Clarifai
Clarifai 是一个 AI 平台，提供从数据探索、数据标注、模型训练、评估和推理到完整 AI 生命周期的功能。上传输入后，Clarifai 应用程序可以用作向量数据库。
此笔记本展示了如何使用与 Clarifai 向量数据库相关的功能。示例展示了文本语义搜索功能。Clarifai 还支持使用图像、视频帧和本地化搜索（参见 排名）和属性搜索（参见 过滤）进行语义搜索。
要使用 Clarifai，您必须拥有一个帐户和一个个人访问令牌 (PAT) 密钥。 点击这里 获取或创建 PAT。
依赖关系
# Install required dependencies
%pip install --upgrade --quiet clarifai langchain-community
导入
在这里我们将设置个人访问令牌。您可以在平台的设置/安全选项中找到您的 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
设置将上传文本数据的用户 ID 和应用程序 ID。注意：在创建应用程序时，请选择合适的基线工作流来索引您的文本文档，例如语言理解工作流。
您需要先在 Clarifai 上创建一个帐户，然后创建一个应用程序。
USER_ID = "USERNAME_ID"
APP_ID = "APPLICATION_ID"
NUMBER_OF_DOCS = 2
从文本
从文本列表创建 Clarifai 向量存储。本节将上传每个文本及其相应的元数据到 Clarifai 应用程序。然后，Clarifai 应用程序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)
]
或者，您可以选择为输入提供自定义输入 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)
您可以创建 clarifai 向量数据库存储并将所有输入直接导入您的应用程序，方法是：
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搜索类似文本。
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']})]
此外，您可以通过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
从文档列表创建 Clarifai 向量存储。本节将上传每个文档及其相应的元数据到 Clarifai 应用程序。然后，Clarifai 应用程序可用于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
创建 clarifai 向量数据库类并将所有文档导入 clarifai 应用程序。
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
从现有应用程序
在 Clarifai 中，我们拥有通过 API 或 UI 将数据添加到应用程序（本质上是项目）的强大工具。大多数用户在与 LangChain 交互之前就已经完成了此操作，因此本示例将使用现有应用程序中的数据来执行搜索。查看我们的 API 文档 和 UI 文档。然后，Clarifai 应用程序可用于语义搜索以查找相关文档。
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."