Embeddings models in LLM Controls

Embedding models convert text into numerical vectors. These embeddings capture the 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

from llmc.components.data import FileComponent
from llmc.components.embeddings import OpenAIEmbeddingsComponent
from llmc.components.processing.split_text import SplitTextComponent
from llmc.components.vectorstores import AstraDBVectorStoreComponent
from llmc.graph import Graph

def ingestion_graph():
    # Document ingestion pipeline
    file_component = FileComponent()
    
    # Split documents into chunks
    text_splitter = SplitTextComponent()
    text_splitter.set(data_inputs=file_component.load_files)
    
    # Initialize OpenAI embeddings model
    openai_embeddings = OpenAIEmbeddingsComponent()
    # Set your OpenAI API key in the component's openai_api_key field
    
    # Connect embeddings to vector database
    vector_store = AstraDBVectorStoreComponent()
    vector_store.set(
        embedding_model=openai_embeddings.build_embeddings,
        ingest_data=text_splitter.split_text,
    )
    
    return Graph(file_component, vector_store)

In this example : FileComponent loads documents from a local machine

SplitTextComponent chunks the documents into smaller pieces
OpenAIEmbeddingsComponent converts text chunks into numerical vectors
AstraDBVectorStoreComponent stores the vectorized data in the vector database

The stored vectors can then be retrieved for similarity searches, RAG applications, and agent-based workflows.

AI/ML

This component generates embeddings using the AI/ML API.

Parameters

Inputs

Name	Type	Description
model_name	String	The name of the AI/ML embedding model to use.
aiml_api_key	SecretString	The API key required 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.

Parameters

Inputs

Name	Type	Description
credentials_profile_name	String	The name of the AWS credentials profile in `~/.aws/credentials` or `~/.aws/config`, which has access keys or role information.
model_id	String	The ID of the model to call, such as `amazon.titan-embed-text-v1`. This is equivalent to the `modelId` property in the `list-foundation-models` API.
endpoint_url	String	The URL to set a specific service endpoint other than the default AWS endpoint.
region_name	String	The AWS region to use, such as `us-west-2`. Falls back to the `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

importantThis component is deprecated as of LLM Controls 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 vectorized embedding provider integration. For more information and instructions, see Embedding Generation.

Parameters

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.

Parameters

Inputs

Name	Type	Description
Model	String	The name of the model to use. Default: `text-embedding-3-small`.
Azure Endpoint	String	Your Azure endpoint, including the resource, such as `https://example-resource.azure.openai.com/`.
Deployment Name	String	The name of the deployment.
API Version	String	The API version to use, with options including various dates.
API Key	String	The API key required 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.

Parameters

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	The number of texts to embed in each batch.
api_base_url	Cloudflare API base URL	The 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.

Parameters

Inputs

Name	Type	Description
cohere_api_key	String	The API key required to authenticate with the Cohere service.
model	String	The 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.

Parameters

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	A 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.

Parameters

Inputs

Name	Display Name	Info
api_key	API Key	The secret API key for accessing Google’s generative AI service. Required.
model_name	Model Name	The name of the embedding model to use. Default: “models/text-embedding-004”.

Outputs

Name	Display Name	Info
embeddings	Embeddings	The built GoogleGenerativeAIEmbeddings object.

Hugging Face Embeddings

noteThis component is deprecated as of LLM Controls 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.

Parameters

Inputs

Name	Display Name	Info
Cache Folder	Cache Folder	The 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	The 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.

Parameters

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 embedding 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:

Create a Vector store RAG flow. There are two embeddings models in this flow that you can replace with Hugging Face embeddings inference components.
Replace both OpenAI embeddings model components with Hugging Face model components.
Connect both Hugging Face components to the Embeddings ports of the Astra DB vector store components.
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.
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.

Parameters

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.

Parameters

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	The API key for authentication with LM Studio.
temperature	Model Temperature	The temperature setting for the model.

Outputs

Name	Display Name	Info
embeddings	Embeddings	The generated embeddings.

MistralAI

This component generates embeddings using MistralAI models.

Parameters

Inputs

Name	Type	Description
model	String	The MistralAI model to use. Default: “mistral-embed”.
mistral_api_key	SecretString	The API key for authenticating with MistralAI.
max_concurrent_requests	Integer	The maximum number of concurrent API requests. Default: 64.
max_retries	Integer	The maximum number of retry attempts for failed requests. Default: 5.
timeout	Integer	The request timeout in seconds. Default: 120.
endpoint	String	The custom API endpoint URL. Default: `https://api.mistral.ai/v1/`).

Outputs

Name	Type	Description
embeddings	Embeddings	A MistralAIEmbeddings instance for generating embeddings.

NVIDIA

This component generates embeddings using NVIDIA models.

Parameters

Inputs

Name	Type	Description
model	String	The NVIDIA model to use for embeddings, such as `nvidia/nv-embed-v1`.
base_url	String	The base URL for the NVIDIA API. Default: `https://integrate.api.nvidia.com/v1`.
nvidia_api_key	SecretString	The API key for authenticating with NVIDIA’s service.
temperature	Float	The model temperature for embedding generation. Default: `0.1`.

Outputs

Name	Type	Description
embeddings	Embeddings	A 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 LLM Controls to your locally running Ollama server and select an embedding model.

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://localhost:11434.
To refresh the server’s list of models, click Refresh.
In the Ollama Model field, select an embedding model. This example uses all-minilm:latest.
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.

Parameters

Inputs

Name	Type	Description
Ollama Model	String	The name of the Ollama model to use. Default: `llama2`.
Ollama Base URL	String	The base URL of the Ollama API. Default: `http://localhost:11434`.
Model Temperature	Float	The 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.

Parameters

Inputs

Name	Type	Description
OpenAI API Key	String	The API key to use for accessing the OpenAI API.
Default Headers	Dict	The default headers for the HTTP requests.
Default Query	NestedDict	The default query parameters for the HTTP requests.
Allowed Special	List	The special tokens allowed for processing. Default: `[]`.
Disallowed Special	List	The special tokens disallowed for processing. Default: `["all"]`.
Chunk Size	Integer	The chunk size for processing. Default: `1000`.
Client	Any	The HTTP client for making requests.
Deployment	String	The deployment name for the model. Default: `text-embedding-3-small`.
Embedding Context Length	Integer	The length of embedding context. Default: `8191`.
Max Retries	Integer	The maximum number of retries for failed requests. Default: `6`.
Model	String	The name of the model to use. Default: `text-embedding-3-small`.
Model Kwargs	NestedDict	Additional keyword arguments for the model.
OpenAI API Base	String	The base URL of the OpenAI API.
OpenAI API Type	String	The type of the OpenAI API.
OpenAI API Version	String	The version of the OpenAI API.
OpenAI Organization	String	The organization associated with the API key.
OpenAI Proxy	String	The proxy server for the requests.
Request Timeout	Float	The 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	The 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.

Parameters

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	A data object containing the original text and its embedding vector.

VertexAI Embeddings

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

Parameters

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	The 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.

Welcome

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Concepts

Components

Integrations

Embeddings models in LLM Controls

Use an embeddings model component in a flow

AI/ML

Amazon Bedrock Embeddings

Astra DB vectorize

Azure OpenAI Embeddings

Cloudflare Workers AI Embeddings

Cohere Embeddings

Embedding similarity

Google generative AI embeddings

Hugging Face Embeddings

Hugging Face embeddings inference

Connect the Hugging Face component to a local embeddings model

IBM watsonx embeddings

Default models

LM Studio Embeddings

MistralAI

NVIDIA

Ollama embeddings

OpenAI Embeddings

Text embedder

VertexAI Embeddings

​Use an embeddings model component in a flow​

​AI/ML

​Amazon Bedrock Embeddings

​Astra DB vectorize

​Azure OpenAI Embeddings

​Cloudflare Workers AI Embeddings

​Cohere Embeddings

​Embedding similarity

​Google generative AI embeddings

​Hugging Face Embeddings

​Hugging Face embeddings inference

​Connect the Hugging Face component to a local embeddings model

​IBM watsonx embeddings

​Default models

​LM Studio Embeddings

​MistralAI

​NVIDIA

​Ollama embeddings

​OpenAI Embeddings

​Text embedder

​VertexAI Embeddings

Use an embeddings model component in a flow

AI/ML

Amazon Bedrock Embeddings

Astra DB vectorize

Azure OpenAI Embeddings

Cloudflare Workers AI Embeddings

Cohere Embeddings

Embedding similarity

Google generative AI embeddings

Hugging Face Embeddings

Hugging Face embeddings inference

Connect the Hugging Face component to a local embeddings model

IBM watsonx embeddings

Default models

LM Studio Embeddings

MistralAI

NVIDIA

Ollama embeddings

OpenAI Embeddings

Text embedder

VertexAI Embeddings