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from agno.agent import Agent
from agno.knowledge.chunking.semantic import SemanticChunking
from agno.knowledge.knowledge import Knowledge
from agno.knowledge.reader.pdf_reader import PDFReader
from agno.vectordb.pgvector import PgVector

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

knowledge = Knowledge(
    vector_db=PgVector(table_name="recipes_semantic_chunking", db_url=db_url),
)
knowledge.insert(
    url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf",
    reader=PDFReader(
        name="Semantic Chunking Reader",
        chunking_strategy=SemanticChunking(
            embedder="text-embedding-3-small",  # When a string is provided, it is used as the model ID for chonkie's built-in embedders
            chunk_size=500,
            similarity_threshold=0.5,
            similarity_window=3,
            min_sentences_per_chunk=1,
            min_characters_per_sentence=24,
            delimiters=[". ", "! ", "? ", "\n"],
            include_delimiters="prev",
            skip_window=0,
            filter_window=5,
            filter_polyorder=3,
            filter_tolerance=0.2,
        ),
    ),
)

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

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

​
Run the Example

# Clone and setup repo
git clone https://github.com/agno-agi/agno.git
cd agno/cookbook/07_knowledge/chunking

# Create and activate virtual environment
./scripts/demo_setup.sh
source .venvs/demo/bin/activate

# Optiona: Run PgVector (needs docker)
./cookbook/scripts/run_pgvector.sh

python semantic_chunking.py