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Available Categories

Agents

Advanced AI components that can perform complex tasks, make decisions, and interact with various tools

API Chain

Facilitates interaction with various types of APIs using natural language processing

Cache

Efficient mechanisms for storing and retrieving frequently accessed data and LLM responses

Chains

Fundamental components that combine language models, memory, and tools for complex tasks

Document Loaders

Components for extracting and processing data from various file types and sources

Embeddings

Vector representations of text that capture semantic meaning for various NLP tasks

LLMs

Integration components for various large language models designed for conversational AI

Memory

Components for maintaining context and storing information across conversations

Moderation

Tools for filtering and controlling content to ensure compliance and safety

Multi-Agents

Systems where multiple specialized agents work together under coordinated supervision

Output Parsers

Components that structure and format language model outputs into specific data formats

Prompts

Tools for creating and managing structured prompts for language models

Record Managers

Components for tracking and managing document operations in vector databases

Retrievers

Components designed to fetch relevant information from various sources

Sequential Agents

Components for creating complex, multi-step conversational workflows

Speech to Text

Tools for converting spoken language into written text

Text Splitters

Components for breaking down large text documents into manageable chunks

Tools

Various components that enhance AI workflows with specialized functionalities

Utilities

Versatile components for workflow flexibility and control flow management

Vector Stores

Components for storing and querying vector embeddings for semantic search

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Key characteristics of nodes:

  • Each node is a microservice with its own environment and scaling controls.
  • Nodes are stateless and do not store data internally.
  • Nodes can be platform-provided or user-created with custom environments and code.

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LangChain

Learn how Ardor integrates with the LangChain framework LangChain is a framework for developing applications powered by language models. It simplifies the process of creating generative AI application, connecting data sources, vectors, memories with LLMs. Ardor complements LangChain by offering a visual interface. Here, nodes are organized into distinct sections, making it easier to build workflows. Currently most of ready to use nodes are from LangChain.