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Knowledge Storage

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

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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"}
        }
    }
)

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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

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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

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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