技巧1:条件分支的性能陷阱
问题: 复杂条件判断导致工作流执行缓慢
{
"condition": "{{#if (and (gt user.score 80) (eq user.level 'premium') (contains user.tags 'vip'))}}true{{/if}}"
}
{
"variable_assignment": {
"is_qualified": "{{user.score > 80 && user.level === 'premium' && user.tags.includes('vip')}}"
},
"condition": "{{is_qualified}}"
}
技巧2:批量处理的内存优化
场景: 处理大量数据时内存溢出
def process_large_dataset(data):
chunk_size = 100
results = []
for i in range(0, len(data), chunk_size):
chunk = data[i:i + chunk_size]
processed = [process_item(item) for item in chunk]
results.extend(processed)
import gc
gc.collect()
return results
技巧3:LLM调用的成本控制
llm_node:
model: "gpt-3.5-turbo"
temperature: 0.1
max_tokens: 150
cache_key: "{{hash(prompt + model + temperature)}}"
fallback:
- model: "gpt-3.5-turbo"
- model: "claude-instant"
- model: "local-llm"
调试神器:工作流日志分析
grep "execution_time" dify.log | awk '{print $3}' | sort -n | tail -10
grep "node_execution" dify.log | jq '.node_name, .duration' | paste - -
grep "token_usage" dify.log | jq '.total_tokens' | awk '{sum+=$1} END {print "Total tokens:", sum}'
生产环境配置
services:
dify-api:
environment:
- CELERY_WORKER_CONCURRENCY=4
- MAX_WORKFLOW_EXECUTION_TIME=300
- ENABLE_WORKFLOW_CACHE=true
deploy:
resources:
limits:
memory: 2G
reservations:
memory: 1G
性能基准
- 简单工作流: < 2秒
- 复杂工作流: < 10秒
- LLM调用: < 5秒
- 批量处理: < 30秒
超过基准?检查节点配置和数据流设计。
记住:工作流设计比模型选择更重要。