基于向量存储的记忆#
VectorStoreRetrieverMemory将记忆存储在VectorDB中，并在每次调用时查询最重要的K个文档。
与大多数其他记忆类不同的是，它不明确跟踪交互的顺序。
在这种情况下，“文档”是先前的对话片段。这可以用来提到AI在对话中早期被告知的相关信息。
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
初始化您的VectorStore#
根据您选择的存储方式，此步骤可能会有所不同。有关更多详细信息，请参阅相关的VectorStore文档。
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({}), {})
创建您的VectorStoreRetrieverMemory#
记忆体对象是从任何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。
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.'