Skip to main content

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.

Knowledge Storage

Knowledge uses vector stores to index and retrieve documents. Choose the right backend for your use case.

Quick Setup

from praisonaiagents import Agent

# Default: Chroma (zero-config, local)
agent = Agent(knowledge=["docs/"])

# With specific provider
agent = Agent(
    knowledge={
        "sources": ["docs/"],
        "vector_store": {
            "provider": "qdrant",
            "config": {"url": "http://localhost:6333"}
        }
    }
)

Supported Vector Stores

Qdrant

High-performance vector search

Chroma

Zero-config local storage

Pinecone

Managed cloud service

Weaviate

Open-source, scalable

LanceDB

Serverless vector DB

Milvus

Enterprise-grade

PGVector

PostgreSQL extension

Cassandra

Distributed storage

ClickHouse

Analytics-optimized

Choosing a Vector Store

Use CaseRecommendedWhy
Local developmentChromaZero config, embedded
Production (hosted)PineconeManaged, scalable
Production (self-hosted)Qdrant/WeaviatePerformant, open-source
PostgreSQL stackPGVectorSame infrastructure
EnterpriseMilvusHigh availability

Configuration Examples

agent = Agent(
    knowledge={
        "sources": ["docs/"],
        "vector_store": {
            "provider": "chroma",
            "config": {
                "collection_name": "my_docs",
                "path": ".praison/chroma"
            }
        }
    }
)

Database Overview

All database backends

Knowledge Quick Start

Get started with knowledge