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Harrison ChaseandShreya Rajpal 985496f4be Docs refactor (#480 )

Big docs refactor! Motivation is to make it easier for people to find
resources they are looking for. To accomplish this, there are now three
main sections:

- Getting Started: steps for getting started, walking through most core
functionality
- Modules: these are different modules of functionality that langchain
provides. Each part here has a "getting started", "how to", "key
concepts" and "reference" section (except in a few select cases where it
didnt easily fit).
- Use Cases: this is to separate use cases (like summarization, question
answering, evaluation, etc) from the modules, and provide a different
entry point to the code base.

There is also a full reference section, as well as extra resources
(glossary, gallery, etc)

Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>

2023-01-02 08:24:09 -08:00

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Adding Memory To an LLMChain

This notebook goes over how to use the Memory class with an LLMChain. For the purposes of this walkthrough, we will add the ConversationBufferMemory class, although this can be any memory class.

In [1]:

from langchain.chains.conversation.memory import ConversationBufferMemory
from langchain import OpenAI, LLMChain, PromptTemplate

The most important step is setting up the prompt correctly. In the below prompt, we have two input keys: one for the actual input, another for the input from the Memory class. Importantly, we make sure the keys in the PromptTemplate and the ConversationBufferMemory match up (chat_history).

In [2]:

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")

In [3]:

llm_chain = LLMChain(
    llm=OpenAI(), 
    prompt=prompt, 
    verbose=True, 
    memory=memory,
)

In [4]:

llm_chain.predict(human_input="Hi there my friend")

Out [4]:


[1m> Entering new LLMChain chain...[0m
Prompt after formatting:
[32;1m[1;3mYou are a chatbot having a conversation with a human.


Human: Hi there my friend
Chatbot:[0m

[1m> Finished LLMChain chain.[0m

' Hi there, how are you doing today?'

In [5]:

llm_chain.predict(human_input="Not to bad - how are you?")

Out [5]:


[1m> Entering new LLMChain chain...[0m
Prompt after formatting:
[32;1m[1;3mYou 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:[0m

[1m> Finished LLMChain chain.[0m

" I'm doing great, thank you for asking!"

In [ ]:

4.1 KiB Raw Blame History

Adding Memory To an LLMChain

4.1 KiB

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