SemaDB
SemaDB 来自 SemaFind 是一个简单易用的向量相似度数据库，用于构建AI应用程序。托管的
SemaDB Cloud提供了一个简单易用的开发者体验，便于入门。
API的完整文档以及示例和交互式演示可在 RapidAPI 上找到。
本笔记本演示了 SemaDB Cloud 向量存储的使用。
您需要使用 pip install -qU langchain-community 安装 langchain-community 才能使用此集成。
加载文档嵌入
为了在本地运行，我们使用 Sentence Transformers ，它们通常用于嵌入句子。您可以使用LangChain提供的任何嵌入模型。
%pip install --upgrade --quiet sentence_transformers
<!--IMPORTS:[{"imported": "HuggingFaceEmbeddings", "source": "langchain_huggingface", "docs": "https://python.langchain.com/api_reference/huggingface/embeddings/langchain_huggingface.embeddings.huggingface.HuggingFaceEmbeddings.html", "title": "SemaDB"}]-->
from langchain_huggingface import HuggingFaceEmbeddings
model_name = "sentence-transformers/all-mpnet-base-v2"
embeddings = HuggingFaceEmbeddings(model_name=model_name)
<!--IMPORTS:[{"imported": "TextLoader", "source": "langchain_community.document_loaders", "docs": "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.text.TextLoader.html", "title": "SemaDB"}, {"imported": "CharacterTextSplitter", "source": "langchain_text_splitters", "docs": "https://python.langchain.com/api_reference/text_splitters/character/langchain_text_splitters.character.CharacterTextSplitter.html", "title": "SemaDB"}]-->
from langchain_community.document_loaders import TextLoader
from langchain_text_splitters import CharacterTextSplitter
loader = TextLoader("../../how_to/state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=400, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
print(len(docs))
114
连接到 SemaDB
SemaDB Cloud 使用 RapidAPI 密钥 进行身份验证。您可以通过创建一个免费的 RapidAPI 账户来获取您的密钥。
import getpass
import os
if "SEMADB_API_KEY" not in os.environ:
os.environ["SEMADB_API_KEY"] = getpass.getpass("SemaDB API Key:")
SemaDB API Key: ········
<!--IMPORTS:[{"imported": "SemaDB", "source": "langchain_community.vectorstores", "docs": "https://python.langchain.com/api_reference/community/vectorstores/langchain_community.vectorstores.semadb.SemaDB.html", "title": "SemaDB"}, {"imported": "DistanceStrategy", "source": "langchain_community.vectorstores.utils", "docs": "https://python.langchain.com/api_reference/community/vectorstores/langchain_community.vectorstores.utils.DistanceStrategy.html", "title": "SemaDB"}]-->
from langchain_community.vectorstores import SemaDB
from langchain_community.vectorstores.utils import DistanceStrategy
SemaDB 向量存储的参数直接反映了 API：
- "mycollection": 是我们将存储这些向量的集合名称。
- 768: 是向量的维度。在我们的案例中，句子变换器嵌入产生 768 维的向量。
- API_KEY: 是您的 RapidAPI 密钥。
- embeddings: 对应于文档、文本和查询的嵌入将如何生成。
- DistanceStrategy: 是使用的距离度量。如果使用 COSINE，包装器会自动对向量进行归一化。
db = SemaDB("mycollection", 768, embeddings, DistanceStrategy.COSINE)
# Create collection if running for the first time. If the collection
# already exists this will fail.
db.create_collection()
True
SemaDB 向量存储包装器将文档文本作为点元数据添加以便后续收集。存储大块文本不推荐。如果您正在索引一个大型集合，我们建议存储对文档的引用，例如外部 ID。
db.add_documents(docs)[:2]
['813c7ef3-9797-466b-8afa-587115592c6c',
'fc392f7f-082b-4932-bfcc-06800db5e017']
相似性搜索
我们使用默认的 LangChain 相似性搜索接口来搜索最相似的句子。
query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query)
print(docs[0].page_content)
And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
docs = db.similarity_search_with_score(query)
docs[0]
(Document(page_content='And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../how_to/state_of_the_union.txt', 'text': 'And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.'}),
0.42369342)
清理
您可以删除集合以移除所有数据。
db.delete_collection()
True
