Memori Penyangga Percakapan
Terapkan memori percakapan dasar untuk menyimpan dan mengambil interaksi sebelumnya dalam konteks agen.
Memori Penyangga Percakapan adalah pelajaran AI Agents with LangChain & Autonomous Workflows gratis di CoddyKit. Ini adalah pelajaran 2 dari 4. Kamu bisa membaca pelajaran lengkapnya di bawah secara gratis — lalu praktikkan langsung di browser dengan editor kode bawaan dan tutor AI 24/7. Ini adalah bagian dari jalur belajar AI Agents with LangChain & Autonomous Workflows, dan progresmu tersinkronisasi di web dan aplikasi CoddyKit. Kursus AI Agents with LangChain & Autonomous Workflows mencakup 4 pelajaran total.
Bagian dari pelajaran ini belum diterjemahkan dan ditampilkan dalam bahasa Inggris.
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!
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- Kursus
- 12
- Pelajaran
- 50
Pertanyaan yang Sering Diajukan
Apakah pelajaran “Memori Penyangga Percakapan” gratis?
Ya — teks lengkap “Memori Penyangga Percakapan” gratis dibaca di sini di web. Untuk praktiknya secara interaktif (editor kode bawaan dan tutor AI 24/7) dan buka sisa kursus AI Agents with LangChain & Autonomous Workflows, upgrade ke CoddyKit PRO. Kursus AI Agents with LangChain & Autonomous Workflows mencakup 4 pelajaran total.
Apa yang akan aku pelajari di “Memori Penyangga Percakapan”?
Terapkan memori percakapan dasar untuk menyimpan dan mengambil interaksi sebelumnya dalam konteks agen. Kamu berlatih AI Agents with LangChain & Autonomous Workflows dengan kode praktik yang langsung kamu jalankan di browser, dan tutor AI 24/7 menjawab pertanyaanmu saat kamu mengerjakan pelajaran ini.
Apakah aku perlu pengalaman untuk memulai AI Agents with LangChain & Autonomous Workflows?
Tidak diperlukan pengalaman sebelumnya. AI Agents with LangChain & Autonomous Workflows di CoddyKit dirancang untuk pemula hingga pelajar tingkat lanjut, jadi kamu bisa memulai di sini atau dari awal dan belajar sesuai kecepatan kamu sendiri. Ini adalah pelajaran 2 dari 4.
Berapa lama pelajaran “Memori Penyangga Percakapan” memakan waktu?
Sebagian besar pelajaran CoddyKit memakan waktu sekitar 5–10 menit. Setiap pelajaran ringkas dan interaktif, jadi kamu membuat kemajuan stabil dan melanjutkan dari tempat kamu tinggalkan di web dan aplikasi.
Bisakah aku menulis dan menjalankan kode dalam pelajaran AI Agents with LangChain & Autonomous Workflows ini?
Ya. Setiap pelajaran AI Agents with LangChain & Autonomous Workflows menyertakan editor kode bawaan, jadi kamu menulis dan menjalankan kode nyata langsung di browser dan mendapatkan umpan balik AI instan — tidak diperlukan penyiapan lokal.
Semua pelajaran dalam kursus ini
- Konsep Memori Agen
- Memori Penyangga Percakapan
- Solusi Memori Tingkat Lanjut
- Strategi Memori Entitas dan Ringkasan