Filtering on load with Pdf-Url

from agno.agent import Agent
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.vectordb.lancedb import LanceDb

# Initialize LanceDB
# By default, it stores data in /tmp/lancedb
vector_db = LanceDb(
    table_name="recipes",
    uri="tmp/lancedb",  # You can change this path to store data elsewhere
)

# Step 1: Initialize knowledge base with documents and metadata
# ------------------------------------------------------------------------------
# When loading the knowledge base, we can attach metadata that will be used for filtering
# This metadata can include user IDs, document types, dates, or any other attributes

# Initialize knowledge base
knowledge_base = PDFUrlKnowledgeBase(
    vector_db=vector_db,
)

knowledge_base.load_document(
    url="https://agno-public.s3.amazonaws.com/recipes/thai_recipes_short.pdf",
    metadata={"cuisine": "Thai", "source": "Thai Cookbook"},
    recreate=False,  # only use at the first run, True/False
)

knowledge_base.load_document(
    url="https://agno-public.s3.amazonaws.com/recipes/cape_recipes_short_2.pdf",
    metadata={"cuisine": "Cape", "source": "Cape Cookbook"},
)


# Option 1: Filters on the Agent
# Initialize the Agent with the knowledge base
agent = Agent(
    knowledge=knowledge_base,
    search_knowledge=True,
    knowledge_filters={
        "cuisine": "Thai"
    },  # This will only return information from documents associated with Thai cuisine
)
agent.print_response(
    "Tell me how to make Pad Thai",
    markdown=True,
)

# # # Option 2: Filters on the run/print_response
# agent = Agent(
#     knowledge=knowledge_base,
#     search_knowledge=True,
# )
# agent.print_response(
#     "Tell me how to make Cape Malay Curry",
#     knowledge_filters={"cuisine": "Cape"},
#     markdown=True,
# )