clickhouse-llm-chat

name	clickhouse-llm-chat
description	Execute LLM inference directly in ClickHouse using OpenAI, Claude, or Perplexity via Python UDFs. Use for text analysis, classification, summarization, or Q&A systems at scale.

ClickHouse LLM Chat Skill

Purpose

Perform LLM inference operations (text generation, classification, analysis) directly within ClickHouse SQL queries using multiple LLM providers with intelligent retry logic and caching.

When to Use

• Perform sentiment analysis on customer reviews
• Classify support tickets or documents
• Generate summaries of long texts
• Extract entities or key information
• Build Q&A systems with RAG
• Analyze text at scale (millions of rows)

Supported Providers

1. OpenAI (openai_chat) - GPT-4, GPT-3.5-turbo
2. Anthropic (claude_chat) - Claude 3.5 Sonnet
3. Perplexity (perplexity_chat) - Sonar with web search

Quick Start

-- Sentiment analysis
SELECT openai_chat('Analyze sentiment: ' || review_text) AS sentiment
FROM customer_reviews
LIMIT 100;

-- Classification
SELECT 
    ticket_id,
    claude_chat('Classify as: technical, billing, general:\n' || message) AS category
FROM support_tickets;

Provider Selection Guide

Provider	Speed	Cost	Quality	Use Case
GPT-3.5	Fast	$	Good	High-volume, simple
GPT-4	Medium	$$$	Excellent	Complex reasoning
Claude 3.5	Fast	$$	Excellent	Analysis, structured
Perplexity	Fast	$	Good	Web-grounded info

Best Practices

1. Cache everything: LLM responses rarely change
2. Batch processing: 10-100x more efficient
3. Right model for task: Don't use GPT-4 for simple classification
4. Structured prompts: JSON output for easier parsing
5. Monitor costs: Track usage per provider

References

• See ../../src/skills/llm_skill.py
• See ../../src/tools/prompt_templates.py
• See ../../examples/sentiment_analysis/