> ## Documentation Index
> Fetch the complete documentation index at: https://ikun.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# 语义分块

语义分块是一种通过分析文本片段之间的语义相似性来将文档分割成更小块的方法，它使用嵌入（embeddings）来实现。该方法利用 chonkie 库，根据可配置的相似度阈值，识别语义含义发生显著变化时的自然断点。与固定长度分块相比，它能更好地保留上下文和意义，通过确保语义相关的内​​容保留在同一块中，并在有意义的主题过渡处进行分割。

```python theme={null}
from agno.agent import Agent
from agno.document.chunking.semantic import SemanticChunking
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.vectordb.pgvector import PgVector

db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"

knowledge_base = PDFUrlKnowledgeBase(
    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
    vector_db=PgVector(table_name="recipes_semantic_chunking", db_url=db_url),
    chunking_strategy=SemanticChunking(),
)
knowledge_base.load(recreate=False)  # Comment out after first run

agent = Agent(
    knowledge_base=knowledge_base,
    search_knowledge=True,
)

agent.print_response("How to make Thai curry?", markdown=True)

```

## 语义分块参数

<Snippet file="chunking-semantic.mdx" />