Implementing Memory in LangChain
LangChain provides easy tools to add memory into your applications, whether for short-term conversation context or long-term user history.
Simple conversation memory
LangChain includes built-in memory types such as ConversationBufferMemory, which allows your LLM to remember previous messages during a session.
Example:
This setup enables the model to recall past inputs and maintain a smoother conversation.
Using vector stores for long-term memory
Here’s how it works:
1. User input is converted to embeddings (numeric vectors).
2. These vectors are stored in a vector database (like FAISS, Pinecone).
3. When needed, LangChain retrieves the most relevant past interactions based on similarity.
Use Case Example:
• Remembering a user’s previous purchase history.
• Recalling facts from uploaded documents or past support tickets.
Benefits:
Better context retention over time.
More scalable and intelligent chatbot applications.
Enables document-based question answering (QA) systems.
Summary
| Feature | Simple Memory | Vector Store Memory |
|---|---|---|
| Scope | Short-term conversation | Long-term history / documents |
| Storage | In-session | Persisted in a database |
| Use Case | Personal chatbot | Knowledge-based systems |
What’s Next?
Now that you’ve learned how to give your AI memory, you’re ready to explore more advanced LangChain concepts like Chains, Tools, and Agents, which allow AI systems to plan and take actions.
