Vectara
Vectara 是值得信赖的AI助手和代理平台，专注于企业关键任务应用的准备。
Vectara 无服务器 RAG 即服务提供了一个易于使用的 API，包含了 RAG 的所有组件，包括：
- 一种从文件（PDF、PPT、DOCX等）中提取文本的方法
- 基于机器学习的分块技术，提供最先进的性能。
- Boomerang 嵌入模型。
- 它自己的内部向量数据库，用于存储文本块和嵌入向量。
- 一个查询服务，自动将查询编码为嵌入，并检索最相关的文本片段（包括支持混合搜索以及多种重新排序选项，如多语言相关性重新排序器、MMR、UDF重新排序器）。
- 一个LLM用于根据检索到的文档（上下文）创建生成摘要，包括引用。
查看Vectara API文档以获取有关如何使用API的更多信息。
本笔记本展示了如何在使用Vectara仅作为向量存储（不进行摘要）时使用基本检索功能，包括：similarity_search 和 similarity_search_with_score 以及使用LangChain的as_retriever功能。
你需要安装 langchain-community 使用 pip install -qU langchain-community 来使用这个集成
入门指南
要开始使用，请按照以下步骤操作：
- 如果您还没有账户，请注册免费试用Vectara。完成注册后，您将获得一个Vectara客户ID。您可以通过点击Vectara控制台窗口右上角的您的名字来找到您的客户ID。
- 在您的账户中，您可以创建一个或多个语料库。每个语料库代表一个区域，用于存储从输入文档中提取的文本数据。要创建语料库，请使用"创建语料库"按钮。然后，您需要为您的语料库提供一个名称和描述。您还可以选择定义过滤属性并应用一些高级选项。如果您点击您创建的语料库，您可以在顶部看到它的名称和语料库ID。
- 接下来，您需要创建API密钥以访问语料库。在语料库视图中点击"访问控制"标签，然后点击"创建API密钥"按钮。为您的密钥命名，并选择您希望密钥是仅查询还是查询+索引。点击“创建”，您现在就有了一个有效的API密钥。请保密此密钥。
要使用LangChain与Vectara，你需要具备以下三个值：customer ID、corpus ID和api_key。
你可以通过两种方式将这些值提供给LangChain：
-
在你的环境中包含这三个变量：
VECTARA_CUSTOMER_ID,VECTARA_CORPUS_ID和VECTARA_API_KEY。例如，你可以使用 os.environ 和 getpass 来设置这些变量，如下所示：
import os
import getpass
os.environ["VECTARA_CUSTOMER_ID"] = getpass.getpass("Vectara Customer ID:")
os.environ["VECTARA_CORPUS_ID"] = getpass.getpass("Vectara Corpus ID:")
os.environ["VECTARA_API_KEY"] = getpass.getpass("Vectara API Key:")
- 将它们添加到
Vectara向量存储构造函数中：
vectara = Vectara(
vectara_customer_id=vectara_customer_id,
vectara_corpus_id=vectara_corpus_id,
vectara_api_key=vectara_api_key
)
在本笔记本中，我们假设它们在环境中提供。
import os
os.environ["VECTARA_API_KEY"] = "<YOUR_VECTARA_API_KEY>"
os.environ["VECTARA_CORPUS_ID"] = "<YOUR_VECTARA_CORPUS_ID>"
os.environ["VECTARA_CUSTOMER_ID"] = "<YOUR_VECTARA_CUSTOMER_ID>"
from langchain_community.vectorstores import Vectara
from langchain_community.vectorstores.vectara import (
RerankConfig,
SummaryConfig,
VectaraQueryConfig,
)
首先，我们将国情咨文文本加载到Vectara中。
请注意，我们使用的是from_files接口，该接口不需要任何本地处理或分块 - Vectara接收文件内容并执行所有必要的预处理、分块和将文件嵌入其知识库中。
在这种情况下，它使用了一个.txt文件，但同样适用于许多其他文件类型。
vectara = Vectara.from_files(["state_of_the_union.txt"])
基础 Vectara RAG（检索增强生成）
我们现在创建一个VectaraQueryConfig对象来控制检索和摘要选项：
- 我们启用摘要功能，指定我们希望LLM选择前7个匹配的块并以英语回应
- 我们在检索过程中启用了MMR（最大边际相关性），并设置了0.2的多样性偏差因子
- 我们希望得到前10个结果，混合搜索配置值为0.025
使用此配置，让我们创建一个LangChain Runnable对象，该对象使用as_rag方法封装完整的Vectara RAG管道：
summary_config = SummaryConfig(is_enabled=True, max_results=7, response_lang="eng")
rerank_config = RerankConfig(reranker="mmr", rerank_k=50, mmr_diversity_bias=0.2)
config = VectaraQueryConfig(
k=10, lambda_val=0.005, rerank_config=rerank_config, summary_config=summary_config
)
query_str = "what did Biden say?"
rag = vectara.as_rag(config)
rag.invoke(query_str)["answer"]
"Biden addressed various topics in his statements. He highlighted the need to confront Putin by building a coalition of nations[1]. He also expressed commitment to investigating the impact of burn pits on soldiers' health, including his son's case[2]. Additionally, Biden outlined a plan to fight inflation by cutting prescription drug costs[3]. He emphasized the importance of continuing to combat COVID-19 and not just accepting living with it[4]. Furthermore, he discussed measures to weaken Russia economically and target Russian oligarchs[6]. Biden also advocated for passing the Equality Act to support LGBTQ+ Americans and condemned state laws targeting transgender individuals[7]."
我们也可以像这样使用流式接口：
output = {}
curr_key = None
for chunk in rag.stream(query_str):
for key in chunk:
if key not in output:
output[key] = chunk[key]
else:
output[key] += chunk[key]
if key == "answer":
print(chunk[key], end="", flush=True)
curr_key = key
Biden addressed various topics in his statements. He highlighted the importance of building coalitions to confront global challenges [1]. He also expressed commitment to investigating the impact of burn pits on soldiers' health, including his son's case [2, 4]. Additionally, Biden outlined his plan to combat inflation by cutting prescription drug costs and reducing the deficit, with support from Nobel laureates and business leaders [3]. He emphasized the ongoing fight against COVID-19 and the need to continue combating the virus [5]. Furthermore, Biden discussed measures taken to weaken Russia's economic and military strength, targeting Russian oligarchs and corrupt leaders [6]. He also advocated for passing the Equality Act to support LGBTQ+ Americans and address discriminatory state laws [7].
幻觉检测和事实一致性评分
Vectara 创建了 HHEM - 一个开源模型，可用于评估 RAG 响应的实际一致性。
作为Vectara RAG的一部分，"事实一致性分数"（或FCS），这是开源HHEM的改进版本，通过API提供。这自动包含在RAG管道的输出中
summary_config = SummaryConfig(is_enabled=True, max_results=5, response_lang="eng")
rerank_config = RerankConfig(reranker="mmr", rerank_k=50, mmr_diversity_bias=0.1)
config = VectaraQueryConfig(
k=10, lambda_val=0.005, rerank_config=rerank_config, summary_config=summary_config
)
rag = vectara.as_rag(config)
resp = rag.invoke(query_str)
print(resp["answer"])
print(f"Vectara FCS = {resp['fcs']}")
Biden addressed various topics in his statements. He highlighted the need to confront Putin by building a coalition of nations[1]. He also expressed his commitment to investigating the impact of burn pits on soldiers' health, referencing his son's experience[2]. Additionally, Biden discussed his plan to fight inflation by cutting prescription drug costs and garnering support from Nobel laureates and business leaders[4]. Furthermore, he emphasized the importance of continuing to combat COVID-19 and not merely accepting living with the virus[5]. Biden's remarks encompassed international relations, healthcare challenges faced by soldiers, economic strategies, and the ongoing battle against the pandemic.
Vectara FCS = 0.41796625
Vectara 作为 langchain 检索器
Vectara组件也可以仅用作检索器。
在这种情况下，它的行为就像任何其他LangChain检索器一样。这种模式的主要用途是进行语义搜索，在这种情况下，我们禁用了摘要功能：
config.summary_config.is_enabled = False
config.k = 3
retriever = vectara.as_retriever(config=config)
retriever.invoke(query_str)
[Document(page_content='He thought the West and NATO wouldn’t respond. And he thought he could divide us at home. We were ready. Here is what we did. We prepared extensively and carefully. We spent months building a coalition of other freedom-loving nations from Europe and the Americas to Asia and Africa to confront Putin.', metadata={'lang': 'eng', 'section': '1', 'offset': '2160', 'len': '36', 'X-TIKA:Parsed-By': 'org.apache.tika.parser.csv.TextAndCSVParser', 'Content-Encoding': 'UTF-8', 'Content-Type': 'text/plain; charset=UTF-8', 'source': 'vectara'}),
Document(page_content='When they came home, many of the world’s fittest and best trained warriors were never the same. Dizziness. \n\nA cancer that would put them in a flag-draped coffin. I know. \n\nOne of those soldiers was my son Major Beau Biden. We don’t know for sure if a burn pit was the cause of his brain cancer, or the diseases of so many of our troops. But I’m committed to finding out everything we can.', metadata={'lang': 'eng', 'section': '1', 'offset': '34652', 'len': '60', 'X-TIKA:Parsed-By': 'org.apache.tika.parser.csv.TextAndCSVParser', 'Content-Encoding': 'UTF-8', 'Content-Type': 'text/plain; charset=UTF-8', 'source': 'vectara'}),
Document(page_content='But cancer from prolonged exposure to burn pits ravaged Heath’s lungs and body. Danielle says Heath was a fighter to the very end. He didn’t know how to stop fighting, and neither did she. Through her pain she found purpose to demand we do better. Tonight, Danielle—we are.', metadata={'lang': 'eng', 'section': '1', 'offset': '35442', 'len': '57', 'X-TIKA:Parsed-By': 'org.apache.tika.parser.csv.TextAndCSVParser', 'Content-Encoding': 'UTF-8', 'Content-Type': 'text/plain; charset=UTF-8', 'source': 'vectara'})]
为了向后兼容，您还可以使用检索器启用摘要功能，在这种情况下，摘要将作为额外的文档对象添加：
config.summary_config.is_enabled = True
config.k = 3
retriever = vectara.as_retriever(config=config)
retriever.invoke(query_str)
[Document(page_content='He thought the West and NATO wouldn’t respond. And he thought he could divide us at home. We were ready. Here is what we did. We prepared extensively and carefully. We spent months building a coalition of other freedom-loving nations from Europe and the Americas to Asia and Africa to confront Putin.', metadata={'lang': 'eng', 'section': '1', 'offset': '2160', 'len': '36', 'X-TIKA:Parsed-By': 'org.apache.tika.parser.csv.TextAndCSVParser', 'Content-Encoding': 'UTF-8', 'Content-Type': 'text/plain; charset=UTF-8', 'source': 'vectara'}),
Document(page_content='When they came home, many of the world’s fittest and best trained warriors were never the same. Dizziness. \n\nA cancer that would put them in a flag-draped coffin. I know. \n\nOne of those soldiers was my son Major Beau Biden. We don’t know for sure if a burn pit was the cause of his brain cancer, or the diseases of so many of our troops. But I’m committed to finding out everything we can.', metadata={'lang': 'eng', 'section': '1', 'offset': '34652', 'len': '60', 'X-TIKA:Parsed-By': 'org.apache.tika.parser.csv.TextAndCSVParser', 'Content-Encoding': 'UTF-8', 'Content-Type': 'text/plain; charset=UTF-8', 'source': 'vectara'}),
Document(page_content='But cancer from prolonged exposure to burn pits ravaged Heath’s lungs and body. Danielle says Heath was a fighter to the very end. He didn’t know how to stop fighting, and neither did she. Through her pain she found purpose to demand we do better. Tonight, Danielle—we are.', metadata={'lang': 'eng', 'section': '1', 'offset': '35442', 'len': '57', 'X-TIKA:Parsed-By': 'org.apache.tika.parser.csv.TextAndCSVParser', 'Content-Encoding': 'UTF-8', 'Content-Type': 'text/plain; charset=UTF-8', 'source': 'vectara'}),
Document(page_content="Biden discussed various topics in his statements. He highlighted the importance of unity and preparation to confront challenges, such as building coalitions to address global issues [1]. Additionally, he shared personal stories about the impact of health issues on soldiers, including his son's experience with brain cancer possibly linked to burn pits [2]. Biden also outlined his plans to combat inflation by cutting prescription drug costs and emphasized the ongoing efforts to combat COVID-19, rejecting the idea of merely living with the virus [4, 5]. Overall, Biden's messages revolved around unity, healthcare challenges faced by soldiers, economic plans, and the ongoing fight against COVID-19.", metadata={'summary': True, 'fcs': 0.54751414})]
使用Vectara进行高级LangChain查询预处理
Vectara的“RAG即服务”在创建问答或聊天机器人链时承担了大量的繁重工作。与LangChain的集成为使用额外功能提供了选项，例如查询预处理，如SelfQueryRetriever或MultiQueryRetriever。让我们来看一个使用MultiQueryRetriever的示例。
由于MQR使用了LLM，我们需要进行设置 - 这里我们选择ChatOpenAI：
from langchain.retrievers.multi_query import MultiQueryRetriever
from langchain_openai.chat_models import ChatOpenAI
llm = ChatOpenAI(temperature=0)
mqr = MultiQueryRetriever.from_llm(retriever=retriever, llm=llm)
def get_summary(documents):
return documents[-1].page_content
(mqr | get_summary).invoke(query_str)
API Reference:MultiQueryRetriever | ChatOpenAI
"Biden's statement highlighted his efforts to unite freedom-loving nations against Putin's aggression, sharing information in advance to counter Russian lies and hold Putin accountable[1]. Additionally, he emphasized his commitment to military families, like Danielle Robinson, and outlined plans for more affordable housing, Pre-K for 3- and 4-year-olds, and ensuring no additional taxes for those earning less than $400,000 a year[2][3]. The statement also touched on the readiness of the West and NATO to respond to Putin's actions, showcasing extensive preparation and coalition-building efforts[4]. Heath Robinson's story, a combat medic who succumbed to cancer from burn pits, was used to illustrate the resilience and fight for better conditions[5]."