1  """PGVector Vector Store - Agent-First Example
 2  
 3  Docker: docker run -d --name pgvector -e POSTGRES_PASSWORD=postgres -p 5433:5432 pgvector/pgvector:pg16
 4  """
 5  import os
 6  from praisonaiagents import Agent
 7  
 8  url = os.getenv("PGVECTOR_URL", "postgresql://postgres:postgres@localhost:5433/postgres")
 9  
10  # Agent-first approach: use knowledge parameter with PGVector
11  agent = Agent(
12      name="Assistant",
13      instructions="You are a helpful assistant with access to documents.",
14      knowledge={"sources": ["./docs/guide.pdf"], "vector_store": {"provider": "pgvector", "url": url}}
15  )
16  
17  # Chat - agent uses knowledge for RAG
18  response = agent.chat("What information do you have?")
19  print(f"Response: {response}")
20  
21  print("PASSED: PGVector with Agent")
22  
23  # --- Advanced: Direct PGVector Usage ---
24  # import psycopg2
25  # conn = psycopg2.connect(url)
26  # cur = conn.cursor()
27  # cur.execute("CREATE EXTENSION IF NOT EXISTS vector")