​
Available Components

AWS Bedrock Embeddings Node

Embeddings using AWS Bedrock

Azure OpenAI Embeddings Node

OpenAI embeddings via Azure

Cohere Embeddings Node

Multilingual embeddings from Cohere

GoogleVertexAI Embeddings Node

Embeddings using Google Vertex AI

HuggingFace Inference Embeddings Node

Embeddings using HuggingFace models

LocalAI Embeddings Node

Open source embeddings using LocalAI

MistralAI Embeddings Node

Embeddings from MistralAI

Ollama Embeddings Node

Local embeddings using Ollama

OpenAI Embeddings Custom

Customizable OpenAI embeddings

OpenAI Embeddings Node

Standard OpenAI embeddings

TogetherAI Embeddings Node

Embeddings from TogetherAI platform

VoyageAI Embeddings Node

High quality embeddings from VoyageAI

​
Use Cases

These embedding components are beneficial for a wide range of applications, including:
  1. Semantic Search: Improve search results by understanding the meaning behind queries and documents.
  2. Text Classification: Categorize text based on its content and meaning.
  3. Document Clustering: Group similar documents together based on their semantic similarity.
  4. Recommendation Systems: Suggest related content or items based on textual descriptions.
  5. Information Retrieval: Enhance document retrieval systems by using semantic embeddings.
  6. Feature Extraction: Prepare text data as input for machine learning models.
  7. Similarity Comparisons: Measure the semantic similarity between different pieces of text.
  8. Natural Language Understanding: Improve the comprehension of text in various NLP tasks.

​
Key Features

  • Multiple Service Options: Choose from various embedding services like OpenAI, Azure, Google, AWS, and open-source alternatives.
  • Model Selection: Many components allow you to select specific models or dynamically load available models.
  • Customization: Some components offer additional parameters for fine-tuning the embedding process.
  • Integration: These components are designed to work within larger NLP and machine learning workflows.
  • Specialized Versions: Some embeddings are tailored for specific frameworks like LlamaIndex.
By leveraging these embedding components, users can enhance their natural language processing capabilities and build more sophisticated AI-powered applications.