基于向量存储的记忆 vectorstore_retriever_memory

VectorStoreRetrieverMemory 在 VectorDB 中存储记忆，并在每次调用时查询最具 "显著性" 的前 K 个文档。

与大多数其他 Memory 类不同的是，它不明确跟踪交互的顺序。

在这种情况下，"docs" 是先前对话片段。这对于引用对话中 AI 之前告知的相关信息非常有用。

from datetime import datetime
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.llms import OpenAI
from langchain.memory import VectorStoreRetrieverMemory
from langchain.chains import ConversationChain
from langchain.prompts import PromptTemplate

初始化你的 VectorStore

根据你选择的存储方式，这一步可能会有所不同。请参考相关的 VectorStore 文档以获取更多详细信息。

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({}), {})

创建你的 VectorStoreRetrieverMemory

内存对象是从任意 VectorStoreRetriever 实例化的。

In actual usage, you would set `k` to be a higher value, but we use k = 1 to show that
# 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"}) #

Notice the first result returned is the memory pertaining to tax help, which the language model deems more semantically relevant
# to a 1099 than the other documents, despite them both containing numbers.
print(memory.load_memory_variables({"prompt": "what sport should i watch?"})["history"])

    input: My favorite sport is soccer
    output: ...

在链中使用

让我们通过一个例子来演示，再次设置 verbose=True 以便我们可以看到提示信息。

llm = OpenAI(temperature=0) # Can be any valid LLM
_DEFAULT_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.

Relevant pieces of previous conversation:
{history}

(You do not need to use these pieces of information if not relevant)

Current conversation:
Human: {input}
AI:"""
PROMPT = PromptTemplate(
    input_variables=["history", "input"], template=_DEFAULT_TEMPLATE
)
conversation_with_summary = ConversationChain(
    llm=llm, 
    prompt=PROMPT,
    # We set a very low max_token_limit for the purposes of testing.
    memory=memory,
    verbose=True
)
conversation_with_summary.predict(input="Hi, my name is Perry, 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.
    
    Relevant pieces of previous conversation:
    input: My favorite food is pizza
    output: thats good to know
    
    (You do not need to use these pieces of information if not relevant)
    
    Current conversation:
    Human: Hi, my name is Perry, what's up?
    AI:
    
    > Finished chain.





    " Hi Perry, I'm doing well. How about you?"

Here, the basketball related content is surfaced
conversation_with_summary.predict(input="what's my favorite sport?")

    
    
    > 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.
    
    Relevant pieces of previous conversation:
    input: My favorite sport is soccer
    output: ...
    
    (You do not need to use these pieces of information if not relevant)
    
    Current conversation:
    Human: what's my favorite sport?
    AI:
    
    > Finished chain.





    ' You told me earlier that your favorite sport is soccer.'

Even though the language model is stateless, since relavent memory is fetched, it can "reason" about the time.
# Timestamping memories and data is useful in general to let the agent determine temporal relevance
conversation_with_summary.predict(input="Whats my favorite food")

    
    
    > 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.
    
    Relevant pieces of previous conversation:
    input: My favorite food is pizza
    output: thats good to know
    
    (You do not need to use these pieces of information if not relevant)
    
    Current conversation:
    Human: Whats my favorite food
    AI:
    
    > Finished chain.





    ' You said your favorite food is pizza.'

The memories from the conversation are automatically stored,
# since this query best matches the introduction chat above,
# the agent is able to 'remember' the user's name.
conversation_with_summary.predict(input="What's my name?")

    
    
    > 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.
    
    Relevant pieces of previous conversation:
    input: Hi, my name is Perry, what's up?
    response:  Hi Perry, I'm doing well. How about you?
    
    (You do not need to use these pieces of information if not relevant)
    
    Current conversation:
    Human: What's my name?
    AI:
    
    > Finished chain.





    ' Your name is Perry.'