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feat(vertex_ai_partner.py): initial working commit for calling vertex ai mistral
Closes https://github.com/BerriAI/litellm/issues/4874
This commit is contained in:
Krrish Dholakia
parent
1a8f45e8da
commit
5b71421a7b
10 changed files with 343 additions and 140 deletions
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@ -1,204 +0,0 @@
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# What is this?
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## Handler for calling llama 3.1 API on Vertex AI
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import copy
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import json
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import os
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import time
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import types
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import uuid
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from enum import Enum
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from typing import Any, Callable, List, Optional, Tuple, Union
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import httpx # type: ignore
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import requests # type: ignore
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import litellm
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from litellm.litellm_core_utils.core_helpers import map_finish_reason
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from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
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from litellm.types.llms.anthropic import (
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AnthropicMessagesTool,
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AnthropicMessagesToolChoice,
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)
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from litellm.types.llms.openai import (
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ChatCompletionToolParam,
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ChatCompletionToolParamFunctionChunk,
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)
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from litellm.types.utils import ResponseFormatChunk
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from litellm.utils import CustomStreamWrapper, ModelResponse, Usage
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from .base import BaseLLM
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from .prompt_templates.factory import (
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construct_tool_use_system_prompt,
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contains_tag,
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custom_prompt,
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extract_between_tags,
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parse_xml_params,
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prompt_factory,
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response_schema_prompt,
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)
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class VertexAIError(Exception):
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def __init__(self, status_code, message):
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self.status_code = status_code
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self.message = message
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self.request = httpx.Request(
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method="POST", url=" https://cloud.google.com/vertex-ai/"
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)
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self.response = httpx.Response(status_code=status_code, request=self.request)
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super().__init__(
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self.message
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) # Call the base class constructor with the parameters it needs
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class VertexAILlama3Config:
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"""
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Reference:https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/llama#streaming
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The class `VertexAILlama3Config` provides configuration for the VertexAI's Llama API interface. Below are the parameters:
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- `max_tokens` Required (integer) max tokens,
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Note: Please make sure to modify the default parameters as required for your use case.
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"""
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max_tokens: Optional[int] = None
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def __init__(
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self,
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max_tokens: Optional[int] = 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 == "max_tokens" and value is None:
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value = self.max_tokens
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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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"max_tokens",
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"stream",
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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 == "max_tokens":
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optional_params["max_tokens"] = value
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if param == "stream":
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optional_params["stream"] = value
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return optional_params
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class VertexAILlama3(BaseLLM):
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def __init__(self) -> None:
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pass
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def create_vertex_llama3_url(
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self, vertex_location: str, vertex_project: str
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) -> str:
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return f"https://{vertex_location}-aiplatform.googleapis.com/v1beta1/projects/{vertex_project}/locations/{vertex_location}/endpoints/openapi"
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def completion(
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self,
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model: str,
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messages: list,
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model_response: ModelResponse,
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print_verbose: Callable,
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encoding,
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logging_obj,
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optional_params: dict,
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custom_prompt_dict: dict,
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headers: Optional[dict],
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timeout: Union[float, httpx.Timeout],
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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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litellm_params=None,
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logger_fn=None,
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acompletion: bool = False,
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client=None,
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):
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try:
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import vertexai
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from google.cloud import aiplatform
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from litellm.llms.openai import OpenAIChatCompletion
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from litellm.llms.vertex_httpx import VertexLLM
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except Exception:
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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 -U "google-cloud-aiplatform>=1.38"`""",
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)
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if not (
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hasattr(vertexai, "preview") or hasattr(vertexai.preview, "language_models")
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):
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raise VertexAIError(
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status_code=400,
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message="""Upgrade vertex ai. Run `pip install "google-cloud-aiplatform>=1.38"`""",
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)
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try:
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vertex_httpx_logic = VertexLLM()
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access_token, project_id = vertex_httpx_logic._ensure_access_token(
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credentials=vertex_credentials, project_id=vertex_project
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)
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openai_chat_completions = OpenAIChatCompletion()
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## Load Config
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# config = litellm.VertexAILlama3.get_config()
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# for k, v in config.items():
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# if k not in optional_params:
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# optional_params[k] = v
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## CONSTRUCT API BASE
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stream: bool = optional_params.get("stream", False) or False
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optional_params["stream"] = stream
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api_base = self.create_vertex_llama3_url(
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vertex_location=vertex_location or "us-central1",
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vertex_project=vertex_project or project_id,
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)
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return openai_chat_completions.completion(
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model=model,
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messages=messages,
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api_base=api_base,
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api_key=access_token,
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custom_prompt_dict=custom_prompt_dict,
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model_response=model_response,
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print_verbose=print_verbose,
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logging_obj=logging_obj,
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optional_params=optional_params,
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acompletion=acompletion,
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litellm_params=litellm_params,
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logger_fn=logger_fn,
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client=client,
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timeout=timeout,
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)
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except Exception as e:
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raise VertexAIError(status_code=500, message=str(e))
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