Memory
Last updated
Last updated
While optional, memory stands out as a crucial component in this particular pipeline, especially when constructing a chatbot. It ensures that all conversations are stored in a buffer, enabling the chatbot to leverage chat history for more contextually relevant responses and facilitating the ability to ask follow-up questions.
Memory plays a vital role in this pipeline, particularly in chatbot construction. It ensures that all conversations are stored in a buffer, allowing the chatbot to utilize chat history. This stored context enables the chatbot to provide responses that are more relevant to the ongoing conversation and facilitates the capability to ask follow-up questions.
Memory serves several crucial purposes:
Enhanced Contextual Responses: By retaining previous conversation history, memory enables the chatbot to better understand the context of current interactions.
Seamless Conversation Flow: With access to past interactions, memory helps maintain continuity in conversations. The chatbot can recall previous topics discussed and smoothly transition between related subjects, creating a more natural and engaging user experience.
Improved User Engagement: Memory enables the chatbot to build rapport with users by recalling previous interactions and incorporating this knowledge into ongoing conversations.
Memory is an optional part in almost all the chains. In order to store the history of the conversation, we make return messages to be true and directly connect to either RetrievalQA or LLMChain chain.
Conclusion
For more information on Memory, check the documentation here.
