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Create Embeddings

Generate vector embeddings from text using an embedding model, useful for semantic search, similarity comparisons, and RAG applications.

QType YAML

models:
  - type: EmbeddingModel
    id: titan_embed
    provider: aws-bedrock
    model_id: amazon.titan-embed-text-v2:0
    dimensions: 1024

flows:
  - type: Flow
    id: main
    steps:
      - type: InvokeEmbedding
        id: embed_text
        model: titan_embed
        inputs: [text]
        outputs: [embedding]

Explanation

  • EmbeddingModel: Defines an embedding model configuration with provider and dimensions
  • dimensions: Size of the embedding vector (must match model output, e.g., 1024 for Titan v2)
  • InvokeEmbedding: Step type that generates embeddings from input text
  • Embedding: Output type containing the vector array and metadata

Complete Example

id: create_embeddings
description: Generate embeddings from text using AWS Bedrock Titan

models:
  - type: EmbeddingModel
    id: titan_embed
    provider: aws-bedrock
    model_id: amazon.titan-embed-text-v2:0
    dimensions: 1024

flows:
  - type: Flow
    id: main
    variables:
      - id: text
        type: text
      - id: embedding
        type: Embedding
    inputs:
      - text
    outputs:
      - embedding
    steps:
      - type: InvokeEmbedding
        id: embed_text
        model: titan_embed
        inputs: [text]
        outputs: [embedding]

Run with: 

qtype run examples/invoke_models/create_embeddings.qtype.yaml \
  -i '{"text": "Your text here"}'

See Also