comfyui_LLM_party

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Available Nodes

Repository Overview

save_redis_memo

ComfyUI Node: Save Redis Memory (save_redis_memo)

Overview

The save_redis_memo node is part of the ComfyUI LLM Party extension aimed at enhancing LLM workflows by facilitating memory management capabilities through Redis. This node specifically allows users to save conversation histories or other data structures into a Redis database. By doing so, users can efficiently manage, retrieve, and extend dynamic content in their AI workflows.

Purpose

This node is designed to store data, typically conversational histories, in a Redis database. It extends existing history data with new entries and updates the stored data accordingly. This feature is crucial for applications where maintaining a comprehensive record of interactions or data states over time is essential, such as in dialogue systems or iterative processes.

Inputs

The save_redis_memo node accepts the following inputs:

  • history (String): The new history data to be appended to the existing stored data.
  • history_key (String): The unique identifier or key for accessing and storing the relevant data in Redis. This key acts as an index for the data.
  • redis_host (String, default: "localhost"): The hostname or IP address of the Redis server. Default is set to local server usage.
  • redis_port (Integer, default: 6379): The port number for the Redis server, with a default value of 6379, which is the standard port for Redis.

Outputs

The node produces the following output:

  • history_key (String): The key associated with the stored history data in Redis. This output can be used to reference or access the stored data elsewhere in the workflow.

Usage in ComfyUI Workflows

In the context of ComfyUI workflows, the save_redis_memo node can be utilized to maintain persistent states or histories across different sessions or stages of the workflow. For example, it can be integrated into a workflow where user interactions with an AI model are logged and updated continually to enrich the conversation or learning context.

Typical use cases include:

  • Chatbots or Dialogue Systems: Saving conversation histories to ensure continuity in user interactions across sessions.
  • Data Logging: Storing various state data or logs that need to be referenced or reviewed later.
  • Dynamic Context Management: Saving context-dependent data that might influence the behavior or response of AI models during inference.

Special Features or Considerations

  • Data Persistence: Utilizing Redis for storage ensures that data is preserved even if the application is restarted, providing robustness to your workflow.
  • Efficiency: Redis is known for its speed and efficiency as an in-memory data store, making it suitable for real-time applications where quick data access and updates are necessary.
  • Flexibility: The node can be configured to connect to different Redis instances by changing the redis_host and redis_port, allowing for scalability and customization based on deployment needs.

When integrating the save_redis_memo node into your workflow, ensure that your Redis server is properly configured and accessible using the specified host and port details.