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

# Recursive Chunking

Recursive chunking is a method of splitting documents into smaller chunks by recursively applying a chunking strategy. This is useful when you want to process large documents in smaller, manageable pieces.

```python theme={null}
from phi.agent import Agent
from phi.document.chunking.recursive import RecursiveChunking
from phi.knowledge.pdf import PDFUrlKnowledgeBase
from phi.vectordb.pgvector import PgVector

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

knowledge_base = PDFUrlKnowledgeBase(
    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
    vector_db=PgVector(table_name="recipes_recursive_chunking", db_url=db_url),
    chunking_strategy=RecursiveChunking(),
)
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)

```

## Params

| Parameter    | Type  | Default | Description                                         |
| ------------ | ----- | ------- | --------------------------------------------------- |
| `chunk_size` | `int` | `5000`  | The maximum size of each chunk.                     |
| `overlap`    | `int` | `0`     | The number of characters to overlap between chunks. |