forked from phoenix/litellm-mirror
Merge branch 'main' into litellm_main_staging
This commit is contained in:
commit
a857f4a8ee
45 changed files with 541 additions and 79 deletions
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@ -102,7 +102,7 @@ spec:
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imagePullPolicy: {{ .Values.image.pullPolicy }}
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env:
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- name: HOST
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value: "0.0.0.0"
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value: "::"
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- name: PORT
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value: {{ .Values.service.port | quote}}
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{{- if .Values.db.deployStandalone }}
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|
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@ -1768,7 +1768,7 @@ LiteLLM supports calling [Vertex AI Text to Speech API](https://console.cloud.go
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Usage
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### Usage - Basic
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<Tabs>
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<TabItem value="sdk" label="SDK">
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@ -1841,6 +1841,150 @@ print("response from proxy", response)
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</Tabs>
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### Usage - `ssml` as input
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Pass your `ssml` as input to the `input` param, if it contains `<speak>`, it will be automatically detected and passed as `ssml` to the Vertex AI API
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If you need to force your `input` to be passed as `ssml`, set `use_ssml=True`
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<Tabs>
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<TabItem value="sdk" label="SDK">
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Vertex AI does not support passing a `model` param - so passing `model=vertex_ai/` is the only required param
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```python
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speech_file_path = Path(__file__).parent / "speech_vertex.mp3"
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ssml = """
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<speak>
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<p>Hello, world!</p>
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<p>This is a test of the <break strength="medium" /> text-to-speech API.</p>
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</speak>
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"""
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response = litellm.speech(
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input=ssml,
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model="vertex_ai/test",
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voice={
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"languageCode": "en-UK",
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"name": "en-UK-Studio-O",
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},
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audioConfig={
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"audioEncoding": "LINEAR22",
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"speakingRate": "10",
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},
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)
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response.stream_to_file(speech_file_path)
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```
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</TabItem>
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<TabItem value="proxy" label="LiteLLM PROXY (Unified Endpoint)">
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```python
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import openai
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client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000")
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ssml = """
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<speak>
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<p>Hello, world!</p>
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<p>This is a test of the <break strength="medium" /> text-to-speech API.</p>
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</speak>
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"""
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# see supported values for "voice" on vertex here:
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# https://console.cloud.google.com/vertex-ai/generative/speech/text-to-speech
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response = client.audio.speech.create(
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model = "vertex-tts",
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input=ssml,
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voice={'languageCode': 'en-US', 'name': 'en-US-Studio-O'},
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)
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print("response from proxy", response)
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```
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</TabItem>
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</Tabs>
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### Forcing SSML Usage
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You can force the use of SSML by setting the `use_ssml` parameter to `True`. This is useful when you want to ensure that your input is treated as SSML, even if it doesn't contain the `<speak>` tags.
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Here are examples of how to force SSML usage:
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<Tabs>
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<TabItem value="sdk" label="SDK">
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Vertex AI does not support passing a `model` param - so passing `model=vertex_ai/` is the only required param
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```python
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speech_file_path = Path(__file__).parent / "speech_vertex.mp3"
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ssml = """
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<speak>
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<p>Hello, world!</p>
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<p>This is a test of the <break strength="medium" /> text-to-speech API.</p>
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</speak>
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"""
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response = litellm.speech(
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input=ssml,
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use_ssml=True,
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model="vertex_ai/test",
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voice={
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"languageCode": "en-UK",
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"name": "en-UK-Studio-O",
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},
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audioConfig={
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"audioEncoding": "LINEAR22",
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"speakingRate": "10",
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},
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)
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response.stream_to_file(speech_file_path)
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```
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</TabItem>
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<TabItem value="proxy" label="LiteLLM PROXY (Unified Endpoint)">
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```python
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import openai
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client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000")
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ssml = """
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<speak>
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<p>Hello, world!</p>
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<p>This is a test of the <break strength="medium" /> text-to-speech API.</p>
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</speak>
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"""
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# see supported values for "voice" on vertex here:
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# https://console.cloud.google.com/vertex-ai/generative/speech/text-to-speech
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response = client.audio.speech.create(
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model = "vertex-tts",
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input=ssml, # pass as None since OpenAI SDK requires this param
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voice={'languageCode': 'en-US', 'name': 'en-US-Studio-O'},
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extra_body={"use_ssml": True},
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)
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print("response from proxy", response)
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```
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</TabItem>
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</Tabs>
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## Extra
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### Using `GOOGLE_APPLICATION_CREDENTIALS`
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|
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@ -349,19 +349,23 @@ class PrometheusLogger(CustomLogger):
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# latency metrics
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total_time: timedelta = kwargs.get("end_time") - kwargs.get("start_time")
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total_time_seconds = total_time.total_seconds()
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api_call_total_time: timedelta = kwargs.get("end_time") - kwargs.get(
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"api_call_start_time"
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)
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api_call_start_time = kwargs.get("api_call_start_time", None)
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api_call_total_time_seconds = api_call_total_time.total_seconds()
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if api_call_start_time is not None and isinstance(
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api_call_start_time, datetime
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):
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api_call_total_time: timedelta = (
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kwargs.get("end_time") - api_call_start_time
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)
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api_call_total_time_seconds = api_call_total_time.total_seconds()
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self.litellm_llm_api_latency_metric.labels(model).observe(
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api_call_total_time_seconds
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)
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# log metrics
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self.litellm_request_total_latency_metric.labels(model).observe(
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total_time_seconds
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)
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self.litellm_llm_api_latency_metric.labels(model).observe(
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api_call_total_time_seconds
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)
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# set x-ratelimit headers
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if premium_user is True:
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|
|
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@ -19,7 +19,8 @@ from litellm.llms.vertex_ai_and_google_ai_studio.gemini.vertex_and_google_ai_stu
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class VertexInput(TypedDict, total=False):
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text: str
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text: Optional[str]
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ssml: Optional[str]
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class VertexVoice(TypedDict, total=False):
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@ -86,10 +87,13 @@ class VertexTextToSpeechAPI(VertexLLM):
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####### Build the request ################
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# API Ref: https://cloud.google.com/text-to-speech/docs/reference/rest/v1/text/synthesize
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vertex_input = VertexInput(text=input)
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# required param
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optional_params = optional_params or {}
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kwargs = kwargs or {}
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optional_params = optional_params or {}
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vertex_input = VertexInput(text=input)
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validate_vertex_input(vertex_input, kwargs, optional_params)
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# required param
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if voice is not None:
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vertex_voice = VertexVoice(**voice)
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elif "voice" in kwargs:
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@ -203,3 +207,34 @@ class VertexTextToSpeechAPI(VertexLLM):
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# Initialize the HttpxBinaryResponseContent instance
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http_binary_response = HttpxBinaryResponseContent(response)
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return http_binary_response
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def validate_vertex_input(
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input_data: VertexInput, kwargs: dict, optional_params: dict
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) -> None:
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# Remove None values
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if input_data.get("text") is None:
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input_data.pop("text", None)
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if input_data.get("ssml") is None:
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input_data.pop("ssml", None)
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# Check if use_ssml is set
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use_ssml = kwargs.get("use_ssml", optional_params.get("use_ssml", False))
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if use_ssml:
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if "text" in input_data:
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input_data["ssml"] = input_data.pop("text")
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elif "ssml" not in input_data:
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raise ValueError("SSML input is required when use_ssml is True.")
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else:
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# LiteLLM will auto-detect if text is in ssml format
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# check if "text" is an ssml - in this case we should pass it as ssml instead of text
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if input_data:
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_text = input_data.get("text", None) or ""
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if "<speak>" in _text:
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input_data["ssml"] = input_data.pop("text")
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if not input_data:
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raise ValueError("Either 'text' or 'ssml' must be provided.")
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if "text" in input_data and "ssml" in input_data:
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raise ValueError("Only one of 'text' or 'ssml' should be provided, not both.")
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|
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@ -1988,17 +1988,33 @@
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"supports_vision": true,
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"source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
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},
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"gemini-experimental": {
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"gemini-pro-experimental": {
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"max_tokens": 8192,
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"max_input_tokens": 1000000,
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"max_output_tokens": 8192,
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"input_cost_per_token": 0,
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"output_cost_per_token": 0,
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"input_cost_per_character": 0,
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"output_cost_per_character": 0,
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"litellm_provider": "vertex_ai-language-models",
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"mode": "chat",
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"supports_function_calling": false,
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"supports_tool_choice": true,
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"source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
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"source": "https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/gemini-experimental"
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},
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"gemini-pro-flash": {
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"max_tokens": 8192,
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"max_input_tokens": 1000000,
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"max_output_tokens": 8192,
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"input_cost_per_token": 0,
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"output_cost_per_token": 0,
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"input_cost_per_character": 0,
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"output_cost_per_character": 0,
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"litellm_provider": "vertex_ai-language-models",
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"mode": "chat",
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"supports_function_calling": false,
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"supports_tool_choice": true,
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"source": "https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/gemini-experimental"
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},
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"gemini-pro-vision": {
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"max_tokens": 2048,
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|
|
1
litellm/proxy/_experimental/out/404.html
Normal file
1
litellm/proxy/_experimental/out/404.html
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@ -1 +1 @@
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<!DOCTYPE html><html id="__next_error__"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="preload" as="script" fetchPriority="low" href="/ui/_next/static/chunks/webpack-193a7eac80c8baba.js" crossorigin=""/><script src="/ui/_next/static/chunks/fd9d1056-f593049e31b05aeb.js" async="" crossorigin=""></script><script src="/ui/_next/static/chunks/69-8316d07d1f41e39f.js" async="" crossorigin=""></script><script src="/ui/_next/static/chunks/main-app-9b4fb13a7db53edf.js" async="" crossorigin=""></script><title>LiteLLM Dashboard</title><meta name="description" content="LiteLLM Proxy Admin UI"/><link rel="icon" href="/ui/favicon.ico" type="image/x-icon" sizes="16x16"/><meta name="next-size-adjust"/><script src="/ui/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js" crossorigin="" noModule=""></script></head><body><script src="/ui/_next/static/chunks/webpack-193a7eac80c8baba.js" crossorigin="" async=""></script><script>(self.__next_f=self.__next_f||[]).push([0]);self.__next_f.push([2,null])</script><script>self.__next_f.push([1,"1:HL[\"/ui/_next/static/media/a34f9d1faa5f3315-s.p.woff2\",\"font\",{\"crossOrigin\":\"\",\"type\":\"font/woff2\"}]\n2:HL[\"/ui/_next/static/css/cd10067a0a3408b4.css\",\"style\",{\"crossOrigin\":\"\"}]\n0:\"$L3\"\n"])</script><script>self.__next_f.push([1,"4:I[47690,[],\"\"]\n6:I[77831,[],\"\"]\n7:I[18018,[\"665\",\"static/chunks/3014691f-b24e8254c7593934.js\",\"936\",\"static/chunks/2f6dbc85-cac2949a76539886.js\",\"505\",\"static/chunks/505-5ff3c318fddfa35c.js\",\"131\",\"static/chunks/131-73d0a4f8e09896fe.js\",\"684\",\"static/chunks/684-16b194c83a169f6d.js\",\"605\",\"static/chunks/605-35a95945041f7699.js\",\"777\",\"static/chunks/777-5360b5460eba0779.js\",\"931\",\"static/chunks/app/page-01641b817a14ea88.js\"],\"\"]\n8:I[5613,[],\"\"]\n9:I[31778,[],\"\"]\nb:I[48955,[],\"\"]\nc:[]\n"])</script><script>self.__next_f.push([1,"3:[[[\"$\",\"link\",\"0\",{\"rel\":\"stylesheet\",\"href\":\"/ui/_next/static/css/cd10067a0a3408b4.css\",\"precedence\":\"next\",\"crossOrigin\":\"\"}]],[\"$\",\"$L4\",null,{\"buildId\":\"LO0Sm6uVF0pa4RdHSL0dN\",\"assetPrefix\":\"/ui\",\"initialCanonicalUrl\":\"/\",\"initialTree\":[\"\",{\"children\":[\"__PAGE__\",{}]},\"$undefined\",\"$undefined\",true],\"initialSeedData\":[\"\",{\"children\":[\"__PAGE__\",{},[\"$L5\",[\"$\",\"$L6\",null,{\"propsForComponent\":{\"params\":{}},\"Component\":\"$7\",\"isStaticGeneration\":true}],null]]},[null,[\"$\",\"html\",null,{\"lang\":\"en\",\"children\":[\"$\",\"body\",null,{\"className\":\"__className_86ef86\",\"children\":[\"$\",\"$L8\",null,{\"parallelRouterKey\":\"children\",\"segmentPath\":[\"children\"],\"loading\":\"$undefined\",\"loadingStyles\":\"$undefined\",\"loadingScripts\":\"$undefined\",\"hasLoading\":false,\"error\":\"$undefined\",\"errorStyles\":\"$undefined\",\"errorScripts\":\"$undefined\",\"template\":[\"$\",\"$L9\",null,{}],\"templateStyles\":\"$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\":[],\"styles\":null}]}]}],null]],\"initialHead\":[false,\"$La\"],\"globalErrorComponent\":\"$b\",\"missingSlots\":\"$Wc\"}]]\n"])</script><script>self.__next_f.push([1,"a:[[\"$\",\"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\"}]]\n5:null\n"])</script><script>self.__next_f.push([1,""])</script></body></html>
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|
||||
# model: sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614
|
||||
# sagemaker_base_url: https://exampleopenaiendpoint-production.up.railway.app/invocations/
|
||||
# api_base: https://exampleopenaiendpoint-production.up.railway.app
|
||||
input_cost_per_token: 10
|
||||
output_cost_per_token: 10
|
||||
|
||||
litellm_settings:
|
||||
max_internal_user_budget: 0.00001
|
||||
internal_user_budget_duration: "3s" # reset every 3seconds
|
||||
|
||||
general_settings:
|
||||
proxy_budget_rescheduler_min_time: 1
|
||||
proxy_budget_rescheduler_max_time: 2
|
||||
model: openai/my-fake-model
|
||||
api_key: my-fake-key
|
||||
api_base: https://exampleopenaiendpoint-production.up.railway.app/
|
||||
|
||||
|
||||
|
||||
|
|
|
@ -40,6 +40,22 @@ else:
|
|||
all_routes = LiteLLMRoutes.openai_routes.value + LiteLLMRoutes.management_routes.value
|
||||
|
||||
|
||||
def is_request_body_safe(request_body: dict) -> bool:
|
||||
"""
|
||||
Check if the request body is safe.
|
||||
|
||||
A malicious user can set the api_base to their own domain and invoke POST /chat/completions to intercept and steal the OpenAI API key.
|
||||
Relevant issue: https://huntr.com/bounties/4001e1a2-7b7a-4776-a3ae-e6692ec3d997
|
||||
"""
|
||||
banned_params = ["api_base", "base_url"]
|
||||
|
||||
for param in banned_params:
|
||||
if param in request_body:
|
||||
raise ValueError(f"BadRequest: {param} is not allowed in request body")
|
||||
|
||||
return True
|
||||
|
||||
|
||||
def common_checks(
|
||||
request_body: dict,
|
||||
team_object: Optional[LiteLLM_TeamTable],
|
||||
|
@ -60,6 +76,7 @@ def common_checks(
|
|||
6. [OPTIONAL] If 'enforce_end_user' enabled - did developer pass in 'user' param for openai endpoints
|
||||
7. [OPTIONAL] If 'litellm.max_budget' is set (>0), is proxy under budget
|
||||
8. [OPTIONAL] If guardrails modified - is request allowed to change this
|
||||
9. Check if request body is safe
|
||||
"""
|
||||
_model = request_body.get("model", None)
|
||||
if team_object is not None and team_object.blocked is True:
|
||||
|
|
45
litellm/proxy/auth/oauth2_proxy_hook.py
Normal file
45
litellm/proxy/auth/oauth2_proxy_hook.py
Normal file
|
@ -0,0 +1,45 @@
|
|||
from typing import Any, Dict
|
||||
|
||||
from fastapi import Request
|
||||
|
||||
from litellm._logging import verbose_proxy_logger
|
||||
from litellm.proxy._types import UserAPIKeyAuth
|
||||
|
||||
|
||||
async def handle_oauth2_proxy_request(request: Request) -> UserAPIKeyAuth:
|
||||
"""
|
||||
Handle request from oauth2 proxy.
|
||||
"""
|
||||
from litellm.proxy.proxy_server import general_settings
|
||||
|
||||
verbose_proxy_logger.debug("Handling oauth2 proxy request")
|
||||
# Define the OAuth2 config mappings
|
||||
oauth2_config_mappings: Dict[str, str] = general_settings.get(
|
||||
"oauth2_config_mappings", None
|
||||
)
|
||||
verbose_proxy_logger.debug(f"Oauth2 config mappings: {oauth2_config_mappings}")
|
||||
|
||||
if not oauth2_config_mappings:
|
||||
raise ValueError("Oauth2 config mappings not found in general_settings")
|
||||
# Initialize a dictionary to store the mapped values
|
||||
auth_data: Dict[str, Any] = {}
|
||||
|
||||
# Extract values from headers based on the mappings
|
||||
for key, header in oauth2_config_mappings.items():
|
||||
value = request.headers.get(header)
|
||||
if value:
|
||||
# Convert max_budget to float if present
|
||||
if key == "max_budget":
|
||||
auth_data[key] = float(value)
|
||||
# Convert models to list if present
|
||||
elif key == "models":
|
||||
auth_data[key] = [model.strip() for model in value.split(",")]
|
||||
else:
|
||||
auth_data[key] = value
|
||||
verbose_proxy_logger.debug(
|
||||
f"Auth data before creating UserAPIKeyAuth object: {auth_data}"
|
||||
)
|
||||
user_api_key_auth = UserAPIKeyAuth(**auth_data)
|
||||
verbose_proxy_logger.debug(f"UserAPIKeyAuth object created: {user_api_key_auth}")
|
||||
# Create and return UserAPIKeyAuth object
|
||||
return user_api_key_auth
|
|
@ -54,6 +54,7 @@ from litellm.proxy.auth.auth_checks import (
|
|||
get_org_object,
|
||||
get_team_object,
|
||||
get_user_object,
|
||||
is_request_body_safe,
|
||||
log_to_opentelemetry,
|
||||
)
|
||||
from litellm.proxy.auth.auth_utils import (
|
||||
|
@ -63,6 +64,7 @@ from litellm.proxy.auth.auth_utils import (
|
|||
route_in_additonal_public_routes,
|
||||
)
|
||||
from litellm.proxy.auth.oauth2_check import check_oauth2_token
|
||||
from litellm.proxy.auth.oauth2_proxy_hook import handle_oauth2_proxy_request
|
||||
from litellm.proxy.common_utils.http_parsing_utils import _read_request_body
|
||||
from litellm.proxy.utils import _to_ns
|
||||
|
||||
|
@ -122,6 +124,9 @@ async def user_api_key_auth(
|
|||
|
||||
try:
|
||||
route: str = get_request_route(request=request)
|
||||
# get the request body
|
||||
request_data = await _read_request_body(request=request)
|
||||
is_request_body_safe(request_body=request_data)
|
||||
|
||||
### LiteLLM Enterprise Security Checks
|
||||
# Check 1. Check if request size is under max_request_size_mb
|
||||
|
@ -213,6 +218,9 @@ async def user_api_key_auth(
|
|||
|
||||
return await check_oauth2_token(token=api_key)
|
||||
|
||||
if general_settings.get("enable_oauth2_proxy_auth", False) is True:
|
||||
return await handle_oauth2_proxy_request(request=request)
|
||||
|
||||
if general_settings.get("enable_jwt_auth", False) is True:
|
||||
is_jwt = jwt_handler.is_jwt(token=api_key)
|
||||
verbose_proxy_logger.debug("is_jwt: %s", is_jwt)
|
||||
|
@ -353,9 +361,6 @@ async def user_api_key_auth(
|
|||
user_info=user_info,
|
||||
)
|
||||
)
|
||||
# get the request body
|
||||
request_data = await _read_request_body(request=request)
|
||||
|
||||
# run through common checks
|
||||
_ = common_checks(
|
||||
request_body=request_data,
|
||||
|
@ -448,7 +453,6 @@ async def user_api_key_auth(
|
|||
)
|
||||
|
||||
## Check END-USER OBJECT
|
||||
request_data = await _read_request_body(request=request)
|
||||
_end_user_object = None
|
||||
end_user_params = {}
|
||||
if "user" in request_data:
|
||||
|
|
|
@ -13,6 +13,15 @@ model_list:
|
|||
model: cohere/rerank-english-v3.0
|
||||
api_key: os.environ/COHERE_API_KEY
|
||||
|
||||
general_settings:
|
||||
enable_oauth2_proxy_auth: True
|
||||
oauth2_config_mappings:
|
||||
token: X-Auth-Token
|
||||
user_id: X-Auth-Client-ID
|
||||
team_id: X-Auth-Team-ID
|
||||
max_budget: X-Auth-Max-Budget
|
||||
models: X-Auth-Allowed-Models
|
||||
|
||||
# default off mode
|
||||
litellm_settings:
|
||||
set_verbose: True
|
|
@ -92,6 +92,7 @@ from litellm.types.router import (
|
|||
RouterErrors,
|
||||
RouterGeneralSettings,
|
||||
RouterRateLimitError,
|
||||
RouterRateLimitErrorBasic,
|
||||
updateDeployment,
|
||||
updateLiteLLMParams,
|
||||
)
|
||||
|
@ -4459,16 +4460,8 @@ class Router:
|
|||
"""
|
||||
|
||||
if _rate_limit_error is True: # allow generic fallback logic to take place
|
||||
model_ids = self.get_model_ids(model_name=model)
|
||||
cooldown_time = self.cooldown_cache.get_min_cooldown(
|
||||
model_ids=model_ids
|
||||
)
|
||||
cooldown_list = self._get_cooldown_deployments()
|
||||
raise RouterRateLimitError(
|
||||
raise RouterRateLimitErrorBasic(
|
||||
model=model,
|
||||
cooldown_time=cooldown_time,
|
||||
enable_pre_call_checks=True,
|
||||
cooldown_list=cooldown_list,
|
||||
)
|
||||
|
||||
elif _context_window_error is True:
|
||||
|
@ -4579,14 +4572,10 @@ class Router:
|
|||
litellm.print_verbose(f"initial list of deployments: {healthy_deployments}")
|
||||
|
||||
if len(healthy_deployments) == 0:
|
||||
model_ids = self.get_model_ids(model_name=model)
|
||||
_cooldown_time = self.cooldown_cache.get_min_cooldown(model_ids=model_ids)
|
||||
_cooldown_list = self._get_cooldown_deployments()
|
||||
raise RouterRateLimitError(
|
||||
model=model,
|
||||
cooldown_time=_cooldown_time,
|
||||
enable_pre_call_checks=self.enable_pre_call_checks,
|
||||
cooldown_list=_cooldown_list,
|
||||
raise ValueError(
|
||||
"{}. You passed in model={}. There is no 'model_name' with this string ".format(
|
||||
RouterErrors.no_deployments_available.value, model
|
||||
)
|
||||
)
|
||||
|
||||
if litellm.model_alias_map and model in litellm.model_alias_map:
|
||||
|
|
|
@ -50,8 +50,9 @@ VERTEX_MODELS_TO_NOT_TEST = [
|
|||
"text-bison@001",
|
||||
"gemini-1.5-pro",
|
||||
"gemini-1.5-pro-preview-0215",
|
||||
"gemini-pro-flash",
|
||||
"gemini-pro-experimental",
|
||||
"gemini-flash-experimental",
|
||||
"gemini-pro-flash",
|
||||
]
|
||||
|
||||
|
||||
|
|
|
@ -214,6 +214,7 @@ async def test_add_message_litellm(sync_mode, provider):
|
|||
[True, False],
|
||||
) #
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.flaky(retries=3, delay=1)
|
||||
async def test_aarun_thread_litellm(sync_mode, provider, is_streaming):
|
||||
"""
|
||||
- Get Assistants
|
||||
|
|
|
@ -243,3 +243,61 @@ async def test_speech_litellm_vertex_async_with_voice():
|
|||
"voice": {"languageCode": "en-UK", "name": "en-UK-Studio-O"},
|
||||
"audioConfig": {"audioEncoding": "LINEAR22", "speakingRate": "10"},
|
||||
}
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_speech_litellm_vertex_async_with_voice_ssml():
|
||||
# Mock the response
|
||||
mock_response = AsyncMock()
|
||||
|
||||
def return_val():
|
||||
return {
|
||||
"audioContent": "dGVzdCByZXNwb25zZQ==",
|
||||
}
|
||||
|
||||
mock_response.json = return_val
|
||||
mock_response.status_code = 200
|
||||
|
||||
ssml = """
|
||||
<speak>
|
||||
<p>Hello, world!</p>
|
||||
<p>This is a test of the <break strength="medium" /> text-to-speech API.</p>
|
||||
</speak>
|
||||
"""
|
||||
|
||||
# Set up the mock for asynchronous calls
|
||||
with patch(
|
||||
"litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
|
||||
new_callable=AsyncMock,
|
||||
) as mock_async_post:
|
||||
mock_async_post.return_value = mock_response
|
||||
model = "vertex_ai/test"
|
||||
|
||||
response = await litellm.aspeech(
|
||||
input=ssml,
|
||||
model=model,
|
||||
voice={
|
||||
"languageCode": "en-UK",
|
||||
"name": "en-UK-Studio-O",
|
||||
},
|
||||
audioConfig={
|
||||
"audioEncoding": "LINEAR22",
|
||||
"speakingRate": "10",
|
||||
},
|
||||
)
|
||||
|
||||
# Assert asynchronous call
|
||||
mock_async_post.assert_called_once()
|
||||
_, kwargs = mock_async_post.call_args
|
||||
print("call args", kwargs)
|
||||
|
||||
assert kwargs["url"] == "https://texttospeech.googleapis.com/v1/text:synthesize"
|
||||
|
||||
assert "x-goog-user-project" in kwargs["headers"]
|
||||
assert kwargs["headers"]["Authorization"] is not None
|
||||
|
||||
assert kwargs["json"] == {
|
||||
"input": {"ssml": ssml},
|
||||
"voice": {"languageCode": "en-UK", "name": "en-UK-Studio-O"},
|
||||
"audioConfig": {"audioEncoding": "LINEAR22", "speakingRate": "10"},
|
||||
}
|
||||
|
|
|
@ -120,6 +120,7 @@ async def test_available_tpm(num_projects, dynamic_rate_limit_handler):
|
|||
|
||||
@pytest.mark.parametrize("num_projects", [1, 2, 100])
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.flaky(retries=3, delay=1)
|
||||
async def test_available_rpm(num_projects, dynamic_rate_limit_handler):
|
||||
model = "my-fake-model"
|
||||
## SET CACHE W/ ACTIVE PROJECTS
|
||||
|
|
|
@ -114,7 +114,7 @@ async def test_async_create_batch(provider):
|
|||
)
|
||||
print("Response from creating file=", file_obj)
|
||||
|
||||
await asyncio.sleep(5)
|
||||
await asyncio.sleep(10)
|
||||
batch_input_file_id = file_obj.id
|
||||
assert (
|
||||
batch_input_file_id is not None
|
||||
|
|
|
@ -11,8 +11,10 @@ from typing import Dict, List, Optional
|
|||
from unittest.mock import MagicMock
|
||||
|
||||
import pytest
|
||||
from starlette.datastructures import URL
|
||||
|
||||
import litellm
|
||||
from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
|
||||
|
||||
|
||||
class Request:
|
||||
|
@ -209,3 +211,38 @@ async def test_user_personal_budgets(key_ownership):
|
|||
except Exception:
|
||||
if key_ownership == "team_key":
|
||||
pytest.fail("Expected this call to work. Key is below team budget.")
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.parametrize("prohibited_param", ["api_base", "base_url"])
|
||||
async def test_user_api_key_auth_fails_with_prohibited_params(prohibited_param):
|
||||
"""
|
||||
Relevant issue: https://huntr.com/bounties/4001e1a2-7b7a-4776-a3ae-e6692ec3d997
|
||||
"""
|
||||
import json
|
||||
|
||||
from fastapi import Request
|
||||
|
||||
# Setup
|
||||
user_key = "sk-1234"
|
||||
|
||||
setattr(litellm.proxy.proxy_server, "master_key", "sk-1234")
|
||||
|
||||
# Create request with prohibited parameter in body
|
||||
request = Request(scope={"type": "http"})
|
||||
request._url = URL(url="/chat/completions")
|
||||
|
||||
async def return_body():
|
||||
body = {prohibited_param: "https://custom-api.com"}
|
||||
return bytes(json.dumps(body), "utf-8")
|
||||
|
||||
request.body = return_body
|
||||
try:
|
||||
response = await user_api_key_auth(
|
||||
request=request, api_key="Bearer " + user_key
|
||||
)
|
||||
except Exception as e:
|
||||
print("error str=", str(e))
|
||||
error_message = str(e.message)
|
||||
print("error message=", error_message)
|
||||
assert "is not allowed in request body" in error_message
|
||||
|
|
|
@ -551,6 +551,20 @@ class RouterGeneralSettings(BaseModel):
|
|||
) # if passed a model not llm_router model list, pass through the request to litellm.acompletion/embedding
|
||||
|
||||
|
||||
class RouterRateLimitErrorBasic(ValueError):
|
||||
"""
|
||||
Raise a basic error inside helper functions.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
model: str,
|
||||
):
|
||||
self.model = model
|
||||
_message = f"{RouterErrors.no_deployments_available.value}."
|
||||
super().__init__(_message)
|
||||
|
||||
|
||||
class RouterRateLimitError(ValueError):
|
||||
def __init__(
|
||||
self,
|
||||
|
|
|
@ -1988,17 +1988,33 @@
|
|||
"supports_vision": true,
|
||||
"source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
|
||||
},
|
||||
"gemini-experimental": {
|
||||
"gemini-pro-experimental": {
|
||||
"max_tokens": 8192,
|
||||
"max_input_tokens": 1000000,
|
||||
"max_output_tokens": 8192,
|
||||
"input_cost_per_token": 0,
|
||||
"output_cost_per_token": 0,
|
||||
"input_cost_per_character": 0,
|
||||
"output_cost_per_character": 0,
|
||||
"litellm_provider": "vertex_ai-language-models",
|
||||
"mode": "chat",
|
||||
"supports_function_calling": false,
|
||||
"supports_tool_choice": true,
|
||||
"source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
|
||||
"source": "https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/gemini-experimental"
|
||||
},
|
||||
"gemini-pro-flash": {
|
||||
"max_tokens": 8192,
|
||||
"max_input_tokens": 1000000,
|
||||
"max_output_tokens": 8192,
|
||||
"input_cost_per_token": 0,
|
||||
"output_cost_per_token": 0,
|
||||
"input_cost_per_character": 0,
|
||||
"output_cost_per_character": 0,
|
||||
"litellm_provider": "vertex_ai-language-models",
|
||||
"mode": "chat",
|
||||
"supports_function_calling": false,
|
||||
"supports_tool_choice": true,
|
||||
"source": "https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/gemini-experimental"
|
||||
},
|
||||
"gemini-pro-vision": {
|
||||
"max_tokens": 2048,
|
||||
|
|
|
@ -1,6 +1,6 @@
|
|||
[tool.poetry]
|
||||
name = "litellm"
|
||||
version = "1.44.8"
|
||||
version = "1.44.9"
|
||||
description = "Library to easily interface with LLM API providers"
|
||||
authors = ["BerriAI"]
|
||||
license = "MIT"
|
||||
|
@ -91,7 +91,7 @@ requires = ["poetry-core", "wheel"]
|
|||
build-backend = "poetry.core.masonry.api"
|
||||
|
||||
[tool.commitizen]
|
||||
version = "1.44.8"
|
||||
version = "1.44.9"
|
||||
version_files = [
|
||||
"pyproject.toml:^version"
|
||||
]
|
||||
|
|
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
|
@ -1 +1 @@
|
|||
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0:["LO0Sm6uVF0pa4RdHSL0dN",[[["",{"children":["onboarding",{"children":["__PAGE__",{}]}]},"$undefined","$undefined",true],["",{"children":["onboarding",{"children":["__PAGE__",{},["$L1",["$","$L2",null,{"propsForComponent":{"params":{}},"Component":"$3","isStaticGeneration":true}],null]]},["$","$L4",null,{"parallelRouterKey":"children","segmentPath":["children","onboarding","children"],"loading":"$undefined","loadingStyles":"$undefined","loadingScripts":"$undefined","hasLoading":false,"error":"$undefined","errorStyles":"$undefined","errorScripts":"$undefined","template":["$","$L5",null,{}],"templateStyles":"$undefined","templateScripts":"$undefined","notFound":"$undefined","notFoundStyles":"$undefined","styles":null}]]},[null,["$","html",null,{"lang":"en","children":["$","body",null,{"className":"__className_86ef86","children":["$","$L4",null,{"parallelRouterKey":"children","segmentPath":["children"],"loading":"$undefined","loadingStyles":"$undefined","loadingScripts":"$undefined","hasLoading":false,"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":[],"styles":null}]}]}],null]],[[["$","link","0",{"rel":"stylesheet","href":"/ui/_next/static/css/cd10067a0a3408b4.css","precedence":"next","crossOrigin":""}]],"$L6"]]]]
|
||||
0:["auooEytIka5iEx4r3srzM",[[["",{"children":["onboarding",{"children":["__PAGE__",{}]}]},"$undefined","$undefined",true],["",{"children":["onboarding",{"children":["__PAGE__",{},["$L1",["$","$L2",null,{"propsForComponent":{"params":{}},"Component":"$3","isStaticGeneration":true}],null]]},["$","$L4",null,{"parallelRouterKey":"children","segmentPath":["children","onboarding","children"],"loading":"$undefined","loadingStyles":"$undefined","loadingScripts":"$undefined","hasLoading":false,"error":"$undefined","errorStyles":"$undefined","errorScripts":"$undefined","template":["$","$L5",null,{}],"templateStyles":"$undefined","templateScripts":"$undefined","notFound":"$undefined","notFoundStyles":"$undefined","styles":null}]]},[null,["$","html",null,{"lang":"en","children":["$","body",null,{"className":"__className_86ef86","children":["$","$L4",null,{"parallelRouterKey":"children","segmentPath":["children"],"loading":"$undefined","loadingStyles":"$undefined","loadingScripts":"$undefined","hasLoading":false,"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":[],"styles":null}]}]}],null]],[[["$","link","0",{"rel":"stylesheet","href":"/ui/_next/static/css/cd10067a0a3408b4.css","precedence":"next","crossOrigin":""}]],"$L6"]]]]
|
||||
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
|
||||
|
|
|
@ -33,6 +33,7 @@ import {
|
|||
InputNumber,
|
||||
message,
|
||||
Select,
|
||||
Tooltip
|
||||
} from "antd";
|
||||
|
||||
import { CopyToClipboard } from "react-copy-to-clipboard";
|
||||
|
@ -87,6 +88,8 @@ interface ItemData {
|
|||
metadata: any;
|
||||
user_id: string | null;
|
||||
expires: any;
|
||||
budget_duration: string | null;
|
||||
budget_reset_at: string | null;
|
||||
// Add any other properties that exist in the item data
|
||||
}
|
||||
|
||||
|
@ -223,7 +226,7 @@ const ViewKeyTable: React.FC<ViewKeyTableProps> = ({
|
|||
<Form
|
||||
form={form}
|
||||
onFinish={handleEditSubmit}
|
||||
initialValues={token} // Pass initial values here
|
||||
initialValues={{...token, budget_duration: token.budget_duration}} // Pass initial values here
|
||||
labelCol={{ span: 8 }}
|
||||
wrapperCol={{ span: 16 }}
|
||||
labelAlign="left"
|
||||
|
@ -297,6 +300,22 @@ const ViewKeyTable: React.FC<ViewKeyTableProps> = ({
|
|||
>
|
||||
<InputNumber step={0.01} precision={2} width={200} />
|
||||
</Form.Item>
|
||||
|
||||
<Form.Item
|
||||
className="mt-8"
|
||||
label="Reset Budget"
|
||||
name="budget_duration"
|
||||
help={`Current Reset Budget: ${
|
||||
token.budget_duration
|
||||
}, budget will be reset: ${token.budget_reset_at ? new Date(token.budget_reset_at).toLocaleString() : 'Never'}`}
|
||||
>
|
||||
<Select placeholder="n/a">
|
||||
<Select.Option value="daily">daily</Select.Option>
|
||||
<Select.Option value="weekly">weekly</Select.Option>
|
||||
<Select.Option value="monthly">monthly</Select.Option>
|
||||
</Select>
|
||||
</Form.Item>
|
||||
|
||||
<Form.Item
|
||||
label="token"
|
||||
name="token"
|
||||
|
@ -305,6 +324,7 @@ const ViewKeyTable: React.FC<ViewKeyTableProps> = ({
|
|||
<Form.Item
|
||||
label="Team"
|
||||
name="team_id"
|
||||
className="mt-8"
|
||||
help="the team this key belongs to"
|
||||
>
|
||||
<Select3 value={token.team_alias}>
|
||||
|
@ -536,7 +556,30 @@ const ViewKeyTable: React.FC<ViewKeyTableProps> = ({
|
|||
}
|
||||
}
|
||||
|
||||
setSelectedToken(token);
|
||||
// Convert the budget_duration to the corresponding select option
|
||||
let budgetDuration = null;
|
||||
if (token.budget_duration) {
|
||||
switch (token.budget_duration) {
|
||||
case "24h":
|
||||
budgetDuration = "daily";
|
||||
break;
|
||||
case "7d":
|
||||
budgetDuration = "weekly";
|
||||
break;
|
||||
case "30d":
|
||||
budgetDuration = "monthly";
|
||||
break;
|
||||
default:
|
||||
budgetDuration = "None";
|
||||
}
|
||||
}
|
||||
|
||||
setSelectedToken({
|
||||
...token,
|
||||
budget_duration: budgetDuration
|
||||
});
|
||||
|
||||
//setSelectedToken(token);
|
||||
setEditModalVisible(true);
|
||||
};
|
||||
|
||||
|
@ -558,6 +601,21 @@ const ViewKeyTable: React.FC<ViewKeyTableProps> = ({
|
|||
const currentKey = formValues.token;
|
||||
formValues.key = currentKey;
|
||||
|
||||
// Convert the budget_duration back to the API expected format
|
||||
if (formValues.budget_duration) {
|
||||
switch (formValues.budget_duration) {
|
||||
case "daily":
|
||||
formValues.budget_duration = "24h";
|
||||
break;
|
||||
case "weekly":
|
||||
formValues.budget_duration = "7d";
|
||||
break;
|
||||
case "monthly":
|
||||
formValues.budget_duration = "30d";
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
console.log("handleEditSubmit:", formValues);
|
||||
|
||||
let newKeyValues = await keyUpdateCall(accessToken, formValues);
|
||||
|
@ -666,6 +724,7 @@ const ViewKeyTable: React.FC<ViewKeyTableProps> = ({
|
|||
<TableHeaderCell>Secret Key</TableHeaderCell>
|
||||
<TableHeaderCell>Spend (USD)</TableHeaderCell>
|
||||
<TableHeaderCell>Budget (USD)</TableHeaderCell>
|
||||
<TableHeaderCell>Budget Reset</TableHeaderCell>
|
||||
<TableHeaderCell>Models</TableHeaderCell>
|
||||
<TableHeaderCell>Rate Limits</TableHeaderCell>
|
||||
<TableHeaderCell>Rate Limits per model</TableHeaderCell>
|
||||
|
@ -722,6 +781,17 @@ const ViewKeyTable: React.FC<ViewKeyTableProps> = ({
|
|||
<Text>Unlimited</Text>
|
||||
)}
|
||||
</TableCell>
|
||||
<TableCell>
|
||||
{item.budget_reset_at != null ? (
|
||||
<div>
|
||||
<p style={{ fontSize: '0.70rem' }}>{new Date(item.budget_reset_at).toLocaleString()}</p>
|
||||
|
||||
</div>
|
||||
|
||||
) : (
|
||||
<p style={{ fontSize: '0.70rem' }}>Never</p>
|
||||
)}
|
||||
</TableCell>
|
||||
{/* <TableCell style={{ maxWidth: '2px' }}>
|
||||
<ViewKeySpendReport
|
||||
token={item.token}
|
||||
|
@ -796,7 +866,7 @@ const ViewKeyTable: React.FC<ViewKeyTableProps> = ({
|
|||
</Text>
|
||||
</TableCell>
|
||||
<TableCell>
|
||||
<Button onClick={() => handleModelLimitClick(item)}>Edit Limits</Button>
|
||||
<Button size="xs" onClick={() => handleModelLimitClick(item)}>Edit Limits</Button>
|
||||
</TableCell>
|
||||
<TableCell>
|
||||
<Icon
|
||||
|
@ -847,7 +917,15 @@ const ViewKeyTable: React.FC<ViewKeyTableProps> = ({
|
|||
<div className="mt-2 flex items-baseline space-x-2.5">
|
||||
<p className="text-tremor font-semibold text-tremor-content-strong dark:text-dark-tremor-content-strong">
|
||||
{selectedToken.max_budget != null ? (
|
||||
<>{selectedToken.max_budget}</>
|
||||
<>
|
||||
{selectedToken.max_budget}
|
||||
{selectedToken.budget_duration && (
|
||||
<>
|
||||
<br />
|
||||
Budget will be reset at {selectedToken.budget_reset_at ? new Date(selectedToken.budget_reset_at).toLocaleString() : 'Never'}
|
||||
</>
|
||||
)}
|
||||
</>
|
||||
) : (
|
||||
<>Unlimited</>
|
||||
)}
|
||||
|
|
Loading…
Add table
Add a link
Reference in a new issue