Скачать или смотреть How LangChain Memory Works: Complete Guide to ConversationBufferMemory | Ep - 11 |

🧠 Understand LangChain Memory Like Never Before – Full Breakdown of ConversationBufferMemory

If you're building AI applications with LangChain and struggling to understand how memory works — especially ConversationBufferMemory — this video is for you!

In this tutorial, we dive deep into what "memory" means in LangChain and why it's such an essential part of building chat-based experiences. You'll learn how memory enables your LLM to remember past messages and maintain context across multiple turns in a conversation.

We break down the entire flow step-by-step — how memory is added to a chain, why it's called twice during execution, and how it stores and retrieves previous interactions. We also explain the key parameters like return_messages, memory_key, and how to connect memory to your prompt using MessagesPlaceholder.

By the end of the video, you’ll know exactly how to:
📌 What memory is and why it matters in conversational apps
📌 How to use ConversationBufferMemory in Python
📌 How to pass memory to PromptTemplate
📌 How to make LangChain remember and use previous chats
📌 Example: Add memory to your PDF chatbot and make it contextual

This isn't just a code walkthrough — it's a complete conceptual guide designed to help you truly understand how and why memory works in LangChain.

If you're serious about building smart, responsive AI apps, this knowledge is a must-have. Subscribe for more deep dives into LangChain, LLMs, and cutting-edge Python projects!

💡 When should you use memory?
Use memory when:
👉 Your app involves back-and-forth conversations
👉 The user's input depends on previous context
👉 You want your AI to behave more human-like and less robotic

🧠 Types of Memory in LangChain:
🔸 ConversationBufferMemory – stores the entire conversation as context
🔸 ConversationSummaryMemory – summarizes past conversations to save space
🔸 ConversationBufferWindowMemory – keeps only the last N messages
🔸 ConversationKGMemory – stores interactions in a knowledge graph
🔸 VectorStoreRetrieverMemory – retrieves context from vector stores (useful with long documents)

✅ By the end of this tutorial, you'll be able to:
✨ Add memory to your LangChain agents
✨ Improve the user experience by maintaining context
✨ Move one step closer to building a fully functional AI like PDF.ai

🎥 Full Playlist:    • LangChain & ChatGPT Masterclass: Build AI ...  

👉 Don’t forget to Like 👍, Subscribe 🔔, and Comment 💬 your thoughts or questions below!

#LangChain #AIChatbot #PythonLangchain #OpenAI #ChatbotDevelopment #MemoryInLangChain #PDFaiClone #LangChainMemory #PromptTemplate #AIInPython #ChatbotWithMemory