Website Knowledge Base - Phidata

The WebsiteKnowledgeBase reads websites, converts them into vector embeddings and loads them to a vector_db.

Usage

We are using a local PgVector database for this example. Make sure it’s running

pip install bs4

knowledge_base.py

from phi.knowledge.website import WebsiteKnowledgeBase
from phi.vectordb.pgvector import PgVector

knowledge_base = WebsiteKnowledgeBase(
    urls=["https://docs.phidata.com/introduction"],
    # Number of links to follow from the seed URLs
    max_links=10,
    # Table name: ai.website_documents
    vector_db=PgVector(
        table_name="website_documents",
        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
    ),
)

Then use the knowledge_base with an Agent:

agent.py

from phi.agent import Agent
from knowledge_base import knowledge_base

agent = Agent(
    knowledge=knowledge_base,
    search_knowledge=True,
)
agent.knowledge.load(recreate=False)

agent.print_response("Ask me about something from the knowledge base")

Params

Parameter	Type	Default	Description
`urls`	`List[str]`	-	URLs to read
`reader`	`WebsiteReader`	-	A `WebsiteReader` that reads the urls and converts them into `Documents` for the vector database.
`max_depth`	`int`	`3`	Maximum depth to crawl.
`max_links`	`int`	`10`	Number of links to crawl.
`vector_db`	`VectorDb`	-	Vector Database for the Knowledge Base.
`num_documents`	`int`	`5`	Number of documents to return on search.
`optimize_on`	`int`	-	Number of documents to optimize the vector db on.
`chunking_strategy`	`ChunkingStrategy`	`FixedSizeChunking`	The chunking strategy to use.