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ClickHouse stores knowledge embeddings for fast vector search at scale — ideal for large document collections and analytics workloads. 
import os
from praisonaiagents import Agent

os.environ["PRAISONAI_KNOWLEDGE_STORE"] = "clickhouse"
os.environ["CLICKHOUSE_HOST"] = "localhost"

agent = Agent(
    name="Analyst",
    instructions="Answer from our knowledge base",
    knowledge=["docs/"],
)
agent.start("Summarise our refund policy")
The user asks from your knowledge base; ClickHouse vector search retrieves relevant chunks at scale.

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Quick Start

1

Simple Usage

pip install clickhouse-connect praisonai
export PRAISONAI_KNOWLEDGE_STORE=clickhouse
export CLICKHOUSE_HOST=localhost
export CLICKHOUSE_PORT=8123

from praisonaiagents import Agent

agent = Agent(
    name="Analyst",
    instructions="Answer from docs",
    knowledge=["docs/"],
)
agent.start("What are our key metrics?")
2

With Configuration

Create the store directly for full control:
from praisonai.persistence import create_knowledge_store

store = create_knowledge_store(
    "clickhouse",
    host="localhost",
    port=8123,
    username="default",
    password="",
    database="praisonai",
    secure=False,
)
store.create_collection("docs", dimension=1536)

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How It Works

ClickHouse is a knowledge store (vector search), not a primary conversation backend. Pair it with SQLite or PostgreSQL for chat history via db().

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Configuration Options

OptionTypeDefaultDescription
hoststr"localhost"ClickHouse server hostname
portint8123HTTP port
usernamestr"default"Database username
passwordstr""Database password
databasestr"praisonai"Database name (created if missing)
secureboolFalseUse HTTPS connection
Environment variables: CLICKHOUSE_HOST, CLICKHOUSE_PORT, CLICKHOUSE_USER, CLICKHOUSE_PASSWORD.

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Docker Setup

docker run -d \
  --name praisonai-clickhouse \
  --ulimit nofile=262144:262144 \
  -p 8123:8123 \
  -p 9000:9000 \
  clickhouse/clickhouse-server:latest

curl "http://localhost:8123/?query=SELECT%20version()"

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Best Practices

Store embeddings and retrieval in ClickHouse; keep conversation history in PostgreSQL, MySQL, or SQLite.
Insert documents in batches of 1000+ rows for better write performance on large corpora.
Use PARTITION BY toYYYYMM(timestamp) when logging retrieval events for analytics.
Enable TLS and authentication when connecting to remote ClickHouse clusters.

Database Persistence

Compare all persistence backends

Persistence Backend Plugins

Register custom storage backends