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如何向多输入链添加内存

大多数内存对象都假设只有一个输入。在这个笔记本中，我们将介绍如何向具有多个输入的链中添加内存。作为这样一个链的示例，我们将向一个问题/回答链添加内存。该链接受相关文档和用户问题作为输入。

from langchain.embeddings.openai import OpenAIEmbeddings  
from langchain.embeddings.cohere import CohereEmbeddings  
from langchain.text_splitter import CharacterTextSplitter  
from langchain.vectorstores.elastic_vector_search import ElasticVectorSearch  
from langchain.vectorstores import Chroma  
from langchain.docstore.document import Document

API 参考：​

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))]  
)  

print("Running Chroma using direct local API.")  
print("Using DuckDB in-memory for database. Data will be transient.")  

query = "What did the president say about Justice Breyer"  
docs = docsearch.similarity_search(query)  

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

API 参考：​

template = """You are a chatbot having a conversation with a human.  
Given the following extracted parts of a long document and a question, create a final answer.  

{context}  

{chat_history}  
Human: {human_input}  
Chatbot:"""  

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 = "What did the president say about Justice Breyer"  
chain({"input_documents": docs, "human_input": query}, return_only_outputs=True)  
print(chain.memory.buffer)  
Human: What did the president say about Justice Breyer  
AI:  Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.