The PgVector Agent uses PgVector as Knowledge Base and Storage for the Agent.

from phi.agent import Agent
from phi.storage.agent.postgres import PgAgentStorage
from phi.knowledge.pdf import PDFUrlKnowledgeBase
from phi.vectordb.pgvector import PgVector

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

agent = Agent(
    storage=PgAgentStorage(table_name="recipe_agent", db_url=db_url),
    knowledge_base=PDFUrlKnowledgeBase(
        urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
        vector_db=PgVector(table_name="recipe_documents", db_url=db_url),
    ),
    # Show tool calls in the response
    show_tool_calls=True,
    # Enable the agent to search the knowledge base
    search_knowledge=True,
    # Enable the agent to read the chat history
    read_chat_history=True,
)
# Comment out after first run
agent.knowledge_base.load(recreate=False)  # type: ignore

agent.print_response("How do I make pad thai?", markdown=True)

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Usage

1

Create a virtual environment

Open the Terminal and create a python virtual environment.

2

Run PgVector

docker run -d \
  -e POSTGRES_DB=ai \
  -e POSTGRES_USER=ai \
  -e POSTGRES_PASSWORD=ai \
  -e PGDATA=/var/lib/postgresql/data/pgdata \
  -v pgvolume:/var/lib/postgresql/data \
  -p 5532:5432 \
  --name pgvector \
  phidata/pgvector:16
3

Install libraries

pip install -U pgvector pypdf "psycopg[binary]" sqlalchemy phidata
4

Run PgVector Agent

​
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