Usage
Agentic Chunking Params
| Parameter | Type | Default | Description |
|---|---|---|---|
model | Model | OpenAIChat | The model to use for chunking. |
max_chunk_size | int | 5000 | The maximum size of each chunk. |
Documentation Index
Fetch the complete documentation index at: /llms.txt
Use this file to discover all available pages before exploring further.
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from agno.agent import Agent
from agno.document.chunking.agentic import AgenticChunking
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_agentic_chunking", db_url=db_url),
chunking_strategy=AgenticChunking(),
)
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)
| Parameter | Type | Default | Description |
|---|---|---|---|
model | Model | OpenAIChat | The model to use for chunking. |
max_chunk_size | int | 5000 | The maximum size of each chunk. |
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