Chatbot Memory for Chat-GPT, Davinci + other LLMs - LangChain #4

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: https://pinecone.io/learn/langchain-conversational-memory/

📌 LangChain Handbook Code: https://github.com/pinecone-io/examples/blob/master/learn/generation/langchain/handbook/03-langchain-conversational-memory.ipynb

🙋🏽‍♂️ Francisco: https://twitter.com/fpingham

Part 1 (Intro): https://youtu.be/BP9fi_0XTlw Part 2 (PromptTemplate): https://youtu.be/RflBcK0oDH0 Part 3 (Chains): https://youtu.be/S8j9Tk0lZHU

🎙️ AI Dev Studio: https://aurelio.ai/

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👾 Discord: https://discord.gg/c5QtDB9RAP

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 ... https://www.youtube.com/watch?v=X05uK0TZozM