Conversation Knowledge Graph

This type of memory uses a knowledge graph to recreate memory.

Using memory with LLM

from langchain.llms import OpenAI
from langchain.memory import ConversationKGMemory

llm = OpenAI(temperature=0)
memory = ConversationKGMemory(llm=llm)
memory.save_context({"input": "say hi to sam"}, {"output": "who is sam"})
memory.save_context({"input": "sam is a friend"}, {"output": "okay"})

memory.load_memory_variables({"input": "who is sam"})

{'history': 'On Sam: Sam is friend.'}

We can also get the history as a list of messages (this is useful if you are using this with a chat model).

memory = ConversationKGMemory(llm=llm, return_messages=True)
memory.save_context({"input": "say hi to sam"}, {"output": "who is sam"})
memory.save_context({"input": "sam is a friend"}, {"output": "okay"})

memory.load_memory_variables({"input": "who is sam"})

{'history': [SystemMessage(content='On Sam: Sam is friend.', additional_kwargs={})]}

We can also more modularly get current entities from a new message (will use previous messages as context).

memory.get_current_entities("what's Sams favorite color?")

['Sam']

We can also more modularly get knowledge triplets from a new message (will use previous messages as context).

memory.get_knowledge_triplets("her favorite color is red")

[KnowledgeTriple(subject='Sam', predicate='favorite color', object_='red')]

Using in a chain

Let’s now use this in a chain!

llm = OpenAI(temperature=0)
from langchain.chains import ConversationChain
from langchain.prompts.prompt import PromptTemplate

template = """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. The AI ONLY uses information contained in the "Relevant Information" section and does not hallucinate.

Relevant Information:

{history}

Conversation:
Human: {input}
AI:"""
prompt = PromptTemplate(input_variables=["history", "input"], template=template)
conversation_with_kg = ConversationChain(
    llm=llm, verbose=True, prompt=prompt, memory=ConversationKGMemory(llm=llm)
)

conversation_with_kg.predict(input="Hi, what's up?")

> Entering new ConversationChain 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. The AI ONLY uses information contained in the "Relevant Information" section and does not hallucinate.

Relevant Information:



Conversation:
Human: Hi, what's up?
AI:

> Finished chain.

" Hi there! I'm doing great. I'm currently in the process of learning about the world around me. I'm learning about different cultures, languages, and customs. It's really fascinating! How about you?"

conversation_with_kg.predict(
    input="My name is James and I'm helping Will. He's an engineer."
)

> Entering new ConversationChain 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. The AI ONLY uses information contained in the "Relevant Information" section and does not hallucinate.

Relevant Information:



Conversation:
Human: My name is James and I'm helping Will. He's an engineer.
AI:

> Finished chain.

" Hi James, it's nice to meet you. I'm an AI and I understand you're helping Will, the engineer. What kind of engineering does he do?"

conversation_with_kg.predict(input="What do you know about Will?")

> Entering new ConversationChain 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. The AI ONLY uses information contained in the "Relevant Information" section and does not hallucinate.

Relevant Information:

On Will: Will is an engineer.

Conversation:
Human: What do you know about Will?
AI:

> Finished chain.

' Will is an engineer.'