mirror of
https://github.com/BerriAI/litellm.git
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224 lines
7.9 KiB
Python
224 lines
7.9 KiB
Python
import os, types
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import json
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from enum import Enum
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import requests # type: ignore
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import time
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from typing import Callable, Optional, Union, List, Any, Tuple
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from litellm.utils import ModelResponse, Usage, CustomStreamWrapper, map_finish_reason
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import litellm, uuid
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import httpx, inspect # type: ignore
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from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
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from .base import BaseLLM
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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 VertexLLM(BaseLLM):
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def __init__(self) -> None:
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super().__init__()
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self.access_token: Optional[str] = None
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self.refresh_token: Optional[str] = None
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self._credentials: Optional[Any] = None
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self.project_id: Optional[str] = None
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self.async_handler: Optional[AsyncHTTPHandler] = None
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def load_auth(self) -> Tuple[Any, str]:
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from google.auth.transport.requests import Request # type: ignore[import-untyped]
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from google.auth.credentials import Credentials # type: ignore[import-untyped]
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import google.auth as google_auth
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credentials, project_id = google_auth.default(
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scopes=["https://www.googleapis.com/auth/cloud-platform"],
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)
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credentials.refresh(Request())
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if not project_id:
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raise ValueError("Could not resolve project_id")
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if not isinstance(project_id, str):
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raise TypeError(
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f"Expected project_id to be a str but got {type(project_id)}"
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)
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return credentials, project_id
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def refresh_auth(self, credentials: Any) -> None:
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from google.auth.transport.requests import Request # type: ignore[import-untyped]
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credentials.refresh(Request())
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def _prepare_request(self, request: httpx.Request) -> None:
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access_token = self._ensure_access_token()
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if request.headers.get("Authorization"):
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# already authenticated, nothing for us to do
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return
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request.headers["Authorization"] = f"Bearer {access_token}"
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def _ensure_access_token(self) -> str:
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if self.access_token is not None:
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return self.access_token
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if not self._credentials:
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self._credentials, project_id = self.load_auth()
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if not self.project_id:
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self.project_id = project_id
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else:
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self.refresh_auth(self._credentials)
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if not self._credentials.token:
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raise RuntimeError("Could not resolve API token from the environment")
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assert isinstance(self._credentials.token, str)
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return self._credentials.token
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def image_generation(
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self,
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prompt: str,
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vertex_project: str,
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vertex_location: str,
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model: Optional[
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str
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] = "imagegeneration", # vertex ai uses imagegeneration as the default model
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client: Optional[AsyncHTTPHandler] = None,
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optional_params: Optional[dict] = None,
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timeout: Optional[int] = None,
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logging_obj=None,
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model_response=None,
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aimg_generation=False,
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):
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if aimg_generation == True:
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response = self.aimage_generation(
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prompt=prompt,
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vertex_project=vertex_project,
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vertex_location=vertex_location,
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model=model,
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client=client,
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optional_params=optional_params,
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timeout=timeout,
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logging_obj=logging_obj,
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model_response=model_response,
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)
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return response
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async def aimage_generation(
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self,
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prompt: str,
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vertex_project: str,
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vertex_location: str,
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model_response: litellm.ImageResponse,
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model: Optional[
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str
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] = "imagegeneration", # vertex ai uses imagegeneration as the default model
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client: Optional[AsyncHTTPHandler] = None,
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optional_params: Optional[dict] = None,
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timeout: Optional[int] = None,
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logging_obj=None,
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):
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response = None
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if client is None:
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_params = {}
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if timeout is not None:
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if isinstance(timeout, float) or isinstance(timeout, int):
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_httpx_timeout = httpx.Timeout(timeout)
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_params["timeout"] = _httpx_timeout
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else:
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_params["timeout"] = httpx.Timeout(timeout=600.0, connect=5.0)
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self.async_handler = AsyncHTTPHandler(**_params) # type: ignore
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else:
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self.async_handler = client # type: ignore
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# make POST request to
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# https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/imagegeneration:predict
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url = f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/{model}:predict"
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"""
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Docs link: https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/imagegeneration?project=adroit-crow-413218
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curl -X POST \
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-H "Authorization: Bearer $(gcloud auth print-access-token)" \
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-H "Content-Type: application/json; charset=utf-8" \
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-d {
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"instances": [
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{
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"prompt": "a cat"
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}
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],
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"parameters": {
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"sampleCount": 1
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}
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} \
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"https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/imagegeneration:predict"
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"""
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auth_header = self._ensure_access_token()
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optional_params = optional_params or {
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"sampleCount": 1
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} # default optional params
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request_data = {
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"instances": [{"prompt": prompt}],
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"parameters": optional_params,
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}
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request_str = f"\n curl -X POST \\\n -H \"Authorization: Bearer {auth_header[:10] + 'XXXXXXXXXX'}\" \\\n -H \"Content-Type: application/json; charset=utf-8\" \\\n -d {request_data} \\\n \"{url}\""
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logging_obj.pre_call(
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input=prompt,
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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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response = await self.async_handler.post(
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url=url,
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headers={
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"Content-Type": "application/json; charset=utf-8",
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"Authorization": f"Bearer {auth_header}",
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},
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data=json.dumps(request_data),
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)
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if response.status_code != 200:
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raise Exception(f"Error: {response.status_code} {response.text}")
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"""
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Vertex AI Image generation response example:
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{
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"predictions": [
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{
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"bytesBase64Encoded": "BASE64_IMG_BYTES",
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"mimeType": "image/png"
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},
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{
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"mimeType": "image/png",
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"bytesBase64Encoded": "BASE64_IMG_BYTES"
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}
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]
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}
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"""
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_json_response = response.json()
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_predictions = _json_response["predictions"]
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_response_data: List[litellm.ImageObject] = []
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for _prediction in _predictions:
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_bytes_base64_encoded = _prediction["bytesBase64Encoded"]
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image_object = litellm.ImageObject(b64_json=_bytes_base64_encoded)
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_response_data.append(image_object)
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model_response.data = _response_data
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return model_response
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