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from agno.agent import Agent
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.vectordb.qdrant import Qdrant
COLLECTION_NAME = "thai-recipes"
vector_db = Qdrant(collection=COLLECTION_NAME, url="http://localhost:6333")
knowledge_base = PDFUrlKnowledgeBase(
urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
vector_db=vector_db,
)
knowledge_base.load(recreate=False) # Comment out after first run
agent = Agent(knowledge=knowledge_base, show_tool_calls=True)
agent.print_response("List down the ingredients to make Massaman Gai", markdown=True)
Create a virtual environment
Terminal and create a python virtual environment.python3 -m venv .venv
source .venv/bin/activate
python3 -m venv .venv
.venv/scripts/activate
Start Qdrant
docker run -p 6333:6333 -p 6334:6334 \
-v $(pwd)/qdrant_storage:/qdrant/storage:z \
qdrant/qdrant
Install libraries
pip install -U qdrant-client pypdf openai agno
Run Agent
python cookbook/agent_concepts/vector_dbs/qdrant_db.py
python cookbook/agent_concepts/vector_dbs/qdrant_db.py
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