📄️ Anthropic Embedding
The Anthropic Embedding node converts text content into numerical vector representations using Anthropic's API through a wrapper implementation. These embeddings capture semantic meaning, enabling similarity comparisons and semantic search operations in workflows. The node supports single text strings or lists of texts for batch processing.
📄️ Google Embedding
The Google Embedding node converts text content into numerical vector representations using Google Generative AI models, with task-specific optimizations for search, similarity, classification, and clustering. It supports multiple transport protocols (REST, gRPC, async gRPC) for different performance requirements. Task-aware optimization produces higher quality embeddings compared to generic, unoptimized vectors.
📄️ HuggingFace Embedding
The HuggingFace Embedding node converts text content into numerical vector representations using Sentence Transformers models that run locally on your infrastructure. No API keys are required—models run entirely on your servers, providing complete data privacy and eliminating per-request costs. Multi-GPU processing supports high-throughput batch operations for large-scale embedding generation.
📄️ Infinity Embedding
The Infinity Embedding node converts text content into numerical vector representations using a self-hosted Infinity embedding server with high-performance optimized batching. It provides an OpenAI-compatible API for easy integration and supports most Sentence Transformers models from HuggingFace. The server architecture enables efficient resource management and concurrent request handling.
📄️ Ollama Embedding
The Ollama Embedding node converts text content into numerical vector representations using embedding models hosted on a local Ollama server. No API keys are required—models run entirely on your infrastructure, providing complete data privacy. Extensive performance tuning options control GPU/CPU resource allocation, context window sizes, and model keep-alive behavior.
📄️ OpenAI Embedding
The OpenAI Embedding node converts text content into numerical vector representations using OpenAI's cloud-based embedding models. It supports both standard OpenAI and Azure OpenAI deployments, with configurable embedding dimensions for storage and performance optimization. Automatic retry logic and batch processing provide reliability and efficiency for large-scale operations.
📄️ VLLM API Embedding
The VLLM API Embedding node connects to a self-hosted VLLM server using an OpenAI-compatible API for high-performance vector generation. It supports dimension reduction on compatible models, automatic retry logic, and batch processing for reliability. The OpenAI-compatible API allows seamless migration between VLLM and OpenAI services by changing the endpoint URL.
📄️ VLLM Embedding
The VLLM Embedding node provides high-performance local embedding generation using VLLM's optimized inference engine with advanced GPU acceleration. It supports tensor parallelism for distributing large models across multiple GPUs, quantization for reduced memory usage, and LoRA adapters for fine-tuned model variants. These optimizations enable production-scale deployments with high throughput and low latency.