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Complete 'requests' library removal (#7350)
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* refactor: initial commit moving watsonx_text to base_llm_http_handler + clarifying new provider directory structure * refactor(watsonx/completion/handler.py): move to using base llm http handler removes 'requests' library usage * fix(watsonx_text/transformation.py): fix result transformation migrates to transformation.py, for usage with base llm http handler * fix(streaming_handler.py): migrate watsonx streaming to transformation.py ensures streaming works with base llm http handler * fix(streaming_handler.py): fix streaming linting errors and remove watsonx conditional logic * fix(watsonx/): fix chat route post completion route refactor * refactor(watsonx/embed): refactor watsonx to use base llm http handler for embedding calls as well * refactor(base.py): remove requests library usage from litellm * build(pyproject.toml): remove requests library usage * fix: fix linting errors * fix: fix linting errors * fix(types/utils.py): fix validation errors for modelresponsestream * fix(replicate/handler.py): fix linting errors * fix(litellm_logging.py): handle modelresponsestream object * fix(streaming_handler.py): fix modelresponsestream args * fix: remove unused imports * test: fix test * fix: fix test * test: fix test * test: fix tests * test: fix test * test: fix patch target * test: fix test
This commit is contained in:
Krish Dholakia
GitHub
parent
8b1ea40e7b
commit
3671829e39
39 changed files with 2147 additions and 2279 deletions
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@ -1,13 +1,15 @@
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from typing import Callable, Dict, Optional, Union, cast
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from typing import Dict, List, Optional, Union, cast
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import httpx
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import litellm
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from litellm import verbose_logger
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from litellm.caching import InMemoryCache
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from litellm.litellm_core_utils.prompt_templates import factory as ptf
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from litellm.llms.base_llm.chat.transformation import BaseLLMException
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from litellm.secret_managers.main import get_secret_str
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from litellm.types.llms.watsonx import WatsonXAPIParams
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from litellm.types.llms.openai import AllMessageValues
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from litellm.types.llms.watsonx import WatsonXAPIParams, WatsonXCredentials
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class WatsonXAIError(BaseLLMException):
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@ -65,18 +67,20 @@ def generate_iam_token(api_key=None, **params) -> str:
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return cast(str, result)
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def _generate_watsonx_token(api_key: Optional[str], token: Optional[str]) -> str:
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if token is not None:
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return token
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token = generate_iam_token(api_key)
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return token
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def _get_api_params(
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params: dict,
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print_verbose: Optional[Callable] = None,
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generate_token: Optional[bool] = True,
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) -> WatsonXAPIParams:
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"""
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Find watsonx.ai credentials in the params or environment variables and return the headers for authentication.
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"""
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# Load auth variables from params
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url = params.pop("url", params.pop("api_base", params.pop("base_url", None)))
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api_key = params.pop("apikey", None)
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token = params.pop("token", None)
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project_id = params.pop(
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"project_id", params.pop("watsonx_project", None)
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) # watsonx.ai project_id - allow 'watsonx_project' to be consistent with how vertex project implementation works -> reduce provider-specific params
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@ -86,29 +90,8 @@ def _get_api_params(
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region_name = params.pop(
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"watsonx_region_name", params.pop("watsonx_region", None)
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) # consistent with how vertex ai + aws regions are accepted
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wx_credentials = params.pop(
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"wx_credentials",
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params.pop(
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"watsonx_credentials", None
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), # follow {provider}_credentials, same as vertex ai
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)
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api_version = params.pop("api_version", litellm.WATSONX_DEFAULT_API_VERSION)
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# Load auth variables from environment variables
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if url is None:
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url = (
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get_secret_str("WATSONX_API_BASE") # consistent with 'AZURE_API_BASE'
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or get_secret_str("WATSONX_URL")
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or get_secret_str("WX_URL")
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or get_secret_str("WML_URL")
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)
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if api_key is None:
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api_key = (
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get_secret_str("WATSONX_APIKEY")
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or get_secret_str("WATSONX_API_KEY")
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or get_secret_str("WX_API_KEY")
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)
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if token is None:
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token = get_secret_str("WATSONX_TOKEN") or get_secret_str("WX_TOKEN")
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if project_id is None:
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project_id = (
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get_secret_str("WATSONX_PROJECT_ID")
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@ -129,34 +112,6 @@ def _get_api_params(
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or get_secret_str("SPACE_ID")
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)
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# credentials parsing
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if wx_credentials is not None:
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url = wx_credentials.get("url", url)
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api_key = wx_credentials.get("apikey", wx_credentials.get("api_key", api_key))
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token = wx_credentials.get(
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"token",
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wx_credentials.get(
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"watsonx_token", token
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), # follow format of {provider}_token, same as azure - e.g. 'azure_ad_token=..'
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)
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# verify that all required credentials are present
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if url is None:
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raise WatsonXAIError(
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status_code=401,
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message="Error: Watsonx URL not set. Set WX_URL in environment variables or pass in as a parameter.",
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)
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if token is None and api_key is not None and generate_token:
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# generate the auth token
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if print_verbose is not None:
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print_verbose("Generating IAM token for Watsonx.ai")
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token = generate_iam_token(api_key)
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elif token is None and api_key is None:
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raise WatsonXAIError(
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status_code=401,
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message="Error: API key or token not found. Set WX_API_KEY or WX_TOKEN in environment variables or pass in as a parameter.",
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)
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if project_id is None:
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raise WatsonXAIError(
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status_code=401,
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@ -164,11 +119,147 @@ def _get_api_params(
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)
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return WatsonXAPIParams(
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url=url,
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api_key=api_key,
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token=cast(str, token),
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project_id=project_id,
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space_id=space_id,
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region_name=region_name,
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api_version=api_version,
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)
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def convert_watsonx_messages_to_prompt(
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model: str,
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messages: List[AllMessageValues],
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provider: str,
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custom_prompt_dict: Dict,
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) -> str:
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# handle anthropic prompts and amazon titan prompts
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if model in custom_prompt_dict:
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# check if the model has a registered custom prompt
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model_prompt_dict = custom_prompt_dict[model]
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prompt = ptf.custom_prompt(
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messages=messages,
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role_dict=model_prompt_dict.get(
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"role_dict", model_prompt_dict.get("roles")
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),
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initial_prompt_value=model_prompt_dict.get("initial_prompt_value", ""),
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final_prompt_value=model_prompt_dict.get("final_prompt_value", ""),
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bos_token=model_prompt_dict.get("bos_token", ""),
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eos_token=model_prompt_dict.get("eos_token", ""),
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)
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return prompt
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elif provider == "ibm-mistralai":
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prompt = ptf.mistral_instruct_pt(messages=messages)
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else:
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prompt: str = ptf.prompt_factory( # type: ignore
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model=model, messages=messages, custom_llm_provider="watsonx"
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)
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return prompt
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# Mixin class for shared IBM Watson X functionality
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class IBMWatsonXMixin:
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def validate_environment(
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self,
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headers: Dict,
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model: str,
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messages: List[AllMessageValues],
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optional_params: Dict,
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api_key: Optional[str] = None,
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) -> Dict:
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headers = {
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"Content-Type": "application/json",
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"Accept": "application/json",
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}
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token = cast(Optional[str], optional_params.get("token"))
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if token:
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headers["Authorization"] = f"Bearer {token}"
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else:
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token = _generate_watsonx_token(api_key=api_key, token=token)
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# build auth headers
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headers["Authorization"] = f"Bearer {token}"
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return headers
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def _get_base_url(self, api_base: Optional[str]) -> str:
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url = (
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api_base
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or get_secret_str("WATSONX_API_BASE") # consistent with 'AZURE_API_BASE'
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or get_secret_str("WATSONX_URL")
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or get_secret_str("WX_URL")
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or get_secret_str("WML_URL")
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)
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if url is None:
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raise WatsonXAIError(
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status_code=401,
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message="Error: Watsonx URL not set. Set WATSONX_API_BASE in environment variables or pass in as parameter - 'api_base='.",
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)
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return url
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def _add_api_version_to_url(self, url: str, api_version: Optional[str]) -> str:
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api_version = api_version or litellm.WATSONX_DEFAULT_API_VERSION
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url = url + f"?version={api_version}"
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return url
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def get_error_class(
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self, error_message: str, status_code: int, headers: Union[Dict, httpx.Headers]
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) -> BaseLLMException:
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return WatsonXAIError(
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status_code=status_code, message=error_message, headers=headers
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)
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@staticmethod
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def get_watsonx_credentials(
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optional_params: dict, api_key: Optional[str], api_base: Optional[str]
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) -> WatsonXCredentials:
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api_key = (
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api_key
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or optional_params.pop("apikey", None)
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or get_secret_str("WATSONX_APIKEY")
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or get_secret_str("WATSONX_API_KEY")
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or get_secret_str("WX_API_KEY")
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)
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api_base = (
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api_base
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or optional_params.pop(
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"url",
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optional_params.pop("api_base", optional_params.pop("base_url", None)),
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)
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or get_secret_str("WATSONX_API_BASE")
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or get_secret_str("WATSONX_URL")
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or get_secret_str("WX_URL")
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or get_secret_str("WML_URL")
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)
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wx_credentials = optional_params.pop(
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"wx_credentials",
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optional_params.pop(
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"watsonx_credentials", None
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), # follow {provider}_credentials, same as vertex ai
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)
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token: Optional[str] = None
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if wx_credentials is not None:
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api_base = wx_credentials.get("url", api_base)
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api_key = wx_credentials.get(
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"apikey", wx_credentials.get("api_key", api_key)
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)
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token = wx_credentials.get(
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"token",
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wx_credentials.get(
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"watsonx_token", None
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), # follow format of {provider}_token, same as azure - e.g. 'azure_ad_token=..'
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)
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if api_key is None or not isinstance(api_key, str):
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raise WatsonXAIError(
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status_code=401,
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message="Error: Watsonx API key not set. Set WATSONX_API_KEY in environment variables or pass in as parameter - 'api_key='.",
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)
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if api_base is None or not isinstance(api_base, str):
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raise WatsonXAIError(
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status_code=401,
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message="Error: Watsonx API base not set. Set WATSONX_API_BASE in environment variables or pass in as parameter - 'api_base='.",
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)
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return WatsonXCredentials(
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api_key=api_key, api_base=api_base, token=cast(Optional[str], token)
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)
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