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Krrish Dholakia 46fe089204 docs(vertex.md): add vertex json schema details to docs

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import Image from '@theme/IdealImage'; import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem';

VertexAI [Anthropic, Gemini, Model Garden]

🆕 `vertex_ai_beta/` route

New vertex_ai_beta/ route. Adds support for system messages, tool_choice params, etc. by moving to httpx client (instead of vertex sdk).

from litellm import completion
import json 

## GET CREDENTIALS 
file_path = 'path/to/vertex_ai_service_account.json'

# Load the JSON file
with open(file_path, 'r') as file:
    vertex_credentials = json.load(file)

# Convert to JSON string
vertex_credentials_json = json.dumps(vertex_credentials)

## COMPLETION CALL 
response = completion(
  model="vertex_ai_beta/gemini-pro",
  messages=[{ "content": "Hello, how are you?","role": "user"}],
  vertex_credentials=vertex_credentials_json
)

System Message

from litellm import completion
import json 

## GET CREDENTIALS 
file_path = 'path/to/vertex_ai_service_account.json'

# Load the JSON file
with open(file_path, 'r') as file:
    vertex_credentials = json.load(file)

# Convert to JSON string
vertex_credentials_json = json.dumps(vertex_credentials)


response = completion(
  model="vertex_ai_beta/gemini-pro",
  messages=[{"content": "You are a good bot.","role": "system"}, {"content": "Hello, how are you?","role": "user"}], 
  vertex_credentials=vertex_credentials_json
)

Function Calling

Force Gemini to make tool calls with tool_choice="required".

from litellm import completion
import json 

## GET CREDENTIALS 
file_path = 'path/to/vertex_ai_service_account.json'

# Load the JSON file
with open(file_path, 'r') as file:
    vertex_credentials = json.load(file)

# Convert to JSON string
vertex_credentials_json = json.dumps(vertex_credentials)


messages = [
    {
        "role": "system",
        "content": "Your name is Litellm Bot, you are a helpful assistant",
    },
    # User asks for their name and weather in San Francisco
    {
        "role": "user",
        "content": "Hello, what is your name and can you tell me the weather?",
    },
]

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the current weather in a given location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA",
                    }
                },
                "required": ["location"],
            },
        },
    }
]

data = {
    "model": "vertex_ai_beta/gemini-1.5-pro-preview-0514"),
    "messages": messages,
    "tools": tools,
    "tool_choice": "required",
    "vertex_credentials": vertex_credentials_json
}

## COMPLETION CALL 
print(completion(**data))

JSON Schema

From v1.40.1+ LiteLLM supports sending response_schema as a param for Gemini-1.5-Pro on Vertex AI. For other models (e.g. gemini-1.5-flash or claude-3-5-sonnet), LiteLLM adds the schema to the message list with a user-controlled prompt.

Response Schema

from litellm import completion 
import json 

## SETUP ENVIRONMENT
# !gcloud auth application-default login - run this to add vertex credentials to your env

messages = [
    {
        "role": "user",
        "content": "List 5 popular cookie recipes."
    }
]

response_schema = {
        "type": "array",
        "items": {
            "type": "object",
            "properties": {
                "recipe_name": {
                    "type": "string",
                },
            },
            "required": ["recipe_name"],
        },
    }


completion(
    model="vertex_ai_beta/gemini-1.5-pro", 
    messages=messages, 
    response_format={"type": "json_object", "response_schema": response_schema} # 👈 KEY CHANGE
    )

print(json.loads(completion.choices[0].message.content))

Add model to config.yaml

model_list:
  - model_name: gemini-pro
    litellm_params:
      model: vertex_ai_beta/gemini-1.5-pro
      vertex_project: "project-id"
      vertex_location: "us-central1"
      vertex_credentials: "/path/to/service_account.json" # [OPTIONAL] Do this OR `!gcloud auth application-default login` - run this to add vertex credentials to your env

Start Proxy

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

Make Request!

curl -X POST 'http://0.0.0.0:4000/chat/completions' \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer sk-1234' \
-D '{
  "model": "gemini-pro",
  "messages": [
        {"role": "user", "content": "List 5 popular cookie recipes."}
    ],
  "response_format": {"type": "json_object", "response_schema": { 
        "type": "array",
        "items": {
            "type": "object",
            "properties": {
                "recipe_name": {
                    "type": "string",
                },
            },
            "required": ["recipe_name"],
        },
    }}
}
'

Validate Schema

To validate the response_schema, set enforce_validation: true.

from litellm import completion, JSONSchemaValidationError
try: 
	completion(
    model="vertex_ai_beta/gemini-1.5-pro", 
    messages=messages, 
    response_format={
        "type": "json_object", 
        "response_schema": response_schema,
        "enforce_validation": true # 👈 KEY CHANGE
    }
	)
except JSONSchemaValidationError as e: 
	print("Raw Response: {}".format(e.raw_response))
	raise e

Add model to config.yaml

model_list:
  - model_name: gemini-pro
    litellm_params:
      model: vertex_ai_beta/gemini-1.5-pro
      vertex_project: "project-id"
      vertex_location: "us-central1"
      vertex_credentials: "/path/to/service_account.json" # [OPTIONAL] Do this OR `!gcloud auth application-default login` - run this to add vertex credentials to your env

Start Proxy

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

Make Request!

curl -X POST 'http://0.0.0.0:4000/chat/completions' \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer sk-1234' \
-D '{
  "model": "gemini-pro",
  "messages": [
        {"role": "user", "content": "List 5 popular cookie recipes."}
    ],
  "response_format": {"type": "json_object", "response_schema": { 
        "type": "array",
        "items": {
            "type": "object",
            "properties": {
                "recipe_name": {
                    "type": "string",
                },
            },
            "required": ["recipe_name"],
        },
    }, 
    "enforce_validation": true
    }
}
'

LiteLLM will validate the response against the schema, and raise a JSONSchemaValidationError if the response does not match the schema.

JSONSchemaValidationError inherits from openai.APIError

Access the raw response with e.raw_response

Add to prompt yourself

from litellm import completion 

## GET CREDENTIALS 
file_path = 'path/to/vertex_ai_service_account.json'

# Load the JSON file
with open(file_path, 'r') as file:
    vertex_credentials = json.load(file)

# Convert to JSON string
vertex_credentials_json = json.dumps(vertex_credentials)

messages = [
    {
        "role": "user",
        "content": """
List 5 popular cookie recipes.

Using this JSON schema:

    Recipe = {"recipe_name": str}

Return a `list[Recipe]`
        """
    }
]

completion(model="vertex_ai_beta/gemini-1.5-flash-preview-0514", messages=messages, response_format={ "type": "json_object" })

Pre-requisites

pip install google-cloud-aiplatform (pre-installed on proxy docker image)

Authentication:

run gcloud auth application-default login See Google Cloud Docs
Alternatively you can set GOOGLE_APPLICATION_CREDENTIALS

Here's how: Jump to Code

- Create a service account on GCP
- Export the credentials as a json
- load the json and json.dump the json as a string
- store the json string in your environment as `GOOGLE_APPLICATION_CREDENTIALS`

Sample Usage

import litellm
litellm.vertex_project = "hardy-device-38811" # Your Project ID
litellm.vertex_location = "us-central1"  # proj location

response = litellm.completion(model="gemini-pro", messages=[{"role": "user", "content": "write code for saying hi from LiteLLM"}])

Usage with LiteLLM Proxy Server

Here's how to use Vertex AI with the LiteLLM Proxy Server

Modify the config.yaml

Use this when you need to set a different location for each vertex model

model_list:
  - model_name: gemini-vision
    litellm_params:
      model: vertex_ai/gemini-1.0-pro-vision-001
      vertex_project: "project-id"
      vertex_location: "us-central1"
  - model_name: gemini-vision
    litellm_params:
      model: vertex_ai/gemini-1.0-pro-vision-001
      vertex_project: "project-id2"
      vertex_location: "us-east"

Use this when you have one vertex location for all models

litellm_settings: 
  vertex_project: "hardy-device-38811" # Your Project ID
  vertex_location: "us-central1" # proj location

model_list: 
  -model_name: team1-gemini-pro
  litellm_params: 
    model: gemini-pro

Start the proxy

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

Send Request to LiteLLM Proxy Server

import openai
client = openai.OpenAI(
    api_key="sk-1234",             # pass litellm proxy key, if you're using virtual keys
    base_url="http://0.0.0.0:4000" # litellm-proxy-base url
)

response = client.chat.completions.create(
    model="team1-gemini-pro",
    messages = [
        {
            "role": "user",
            "content": "what llm are you"
        }
    ],
)

print(response)

curl --location 'http://0.0.0.0:4000/chat/completions' \
    --header 'Authorization: Bearer sk-1234' \
    --header 'Content-Type: application/json' \
    --data '{
    "model": "team1-gemini-pro",
    "messages": [
        {
        "role": "user",
        "content": "what llm are you"
        }
    ],
}'

Specifying Safety Settings

In certain use-cases you may need to make calls to the models and pass safety settigns different from the defaults. To do so, simple pass the safety_settings argument to completion or acompletion. For example:

response = completion(
    model="vertex_ai/gemini-pro", 
    messages=[{"role": "user", "content": "write code for saying hi from LiteLLM"}]
    safety_settings=[
        {
            "category": "HARM_CATEGORY_HARASSMENT",
            "threshold": "BLOCK_NONE",
        },
        {
            "category": "HARM_CATEGORY_HATE_SPEECH",
            "threshold": "BLOCK_NONE",
        },
        {
            "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
            "threshold": "BLOCK_NONE",
        },
        {
            "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
            "threshold": "BLOCK_NONE",
        },
    ]
)

Option 1: Set in config

model_list:
  - model_name: gemini-experimental
    litellm_params:
      model: vertex_ai/gemini-experimental
      vertex_project: litellm-epic
      vertex_location: us-central1
      safety_settings:
      - category: HARM_CATEGORY_HARASSMENT
        threshold: BLOCK_NONE
      - category: HARM_CATEGORY_HATE_SPEECH
        threshold: BLOCK_NONE
      - category: HARM_CATEGORY_SEXUALLY_EXPLICIT
        threshold: BLOCK_NONE
      - category: HARM_CATEGORY_DANGEROUS_CONTENT
        threshold: BLOCK_NONE

Option 2: Set on call

response = client.chat.completions.create(
    model="gemini-experimental",
    messages=[
        {
            "role": "user",
            "content": "Can you write exploits?",
        }
    ],
    max_tokens=8192,
    stream=False,
    temperature=0.0,

    extra_body={
        "safety_settings": [
            {
                "category": "HARM_CATEGORY_HARASSMENT",
                "threshold": "BLOCK_NONE",
            },
            {
                "category": "HARM_CATEGORY_HATE_SPEECH",
                "threshold": "BLOCK_NONE",
            },
            {
                "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
                "threshold": "BLOCK_NONE",
            },
            {
                "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
                "threshold": "BLOCK_NONE",
            },
        ],
    }
)

Set Vertex Project & Vertex Location

All calls using Vertex AI require the following parameters:

Your Project ID

import os, litellm 

# set via env var
os.environ["VERTEXAI_PROJECT"] = "hardy-device-38811" # Your Project ID`

### OR ###

# set directly on module 
litellm.vertex_project = "hardy-device-38811" # Your Project ID`

Your Project Location

import os, litellm 

# set via env var
os.environ["VERTEXAI_LOCATION"] = "us-central1 # Your Location

### OR ###

# set directly on module 
litellm.vertex_location = "us-central1 # Your Location

Anthropic

Model Name	Function Call
claude-3-opus@20240229	`completion('vertex_ai/claude-3-opus@20240229', messages)`
claude-3-5-sonnet@20240620	`completion('vertex_ai/claude-3-5-sonnet@20240620', messages)`
claude-3-sonnet@20240229	`completion('vertex_ai/claude-3-sonnet@20240229', messages)`
claude-3-haiku@20240307	`completion('vertex_ai/claude-3-haiku@20240307', messages)`

Usage

from litellm import completion
import os

os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = ""

model = "claude-3-sonnet@20240229"

vertex_ai_project = "your-vertex-project" # can also set this as os.environ["VERTEXAI_PROJECT"]
vertex_ai_location = "your-vertex-location" # can also set this as os.environ["VERTEXAI_LOCATION"]

response = completion(
    model="vertex_ai/" + model,
    messages=[{"role": "user", "content": "hi"}],
    temperature=0.7,
    vertex_ai_project=vertex_ai_project,
    vertex_ai_location=vertex_ai_location,
)
print("\nModel Response", response)

1. Add to config

model_list:
    - model_name: anthropic-vertex
      litellm_params:
        model: vertex_ai/claude-3-sonnet@20240229
        vertex_ai_project: "my-test-project"
        vertex_ai_location: "us-east-1"
    - model_name: anthropic-vertex
      litellm_params:
        model: vertex_ai/claude-3-sonnet@20240229
        vertex_ai_project: "my-test-project"
        vertex_ai_location: "us-west-1"

2. Start proxy

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

# RUNNING at http://0.0.0.0:4000

3. Test it!

curl --location 'http://0.0.0.0:4000/chat/completions' \
      --header 'Authorization: Bearer sk-1234' \
      --header 'Content-Type: application/json' \
      --data '{
            "model": "anthropic-vertex", # 👈 the 'model_name' in config
            "messages": [
                {
                "role": "user",
                "content": "what llm are you"
                }
            ],
        }'

Model Garden

Model Name	Function Call
llama2	`completion('vertex_ai/<endpoint_id>', messages)`

Using Model Garden

from litellm import completion
import os

## set ENV variables
os.environ["VERTEXAI_PROJECT"] = "hardy-device-38811"
os.environ["VERTEXAI_LOCATION"] = "us-central1"

response = completion(
  model="vertex_ai/<your-endpoint-id>", 
  messages=[{ "content": "Hello, how are you?","role": "user"}]
)

Gemini Pro

Model Name	Function Call
gemini-pro	`completion('gemini-pro', messages)`, `completion('vertex_ai/gemini-pro', messages)`

Gemini Pro Vision

Model Name	Function Call
gemini-pro-vision	`completion('gemini-pro-vision', messages)`, `completion('vertex_ai/gemini-pro-vision', messages)`

Gemini 1.5 Pro (and Vision)

Model Name	Function Call
gemini-1.5-pro	`completion('gemini-1.5-pro', messages)`, `completion('vertex_ai/gemini-1.5-pro', messages)`
gemini-1.5-flash-preview-0514	`completion('gemini-1.5-flash-preview-0514', messages)`, `completion('vertex_ai/gemini-1.5-flash-preview-0514', messages)`
gemini-1.5-pro-preview-0514	`completion('gemini-1.5-pro-preview-0514', messages)`, `completion('vertex_ai/gemini-1.5-pro-preview-0514', messages)`

Using Gemini Pro Vision

Call gemini-pro-vision in the same input/output format as OpenAI gpt-4-vision

LiteLLM Supports the following image types passed in url

Images with Cloud Storage URIs - gs://cloud-samples-data/generative-ai/image/boats.jpeg
Images with direct links - https://storage.googleapis.com/github-repo/img/gemini/intro/landmark3.jpg
Videos with Cloud Storage URIs - https://storage.googleapis.com/github-repo/img/gemini/multimodality_usecases_overview/pixel8.mp4
Base64 Encoded Local Images

Example Request - image url

import litellm

response = litellm.completion(
  model = "vertex_ai/gemini-pro-vision",
  messages=[
      {
          "role": "user",
          "content": [
                          {
                              "type": "text",
                              "text": "Whats in this image?"
                          },
                          {
                              "type": "image_url",
                              "image_url": {
                              "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
                              }
                          }
                      ]
      }
  ],
)
print(response)

import litellm

def encode_image(image_path):
    import base64

    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode("utf-8")

image_path = "cached_logo.jpg"
# Getting the base64 string
base64_image = encode_image(image_path)
response = litellm.completion(
    model="vertex_ai/gemini-pro-vision",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Whats in this image?"},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "data:image/jpeg;base64," + base64_image
                    },
                },
            ],
        }
    ],
)
print(response)

Usage - Function Calling

LiteLLM supports Function Calling for Vertex AI gemini models.

from litellm import completion
import os
# set env
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = ".."
os.environ["VERTEX_AI_PROJECT"] = ".."
os.environ["VERTEX_AI_LOCATION"] = ".."

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_current_weather",
            "description": "Get the current weather in a given location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA",
                    },
                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
                },
                "required": ["location"],
            },
        },
    }
]
messages = [{"role": "user", "content": "What's the weather like in Boston today?"}]

response = completion(
    model="vertex_ai/gemini-pro-vision",
    messages=messages,
    tools=tools,
)
# Add any assertions, here to check response args
print(response)
assert isinstance(response.choices[0].message.tool_calls[0].function.name, str)
assert isinstance(
    response.choices[0].message.tool_calls[0].function.arguments, str
)

Usage - PDF / Videos / etc. Files

Pass any file supported by Vertex AI, through LiteLLM.

from litellm import completion

response = completion(
    model="vertex_ai/gemini-1.5-flash",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "You are a very professional document summarization specialist. Please summarize the given document."},
                {
                    "type": "image_url",
                    "image_url": "gs://cloud-samples-data/generative-ai/pdf/2403.05530.pdf",
                },
            ],
        }
    ],
    max_tokens=300,
)

print(response.choices[0])

Add model to config

- model_name: gemini-1.5-flash
  litellm_params:
    model: vertex_ai/gemini-1.5-flash
    vertex_credentials: "/path/to/service_account.json"

Start Proxy

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

Test it!

curl http://0.0.0.0:4000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer <YOUR-LITELLM-KEY>" \
  -d '{
    "model": "gemini-1.5-flash",
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "You are a very professional document summarization specialist. Please summarize the given document"
          },
          {
                "type": "image_url",
                "image_url": "gs://cloud-samples-data/generative-ai/pdf/2403.05530.pdf",
            },
          }
        ]
      }
    ],
    "max_tokens": 300
  }'

Chat Models

Model Name	Function Call
chat-bison-32k	`completion('chat-bison-32k', messages)`
chat-bison	`completion('chat-bison', messages)`
chat-bison@001	`completion('chat-bison@001', messages)`

Code Chat Models

Model Name	Function Call
codechat-bison	`completion('codechat-bison', messages)`
codechat-bison-32k	`completion('codechat-bison-32k', messages)`
codechat-bison@001	`completion('codechat-bison@001', messages)`

Text Models

Model Name	Function Call
text-bison	`completion('text-bison', messages)`
text-bison@001	`completion('text-bison@001', messages)`

Code Text Models

Model Name	Function Call
code-bison	`completion('code-bison', messages)`
code-bison@001	`completion('code-bison@001', messages)`
code-gecko@001	`completion('code-gecko@001', messages)`
code-gecko@latest	`completion('code-gecko@latest', messages)`

Embedding Models

Usage - Embedding

import litellm
from litellm import embedding
litellm.vertex_project = "hardy-device-38811" # Your Project ID
litellm.vertex_location = "us-central1"  # proj location

response = embedding(
    model="vertex_ai/textembedding-gecko",
    input=["good morning from litellm"],
)
print(response)

Supported Embedding Models

All models listed here are supported

Model Name	Function Call
text-embedding-004	`embedding(model="vertex_ai/text-embedding-004", input)`
text-multilingual-embedding-002	`embedding(model="vertex_ai/text-multilingual-embedding-002", input)`
textembedding-gecko	`embedding(model="vertex_ai/textembedding-gecko", input)`
textembedding-gecko-multilingual	`embedding(model="vertex_ai/textembedding-gecko-multilingual", input)`
textembedding-gecko-multilingual@001	`embedding(model="vertex_ai/textembedding-gecko-multilingual@001", input)`
textembedding-gecko@001	`embedding(model="vertex_ai/textembedding-gecko@001", input)`
textembedding-gecko@003	`embedding(model="vertex_ai/textembedding-gecko@003", input)`
text-embedding-preview-0409	`embedding(model="vertex_ai/text-embedding-preview-0409", input)`
text-multilingual-embedding-preview-0409	`embedding(model="vertex_ai/text-multilingual-embedding-preview-0409", input)`

Advanced Use `task_type` and `title` (Vertex Specific Params)

👉 task_type and title are vertex specific params

LiteLLM Supported Vertex Specific Params

auto_truncate: Optional[bool] = None
task_type: Optional[Literal["RETRIEVAL_QUERY","RETRIEVAL_DOCUMENT", "SEMANTIC_SIMILARITY", "CLASSIFICATION", "CLUSTERING", "QUESTION_ANSWERING", "FACT_VERIFICATION"]] = None
title: Optional[str] = None # The title of the document to be embedded. (only valid with task_type=RETRIEVAL_DOCUMENT).

Example Usage with LiteLLM

response = litellm.embedding(
    model="vertex_ai/text-embedding-004",
    input=["good morning from litellm", "gm"]
    task_type = "RETRIEVAL_DOCUMENT",
    dimensions=1,
    auto_truncate=True,
)

Image Generation Models

Usage

response = await litellm.aimage_generation(
    prompt="An olympic size swimming pool",
    model="vertex_ai/imagegeneration@006",
    vertex_ai_project="adroit-crow-413218",
    vertex_ai_location="us-central1",
)

Generating multiple images

Use the n parameter to pass how many images you want generated

response = await litellm.aimage_generation(
    prompt="An olympic size swimming pool",
    model="vertex_ai/imagegeneration@006",
    vertex_ai_project="adroit-crow-413218",
    vertex_ai_location="us-central1",
    n=1,
)

Extra

Using `GOOGLE_APPLICATION_CREDENTIALS`

Here's the code for storing your service account credentials as GOOGLE_APPLICATION_CREDENTIALS environment variable:

def load_vertex_ai_credentials():
  # Define the path to the vertex_key.json file
  print("loading vertex ai credentials")
  filepath = os.path.dirname(os.path.abspath(__file__))
  vertex_key_path = filepath + "/vertex_key.json"

  # Read the existing content of the file or create an empty dictionary
  try:
      with open(vertex_key_path, "r") as file:
          # Read the file content
          print("Read vertexai file path")
          content = file.read()

          # If the file is empty or not valid JSON, create an empty dictionary
          if not content or not content.strip():
              service_account_key_data = {}
          else:
              # Attempt to load the existing JSON content
              file.seek(0)
              service_account_key_data = json.load(file)
  except FileNotFoundError:
      # If the file doesn't exist, create an empty dictionary
      service_account_key_data = {}

  # Create a temporary file
  with tempfile.NamedTemporaryFile(mode="w+", delete=False) as temp_file:
      # Write the updated content to the temporary file
      json.dump(service_account_key_data, temp_file, indent=2)

  # Export the temporary file as GOOGLE_APPLICATION_CREDENTIALS
  os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = os.path.abspath(temp_file.name)

Using GCP Service Account

:::info

Trying to deploy LiteLLM on Google Cloud Run? Tutorial here

:::

Figure out the Service Account bound to the Google Cloud Run service

Get the FULL EMAIL address of the corresponding Service Account
Next, go to IAM & Admin > Manage Resources , select your top-level project that houses your Google Cloud Run Service

Click Add Principal

Specify the Service Account as the principal and Vertex AI User as the role

Once that's done, when you deploy the new container in the Google Cloud Run service, LiteLLM will have automatic access to all Vertex AI endpoints.

s/o @Darien Kindlund for this tutorial

30 KiB Raw Blame History

VertexAI [Anthropic, Gemini, Model Garden]

🆕 vertex_ai_beta/ route

System Message

Function Calling

JSON Schema

Pre-requisites

Sample Usage

Usage with LiteLLM Proxy Server

Specifying Safety Settings

Set Vertex Project & Vertex Location

Anthropic

Usage

Model Garden

Using Model Garden

Gemini Pro

Gemini Pro Vision

Gemini 1.5 Pro (and Vision)

Using Gemini Pro Vision

Usage - Function Calling

Usage - PDF / Videos / etc. Files

Chat Models

Code Chat Models

Text Models

Code Text Models

Embedding Models

Usage - Embedding

Supported Embedding Models

Advanced Use task_type and title (Vertex Specific Params)

Image Generation Models

Extra

Using GOOGLE_APPLICATION_CREDENTIALS

Using GCP Service Account

30 KiB

Raw Blame History

🆕 `vertex_ai_beta/` route

Advanced Use `task_type` and `title` (Vertex Specific Params)

Using `GOOGLE_APPLICATION_CREDENTIALS`