Embeddings models in Langflow

Embeddings models convert text into numerical vectors. These embeddings capture semantic meaning of the input text, and allow LLMs to understand context.

Refer to your specific component's documentation for more information on parameters.

Use an embeddings model component in a flow

In this example of a document ingestion pipeline, the OpenAI embeddings model is connected to a vector database. The component converts the text chunks into vectors and stores them in the vector database. The vectorized data can be used to inform AI workloads like chatbots, similarity searches, and agents.

This embeddings component uses an OpenAI API key for authentication. Refer to your specific embeddings component's documentation for more information on authentication.

AI/ML

This component generates embeddings using the AI/ML API.

Inputs

Name	Type	Description
model_name	String	The name of the AI/ML embedding model to use
aiml_api_key	SecretString	API key for authenticating with the AI/ML service

Outputs

Name	Type	Description
embeddings	Embeddings	An instance of AIMLEmbeddingsImpl for generating embeddings

Amazon Bedrock Embeddings

This component is used to load embedding models from Amazon Bedrock.

Inputs

Name	Type	Description
credentials_profile_name	String	Name of the AWS credentials profile in ~/.aws/credentials or ~/.aws/config, which has access keys or role information
model_id	String	ID of the model to call, e.g., amazon.titan-embed-text-v1. This is equivalent to the modelId property in the list-foundation-models API
endpoint_url	String	URL to set a specific service endpoint other than the default AWS endpoint
region_name	String	AWS region to use, e.g., us-west-2. Falls back to AWS_DEFAULT_REGION environment variable or region specified in ~/.aws/config if not provided

Outputs

Name	Type	Description
embeddings	Embeddings	An instance for generating embeddings using Amazon Bedrock

Astra DB vectorize

important

This component is deprecated as of Langflow version 1.1.2. Instead, use the Astra DB vector store component

Connect this component to the Embeddings port of the Astra DB vector store component to generate embeddings.

This component requires that your Astra DB database has a collection that uses a vectorize embedding provider integration. For more information and instructions, see Embedding Generation.

Inputs

Name	Display Name	Info
provider	Embedding Provider	The embedding provider to use
model_name	Model Name	The embedding model to use
authentication	Authentication	The name of the API key in Astra that stores your vectorize embedding provider credentials. (Not required if using an Astra-hosted embedding provider.)
provider_api_key	Provider API Key	As an alternative to authentication, directly provide your embedding provider credentials.
model_parameters	Model Parameters	Additional model parameters

Outputs

Name	Type	Description
embeddings	Embeddings	An instance for generating embeddings using Astra vectorize

Azure OpenAI Embeddings

This component generates embeddings using Azure OpenAI models.

Inputs

Name	Type	Description
Model	String	Name of the model to use (default: text-embedding-3-small)
Azure Endpoint	String	Your Azure endpoint, including the resource. Example: https://example-resource.azure.openai.com/
Deployment Name	String	The name of the deployment
API Version	String	The API version to use, options include various dates
API Key	String	The API key to access the Azure OpenAI service

Outputs

Name	Type	Description
embeddings	Embeddings	An instance for generating embeddings using Azure OpenAI

Cloudflare Workers AI Embeddings

This component generates embeddings using Cloudflare Workers AI models.

Inputs

Name	Display Name	Info
account_id	Cloudflare account ID	Find your Cloudflare account ID
api_token	Cloudflare API token	Create an API token
model_name	Model Name	List of supported models
strip_new_lines	Strip New Lines	Whether to strip new lines from the input text
batch_size	Batch Size	Number of texts to embed in each batch
api_base_url	Cloudflare API base URL	Base URL for the Cloudflare API
headers	Headers	Additional request headers

Outputs

Name	Display Name	Info
embeddings	Embeddings	An instance for generating embeddings using Cloudflare Workers

Cohere Embeddings

This component is used to load embedding models from Cohere.

Inputs

Name	Type	Description
cohere_api_key	String	API key required to authenticate with the Cohere service
model	String	Language model used for embedding text documents and performing queries (default: embed-english-v2.0)
truncate	Boolean	Whether to truncate the input text to fit within the model's constraints (default: False)

Outputs

Name	Type	Description
embeddings	Embeddings	An instance for generating embeddings using Cohere

Embedding similarity

This component computes selected forms of similarity between two embedding vectors.

Inputs

Name	Display Name	Info
embedding_vectors	Embedding Vectors	A list containing exactly two data objects with embedding vectors to compare.
similarity_metric	Similarity Metric	Select the similarity metric to use. Options: "Cosine Similarity", "Euclidean Distance", "Manhattan Distance".

Outputs

Name	Display Name	Info
similarity_data	Similarity Data	Data object containing the computed similarity score and additional information.

Google generative AI embeddings

This component connects to Google's generative AI embedding service using the GoogleGenerativeAIEmbeddings class from the langchain-google-genai package.

Inputs

Name	Display Name	Info
api_key	API Key	Secret API key for accessing Google's generative AI service (required)
model_name	Model Name	Name of the embedding model to use (default: "models/text-embedding-004")

Outputs

Name	Display Name	Info
embeddings	Embeddings	Built GoogleGenerativeAIEmbeddings object

Hugging Face Embeddings

note

This component is deprecated as of Langflow version 1.0.18. Instead, use the Hugging Face Embeddings Inference component.

This component loads embedding models from HuggingFace.

Use this component to generate embeddings using locally downloaded Hugging Face models. Ensure you have sufficient computational resources to run the models.

Inputs

Name	Display Name	Info
Cache Folder	Cache Folder	Folder path to cache HuggingFace models
Encode Kwargs	Encoding Arguments	Additional arguments for the encoding process
Model Kwargs	Model Arguments	Additional arguments for the model
Model Name	Model Name	Name of the HuggingFace model to use
Multi Process	Multi-Process	Whether to use multiple processes

Outputs

Name	Display Name	Info
embeddings	Embeddings	The generated embeddings

Hugging Face embeddings inference

This component generates embeddings using Hugging Face Inference API models and requires a Hugging Face API token to authenticate. Local inference models do not require an API key.

Use this component to create embeddings with Hugging Face's hosted models, or to connect to your own locally hosted models.

Inputs

Name	Display Name	Info
API Key	API Key	The API key for accessing the Hugging Face Inference API.
API URL	API URL	The URL of the Hugging Face Inference API.
Model Name	Model Name	The name of the model to use for embeddings.

Outputs

Name	Display Name	Info
embeddings	Embeddings	The generated embeddings.

Connect the Hugging Face component to a local embeddings model

To run an embeddings inference locally, see the HuggingFace documentation.

To connect the local Hugging Face model to the Hugging Face embeddings inference component and use it in a flow, follow these steps:

1. Create a Vector store RAG flow. There are two embeddings models in this flow that you can replace with Hugging Face embeddings inference components.
2. Replace both OpenAI embeddings model components with Hugging Face model components.
3. Connect both Hugging Face components to the Embeddings ports of the Astra DB vector store components.
4. In the Hugging Face components, set the Inference Endpoint field to the URL of your local inference model. The API Key field is not required for local inference.
5. Run the flow. The local inference models generate embeddings for the input text.

IBM watsonx embeddings

This component generates text using IBM watsonx.ai foundation models.

To use IBM watsonx.ai embeddings components, replace an embeddings component with the IBM watsonx.ai component in a flow.

An example document processing flow looks like the following:

This flow loads a PDF file from local storage and splits the text into chunks.

The IBM watsonx embeddings component converts the text chunks into embeddings, which are then stored in a Chroma DB vector store.

The values for API endpoint, Project ID, API key, and Model Name are found in your IBM watsonx.ai deployment. For more information, see the Langchain documentation.

Default models

The component supports several default models with the following vector dimensions:

• sentence-transformers/all-minilm-l12-v2: 384-dimensional embeddings
• ibm/slate-125m-english-rtrvr-v2: 768-dimensional embeddings
• ibm/slate-30m-english-rtrvr-v2: 768-dimensional embeddings
• intfloat/multilingual-e5-large: 1024-dimensional embeddings

The component automatically fetches and updates the list of available models from your watsonx.ai instance when you provide your API endpoint and credentials.

Inputs

Name	Display Name	Info
url	watsonx API Endpoint	The base URL of the API.
project_id	watsonx project id	The project ID for your watsonx.ai instance.
api_key	API Key	The API Key to use for the model.
model_name	Model Name	The name of the embedding model to use.
truncate_input_tokens	Truncate Input Tokens	The maximum number of tokens to process. Default: 200.
input_text	Include the original text in the output	Determines if the original text is included in the output. Default: True.

Outputs

Name	Display Name	Info
embeddings	Embeddings	An instance for generating embeddings using watsonx.ai

LM Studio Embeddings

This component generates embeddings using LM Studio models.

Inputs

Name	Display Name	Info
model	Model	The LM Studio model to use for generating embeddings
base_url	LM Studio Base URL	The base URL for the LM Studio API
api_key	LM Studio API Key	API key for authentication with LM Studio
temperature	Model Temperature	Temperature setting for the model

Outputs

Name	Display Name	Info
embeddings	Embeddings	The generated embeddings

MistralAI

This component generates embeddings using MistralAI models.

Inputs

Name	Type	Description
model	String	The MistralAI model to use (default: "mistral-embed")
mistral_api_key	SecretString	API key for authenticating with MistralAI
max_concurrent_requests	Integer	Maximum number of concurrent API requests (default: 64)
max_retries	Integer	Maximum number of retry attempts for failed requests (default: 5)
timeout	Integer	Request timeout in seconds (default: 120)
endpoint	String	Custom API endpoint URL (default: https://api.mistral.ai/v1/)

Outputs

Name	Type	Description
embeddings	Embeddings	MistralAIEmbeddings instance for generating embeddings

NVIDIA

This component generates embeddings using NVIDIA models.

Inputs

Name	Type	Description
model	String	The NVIDIA model to use for embeddings (e.g., nvidia/nv-embed-v1)
base_url	String	Base URL for the NVIDIA API (default: https://integrate.api.nvidia.com/v1)
nvidia_api_key	SecretString	API key for authenticating with NVIDIA's service
temperature	Float	Model temperature for embedding generation (default: 0.1)

Outputs

Name	Type	Description
embeddings	Embeddings	NVIDIAEmbeddings instance for generating embeddings

Ollama embeddings

This component generates embeddings using Ollama models.

For a list of Ollama embeddings models, see the Ollama documentation.

To use this component in a flow, connect Langflow to your locally running Ollama server and select an embeddings model.

1. In the Ollama component, in the Ollama Base URL field, enter the address for your locally running Ollama server. This value is set as the OLLAMA_HOST environment variable in Ollama. The default base URL is http://127.0.0.1:11434.
2. To refresh the server's list of models, click .
3. In the Ollama Model field, select an embeddings model. This example uses all-minilm:latest.
4. Connect the Ollama embeddings component to a flow. For example, this flow connects a local Ollama server running a all-minilm:latest embeddings model to a Chroma DB vector store to generate embeddings for split text.

For more information, see the Ollama documentation.

Inputs

Name	Type	Description
Ollama Model	String	Name of the Ollama model to use (default: llama2)
Ollama Base URL	String	Base URL of the Ollama API (default: http://localhost:11434)
Model Temperature	Float	Temperature parameter for the model. Adjusts the randomness in the generated embeddings

Outputs

Name	Type	Description
embeddings	Embeddings	An instance for generating embeddings using Ollama

OpenAI Embeddings

This component is used to load embedding models from OpenAI.

Inputs

Name	Type	Description
OpenAI API Key	String	The API key to use for accessing the OpenAI API
Default Headers	Dict	Default headers for the HTTP requests
Default Query	NestedDict	Default query parameters for the HTTP requests
Allowed Special	List	Special tokens allowed for processing (default: [])
Disallowed Special	List	Special tokens disallowed for processing (default: ["all"])
Chunk Size	Integer	Chunk size for processing (default: 1000)
Client	Any	HTTP client for making requests
Deployment	String	Deployment name for the model (default: text-embedding-3-small)
Embedding Context Length	Integer	Length of embedding context (default: 8191)
Max Retries	Integer	Maximum number of retries for failed requests (default: 6)
Model	String	Name of the model to use (default: text-embedding-3-small)
Model Kwargs	NestedDict	Additional keyword arguments for the model
OpenAI API Base	String	Base URL of the OpenAI API
OpenAI API Type	String	Type of the OpenAI API
OpenAI API Version	String	Version of the OpenAI API
OpenAI Organization	String	Organization associated with the API key
OpenAI Proxy	String	Proxy server for the requests
Request Timeout	Float	Timeout for the HTTP requests
Show Progress Bar	Boolean	Whether to show a progress bar for processing (default: False)
Skip Empty	Boolean	Whether to skip empty inputs (default: False)
TikToken Enable	Boolean	Whether to enable TikToken (default: True)
TikToken Model Name	String	Name of the TikToken model

Outputs

Name	Type	Description
embeddings	Embeddings	An instance for generating embeddings using OpenAI

Text embedder

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

Inputs

Name	Display Name	Info
embedding_model	Embedding Model	The embedding model to use for generating embeddings.
message	Message	The message for which to generate embeddings.

Outputs

Name	Display Name	Info
embeddings	Embedding Data	Data object containing the original text and its embedding vector.

VertexAI Embeddings

This component is a wrapper around Google Vertex AI Embeddings API.

Inputs

Name	Type	Description
credentials	Credentials	The default custom credentials to use
location	String	The default location to use when making API calls (default: us-central1)
max_output_tokens	Integer	Token limit determines the maximum amount of text output from one prompt (default: 128)
model_name	String	The name of the Vertex AI large language model (default: text-bison)
project	String	The default GCP project to use when making Vertex API calls
request_parallelism	Integer	The amount of parallelism allowed for requests issued to VertexAI models (default: 5)
temperature	Float	Tunes the degree of randomness in text generations. Should be a non-negative value (default: 0)
top_k	Integer	How the model selects tokens for output, the next token is selected from the top k tokens (default: 40)
top_p	Float	Tokens are selected from the most probable to least until the sum of their probabilities exceeds the top p value (default: 0.95)
tuned_model_name	String	The name of a tuned model. If provided, model_name is ignored
verbose	Boolean	This parameter controls the level of detail in the output. When set to True, it prints internal states of the chain to help debug (default: False)

Outputs

Name	Type	Description
embeddings	Embeddings	An instance for generating embeddings using VertexAI