forked from phoenix/litellm-mirror
Merge branch 'main' into litellm_return-response_headers
This commit is contained in:
Ishaan Jaff
GitHub
commit
f6225623e9
37 changed files with 570 additions and 89 deletions
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@ -22,6 +22,7 @@ Anthropic API fails requests when `max_tokens` are not passed. Due to this litel
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import os
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os.environ["ANTHROPIC_API_KEY"] = "your-api-key"
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# os.environ["ANTHROPIC_API_BASE"] = "" # [OPTIONAL] or 'ANTHROPIC_BASE_URL'
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```
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## Usage
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|
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@ -1,3 +1,6 @@
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import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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# Mistral AI API
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https://docs.mistral.ai/api/
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@ -41,9 +44,106 @@ for chunk in response:
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```
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## Usage with LiteLLM Proxy
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### 1. Set Mistral Models on config.yaml
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```yaml
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model_list:
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- model_name: mistral-small-latest
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litellm_params:
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model: mistral/mistral-small-latest
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api_key: "os.environ/MISTRAL_API_KEY" # ensure you have `MISTRAL_API_KEY` in your .env
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```
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### 2. Start Proxy
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```
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litellm --config config.yaml
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```
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### 3. Test it
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<Tabs>
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<TabItem value="Curl" label="Curl Request">
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```shell
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curl --location 'http://0.0.0.0:4000/chat/completions' \
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--header 'Content-Type: application/json' \
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--data ' {
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"model": "mistral-small-latest",
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"messages": [
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{
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"role": "user",
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"content": "what llm are you"
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}
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]
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}
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'
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```
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</TabItem>
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<TabItem value="openai" label="OpenAI v1.0.0+">
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```python
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import openai
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client = openai.OpenAI(
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api_key="anything",
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base_url="http://0.0.0.0:4000"
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)
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response = client.chat.completions.create(model="mistral-small-latest", messages = [
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{
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"role": "user",
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"content": "this is a test request, write a short poem"
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}
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])
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print(response)
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```
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</TabItem>
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<TabItem value="langchain" label="Langchain">
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```python
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from langchain.chat_models import ChatOpenAI
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from langchain.prompts.chat import (
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ChatPromptTemplate,
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HumanMessagePromptTemplate,
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SystemMessagePromptTemplate,
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)
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from langchain.schema import HumanMessage, SystemMessage
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chat = ChatOpenAI(
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openai_api_base="http://0.0.0.0:4000", # set openai_api_base to the LiteLLM Proxy
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model = "mistral-small-latest",
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temperature=0.1
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)
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messages = [
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SystemMessage(
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content="You are a helpful assistant that im using to make a test request to."
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),
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HumanMessage(
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content="test from litellm. tell me why it's amazing in 1 sentence"
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),
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]
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response = chat(messages)
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print(response)
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```
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</TabItem>
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</Tabs>
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## Supported Models
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:::info
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All models listed here https://docs.mistral.ai/platform/endpoints are supported. We actively maintain the list of models, pricing, token window, etc. [here](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json).
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:::
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| Model Name | Function Call |
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|----------------|--------------------------------------------------------------|
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| Mistral Small | `completion(model="mistral/mistral-small-latest", messages)` |
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@ -53,6 +153,10 @@ All models listed here https://docs.mistral.ai/platform/endpoints are supported.
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| Mixtral 8x7B | `completion(model="mistral/open-mixtral-8x7b", messages)` |
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| Mixtral 8x22B | `completion(model="mistral/open-mixtral-8x22b", messages)` |
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| Codestral | `completion(model="mistral/codestral-latest", messages)` |
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| Mistral NeMo | `completion(model="mistral/open-mistral-nemo", messages)` |
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| Mistral NeMo 2407 | `completion(model="mistral/open-mistral-nemo-2407", messages)` |
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| Codestral Mamba | `completion(model="mistral/open-codestral-mamba", messages)` |
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| Codestral Mamba | `completion(model="mistral/codestral-mamba-latest"", messages)` |
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## Function Calling
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@ -831,6 +831,7 @@ class OpenAIChatCompletion(BaseLLM):
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client=None,
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organization: Optional[str] = None,
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custom_llm_provider: Optional[str] = None,
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drop_params: Optional[bool] = None,
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):
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super().completion()
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exception_mapping_worked = False
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@ -886,6 +887,7 @@ class OpenAIChatCompletion(BaseLLM):
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client=client,
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max_retries=max_retries,
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organization=organization,
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drop_params=drop_params,
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)
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else:
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return self.acompletion(
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@ -899,6 +901,7 @@ class OpenAIChatCompletion(BaseLLM):
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client=client,
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max_retries=max_retries,
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organization=organization,
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drop_params=drop_params,
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)
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elif optional_params.get("stream", False):
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return self.streaming(
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@ -962,6 +965,33 @@ class OpenAIChatCompletion(BaseLLM):
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model_response_object=model_response,
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_response_headers=headers,
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)
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except openai.UnprocessableEntityError as e:
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## check if body contains unprocessable params - related issue https://github.com/BerriAI/litellm/issues/4800
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if litellm.drop_params is True or drop_params is True:
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if e.body is not None and e.body.get("detail"): # type: ignore
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detail = e.body.get("detail") # type: ignore
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invalid_params: List[str] = []
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if (
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isinstance(detail, List)
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and len(detail) > 0
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and isinstance(detail[0], dict)
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):
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for error_dict in detail:
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if (
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error_dict.get("loc")
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and isinstance(error_dict.get("loc"), list)
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and len(error_dict.get("loc")) == 2
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):
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invalid_params.append(error_dict["loc"][1])
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new_data = {}
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for k, v in optional_params.items():
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if k not in invalid_params:
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new_data[k] = v
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optional_params = new_data
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else:
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raise e
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# e.message
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except Exception as e:
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if print_verbose is not None:
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print_verbose(f"openai.py: Received openai error - {str(e)}")
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@ -1019,30 +1049,36 @@ class OpenAIChatCompletion(BaseLLM):
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client=None,
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max_retries=None,
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headers=None,
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drop_params: Optional[bool] = None,
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):
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response = None
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try:
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openai_aclient = self._get_openai_client(
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is_async=True,
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api_key=api_key,
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api_base=api_base,
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timeout=timeout,
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max_retries=max_retries,
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organization=organization,
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client=client,
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)
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for _ in range(
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2
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): # if call fails due to alternating messages, retry with reformatted message
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try:
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openai_aclient = self._get_openai_client(
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is_async=True,
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api_key=api_key,
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api_base=api_base,
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timeout=timeout,
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max_retries=max_retries,
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organization=organization,
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client=client,
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)
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## LOGGING
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logging_obj.pre_call(
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input=data["messages"],
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api_key=openai_aclient.api_key,
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additional_args={
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"headers": {"Authorization": f"Bearer {openai_aclient.api_key}"},
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"api_base": openai_aclient._base_url._uri_reference,
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"acompletion": True,
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"complete_input_dict": data,
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},
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)
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## LOGGING
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logging_obj.pre_call(
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input=data["messages"],
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api_key=openai_aclient.api_key,
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additional_args={
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"headers": {
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"Authorization": f"Bearer {openai_aclient.api_key}"
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},
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"api_base": openai_aclient._base_url._uri_reference,
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"acompletion": True,
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"complete_input_dict": data,
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},
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)
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headers, response = await self.make_openai_chat_completion_request(
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openai_aclient=openai_aclient, data=data, timeout=timeout
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@ -1063,6 +1099,51 @@ class OpenAIChatCompletion(BaseLLM):
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)
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except Exception as e:
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raise e
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headers, response = await self.make_openai_chat_completion_request(
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openai_aclient=openai_aclient, data=data, timeout=timeout
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)
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stringified_response = response.model_dump()
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logging_obj.post_call(
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input=data["messages"],
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api_key=api_key,
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original_response=stringified_response,
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additional_args={"complete_input_dict": data},
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)
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logging_obj.model_call_details["response_headers"] = headers
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return convert_to_model_response_object(
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response_object=stringified_response,
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model_response_object=model_response,
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hidden_params={"headers": headers},
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)
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except openai.UnprocessableEntityError as e:
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## check if body contains unprocessable params - related issue https://github.com/BerriAI/litellm/issues/4800
|
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if litellm.drop_params is True or drop_params is True:
|
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if e.body is not None and e.body.get("detail"): # type: ignore
|
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detail = e.body.get("detail") # type: ignore
|
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invalid_params: List[str] = []
|
||||
if (
|
||||
isinstance(detail, List)
|
||||
and len(detail) > 0
|
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and isinstance(detail[0], dict)
|
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):
|
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for error_dict in detail:
|
||||
if (
|
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error_dict.get("loc")
|
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and isinstance(error_dict.get("loc"), list)
|
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and len(error_dict.get("loc")) == 2
|
||||
):
|
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invalid_params.append(error_dict["loc"][1])
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|
||||
new_data = {}
|
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for k, v in data.items():
|
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if k not in invalid_params:
|
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new_data[k] = v
|
||||
data = new_data
|
||||
else:
|
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raise e
|
||||
# e.message
|
||||
except Exception as e:
|
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raise e
|
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|
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def streaming(
|
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self,
|
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|
|
@ -1126,29 +1207,31 @@ class OpenAIChatCompletion(BaseLLM):
|
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client=None,
|
||||
max_retries=None,
|
||||
headers=None,
|
||||
drop_params: Optional[bool] = None,
|
||||
):
|
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response = None
|
||||
try:
|
||||
openai_aclient = self._get_openai_client(
|
||||
is_async=True,
|
||||
api_key=api_key,
|
||||
api_base=api_base,
|
||||
timeout=timeout,
|
||||
max_retries=max_retries,
|
||||
organization=organization,
|
||||
client=client,
|
||||
)
|
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## LOGGING
|
||||
logging_obj.pre_call(
|
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input=data["messages"],
|
||||
api_key=api_key,
|
||||
additional_args={
|
||||
"headers": headers,
|
||||
"api_base": api_base,
|
||||
"acompletion": True,
|
||||
"complete_input_dict": data,
|
||||
},
|
||||
)
|
||||
for _ in range(2):
|
||||
try:
|
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openai_aclient = self._get_openai_client(
|
||||
is_async=True,
|
||||
api_key=api_key,
|
||||
api_base=api_base,
|
||||
timeout=timeout,
|
||||
max_retries=max_retries,
|
||||
organization=organization,
|
||||
client=client,
|
||||
)
|
||||
## LOGGING
|
||||
logging_obj.pre_call(
|
||||
input=data["messages"],
|
||||
api_key=api_key,
|
||||
additional_args={
|
||||
"headers": headers,
|
||||
"api_base": api_base,
|
||||
"acompletion": True,
|
||||
"complete_input_dict": data,
|
||||
},
|
||||
)
|
||||
|
||||
headers, response = await self.make_openai_chat_completion_request(
|
||||
openai_aclient=openai_aclient, data=data, timeout=timeout
|
||||
|
|
@ -1170,14 +1253,63 @@ class OpenAIChatCompletion(BaseLLM):
|
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raise OpenAIError(
|
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status_code=500,
|
||||
message=f"{str(e)}\n\nOriginal Response: {response.text}",
|
||||
|
||||
headers, response = await self.make_openai_chat_completion_request(
|
||||
openai_aclient=openai_aclient, data=data, timeout=timeout
|
||||
|
||||
)
|
||||
else:
|
||||
if type(e).__name__ == "ReadTimeout":
|
||||
raise OpenAIError(status_code=408, message=f"{type(e).__name__}")
|
||||
elif hasattr(e, "status_code"):
|
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raise OpenAIError(status_code=e.status_code, message=str(e))
|
||||
logging_obj.model_call_details["response_headers"] = headers
|
||||
streamwrapper = CustomStreamWrapper(
|
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completion_stream=response,
|
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model=model,
|
||||
custom_llm_provider="openai",
|
||||
logging_obj=logging_obj,
|
||||
stream_options=data.get("stream_options", None),
|
||||
)
|
||||
return streamwrapper
|
||||
except openai.UnprocessableEntityError as e:
|
||||
## check if body contains unprocessable params - related issue https://github.com/BerriAI/litellm/issues/4800
|
||||
if litellm.drop_params is True or drop_params is True:
|
||||
if e.body is not None and e.body.get("detail"): # type: ignore
|
||||
detail = e.body.get("detail") # type: ignore
|
||||
invalid_params: List[str] = []
|
||||
if (
|
||||
isinstance(detail, List)
|
||||
and len(detail) > 0
|
||||
and isinstance(detail[0], dict)
|
||||
):
|
||||
for error_dict in detail:
|
||||
if (
|
||||
error_dict.get("loc")
|
||||
and isinstance(error_dict.get("loc"), list)
|
||||
and len(error_dict.get("loc")) == 2
|
||||
):
|
||||
invalid_params.append(error_dict["loc"][1])
|
||||
|
||||
new_data = {}
|
||||
for k, v in data.items():
|
||||
if k not in invalid_params:
|
||||
new_data[k] = v
|
||||
data = new_data
|
||||
else:
|
||||
raise OpenAIError(status_code=500, message=f"{str(e)}")
|
||||
raise e
|
||||
except (
|
||||
Exception
|
||||
) as e: # need to exception handle here. async exceptions don't get caught in sync functions.
|
||||
if response is not None and hasattr(response, "text"):
|
||||
raise OpenAIError(
|
||||
status_code=500,
|
||||
message=f"{str(e)}\n\nOriginal Response: {response.text}",
|
||||
)
|
||||
else:
|
||||
if type(e).__name__ == "ReadTimeout":
|
||||
raise OpenAIError(
|
||||
status_code=408, message=f"{type(e).__name__}"
|
||||
)
|
||||
elif hasattr(e, "status_code"):
|
||||
raise OpenAIError(status_code=e.status_code, message=str(e))
|
||||
else:
|
||||
raise OpenAIError(status_code=500, message=f"{str(e)}")
|
||||
|
||||
# Embedding
|
||||
async def make_openai_embedding_request(
|
||||
|
|
|
|||
|
|
@ -1176,6 +1176,7 @@ def completion(
|
|||
client=client, # pass AsyncOpenAI, OpenAI client
|
||||
organization=organization,
|
||||
custom_llm_provider=custom_llm_provider,
|
||||
drop_params=non_default_params.get("drop_params"),
|
||||
)
|
||||
except Exception as e:
|
||||
## LOGGING - log the original exception returned
|
||||
|
|
@ -1487,6 +1488,7 @@ def completion(
|
|||
api_base
|
||||
or litellm.api_base
|
||||
or get_secret("ANTHROPIC_API_BASE")
|
||||
or get_secret("ANTHROPIC_BASE_URL")
|
||||
or "https://api.anthropic.com/v1/complete"
|
||||
)
|
||||
response = anthropic_text_completions.completion(
|
||||
|
|
@ -1512,6 +1514,7 @@ def completion(
|
|||
api_base
|
||||
or litellm.api_base
|
||||
or get_secret("ANTHROPIC_API_BASE")
|
||||
or get_secret("ANTHROPIC_BASE_URL")
|
||||
or "https://api.anthropic.com/v1/messages"
|
||||
)
|
||||
response = anthropic_chat_completions.completion(
|
||||
|
|
|
|||
|
|
@ -929,6 +929,46 @@
|
|||
"litellm_provider": "mistral",
|
||||
"mode": "chat"
|
||||
},
|
||||
"mistral/open-mistral-nemo": {
|
||||
"max_tokens": 128000,
|
||||
"max_input_tokens": 128000,
|
||||
"max_output_tokens": 128000,
|
||||
"input_cost_per_token": 0.0000003,
|
||||
"output_cost_per_token": 0.0000003,
|
||||
"litellm_provider": "mistral",
|
||||
"mode": "chat",
|
||||
"source": "https://mistral.ai/technology/"
|
||||
},
|
||||
"mistral/open-mistral-nemo-2407": {
|
||||
"max_tokens": 128000,
|
||||
"max_input_tokens": 128000,
|
||||
"max_output_tokens": 128000,
|
||||
"input_cost_per_token": 0.0000003,
|
||||
"output_cost_per_token": 0.0000003,
|
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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
|
||||
|
|
|
|||
File diff suppressed because one or more lines are too long
|
|
@ -2,6 +2,6 @@
|
|||
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|
||||
4:I[5613,[],""]
|
||||
5:I[31778,[],""]
|
||||
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|
||||
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
|
||||
|
|
|
|||
|
|
@ -1,13 +1,9 @@
|
|||
model_list:
|
||||
- model_name: bad-azure-model
|
||||
- model_name: azure-mistral
|
||||
litellm_params:
|
||||
model: azure/chatgpt-v-2
|
||||
azure_ad_token: ""
|
||||
api_base: os.environ/AZURE_API_BASE
|
||||
|
||||
- model_name: good-openai-model
|
||||
litellm_params:
|
||||
model: gpt-3.5-turbo
|
||||
model: azure_ai/mistral
|
||||
api_key: os.environ/AZURE_AI_MISTRAL_API_KEY
|
||||
api_base: os.environ/AZURE_AI_MISTRAL_API_BASE
|
||||
|
||||
litellm_settings:
|
||||
fallbacks: [{"bad-azure-model": ["good-openai-model"]}]
|
||||
drop_params: true
|
||||
|
|
@ -886,6 +886,7 @@ class LiteLLM_TeamTable(TeamBase):
|
|||
budget_duration: Optional[str] = None
|
||||
budget_reset_at: Optional[datetime] = None
|
||||
model_id: Optional[int] = None
|
||||
last_refreshed_at: Optional[float] = None
|
||||
|
||||
model_config = ConfigDict(protected_namespaces=())
|
||||
|
||||
|
|
@ -1238,6 +1239,9 @@ class LiteLLM_VerificationTokenView(LiteLLM_VerificationToken):
|
|||
end_user_rpm_limit: Optional[int] = None
|
||||
end_user_max_budget: Optional[float] = None
|
||||
|
||||
# Time stamps
|
||||
last_refreshed_at: Optional[float] = None # last time joint view was pulled from db
|
||||
|
||||
|
||||
class UserAPIKeyAuth(
|
||||
LiteLLM_VerificationTokenView
|
||||
|
|
|
|||
|
|
@ -59,7 +59,7 @@ def common_checks(
|
|||
6. [OPTIONAL] If 'litellm.max_budget' is set (>0), is proxy under budget
|
||||
"""
|
||||
_model = request_body.get("model", None)
|
||||
if team_object is not None and team_object.blocked == True:
|
||||
if team_object is not None and team_object.blocked is True:
|
||||
raise Exception(
|
||||
f"Team={team_object.team_id} is blocked. Update via `/team/unblock` if your admin."
|
||||
)
|
||||
|
|
@ -349,6 +349,15 @@ async def get_user_object(
|
|||
)
|
||||
|
||||
|
||||
async def _cache_team_object(
|
||||
team_id: str,
|
||||
team_table: LiteLLM_TeamTable,
|
||||
user_api_key_cache: DualCache,
|
||||
):
|
||||
key = "team_id:{}".format(team_id)
|
||||
await user_api_key_cache.async_set_cache(key=key, value=team_table)
|
||||
|
||||
|
||||
@log_to_opentelemetry
|
||||
async def get_team_object(
|
||||
team_id: str,
|
||||
|
|
@ -386,7 +395,9 @@ async def get_team_object(
|
|||
|
||||
_response = LiteLLM_TeamTable(**response.dict())
|
||||
# save the team object to cache
|
||||
await user_api_key_cache.async_set_cache(key=key, value=_response)
|
||||
await _cache_team_object(
|
||||
team_id=team_id, team_table=_response, user_api_key_cache=user_api_key_cache
|
||||
)
|
||||
|
||||
return _response
|
||||
except Exception as e:
|
||||
|
|
|
|||
|
|
@ -453,6 +453,32 @@ async def user_api_key_auth(
|
|||
|
||||
return valid_token
|
||||
|
||||
if (
|
||||
valid_token is not None
|
||||
and isinstance(valid_token, UserAPIKeyAuth)
|
||||
and valid_token.team_id is not None
|
||||
and user_api_key_cache.get_cache(
|
||||
key="team_id:{}".format(valid_token.team_id)
|
||||
)
|
||||
is not None
|
||||
):
|
||||
## UPDATE TEAM VALUES BASED ON CACHED TEAM OBJECT - allows `/team/update` values to work for cached token
|
||||
team_obj: LiteLLM_TeamTable = user_api_key_cache.get_cache(
|
||||
key="team_id:{}".format(valid_token.team_id)
|
||||
)
|
||||
|
||||
if (
|
||||
team_obj.last_refreshed_at is not None
|
||||
and valid_token.last_refreshed_at is not None
|
||||
and team_obj.last_refreshed_at > valid_token.last_refreshed_at
|
||||
):
|
||||
team_obj_dict = team_obj.__dict__
|
||||
|
||||
for k, v in team_obj_dict.items():
|
||||
field_name = f"team_{k}"
|
||||
if field_name in valid_token.__fields__:
|
||||
setattr(valid_token, field_name, v)
|
||||
|
||||
try:
|
||||
is_master_key_valid = secrets.compare_digest(api_key, master_key) # type: ignore
|
||||
except Exception as e:
|
||||
|
|
@ -504,7 +530,6 @@ async def user_api_key_auth(
|
|||
raise Exception("No connected db.")
|
||||
|
||||
## check for cache hit (In-Memory Cache)
|
||||
original_api_key = api_key # (Patch: For DynamoDB Backwards Compatibility)
|
||||
_user_role = None
|
||||
if api_key.startswith("sk-"):
|
||||
api_key = hash_token(token=api_key)
|
||||
|
|
@ -521,10 +546,13 @@ async def user_api_key_auth(
|
|||
parent_otel_span=parent_otel_span,
|
||||
proxy_logging_obj=proxy_logging_obj,
|
||||
)
|
||||
|
||||
if _valid_token is not None:
|
||||
## update cached token
|
||||
valid_token = UserAPIKeyAuth(
|
||||
**_valid_token.model_dump(exclude_none=True)
|
||||
)
|
||||
|
||||
verbose_proxy_logger.debug("Token from db: %s", valid_token)
|
||||
elif valid_token is not None and isinstance(valid_token, UserAPIKeyAuth):
|
||||
verbose_proxy_logger.debug("API Key Cache Hit!")
|
||||
|
|
|
|||
|
|
@ -328,11 +328,13 @@ async def update_team(
|
|||
}'
|
||||
```
|
||||
"""
|
||||
from litellm.proxy.auth.auth_checks import _cache_team_object
|
||||
from litellm.proxy.proxy_server import (
|
||||
_duration_in_seconds,
|
||||
create_audit_log_for_update,
|
||||
litellm_proxy_admin_name,
|
||||
prisma_client,
|
||||
user_api_key_cache,
|
||||
)
|
||||
|
||||
if prisma_client is None:
|
||||
|
|
@ -361,11 +363,22 @@ async def update_team(
|
|||
# set the budget_reset_at in DB
|
||||
updated_kv["budget_reset_at"] = reset_at
|
||||
|
||||
team_row = await prisma_client.update_data(
|
||||
update_key_values=updated_kv,
|
||||
data=updated_kv,
|
||||
table_name="team",
|
||||
team_id=data.team_id,
|
||||
team_row: Optional[
|
||||
LiteLLM_TeamTable
|
||||
] = await prisma_client.db.litellm_teamtable.update(
|
||||
where={"team_id": data.team_id}, data=updated_kv # type: ignore
|
||||
)
|
||||
|
||||
if team_row is None or team_row.team_id is None:
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail={"error": "Team doesn't exist. Got={}".format(team_row)},
|
||||
)
|
||||
|
||||
await _cache_team_object(
|
||||
team_id=team_row.team_id,
|
||||
team_table=team_row,
|
||||
user_api_key_cache=user_api_key_cache,
|
||||
)
|
||||
|
||||
# Enterprise Feature - Audit Logging. Enable with litellm.store_audit_logs = True
|
||||
|
|
@ -392,7 +405,7 @@ async def update_team(
|
|||
)
|
||||
)
|
||||
|
||||
return team_row
|
||||
return {"team_id": team_row.team_id, "data": team_row}
|
||||
|
||||
|
||||
@router.post(
|
||||
|
|
|
|||
|
|
@ -1331,7 +1331,9 @@ class PrismaClient:
|
|||
response["team_models"] = []
|
||||
if response["team_blocked"] is None:
|
||||
response["team_blocked"] = False
|
||||
response = LiteLLM_VerificationTokenView(**response)
|
||||
response = LiteLLM_VerificationTokenView(
|
||||
**response, last_refreshed_at=time.time()
|
||||
)
|
||||
# for prisma we need to cast the expires time to str
|
||||
if response.expires is not None and isinstance(
|
||||
response.expires, datetime
|
||||
|
|
|
|||
|
|
@ -417,14 +417,15 @@ class Router:
|
|||
litellm.failure_callback.append(self.deployment_callback_on_failure)
|
||||
else:
|
||||
litellm.failure_callback = [self.deployment_callback_on_failure]
|
||||
print( # noqa
|
||||
verbose_router_logger.debug(
|
||||
f"Intialized router with Routing strategy: {self.routing_strategy}\n\n"
|
||||
f"Routing enable_pre_call_checks: {self.enable_pre_call_checks}\n\n"
|
||||
f"Routing fallbacks: {self.fallbacks}\n\n"
|
||||
f"Routing content fallbacks: {self.content_policy_fallbacks}\n\n"
|
||||
f"Routing context window fallbacks: {self.context_window_fallbacks}\n\n"
|
||||
f"Router Redis Caching={self.cache.redis_cache}\n"
|
||||
) # noqa
|
||||
)
|
||||
|
||||
self.routing_strategy_args = routing_strategy_args
|
||||
self.retry_policy: Optional[RetryPolicy] = retry_policy
|
||||
self.model_group_retry_policy: Optional[Dict[str, RetryPolicy]] = (
|
||||
|
|
|
|||
|
|
@ -142,6 +142,36 @@ def test_completion_azure_ai_command_r():
|
|||
pytest.fail(f"Error occurred: {e}")
|
||||
|
||||
|
||||
@pytest.mark.parametrize("sync_mode", [True, False])
|
||||
@pytest.mark.asyncio
|
||||
async def test_completion_azure_ai_mistral_invalid_params(sync_mode):
|
||||
try:
|
||||
import os
|
||||
|
||||
litellm.set_verbose = True
|
||||
|
||||
os.environ["AZURE_AI_API_BASE"] = os.getenv("AZURE_MISTRAL_API_BASE", "")
|
||||
os.environ["AZURE_AI_API_KEY"] = os.getenv("AZURE_MISTRAL_API_KEY", "")
|
||||
|
||||
data = {
|
||||
"model": "azure_ai/mistral",
|
||||
"messages": [{"role": "user", "content": "What is the meaning of life?"}],
|
||||
"frequency_penalty": 0.1,
|
||||
"presence_penalty": 0.1,
|
||||
"drop_params": True,
|
||||
}
|
||||
if sync_mode:
|
||||
response: litellm.ModelResponse = completion(**data) # type: ignore
|
||||
else:
|
||||
response: litellm.ModelResponse = await litellm.acompletion(**data) # type: ignore
|
||||
|
||||
assert "azure_ai" in response.model
|
||||
except litellm.Timeout as e:
|
||||
pass
|
||||
except Exception as e:
|
||||
pytest.fail(f"Error occurred: {e}")
|
||||
|
||||
|
||||
def test_completion_azure_command_r():
|
||||
try:
|
||||
litellm.set_verbose = True
|
||||
|
|
|
|||
|
|
@ -2881,6 +2881,40 @@ def test_azure_streaming_and_function_calling():
|
|||
raise e
|
||||
|
||||
|
||||
@pytest.mark.parametrize("sync_mode", [True, False])
|
||||
@pytest.mark.asyncio
|
||||
async def test_completion_azure_ai_mistral_invalid_params(sync_mode):
|
||||
try:
|
||||
import os
|
||||
|
||||
litellm.set_verbose = True
|
||||
|
||||
os.environ["AZURE_AI_API_BASE"] = os.getenv("AZURE_MISTRAL_API_BASE", "")
|
||||
os.environ["AZURE_AI_API_KEY"] = os.getenv("AZURE_MISTRAL_API_KEY", "")
|
||||
|
||||
data = {
|
||||
"model": "azure_ai/mistral",
|
||||
"messages": [{"role": "user", "content": "What is the meaning of life?"}],
|
||||
"frequency_penalty": 0.1,
|
||||
"presence_penalty": 0.1,
|
||||
"drop_params": True,
|
||||
"stream": True,
|
||||
}
|
||||
if sync_mode:
|
||||
response: litellm.ModelResponse = completion(**data) # type: ignore
|
||||
for chunk in response:
|
||||
print(chunk)
|
||||
else:
|
||||
response: litellm.ModelResponse = await litellm.acompletion(**data) # type: ignore
|
||||
|
||||
async for chunk in response:
|
||||
print(chunk)
|
||||
except litellm.Timeout as e:
|
||||
pass
|
||||
except Exception as e:
|
||||
pytest.fail(f"Error occurred: {e}")
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_azure_astreaming_and_function_calling():
|
||||
import uuid
|
||||
|
|
|
|||
|
|
@ -44,3 +44,48 @@ def test_check_valid_ip(
|
|||
request = Request(client_ip)
|
||||
|
||||
assert _check_valid_ip(allowed_ips, request) == expected_result # type: ignore
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_check_blocked_team():
|
||||
"""
|
||||
cached valid_token obj has team_blocked = true
|
||||
|
||||
cached team obj has team_blocked = false
|
||||
|
||||
assert team is not blocked
|
||||
"""
|
||||
import asyncio
|
||||
import time
|
||||
|
||||
from fastapi import Request
|
||||
from starlette.datastructures import URL
|
||||
|
||||
from litellm.proxy._types import LiteLLM_TeamTable, UserAPIKeyAuth
|
||||
from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
|
||||
from litellm.proxy.proxy_server import hash_token, user_api_key_cache
|
||||
|
||||
_team_id = "1234"
|
||||
user_key = "sk-12345678"
|
||||
|
||||
valid_token = UserAPIKeyAuth(
|
||||
team_id=_team_id,
|
||||
team_blocked=True,
|
||||
token=hash_token(user_key),
|
||||
last_refreshed_at=time.time(),
|
||||
)
|
||||
await asyncio.sleep(1)
|
||||
team_obj = LiteLLM_TeamTable(
|
||||
team_id=_team_id, blocked=False, last_refreshed_at=time.time()
|
||||
)
|
||||
user_api_key_cache.set_cache(key=hash_token(user_key), value=valid_token)
|
||||
user_api_key_cache.set_cache(key="team_id:{}".format(_team_id), value=team_obj)
|
||||
|
||||
setattr(litellm.proxy.proxy_server, "user_api_key_cache", user_api_key_cache)
|
||||
setattr(litellm.proxy.proxy_server, "master_key", "sk-1234")
|
||||
setattr(litellm.proxy.proxy_server, "prisma_client", "hello-world")
|
||||
|
||||
request = Request(scope={"type": "http"})
|
||||
request._url = URL(url="/chat/completions")
|
||||
|
||||
await user_api_key_auth(request=request, api_key="Bearer " + user_key)
|
||||
|
|
|
|||
|
|
@ -929,6 +929,46 @@
|
|||
"litellm_provider": "mistral",
|
||||
"mode": "chat"
|
||||
},
|
||||
"mistral/open-mistral-nemo": {
|
||||
"max_tokens": 128000,
|
||||
"max_input_tokens": 128000,
|
||||
"max_output_tokens": 128000,
|
||||
"input_cost_per_token": 0.0000003,
|
||||
"output_cost_per_token": 0.0000003,
|
||||
"litellm_provider": "mistral",
|
||||
"mode": "chat",
|
||||
"source": "https://mistral.ai/technology/"
|
||||
},
|
||||
"mistral/open-mistral-nemo-2407": {
|
||||
"max_tokens": 128000,
|
||||
"max_input_tokens": 128000,
|
||||
"max_output_tokens": 128000,
|
||||
"input_cost_per_token": 0.0000003,
|
||||
"output_cost_per_token": 0.0000003,
|
||||
"litellm_provider": "mistral",
|
||||
"mode": "chat",
|
||||
"source": "https://mistral.ai/technology/"
|
||||
},
|
||||
"mistral/open-codestral-mamba": {
|
||||
"max_tokens": 256000,
|
||||
"max_input_tokens": 256000,
|
||||
"max_output_tokens": 256000,
|
||||
"input_cost_per_token": 0.00000025,
|
||||
"output_cost_per_token": 0.00000025,
|
||||
"litellm_provider": "mistral",
|
||||
"mode": "chat",
|
||||
"source": "https://mistral.ai/technology/"
|
||||
},
|
||||
"mistral/codestral-mamba-latest": {
|
||||
"max_tokens": 256000,
|
||||
"max_input_tokens": 256000,
|
||||
"max_output_tokens": 256000,
|
||||
"input_cost_per_token": 0.00000025,
|
||||
"output_cost_per_token": 0.00000025,
|
||||
"litellm_provider": "mistral",
|
||||
"mode": "chat",
|
||||
"source": "https://mistral.ai/technology/"
|
||||
},
|
||||
"mistral/mistral-embed": {
|
||||
"max_tokens": 8192,
|
||||
"max_input_tokens": 8192,
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
[tool.poetry]
|
||||
name = "litellm"
|
||||
version = "1.41.25"
|
||||
version = "1.41.26"
|
||||
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 = "1.41.25"
|
||||
version = "1.41.26"
|
||||
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
|
|
@ -1 +1 @@
|
|||
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Loading…
Add table
Add a link
Reference in a new issue