Google Cloud Vertex AI 重新排序器
Vertex Search Ranking API 是 Vertex AI Agent Builder 中的一个独立 API。它接收一个文档列表，并根据这些文档与查询的相关性对它们进行重新排序。与仅关注文档和查询语义相似性的嵌入相比，排名 API 可以为您提供文档回答给定查询的精确分数。排名 API 可用于在检索到一组初始候选文档后提高搜索结果的质量。
排名API是无状态的，因此在调用API之前不需要索引文档。您只需要传入查询和文档即可。这使得API非常适合从任何文档检索器中重新排名文档。
欲了解更多信息，请参阅Rank and rerank documents。
%pip install --upgrade --quiet langchain langchain-community langchain-google-community langchain-google-community[vertexaisearch] langchain-google-vertexai langchain-chroma langchain-text-splitters beautifulsoup4
设置
PROJECT_ID = ""
REGION = ""
RANKING_LOCATION_ID = "global" # @param {type:"string"}
# Initialize GCP project for Vertex AI
from google.cloud import aiplatform
aiplatform.init(project=PROJECT_ID, location=REGION)
加载和准备数据
对于这个例子，我们将使用Google Wiki页面来演示Vertex Ranking API的工作原理。
我们使用一个标准的流程：load -> split -> embed data。
嵌入是使用Vertex Embeddings API模型 - textembedding-gecko@003创建的
from langchain_chroma import Chroma
from langchain_community.document_loaders import WebBaseLoader
from langchain_google_vertexai import VertexAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
vectordb = None
# Load wiki page
loader = WebBaseLoader("https://en.wikipedia.org/wiki/Google")
data = loader.load()
# Split doc into chunks
text_splitter = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=5)
splits = text_splitter.split_documents(data)
print(f"Your {len(data)} documents have been split into {len(splits)} chunks")
if vectordb is not None: # delete existing vectordb if it already exists
vectordb.delete_collection()
embedding = VertexAIEmbeddings(model_name="textembedding-gecko@003")
vectordb = Chroma.from_documents(documents=splits, embedding=embedding)
API Reference:WebBaseLoader | RecursiveCharacterTextSplitter
Your 1 documents have been split into 266 chunks
import pandas as pd
from langchain.retrievers.contextual_compression import ContextualCompressionRetriever
from langchain_google_community.vertex_rank import VertexAIRank
# Instantiate the VertexAIReranker with the SDK manager
reranker = VertexAIRank(
project_id=PROJECT_ID,
location_id=RANKING_LOCATION_ID,
ranking_config="default_ranking_config",
title_field="source",
top_n=5,
)
basic_retriever = vectordb.as_retriever(search_kwargs={"k": 5}) # fetch top 5 documents
# Create the ContextualCompressionRetriever with the VertexAIRanker as a Reranker
retriever_with_reranker = ContextualCompressionRetriever(
base_compressor=reranker, base_retriever=basic_retriever
)
API Reference:ContextualCompressionRetriever | VertexAIRank
测试顶点排名API
让我们用相同的查询来查询basic_retriever和retriever_with_reranker，并比较检索到的文档。
排名API接收来自basic_retriever的输入并将其传递给排名API。
排名API用于提高排名的质量，并确定一个分数，该分数表示每条记录与查询的相关性。
你可以看到未排名文档和排名文档之间的区别。Ranking API 将语义上最相关的文档移动到LLM上下文窗口的顶部，从而帮助其形成更好的推理答案。
import pandas as pd
# Use the basic_retriever and the retriever_with_reranker to get relevant documents
query = "how did the name google originate?"
retrieved_docs = basic_retriever.invoke(query)
reranked_docs = retriever_with_reranker.invoke(query)
# Create two lists of results for unranked and ranked docs
unranked_docs_content = [docs.page_content for docs in retrieved_docs]
ranked_docs_content = [docs.page_content for docs in reranked_docs]
# Create a comparison DataFrame using the padded lists
comparison_df = pd.DataFrame(
{
"Unranked Documents": unranked_docs_content,
"Ranked Documents": ranked_docs_content,
}
)
comparison_df
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<div>
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Unranked Documents</th>
<th>Ranked Documents</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>^ a b Brin, Sergey; Page, Lawrence (1998). "The anatomy of a large-scale hypertextual Web search engine" (PDF). Computer Networks and ISDN Systems. 30 (1–7): 107–117. CiteSeerX 10.1.1.115.5930. doi:10.1016/S0169-7552(98)00110-X. ISSN 0169-7552. S2CID 7587743. Archived (PDF) from the original on September 27, 2015. Retrieved April 7, 2019.\n\n^ "About: RankDex". Archived from the original on January 20, 2012. Retrieved September 29, 2010., RankDex\n\n^ "Method for node ranking in a linked database". Google Patents. Archived from the original on October 15, 2015. Retrieved October 19, 2015.\n\n^ Koller, David (January 2004). "Origin of the name "Google"". Stanford University. Archived from the original on June 27, 2012.</td>
<td>The name "Google" originated from a misspelling of "googol",[211][212] which refers to the number represented by a 1 followed by one-hundred zeros. Page and Brin write in their original paper on PageRank:[33] "We chose our system name, Google, because it is a common spelling of googol, or 10100[,] and fits well with our goal of building very large-scale search engines." Having found its way increasingly into everyday language, the verb "google" was added to the Merriam Webster Collegiate Dictionary and the Oxford English Dictionary in 2006, meaning "to use the Google search engine to obtain information on the Internet."[213][214] Google's mission statement, from the outset, was "to organize the world's information and make it universally accessible and useful",[215] and its unofficial</td>
</tr>
<tr>
<th>1</th>
<td>Eventually, they changed the name to Google; the name of the search engine was a misspelling of the word googol,[21][36][37] a very large number written 10100 (1 followed by 100 zeros), picked to signify that the search engine was intended to provide large quantities of information.[38]</td>
<td>Eventually, they changed the name to Google; the name of the search engine was a misspelling of the word googol,[21][36][37] a very large number written 10100 (1 followed by 100 zeros), picked to signify that the search engine was intended to provide large quantities of information.[38]</td>
</tr>
<tr>
<th>2</th>
<td>The name "Google" originated from a misspelling of "googol",[211][212] which refers to the number represented by a 1 followed by one-hundred zeros. Page and Brin write in their original paper on PageRank:[33] "We chose our system name, Google, because it is a common spelling of googol, or 10100[,] and fits well with our goal of building very large-scale search engines." Having found its way increasingly into everyday language, the verb "google" was added to the Merriam Webster Collegiate Dictionary and the Oxford English Dictionary in 2006, meaning "to use the Google search engine to obtain information on the Internet."[213][214] Google's mission statement, from the outset, was "to organize the world's information and make it universally accessible and useful",[215] and its unofficial</td>
<td>^ Meijer, Bart (January 3, 2019). "Google shifted $23 billion to tax haven Bermuda in 2017: filing". Reuters. Archived from the original on January 3, 2019. Retrieved January 3, 2019. Google moved 19.9 billion euros ($22.7 billion) through a Dutch shell company to Bermuda in 2017, as part of an arrangement that allows it to reduce its foreign tax bill\n\n^ Hamburger, Tom; Gold, Matea (April 13, 2014). "Google, once disdainful of lobbying, now a master of Washington influence". The Washington Post. Archived from the original on October 27, 2017. Retrieved August 22, 2017.\n\n^ Koller, David (January 2004). "Origin of the name, "Google."". Stanford University. Archived from the original on June 27, 2012. Retrieved May 28, 2006.</td>
</tr>
<tr>
<th>3</th>
<td>^ Meijer, Bart (January 3, 2019). "Google shifted $23 billion to tax haven Bermuda in 2017: filing". Reuters. Archived from the original on January 3, 2019. Retrieved January 3, 2019. Google moved 19.9 billion euros ($22.7 billion) through a Dutch shell company to Bermuda in 2017, as part of an arrangement that allows it to reduce its foreign tax bill\n\n^ Hamburger, Tom; Gold, Matea (April 13, 2014). "Google, once disdainful of lobbying, now a master of Washington influence". The Washington Post. Archived from the original on October 27, 2017. Retrieved August 22, 2017.\n\n^ Koller, David (January 2004). "Origin of the name, "Google."". Stanford University. Archived from the original on June 27, 2012. Retrieved May 28, 2006.</td>
<td>^ a b Brin, Sergey; Page, Lawrence (1998). "The anatomy of a large-scale hypertextual Web search engine" (PDF). Computer Networks and ISDN Systems. 30 (1–7): 107–117. CiteSeerX 10.1.1.115.5930. doi:10.1016/S0169-7552(98)00110-X. ISSN 0169-7552. S2CID 7587743. Archived (PDF) from the original on September 27, 2015. Retrieved April 7, 2019.\n\n^ "About: RankDex". Archived from the original on January 20, 2012. Retrieved September 29, 2010., RankDex\n\n^ "Method for node ranking in a linked database". Google Patents. Archived from the original on October 15, 2015. Retrieved October 19, 2015.\n\n^ Koller, David (January 2004). "Origin of the name "Google"". Stanford University. Archived from the original on June 27, 2012.</td>
</tr>
<tr>
<th>4</th>
<td>^ Swant, Marty. "The World's Valuable Brands". Forbes. Archived from the original on October 18, 2020. Retrieved January 19, 2022.\n\n^ "Best Global Brands". Interbrand. Archived from the original on February 1, 2022. Retrieved March 7, 2011.\n\n^ a b c d "How we started and where we are today – Google". about.google. Archived from the original on April 22, 2020. Retrieved April 24, 2021.\n\n^ Brezina, Corona (2013). Sergey Brin, Larry Page, Eric Schmidt, and Google (1st ed.). New York: Rosen Publishing Group. p. 18. ISBN 978-1-4488-6911-4. LCCN 2011039480.\n\n^ a b c "Our history in depth". Google Company. Archived from the original on April 1, 2012. Retrieved July 15, 2017.</td>
<td>^ Swant, Marty. "The World's Valuable Brands". Forbes. Archived from the original on October 18, 2020. Retrieved January 19, 2022.\n\n^ "Best Global Brands". Interbrand. Archived from the original on February 1, 2022. Retrieved March 7, 2011.\n\n^ a b c d "How we started and where we are today – Google". about.google. Archived from the original on April 22, 2020. Retrieved April 24, 2021.\n\n^ Brezina, Corona (2013). Sergey Brin, Larry Page, Eric Schmidt, and Google (1st ed.). New York: Rosen Publishing Group. p. 18. ISBN 978-1-4488-6911-4. LCCN 2011039480.\n\n^ a b c "Our history in depth". Google Company. Archived from the original on April 1, 2012. Retrieved July 15, 2017.</td>
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让我们检查几个重新排序的文档。我们观察到检索器仍然返回相关的Langchain类型文档，但作为元数据字段的一部分，我们还从Ranking API接收到了relevance_score。
for i in range(2):
print(f"Document {i}")
print(reranked_docs[i])
print("----------------------------------------------------------\n")
<style>
pre {
white-space: pre-wrap;
}
</style>
Document 0
page_content='The name "Google" originated from a misspelling of "googol",[211][212] which refers to the number represented by a 1 followed by one-hundred zeros. Page and Brin write in their original paper on PageRank:[33] "We chose our system name, Google, because it is a common spelling of googol, or 10100[,] and fits well with our goal of building very large-scale search engines." Having found its way increasingly into everyday language, the verb "google" was added to the Merriam Webster Collegiate Dictionary and the Oxford English Dictionary in 2006, meaning "to use the Google search engine to obtain information on the Internet."[213][214] Google\'s mission statement, from the outset, was "to organize the world\'s information and make it universally accessible and useful",[215] and its unofficial' metadata={'id': '2', 'relevance_score': 0.9800000190734863, 'source': 'https://en.wikipedia.org/wiki/Google'}
----------------------------------------------------------
Document 1
page_content='Eventually, they changed the name to Google; the name of the search engine was a misspelling of the word googol,[21][36][37] a very large number written 10100 (1 followed by 100 zeros), picked to signify that the search engine was intended to provide large quantities of information.[38]' metadata={'id': '1', 'relevance_score': 0.75, 'source': 'https://en.wikipedia.org/wiki/Google'}
----------------------------------------------------------
将所有内容整合在一起
这展示了一个完整的RAG链的示例，其中包含一个简单的提示模板，说明如何使用Vertex Ranking API执行重新排名。
from langchain.chains import LLMChain
from langchain_core.documents import Document
from langchain_core.prompts import PromptTemplate
from langchain_core.runnables import RunnableParallel, RunnablePassthrough
from langchain_google_vertexai import VertexAI
llm = VertexAI(model_name="gemini-1.0-pro-002")
# Instantiate the VertexAIReranker with the SDK manager
reranker = VertexAIRank(
project_id=PROJECT_ID,
location_id=RANKING_LOCATION_ID,
ranking_config="default_ranking_config",
title_field="source", # metadata field key from your existing documents
top_n=5,
)
# value of k can be set to a higher value as well for tweaking performance
# eg: # of docs: basic_retriever(100) -> reranker(5)
basic_retriever = vectordb.as_retriever(search_kwargs={"k": 5}) # fetch top 5 documents
# Create the ContextualCompressionRetriever with the VertexAIRanker as a Reranker
retriever_with_reranker = ContextualCompressionRetriever(
base_compressor=reranker, base_retriever=basic_retriever
)
template = """
<context>
{context}
</context>
Question:
{query}
Don't give information outside the context or repeat your findings.
Answer:
"""
prompt = PromptTemplate.from_template(template)
reranker_setup_and_retrieval = RunnableParallel(
{"context": retriever_with_reranker, "query": RunnablePassthrough()}
)
chain = reranker_setup_and_retrieval | prompt | llm
<style>
pre {
white-space: pre-wrap;
}
</style>
query = "how did the name google originate?"
<style>
pre {
white-space: pre-wrap;
}
</style>
chain.invoke(query)
<style>
pre {
white-space: pre-wrap;
}
</style>
'The name "Google" originated as a misspelling of the word "googol," a mathematical term for the number 1 followed by 100 zeros. Larry Page and Sergey Brin, the founders of Google, chose the name because it reflected their goal of building a search engine that could handle massive amounts of information. \n'