Conversational memory is how a chatbot can respond to multiple queries in a chat-like manner. It enables a coherent conversation, and without it, every query would be treated as an entirely independent input without considering past interactions.
The memory allows a Large Language Model (LLM) to remember previous interactions with the user. By default, LLMs are *stateless* — meaning each incoming query is processed independently of other interactions. The only thing that exists for a stateless agent is the current input, nothing else.
There are many applications where remembering previous interactions is very important, such as chatbots. Conversational memory allows us to do that.
There are several ways that we can implement conversational memory. In the context of LangChain, they are all built on top of the `ConversationChain`.
🌲 Pinecone article:
[ Ссылка ]
📌 LangChain Handbook Code:
[ Ссылка ]
🙋🏽♂️ Francisco:
[ Ссылка ]
Part 1 (Intro): [ Ссылка ]
Part 2 (PromptTemplate): [ Ссылка ]
Part 3 (Chains): [ Ссылка ]
🎙️ AI Dev Studio:
[ Ссылка ]
🎉 Subscribe for Article and Video Updates!
[ Ссылка ]
[ Ссылка ]
👾 Discord:
[ Ссылка ]
00:00 Conversational memory for chatbots
00:28 Why we need conversational memory for chatbots
01:45 Implementation of conversational memory
04:05 LangChain's Conversation Chain
12:00 Conversation Summary Memory in LangChain
19:06 Conversation Buffer Window Memory in LangChain
21:35 Conversation Summary Buffer Memory in LangChain
24:33 Other LangChain Memory Types
25:25 Final thoughts on conversational memory
#artificialintelligence #nlp #openai #deeplearning #langchain
