如何向LLMChain添加内存#
本教程将介绍如何使用Memory类与LLMChain。在本次演示中，我们将添加ConversationBufferMemory类，但这可以是任何内存类。
from langchain.memory import ConversationBufferMemory
from langchain import OpenAI, LLMChain, PromptTemplate
最重要的步骤是正确设置提示。在下面的提示中，我们有两个输入键：一个用于实际输入，另一个用于来自Memory类的输入。重要的是，确保PromptTemplate和ConversationBufferMemory中的键匹配(chat_history)。
template = """You are a chatbot having a conversation with a human.
{chat_history}
Human: {human_input}
Chatbot:"""
prompt = PromptTemplate(
input_variables=["chat_history", "human_input"],
template=template
)
memory = ConversationBufferMemory(memory_key="chat_history")
llm_chain = LLMChain(
llm=OpenAI(),
prompt=prompt,
verbose=True,
memory=memory,
)
llm_chain.predict(human_input="Hi there my friend")
> Entering new LLMChain chain...
Prompt after formatting:
You are a chatbot having a conversation with a human.
Human: Hi there my friend
Chatbot:
> Finished LLMChain chain.
' Hi there, how are you doing today?'
llm_chain.predict(human_input="Not too bad - how are you?")
> Entering new LLMChain chain...
Prompt after formatting:
You are a chatbot having a conversation with a human.
Human: Hi there my friend
AI: Hi there, how are you doing today?
Human: Not to bad - how are you?
Chatbot:
> Finished LLMChain chain.
" I'm doing great, thank you for asking!"