如何让AI Agent在多轮对话中保持长期记忆？7种关键优化方法解析 原创

from langchain.memory import ConversationBufferMemory
memory = ConversationBufferMemory()
memory.save_context({"input": "你好"}, {"output": "怎么了"})
print(memory.load_memory_variables({}))

from langchain.memory import ConversationBufferWindowMemory
memory = ConversationBufferWindowMemory(k=1)
memory.save_context({"input": "iphone15续航"}, {"output": "续航一般"})
memory.save_context({"input": "配送"}, {"output": "很快"})
# {'history': 'Human: 配送\nAI: 很快'}
print(memory.load_memory_variables({}))

llm = ChatOpenAI(temperature=0, model="gpt-4o")


memory = ConversationEntityMemory(
    llm=llm,
    return_messages=True,
)
print(memory.load_memory_variables(inputs={"input": "good!  busy working on Langchain.  lots to do."}))
memory.save_context({"input": "good!  busy working on Langchain.  lots to do."}, {"output": "That sounds like a lot of work!  What kind of things are you doing to make Langchain better?"})
print(memory.load_memory_variables(inputs={"input": "i'm trying to improve Langchain's interfaces, the UX, its integrations with various products the user might want ...  a lot of stuff"}))
memory.save_context(inputs={"input": "i'm trying to improve Langchain's interfaces, the UX, its integrations with various products the user might want ...  a lot of stuff"}, outputs={"output": "that sounds great job"})
print(memory.load_memory_variables(inputs={"input": "what is langchain"}))

from langchain_community.memory.kg import ConversationKGMemory

llm = ChatOpenAI(temperature=0, model="gpt-4o")

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"})
print(memory.load_memory_variables({"input": "who is sam"}))  # {'history': 'On Sam: Sam is a friend.'}
print(memory.get_current_entities("what's Sams favorite color?"))  # ['Sam']

llm = ChatOpenAI(temperature=0, model="gpt-4o")
memory = ConversationSummaryMemory(llm=llm)
memory.save_context({"input": "hi"}, {"output": "whats up"})
print(memory.load_memory_variables({}))  # {'history': 'The human greets the AI with "hi," and the AI responds with "what\'s up."'}

llm = ChatOpenAI(temperature=0, model="gpt-4o")
memory = ConversationSummaryBufferMemory(llm=llm, max_token_limit=10)
memory.save_context({"input": "hi"}, {"output": "whats up"})
memory.save_context({"input": "not much you"}, {"output": "not much"})
# {'history': 'System: The human greets with "hi." The AI responds with "what\'s up," and the human replies with "not much, you?"\nAI: not much'}
print(memory.load_memory_variables({}))

import faiss

from langchain.docstore import InMemoryDocstore
from langchain.vectorstores import FAISS


embedding_size = 1536 # Dimensions of the OpenAIEmbeddings
index = faiss.IndexFlatL2(embedding_size)
embedding_fn = OpenAIEmbeddings().embed_query
vectorstore = FAISS(embedding_fn, index, InMemoryDocstore({}), {})


# the vector lookup still returns the semantically relevant information
retriever = vectorstore.as_retriever(search_kwargs=dict(k=1))
memory = VectorStoreRetrieverMemory(retriever=retriever)

# When added to an agent, the memory object can save pertinent information from conversations or used tools
memory.save_context({"input": "My favorite food is pizza"}, {"output": "thats good to know"})
memory.save_context({"input": "My favorite sport is soccer"}, {"output": "..."})
memory.save_context({"input": "I don't the Celtics"}, {"output": "ok"})