Embedding Model

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Embedding Model components in Robility flow generate text embeddings using a specified Large Language Model (LLM).

Robility flow includes an Embedding Model core component that has built-in support for some LLMs. Alternatively, you can use additional embedding models in place of the core Embedding Model component.

Use Embedding Model components in a flow

Use Embedding Model components anywhere you need to generate embedding in a flow.

This example shows how to use an Embedding Model component in a flow to create a semantic search system. This flow loads a text file, splits the text into chunks, generates embeddings for each chunk, and then loads the chunks and embeddings into a vector store. The Input and Output components allow a user to query the vector store through a chat interface.

1. Create a flow, add a File component, and then select a file containing text data, such as a PDF, that you can use to test the flow.
2. Add an Embedding Model component and then provide a valid OpenAI API key. You can enter component API keys directly or use Robility flow global variables to reference your API keys.

Points to note

If your preferred embedding model provider or model isn’t supported by the Embedding Model core component, you can use additional embedding models in place of the core component.

Search the Components menu for your preferred provider to find additional embedding models, such as the Hugging Face Embeddings Inference component.

3. Add a Split Text component to your flow. This component splits text input into smaller chunks to be processed into embeddings.
4. Add a Vector Store component, such as the Chroma DB component, to your flow, and then configure the component to connect to your vector store database. This component stores the generated embeddings so they can be used for similarity search.
5. Connect the components:

a. Connect the File component’s Loaded Files output to the Split Text component’s Data or DataFrame input.
b. Connect the Split Text component’s Chunks output to the Vector Store component’s Ingest Data input.
c. Connect the Embedding Model component’s Embeddings output to the Vector Store component’s Embedding input.

6. To query the vector store, add Chat Input and Output components:

a. Connect the Chat Input component to the Vector Store component’s Search Query input.
b. Connect the Vector Store component’s Search Results output to the Chat Output component.

7. Click Playground and then enter a search query to retrieve text chunks that are most semantically similar to your query.

Embedding Model

Some Embedding Model component input parameters are hidden by default in the visual editor. You can toggle parameters through the Controls in the component’s header menu.

Name	Display Name	Type	Description
provider	Model Provider	List	Input parameter. Select the embedding model provider.
model	Model Name	List	Input parameter. Select the embedding model to use.
api_key	OpenAI API Key	Secret[String]	Input parameter. The API key required for authenticating with the provider.
api_base	API Base URL	String	Input parameter. Base URL for the API. Leave empty for default.
dimensions	Dimensions	Integer	Input parameter. The number of dimensions for the output embeddings.
chunk_size	Chunk Size	Integer	Input parameter. The size of text chunks to process. Default: 1000.
request_timeout	Request Timeout	Float	Input parameter. Timeout for API requests.
max_retries	Max Retries	Integer	Input parameter. Maximum number of retry attempts. Default: 3.
show_progress_bar	Show Progress Bar	Boolean	Input parameter. Whether to display a progress bar during embedding generation.
model_kwargs	Model Kwargs	Dictionary	Input parameter. Additional keyword arguments to pass to the model.
embeddings	Embeddings	Embeddings	Output parameter. An instance for generating embeddings using the selected provider.

Additional embedding models

If your provider or model isn’t supported by the Embedding Model core component, additional provider-specific Embedding Model components are available in the Bundles section of the Components menu.

Legacy embedding components

The following components are legacy components. You can still use them in your flows, but they are no longer maintained, and they can be removed in future releases.

Embedding Similarity

The Embedding Similarity component is replaced by built-in similarity search functionality in Vector Store components.

This component calculates similarity scores for two embedding vectors.

It accepts the following parameters:

Name	Display Name	Info
embedding_vectors	Embedding Vectors	Input parameter. A list containing exactly two data objects with embedding vectors to compare.
similarity_metric	Similarity Metric	Input parameter. Select the similarity metric to use. Options: "Cosine Similarity", "Euclidean Distance", "Manhattan Distance".
similarity_data	Similarity Data	Output parameter. A data object containing the computed similarity score and additional information.

Text Embedder

The Text Embedder component is replaced by the Embedding Model component.

This component generates embeddings for a given message using a specified embedding model.

It accepts the following parameters:

Name	Display Name	Info
embedding_model	Embedding Model	Input parameter. The embedding model to use for generating embeddings.
message	Message	Input parameter. The message for which to generate embeddings.
embeddings	Embedding Data	Output parameter. A data object containing the original text and its embedding vector.

Embedding Model

Use Embedding Model components in a flow

Embedding Model

Additional embedding models

Legacy embedding components

Embedding Similarity

Text Embedder

Embedding Model

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