AI Agents with LangChain & Autonomous Workflows · レッスン

会話バッファメモリ

基本的な会話メモリを実装し、エージェントのコンテキスト内で過去のやり取りを保存・取得します。

「会話バッファメモリ」はCoddyKit上の無料AI Agents with LangChain & Autonomous Workflowsレッスンです。これはレッスン2/4です。下記で完全なレッスンを無料で読むことができます。その後、ブラウザ内の組み込みコードエディタと24時間対応のAIチューターでハンズオン演習できます。これはAI Agents with LangChain & Autonomous Workflows学習パスの一部であり、ウェブとCoddyKitアプリ全体で進捗が同期されます。 AI Agents with LangChain & Autonomous Workflowsコースには全4レッスンが含まれています。

このレッスンの一部はまだ翻訳されておらず、英語で表示されています。

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_context to add turns and load_memory_variables to retrieve them.
It's ideal for short, direct conversations but can quickly hit LLM context limits with longer chats.
Remember to set memory_key if your prompt expects a different variable name for history.

Next, we'll dive into more advanced memory solutions that handle longer conversations better!

よくある質問

「会話バッファメモリ」レッスンは無料ですか？

はい。「会話バッファメモリ」の完全なテキストはこのウェブで無料で読めます。インタラクティブに演習し（組み込みコードエディタと24時間対応のAIチューター）、AI Agents with LangChain & Autonomous Workflowsコースの残りをアンロックするには、CoddyKit PROにアップグレードしてください。 AI Agents with LangChain & Autonomous Workflowsコースには全4レッスンが含まれています。

「会話バッファメモリ」で何を学びますか？

基本的な会話メモリを実装し、エージェントのコンテキスト内で過去のやり取りを保存・取得します。ブラウザで直接実行するハンズオンコードでAI Agents with LangChain & Autonomous Workflowsを演習し、24時間対応のAIチューターがレッスンを進める中での質問に答えます。

AI Agents with LangChain & Autonomous Workflowsを始めるのに経験は必要ですか？

事前経験は必要ありません。CoddyKitのAI Agents with LangChain & Autonomous Workflowsは初級者から上級者向けに構成されているため、ここから始めるか最初から始めて、自分のペースで進むことができます。これはレッスン2/4です。

「会話バッファメモリ」レッスンにはどのくらい時間がかかりますか？

ほとんどのCoddyKitレッスンは約5～10分かかります。各レッスンはコンパクトでインタラクティブなので、着実に進歩し、ウェブとアプリ全体で正確に前回の場所から再開できます。

このAI Agents with LangChain & Autonomous Workflowsレッスンでコードを書いて実行できますか？

はい。すべてのAI Agents with LangChain & Autonomous Workflowsレッスンに組み込みコードエディタが含まれているため、ブラウザでリアルコードを書いて実行し、即座のAIフィードバックを取得できます。ローカル設定は不要です。

このコースのすべてのレッスン

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