto-http = "1.25.0"
|
||||
|
||||
[[package]]
|
||||
name = "opentelemetry-exporter-otlp-proto-common"
|
||||
version = "1.25.0"
|
||||
description = "OpenTelemetry Protobuf encoding"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
groups = ["dev", "proxy-dev"]
|
||||
files = [
|
||||
{file = "opentelemetry_exporter_otlp_proto_common-1.25.0-py3-none-any.whl", hash = "sha256:15637b7d580c2675f70246563363775b4e6de947871e01d0f4e3881d1848d693"},
|
||||
{file = "opentelemetry_exporter_otlp_proto_common-1.25.0.tar.gz", hash = "sha256:c93f4e30da4eee02bacd1e004eb82ce4da143a2f8e15b987a9f603e0a85407d3"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
opentelemetry-proto = "1.25.0"
|
||||
|
||||
[[package]]
|
||||
name = "opentelemetry-exporter-otlp-proto-grpc"
|
||||
version = "1.25.0"
|
||||
description = "OpenTelemetry Collector Protobuf over gRPC Exporter"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
groups = ["dev", "proxy-dev"]
|
||||
files = [
|
||||
{file = "opentelemetry_exporter_otlp_proto_grpc-1.25.0-py3-none-any.whl", hash = "sha256:3131028f0c0a155a64c430ca600fd658e8e37043cb13209f0109db5c1a3e4eb4"},
|
||||
{file = "opentelemetry_exporter_otlp_proto_grpc-1.25.0.tar.gz", hash = "sha256:c0b1661415acec5af87625587efa1ccab68b873745ca0ee96b69bb1042087eac"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
deprecated = ">=1.2.6"
|
||||
googleapis-common-protos = ">=1.52,<2.0"
|
||||
grpcio = ">=1.0.0,<2.0.0"
|
||||
opentelemetry-api = ">=1.15,<2.0"
|
||||
opentelemetry-exporter-otlp-proto-common = "1.25.0"
|
||||
opentelemetry-proto = "1.25.0"
|
||||
opentelemetry-sdk = ">=1.25.0,<1.26.0"
|
||||
|
||||
[[package]]
|
||||
name = "opentelemetry-exporter-otlp-proto-http"
|
||||
version = "1.25.0"
|
||||
description = "OpenTelemetry Collector Protobuf over HTTP Exporter"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
groups = ["dev", "proxy-dev"]
|
||||
files = [
|
||||
{file = "opentelemetry_exporter_otlp_proto_http-1.25.0-py3-none-any.whl", hash = "sha256:2eca686ee11b27acd28198b3ea5e5863a53d1266b91cda47c839d95d5e0541a6"},
|
||||
{file = "opentelemetry_exporter_otlp_proto_http-1.25.0.tar.gz", hash = "sha256:9f8723859e37c75183ea7afa73a3542f01d0fd274a5b97487ea24cb683d7d684"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
deprecated = ">=1.2.6"
|
||||
googleapis-common-protos = ">=1.52,<2.0"
|
||||
opentelemetry-api = ">=1.15,<2.0"
|
||||
opentelemetry-exporter-otlp-proto-common = "1.25.0"
|
||||
opentelemetry-proto = "1.25.0"
|
||||
opentelemetry-sdk = ">=1.25.0,<1.26.0"
|
||||
requests = ">=2.7,<3.0"
|
||||
|
||||
[[package]]
|
||||
name = "opentelemetry-proto"
|
||||
version = "1.25.0"
|
||||
description = "OpenTelemetry Python Proto"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
groups = ["dev", "proxy-dev"]
|
||||
files = [
|
||||
{file = "opentelemetry_proto-1.25.0-py3-none-any.whl", hash = "sha256:f07e3341c78d835d9b86665903b199893befa5e98866f63d22b00d0b7ca4972f"},
|
||||
{file = "opentelemetry_proto-1.25.0.tar.gz", hash = "sha256:35b6ef9dc4a9f7853ecc5006738ad40443701e52c26099e197895cbda8b815a3"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
protobuf = ">=3.19,<5.0"
|
||||
|
||||
[[package]]
|
||||
name = "opentelemetry-sdk"
|
||||
version = "1.25.0"
|
||||
description = "OpenTelemetry Python SDK"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
groups = ["dev", "proxy-dev"]
|
||||
files = [
|
||||
{file = "opentelemetry_sdk-1.25.0-py3-none-any.whl", hash = "sha256:d97ff7ec4b351692e9d5a15af570c693b8715ad78b8aafbec5c7100fe966b4c9"},
|
||||
{file = "opentelemetry_sdk-1.25.0.tar.gz", hash = "sha256:ce7fc319c57707ef5bf8b74fb9f8ebdb8bfafbe11898410e0d2a761d08a98ec7"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
opentelemetry-api = "1.25.0"
|
||||
opentelemetry-semantic-conventions = "0.46b0"
|
||||
typing-extensions = ">=3.7.4"
|
||||
|
||||
[[package]]
|
||||
name = "opentelemetry-semantic-conventions"
|
||||
version = "0.46b0"
|
||||
description = "OpenTelemetry Semantic Conventions"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
groups = ["dev", "proxy-dev"]
|
||||
files = [
|
||||
{file = "opentelemetry_semantic_conventions-0.46b0-py3-none-any.whl", hash = "sha256:6daef4ef9fa51d51855d9f8e0ccd3a1bd59e0e545abe99ac6203804e36ab3e07"},
|
||||
{file = "opentelemetry_semantic_conventions-0.46b0.tar.gz", hash = "sha256:fbc982ecbb6a6e90869b15c1673be90bd18c8a56ff1cffc0864e38e2edffaefa"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
opentelemetry-api = "1.25.0"
|
||||
|
||||
[[package]]
|
||||
name = "orjson"
|
||||
version = "3.10.15"
|
||||
|
|
@ -2668,25 +2800,25 @@ testing = ["google-api-core (>=1.31.5)"]
|
|||
|
||||
[[package]]
|
||||
name = "protobuf"
|
||||
version = "5.29.4"
|
||||
version = "4.25.6"
|
||||
description = ""
|
||||
optional = true
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"extra-proxy\""
|
||||
groups = ["main", "dev", "proxy-dev"]
|
||||
files = [
|
||||
{file = "protobuf-5.29.4-cp310-abi3-win32.whl", hash = "sha256:13eb236f8eb9ec34e63fc8b1d6efd2777d062fa6aaa68268fb67cf77f6839ad7"},
|
||||
{file = "protobuf-5.29.4-cp310-abi3-win_amd64.whl", hash = "sha256:bcefcdf3976233f8a502d265eb65ea740c989bacc6c30a58290ed0e519eb4b8d"},
|
||||
{file = "protobuf-5.29.4-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:307ecba1d852ec237e9ba668e087326a67564ef83e45a0189a772ede9e854dd0"},
|
||||
{file = "protobuf-5.29.4-cp38-abi3-manylinux2014_aarch64.whl", hash = "sha256:aec4962f9ea93c431d5714ed1be1c93f13e1a8618e70035ba2b0564d9e633f2e"},
|
||||
{file = "protobuf-5.29.4-cp38-abi3-manylinux2014_x86_64.whl", hash = "sha256:d7d3f7d1d5a66ed4942d4fefb12ac4b14a29028b209d4bfb25c68ae172059922"},
|
||||
{file = "protobuf-5.29.4-cp38-cp38-win32.whl", hash = "sha256:1832f0515b62d12d8e6ffc078d7e9eb06969aa6dc13c13e1036e39d73bebc2de"},
|
||||
{file = "protobuf-5.29.4-cp38-cp38-win_amd64.whl", hash = "sha256:476cb7b14914c780605a8cf62e38c2a85f8caff2e28a6a0bad827ec7d6c85d68"},
|
||||
{file = "protobuf-5.29.4-cp39-cp39-win32.whl", hash = "sha256:fd32223020cb25a2cc100366f1dedc904e2d71d9322403224cdde5fdced0dabe"},
|
||||
{file = "protobuf-5.29.4-cp39-cp39-win_amd64.whl", hash = "sha256:678974e1e3a9b975b8bc2447fca458db5f93a2fb6b0c8db46b6675b5b5346812"},
|
||||
{file = "protobuf-5.29.4-py3-none-any.whl", hash = "sha256:3fde11b505e1597f71b875ef2fc52062b6a9740e5f7c8997ce878b6009145862"},
|
||||
{file = "protobuf-5.29.4.tar.gz", hash = "sha256:4f1dfcd7997b31ef8f53ec82781ff434a28bf71d9102ddde14d076adcfc78c99"},
|
||||
{file = "protobuf-4.25.6-cp310-abi3-win32.whl", hash = "sha256:61df6b5786e2b49fc0055f636c1e8f0aff263808bb724b95b164685ac1bcc13a"},
|
||||
{file = "protobuf-4.25.6-cp310-abi3-win_amd64.whl", hash = "sha256:b8f837bfb77513fe0e2f263250f423217a173b6d85135be4d81e96a4653bcd3c"},
|
||||
{file = "protobuf-4.25.6-cp37-abi3-macosx_10_9_universal2.whl", hash = "sha256:6d4381f2417606d7e01750e2729fe6fbcda3f9883aa0c32b51d23012bded6c91"},
|
||||
{file = "protobuf-4.25.6-cp37-abi3-manylinux2014_aarch64.whl", hash = "sha256:5dd800da412ba7f6f26d2c08868a5023ce624e1fdb28bccca2dc957191e81fb5"},
|
||||
{file = "protobuf-4.25.6-cp37-abi3-manylinux2014_x86_64.whl", hash = "sha256:4434ff8bb5576f9e0c78f47c41cdf3a152c0b44de475784cd3fd170aef16205a"},
|
||||
{file = "protobuf-4.25.6-cp38-cp38-win32.whl", hash = "sha256:8bad0f9e8f83c1fbfcc34e573352b17dfce7d0519512df8519994168dc015d7d"},
|
||||
{file = "protobuf-4.25.6-cp38-cp38-win_amd64.whl", hash = "sha256:b6905b68cde3b8243a198268bb46fbec42b3455c88b6b02fb2529d2c306d18fc"},
|
||||
{file = "protobuf-4.25.6-cp39-cp39-win32.whl", hash = "sha256:3f3b0b39db04b509859361ac9bca65a265fe9342e6b9406eda58029f5b1d10b2"},
|
||||
{file = "protobuf-4.25.6-cp39-cp39-win_amd64.whl", hash = "sha256:6ef2045f89d4ad8d95fd43cd84621487832a61d15b49500e4c1350e8a0ef96be"},
|
||||
{file = "protobuf-4.25.6-py3-none-any.whl", hash = "sha256:07972021c8e30b870cfc0863409d033af940213e0e7f64e27fe017b929d2c9f7"},
|
||||
{file = "protobuf-4.25.6.tar.gz", hash = "sha256:f8cfbae7c5afd0d0eaccbe73267339bff605a2315860bb1ba08eb66670a9a91f"},
|
||||
]
|
||||
markers = {main = "extra == \"extra-proxy\""}
|
||||
|
||||
[[package]]
|
||||
name = "pyasn1"
|
||||
|
|
@ -3342,7 +3474,7 @@ version = "2.31.0"
|
|||
description = "Python HTTP for Humans."
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
groups = ["main"]
|
||||
groups = ["main", "dev", "proxy-dev"]
|
||||
files = [
|
||||
{file = "requests-2.31.0-py3-none-any.whl", hash = "sha256:58cd2187c01e70e6e26505bca751777aa9f2ee0b7f4300988b709f44e013003f"},
|
||||
{file = "requests-2.31.0.tar.gz", hash = "sha256:942c5a758f98d790eaed1a29cb6eefc7ffb0d1cf7af05c3d2791656dbd6ad1e1"},
|
||||
|
|
@ -4027,7 +4159,7 @@ version = "1.26.20"
|
|||
description = "HTTP library with thread-safe connection pooling, file post, and more."
|
||||
optional = false
|
||||
python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
|
||||
groups = ["main"]
|
||||
groups = ["main", "dev", "proxy-dev"]
|
||||
markers = "python_version < \"3.10\""
|
||||
files = [
|
||||
{file = "urllib3-1.26.20-py2.py3-none-any.whl", hash = "sha256:0ed14ccfbf1c30a9072c7ca157e4319b70d65f623e91e7b32fadb2853431016e"},
|
||||
|
|
@ -4045,7 +4177,7 @@ version = "2.2.3"
|
|||
description = "HTTP library with thread-safe connection pooling, file post, and more."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
groups = ["main", "dev"]
|
||||
groups = ["main", "dev", "proxy-dev"]
|
||||
markers = "python_version >= \"3.10\""
|
||||
files = [
|
||||
{file = "urllib3-2.2.3-py3-none-any.whl", hash = "sha256:ca899ca043dcb1bafa3e262d73aa25c465bfb49e0bd9dd5d59f1d0acba2f8fac"},
|
||||
|
|
@ -4229,6 +4361,95 @@ files = [
|
|||
{file = "websockets-13.1.tar.gz", hash = "sha256:a3b3366087c1bc0a2795111edcadddb8b3b59509d5db5d7ea3fdd69f954a8878"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "wrapt"
|
||||
version = "1.17.2"
|
||||
description = "Module for decorators, wrappers and monkey patching."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
groups = ["dev", "proxy-dev"]
|
||||
files = [
|
||||
{file = "wrapt-1.17.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:3d57c572081fed831ad2d26fd430d565b76aa277ed1d30ff4d40670b1c0dd984"},
|
||||
{file = "wrapt-1.17.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:b5e251054542ae57ac7f3fba5d10bfff615b6c2fb09abeb37d2f1463f841ae22"},
|
||||
{file = "wrapt-1.17.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:80dd7db6a7cb57ffbc279c4394246414ec99537ae81ffd702443335a61dbf3a7"},
|
||||
{file = "wrapt-1.17.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0a6e821770cf99cc586d33833b2ff32faebdbe886bd6322395606cf55153246c"},
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
{file = "wrapt-1.17.2-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:d5e2439eecc762cd85e7bd37161d4714aa03a33c5ba884e26c81559817ca0925"},
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||||
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|
||||
{file = "wrapt-1.17.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:8fdbdb757d5390f7c675e558fd3186d590973244fab0c5fe63d373ade3e99d40"},
|
||||
{file = "wrapt-1.17.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5bb1d0dbf99411f3d871deb6faa9aabb9d4e744d67dcaaa05399af89d847a91d"},
|
||||
{file = "wrapt-1.17.2-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d18a4865f46b8579d44e4fe1e2bcbc6472ad83d98e22a26c963d46e4c125ef0b"},
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||||
{file = "wrapt-1.17.2-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bc570b5f14a79734437cb7b0500376b6b791153314986074486e0b0fa8d71d98"},
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||||
{file = "wrapt-1.17.2-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:6d9187b01bebc3875bac9b087948a2bccefe464a7d8f627cf6e48b1bbae30f82"},
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{file = "wrapt-1.17.2-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:9e8659775f1adf02eb1e6f109751268e493c73716ca5761f8acb695e52a756ae"},
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{file = "wrapt-1.17.2-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:e8b2816ebef96d83657b56306152a93909a83f23994f4b30ad4573b00bd11bb9"},
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{file = "wrapt-1.17.2-cp312-cp312-win32.whl", hash = "sha256:468090021f391fe0056ad3e807e3d9034e0fd01adcd3bdfba977b6fdf4213ea9"},
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{file = "wrapt-1.17.2-cp312-cp312-win_amd64.whl", hash = "sha256:ec89ed91f2fa8e3f52ae53cd3cf640d6feff92ba90d62236a81e4e563ac0e991"},
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{file = "wrapt-1.17.2-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:6ed6ffac43aecfe6d86ec5b74b06a5be33d5bb9243d055141e8cabb12aa08125"},
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{file = "wrapt-1.17.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:a604bf7a053f8362d27eb9fefd2097f82600b856d5abe996d623babd067b1ab5"},
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{file = "wrapt-1.17.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5cbabee4f083b6b4cd282f5b817a867cf0b1028c54d445b7ec7cfe6505057cf8"},
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{file = "wrapt-1.17.2-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:49703ce2ddc220df165bd2962f8e03b84c89fee2d65e1c24a7defff6f988f4d6"},
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||||
{file = "wrapt-1.17.2-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8112e52c5822fc4253f3901b676c55ddf288614dc7011634e2719718eaa187dc"},
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||||
{file = "wrapt-1.17.2-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:9fee687dce376205d9a494e9c121e27183b2a3df18037f89d69bd7b35bcf59e2"},
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||||
{file = "wrapt-1.17.2-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:18983c537e04d11cf027fbb60a1e8dfd5190e2b60cc27bc0808e653e7b218d1b"},
|
||||
{file = "wrapt-1.17.2-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:703919b1633412ab54bcf920ab388735832fdcb9f9a00ae49387f0fe67dad504"},
|
||||
{file = "wrapt-1.17.2-cp313-cp313-win32.whl", hash = "sha256:abbb9e76177c35d4e8568e58650aa6926040d6a9f6f03435b7a522bf1c487f9a"},
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||||
{file = "wrapt-1.17.2-cp313-cp313-win_amd64.whl", hash = "sha256:69606d7bb691b50a4240ce6b22ebb319c1cfb164e5f6569835058196e0f3a845"},
|
||||
{file = "wrapt-1.17.2-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:4a721d3c943dae44f8e243b380cb645a709ba5bd35d3ad27bc2ed947e9c68192"},
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||||
{file = "wrapt-1.17.2-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:766d8bbefcb9e00c3ac3b000d9acc51f1b399513f44d77dfe0eb026ad7c9a19b"},
|
||||
{file = "wrapt-1.17.2-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:e496a8ce2c256da1eb98bd15803a79bee00fc351f5dfb9ea82594a3f058309e0"},
|
||||
{file = "wrapt-1.17.2-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:40d615e4fe22f4ad3528448c193b218e077656ca9ccb22ce2cb20db730f8d306"},
|
||||
{file = "wrapt-1.17.2-cp313-cp313t-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a5aaeff38654462bc4b09023918b7f21790efb807f54c000a39d41d69cf552cb"},
|
||||
{file = "wrapt-1.17.2-cp313-cp313t-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9a7d15bbd2bc99e92e39f49a04653062ee6085c0e18b3b7512a4f2fe91f2d681"},
|
||||
{file = "wrapt-1.17.2-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:e3890b508a23299083e065f435a492b5435eba6e304a7114d2f919d400888cc6"},
|
||||
{file = "wrapt-1.17.2-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:8c8b293cd65ad716d13d8dd3624e42e5a19cc2a2f1acc74b30c2c13f15cb61a6"},
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||||
{file = "wrapt-1.17.2-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:4c82b8785d98cdd9fed4cac84d765d234ed3251bd6afe34cb7ac523cb93e8b4f"},
|
||||
{file = "wrapt-1.17.2-cp313-cp313t-win32.whl", hash = "sha256:13e6afb7fe71fe7485a4550a8844cc9ffbe263c0f1a1eea569bc7091d4898555"},
|
||||
{file = "wrapt-1.17.2-cp313-cp313t-win_amd64.whl", hash = "sha256:eaf675418ed6b3b31c7a989fd007fa7c3be66ce14e5c3b27336383604c9da85c"},
|
||||
{file = "wrapt-1.17.2-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:5c803c401ea1c1c18de70a06a6f79fcc9c5acfc79133e9869e730ad7f8ad8ef9"},
|
||||
{file = "wrapt-1.17.2-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:f917c1180fdb8623c2b75a99192f4025e412597c50b2ac870f156de8fb101119"},
|
||||
{file = "wrapt-1.17.2-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:ecc840861360ba9d176d413a5489b9a0aff6d6303d7e733e2c4623cfa26904a6"},
|
||||
{file = "wrapt-1.17.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bb87745b2e6dc56361bfde481d5a378dc314b252a98d7dd19a651a3fa58f24a9"},
|
||||
{file = "wrapt-1.17.2-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:58455b79ec2661c3600e65c0a716955adc2410f7383755d537584b0de41b1d8a"},
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||||
{file = "wrapt-1.17.2-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b4e42a40a5e164cbfdb7b386c966a588b1047558a990981ace551ed7e12ca9c2"},
|
||||
{file = "wrapt-1.17.2-cp38-cp38-musllinux_1_2_aarch64.whl", hash = "sha256:91bd7d1773e64019f9288b7a5101f3ae50d3d8e6b1de7edee9c2ccc1d32f0c0a"},
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||||
{file = "wrapt-1.17.2-cp38-cp38-musllinux_1_2_i686.whl", hash = "sha256:bb90fb8bda722a1b9d48ac1e6c38f923ea757b3baf8ebd0c82e09c5c1a0e7a04"},
|
||||
{file = "wrapt-1.17.2-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:08e7ce672e35efa54c5024936e559469436f8b8096253404faeb54d2a878416f"},
|
||||
{file = "wrapt-1.17.2-cp38-cp38-win32.whl", hash = "sha256:410a92fefd2e0e10d26210e1dfb4a876ddaf8439ef60d6434f21ef8d87efc5b7"},
|
||||
{file = "wrapt-1.17.2-cp38-cp38-win_amd64.whl", hash = "sha256:95c658736ec15602da0ed73f312d410117723914a5c91a14ee4cdd72f1d790b3"},
|
||||
{file = "wrapt-1.17.2-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:99039fa9e6306880572915728d7f6c24a86ec57b0a83f6b2491e1d8ab0235b9a"},
|
||||
{file = "wrapt-1.17.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:2696993ee1eebd20b8e4ee4356483c4cb696066ddc24bd70bcbb80fa56ff9061"},
|
||||
{file = "wrapt-1.17.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:612dff5db80beef9e649c6d803a8d50c409082f1fedc9dbcdfde2983b2025b82"},
|
||||
{file = "wrapt-1.17.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:62c2caa1585c82b3f7a7ab56afef7b3602021d6da34fbc1cf234ff139fed3cd9"},
|
||||
{file = "wrapt-1.17.2-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c958bcfd59bacc2d0249dcfe575e71da54f9dcf4a8bdf89c4cb9a68a1170d73f"},
|
||||
{file = "wrapt-1.17.2-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fc78a84e2dfbc27afe4b2bd7c80c8db9bca75cc5b85df52bfe634596a1da846b"},
|
||||
{file = "wrapt-1.17.2-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:ba0f0eb61ef00ea10e00eb53a9129501f52385c44853dbd6c4ad3f403603083f"},
|
||||
{file = "wrapt-1.17.2-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:1e1fe0e6ab7775fd842bc39e86f6dcfc4507ab0ffe206093e76d61cde37225c8"},
|
||||
{file = "wrapt-1.17.2-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:c86563182421896d73858e08e1db93afdd2b947a70064b813d515d66549e15f9"},
|
||||
{file = "wrapt-1.17.2-cp39-cp39-win32.whl", hash = "sha256:f393cda562f79828f38a819f4788641ac7c4085f30f1ce1a68672baa686482bb"},
|
||||
{file = "wrapt-1.17.2-cp39-cp39-win_amd64.whl", hash = "sha256:36ccae62f64235cf8ddb682073a60519426fdd4725524ae38874adf72b5f2aeb"},
|
||||
{file = "wrapt-1.17.2-py3-none-any.whl", hash = "sha256:b18f2d1533a71f069c7f82d524a52599053d4c7166e9dd374ae2136b7f40f7c8"},
|
||||
{file = "wrapt-1.17.2.tar.gz", hash = "sha256:41388e9d4d1522446fe79d3213196bd9e3b301a336965b9e27ca2788ebd122f3"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "wsproto"
|
||||
version = "1.2.0"
|
||||
|
|
@ -4363,7 +4584,7 @@ version = "3.20.2"
|
|||
description = "Backport of pathlib-compatible object wrapper for zip files"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
groups = ["main"]
|
||||
groups = ["main", "dev", "proxy-dev"]
|
||||
files = [
|
||||
{file = "zipp-3.20.2-py3-none-any.whl", hash = "sha256:a817ac80d6cf4b23bf7f2828b7cabf326f15a001bea8b1f9b49631780ba28350"},
|
||||
{file = "zipp-3.20.2.tar.gz", hash = "sha256:bc9eb26f4506fda01b81bcde0ca78103b6e62f991b381fec825435c836edbc29"},
|
||||
|
|
@ -4384,4 +4605,4 @@ proxy = ["PyJWT", "apscheduler", "backoff", "boto3", "cryptography", "fastapi",
|
|||
[metadata]
|
||||
lock-version = "2.1"
|
||||
python-versions = ">=3.8.1,<4.0, !=3.9.7"
|
||||
content-hash = "40074b2e47aae8ece058be9a42eda3ca0618e27e4fc9d6529793816df7adb6c8"
|
||||
content-hash = "adefc5c35b625ab156ff674c880256643a22880012451d4ade7fa2ef11f5885d"
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
[tool.poetry]
|
||||
name = "litellm"
|
||||
version = "1.67.1"
|
||||
version = "1.67.2"
|
||||
description = "Library to easily interface with LLM API providers"
|
||||
authors = ["BerriAI"]
|
||||
license = "MIT"
|
||||
|
|
@ -107,18 +107,24 @@ types-requests = "*"
|
|||
types-setuptools = "*"
|
||||
types-redis = "*"
|
||||
types-PyYAML = "*"
|
||||
opentelemetry-api = "1.25.0"
|
||||
opentelemetry-sdk = "1.25.0"
|
||||
opentelemetry-exporter-otlp = "1.25.0"
|
||||
|
||||
[tool.poetry.group.proxy-dev.dependencies]
|
||||
prisma = "0.11.0"
|
||||
hypercorn = "^0.15.0"
|
||||
prometheus-client = "0.20.0"
|
||||
opentelemetry-api = "1.25.0"
|
||||
opentelemetry-sdk = "1.25.0"
|
||||
opentelemetry-exporter-otlp = "1.25.0"
|
||||
|
||||
[build-system]
|
||||
requires = ["poetry-core", "wheel"]
|
||||
build-backend = "poetry.core.masonry.api"
|
||||
|
||||
[tool.commitizen]
|
||||
version = "1.67.1"
|
||||
version = "1.67.2"
|
||||
version_files = [
|
||||
"pyproject.toml:^version"
|
||||
]
|
||||
|
|
|
|||
|
|
@ -22,9 +22,6 @@
|
|||
- Audit Logs with retention policy
|
||||
- Control Allowed IP Addresses that can access your Cloud LiteLLM Instance
|
||||
|
||||
For security inquiries, please contact us at support@berri.ai
|
||||
|
||||
|
||||
For security inquiries, please contact us at support@berri.ai
|
||||
|
||||
#### Supported data regions for LiteLLM Cloud
|
||||
|
|
|
|||
4
test-results/.last-run.json
Normal file
4
test-results/.last-run.json
Normal file
|
|
@ -0,0 +1,4 @@
|
|||
{
|
||||
"status": "failed",
|
||||
"failedTests": []
|
||||
}
|
||||
231
tests/litellm/integrations/arize/test_arize_utils.py
Normal file
231
tests/litellm/integrations/arize/test_arize_utils.py
Normal file
|
|
@ -0,0 +1,231 @@
|
|||
import json
|
||||
import os
|
||||
import sys
|
||||
from typing import Optional
|
||||
|
||||
# Adds the grandparent directory to sys.path to allow importing project modules
|
||||
sys.path.insert(0, os.path.abspath("../.."))
|
||||
|
||||
import asyncio
|
||||
import litellm
|
||||
import pytest
|
||||
from litellm.integrations.arize.arize import ArizeLogger
|
||||
from litellm.integrations.custom_logger import CustomLogger
|
||||
from litellm.integrations._types.open_inference import (
|
||||
SpanAttributes,
|
||||
MessageAttributes,
|
||||
ToolCallAttributes,
|
||||
)
|
||||
from litellm.types.utils import Choices, StandardCallbackDynamicParams
|
||||
|
||||
|
||||
def test_arize_set_attributes():
|
||||
"""
|
||||
Test setting attributes for Arize, including all custom LLM attributes.
|
||||
Ensures that the correct span attributes are being added during a request.
|
||||
"""
|
||||
from unittest.mock import MagicMock
|
||||
from litellm.types.utils import ModelResponse
|
||||
|
||||
span = MagicMock() # Mocked tracing span to test attribute setting
|
||||
|
||||
# Construct kwargs to simulate a real LLM request scenario
|
||||
kwargs = {
|
||||
"model": "gpt-4o",
|
||||
"messages": [{"role": "user", "content": "Basic Request Content"}],
|
||||
"standard_logging_object": {
|
||||
"model_parameters": {"user": "test_user"},
|
||||
"metadata": {"key_1": "value_1", "key_2": None},
|
||||
"call_type": "completion",
|
||||
},
|
||||
"optional_params": {
|
||||
"max_tokens": "100",
|
||||
"temperature": "1",
|
||||
"top_p": "5",
|
||||
"stream": False,
|
||||
"user": "test_user",
|
||||
"tools": [
|
||||
{
|
||||
"function": {
|
||||
"name": "get_weather",
|
||||
"description": "Fetches weather details.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"location": {
|
||||
"type": "string",
|
||||
"description": "City name",
|
||||
}
|
||||
},
|
||||
"required": ["location"],
|
||||
},
|
||||
}
|
||||
}
|
||||
],
|
||||
"functions": [{"name": "get_weather"}, {"name": "get_stock_price"}],
|
||||
},
|
||||
"litellm_params": {"custom_llm_provider": "openai"},
|
||||
}
|
||||
|
||||
# Simulated LLM response object
|
||||
response_obj = ModelResponse(
|
||||
usage={"total_tokens": 100, "completion_tokens": 60, "prompt_tokens": 40},
|
||||
choices=[
|
||||
Choices(message={"role": "assistant", "content": "Basic Response Content"})
|
||||
],
|
||||
model="gpt-4o",
|
||||
id="chatcmpl-ID",
|
||||
)
|
||||
|
||||
# Apply attribute setting via ArizeLogger
|
||||
ArizeLogger.set_arize_attributes(span, kwargs, response_obj)
|
||||
|
||||
# Validate that the expected number of attributes were set
|
||||
assert span.set_attribute.call_count == 28
|
||||
|
||||
# Metadata attached to the span
|
||||
span.set_attribute.assert_any_call(
|
||||
SpanAttributes.METADATA, json.dumps({"key_1": "value_1", "key_2": None})
|
||||
)
|
||||
|
||||
# Basic LLM information
|
||||
span.set_attribute.assert_any_call(SpanAttributes.LLM_MODEL_NAME, "gpt-4o")
|
||||
span.set_attribute.assert_any_call("llm.request.type", "completion")
|
||||
span.set_attribute.assert_any_call(SpanAttributes.LLM_PROVIDER, "openai")
|
||||
|
||||
# LLM generation parameters
|
||||
span.set_attribute.assert_any_call("llm.request.max_tokens", "100")
|
||||
span.set_attribute.assert_any_call("llm.request.temperature", "1")
|
||||
span.set_attribute.assert_any_call("llm.request.top_p", "5")
|
||||
|
||||
# Streaming and user info
|
||||
span.set_attribute.assert_any_call("llm.is_streaming", "False")
|
||||
span.set_attribute.assert_any_call("llm.user", "test_user")
|
||||
|
||||
# Response metadata
|
||||
span.set_attribute.assert_any_call("llm.response.id", "chatcmpl-ID")
|
||||
span.set_attribute.assert_any_call("llm.response.model", "gpt-4o")
|
||||
span.set_attribute.assert_any_call(SpanAttributes.OPENINFERENCE_SPAN_KIND, "LLM")
|
||||
|
||||
# Request message content and metadata
|
||||
span.set_attribute.assert_any_call(
|
||||
SpanAttributes.INPUT_VALUE, "Basic Request Content"
|
||||
)
|
||||
span.set_attribute.assert_any_call(
|
||||
f"{SpanAttributes.LLM_INPUT_MESSAGES}.0.{MessageAttributes.MESSAGE_ROLE}",
|
||||
"user",
|
||||
)
|
||||
span.set_attribute.assert_any_call(
|
||||
f"{SpanAttributes.LLM_INPUT_MESSAGES}.0.{MessageAttributes.MESSAGE_CONTENT}",
|
||||
"Basic Request Content",
|
||||
)
|
||||
|
||||
# Tool call definitions and function names
|
||||
span.set_attribute.assert_any_call(
|
||||
f"{SpanAttributes.LLM_TOOLS}.0.{SpanAttributes.TOOL_NAME}", "get_weather"
|
||||
)
|
||||
span.set_attribute.assert_any_call(
|
||||
f"{SpanAttributes.LLM_TOOLS}.0.{SpanAttributes.TOOL_DESCRIPTION}",
|
||||
"Fetches weather details.",
|
||||
)
|
||||
span.set_attribute.assert_any_call(
|
||||
f"{SpanAttributes.LLM_TOOLS}.0.{SpanAttributes.TOOL_PARAMETERS}",
|
||||
json.dumps(
|
||||
{
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"location": {"type": "string", "description": "City name"}
|
||||
},
|
||||
"required": ["location"],
|
||||
}
|
||||
),
|
||||
)
|
||||
|
||||
# Tool calls captured from optional_params
|
||||
span.set_attribute.assert_any_call(
|
||||
f"{MessageAttributes.MESSAGE_TOOL_CALLS}.0.{ToolCallAttributes.TOOL_CALL_FUNCTION_NAME}",
|
||||
"get_weather",
|
||||
)
|
||||
span.set_attribute.assert_any_call(
|
||||
f"{MessageAttributes.MESSAGE_TOOL_CALLS}.1.{ToolCallAttributes.TOOL_CALL_FUNCTION_NAME}",
|
||||
"get_stock_price",
|
||||
)
|
||||
|
||||
# Invocation parameters
|
||||
span.set_attribute.assert_any_call(
|
||||
SpanAttributes.LLM_INVOCATION_PARAMETERS, '{"user": "test_user"}'
|
||||
)
|
||||
|
||||
# User ID
|
||||
span.set_attribute.assert_any_call(SpanAttributes.USER_ID, "test_user")
|
||||
|
||||
# Output message content
|
||||
span.set_attribute.assert_any_call(
|
||||
SpanAttributes.OUTPUT_VALUE, "Basic Response Content"
|
||||
)
|
||||
span.set_attribute.assert_any_call(
|
||||
f"{SpanAttributes.LLM_OUTPUT_MESSAGES}.0.{MessageAttributes.MESSAGE_ROLE}",
|
||||
"assistant",
|
||||
)
|
||||
span.set_attribute.assert_any_call(
|
||||
f"{SpanAttributes.LLM_OUTPUT_MESSAGES}.0.{MessageAttributes.MESSAGE_CONTENT}",
|
||||
"Basic Response Content",
|
||||
)
|
||||
|
||||
# Token counts
|
||||
span.set_attribute.assert_any_call(SpanAttributes.LLM_TOKEN_COUNT_TOTAL, 100)
|
||||
span.set_attribute.assert_any_call(SpanAttributes.LLM_TOKEN_COUNT_COMPLETION, 60)
|
||||
span.set_attribute.assert_any_call(SpanAttributes.LLM_TOKEN_COUNT_PROMPT, 40)
|
||||
|
||||
|
||||
class TestArizeLogger(CustomLogger):
|
||||
"""
|
||||
Custom logger implementation to capture standard_callback_dynamic_params.
|
||||
Used to verify that dynamic config keys are being passed to callbacks.
|
||||
"""
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.standard_callback_dynamic_params: Optional[
|
||||
StandardCallbackDynamicParams
|
||||
] = None
|
||||
|
||||
async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
|
||||
# Capture dynamic params and print them for verification
|
||||
print("logged kwargs", json.dumps(kwargs, indent=4, default=str))
|
||||
self.standard_callback_dynamic_params = kwargs.get(
|
||||
"standard_callback_dynamic_params"
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_arize_dynamic_params():
|
||||
"""
|
||||
Test to ensure that dynamic Arize keys (API key and space key)
|
||||
are received inside the callback logger at runtime.
|
||||
"""
|
||||
test_arize_logger = TestArizeLogger()
|
||||
litellm.callbacks = [test_arize_logger]
|
||||
|
||||
# Perform a mocked async completion call to trigger logging
|
||||
await litellm.acompletion(
|
||||
model="gpt-4o",
|
||||
messages=[{"role": "user", "content": "Basic Request Content"}],
|
||||
mock_response="test",
|
||||
arize_api_key="test_api_key_dynamic",
|
||||
arize_space_key="test_space_key_dynamic",
|
||||
)
|
||||
|
||||
# Allow for async propagation
|
||||
await asyncio.sleep(2)
|
||||
|
||||
# Assert dynamic parameters were received in the callback
|
||||
assert test_arize_logger.standard_callback_dynamic_params is not None
|
||||
assert (
|
||||
test_arize_logger.standard_callback_dynamic_params.get("arize_api_key")
|
||||
== "test_api_key_dynamic"
|
||||
)
|
||||
assert (
|
||||
test_arize_logger.standard_callback_dynamic_params.get("arize_space_key")
|
||||
== "test_space_key_dynamic"
|
||||
)
|
||||
98
tests/litellm/integrations/test_agentops.py
Normal file
98
tests/litellm/integrations/test_agentops.py
Normal file
|
|
@ -0,0 +1,98 @@
|
|||
import os
|
||||
import sys
|
||||
import pytest
|
||||
from unittest.mock import patch, MagicMock
|
||||
|
||||
sys.path.insert(0, os.path.abspath("../..")) # Adds the parent directory to the system-path
|
||||
|
||||
from litellm.integrations.agentops.agentops import AgentOps, AgentOpsConfig
|
||||
|
||||
@pytest.fixture
|
||||
def mock_auth_response():
|
||||
return {
|
||||
"token": "test_jwt_token",
|
||||
"project_id": "test_project_id"
|
||||
}
|
||||
|
||||
@pytest.fixture
|
||||
def agentops_config():
|
||||
return AgentOpsConfig(
|
||||
endpoint="https://otlp.agentops.cloud/v1/traces",
|
||||
api_key="test_api_key",
|
||||
service_name="test_service",
|
||||
deployment_environment="test_env",
|
||||
auth_endpoint="https://api.agentops.ai/v3/auth/token"
|
||||
)
|
||||
|
||||
def test_agentops_config_from_env():
|
||||
"""Test that AgentOpsConfig correctly reads from environment variables"""
|
||||
with patch.dict(os.environ, {
|
||||
"AGENTOPS_API_KEY": "test_key",
|
||||
"AGENTOPS_SERVICE_NAME": "test_service",
|
||||
"AGENTOPS_ENVIRONMENT": "test_env"
|
||||
}):
|
||||
config = AgentOpsConfig.from_env()
|
||||
assert config.api_key == "test_key"
|
||||
assert config.service_name == "test_service"
|
||||
assert config.deployment_environment == "test_env"
|
||||
assert config.endpoint == "https://otlp.agentops.cloud/v1/traces"
|
||||
assert config.auth_endpoint == "https://api.agentops.ai/v3/auth/token"
|
||||
|
||||
def test_agentops_config_defaults():
|
||||
"""Test that AgentOpsConfig uses correct default values"""
|
||||
config = AgentOpsConfig()
|
||||
assert config.service_name is None
|
||||
assert config.deployment_environment is None
|
||||
assert config.api_key is None
|
||||
assert config.endpoint == "https://otlp.agentops.cloud/v1/traces"
|
||||
assert config.auth_endpoint == "https://api.agentops.ai/v3/auth/token"
|
||||
|
||||
@patch('litellm.integrations.agentops.agentops.AgentOps._fetch_auth_token')
|
||||
def test_fetch_auth_token_success(mock_fetch_auth_token, mock_auth_response):
|
||||
"""Test successful JWT token fetch"""
|
||||
mock_fetch_auth_token.return_value = mock_auth_response
|
||||
|
||||
config = AgentOpsConfig(api_key="test_key")
|
||||
agentops = AgentOps(config=config)
|
||||
|
||||
mock_fetch_auth_token.assert_called_once_with("test_key", "https://api.agentops.ai/v3/auth/token")
|
||||
assert agentops.resource_attributes.get("project.id") == mock_auth_response.get("project_id")
|
||||
|
||||
@patch('litellm.integrations.agentops.agentops.AgentOps._fetch_auth_token')
|
||||
def test_fetch_auth_token_failure(mock_fetch_auth_token):
|
||||
"""Test failed JWT token fetch"""
|
||||
mock_fetch_auth_token.side_effect = Exception("Failed to fetch auth token: Unauthorized")
|
||||
|
||||
config = AgentOpsConfig(api_key="test_key")
|
||||
agentops = AgentOps(config=config)
|
||||
|
||||
mock_fetch_auth_token.assert_called_once()
|
||||
assert "project.id" not in agentops.resource_attributes
|
||||
|
||||
@patch('litellm.integrations.agentops.agentops.AgentOps._fetch_auth_token')
|
||||
def test_agentops_initialization(mock_fetch_auth_token, agentops_config, mock_auth_response):
|
||||
"""Test AgentOps initialization with config"""
|
||||
mock_fetch_auth_token.return_value = mock_auth_response
|
||||
|
||||
agentops = AgentOps(config=agentops_config)
|
||||
|
||||
assert agentops.resource_attributes["service.name"] == "test_service"
|
||||
assert agentops.resource_attributes["deployment.environment"] == "test_env"
|
||||
assert agentops.resource_attributes["telemetry.sdk.name"] == "agentops"
|
||||
assert agentops.resource_attributes["project.id"] == "test_project_id"
|
||||
|
||||
def test_agentops_initialization_no_auth():
|
||||
"""Test AgentOps initialization without authentication"""
|
||||
test_config = AgentOpsConfig(
|
||||
endpoint="https://otlp.agentops.cloud/v1/traces",
|
||||
api_key=None, # No API key
|
||||
service_name="test_service",
|
||||
deployment_environment="test_env"
|
||||
)
|
||||
|
||||
agentops = AgentOps(config=test_config)
|
||||
|
||||
assert agentops.resource_attributes["service.name"] == "test_service"
|
||||
assert agentops.resource_attributes["deployment.environment"] == "test_env"
|
||||
assert agentops.resource_attributes["telemetry.sdk.name"] == "agentops"
|
||||
assert "project.id" not in agentops.resource_attributes
|
||||
|
|
@ -11,6 +11,7 @@ sys.path.insert(
|
|||
|
||||
from litellm.litellm_core_utils.prompt_templates.common_utils import (
|
||||
get_format_from_file_id,
|
||||
handle_any_messages_to_chat_completion_str_messages_conversion,
|
||||
update_messages_with_model_file_ids,
|
||||
)
|
||||
|
||||
|
|
@ -64,3 +65,63 @@ def test_update_messages_with_model_file_ids():
|
|||
],
|
||||
}
|
||||
]
|
||||
|
||||
|
||||
def test_handle_any_messages_to_chat_completion_str_messages_conversion_list():
|
||||
# Test with list of messages
|
||||
messages = [
|
||||
{"role": "user", "content": "Hello"},
|
||||
{"role": "assistant", "content": "Hi there"},
|
||||
]
|
||||
result = handle_any_messages_to_chat_completion_str_messages_conversion(messages)
|
||||
assert len(result) == 2
|
||||
assert result[0] == messages[0]
|
||||
assert result[1] == messages[1]
|
||||
|
||||
|
||||
def test_handle_any_messages_to_chat_completion_str_messages_conversion_list_infinite_loop():
|
||||
# Test that list handling doesn't cause infinite recursion
|
||||
messages = [
|
||||
{"role": "user", "content": "Hello"},
|
||||
{"role": "assistant", "content": "Hi there"},
|
||||
]
|
||||
# This should complete without stack overflow
|
||||
result = handle_any_messages_to_chat_completion_str_messages_conversion(messages)
|
||||
assert len(result) == 2
|
||||
assert result[0] == messages[0]
|
||||
assert result[1] == messages[1]
|
||||
|
||||
|
||||
def test_handle_any_messages_to_chat_completion_str_messages_conversion_dict():
|
||||
# Test with single dictionary message
|
||||
message = {"role": "user", "content": "Hello"}
|
||||
result = handle_any_messages_to_chat_completion_str_messages_conversion(message)
|
||||
assert len(result) == 1
|
||||
assert result[0]["input"] == json.dumps(message)
|
||||
|
||||
|
||||
def test_handle_any_messages_to_chat_completion_str_messages_conversion_str():
|
||||
# Test with string message
|
||||
message = "Hello"
|
||||
result = handle_any_messages_to_chat_completion_str_messages_conversion(message)
|
||||
assert len(result) == 1
|
||||
assert result[0]["input"] == message
|
||||
|
||||
|
||||
def test_handle_any_messages_to_chat_completion_str_messages_conversion_other():
|
||||
# Test with non-string/dict/list type
|
||||
message = 123
|
||||
result = handle_any_messages_to_chat_completion_str_messages_conversion(message)
|
||||
assert len(result) == 1
|
||||
assert result[0]["input"] == "123"
|
||||
|
||||
|
||||
def test_handle_any_messages_to_chat_completion_str_messages_conversion_complex():
|
||||
# Test with complex nested structure
|
||||
message = {
|
||||
"role": "user",
|
||||
"content": {"text": "Hello", "metadata": {"timestamp": "2024-01-01"}},
|
||||
}
|
||||
result = handle_any_messages_to_chat_completion_str_messages_conversion(message)
|
||||
assert len(result) == 1
|
||||
assert result[0]["input"] == json.dumps(message)
|
||||
|
|
|
|||
|
|
@ -203,9 +203,6 @@ class TestOpenAIResponsesAPIConfig:
|
|||
|
||||
result = self.config.get_complete_url(
|
||||
api_base=api_base,
|
||||
model=self.model,
|
||||
api_key="test_api_key",
|
||||
optional_params={},
|
||||
litellm_params={},
|
||||
)
|
||||
|
||||
|
|
@ -215,9 +212,6 @@ class TestOpenAIResponsesAPIConfig:
|
|||
with patch("litellm.api_base", "https://litellm-api-base.example.com/v1"):
|
||||
result = self.config.get_complete_url(
|
||||
api_base=None,
|
||||
model=self.model,
|
||||
api_key="test_api_key",
|
||||
optional_params={},
|
||||
litellm_params={},
|
||||
)
|
||||
|
||||
|
|
@ -231,9 +225,6 @@ class TestOpenAIResponsesAPIConfig:
|
|||
):
|
||||
result = self.config.get_complete_url(
|
||||
api_base=None,
|
||||
model=self.model,
|
||||
api_key="test_api_key",
|
||||
optional_params={},
|
||||
litellm_params={},
|
||||
)
|
||||
|
||||
|
|
@ -247,9 +238,6 @@ class TestOpenAIResponsesAPIConfig:
|
|||
):
|
||||
result = self.config.get_complete_url(
|
||||
api_base=None,
|
||||
model=self.model,
|
||||
api_key="test_api_key",
|
||||
optional_params={},
|
||||
litellm_params={},
|
||||
)
|
||||
|
||||
|
|
@ -260,10 +248,26 @@ class TestOpenAIResponsesAPIConfig:
|
|||
|
||||
result = self.config.get_complete_url(
|
||||
api_base=api_base,
|
||||
model=self.model,
|
||||
api_key="test_api_key",
|
||||
optional_params={},
|
||||
litellm_params={},
|
||||
)
|
||||
|
||||
assert result == "https://custom-openai.example.com/v1/responses"
|
||||
|
||||
def test_get_event_model_class_generic_event(self):
|
||||
"""Test that get_event_model_class returns the correct event model class"""
|
||||
from litellm.types.llms.openai import GenericEvent
|
||||
|
||||
event_type = "test"
|
||||
result = self.config.get_event_model_class(event_type)
|
||||
assert result == GenericEvent
|
||||
|
||||
def test_transform_streaming_response_generic_event(self):
|
||||
"""Test that transform_streaming_response returns the correct event model class"""
|
||||
from litellm.types.llms.openai import GenericEvent
|
||||
|
||||
chunk = {"type": "test", "test": "test"}
|
||||
result = self.config.transform_streaming_response(
|
||||
model=self.model, parsed_chunk=chunk, logging_obj=self.logging_obj
|
||||
)
|
||||
assert isinstance(result, GenericEvent)
|
||||
assert result.type == "test"
|
||||
|
|
|
|||
|
|
@ -153,3 +153,19 @@ async def test_get_users_includes_timestamps(mocker):
|
|||
assert user_response.created_at == mock_user_data["created_at"]
|
||||
assert user_response.updated_at == mock_user_data["updated_at"]
|
||||
assert user_response.key_count == 0
|
||||
|
||||
|
||||
def test_validate_sort_params():
|
||||
"""
|
||||
Test that validate_sort_params returns None if sort_by is None
|
||||
"""
|
||||
from litellm.proxy.management_endpoints.internal_user_endpoints import (
|
||||
_validate_sort_params,
|
||||
)
|
||||
|
||||
assert _validate_sort_params(None, "asc") is None
|
||||
assert _validate_sort_params(None, "desc") is None
|
||||
assert _validate_sort_params("user_id", "asc") == {"user_id": "asc"}
|
||||
assert _validate_sort_params("user_id", "desc") == {"user_id": "desc"}
|
||||
with pytest.raises(Exception):
|
||||
_validate_sort_params("user_id", "invalid")
|
||||
|
|
|
|||
21
tests/litellm/types/llms/test_types_llms_openai.py
Normal file
21
tests/litellm/types/llms/test_types_llms_openai.py
Normal file
|
|
@ -0,0 +1,21 @@
|
|||
import asyncio
|
||||
import os
|
||||
import sys
|
||||
from typing import Optional
|
||||
from unittest.mock import AsyncMock, patch
|
||||
|
||||
import pytest
|
||||
|
||||
sys.path.insert(0, os.path.abspath("../../.."))
|
||||
import json
|
||||
|
||||
import litellm
|
||||
|
||||
|
||||
def test_generic_event():
|
||||
from litellm.types.llms.openai import GenericEvent
|
||||
|
||||
event = {"type": "test", "test": "test"}
|
||||
event = GenericEvent(**event)
|
||||
assert event.type == "test"
|
||||
assert event.test == "test"
|
||||
|
|
@ -189,6 +189,90 @@ class BaseResponsesAPITest(ABC):
|
|||
|
||||
|
||||
|
||||
@pytest.mark.parametrize("sync_mode", [False, True])
|
||||
@pytest.mark.asyncio
|
||||
async def test_basic_openai_responses_delete_endpoint(self, sync_mode):
|
||||
litellm._turn_on_debug()
|
||||
litellm.set_verbose = True
|
||||
base_completion_call_args = self.get_base_completion_call_args()
|
||||
if sync_mode:
|
||||
response = litellm.responses(
|
||||
input="Basic ping", max_output_tokens=20,
|
||||
**base_completion_call_args
|
||||
)
|
||||
|
||||
# delete the response
|
||||
if isinstance(response, ResponsesAPIResponse):
|
||||
litellm.delete_responses(
|
||||
response_id=response.id,
|
||||
**base_completion_call_args
|
||||
)
|
||||
else:
|
||||
raise ValueError("response is not a ResponsesAPIResponse")
|
||||
else:
|
||||
response = await litellm.aresponses(
|
||||
input="Basic ping", max_output_tokens=20,
|
||||
**base_completion_call_args
|
||||
)
|
||||
|
||||
# async delete the response
|
||||
if isinstance(response, ResponsesAPIResponse):
|
||||
await litellm.adelete_responses(
|
||||
response_id=response.id,
|
||||
**base_completion_call_args
|
||||
)
|
||||
else:
|
||||
raise ValueError("response is not a ResponsesAPIResponse")
|
||||
|
||||
|
||||
@pytest.mark.parametrize("sync_mode", [True, False])
|
||||
@pytest.mark.asyncio
|
||||
async def test_basic_openai_responses_streaming_delete_endpoint(self, sync_mode):
|
||||
#litellm._turn_on_debug()
|
||||
#litellm.set_verbose = True
|
||||
base_completion_call_args = self.get_base_completion_call_args()
|
||||
response_id = None
|
||||
if sync_mode:
|
||||
response_id = None
|
||||
response = litellm.responses(
|
||||
input="Basic ping", max_output_tokens=20,
|
||||
stream=True,
|
||||
**base_completion_call_args
|
||||
)
|
||||
for event in response:
|
||||
print("litellm response=", json.dumps(event, indent=4, default=str))
|
||||
if "response" in event:
|
||||
response_obj = event.get("response")
|
||||
if response_obj is not None:
|
||||
response_id = response_obj.get("id")
|
||||
print("got response_id=", response_id)
|
||||
|
||||
# delete the response
|
||||
assert response_id is not None
|
||||
litellm.delete_responses(
|
||||
response_id=response_id,
|
||||
**base_completion_call_args
|
||||
)
|
||||
else:
|
||||
response = await litellm.aresponses(
|
||||
input="Basic ping", max_output_tokens=20,
|
||||
stream=True,
|
||||
**base_completion_call_args
|
||||
)
|
||||
async for event in response:
|
||||
print("litellm response=", json.dumps(event, indent=4, default=str))
|
||||
if "response" in event:
|
||||
response_obj = event.get("response")
|
||||
if response_obj is not None:
|
||||
response_id = response_obj.get("id")
|
||||
print("got response_id=", response_id)
|
||||
|
||||
# delete the response
|
||||
assert response_id is not None
|
||||
await litellm.adelete_responses(
|
||||
response_id=response_id,
|
||||
**base_completion_call_args
|
||||
)
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -29,6 +29,12 @@ class TestAnthropicResponsesAPITest(BaseResponsesAPITest):
|
|||
return {
|
||||
"model": "anthropic/claude-3-5-sonnet-latest",
|
||||
}
|
||||
|
||||
async def test_basic_openai_responses_delete_endpoint(self, sync_mode=False):
|
||||
pass
|
||||
|
||||
async def test_basic_openai_responses_streaming_delete_endpoint(self, sync_mode=False):
|
||||
pass
|
||||
|
||||
|
||||
def test_multiturn_tool_calls():
|
||||
|
|
|
|||
|
|
@ -804,7 +804,7 @@ async def test_openai_o1_pro_response_api(sync_mode):
|
|||
print("Response:", json.dumps(response, indent=4, default=str))
|
||||
|
||||
# Check that the response has the expected structure
|
||||
assert response["id"] == mock_response["id"]
|
||||
assert response["id"] is not None
|
||||
assert response["status"] == "incomplete"
|
||||
assert response["incomplete_details"].reason == "max_output_tokens"
|
||||
assert response["max_output_tokens"] == 20
|
||||
|
|
|
|||
|
|
@ -470,3 +470,4 @@ class TestOpenAIGPT4OAudioTranscription(BaseLLMAudioTranscriptionTest):
|
|||
|
||||
def get_custom_llm_provider(self) -> litellm.LlmProviders:
|
||||
return litellm.LlmProviders.OPENAI
|
||||
|
||||
|
|
|
|||
|
|
@ -147,7 +147,7 @@ async def test_get_response():
|
|||
prompt = '\ndef count_nums(arr):\n """\n Write a function count_nums which takes an array of integers and returns\n the number of elements which has a sum of digits > 0.\n If a number is negative, then its first signed digit will be negative:\n e.g. -123 has signed digits -1, 2, and 3.\n >>> count_nums([]) == 0\n >>> count_nums([-1, 11, -11]) == 1\n >>> count_nums([1, 1, 2]) == 3\n """\n'
|
||||
try:
|
||||
response = await acompletion(
|
||||
model="gemini-pro",
|
||||
model="gemini-1.5-flash",
|
||||
messages=[
|
||||
{
|
||||
"role": "system",
|
||||
|
|
@ -454,6 +454,8 @@ async def test_async_vertexai_response():
|
|||
or "002" in model
|
||||
or "gemini-2.0-flash-thinking-exp" in model
|
||||
or "gemini-2.0-pro-exp-02-05" in model
|
||||
or "gemini-pro" in model
|
||||
or "gemini-1.0-pro" in model
|
||||
):
|
||||
# our account does not have access to this model
|
||||
continue
|
||||
|
|
@ -464,6 +466,8 @@ async def test_async_vertexai_response():
|
|||
model=model, messages=messages, temperature=0.7, timeout=5
|
||||
)
|
||||
print(f"response: {response}")
|
||||
except litellm.NotFoundError as e:
|
||||
pass
|
||||
except litellm.RateLimitError as e:
|
||||
pass
|
||||
except litellm.Timeout as e:
|
||||
|
|
@ -501,6 +505,8 @@ async def test_async_vertexai_streaming_response():
|
|||
or "002" in model
|
||||
or "gemini-2.0-flash-thinking-exp" in model
|
||||
or "gemini-2.0-pro-exp-02-05" in model
|
||||
or "gemini-pro" in model
|
||||
or "gemini-1.0-pro" in model
|
||||
):
|
||||
# our account does not have access to this model
|
||||
continue
|
||||
|
|
@ -522,6 +528,8 @@ async def test_async_vertexai_streaming_response():
|
|||
complete_response += chunk.choices[0].delta.content
|
||||
print(f"complete_response: {complete_response}")
|
||||
assert len(complete_response) > 0
|
||||
except litellm.NotFoundError as e:
|
||||
pass
|
||||
except litellm.RateLimitError as e:
|
||||
pass
|
||||
except litellm.APIConnectionError:
|
||||
|
|
@ -1776,7 +1784,7 @@ async def test_gemini_pro_function_calling_streaming(sync_mode):
|
|||
load_vertex_ai_credentials()
|
||||
litellm.set_verbose = True
|
||||
data = {
|
||||
"model": "vertex_ai/gemini-pro",
|
||||
"model": "vertex_ai/gemini-1.5-flash",
|
||||
"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
|
|
@ -1836,57 +1844,6 @@ async def test_gemini_pro_function_calling_streaming(sync_mode):
|
|||
pass
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.flaky(retries=3, delay=1)
|
||||
async def test_gemini_pro_async_function_calling():
|
||||
load_vertex_ai_credentials()
|
||||
litellm.set_verbose = True
|
||||
try:
|
||||
tools = [
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "get_current_weather",
|
||||
"description": "Get the current weather in a given location.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"location": {
|
||||
"type": "string",
|
||||
"description": "The city and state, e.g. San Francisco, CA",
|
||||
},
|
||||
"unit": {
|
||||
"type": "string",
|
||||
"enum": ["celsius", "fahrenheit"],
|
||||
},
|
||||
},
|
||||
"required": ["location"],
|
||||
},
|
||||
},
|
||||
}
|
||||
]
|
||||
messages = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": "What's the weather like in Boston today in fahrenheit?",
|
||||
}
|
||||
]
|
||||
completion = await litellm.acompletion(
|
||||
model="gemini-pro", messages=messages, tools=tools, tool_choice="auto"
|
||||
)
|
||||
print(f"completion: {completion}")
|
||||
print(f"message content: {completion.choices[0].message.content}")
|
||||
assert completion.choices[0].message.content is None
|
||||
assert len(completion.choices[0].message.tool_calls) == 1
|
||||
|
||||
# except litellm.APIError as e:
|
||||
# pass
|
||||
except litellm.RateLimitError as e:
|
||||
pass
|
||||
except Exception as e:
|
||||
pytest.fail(f"An exception occurred - {str(e)}")
|
||||
# raise Exception("it worked!")
|
||||
|
||||
|
||||
# asyncio.run(gemini_pro_async_function_calling())
|
||||
|
||||
|
|
|
|||
|
|
@ -1,111 +0,0 @@
|
|||
import os
|
||||
import sys
|
||||
import time
|
||||
from unittest.mock import Mock, patch
|
||||
import json
|
||||
import opentelemetry.exporter.otlp.proto.grpc.trace_exporter
|
||||
from typing import Optional
|
||||
|
||||
sys.path.insert(
|
||||
0, os.path.abspath("../..")
|
||||
) # Adds the parent directory to the system-path
|
||||
from litellm.integrations._types.open_inference import SpanAttributes
|
||||
from litellm.integrations.arize.arize import ArizeConfig, ArizeLogger
|
||||
from litellm.integrations.custom_logger import CustomLogger
|
||||
from litellm.main import completion
|
||||
import litellm
|
||||
from litellm.types.utils import Choices, StandardCallbackDynamicParams
|
||||
import pytest
|
||||
import asyncio
|
||||
|
||||
|
||||
def test_arize_set_attributes():
|
||||
"""
|
||||
Test setting attributes for Arize
|
||||
"""
|
||||
from unittest.mock import MagicMock
|
||||
from litellm.types.utils import ModelResponse
|
||||
|
||||
span = MagicMock()
|
||||
kwargs = {
|
||||
"role": "user",
|
||||
"content": "simple arize test",
|
||||
"model": "gpt-4o",
|
||||
"messages": [{"role": "user", "content": "basic arize test"}],
|
||||
"standard_logging_object": {
|
||||
"model_parameters": {"user": "test_user"},
|
||||
"metadata": {"key": "value", "key2": None},
|
||||
},
|
||||
}
|
||||
response_obj = ModelResponse(
|
||||
usage={"total_tokens": 100, "completion_tokens": 60, "prompt_tokens": 40},
|
||||
choices=[Choices(message={"role": "assistant", "content": "response content"})],
|
||||
)
|
||||
|
||||
ArizeLogger.set_arize_attributes(span, kwargs, response_obj)
|
||||
|
||||
assert span.set_attribute.call_count == 14
|
||||
span.set_attribute.assert_any_call(
|
||||
SpanAttributes.METADATA, json.dumps({"key": "value", "key2": None})
|
||||
)
|
||||
span.set_attribute.assert_any_call(SpanAttributes.LLM_MODEL_NAME, "gpt-4o")
|
||||
span.set_attribute.assert_any_call(SpanAttributes.OPENINFERENCE_SPAN_KIND, "LLM")
|
||||
span.set_attribute.assert_any_call(SpanAttributes.INPUT_VALUE, "basic arize test")
|
||||
span.set_attribute.assert_any_call("llm.input_messages.0.message.role", "user")
|
||||
span.set_attribute.assert_any_call(
|
||||
"llm.input_messages.0.message.content", "basic arize test"
|
||||
)
|
||||
span.set_attribute.assert_any_call(
|
||||
SpanAttributes.LLM_INVOCATION_PARAMETERS, '{"user": "test_user"}'
|
||||
)
|
||||
span.set_attribute.assert_any_call(SpanAttributes.USER_ID, "test_user")
|
||||
span.set_attribute.assert_any_call(SpanAttributes.OUTPUT_VALUE, "response content")
|
||||
span.set_attribute.assert_any_call(
|
||||
"llm.output_messages.0.message.role", "assistant"
|
||||
)
|
||||
span.set_attribute.assert_any_call(
|
||||
"llm.output_messages.0.message.content", "response content"
|
||||
)
|
||||
span.set_attribute.assert_any_call(SpanAttributes.LLM_TOKEN_COUNT_TOTAL, 100)
|
||||
span.set_attribute.assert_any_call(SpanAttributes.LLM_TOKEN_COUNT_COMPLETION, 60)
|
||||
span.set_attribute.assert_any_call(SpanAttributes.LLM_TOKEN_COUNT_PROMPT, 40)
|
||||
|
||||
|
||||
class TestArizeLogger(CustomLogger):
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.standard_callback_dynamic_params: Optional[
|
||||
StandardCallbackDynamicParams
|
||||
] = None
|
||||
|
||||
async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
|
||||
print("logged kwargs", json.dumps(kwargs, indent=4, default=str))
|
||||
self.standard_callback_dynamic_params = kwargs.get(
|
||||
"standard_callback_dynamic_params"
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_arize_dynamic_params():
|
||||
"""verify arize ai dynamic params are recieved by a callback"""
|
||||
test_arize_logger = TestArizeLogger()
|
||||
litellm.callbacks = [test_arize_logger]
|
||||
await litellm.acompletion(
|
||||
model="gpt-4o",
|
||||
messages=[{"role": "user", "content": "basic arize test"}],
|
||||
mock_response="test",
|
||||
arize_api_key="test_api_key_dynamic",
|
||||
arize_space_key="test_space_key_dynamic",
|
||||
)
|
||||
|
||||
await asyncio.sleep(2)
|
||||
|
||||
assert test_arize_logger.standard_callback_dynamic_params is not None
|
||||
assert (
|
||||
test_arize_logger.standard_callback_dynamic_params.get("arize_api_key")
|
||||
== "test_api_key_dynamic"
|
||||
)
|
||||
assert (
|
||||
test_arize_logger.standard_callback_dynamic_params.get("arize_space_key")
|
||||
== "test_space_key_dynamic"
|
||||
)
|
||||
|
|
@ -38,6 +38,7 @@ from litellm.integrations.langfuse.langfuse_prompt_management import (
|
|||
LangfusePromptManagement,
|
||||
)
|
||||
from litellm.integrations.azure_storage.azure_storage import AzureBlobStorageLogger
|
||||
from litellm.integrations.agentops import AgentOps
|
||||
from litellm.integrations.humanloop import HumanloopLogger
|
||||
from litellm.proxy.hooks.dynamic_rate_limiter import _PROXY_DynamicRateLimitHandler
|
||||
from unittest.mock import patch
|
||||
|
|
@ -75,6 +76,7 @@ callback_class_str_to_classType = {
|
|||
"pagerduty": PagerDutyAlerting,
|
||||
"gcs_pubsub": GcsPubSubLogger,
|
||||
"anthropic_cache_control_hook": AnthropicCacheControlHook,
|
||||
"agentops": AgentOps,
|
||||
}
|
||||
|
||||
expected_env_vars = {
|
||||
|
|
|
|||
Binary file not shown.
|
After Width: | Height: | Size: 48 KiB |
|
|
@ -0,0 +1,31 @@
|
|||
// tests/auth.spec.ts
|
||||
import { test, expect } from "@playwright/test";
|
||||
|
||||
test.describe("Authentication Checks", () => {
|
||||
test("should redirect unauthenticated user from a protected page", async ({ page }) => {
|
||||
test.setTimeout(30000);
|
||||
|
||||
page.on("console", (msg) => console.log("PAGE LOG:", msg.text()));
|
||||
|
||||
const protectedPageUrl = "http://localhost:4000/ui?page=llm-playground";
|
||||
const expectedRedirectUrl = "http://localhost:4000/sso/key/generate";
|
||||
|
||||
console.log(`Attempting to navigate to protected page: ${protectedPageUrl}`);
|
||||
|
||||
await page.goto(protectedPageUrl);
|
||||
|
||||
console.log(`Navigation initiated. Current URL: ${page.url()}`);
|
||||
|
||||
try {
|
||||
await page.waitForURL(expectedRedirectUrl, { timeout: 10000 });
|
||||
console.log(`Waited for URL. Current URL is now: ${page.url()}`);
|
||||
} catch (error) {
|
||||
console.error(`Timeout waiting for URL: ${expectedRedirectUrl}. Current URL: ${page.url()}`);
|
||||
await page.screenshot({ path: "redirect-fail-screenshot.png" });
|
||||
throw error;
|
||||
}
|
||||
|
||||
await expect(page).toHaveURL(expectedRedirectUrl);
|
||||
console.log(`Assertion passed: Page URL is ${expectedRedirectUrl}`);
|
||||
});
|
||||
});
|
||||
214
tests/proxy_admin_ui_tests/e2e_ui_tests/search_users.spec.ts
Normal file
214
tests/proxy_admin_ui_tests/e2e_ui_tests/search_users.spec.ts
Normal file
|
|
@ -0,0 +1,214 @@
|
|||
/*
|
||||
Search Users in Admin UI
|
||||
E2E Test for user search functionality
|
||||
|
||||
Tests:
|
||||
1. Navigate to Internal Users tab
|
||||
2. Verify search input exists
|
||||
3. Test search functionality
|
||||
4. Verify results update
|
||||
5. Test filtering by email, user ID, and SSO user ID
|
||||
*/
|
||||
|
||||
import { test, expect } from "@playwright/test";
|
||||
|
||||
test("user search test", async ({ page }) => {
|
||||
// Set a longer timeout for the entire test
|
||||
test.setTimeout(60000);
|
||||
|
||||
// Enable console logging
|
||||
page.on("console", (msg) => console.log("PAGE LOG:", msg.text()));
|
||||
|
||||
// Login first
|
||||
await page.goto("http://localhost:4000/ui");
|
||||
console.log("Navigated to login page");
|
||||
|
||||
// Wait for login form to be visible
|
||||
await page.waitForSelector('input[name="username"]', { timeout: 10000 });
|
||||
console.log("Login form is visible");
|
||||
|
||||
await page.fill('input[name="username"]', "admin");
|
||||
await page.fill('input[name="password"]', "gm");
|
||||
console.log("Filled login credentials");
|
||||
|
||||
const loginButton = page.locator('input[type="submit"]');
|
||||
await expect(loginButton).toBeEnabled();
|
||||
await loginButton.click();
|
||||
console.log("Clicked login button");
|
||||
|
||||
// Wait for navigation to complete and dashboard to load
|
||||
await page.waitForLoadState("networkidle");
|
||||
console.log("Page loaded after login");
|
||||
|
||||
// Take a screenshot for debugging
|
||||
await page.screenshot({ path: "after-login.png" });
|
||||
console.log("Took screenshot after login");
|
||||
|
||||
// Try to find the Internal User tab with more debugging
|
||||
console.log("Looking for Internal User tab...");
|
||||
const internalUserTab = page.locator("span.ant-menu-title-content", {
|
||||
hasText: "Internal User",
|
||||
});
|
||||
|
||||
// Wait for the tab to be visible
|
||||
await internalUserTab.waitFor({ state: "visible", timeout: 10000 });
|
||||
console.log("Internal User tab is visible");
|
||||
|
||||
// Take another screenshot before clicking
|
||||
await page.screenshot({ path: "before-tab-click.png" });
|
||||
console.log("Took screenshot before tab click");
|
||||
|
||||
await internalUserTab.click();
|
||||
console.log("Clicked Internal User tab");
|
||||
|
||||
// Wait for the page to load and table to be visible
|
||||
await page.waitForSelector("tbody tr", { timeout: 30000 });
|
||||
await page.waitForTimeout(2000); // Additional wait for table to stabilize
|
||||
console.log("Table is visible");
|
||||
|
||||
// Take a final screenshot
|
||||
await page.screenshot({ path: "after-tab-click.png" });
|
||||
console.log("Took screenshot after tab click");
|
||||
|
||||
// Verify search input exists
|
||||
const searchInput = page.locator('input[placeholder="Search by email..."]');
|
||||
await expect(searchInput).toBeVisible();
|
||||
console.log("Search input is visible");
|
||||
|
||||
// Test search functionality
|
||||
const initialUserCount = await page.locator("tbody tr").count();
|
||||
console.log(`Initial user count: ${initialUserCount}`);
|
||||
|
||||
// Perform a search
|
||||
const testEmail = "test@";
|
||||
await searchInput.fill(testEmail);
|
||||
console.log("Filled search input");
|
||||
|
||||
// Wait for the debounced search to complete
|
||||
await page.waitForTimeout(500);
|
||||
console.log("Waited for debounce");
|
||||
|
||||
// Wait for the results count to update
|
||||
await page.waitForFunction((initialCount) => {
|
||||
const currentCount = document.querySelectorAll("tbody tr").length;
|
||||
return currentCount !== initialCount;
|
||||
}, initialUserCount);
|
||||
console.log("Results updated");
|
||||
|
||||
const filteredUserCount = await page.locator("tbody tr").count();
|
||||
console.log(`Filtered user count: ${filteredUserCount}`);
|
||||
|
||||
expect(filteredUserCount).toBeDefined();
|
||||
|
||||
// Clear the search
|
||||
await searchInput.clear();
|
||||
console.log("Cleared search");
|
||||
|
||||
await page.waitForTimeout(500);
|
||||
console.log("Waited for debounce after clear");
|
||||
|
||||
await page.waitForFunction((initialCount) => {
|
||||
const currentCount = document.querySelectorAll("tbody tr").length;
|
||||
return currentCount === initialCount;
|
||||
}, initialUserCount);
|
||||
console.log("Results reset");
|
||||
|
||||
const resetUserCount = await page.locator("tbody tr").count();
|
||||
console.log(`Reset user count: ${resetUserCount}`);
|
||||
|
||||
expect(resetUserCount).toBe(initialUserCount);
|
||||
});
|
||||
|
||||
test("user filter test", async ({ page }) => {
|
||||
// Set a longer timeout for the entire test
|
||||
test.setTimeout(60000);
|
||||
|
||||
// Enable console logging
|
||||
page.on("console", (msg) => console.log("PAGE LOG:", msg.text()));
|
||||
|
||||
// Login first
|
||||
await page.goto("http://localhost:4000/ui");
|
||||
console.log("Navigated to login page");
|
||||
|
||||
// Wait for login form to be visible
|
||||
await page.waitForSelector('input[name="username"]', { timeout: 10000 });
|
||||
console.log("Login form is visible");
|
||||
|
||||
await page.fill('input[name="username"]', "admin");
|
||||
await page.fill('input[name="password"]', "gm");
|
||||
console.log("Filled login credentials");
|
||||
|
||||
const loginButton = page.locator('input[type="submit"]');
|
||||
await expect(loginButton).toBeEnabled();
|
||||
await loginButton.click();
|
||||
console.log("Clicked login button");
|
||||
|
||||
// Wait for navigation to complete and dashboard to load
|
||||
await page.waitForLoadState("networkidle");
|
||||
console.log("Page loaded after login");
|
||||
|
||||
// Navigate to Internal Users tab
|
||||
const internalUserTab = page.locator("span.ant-menu-title-content", {
|
||||
hasText: "Internal User",
|
||||
});
|
||||
await internalUserTab.waitFor({ state: "visible", timeout: 10000 });
|
||||
await internalUserTab.click();
|
||||
console.log("Clicked Internal User tab");
|
||||
|
||||
// Wait for the page to load and table to be visible
|
||||
await page.waitForSelector("tbody tr", { timeout: 30000 });
|
||||
await page.waitForTimeout(2000); // Additional wait for table to stabilize
|
||||
console.log("Table is visible");
|
||||
|
||||
// Get initial user count
|
||||
const initialUserCount = await page.locator("tbody tr").count();
|
||||
console.log(`Initial user count: ${initialUserCount}`);
|
||||
|
||||
// Click the filter button to show additional filters
|
||||
const filterButton = page.getByRole("button", {
|
||||
name: "Filters",
|
||||
exact: true,
|
||||
});
|
||||
await filterButton.click();
|
||||
console.log("Clicked filter button");
|
||||
await page.waitForTimeout(500); // Wait for filters to appear
|
||||
|
||||
// Test user ID filter
|
||||
const userIdInput = page.locator('input[placeholder="Filter by User ID"]');
|
||||
await expect(userIdInput).toBeVisible();
|
||||
console.log("User ID filter is visible");
|
||||
|
||||
await userIdInput.fill("user");
|
||||
console.log("Filled user ID filter");
|
||||
await page.waitForTimeout(1000);
|
||||
const userIdFilteredCount = await page.locator("tbody tr").count();
|
||||
console.log(`User ID filtered count: ${userIdFilteredCount}`);
|
||||
expect(userIdFilteredCount).toBeLessThan(initialUserCount);
|
||||
|
||||
// Clear user ID filter
|
||||
await userIdInput.clear();
|
||||
await page.waitForTimeout(1000);
|
||||
console.log("Cleared user ID filter");
|
||||
|
||||
// Test SSO user ID filter
|
||||
const ssoUserIdInput = page.locator('input[placeholder="Filter by SSO ID"]');
|
||||
await expect(ssoUserIdInput).toBeVisible();
|
||||
console.log("SSO user ID filter is visible");
|
||||
|
||||
await ssoUserIdInput.fill("sso");
|
||||
console.log("Filled SSO user ID filter");
|
||||
await page.waitForTimeout(1000);
|
||||
const ssoUserIdFilteredCount = await page.locator("tbody tr").count();
|
||||
console.log(`SSO user ID filtered count: ${ssoUserIdFilteredCount}`);
|
||||
expect(ssoUserIdFilteredCount).toBeLessThan(initialUserCount);
|
||||
|
||||
// Clear SSO user ID filter
|
||||
await ssoUserIdInput.clear();
|
||||
await page.waitForTimeout(5000);
|
||||
console.log("Cleared SSO user ID filter");
|
||||
|
||||
// Verify count returns to initial after clearing all filters
|
||||
const finalUserCount = await page.locator("tbody tr").count();
|
||||
console.log(`Final user count: ${finalUserCount}`);
|
||||
expect(finalUserCount).toBe(initialUserCount);
|
||||
});
|
||||
|
|
@ -2,45 +2,74 @@
|
|||
Test view internal user page
|
||||
*/
|
||||
|
||||
import { test, expect } from '@playwright/test';
|
||||
import { test, expect } from "@playwright/test";
|
||||
|
||||
test('view internal user page', async ({ page }) => {
|
||||
test("view internal user page", async ({ page }) => {
|
||||
// Go to the specified URL
|
||||
await page.goto('http://localhost:4000/ui');
|
||||
await page.goto("http://localhost:4000/ui");
|
||||
|
||||
// Enter "admin" in the username input field
|
||||
await page.fill('input[name="username"]', 'admin');
|
||||
await page.fill('input[name="username"]', "admin");
|
||||
|
||||
// Enter "gm" in the password input field
|
||||
await page.fill('input[name="password"]', 'gm');
|
||||
await page.fill('input[name="password"]', "gm");
|
||||
|
||||
// Optionally, you can add an assertion to verify the login button is enabled
|
||||
// Click the login button
|
||||
const loginButton = page.locator('input[type="submit"]');
|
||||
await expect(loginButton).toBeEnabled();
|
||||
|
||||
// Optionally, you can click the login button to submit the form
|
||||
await loginButton.click();
|
||||
|
||||
const tabElement = page.locator('span.ant-menu-title-content', { hasText: 'Internal User' });
|
||||
// Wait for the Internal User tab and click it
|
||||
const tabElement = page.locator("span.ant-menu-title-content", {
|
||||
hasText: "Internal User",
|
||||
});
|
||||
await tabElement.click();
|
||||
|
||||
// try to click on button
|
||||
// <button class="bg-blue-500 hover:bg-blue-700 text-white font-bold py-2 px-4 rounded-l focus:outline-none" disabled="">← Prev</button>
|
||||
// wait 1-2 seconds
|
||||
await page.waitForTimeout(10000);
|
||||
// Wait for the table to load
|
||||
await page.waitForSelector("tbody tr", { timeout: 10000 });
|
||||
await page.waitForTimeout(2000); // Additional wait for table to stabilize
|
||||
|
||||
// Test all expected fields are present
|
||||
// number of keys owned by user
|
||||
const keysBadges = page.locator('p.tremor-Badge-text.text-sm.whitespace-nowrap', { hasText: 'Keys' });
|
||||
const keysCountArray = await keysBadges.evaluateAll(elements => elements.map(el => parseInt(el.textContent.split(' ')[0], 10)));
|
||||
// Test all expected fields are present
|
||||
// number of keys owned by user
|
||||
const keysBadges = page.locator(
|
||||
"p.tremor-Badge-text.text-sm.whitespace-nowrap",
|
||||
{ hasText: "Keys" }
|
||||
);
|
||||
const keysCountArray = await keysBadges.evaluateAll((elements) =>
|
||||
elements.map((el) => {
|
||||
const text = el.textContent;
|
||||
return text ? parseInt(text.split(" ")[0], 10) : 0;
|
||||
})
|
||||
);
|
||||
|
||||
const hasNonZeroKeys = keysCountArray.some(count => count > 0);
|
||||
const hasNonZeroKeys = keysCountArray.some((count) => count > 0);
|
||||
expect(hasNonZeroKeys).toBe(true);
|
||||
|
||||
// test pagination
|
||||
const prevButton = page.locator('button.px-3.py-1.text-sm.border.rounded-md.hover\\:bg-gray-50.disabled\\:opacity-50.disabled\\:cursor-not-allowed', { hasText: 'Previous' });
|
||||
await expect(prevButton).toBeDisabled();
|
||||
// Wait for pagination controls to be visible
|
||||
await page.waitForSelector(".flex.justify-between.items-center", {
|
||||
timeout: 5000,
|
||||
});
|
||||
|
||||
const nextButton = page.locator('button.px-3.py-1.text-sm.border.rounded-md.hover\\:bg-gray-50.disabled\\:opacity-50.disabled\\:cursor-not-allowed', { hasText: 'Next' });
|
||||
await expect(nextButton).toBeEnabled();
|
||||
// Check if we're on the first page by looking at the results count
|
||||
const resultsText =
|
||||
(await page.locator(".text-sm.text-gray-700").textContent()) || "";
|
||||
const isFirstPage = resultsText.includes("1 -");
|
||||
|
||||
if (isFirstPage) {
|
||||
// On first page, previous button should be disabled
|
||||
const prevButton = page.locator("button", { hasText: "Previous" });
|
||||
await expect(prevButton).toBeDisabled();
|
||||
}
|
||||
|
||||
// Next button should be enabled if there are more pages
|
||||
const nextButton = page.locator("button", { hasText: "Next" });
|
||||
const totalResults =
|
||||
(await page.locator(".text-sm.text-gray-700").textContent()) || "";
|
||||
const hasMorePages =
|
||||
totalResults.includes("of") && !totalResults.includes("1 - 25 of 25");
|
||||
|
||||
if (hasMorePages) {
|
||||
await expect(nextButton).toBeEnabled();
|
||||
}
|
||||
});
|
||||
|
|
|
|||
|
|
@ -0,0 +1,82 @@
|
|||
import { test, expect } from "@playwright/test";
|
||||
import { loginToUI } from "../utils/login";
|
||||
|
||||
test.describe("User Info View", () => {
|
||||
test.beforeEach(async ({ page }) => {
|
||||
await loginToUI(page);
|
||||
// Navigate to users page
|
||||
await page.goto("http://localhost:4000/ui?page=users");
|
||||
});
|
||||
|
||||
test("should display user info when clicking on user ID", async ({
|
||||
page,
|
||||
}) => {
|
||||
// Wait for users table to load
|
||||
await page.waitForSelector("table");
|
||||
|
||||
// Get the first user ID cell
|
||||
const firstUserIdCell = page.locator(
|
||||
"table tbody tr:first-child td:first-child"
|
||||
);
|
||||
const userId = await firstUserIdCell.textContent();
|
||||
console.log("Found user ID:", userId);
|
||||
|
||||
// Click on the user ID
|
||||
await firstUserIdCell.click();
|
||||
|
||||
// Wait for user info view to load
|
||||
await page.waitForSelector('h1:has-text("User")');
|
||||
console.log("User info view loaded");
|
||||
|
||||
// Check for tabs
|
||||
await expect(page.locator('button:has-text("Overview")')).toBeVisible();
|
||||
await expect(page.locator('button:has-text("Details")')).toBeVisible();
|
||||
|
||||
// Switch to details tab
|
||||
await page.locator('button:has-text("Details")').click();
|
||||
|
||||
// Check details section
|
||||
await expect(page.locator("text=User ID")).toBeVisible();
|
||||
await expect(page.locator("text=Email")).toBeVisible();
|
||||
|
||||
// Go back to users list
|
||||
await page.locator('button:has-text("Back to Users")').click();
|
||||
|
||||
// Verify we're back on the users page
|
||||
await expect(page.locator('h1:has-text("Users")')).toBeVisible();
|
||||
});
|
||||
|
||||
// test("should handle user deletion", async ({ page }) => {
|
||||
// // Wait for users table to load
|
||||
// await page.waitForSelector("table");
|
||||
|
||||
// // Get the first user ID cell
|
||||
// const firstUserIdCell = page.locator(
|
||||
// "table tbody tr:first-child td:first-child"
|
||||
// );
|
||||
// const userId = await firstUserIdCell.textContent();
|
||||
|
||||
// // Click on the user ID
|
||||
// await firstUserIdCell.click();
|
||||
|
||||
// // Wait for user info view to load
|
||||
// await page.waitForSelector('h1:has-text("User")');
|
||||
|
||||
// // Click delete button
|
||||
// await page.locator('button:has-text("Delete User")').click();
|
||||
|
||||
// // Confirm deletion in modal
|
||||
// await page.locator('button:has-text("Delete")').click();
|
||||
|
||||
// // Verify success message
|
||||
// await expect(page.locator("text=User deleted successfully")).toBeVisible();
|
||||
|
||||
// // Verify we're back on the users page
|
||||
// await expect(page.locator('h1:has-text("Users")')).toBeVisible();
|
||||
|
||||
// // Verify user is no longer in the table
|
||||
// if (userId) {
|
||||
// await expect(page.locator(`text=${userId}`)).not.toBeVisible();
|
||||
// }
|
||||
// });
|
||||
});
|
||||
4
tests/proxy_admin_ui_tests/test-results/.last-run.json
Normal file
4
tests/proxy_admin_ui_tests/test-results/.last-run.json
Normal file
|
|
@ -0,0 +1,4 @@
|
|||
{
|
||||
"status": "passed",
|
||||
"failedTests": []
|
||||
}
|
||||
23
tests/proxy_admin_ui_tests/utils/login.ts
Normal file
23
tests/proxy_admin_ui_tests/utils/login.ts
Normal file
|
|
@ -0,0 +1,23 @@
|
|||
import { Page, expect } from "@playwright/test";
|
||||
|
||||
export async function loginToUI(page: Page) {
|
||||
// Login first
|
||||
await page.goto("http://localhost:4000/ui");
|
||||
console.log("Navigated to login page");
|
||||
|
||||
// Wait for login form to be visible
|
||||
await page.waitForSelector('input[name="username"]', { timeout: 10000 });
|
||||
console.log("Login form is visible");
|
||||
|
||||
await page.fill('input[name="username"]', "admin");
|
||||
await page.fill('input[name="password"]', "gm");
|
||||
console.log("Filled login credentials");
|
||||
|
||||
const loginButton = page.locator('input[type="submit"]');
|
||||
await expect(loginButton).toBeEnabled();
|
||||
await loginButton.click();
|
||||
console.log("Clicked login button");
|
||||
|
||||
// Wait for navigation to complete
|
||||
await page.waitForURL("**/*");
|
||||
}
|
||||
|
|
@ -109,6 +109,18 @@ async def test_key_gen():
|
|||
await asyncio.gather(*tasks)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_simple_key_gen():
|
||||
async with aiohttp.ClientSession() as session:
|
||||
key_data = await generate_key(session, i=0)
|
||||
key = key_data["key"]
|
||||
assert key_data["token"] is not None
|
||||
assert key_data["token"] != key
|
||||
assert key_data["token_id"] is not None
|
||||
assert key_data["created_at"] is not None
|
||||
assert key_data["updated_at"] is not None
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_key_gen_bad_key():
|
||||
"""
|
||||
|
|
|
|||
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|
|
@ -1 +1 @@
|
|||
(self.webpackChunk_N_E=self.webpackChunk_N_E||[]).push([[185],{96443:function(n,e,t){Promise.resolve().then(t.t.bind(t,39974,23)),Promise.resolve().then(t.t.bind(t,2778,23))},2778:function(){},39974:function(n){n.exports={style:{fontFamily:"'__Inter_cf7686', '__Inter_Fallback_cf7686'",fontStyle:"normal"},className:"__className_cf7686"}}},function(n){n.O(0,[919,986,971,117,744],function(){return n(n.s=96443)}),_N_E=n.O()}]);
|
||||
(self.webpackChunk_N_E=self.webpackChunk_N_E||[]).push([[185],{35677:function(n,e,t){Promise.resolve().then(t.t.bind(t,39974,23)),Promise.resolve().then(t.t.bind(t,2778,23))},2778:function(){},39974:function(n){n.exports={style:{fontFamily:"'__Inter_cf7686', '__Inter_Fallback_cf7686'",fontStyle:"normal"},className:"__className_cf7686"}}},function(n){n.O(0,[919,986,971,117,744],function(){return n(n.s=35677)}),_N_E=n.O()}]);
|
||||
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