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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程序的就绪性。

Vectara 无服务器 RAG-as-a-service 提供易于使用的 API 背后的所有 RAG 组件，包括

  1. 一种从文件（PDF、PPT、DOCX 等）中提取文本的方法
  2. 基于机器学习的分块，可提供最先进的性能。
  3. Boomerang 嵌入模型。
  4. 其自身的内部向量数据库，用于存储文本块和嵌入向量。
  5. 一种查询服务，可自动将查询编码为嵌入，并检索最相关的文本段（包括对 混合搜索 以及多种重新排序选项的支持，例如 多语言相关性重排序器、MMR、UDF 重排序器）。
  6. 一个 LLM，用于基于检索到的文档（上下文）创建生成式摘要，包括引文。

请参阅 Vectara API 文档，以获取有关如何使用 API 的更多信息。

本笔记本演示了在使用 Vectara 仅作为向量存储（不进行摘要）时如何使用基本检索功能，包括：similarity_search 和 similarity_search_with_score 以及使用 LangChain as_retriever 功能。

您需要使用 `pip install -qU langchain-community` 安装 `langchain-community` 才能使用此集成

开始入门

要开始入门，请按照以下步骤操作

  1. 如果您还没有 Vectara 免费试用帐户，请注册。完成注册后，您将拥有一个 Vectara 客户 ID。您可以通过单击 Vectara 控制台窗口右上角的您的姓名来找到您的客户 ID。
  2. 在您的帐户中，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名称和描述。您可以选择定义筛选属性并应用一些高级选项。如果您单击您创建的语料库，您可以在顶部看到其名称和语料库 ID。
  3. 接下来，您需要创建 API 密钥才能访问语料库。单击语料库视图中的**“访问控制”**选项卡，然后单击**“创建 API 密钥”**按钮。为您的密钥命名，然后选择您想要查询专用密钥还是查询+索引密钥。单击“创建”，您现在就拥有了一个活动的 API 密钥。请务必对该密钥保密。

要将 LangChain 与 Vectara 结合使用，您需要拥有以下三个值：客户 ID、语料库 ID 和 api_key。您可以通过两种方式将这些值提供给 LangChain

  1. 在您的环境中包含以下三个变量：VECTARA_CUSTOMER_ID、VECTARA_CORPUS_ID 和 VECTARA_API_KEY。

    例如，您可以使用 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:")
  1. 将它们添加到 Vectara 向量存储构造器中
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 from_files 接口，它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、分块以及将文件嵌入到其知识库中。

在本例中，它使用 .txt 文件，但这同样适用于许多其他文件类型。

vectara = Vectara.from_files(["state_of_the_union.txt"])

基本 Vectara RAG（检索增强生成）​

现在，我们创建一个 VectaraQueryConfig 对象来控制检索和摘要选项

  • 我们启用摘要，指定我们希望 LLM 选择前 7 个匹配的块并以英语回复
  • 我们在检索过程中启用 MMR（最大边际相关性），多样性偏差因子为 0.2
  • 我们想要前 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，该对象封装了完整的 Vectara RAG 管道，使用 as_rag 方法

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 - 一种开源模型，可用于评估 RAG 响应的事实一致性。

作为 Vectara RAG 的一部分，“事实一致性评分”（或 FCS），它是开源 HHEM 的改进版本，通过 API 提供。这会自动包含在 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。此模式的主要用途是语义搜索，在这种情况下，我们禁用摘要

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，在这种情况下，摘要将作为额外的 Document 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 as a service”在创建问答或聊天机器人链方面做了大量繁重的工作。与 LangChain 的集成提供了使用其他功能（如查询预处理，例如 SelfQueryRetriever 或 MultiQueryRetriever）的选项。让我们看一个使用 MultiQueryRetriever 的示例。

由于 MQR 使用 LLM，我们必须对其进行设置 - 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)
"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]."

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