Skip to main content
Open In ColabOpen on GitHub

Clarifai

Clarifai 是一个提供从数据探索、数据标注、模型训练、评估到推理的完整AI生命周期的AI平台。上传输入后，一个Clarifai应用程序可以作为向量数据库使用。

这个笔记本展示了如何使用与Clarifai向量数据库相关的功能。示例用于演示文本语义搜索能力。Clarifai还支持基于图像、视频帧和局部搜索的语义搜索（参见排名）以及属性搜索（参见过滤器）。

要使用 Clarifai，您必须拥有一个账户和一个个人访问令牌 (PAT)。 在此处 获取或创建 PAT。

依赖项

# Install required dependencies
%pip install --upgrade --quiet  clarifai langchain-community

Imports

在这里我们将设置个人访问令牌。您可以在平台的设置/安全中找到您的PAT。

# Please login and get your API key from  https://clarifai.com/settings/security
from getpass import getpass

CLARIFAI_PAT = getpass()
 ········
# Import the required modules
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Clarifai
from langchain_text_splitters import CharacterTextSplitter

设置

设置将要上传文本数据的用户ID和应用ID。注意：在创建该应用程序时，请选择一个适合索引您的文本文档的基本工作流，例如语言理解工作流。

您首先需要在Clarifai上创建一个账户，然后创建一个应用。

USER_ID = "USERNAME_ID"
APP_ID = "APPLICATION_ID"
NUMBER_OF_DOCS = 2

从文本 ​

从文本列表创建一个Clarifai向量存储。本节将把每条文本及其相应的元数据上传到一个Clarifai应用中。然后，该Clarifai应用可用于语义搜索，以查找相关文本。

texts = [
    "I really enjoy spending time with you",
    "I hate spending time with my dog",
    "I want to go for a run",
    "I went to the movies yesterday",
    "I love playing soccer with my friends",
]

metadatas = [
    {"id": i, "text": text, "source": "book 1", "category": ["books", "modern"]}
    for i, text in enumerate(texts)
]

您可以选择给输入框指定自定义的输入ID。

idlist = ["text1", "text2", "text3", "text4", "text5"]
metadatas = [
    {"id": idlist[i], "text": text, "source": "book 1", "category": ["books", "modern"]}
    for i, text in enumerate(texts)
]
# There is an option to initialize clarifai vector store with pat as argument!
clarifai_vector_db = Clarifai(
    user_id=USER_ID,
    app_id=APP_ID,
    number_of_docs=NUMBER_OF_DOCS,
)

将数据上传到clarifai应用程序。

# upload with metadata and custom input ids.
response = clarifai_vector_db.add_texts(texts=texts, ids=idlist, metadatas=metadatas)

# upload without metadata (Not recommended)- Since you will not be able to perform Search operation with respect to metadata.
# custom input_id (optional)
response = clarifai_vector_db.add_texts(texts=texts)

您可以直接将clarifai向量DB存储并导入所有输入到您的应用程序中，通过,

clarifai_vector_db = Clarifai.from_texts(
    user_id=USER_ID,
    app_id=APP_ID,
    texts=texts,
    metadatas=metadatas,
)

使用相似性搜索功能查找相似文本。

docs = clarifai_vector_db.similarity_search("I would like to see you")
docs
[Document(page_content='I really enjoy spending time with you', metadata={'text': 'I really enjoy spending time with you', 'id': 'text1', 'source': 'book 1', 'category': ['books', 'modern']})]

进一步您可以根据元数据过滤搜索结果。

# There is lots powerful filtering you can do within an app by leveraging metadata filters.
# This one will limit the similarity query to only the texts that have key of "source" matching value of "book 1"
book1_similar_docs = clarifai_vector_db.similarity_search(
    "I would love to see you", filter={"source": "book 1"}
)

# you can also use lists in the input's metadata and then select things that match an item in the list. This is useful for categories like below:
book_category_similar_docs = clarifai_vector_db.similarity_search(
    "I would love to see you", filter={"category": ["books"]}
)

从文档​

创建一个来自文档列表的Clarifai向量存储。此部分将上传每个文档及其相应的元数据到Clarifai应用程序。然后，可以使用Clarifai应用程序进行语义搜索以查找相关文档。

loader = TextLoader("your_local_file_path.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
USER_ID = "USERNAME_ID"
APP_ID = "APPLICATION_ID"
NUMBER_OF_DOCS = 4

将所有文档导入clarifai App，创建一个clarifai向量DB类。

clarifai_vector_db = Clarifai.from_documents(
    user_id=USER_ID,
    app_id=APP_ID,
    documents=docs,
    number_of_docs=NUMBER_OF_DOCS,
)
docs = clarifai_vector_db.similarity_search("Texts related to population")
docs

从现有应用出发​

在Clarifai中，我们有很好的工具可以通过API或UI将数据添加到应用程序（实际上是项目）中。大多数用户在与LangChain互动之前已经完成了这一操作，因此此示例将使用现有应用中的数据来执行搜索。请查阅我们的API文档和UI文档。然后可以使用Clarifai应用程序进行语义搜索以找到相关文档。

USER_ID = "USERNAME_ID"
APP_ID = "APPLICATION_ID"
NUMBER_OF_DOCS = 4
clarifai_vector_db = Clarifai(
    user_id=USER_ID,
    app_id=APP_ID,
    number_of_docs=NUMBER_OF_DOCS,
)
docs = clarifai_vector_db.similarity_search(
    "Texts related to ammuniction and president wilson"
)
docs[0].page_content
"President Wilson, generally acclaimed as the leader of the world's democracies,\nphrased for civilization the arguments against autocracy in the great peace conference\nafter the war. The President headed the American delegation to that conclave of world\nre-construction. With him as delegates to the conference were Robert Lansing, Secretary\nof State; Henry White, former Ambassador to France and Italy; Edward M. House and\nGeneral Tasker H. Bliss.\nRepresenting American Labor at the International Labor conference held in Paris\nsimultaneously with the Peace Conference were Samuel Gompers, president of the\nAmerican Federation of Labor; William Green, secretary-treasurer of the United Mine\nWorkers of America; John R. Alpine, president of the Plumbers' Union; James Duncan,\npresident of the International Association of Granite Cutters; Frank Duffy, president of\nthe United Brotherhood of Carpenters and Joiners, and Frank Morrison, secretary of the\nAmerican Federation of Labor.\nEstimating the share of each Allied nation in the great victory, mankind will\nconclude that the heaviest cost in proportion to prewar population and treasure was paid\nby the nations that first felt the shock of war, Belgium, Serbia, Poland and France. All\nfour were the battle-grounds of huge armies, oscillating in a bloody frenzy over once\nfertile fields and once prosperous towns.\nBelgium, with a population of 8,000,000, had a casualty list of more than 350,000;\nFrance, with its casualties of 4,000,000 out of a population (including its colonies) of\n90,000,000, is really the martyr nation of the world. Her gallant poilus showed the world\nhow cheerfully men may die in defense of home and liberty. Huge Russia, including\nhapless Poland, had a casualty list of 7,000,000 out of its entire population of\n180,000,000. The United States out of a population of 110,000,000 had a casualty list of\n236,117 for nineteen months of war; of these 53,169 were killed or died of disease;\n179,625 were wounded; and 3,323 prisoners or missing."