Memória de buffer de conversação
Implemente uma memória conversacional básica para armazenar e recuperar interações anteriores no contexto de um agente.
Memória de buffer de conversação é uma aula grátis de AI Agents with LangChain & Autonomous Workflows no CoddyKit. Esta é a aula 2 de 4. Você pode ler a aula completa abaixo gratuitamente — depois pratica ao vivo no navegador com um editor de código integrado e um tutor de IA 24/7. Faz parte do caminho de aprendizado de AI Agents with LangChain & Autonomous Workflows, e seu progresso é sincronizado entre a web e o app CoddyKit. O curso de AI Agents with LangChain & Autonomous Workflows inclui 4 aulas no total.
Partes desta aula ainda não foram traduzidas e aparecem em inglês.
Why Agents Need Memory
Imagine having a conversation where you instantly forget everything said just a moment ago. That's what happens to an AI agent without memory!
Agents often need to recall past interactions to maintain context. Without memory, each turn is a fresh start, leading to repetitive or nonsensical conversations.
What is Buffer Memory?
ConversationBufferMemory is LangChain's simplest memory type. It stores the raw, unsummarized conversation history directly.
Think of it like keeping a full transcript of everything said, in the exact order it was said. It's straightforward and easy to use for basic conversational recall.
Initializing Buffer Memory
To use ConversationBufferMemory, you simply import it and create an instance. It's often integrated into a Chain, but we can explore it standalone first.
Let's initialize a memory object and peek at its initial (empty) state:
from langchain.memory import ConversationBufferMemory
# Initialize the memory
memory = ConversationBufferMemory()
print("Memory initialized!")
# You can see its content (it will be empty at first)
print(memory.load_memory_variables({}))Saving Conversation Context
The save_context method is how you add new user inputs and AI outputs to the memory. It takes two dictionaries: one for inputs and one for outputs.
This method is crucial because it's how the memory 'learns' from the ongoing conversation and builds its history.
Code Demo: Saving Context
Let's add a simple interaction to our memory object and see how it stores the conversation turn.
Notice how the 'input' and 'output' are paired and stored as 'Human' and 'AI' messages.
from langchain.memory import ConversationBufferMemory
memory = ConversationBufferMemory()
# Simulate a user input and an AI response
memory.save_context(
{"input": "Hi there!"},
{"output": "Hello! How can I help you?"}
)
print("Memory after one turn:")
print(memory.load_memory_variables({}))Retrieving Stored History
To access the stored conversation history, you use the load_memory_variables method. It returns a dictionary containing the memory content.
By default, the conversation history is stored under the key 'history'. This key is what you'll typically pass into your LLM's prompt template.
Customizing the Memory Key
By default, ConversationBufferMemory stores history under the key 'history'. However, your prompt template might expect a different variable name, like 'chat_history'.
You can change this using the memory_key parameter during initialization:
from langchain.memory import ConversationBufferMemory
# Initialize with a custom memory_key
memory = ConversationBufferMemory(memory_key="my_chat_history")
memory.save_context(
{"input": "What's up?"},
{"output": "Not much, just coding!"}
)
print("Memory with custom key:")
print(memory.load_memory_variables({}))Integrating with an LLM Chain
Here's how you integrate ConversationBufferMemory into an LLMChain. The memory_key in the ConversationBufferMemory must match the variable name in your PromptTemplate (e.g., chat_history).
Remember to replace 'YOUR_API_KEY' with your actual OpenAI key if you want to run this example fully.
from langchain.memory import ConversationBufferMemory
from langchain.chains import LLMChain
from langchain_openai import OpenAI
from langchain.prompts import PromptTemplate
import os
# Set your OpenAI API key (replace with your actual key or env var)
# os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"
# Initialize LLM (using a dummy if API key not set)
llm = OpenAI(temperature=0) # You might need to set openai_api_key=os.environ.get("OPENAI_API_KEY")
# Initialize memory with a key matching the prompt template
memory = ConversationBufferMemory(memory_key="chat_history")
# Define a prompt template that expects 'chat_history'
template = """You are a friendly chatbot.
{chat_history}
Human: {human_input}
AI:"""
prompt = PromptTemplate(
input_variables=["chat_history", "human_input"],
template=template
)
# Create an LLMChain with the memory
conversation = LLMChain(
llm=llm,
prompt=prompt,
verbose=False,
memory=memory
)
# First turn
print("Human: What is your capital?")
response1 = conversation.predict(human_input="What is your capital?")
print(f"AI: {response1}")
# Second turn - the AI should remember the context
print("\nHuman: And what about its population?")
response2 = conversation.predict(human_input="And what about its population?")
print(f"AI: {response2}")When to Use Buffer Memory
ConversationBufferMemory is excellent for short, direct conversations where you need exact recall of recent turns.
- Pros: Simple to implement, stores full, unedited conversation details.
- Cons: Can quickly exceed the LLM's context window for longer chats, no summarization or filtering, leading to higher token usage and costs.
Quick Check
Let's test your understanding of ConversationBufferMemory.
Buffer Memory Summary
We've successfully explored ConversationBufferMemory, LangChain's simplest way to give agents a basic form of memory:
- It stores raw conversation history.
- You use
save_contextto add turns andload_memory_variablesto retrieve them. - It's ideal for short, direct conversations but can quickly hit LLM context limits with longer chats.
- Remember to set
memory_keyif your prompt expects a different variable name for history.
Next, we'll dive into more advanced memory solutions that handle longer conversations better!
Perguntas Frequentes
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O que vou aprender em “Memória de buffer de conversação”?
Implemente uma memória conversacional básica para armazenar e recuperar interações anteriores no contexto de um agente. Você pratica AI Agents with LangChain & Autonomous Workflows com código prático que executa diretamente no navegador, e um tutor de IA 24/7 responde suas dúvidas enquanto trabalha na aula.
Preciso ter experiência prévia para começar AI Agents with LangChain & Autonomous Workflows?
Nenhuma experiência prévia é necessária. AI Agents with LangChain & Autonomous Workflows no CoddyKit é estruturado para alunos iniciantes até avançados, então você pode começar aqui ou desde o início e aprender no seu ritmo. Esta é a aula 2 de 4.
Quanto tempo leva a aula “Memória de buffer de conversação”?
A maioria das aulas CoddyKit leva cerca de 5–10 minutos. Cada uma é compacta e interativa, então você faz progresso constante e retoma exatamente de onde parou entre web e app.
Posso escrever e executar código nesta aula de AI Agents with LangChain & Autonomous Workflows?
Sim. Cada aula de AI Agents with LangChain & Autonomous Workflows inclui um editor de código integrado, então você escreve e executa código real direto no navegador e recebe feedback de IA instantaneamente — nenhuma configuração local necessária.
Todas as aulas deste curso
- Conceitos de memória dos agentes
- Memória de buffer de conversação
- Soluções avançadas de memória
- Estratégias de memória de entidades e resumos