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LangChain concepts in Ensemble#

This page explains how LangChain concepts and features map to Ensemble nodes.

This page includes lists of the LangChain-focused nodes in Ensemble. You can use any Ensemble node in a pathway where you interact with LangChain, to link LangChain to other services. The LangChain features uses Ensemble's Cluster nodes.

Ensemble implements LangChain JS

This feature is Ensemble's implementation of LangChain's JavaScript framework.

Trigger nodes#

On new manual Chat Message

Cluster nodes#

Cluster nodes are node groups that work together to provide functionality in an Ensemble workflow. Instead of using a single node, you use a root node and one or more sub-nodes. This allows extensive configuration of node functionality.

Root nodes#

Each cluster starts with one root node.

Chains#

A chain is a series of LLMs, and related tools, linked together to support functionality that can't be provided by a single LLM alone.

Available nodes:

Learn more about Chains in LangChain.

Agents#

An agent has access to a suite of tools, and determines which ones to use depending on the user input. Agents can use multiple tools, and use the output of one tool as the input to the next. Source

Available nodes:

Learn more about Agents in LangChain.

Vector stores#

Miscellaneous#

Utility nodes.

LangChain Code: import LangChain. This means if there is functionality you need that Ensemble hasn't created a node for, you can still use it.

Sub-nodes#

Each root node can have one or more sub-nodes attached to it.

Document loaders#

Document loaders add data to your chain as documents. The data source can be a file or web service.

Available nodes:

Learn more about Document loaders in LangChain.

Language models#

LLMs (large language models) are programs that analyze datasets. They're the key element of working with AI.

Available nodes:

Learn more about Language models in LangChain.

Memory#

Memory retains information about previous queries in a series of queries. For example, when a user interacts with a chat model, it's useful if your application can remember and call on the full conversation, not just the most recent query entered by the user.

Available nodes:

Learn more about Memory in LangChain.

Output parsers#

Output parsers take the text generated by an LLM and format it to match the structure you require.

Available nodes:

Learn more about Output parsers in LangChain.

Retrievers#

Text splitters#

Text splitters break down data (documents), making it easier for the LLM to process the information and return accurate results.

Available nodes:

Ensemble's text splitter nodes implements parts of LangChain's text_splitter API.

Tools#

Utility tools.

Embeddings#

Miscellaneous#