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Conversation Knowledge Graph Memory
 Contents 
Using in a chain
Conversation Knowledge Graph Memory#
This type of memory uses a knowledge graph to recreate memory.
Let’s first walk through how to use the utilities
from langchain.memory import ConversationKGMemory
from langchain.llms import OpenAI
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)
rtdocs_stable/api.python.langchain.com/en/stable/modules/memory/types/kg.html