Documentation Index
Fetch the complete documentation index at: https://docs.praison.ai/llms.txt
Use this file to discover all available pages before exploring further.
PGVector
Vector search in PostgreSQL using pgvector extension.
Setup
# Docker with pgvector
docker run -d -p 5432:5432 -e POSTGRES_PASSWORD=password ankane/pgvector
pip install psycopg2-binary
Quick Start (Agent with Knowledge)
Use PGVector as a knowledge store with an agent:
from praisonaiagents import Agent
agent = Agent(
name="Assistant",
instructions="You are a helpful assistant with access to documents.",
knowledge=["./docs/guide.pdf"]
)
agent.chat("What does the guide say?")
Advanced Usage (Direct Store)
from praisonai.persistence.factory import create_knowledge_store
store = create_knowledge_store(
"pgvector",
url="postgresql://postgres:password@localhost:5432/praisonai"
)
store.create_collection("documents", dimension=384)
store.insert("documents", [doc])
results = store.search("documents", query_embedding, limit=5)
Configuration
| Option | Description |
|---|
url | PostgreSQL connection URL |
schema | Schema name (default: public) |
table_prefix | Table prefix (default: praison_) |