--- title: "Vertex AI Embedding" url: "/docs/providers/vertex_embedding" canonical_url: "https://docs.litellm.ai/docs/providers/vertex_embedding" type: "docs" last_updated: "2026-10-06" related: - "/docs/providers/vertex_self_deployed" - "/docs/providers/vertex_image" --- # Vertex AI Embedding > Index of all LiteLLM docs: https://docs.litellm.ai/llms.txt ## Usage - Embedding **SDK** ```python 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) ``` **LiteLLM PROXY** 1. Add model to config.yaml ```yaml model_list: - model_name: snowflake-arctic-embed-m-long-1731622468876 litellm_params: model: vertex_ai/ vertex_project: "adroit-crow-413218" vertex_location: "us-central1" vertex_credentials: adroit-crow-413218-a956eef1a2a8.json litellm_settings: drop_params: True ``` 2. Start Proxy ``` $ litellm --config /path/to/config.yaml ``` 3. Make Request using OpenAI Python SDK, Langchain Python SDK ```python import openai client = openai.OpenAI(api_key="sk-", base_url="http://0.0.0.0:4000") response = client.embeddings.create( model="snowflake-arctic-embed-m-long-1731622468876", input = ["good morning from litellm", "this is another item"], ) print(response) ``` #### Supported Embedding Models All models listed [here](https://github.com/BerriAI/litellm/blob/57f37f743886a0249f630a6792d49dffc2c5d9b7/model_prices_and_context_window.json#L835) 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)` | | gemini-embedding-2-preview | `embedding(model="vertex_ai/gemini-embedding-2-preview", input)` | [Multimodal docs](#gemini-embedding-2-preview-multimodal) | | gemini-embedding-2 *(GA)* | `embedding(model="vertex_ai/gemini-embedding-2", input)` | [Multimodal docs](#gemini-embedding-2-preview-multimodal) ยท [GA notes](/blog/gemini_embedding_2_ga) | | Fine-tuned OR Custom Embedding models | `embedding(model="vertex_ai/", input)` | ### Supported OpenAI (Unified) Params | [param](../embedding/supported_embedding.md#input-params-for-litellmembedding) | type | [vertex equivalent](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/text-embeddings-api) | |-------|-------------|--------------------| | `input` | **string or List[string]** | `instances` | | `dimensions` | **int** | `output_dimensionality` | | `input_type` | **Literal["RETRIEVAL_QUERY","RETRIEVAL_DOCUMENT", "SEMANTIC_SIMILARITY", "CLASSIFICATION", "CLUSTERING", "QUESTION_ANSWERING", "FACT_VERIFICATION"]** | `task_type` | #### Usage with OpenAI (Unified) Params **SDK** ```python response = litellm.embedding( model="vertex_ai/text-embedding-004", input=["good morning from litellm", "gm"], input_type = "RETRIEVAL_DOCUMENT", dimensions=1, ) ``` **LiteLLM PROXY** ```python import openai client = openai.OpenAI(api_key="sk-", base_url="http://0.0.0.0:4000") response = client.embeddings.create( model="text-embedding-004", input = ["good morning from litellm", "gm"], dimensions=1, extra_body = { "input_type": "RETRIEVAL_QUERY", } ) print(response) ``` ### Supported Vertex Specific Params | param | type | |-------|-------------| | `auto_truncate` | **bool** | | `task_type` | **Literal["RETRIEVAL_QUERY","RETRIEVAL_DOCUMENT", "SEMANTIC_SIMILARITY", "CLASSIFICATION", "CLUSTERING", "QUESTION_ANSWERING", "FACT_VERIFICATION"]** | | `title` | **str** | #### Usage with Vertex Specific Params (Use `task_type` and `title`) You can pass any vertex specific params to the embedding model. Just pass them to the embedding function like this: [Relevant Vertex AI doc with all embedding params](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/text-embeddings-api#request_body) **SDK** ```python response = litellm.embedding( model="vertex_ai/text-embedding-004", input=["good morning from litellm", "gm"], task_type = "RETRIEVAL_DOCUMENT", title = "test", dimensions=1, auto_truncate=True, ) ``` **LiteLLM PROXY** ```python import openai client = openai.OpenAI(api_key="sk-", base_url="http://0.0.0.0:4000") response = client.embeddings.create( model="text-embedding-004", input = ["good morning from litellm", "gm"], dimensions=1, extra_body = { "task_type": "RETRIEVAL_QUERY", "auto_truncate": True, "title": "test", } ) print(response) ``` ## **BGE Embeddings** Use BGE (Baidu General Embedding) models deployed on Vertex AI. ### Usage **SDK** ```python showLineNumbers title="Using BGE on Vertex AI" import litellm response = litellm.embedding( model="vertex_ai/bge/", input=["Hello", "World"], vertex_project="your-project-id", vertex_location="your-location" ) print(response) ``` **LiteLLM PROXY** 1. Add model to config.yaml ```yaml showLineNumbers title="config.yaml" model_list: - model_name: bge-embedding litellm_params: model: vertex_ai/bge/ vertex_project: "your-project-id" vertex_location: "us-central1" vertex_credentials: your-credentials.json litellm_settings: drop_params: True ``` 2. Start Proxy ```bash $ litellm --config /path/to/config.yaml ``` 3. Make Request using OpenAI Python SDK ```python showLineNumbers title="Making requests to BGE" import openai client = openai.OpenAI(api_key="sk-", base_url="http://0.0.0.0:4000") response = client.embeddings.create( model="bge-embedding", input=["good morning from litellm", "this is another item"] ) print(response) ``` Using a Private Service Connect (PSC) endpoint ```yaml showLineNumbers title="config.yaml (PSC)" model_list: - model_name: bge-small-en-v1.5 litellm_params: model: vertex_ai/bge/1234567890 api_base: http://10.96.32.8 # Your PSC IP vertex_project: my-project-id #optional vertex_location: us-central1 #optional ``` ## **Multi-Modal Embeddings** ### Gemini Embedding 2 Preview (Multimodal) `gemini-embedding-2-preview` supports **unified multimodal embeddings**: text, images, audio, video, and PDF in a single request. See [blog post](/blog/gemini_embedding_2_multimodal) for details. The GA model id `gemini-embedding-2` exposes the same behavior, so swap the model name in any example below. See [GA blog](/blog/gemini_embedding_2_ga) for cost-map coverage and pricing notes. :::warning[Response shape: Vertex returns one combined vector] Vertex AI's Gemini embedding endpoint only exposes single-content `embedContent` (no `batchEmbedContents`), so passing `N` items in `input=[...]` returns **1 unified embedding** that fuses all parts rather than N separate vectors. To get one vector per item, call `embedding(...)` once per input. This differs from the Gemini API path (`gemini/gemini-embedding-2-preview`), which returns one embedding per input element (OpenAI-compatible). See [Gemini embedding docs](../embedding/supported_embedding#gemini-embedding-2-preview-multimodal). ::: **Input formats:** - **Data URIs:** `data:image/png;base64,` - **GCS URLs:** `gs://bucket/path/to/file.png` (MIME type inferred from extension) **Supported MIME types:** `image/png`, `image/jpeg`, `audio/mpeg`, `audio/wav`, `video/mp4`, `video/quicktime`, `application/pdf` **SDK** ```python import litellm from litellm import embedding litellm.vertex_project = "your-project-id" litellm.vertex_location = "us-central1" # Text + Image (GCS URL) response = embedding( model="vertex_ai/gemini-embedding-2-preview", input=[ "Describe this image", "gs://my-bucket/images/photo.png" ], ) # Text + Image (base64) response = embedding( model="vertex_ai/gemini-embedding-2-preview", input=[ "The food was delicious", "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAgAAAAIAQMAAAD+wSzIAAAABlBMVEX///+/v7+jQ3Y5AAAADklEQVQI12P4AIX8EAgALgAD/aNpbtEAAAAASUVORK5CYII" ], ) ``` **LiteLLM PROXY** ```yaml model_list: - model_name: vertex-gemini-embedding-2-preview litellm_params: model: vertex_ai/gemini-embedding-2-preview vertex_project: "your-project-id" vertex_location: "us-central1" ``` ```bash curl -X POST http://localhost:4000/embeddings \ -H "Authorization: Bearer $LITELLM_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "vertex-gemini-embedding-2-preview", "input": ["Describe this", "gs://bucket/image.png"] }' ``` ### multimodalembedding@001 (Legacy) Known Limitations: - Only supports 1 image / video / image per request - Only supports GCS or base64 encoded images / videos ### Usage **SDK** Using GCS Images ```python response = await litellm.aembedding( model="vertex_ai/multimodalembedding@001", input="gs://cloud-samples-data/vertex-ai/llm/prompts/landmark1.png" # will be sent as a gcs image ) ``` Using base 64 encoded images ```python response = await litellm.aembedding( model="vertex_ai/multimodalembedding@001", input="data:image/jpeg;base64,..." # will be sent as a base64 encoded image ) ``` **LiteLLM PROXY (Unified Endpoint)** 1. Add model to config.yaml ```yaml model_list: - model_name: multimodalembedding@001 litellm_params: model: vertex_ai/multimodalembedding@001 vertex_project: "adroit-crow-413218" vertex_location: "us-central1" vertex_credentials: adroit-crow-413218-a956eef1a2a8.json litellm_settings: drop_params: True ``` 2. Start Proxy ``` $ litellm --config /path/to/config.yaml ``` 3. Make Request use OpenAI Python SDK, Langchain Python SDK **OpenAI SDK** Requests with GCS Image / Video URI ```python import openai client = openai.OpenAI(api_key="sk-", base_url="http://0.0.0.0:4000") # # request sent to model set on litellm proxy, `litellm --model` response = client.embeddings.create( model="multimodalembedding@001", input = "gs://cloud-samples-data/vertex-ai/llm/prompts/landmark1.png", ) print(response) ``` Requests with base64 encoded images ```python import openai client = openai.OpenAI(api_key="sk-", base_url="http://0.0.0.0:4000") # # request sent to model set on litellm proxy, `litellm --model` response = client.embeddings.create( model="multimodalembedding@001", input = "data:image/jpeg;base64,...", ) print(response) ``` **Langchain** Requests with GCS Image / Video URI ```python from langchain_openai import OpenAIEmbeddings embeddings_models = "multimodalembedding@001" embeddings = OpenAIEmbeddings( model="multimodalembedding@001", base_url="http://0.0.0.0:4000", api_key="sk-", # type: ignore ) query_result = embeddings.embed_query( "gs://cloud-samples-data/vertex-ai/llm/prompts/landmark1.png" ) print(query_result) ``` Requests with base64 encoded images ```python from langchain_openai import OpenAIEmbeddings embeddings_models = "multimodalembedding@001" embeddings = OpenAIEmbeddings( model="multimodalembedding@001", base_url="http://0.0.0.0:4000", api_key="sk-", # type: ignore ) query_result = embeddings.embed_query( "data:image/jpeg;base64,..." ) print(query_result) ``` **LiteLLM PROXY (Vertex SDK)** 1. Add model to config.yaml ```yaml default_vertex_config: vertex_project: "adroit-crow-413218" vertex_location: "us-central1" vertex_credentials: adroit-crow-413218-a956eef1a2a8.json ``` 2. Start Proxy ``` $ litellm --config /path/to/config.yaml ``` 3. Make Request use OpenAI Python SDK ```python import vertexai from vertexai.vision_models import Image, MultiModalEmbeddingModel, Video from vertexai.vision_models import VideoSegmentConfig from google.auth.credentials import Credentials LITELLM_PROXY_API_KEY = "sk-" 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 = MultiModalEmbeddingModel.from_pretrained("multimodalembedding") image = Image.load_from_file( "gs://cloud-samples-data/vertex-ai/llm/prompts/landmark1.png" ) embeddings = model.get_embeddings( image=image, contextual_text="Colosseum", dimension=1408, ) print(f"Image Embedding: {embeddings.image_embedding}") print(f"Text Embedding: {embeddings.text_embedding}") ``` ### Text + Image + Video Embeddings **SDK** Text + Image ```python response = await litellm.aembedding( model="vertex_ai/multimodalembedding@001", input=["hey", "gs://cloud-samples-data/vertex-ai/llm/prompts/landmark1.png"] # will be sent as a gcs image ) ``` Text + Video ```python response = await litellm.aembedding( model="vertex_ai/multimodalembedding@001", input=["hey", "gs://my-bucket/embeddings/supermarket-video.mp4"] # will be sent as a gcs image ) ``` Image + Video ```python response = await litellm.aembedding( model="vertex_ai/multimodalembedding@001", input=["gs://cloud-samples-data/vertex-ai/llm/prompts/landmark1.png", "gs://my-bucket/embeddings/supermarket-video.mp4"] # will be sent as a gcs image ) ``` **LiteLLM PROXY (Unified Endpoint)** 1. Add model to config.yaml ```yaml model_list: - model_name: multimodalembedding@001 litellm_params: model: vertex_ai/multimodalembedding@001 vertex_project: "adroit-crow-413218" vertex_location: "us-central1" vertex_credentials: adroit-crow-413218-a956eef1a2a8.json litellm_settings: drop_params: True ``` 2. Start Proxy ``` $ litellm --config /path/to/config.yaml ``` 3. Make Request use OpenAI Python SDK, Langchain Python SDK Text + Image ```python import openai client = openai.OpenAI(api_key="sk-", base_url="http://0.0.0.0:4000") # # request sent to model set on litellm proxy, `litellm --model` response = client.embeddings.create( model="multimodalembedding@001", input = ["hey", "gs://cloud-samples-data/vertex-ai/llm/prompts/landmark1.png"], ) print(response) ``` Text + Video ```python import openai client = openai.OpenAI(api_key="sk-", base_url="http://0.0.0.0:4000") # # request sent to model set on litellm proxy, `litellm --model` response = client.embeddings.create( model="multimodalembedding@001", input = ["hey", "gs://my-bucket/embeddings/supermarket-video.mp4"], ) print(response) ``` Image + Video ```python import openai client = openai.OpenAI(api_key="sk-", base_url="http://0.0.0.0:4000") # # request sent to model set on litellm proxy, `litellm --model` response = client.embeddings.create( model="multimodalembedding@001", input = ["gs://cloud-samples-data/vertex-ai/llm/prompts/landmark1.png", "gs://my-bucket/embeddings/supermarket-video.mp4"], ) print(response) ``` ## Related pages - [Vertex AI - Self Deployed Models](https://docs.litellm.ai/docs/providers/vertex_self_deployed.md) - [Vertex AI Image Generation](https://docs.litellm.ai/docs/providers/vertex_image.md)