Embedding models

important

In Langflow version 1.5, the singular Embedding model component replaces many provider-specific embedding models components. Any provider-specific embedding model components that weren't incorporated into the singular component were moved to Bundles.

Embedding model components in Langflow generate text embeddings using the selected Large Language Model (LLM). The core Embedding model component supports many LLM providers, models, and use cases. For additional providers and models not supported by the core Embedding model component, see Bundles.

The core Language Model and Embedding Model components are adequate for most use cases.

Use an Embedding Model component in a flow

Create a semantic search system with the Embedding model component.

1. Add the Embedding model component to your flow. The default model is OpenAI's text-embedding-3-small, which is a balanced model, based on OpenAI's recommendations.
2. In the OpenAI API Key field, enter your OpenAI API key.
3. Add a Split text component to your flow. This component splits your input text into smaller chunks to be processed into embeddings.
4. Add a Chroma DB vector store component to your flow. This component stores your text embeddings for later retrieval.
5. Connect the Text Splitter component's Chunks output to the Chroma DB component's Ingest Data input.
6. Connect the Embedding model component's Embeddings output to the Chroma DB component's Embeddings input.

This flow loads a file from the File loader, splits the text, and embeds the split text into the local Chroma vector store using the text-embedding-3-small model.

To query the vector store, include Chat Input and Chat Output components.

7. Connect a Chat Input component to the Search Query input of the Chroma DB vector store.
8. Connect a Chat Output component to the Search Results port of the Chroma DB vector store.

Your flow looks like the following:

9. Open the Playground and enter a search query. The Playground returns the most semantically similar text chunks.

Parameters

Inputs

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

Outputs

Name	Type	Description
embeddings	Embeddings	An instance for generating embeddings using the selected provider.

Embedding models bundles

If your provider or model isn't supported by the core Embedding model component, see Bundles for additional language model and embedding model components developed by third-party contributors.