deepset-ai--haystack/docs-website/versioned_docs/version-2.26/pipeline-components/embedders/vertexaitextembedder.mdx

---
title: "VertexAITextEmbedder"
id: vertexaitextembedder
slug: "/vertexaitextembedder"
description: "This component computes embeddings for text (such as a query) using models through VertexAI Embeddings API."
---

# VertexAITextEmbedder

This component computes embeddings for text (such as a query) using models through VertexAI Embeddings API.

:::warning[Deprecation Notice]

This integration uses the deprecated google-generativeai SDK, which will lose support after August 2025.

We recommend switching to the new [GoogleGenAITextEmbedder](googlegenaitextembedder.mdx) integration instead.
:::

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | Before an embedding [Retriever](../retrievers.mdx) in a query/RAG pipeline                         |
| **Mandatory init variables**           | `model`: The model used through the VertexAI Embeddings API                                     |
| **Mandatory run variables**            | `text`: A string                                                                                |
| **Output variables**                   | `embedding`: A list of float numbers                                                            |
| **API reference**                      | [Google Vertex](/reference/integrations-google-vertex)                                                 |
| **GitHub link**                        | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/google_vertex |

</div>

## Overview

`VertexAITextEmbedder` embeds a simple string (such as a query) into a vector. For embedding lists of documents, use the [`VertexAIDocumentEmbedder`](vertexaidocumentembedder.mdx) which enriches the document with the computed embedding, also known as vector.

To start using the `VertexAITextEmbedder`, initialize it with:

- `model`: The supported models are:
  - "text-embedding-004"
  - "text-embedding-005"
  - "textembedding-gecko-multilingual@001"
  - "text-multilingual-embedding-002"
  - "text-embedding-large-exp-03-07"
- `task_type`: "RETRIEVAL_QUERY” is the default. You can find all task types in the official [Google documentation](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/text-embeddings-api#tasktype).

### Authentication

`VertexAITextEmbedder` uses Google Cloud Application Default Credentials (ADCs) for authentication. For more information on how to set up ADCs, see the [official documentation](https://cloud.google.com/docs/authentication/provide-credentials-adc).

Keep in mind that it’s essential to use an account that has access to a project authorized to use Google Vertex AI endpoints.

You can find your project ID in the [GCP resource manager](https://console.cloud.google.com/cloud-resource-manager) or locally by running `gcloud projects list` in your terminal. For more info on the gcloud CLI, see its [official documentation](https://cloud.google.com/cli).

## Usage

Install the `google-vertex-haystack` package to use this Embedder:

```shell
pip install google-vertex-haystack
```

### On its own

```python
from haystack_integrations.components.embedders.google_vertex import (
    VertexAITextEmbedder,
)

text_to_embed = "I love pizza!"

text_embedder = VertexAITextEmbedder(model="text-embedding-005")

print(text_embedder.run(text_to_embed))
## {'embedding': [-0.08127457648515701, 0.03399784862995148, -0.05116401985287666, ...]
```

### In a pipeline

```python
from haystack import Document
from haystack import Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack_integrations.components.embedders.google_vertex import (
    VertexAITextEmbedder,
)
from haystack_integrations.components.embedders.google_vertex import (
    VertexAIDocumentEmbedder,
)
from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever

document_store = InMemoryDocumentStore(embedding_similarity_function="cosine")

documents = [
    Document(content="My name is Wolfgang and I live in Berlin"),
    Document(content="I saw a black horse running"),
    Document(content="Germany has many big cities"),
]

document_embedder = VertexAIDocumentEmbedder(model="text-embedding-005")
documents_with_embeddings = document_embedder.run(documents)["documents"]
document_store.write_documents(documents_with_embeddings)

query_pipeline = Pipeline()
query_pipeline.add_component(
    "text_embedder",
    VertexAITextEmbedder(model="text-embedding-005"),
)
query_pipeline.add_component(
    "retriever",
    InMemoryEmbeddingRetriever(document_store=document_store),
)
query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")

query = "Who lives in Berlin?"

result = query_pipeline.run({"text_embedder": {"text": query}})

print(result["retriever"]["documents"][0])

## Document(id=..., content: 'My name is Wolfgang and I live in Berlin')
```