mirror of
https://github.com/BerriAI/litellm.git
synced 2025-04-26 11:14:04 +00:00
fix map input_type to task_type for vertex ai
This commit is contained in:
Ishaan Jaff
parent
6a5992ed2d
commit
ea12519b98
7 changed files with 311 additions and 271 deletions
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@ -861,7 +861,7 @@ from .llms.vertex_ai_and_google_ai_studio.gemini.vertex_and_google_ai_studio_gem
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GoogleAIStudioGeminiConfig,
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VertexAIConfig,
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)
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from .llms.vertex_ai_and_google_ai_studio.vertex_ai_non_gemini import (
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from .llms.vertex_ai_and_google_ai_studio.vertex_embeddings.embedding_handler import (
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VertexAITextEmbeddingConfig,
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)
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from .llms.vertex_ai_and_google_ai_studio.vertex_ai_anthropic import (
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@ -1096,269 +1096,3 @@ async def async_streaming(
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)
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return streamwrapper
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class VertexAITextEmbeddingConfig(BaseModel):
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"""
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Reference: https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/text-embeddings-api#TextEmbeddingInput
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Args:
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auto_truncate: Optional(bool) If True, will truncate input text to fit within the model's max input length.
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task_type: Optional(str) The type of task to be performed. The default is "RETRIEVAL_QUERY".
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title: Optional(str) The title of the document to be embedded. (only valid with task_type=RETRIEVAL_DOCUMENT).
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"""
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auto_truncate: Optional[bool] = None
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task_type: Optional[
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Literal[
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"RETRIEVAL_QUERY",
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"RETRIEVAL_DOCUMENT",
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"SEMANTIC_SIMILARITY",
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"CLASSIFICATION",
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"CLUSTERING",
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"QUESTION_ANSWERING",
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"FACT_VERIFICATION",
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]
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] = None
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title: Optional[str] = None
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def __init__(
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self,
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auto_truncate: Optional[bool] = None,
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task_type: Optional[
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Literal[
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"RETRIEVAL_QUERY",
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"RETRIEVAL_DOCUMENT",
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"SEMANTIC_SIMILARITY",
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"CLASSIFICATION",
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"CLUSTERING",
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"QUESTION_ANSWERING",
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"FACT_VERIFICATION",
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]
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] = None,
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title: Optional[str] = None,
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) -> None:
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locals_ = locals()
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for key, value in locals_.items():
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if key != "self" and value is not None:
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setattr(self.__class__, key, value)
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@classmethod
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def get_config(cls):
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return {
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k: v
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for k, v in cls.__dict__.items()
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if not k.startswith("__")
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and not isinstance(
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v,
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(
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types.FunctionType,
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types.BuiltinFunctionType,
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classmethod,
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staticmethod,
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),
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)
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and v is not None
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}
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def get_supported_openai_params(self):
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return [
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"dimensions",
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]
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def map_openai_params(self, non_default_params: dict, optional_params: dict):
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for param, value in non_default_params.items():
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if param == "dimensions":
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optional_params["output_dimensionality"] = value
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return optional_params
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def get_mapped_special_auth_params(self) -> dict:
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"""
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Common auth params across bedrock/vertex_ai/azure/watsonx
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"""
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return {"project": "vertex_project", "region_name": "vertex_location"}
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def map_special_auth_params(self, non_default_params: dict, optional_params: dict):
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mapped_params = self.get_mapped_special_auth_params()
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for param, value in non_default_params.items():
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if param in mapped_params:
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optional_params[mapped_params[param]] = value
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return optional_params
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def embedding(
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model: str,
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input: Union[list, str],
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print_verbose,
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model_response: litellm.EmbeddingResponse,
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optional_params: dict,
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api_key: Optional[str] = None,
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logging_obj=None,
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encoding=None,
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vertex_project=None,
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vertex_location=None,
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vertex_credentials=None,
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aembedding=False,
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):
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# logic for parsing in - calling - parsing out model embedding calls
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try:
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import vertexai
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except:
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raise VertexAIError(
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status_code=400,
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message="vertexai import failed please run `pip install google-cloud-aiplatform`",
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)
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import google.auth # type: ignore
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from vertexai.language_models import TextEmbeddingInput, TextEmbeddingModel
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## Load credentials with the correct quota project ref: https://github.com/googleapis/python-aiplatform/issues/2557#issuecomment-1709284744
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try:
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print_verbose(
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f"VERTEX AI: vertex_project={vertex_project}; vertex_location={vertex_location}"
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)
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if vertex_credentials is not None and isinstance(vertex_credentials, str):
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import google.oauth2.service_account
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json_obj = json.loads(vertex_credentials)
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creds = google.oauth2.service_account.Credentials.from_service_account_info(
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json_obj,
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scopes=["https://www.googleapis.com/auth/cloud-platform"],
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)
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else:
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creds, _ = google.auth.default(quota_project_id=vertex_project)
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print_verbose(
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f"VERTEX AI: creds={creds}; google application credentials: {os.getenv('GOOGLE_APPLICATION_CREDENTIALS')}"
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)
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vertexai.init(
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project=vertex_project, location=vertex_location, credentials=creds
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)
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except Exception as e:
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raise VertexAIError(status_code=401, message=str(e))
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if isinstance(input, str):
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input = [input]
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if optional_params is not None and isinstance(optional_params, dict):
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if optional_params.get("task_type") or optional_params.get("title"):
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# if user passed task_type or title, cast to TextEmbeddingInput
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_task_type = optional_params.pop("task_type", None)
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_title = optional_params.pop("title", None)
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input = [
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TextEmbeddingInput(text=x, task_type=_task_type, title=_title)
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for x in input
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]
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try:
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llm_model = TextEmbeddingModel.from_pretrained(model)
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except Exception as e:
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raise VertexAIError(status_code=422, message=str(e))
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if aembedding == True:
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return async_embedding(
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model=model,
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client=llm_model,
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input=input,
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logging_obj=logging_obj,
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model_response=model_response,
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optional_params=optional_params,
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encoding=encoding,
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)
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_input_dict = {"texts": input, **optional_params}
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request_str = f"""embeddings = llm_model.get_embeddings({_input_dict})"""
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## LOGGING PRE-CALL
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logging_obj.pre_call(
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input=input,
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api_key=None,
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additional_args={
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"complete_input_dict": optional_params,
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"request_str": request_str,
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},
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)
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try:
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embeddings = llm_model.get_embeddings(**_input_dict)
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except Exception as e:
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raise VertexAIError(status_code=500, message=str(e))
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## LOGGING POST-CALL
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logging_obj.post_call(input=input, api_key=None, original_response=embeddings)
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## Populate OpenAI compliant dictionary
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embedding_response = []
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input_tokens: int = 0
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for idx, embedding in enumerate(embeddings):
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embedding_response.append(
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{
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"object": "embedding",
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"index": idx,
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"embedding": embedding.values,
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}
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)
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input_tokens += embedding.statistics.token_count # type: ignore
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model_response.object = "list"
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model_response.data = embedding_response
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model_response.model = model
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usage = Usage(
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prompt_tokens=input_tokens, completion_tokens=0, total_tokens=input_tokens
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)
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setattr(model_response, "usage", usage)
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return model_response
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async def async_embedding(
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model: str,
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input: Union[list, str],
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model_response: litellm.EmbeddingResponse,
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logging_obj=None,
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optional_params=None,
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encoding=None,
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client=None,
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):
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"""
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Async embedding implementation
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"""
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_input_dict = {"texts": input, **optional_params}
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request_str = f"""embeddings = llm_model.get_embeddings({_input_dict})"""
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## LOGGING PRE-CALL
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logging_obj.pre_call(
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input=input,
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api_key=None,
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additional_args={
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"complete_input_dict": optional_params,
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"request_str": request_str,
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},
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)
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try:
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embeddings = await client.get_embeddings_async(**_input_dict)
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except Exception as e:
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raise VertexAIError(status_code=500, message=str(e))
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## LOGGING POST-CALL
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logging_obj.post_call(input=input, api_key=None, original_response=embeddings)
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## Populate OpenAI compliant dictionary
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embedding_response = []
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input_tokens: int = 0
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for idx, embedding in enumerate(embeddings):
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embedding_response.append(
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{
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"object": "embedding",
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"index": idx,
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"embedding": embedding.values,
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}
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)
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input_tokens += embedding.statistics.token_count
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model_response.object = "list"
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model_response.data = embedding_response
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model_response.model = model
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usage = Usage(
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prompt_tokens=input_tokens, completion_tokens=0, total_tokens=input_tokens
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)
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setattr(model_response, "usage", usage)
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return model_response
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@ -0,0 +1,280 @@
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import json
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import os
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import types
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from typing import Literal, Optional, Union
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from pydantic import BaseModel
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import litellm
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from litellm._logging import verbose_logger
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from litellm.llms.vertex_ai_and_google_ai_studio.vertex_ai_non_gemini import (
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VertexAIError,
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)
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from litellm.types.llms.vertex_ai import *
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from litellm.utils import Usage
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class VertexAITextEmbeddingConfig(BaseModel):
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"""
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Reference: https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/text-embeddings-api#TextEmbeddingInput
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Args:
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auto_truncate: Optional(bool) If True, will truncate input text to fit within the model's max input length.
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task_type: Optional(str) The type of task to be performed. The default is "RETRIEVAL_QUERY".
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title: Optional(str) The title of the document to be embedded. (only valid with task_type=RETRIEVAL_DOCUMENT).
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"""
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auto_truncate: Optional[bool] = None
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task_type: Optional[
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Literal[
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"RETRIEVAL_QUERY",
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"RETRIEVAL_DOCUMENT",
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"SEMANTIC_SIMILARITY",
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"CLASSIFICATION",
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"CLUSTERING",
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"QUESTION_ANSWERING",
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"FACT_VERIFICATION",
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]
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] = None
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title: Optional[str] = None
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def __init__(
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self,
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auto_truncate: Optional[bool] = None,
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task_type: Optional[
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Literal[
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"RETRIEVAL_QUERY",
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"RETRIEVAL_DOCUMENT",
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"SEMANTIC_SIMILARITY",
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"CLASSIFICATION",
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"CLUSTERING",
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"QUESTION_ANSWERING",
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"FACT_VERIFICATION",
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]
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] = None,
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title: Optional[str] = None,
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) -> None:
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locals_ = locals()
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for key, value in locals_.items():
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if key != "self" and value is not None:
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setattr(self.__class__, key, value)
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@classmethod
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def get_config(cls):
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return {
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k: v
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for k, v in cls.__dict__.items()
|
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if not k.startswith("__")
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and not isinstance(
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v,
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(
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types.FunctionType,
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types.BuiltinFunctionType,
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classmethod,
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staticmethod,
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),
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)
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and v is not None
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}
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def get_supported_openai_params(self):
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return ["dimensions", "input_type"]
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def map_openai_params(self, non_default_params: dict, optional_params: dict):
|
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for param, value in non_default_params.items():
|
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if param == "dimensions":
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optional_params["output_dimensionality"] = value
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if param == "input_type":
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optional_params["task_type"] = value
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return optional_params
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def get_mapped_special_auth_params(self) -> dict:
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"""
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Common auth params across bedrock/vertex_ai/azure/watsonx
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"""
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return {"project": "vertex_project", "region_name": "vertex_location"}
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def map_special_auth_params(self, non_default_params: dict, optional_params: dict):
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mapped_params = self.get_mapped_special_auth_params()
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for param, value in non_default_params.items():
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if param in mapped_params:
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optional_params[mapped_params[param]] = value
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return optional_params
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def embedding(
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model: str,
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input: Union[list, str],
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print_verbose,
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model_response: litellm.EmbeddingResponse,
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optional_params: dict,
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api_key: Optional[str] = None,
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logging_obj=None,
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encoding=None,
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vertex_project=None,
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vertex_location=None,
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vertex_credentials=None,
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aembedding=False,
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):
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# logic for parsing in - calling - parsing out model embedding calls
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try:
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import vertexai
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except:
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raise VertexAIError(
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status_code=400,
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message="vertexai import failed please run `pip install google-cloud-aiplatform`",
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)
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import google.auth # type: ignore
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from vertexai.language_models import TextEmbeddingInput, TextEmbeddingModel
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|
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## Load credentials with the correct quota project ref: https://github.com/googleapis/python-aiplatform/issues/2557#issuecomment-1709284744
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try:
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print_verbose(
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f"VERTEX AI: vertex_project={vertex_project}; vertex_location={vertex_location}"
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)
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if vertex_credentials is not None and isinstance(vertex_credentials, str):
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import google.oauth2.service_account
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json_obj = json.loads(vertex_credentials)
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creds = google.oauth2.service_account.Credentials.from_service_account_info(
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json_obj,
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scopes=["https://www.googleapis.com/auth/cloud-platform"],
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)
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else:
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creds, _ = google.auth.default(quota_project_id=vertex_project)
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print_verbose(
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f"VERTEX AI: creds={creds}; google application credentials: {os.getenv('GOOGLE_APPLICATION_CREDENTIALS')}"
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)
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vertexai.init(
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project=vertex_project, location=vertex_location, credentials=creds
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)
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except Exception as e:
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raise VertexAIError(status_code=401, message=str(e))
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|
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if isinstance(input, str):
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input = [input]
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|
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if optional_params is not None and isinstance(optional_params, dict):
|
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if optional_params.get("task_type") or optional_params.get("title"):
|
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# if user passed task_type or title, cast to TextEmbeddingInput
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_task_type = optional_params.pop("task_type", None)
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_title = optional_params.pop("title", None)
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input = [
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TextEmbeddingInput(text=x, task_type=_task_type, title=_title)
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for x in input
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]
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try:
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llm_model = TextEmbeddingModel.from_pretrained(model)
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except Exception as e:
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raise VertexAIError(status_code=422, message=str(e))
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|
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if aembedding == True:
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return async_embedding(
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model=model,
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client=llm_model,
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input=input,
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logging_obj=logging_obj,
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model_response=model_response,
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optional_params=optional_params,
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encoding=encoding,
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)
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_input_dict = {"texts": input, **optional_params}
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request_str = f"""embeddings = llm_model.get_embeddings({_input_dict})"""
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## LOGGING PRE-CALL
|
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logging_obj.pre_call(
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input=input,
|
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api_key=None,
|
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additional_args={
|
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"complete_input_dict": optional_params,
|
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"request_str": request_str,
|
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},
|
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)
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try:
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embeddings = llm_model.get_embeddings(**_input_dict)
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except Exception as e:
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raise VertexAIError(status_code=500, message=str(e))
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## LOGGING POST-CALL
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logging_obj.post_call(input=input, api_key=None, original_response=embeddings)
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## Populate OpenAI compliant dictionary
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embedding_response = []
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input_tokens: int = 0
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for idx, embedding in enumerate(embeddings):
|
||||
embedding_response.append(
|
||||
{
|
||||
"object": "embedding",
|
||||
"index": idx,
|
||||
"embedding": embedding.values,
|
||||
}
|
||||
)
|
||||
input_tokens += embedding.statistics.token_count # type: ignore
|
||||
model_response.object = "list"
|
||||
model_response.data = embedding_response
|
||||
model_response.model = model
|
||||
|
||||
usage = Usage(
|
||||
prompt_tokens=input_tokens, completion_tokens=0, total_tokens=input_tokens
|
||||
)
|
||||
setattr(model_response, "usage", usage)
|
||||
|
||||
return model_response
|
||||
|
||||
|
||||
async def async_embedding(
|
||||
model: str,
|
||||
input: Union[list, str],
|
||||
model_response: litellm.EmbeddingResponse,
|
||||
logging_obj=None,
|
||||
optional_params=None,
|
||||
encoding=None,
|
||||
client=None,
|
||||
):
|
||||
"""
|
||||
Async embedding implementation
|
||||
"""
|
||||
_input_dict = {"texts": input, **optional_params}
|
||||
request_str = f"""embeddings = llm_model.get_embeddings({_input_dict})"""
|
||||
## LOGGING PRE-CALL
|
||||
logging_obj.pre_call(
|
||||
input=input,
|
||||
api_key=None,
|
||||
additional_args={
|
||||
"complete_input_dict": optional_params,
|
||||
"request_str": request_str,
|
||||
},
|
||||
)
|
||||
|
||||
try:
|
||||
embeddings = await client.get_embeddings_async(**_input_dict)
|
||||
except Exception as e:
|
||||
raise VertexAIError(status_code=500, message=str(e))
|
||||
|
||||
## LOGGING POST-CALL
|
||||
logging_obj.post_call(input=input, api_key=None, original_response=embeddings)
|
||||
## Populate OpenAI compliant dictionary
|
||||
embedding_response = []
|
||||
input_tokens: int = 0
|
||||
for idx, embedding in enumerate(embeddings):
|
||||
embedding_response.append(
|
||||
{
|
||||
"object": "embedding",
|
||||
"index": idx,
|
||||
"embedding": embedding.values,
|
||||
}
|
||||
)
|
||||
input_tokens += embedding.statistics.token_count
|
||||
|
||||
model_response.object = "list"
|
||||
model_response.data = embedding_response
|
||||
model_response.model = model
|
||||
usage = Usage(
|
||||
prompt_tokens=input_tokens, completion_tokens=0, total_tokens=input_tokens
|
||||
)
|
||||
setattr(model_response, "usage", usage)
|
||||
return model_response
|
||||
|
|
@ -126,18 +126,21 @@ from .llms.vertex_ai_and_google_ai_studio import (
|
|||
vertex_ai_anthropic,
|
||||
vertex_ai_non_gemini,
|
||||
)
|
||||
from .llms.vertex_ai_and_google_ai_studio.embeddings.batch_embed_content_handler import (
|
||||
GoogleBatchEmbeddings,
|
||||
)
|
||||
from .llms.vertex_ai_and_google_ai_studio.gemini.vertex_and_google_ai_studio_gemini import (
|
||||
VertexLLM,
|
||||
)
|
||||
from .llms.vertex_ai_and_google_ai_studio.gemini_embeddings.batch_embed_content_handler import (
|
||||
GoogleBatchEmbeddings,
|
||||
)
|
||||
from .llms.vertex_ai_and_google_ai_studio.multimodal_embeddings.embedding_handler import (
|
||||
VertexMultimodalEmbedding,
|
||||
)
|
||||
from .llms.vertex_ai_and_google_ai_studio.vertex_ai_partner_models.main import (
|
||||
VertexAIPartnerModels,
|
||||
)
|
||||
from .llms.vertex_ai_and_google_ai_studio.vertex_embeddings import (
|
||||
embedding_handler as vertex_ai_embedding_handler,
|
||||
)
|
||||
from .llms.watsonx import IBMWatsonXAI
|
||||
from .types.llms.openai import HttpxBinaryResponseContent
|
||||
from .types.utils import (
|
||||
|
|
@ -3606,7 +3609,7 @@ def embedding(
|
|||
custom_llm_provider="vertex_ai",
|
||||
)
|
||||
else:
|
||||
response = vertex_ai_non_gemini.embedding(
|
||||
response = vertex_ai_embedding_handler.embedding(
|
||||
model=model,
|
||||
input=input,
|
||||
encoding=encoding,
|
||||
|
|
|
|||
|
|
@ -2014,6 +2014,29 @@ def test_vertexai_embedding_embedding_latest():
|
|||
pytest.fail(f"Error occurred: {e}")
|
||||
|
||||
|
||||
# @pytest.mark.skip(
|
||||
# reason="new test - works locally running into vertex version issues on ci/cd"
|
||||
# )
|
||||
def test_vertexai_embedding_embedding_latest_input_type():
|
||||
try:
|
||||
load_vertex_ai_credentials()
|
||||
litellm.set_verbose = True
|
||||
|
||||
response = embedding(
|
||||
model="vertex_ai/text-embedding-004",
|
||||
input=["hi"],
|
||||
input_type="RETRIEVAL_QUERY",
|
||||
)
|
||||
|
||||
assert len(response.data[0]["embedding"]) == 1
|
||||
assert response.usage.prompt_tokens > 0
|
||||
print(f"response:", response)
|
||||
except litellm.RateLimitError as e:
|
||||
pass
|
||||
except Exception as e:
|
||||
pytest.fail(f"Error occurred: {e}")
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.flaky(retries=3, delay=1)
|
||||
async def test_vertexai_aembedding():
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue