在多输入链中的记忆

from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter

with open("../../state_of_the_union.txt") as f:
    state_of_the_union = f.read()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_text(state_of_the_union)
 
embeddings = OpenAIEmbeddings()

docsearch = Chroma.from_texts(
    texts, embeddings, metadatas=[{"source": i} for i in range(len(texts))]
)

query = "The president said about Justice Breyer"
docs = docsearch.similarity_search(query)

from langchain.chains.question_answering import load_qa_chain
from langchain.memory import ConversationBufferMemory
from langchain_core.prompts import PromptTemplate
from langchain_openai import OpenAI

template = """您是一个与人对话的聊天机器人。
 
给定以下长文档的提取部分和一个问题，请生成最终的答案。
 
{context}
 
{chat_history}
人类: {human_input}
聊天机器人:"""
 
prompt = PromptTemplate(
    input_variables=["chat_history", "human_input", "context"], template=template
)
memory = ConversationBufferMemory(memory_key="chat_history", input_key="human_input")
chain = load_qa_chain(
    OpenAI(temperature=0), chain_type="stuff", memory=memory, prompt=prompt
)

query = "The president said about Justice Breyer"
chain({"input_documents": docs, "human_input": query}, return_only_outputs=True)

print(chain.memory.buffer)