[BETA] Vertex AI Endpoints

Use VertexAI SDK to call endpoints on LiteLLM Gateway (native provider format)

tip

Looking for the Unified API (OpenAI format) for VertexAI ? Go here - using vertexAI with LiteLLM SDK or LiteLLM Proxy Server

Supported API Endpoints

• Gemini API
• Embeddings API
• Imagen API
• Code Completion API
• Batch prediction API
• Tuning API
• CountTokens API

Quick Start Usage

1. Set default_vertex_config on your config.yaml

Add the following credentials to your litellm config.yaml to use the Vertex AI endpoints.

default_vertex_config:
  vertex_project: "adroit-crow-413218"
  vertex_location: "us-central1"
  vertex_credentials: "/Users/ishaanjaffer/Downloads/adroit-crow-413218-a956eef1a2a8.json" # Add path to service account.json

2. Start litellm proxy

litellm --config /path/to/config.yaml

3. Test it

import vertexai
from google.auth.credentials import Credentials
from vertexai.generative_models import GenerativeModel

LITELLM_PROXY_API_KEY = "sk-1234"
LITELLM_PROXY_BASE = "http://0.0.0.0:4000/vertex-ai"

import datetime


class CredentialsWrapper(Credentials):
    def __init__(self, token=None):
        super().__init__()
        self.token = token
        self.expiry = None  # or set to a future date if needed

    def refresh(self, request):
        pass

    def apply(self, headers, token=None):
        headers["Authorization"] = f"Bearer {self.token}"

    @property
    def expired(self):
        return False  # Always consider the token as non-expired

    @property
    def valid(self):
        return True  # Always consider the credentials as valid


credentials = CredentialsWrapper(token=LITELLM_PROXY_API_KEY)

vertexai.init(
    project="adroit-crow-413218",
    location="us-central1",
    api_endpoint=LITELLM_PROXY_BASE,
    credentials=credentials,
    api_transport="rest",
)

model = GenerativeModel("gemini-1.5-flash-001")

response = model.generate_content(
    "What's a good name for a flower shop that specializes in selling bouquets of dried flowers?"
)

print(response.text)

Usage Examples

Gemini API (Generate Content)

Vertex Python SDK

import vertexai
from google.auth.credentials import Credentials
from vertexai.generative_models import GenerativeModel

LITELLM_PROXY_API_KEY = "sk-1234"
LITELLM_PROXY_BASE = "http://0.0.0.0:4000/vertex-ai"

import datetime


class CredentialsWrapper(Credentials):
    def __init__(self, token=None):
        super().__init__()
        self.token = token
        self.expiry = None  # or set to a future date if needed

    def refresh(self, request):
        pass

    def apply(self, headers, token=None):
        headers["Authorization"] = f"Bearer {self.token}"

    @property
    def expired(self):
        return False  # Always consider the token as non-expired

    @property
    def valid(self):
        return True  # Always consider the credentials as valid


credentials = CredentialsWrapper(token=LITELLM_PROXY_API_KEY)

vertexai.init(
    project="adroit-crow-413218",
    location="us-central1",
    api_endpoint=LITELLM_PROXY_BASE,
    credentials=credentials,
    api_transport="rest",
   
)

model = GenerativeModel("gemini-1.5-flash-001")

response = model.generate_content(
    "What's a good name for a flower shop that specializes in selling bouquets of dried flowers?"
)

print(response.text)

Curl

curl http://localhost:4000/vertex-ai/publishers/google/models/gemini-1.5-flash-001:generateContent \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-1234" \
  -d '{"contents":[{"role": "user", "parts":[{"text": "hi"}]}]}'

Embeddings API

Vertex Python SDK

from typing import List, Optional
from vertexai.language_models import TextEmbeddingInput, TextEmbeddingModel
import vertexai
from google.auth.credentials import Credentials
from vertexai.generative_models import GenerativeModel

LITELLM_PROXY_API_KEY = "sk-1234"
LITELLM_PROXY_BASE = "http://0.0.0.0:4000/vertex-ai"

import datetime


class CredentialsWrapper(Credentials):
    def __init__(self, token=None):
        super().__init__()
        self.token = token
        self.expiry = None  # or set to a future date if needed

    def refresh(self, request):
        pass

    def apply(self, headers, token=None):
        headers["Authorization"] = f"Bearer {self.token}"

    @property
    def expired(self):
        return False  # Always consider the token as non-expired

    @property
    def valid(self):
        return True  # Always consider the credentials as valid


credentials = CredentialsWrapper(token=LITELLM_PROXY_API_KEY)

vertexai.init(
    project="adroit-crow-413218",
    location="us-central1",
    api_endpoint=LITELLM_PROXY_BASE,
    credentials=credentials,
    api_transport="rest",
)


def embed_text(
    texts: List[str] = ["banana muffins? ", "banana bread? banana muffins?"],
    task: str = "RETRIEVAL_DOCUMENT",
    model_name: str = "text-embedding-004",
    dimensionality: Optional[int] = 256,
) -> List[List[float]]:
    """Embeds texts with a pre-trained, foundational model."""
    model = TextEmbeddingModel.from_pretrained(model_name)
    inputs = [TextEmbeddingInput(text, task) for text in texts]
    kwargs = dict(output_dimensionality=dimensionality) if dimensionality else {}
    embeddings = model.get_embeddings(inputs, **kwargs)
    return [embedding.values for embedding in embeddings]

Curl

curl http://localhost:4000/vertex-ai/publishers/google/models/textembedding-gecko@001:predict \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-1234" \
  -d '{"instances":[{"content": "gm"}]}'

Imagen API

Vertex Python SDK

from typing import List, Optional
from vertexai.preview.vision_models import ImageGenerationModel
import vertexai
from google.auth.credentials import Credentials

LITELLM_PROXY_API_KEY = "sk-1234"
LITELLM_PROXY_BASE = "http://0.0.0.0:4000/vertex-ai"

import datetime


class CredentialsWrapper(Credentials):
    def __init__(self, token=None):
        super().__init__()
        self.token = token
        self.expiry = None  # or set to a future date if needed

    def refresh(self, request):
        pass

    def apply(self, headers, token=None):
        headers["Authorization"] = f"Bearer {self.token}"

    @property
    def expired(self):
        return False  # Always consider the token as non-expired

    @property
    def valid(self):
        return True  # Always consider the credentials as valid


credentials = CredentialsWrapper(token=LITELLM_PROXY_API_KEY)

vertexai.init(
    project="adroit-crow-413218",
    location="us-central1",
    api_endpoint=LITELLM_PROXY_BASE,
    credentials=credentials,
    api_transport="rest",
)

model = ImageGenerationModel.from_pretrained("imagen-3.0-generate-001")

images = model.generate_images(
    prompt=prompt,
    # Optional parameters
    number_of_images=1,
    language="en",
    # You can't use a seed value and watermark at the same time.
    # add_watermark=False,
    # seed=100,
    aspect_ratio="1:1",
    safety_filter_level="block_some",
    person_generation="allow_adult",
)

images[0].save(location=output_file, include_generation_parameters=False)

# Optional. View the generated image in a notebook.
# images[0].show()

print(f"Created output image using {len(images[0]._image_bytes)} bytes")

Curl

curl http://localhost:4000/vertex-ai/publishers/google/models/imagen-3.0-generate-001:predict \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-1234" \
  -d '{"instances":[{"prompt": "make an otter"}], "parameters": {"sampleCount": 1}}'

Count Tokens API

Vertex Python SDK

from typing import List, Optional
from vertexai.generative_models import GenerativeModel
import vertexai
from google.auth.credentials import Credentials

LITELLM_PROXY_API_KEY = "sk-1234"
LITELLM_PROXY_BASE = "http://0.0.0.0:4000/vertex-ai"

import datetime


class CredentialsWrapper(Credentials):
    def __init__(self, token=None):
        super().__init__()
        self.token = token
        self.expiry = None  # or set to a future date if needed

    def refresh(self, request):
        pass

    def apply(self, headers, token=None):
        headers["Authorization"] = f"Bearer {self.token}"

    @property
    def expired(self):
        return False  # Always consider the token as non-expired

    @property
    def valid(self):
        return True  # Always consider the credentials as valid


credentials = CredentialsWrapper(token=LITELLM_PROXY_API_KEY)

vertexai.init(
    project="adroit-crow-413218",
    location="us-central1",
    api_endpoint=LITELLM_PROXY_BASE,
    credentials=credentials,
    api_transport="rest",
)


model = GenerativeModel("gemini-1.5-flash-001")

prompt = "Why is the sky blue?"

# Prompt tokens count
response = model.count_tokens(prompt)
print(f"Prompt Token Count: {response.total_tokens}")
print(f"Prompt Character Count: {response.total_billable_characters}")

# Send text to Gemini
response = model.generate_content(prompt)

# Response tokens count
usage_metadata = response.usage_metadata
print(f"Prompt Token Count: {usage_metadata.prompt_token_count}")
print(f"Candidates Token Count: {usage_metadata.candidates_token_count}")
print(f"Total Token Count: {usage_metadata.total_token_count}")

Curl

curl http://localhost:4000/vertex-ai/publishers/google/models/gemini-1.5-flash-001:countTokens \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-1234" \
  -d '{"contents":[{"role": "user", "parts":[{"text": "hi"}]}]}'

Tuning API

Create Fine Tuning Job

Vertex Python SDK

from typing import List, Optional
from vertexai.preview.tuning import sft
import vertexai
from google.auth.credentials import Credentials

LITELLM_PROXY_API_KEY = "sk-1234"
LITELLM_PROXY_BASE = "http://0.0.0.0:4000/vertex-ai"

import datetime


class CredentialsWrapper(Credentials):
    def __init__(self, token=None):
        super().__init__()
        self.token = token
        self.expiry = None  # or set to a future date if needed

    def refresh(self, request):
        pass

    def apply(self, headers, token=None):
        headers["Authorization"] = f"Bearer {self.token}"

    @property
    def expired(self):
        return False  # Always consider the token as non-expired

    @property
    def valid(self):
        return True  # Always consider the credentials as valid


credentials = CredentialsWrapper(token=LITELLM_PROXY_API_KEY)

vertexai.init(
    project="adroit-crow-413218",
    location="us-central1",
    api_endpoint=LITELLM_PROXY_BASE,
    credentials=credentials,
    api_transport="rest",
)


# TODO(developer): Update project
vertexai.init(project=PROJECT_ID, location="us-central1")

sft_tuning_job = sft.train(
    source_model="gemini-1.0-pro-002",
    train_dataset="gs://cloud-samples-data/ai-platform/generative_ai/sft_train_data.jsonl",
)

# Polling for job completion
while not sft_tuning_job.has_ended:
    time.sleep(60)
    sft_tuning_job.refresh()

print(sft_tuning_job.tuned_model_name)
print(sft_tuning_job.tuned_model_endpoint_name)
print(sft_tuning_job.experiment)

Curl

curl http://localhost:4000/vertex-ai/tuningJobs \
      -H "Content-Type: application/json" \
      -H "Authorization: Bearer sk-1234" \
      -d '{
  "baseModel": "gemini-1.0-pro-002",
  "supervisedTuningSpec" : {
      "training_dataset_uri": "gs://cloud-samples-data/ai-platform/generative_ai/sft_train_data.jsonl"
  }
}'