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SpaCy

spaCy 是一个用于高级自然语言处理的开源软件库,使用编程语言 Python 和 Cython 编写。

安装和设置

%pip install --upgrade --quiet  spacy

导入必要的类

<!--IMPORTS:[{"imported": "SpacyEmbeddings", "source": "langchain_community.embeddings.spacy_embeddings", "docs": "https://python.langchain.com/api_reference/community/embeddings/langchain_community.embeddings.spacy_embeddings.SpacyEmbeddings.html", "title": "SpaCy"}]-->
from langchain_community.embeddings.spacy_embeddings import SpacyEmbeddings

示例

初始化 SpacyEmbeddings。这将把 Spacy 模型加载到内存中。

embedder = SpacyEmbeddings(model_name="en_core_web_sm")

定义一些示例文本。这些可以是您想要分析的任何文档,例如新闻文章、社交媒体帖子或产品评论。

texts = [
"The quick brown fox jumps over the lazy dog.",
"Pack my box with five dozen liquor jugs.",
"How vexingly quick daft zebras jump!",
"Bright vixens jump; dozy fowl quack.",
]

为文本生成并打印嵌入。SpacyEmbeddings 类为每个文档生成一个嵌入,这是文档内容的数值表示。这些嵌入可以用于各种自然语言处理任务,例如文档相似性比较或文本分类。

embeddings = embedder.embed_documents(texts)
for i, embedding in enumerate(embeddings):
print(f"Embedding for document {i+1}: {embedding}")

为单个文本生成并打印嵌入。您还可以为单个文本生成嵌入,例如搜索查询。这对于信息检索等任务非常有用,您希望找到与给定查询相似的文档。

query = "Quick foxes and lazy dogs."
query_embedding = embedder.embed_query(query)
print(f"Embedding for query: {query_embedding}")

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