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108 lines
4.7 KiB
Plaintext
108 lines
4.7 KiB
Plaintext
---
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title: "VertexAIDocumentEmbedder"
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id: vertexaidocumentembedder
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slug: "/vertexaidocumentembedder"
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description: "This component computes embeddings for documents using models through VertexAI Embeddings API."
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---
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# VertexAIDocumentEmbedder
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This component computes embeddings for documents using models through VertexAI Embeddings API.
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:::warning Deprecation Notice
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This integration uses the deprecated google-generativeai SDK, which will lose support after August 2025.
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We recommend switching to the new [GoogleGenAIDocumentEmbedder](googlegenaidocumentembedder.mdx) integration instead.
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:::
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<div className="key-value-table">
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| | |
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| --- | --- |
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| **Most common position in a pipeline** | Before a [DocumentWriter](../writers/documentwriter.mdx) in an indexing pipeline |
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| **Mandatory init variables** | `model`: The model used through the VertexAI Embeddings API |
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| **Mandatory run variables** | `documents`: A list of documents to be embedded |
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| **Output variables** | `documents`: A list of documents enriched with embeddings |
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| **API reference** | [Google Vertex](/reference/integrations-google-vertex) |
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| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/google_vertex |
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</div>
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`VertexAIDocumentEmbedder` enriches the metadata of documents with an embedding of their content. To embed a string, use the [`VertexAITextEmbedder`](vertexaitextembedder.mdx).
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To use the `VertexAIDocumentEmbedder`, initialize it with:
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- `model`: The supported models are:
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- "text-embedding-004"
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- "text-embedding-005"
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- "textembedding-gecko-multilingual@001"
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- "text-multilingual-embedding-002"
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- "text-embedding-large-exp-03-07"
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- `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).
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### Authentication
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`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).
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Keep in mind that it’s essential to use an account that has access to a project authorized to use Google Vertex AI endpoints.
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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).
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## Usage
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Install the `google-vertex-haystack` package to use this Embedder:
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```shell
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pip install google-vertex-haystack
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```
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### On its own
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```python
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from haystack import Document
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from haystack_integrations.components.embedders.google_vertex import VertexAIDocumentEmbedder
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doc = Document(content="I love pizza!")
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document_embedder = VertexAIDocumentEmbedder(model="text-embedding-005")
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result = document_embedder.run([doc])
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print(result['documents'][0].embedding)
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## [-0.044606007635593414, 0.02857724390923977, -0.03549133986234665,
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```
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### In a pipeline
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```python
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from haystack import Document
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from haystack import Pipeline
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from haystack.document_stores.in_memory import InMemoryDocumentStore
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from haystack_integrations.components.embedders.google_vertex import VertexAITextEmbedder
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from haystack_integrations.components.embedders.google_vertex import VertexAIDocumentEmbedder
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from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
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document_store = InMemoryDocumentStore(embedding_similarity_function="cosine")
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documents = [Document(content="My name is Wolfgang and I live in Berlin"),
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Document(content="I saw a black horse running"),
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Document(content="Germany has many big cities")]
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document_embedder = VertexAIDocumentEmbedder(model="text-embedding-005")
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documents_with_embeddings = document_embedder.run(documents)['documents']
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document_store.write_documents(documents_with_embeddings)
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query_pipeline = Pipeline()
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query_pipeline.add_component("text_embedder", VertexAITextEmbedder(model="text-embedding-005"))
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query_pipeline.add_component("retriever", InMemoryEmbeddingRetriever(document_store=document_store))
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query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
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query = "Who lives in Berlin?"
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result = query_pipeline.run({"text_embedder":{"text": query}})
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print(result['retriever']['documents'][0])
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## Document(id=..., content: 'My name is Wolfgang and I live in Berlin')
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```
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