Wikipedia KnowledgeBase - Phidata

The WikipediaKnowledgeBase reads wikipedia topics, converts them into vector embeddings and loads them to a vector databse.

Usage

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

pip install wikipedia

knowledge_base.py

from phi.knowledge.wikipedia import WikipediaKnowledgeBase
from phi.vectordb.pgvector import PgVector

knowledge_base = WikipediaKnowledgeBase(
    topics=["Manchester United", "Real Madrid"],
    # Table name: ai.wikipedia_documents
    vector_db=PgVector(
        table_name="wikipedia_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
`topics`	`List[str]`	-	Topics to read
`vector_db`	`VectorDb`	-	Vector Database for the Knowledge Base.
`reader`	`Reader`	-	A `Reader` that reads the topics and converts them into `Documents` for the vector database.
`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.