Buffer Window Memory

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Node Details

• Name: bufferWindowMemory
• Type: BufferWindowMemory
• Version: 2.0
• Category: Memory
• Icon: memory.svg

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Description

This node uses a window of size k to surface the last k back-and-forth exchanges to use as memory in a conversation. It’s useful for maintaining recent context without storing the entire conversation history.

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Base Classes

• BufferWindowMemory
• (Additional base classes from langchain’s BufferWindowMemory)

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Parameters

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Inputs

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Inputs

1. Size (k)

   • Type: number
   • Default: 4
   • Description: The number of recent message pairs (back-and-forth exchanges) to keep in memory
   • Description: The number of recent message pairs (back-and-forth exchanges) to keep in memory

2. Session Id (optional)

3. Session Id (optional)

   • Type: string
   • Description: A unique identifier for the conversation session. If not specified, a random id will be used
   • Description: A unique identifier for the conversation session. If not specified, a random id will be used

4. Memory Key

   • Type: string
   • Default: “chat_history”
   • Description: The key used to store and retrieve the chat history in the memory
   • Description: The key used to store and retrieve the chat history in the memory

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Functionality

1. Initialization:

   • Creates a BufferWindowMemoryExtended instance with the specified parameters.
   • Connects to a database using the provided DataSource.

2. Message Retrieval:

   • Fetches messages from the database based on the session ID and chatflow ID.
   • Limits the number of messages based on the specified window size (k).
   • Can return messages as either IMessage or BaseMessage types.

3. Message Storage:

   • Actual message storage is handled at the server level, not within this node.

4. Memory Clearing:

   • Clearing of chat messages is also handled at the server level.

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Use Cases

• Maintaining recent context in chatbots or conversational AI systems.
• Limiting the amount of historical data passed to language models to reduce token usage and improve relevance.
• Providing a sliding window of conversation history for context-aware responses.

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Integration

This node is designed to work within a larger system, likely a conversational AI framework. It interfaces with a database to persist and retrieve conversation history, making it suitable for applications that require stateful conversations across multiple interactions or sessions.