Google Cloud Vertex AI Reranker
Vertex Search Ranking API 是 Vertex AI Agent Builder 中一个独立的 API。它接收文档列表，并根据文档与查询的相关性对这些文档进行重新排序。与仅查看文档和查询的语义相似性的 embeddings 相比，ranking API 可以为你提供关于文档如何很好地回答给定查询的精确分数。ranking API 可用于在检索到初始候选文档集后提高搜索结果的质量。
ranking API 是无状态的，因此在调用 API 之前无需索引文档。你只需传入查询和文档即可。这使得该 API 非常适合对来自任何文档检索器的文档进行重新排序。
有关更多信息，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
在本示例中，我们将使用 Google Wiki 页面来演示 Vertex Ranking API 的工作原理。
我们使用标准的 load -> split -> embed data 流程。
这些嵌入是使用 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)
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 Vertex Ranking 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。
Ranking API 接收来自 basic_retriever 的输入，并将其传递给 Ranking API。
Ranking API 用于提高排序质量，并确定一个分数，该分数指示每个记录与查询的相关性。
您可以看到未排序文档和已排序文档之间的区别。Ranking API 将最相关的文档移动到 LLM 的上下文窗口的顶部，从而帮助它通过推理形成更好的答案。
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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<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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让我们检查几篇重新排序过的文档。我们观察到检索器仍然返回相关的 Langchain Document 类型（文档），但在元数据字段中，我们也收到了来自 Ranking API 的 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'}
----------------------------------------------------------
整合所有内容
这展示了一个完整的 RAG 链示例，其中包含一个简单的提示模板，说明如何使用 Vertex Ranking API 执行重排。
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
API Reference:LLMChain | Document | PromptTemplate | RunnableParallel | RunnablePassthrough | VertexAI
<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'