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gcp_vertex_ai_embeddings

Generates vector embeddings to represent input text, using the Vertex AI API.

This processor sends text strings to the Vertex AI API, which generates vector embeddings. By default, the processor submits the entire payload of each message as a string, unless you use the text configuration field to customize it.

For more information, see the Vertex AI documentation.

  • Common

  • Advanced

processor:
  label: ""
  gcp_vertex_ai_embeddings:
    project: "" # No default (required)
    credentials_json: "" # No default (optional)
    location: us-central1
    model: "" # No default (required)
    task_type: RETRIEVAL_DOCUMENT
    text: "" # No default (optional)
    output_dimensions: 0 # No default (optional)
processor:
  label: ""
  gcp_vertex_ai_embeddings:
    project: "" # No default (required)
    credentials_json: "" # No default (optional)
    location: us-central1
    model: "" # No default (required)
    task_type: RETRIEVAL_DOCUMENT
    text: "" # No default (optional)
    output_dimensions: 0 # No default (optional)

Fields

credentials_json

Set your Google Service Account Credentials as JSON (optional).

This field contains sensitive information that usually shouldn’t be added to a configuration directly. For more information, see Manage Secrets before adding it to your configuration.

Type: string

location

The location of the Vertex AI model that you want to use.

Type: string

Default: us-central1

model

The name of the embedding model to use. For a full list of models, see the Vertex AI Model Garden.

Type: string

# Examples:
model: text-embedding-004

# ---

model: text-multilingual-embedding-002

output_dimensions

The maximum length of a generated vector embedding. If this value is set, generated embeddings are truncated to this size.

Type: int

project

The ID of your Google Cloud project.

Type: string

task_type

Use the following options to optimize embeddings that the model generates for specific use cases.

Type: string

Default: RETRIEVAL_DOCUMENT

Option Summary

CLASSIFICATION

optimize for being able classify texts according to preset labels

CLUSTERING

optimize for clustering texts based on their similarities

FACT_VERIFICATION

optimize for queries that are proving or disproving a fact such as "apples grow underground"

QUESTION_ANSWERING

optimize for search proper questions such as "Why is the sky blue?"

RETRIEVAL_DOCUMENT

optimize for documents that will be searched (also known as a corpus)

RETRIEVAL_QUERY

optimize for queries such as "What is the best fish recipe?" or "best restaurant in Chicago"

SEMANTIC_SIMILARITY

optimize for text similarity

text

The text you want to generate vector embeddings for. By default, the processor submits the entire payload of each message as a string.

This field supports interpolation functions.

Type: string