Memory allows LLMs to remember previous interactions.
Learn to implement different memory types in LangChain.
Memory Types
ConversationBufferMemory: Stores all messages
ConversationSummaryMemory: Summarizes conversations
VectorStoreMemory: Uses vector similarity
Implementation
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationChain
memory = ConversationBufferMemory()
conversation = ConversationChain(llm=llm, memory=memory)
conversation.predict(input=”Hi, I’m learning LangChain”)
conversation.predict(input=”What did I say my name was?”)
Memory Management
Limit memory size to control token usage.
Conclusion
Memory enables contextual conversations!