RetrievalQAWithSourcesChain
本教程介绍如何使用索引对问题进行基于来源的问答。它通过使用RetrievalQAWithSourcesChain来完成从索引中查找文档的工作。
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
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": f"{i}-pl"} for i in range(len(texts))])
Running Chroma using direct local API.
Using DuckDB in-memory for database. Data will be transient.
from langchain.chains import RetrievalQAWithSourcesChain
from langchain import OpenAI
chain = RetrievalQAWithSourcesChain.from_chain_type(OpenAI(temperature=0), chain_type="stuff", retriever=docsearch.as_retriever())
chain({"question": "What did the president say about Justice Breyer"}, return_only_outputs=True)
{'answer': ' The president honored Justice Breyer for his service and mentioned his legacy of excellence.\n',
'sources': '31-pl'}
链式类型#
您可以轻松指定要加载和使用的不同链式类型。有关这些类型的更详细演示，请参见本教程。
有两种加载不同链式类型的方法。首先，您可以在from_chain_type方法中指定链式类型参数。这允许您传递要使用的链式类型的名称。例如，在下面的示例中，我们将链式类型更改为map_reduce。
chain = RetrievalQAWithSourcesChain.from_chain_type(OpenAI(temperature=0), chain_type="map_reduce", retriever=docsearch.as_retriever())
chain({"question": "What did the president say about Justice Breyer"}, return_only_outputs=True)
{'answer': ' The president said "Justice Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service."\n',
'sources': '31-pl'}
The above way allows you to really simply change the chain_type, but it does provide a ton of flexibility over parameters to that chain type. If you want to control those parameters, you can load the chain directly (as you did in this notebook) and then pass that directly to the the RetrievalQAWithSourcesChain chain with the combine_documents_chain parameter. For example:
from langchain.chains.qa_with_sources import load_qa_with_sources_chain
qa_chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type="stuff")
qa = RetrievalQAWithSourcesChain(combine_documents_chain=qa_chain, retriever=docsearch.as_retriever())
qa({"question": "What did the president say about Justice Breyer"}, return_only_outputs=True)
{'answer': ' The president honored Justice Breyer for his service and mentioned his legacy of excellence.\n',
'sources': '31-pl'}