langchain/docs/getting_started/memory.ipynb

Memory

So far, all the chains and agents we've gone through have been stateless. But often, you may want a chain or agent to have some concept of "memory" so that it may remember information about its previous interactions. The most clear and simple example of this is when designing a chatbot - you want it to remember previous messages so it can use context from that to have a better conversation. This would be a type of "short-term memory". On the more complex side, you could imagine a chain/agent remembering key pieces of information over time - this would be a form of "long-term memory".

LangChain provides several specially created chains just for this purpose. This notebook walk throughs using one of those chains (the ConversationChain) with two different types of memory.

ConversationChain with default memory

By default, the ConversationChain has a simple type of memory which remebers all previes inputs/outputs and adds them to the context that is passed. Let's take a look at using this chain (setting verbose=True so we can see the prompt).

In [1]:

from langchain import OpenAI, ConversationChain

llm = OpenAI(temperature=0)
conversation = ConversationChain(llm=llm, verbose=True)

conversation.predict(input="Hi there!")

Out [1]:

> Entering new chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.

Current conversation:

Human: Hi there!
AI:

> Finished chain.

' Hello! How are you today?'

In [2]:

conversation.predict(input="I'm doing well! Just having a conversation with an AI.")

Out [2]:

> Entering new chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.

Current conversation:

Human: Hi there!
AI:  Hello! How are you today?
Human: I'm doing well! Just having a conversation with an AI.
AI:

> Finished chain.

" That's great! What would you like to talk about?"

In [3]:

conversation.predict(input="Tell me about yourself.")

Out [3]:

> Entering new chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.

Current conversation:

Human: Hi there!
AI:  Hello! How are you today?
Human: I'm doing well! Just having a conversation with an AI.
AI:  That's great! What would you like to talk about?
Human: Tell me about yourself.
AI:

> Finished chain.

'  I am an AI created to provide information and support to humans. I enjoy learning and exploring new things.'

ConversationChain with ConversationSummaryMemory

Now lets take a look at using a slightly more complex type of memory - ConversationSummaryMemory. This type of memory creates a summary of the conversation over time. This can be useful for condensing information from the conversation over time.

Let's walk through an example, again setting verbose=True so we can see the prompt.

In [4]:

from langchain.chains.conversation.memory import ConversationSummaryMemory

In [5]:

conversation_with_summary = ConversationChain(llm=llm, memory=ConversationSummaryMemory(llm=OpenAI()), verbose=True)
conversation_with_summary.predict(input="Hi, what's up?")

Out [5]:

> Entering new chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.

Current conversation:

Human: Hi, what's up?
AI:

> Finished chain.

"\n\nI'm doing well, thank you for asking. I'm currently working on a project that I'm really excited about."

In [6]:

conversation_with_summary.predict(input="Tell me more about it!")

Out [6]:

> Entering new chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.

Current conversation:

The human and artificial intelligence are talking. The human asked the AI what it is doing, and the AI said that it is working on a project that it is excited about.
Human: Tell me more about it!
AI:

> Finished chain.

"\n\nI'm working on a project that I'm really excited about. It's a lot of work, but I think it's going to be really great when it's finished. I can't wait to show it to you!"

In [7]:

conversation_with_summary.predict(input="Very cool -- what is the scope of the project?")

Out [7]:

> Entering new chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.

Current conversation:


The human and artificial intelligence are talking. The human asked the AI what it is doing, and the AI said that it is working on a project that it is excited about. The AI said that the project is a lot of work, but it is going to be great when it is finished.
Human: Very cool -- what is the scope of the project?
AI:

> Finished chain.

'\n\nThe project is quite large in scope. It involves a lot of data analysis and work with artificial intelligence algorithms.'

More Resources on Memory

This just scratches the surface of what you can do with memory. For more examples on things like how to implement custom memory classes, how to add memory to a custom LLM chain and how to use memory with and agent, please see the How-To: Memory section.

In [ ]: