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.
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.
This component generates embeddings using the AI/ML API.
| 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 |
| Name | Type | Description |
|---|
| embeddings | Embeddings | An instance of AIMLEmbeddingsImpl for generating embeddings |
This component is used to load embedding models from Amazon Bedrock.
| 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 |
| Name | Type | Description |
|---|
| embeddings | Embeddings | An instance for generating embeddings using Amazon Bedrock |
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.
| 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 |
| Name | Type | Description |
|---|
| embeddings | Embeddings | An instance for generating embeddings using Astra vectorize |
This component generates embeddings using Azure OpenAI models.
| 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 |
| Name | Type | Description |
|---|
| embeddings | Embeddings | An instance for generating embeddings using Azure OpenAI |
This component is used to load embedding models from Cohere.
| 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) |
| Name | Type | Description |
|---|
| embeddings | Embeddings | An instance for generating embeddings using Cohere |
This component computes selected forms of similarity between two embedding vectors.
| 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". |
| Name | Display Name | Info |
|---|
| similarity_data | Similarity Data | Data object containing the computed similarity score and additional information. |
This component connects to Google's generative AI embedding service using the GoogleGenerativeAIEmbeddings class from the langchain-google-genai package.
| 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") |
| Name | Display Name | Info |
|---|
| embeddings | Embeddings | Built GoogleGenerativeAIEmbeddings object |
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.
| 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 |
This component generates embeddings using Hugging Face Inference API models.
Use this component to create embeddings with Hugging Face's hosted models. Ensure you have a valid Hugging Face API key.
| Name | Display Name | Info |
|---|
| API Key | API Key | API key for accessing the Hugging Face Inference API |
| API URL | API URL | URL of the Hugging Face Inference API |
| Model Name | Model Name | Name of the model to use for embeddings |
| Cache Folder | Cache Folder | Folder path to cache Hugging Face models |
| Encode Kwargs | Encoding Arguments | Additional arguments for the encoding process |
| Model Kwargs | Model Arguments | Additional arguments for the model |
| Multi Process | Multi-Process | Whether to use multiple processes |
This component generates embeddings using MistralAI models.
| 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/") |
| Name | Type | Description |
|---|
| embeddings | Embeddings | MistralAIEmbeddings instance for generating embeddings |
This component generates embeddings using NVIDIA models.
| 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) |
| Name | Type | Description |
|---|
| embeddings | Embeddings | NVIDIAEmbeddings instance for generating embeddings |
This component generates embeddings using Ollama models.
| 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 |
| Name | Type | Description |
|---|
| embeddings | Embeddings | An instance for generating embeddings using Ollama |
This component is used to load embedding models from OpenAI.
| 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 |
| 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.
| 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. |
| Name | Display Name | Info |
|---|
| embeddings | Embedding Data | Data object containing the original text and its embedding vector. |
This component is a wrapper around Google Vertex AI Embeddings API.
| 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) |
| Name | Type | Description |
|---|
| embeddings | Embeddings | An instance for generating embeddings using VertexAI |
