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Window Buffer Memory node#

Use the Window Buffer Memory node to persist chat history in your workflow.

On this page, you'll find a list of operations the Window Buffer Memory node supports, and links to more resources.

Don't use this node if running Localmind Automate in queue mode

This node may not work in an active production workflow. This is because Localmind Automate can't guarantee that every call to Window Buffer Memory will go to the same worker.

Parameter resolution in sub-nodes

Sub-nodes behave differently to other nodes when processing multiple items using an expression.

Most nodes, including root nodes, take any number of items as input, process these items, and output the results. You can use expressions to refer to input items, and the node resolves the expression for each item in turn. For example, given an input of five name values, the expression {{ $json.name }} resolves to each name in turn.

In sub-nodes, the expression always resolves to the first item. For example, given an input of five name values, the expression {{ $json.name }} always resolves to the first name.

Node parameters#

Configure these parameters to configure the node:

  • Session Key: Enter the key to use to store the memory in the workflow data.
  • Context Window Length: Enter the number of previous interactions to consider for context.

Refer to LangChain's Buffer Window Memory documentation for more information about the service.

View Localmind Automate's Advanced AI documentation.

Common issues#

For common questions or issues and suggested solutions, refer to Common issues.

AI glossary#

  • completion: Completions are the responses generated by a model like GPT.
  • hallucinations: Hallucination in AI is when an LLM (large language model) mistakenly perceives patterns or objects that don't exist.
  • vector database: A vector database stores mathematical representations of information. Use with embeddings and retrievers to create a database that your AI can access when answering questions.
  • vector store: A vector store, or vector database, stores mathematical representations of information. Use with embeddings and retrievers to create a database that your AI can access when answering questions.