在内存中的文档上进行问答

这里我们将介绍如何使用LangChain在文档列表上进行问答。在底层，我们将使用我们的文档链。

准备数据

首先我们准备数据。在这个例子中，我们在一个向量数据库上进行相似性搜索，但是这些文档可以以任何方式获取（这个笔记本的重点是在获取文档之后要做什么）。

from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.docstore.document import Document
from langchain.prompts import PromptTemplate
from langchain.indexes.vectorstore import VectorstoreIndexCreator

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": str(i)} for i in range(len(texts))]).as_retriever()

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

from langchain.chains.question_answering import load_qa_chain
from langchain.llms import OpenAI

快速开始

如果您只想尽快开始，这是推荐的方法：

chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff")
query = "What did the president say about Justice Breyer"
chain.run(input_documents=docs, question=query)

输出结果为：

' The president said that Justice Breyer has dedicated his life to serve the country and thanked him for his service.'

如果您想更多地控制和了解正在发生的事情，请参阅下面的信息。

stuff链

这部分展示了使用stuff链进行问答的结果。

chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff")

query = "What did the president say about Justice Breyer"
chain({"input_documents": docs, "question": query}, return_only_outputs=True)

输出结果为：

{'output_text': ' The president said that Justice Breyer has dedicated his life to serve the country and thanked him for his service.'}

自定义提示

您也可以在这个链上使用自己的提示。在这个例子中，我们将用意大利语回答。

prompt_template = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.

{context}

Question: {question}
Answer in Italian:"""
PROMPT = PromptTemplate(
    template=prompt_template, input_variables=["context", "question"]
)
chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff", prompt=PROMPT)
chain({"input_documents": docs, "question": query}, return_only_outputs=True)

输出结果为：

{'output_text': ' Il presidente ha detto che Justice Breyer ha dedicato la sua vita a servire questo paese e ha ricevuto una vasta gamma di supporto.'}

map_reduce链

这部分展示了使用map_reduce链进行问答的结果。

chain = load_qa_chain(OpenAI(temperature=0), chain_type="map_reduce")

query = "What did the president say about Justice Breyer"
chain({"input_documents": docs, "question": query}, return_only_outputs=True)

输出结果为：

{'output_text': ' The president said that Justice Breyer is an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court, and thanked him for his service.'}

中间步骤

我们还可以返回map_reduce链的中间步骤，以便进行检查。这可以通过设置return_map_steps变量来实现。

chain = load_qa_chain(OpenAI(temperature=0), chain_type="map_reduce", return_map_steps=True)

chain({"input_documents": docs, "question": query}, return_only_outputs=True)

输出结果为：

{'intermediate_steps': [' "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."', ' A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.', ' None', ' None'], 'output_text': ' The president said that Justice Breyer is an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court, and thanked him for his service.'}

自定义提示

您也可以在这个链上使用自己的提示。在这个例子中，我们将用意大利语回答。

question_prompt_template = """Use the following portion of a long document to see if any of the text is relevant to answer the question.   
Return any relevant text translated into italian.  
{context}  
Question: {question}  
Relevant text, if any, in Italian:"""
QUESTION_PROMPT = PromptTemplate(
    template=question_prompt_template, input_variables=["context", "question"]
)

combine_prompt_template = """Given the following extracted parts of a long document and a question, create a final answer italian.   
If you don't know the answer, just say that you don't know. Don't try to make up an answer.  

QUESTION: {question}  
==========  
{summaries}  
==========  
Answer in Italian:"""
COMBINE_PROMPT = PromptTemplate(
    template=combine_prompt_template, input_variables=["summaries", "question"]
)

chain = load_qa_chain(OpenAI(temperature=0), chain_type="map_reduce", return_map_steps=True, question_prompt=QUESTION_PROMPT, combine_prompt=COMBINE_PROMPT)
chain({"input_documents": docs, "question": query}, return_only_outputs=True)

输出结果为：

{'intermediate_steps': ["\nStasera vorrei onorare qualcuno che ha dedicato la sua vita a servire questo paese: il giustizia Stephen Breyer - un veterano dell'esercito, uno studioso costituzionale e un giustizia in uscita della Corte Suprema degli Stati Uniti. Giustizia Breyer, grazie per il tuo servizio.", '\nNessun testo pertinente.', ' Non ha detto nulla riguardo a Justice Breyer.', " Non c'è testo pertinente."], 'output_text': ' Non ha detto nulla riguardo a Justice Breyer.'}

批次 使用 map_reduce 链时，需要记住的一件事是在映射步骤中使用的批量大小。 如果太高，可能会导致速率限制错误。 您可以通过设置所使用的 LLM 的批量大小来控制这一点。 请注意，这仅适用于具有此参数的LLM。 下面是这样做的一个示例：

llm = OpenAI(batch_size=5, temperature=0)

refine链

这部分展示了使用refine链进行问答的结果。

chain = load_qa_chain(OpenAI(temperature=0), chain_type="refine")

query = "What did the president say about Justice Breyer"
chain({"input_documents": docs, "question": query}, return_only_outputs=True)

输出结果为：

{'output_text': '\n\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his support of the Equality Act and his commitment to protecting the rights of LGBTQ+ Americans. He also praised Justice Breyer for his role in helping to pass the Bipartisan Infrastructure Law, which he said would be the most sweeping investment to rebuild America in history and would help the country compete for the jobs of the 21st Century.'}

中间步骤

我们还可以返回refine链的中间步骤，以便进行检查。这可以通过设置return_refine_steps变量来实现。

chain = load_qa_chain(OpenAI(temperature=0), chain_type="refine", return_refine_steps=True)

chain({"input_documents": docs, "question": query}, return_only_outputs=True)

输出结果为：

{'intermediate_steps': ['\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country and his legacy of excellence.', '\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice.', '\n\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his support of the Equality Act and his commitment to protecting the rights of LGBTQ+ Americans.', '\n\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his support of the Equality Act and his commitment to protecting the rights of LGBTQ+ Americans. He also praised Justice Breyer for his role in helping to pass the Bipartisan Infrastructure Law, which is the most sweeping investment to rebuild America in history.'], 'output_text': '\n\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his support of the Equality Act and his commitment to protecting the rights of LGBTQ+ Americans. He also praised Justice Breyer for his role in helping to pass the Bipartisan Infrastructure Law, which is the most sweeping investment to rebuild America in history.'}

自定义提示

您也可以在这个链上使用自己的提示。在这个例子中，我们将用意大利语回答。

refine_prompt_template = (  
    "The original question is as follows: {question}\n"  
    "We have provided an existing answer: {existing_answer}\n"  
    "We have the opportunity to refine the existing answer"  
    "(only if needed) with some more context below.\n"  
    "------------\n"  
    "{context_str}\n"  
    "------------\n"  
    "Given the new context, refine the original answer to better "  
    "answer the question. "  
    "If the context isn't useful, return the original answer. Reply in Italian."  
)  
refine_prompt = PromptTemplate(  
    input_variables=["question", "existing_answer", "context_str"],  
    template=refine_prompt_template,  
)  
  
initial_qa_template = (  
    "Context information is below. \n"  
    "---------------------\n"  
    "{context_str}"  
    "\n---------------------\n"  
    "Given the context information and not prior knowledge, "  
    "answer the question: {question}\nYour answer should be in Italian.\n"  
)  
initial_qa_prompt = PromptTemplate(  
    input_variables=["context_str", "question"], template=initial_qa_template  
)  
chain = load_qa_chain(OpenAI(temperature=0), chain_type="refine", return_refine_steps=True,  
                     question_prompt=initial_qa_prompt, refine_prompt=refine_prompt)  
chain({"input_documents": docs, "question": query}, return_only_outputs=True)

输出结果为：

{'intermediate_steps': ['\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese e ha reso omaggio al suo servizio.', "\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha reso omaggio al suo servizio e ha sostenuto la nomina di una top litigatrice in pratica privata, un ex difensore pubblico federale e una famiglia di insegnanti e agenti di polizia delle scuole pubbliche. Ha anche sottolineato l'importanza di avanzare la libertà e la giustizia attraverso la sicurezza delle frontiere e la risoluzione del sistema di immigrazione.", "\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha reso omaggio al suo servizio e ha sostenuto la nomina di una top litigatrice in pratica privata, un ex difensore pubblico federale e una famiglia di insegnanti e agenti di polizia delle scuole pubbliche. Ha anche sottolineato l'importanza di avanzare la libertà e la giustizia attraverso la sicurezza delle frontiere, la risoluzione del sistema di immigrazione, la protezione degli americani LGBTQ+ e l'approvazione dell'Equality Act. Ha inoltre sottolineato l'importanza di lavorare insieme per sconfiggere l'epidemia di oppiacei.", "\n\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha reso omaggio al suo servizio e ha sostenuto la nomina di una top litigatrice in pratica privata, un ex difensore pubblico federale e una famiglia di insegnanti e agenti di polizia delle scuole pubbliche. Ha anche sottolineato l'importanza di avanzare la libertà e la giustizia attraverso la sicurezza delle frontiere, la risoluzione del sistema di immigrazione, la protezione degli americani LGBTQ+ e l'approvazione dell'Equality Act. Ha inoltre sottolineato l'importanza di lavorare insieme per sconfiggere l'epidemia di oppiacei e per investire in America, educare gli americani, far crescere la forza lavoro e costruire l'economia dal"], 'output_text': "\n\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha reso omaggio al suo servizio e ha sostenuto la nomina di una top litigatrice in pratica privata, un ex difensore pubblico federale e una famiglia di insegnanti e agenti di polizia delle scuole pubbliche. Ha anche sottolineato l'importanza di avanzare la libertà e la giustizia attraverso la sicurezza delle frontiere, la risoluzione del sistema di immigrazione, la protezione degli americani LGBTQ+ e l'approvazione dell'Equality Act. Ha inoltre sottolineato l'importanza di lavorare insieme per sconfiggere l'epidemia di oppiacei e per investire in America, educare gli americani, far crescere la forza lavoro e costruire l'economia dal}

map-rerank链

这部分展示了使用map-rerank链进行问答的结果。

chain = load_qa_chain(OpenAI(temperature=0), chain_type="map_rerank", return_intermediate_steps=True)

query = "What did the president say about Justice Breyer"
results = chain({"input_documents": docs, "question": query}, return_only_outputs=True)

results["output_text"]

输出结果为：

' The President thanked Justice Breyer for his service and honored him for dedicating his life to serve the country.'

results["intermediate_steps"]

输出结果为：

[{'answer': ' The President thanked Justice Breyer for his service and honored him for dedicating his life to serve the country.', 'score': '100'}, {'answer': ' This document does not answer the question', 'score': '0'}, {'answer': ' This document does not answer the question', 'score': '0'}, {'answer': ' This document does not answer the question', 'score': '0'}]

自定义提示

您也可以在这个链上使用自己的提示。在这个例子中，我们将用意大利语回答。

from langchain.output_parsers import RegexParser

output_parser = RegexParser(
    regex=r"(.*?)\nScore: (.*)",
    output_keys=["answer", "score"],
)

prompt_template = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.

In addition to giving an answer, also return a score of how fully it answered the user's question. This should be in the following format:

Question: [question here]
Helpful Answer In Italian: [answer here]
Score: [score between 0 and 100]

Begin!

Context:
---------
{context}
---------
Question: {question}
Helpful Answer In Italian:"""
PROMPT = PromptTemplate(
    template=prompt_template,
    input_variables=["context", "question"],
    output_parser=output_parser,
)

chain = load_qa_chain(OpenAI(temperature=0), chain_type="map_rerank", return_intermediate_steps=True, prompt=PROMPT)
query = "What did the president say about Justice Breyer"
chain({"input_documents": docs, "question": query}, return_only_outputs=True)

输出结果为：

{'intermediate_steps': [{'answer': ' Il presidente ha detto che Justice Breyer ha dedicato la sua vita a servire questo paese.', 'score': '100'}, {'answer': ' Il presidente non ha detto nulla sulla Giustizia Breyer.', 'score': '100'}, {'answer': ' Non so.', 'score': '0'}, {'answer': ' Non so.', 'score': '0'}], 'output_text': ' Il presidente ha detto che Justice Breyer ha dedicato la sua vita a servire questo paese.'}

带来源的文档问答

我们还可以执行文档问答并返回用于回答问题的来源。为此，我们只需要确保每个文档的元数据中有一个"source"键，并使用load_qa_with_sources辅助函数构建我们的链：

docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{"source": str(i)} for i in range(len(texts))])
query = "What did the president say about Justice Breyer"
docs = docsearch.similarity_search(query)

from langchain.chains.qa_with_sources import load_qa_with_sources_chain

chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type="stuff")
query = "What did the president say about Justice Breyer"
chain({"input_documents": docs, "question": query}, return_only_outputs=True)

输出结果为：

{'output_text': ' The president thanked Justice Breyer for his service.\nSOURCES: 30-pl'}