Embedding Model
Embedding Model components in Langflow generate text embeddings using a specified Large Language Model (LLM).
Langflow includes an Embedding Model core component that has built-in support for some LLMs. -Alternatively, you can use any additional Embedding Model component in place of the core Embedding Model component.
-The built-in LLMs are appropriate for most text-based embedding model use cases in Langflow.
+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 embeddings 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.
This example uses the Embedding Model core component.
To use another model, you can replace the Embedding Model core component with any additional Embedding Model component in these steps. -However, your component might have different parameters than the Embedding Model core component.
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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.
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Add an Embedding Model component, and then provide a valid OpenAI API key.
-By default, the Embedding Model component uses an OpenAI model. -If you want to use a different model, edit the Model Name, and API Key fields accordingly. -Or, see Additional Embedding Model components for other components that you can use in place of the Embedding Model core component.
-You can enter component API keys directly or use Langflow global variables to reference your API keys.
+Add an Embedding Model component, and then provide a valid OpenAI API key. +You can enter component API keys directly or use Langflow global variables to reference your API keys.
+tipIf 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.
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Add a Split Text component to your flow. @@ -100,13 +95,13 @@ This component stores the generated embeddings so they can be used for similarit
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 Model components​
-If your provider or model isn't supported by the Embedding Model core component, additional single-provider Embedding Model components are available in the Bundles section of the Components menu.
+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.