---
title: "VertexAIDocumentEmbedder"
id: vertexaidocumentembedder
slug: "/vertexaidocumentembedder"
description: "This component computes embeddings for documents using models through VertexAI Embeddings API."
---

# VertexAIDocumentEmbedder

This component computes embeddings for documents 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 [GoogleGenAIDocumentEmbedder](googlegenaidocumentembedder.mdx) integration instead.
:::

|                                        |                                                                                                 |
| -------------------------------------- | ----------------------------------------------------------------------------------------------- |
| **Most common position in a pipeline** | Before a [DocumentWriter](../writers/documentwriter.mdx) in an indexing pipeline                           |
| **Mandatory init variables**           | "model": The model used through the VertexAI Embeddings API                                     |
| **Mandatory run variables**            | “documents”: A list of documents to be embedded                                                 |
| **Output variables**                   | “documents”: A list of documents enriched with embeddings                                       |
| **API reference**                      | [Google Vertex](/reference/integrations-google-vertex)                                                 |
| **GitHub link**                        | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/google_vertex |

`VertexAIDocumentEmbedder` enriches the metadata of documents with an embedding of their content. To embed a string, use the [`VertexAITextEmbedder`](vertexaitextembedder.mdx).

To use the `VertexAIDocumentEmbedder`, 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_DOCUMENT” 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

`VertexAIDocumentEmbedder` 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 import Document
from haystack_integrations.components.embedders.google_vertex import (
    VertexAIDocumentEmbedder,
)

doc = Document(content="I love pizza!")

document_embedder = VertexAIDocumentEmbedder(model="text-embedding-005")

result = document_embedder.run([doc])
print(result["documents"][0].embedding)
## [-0.044606007635593414, 0.02857724390923977, -0.03549133986234665,
```

### 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')
```
