Moving LangChain applications from development to production introduces significant operational demands. While functionality is key, real-world use requires applications that are responsive, cost-effective, and capable of handling user load. This chapter focuses on the techniques needed to achieve these goals.
You will learn to identify performance bottlenecks within your LangChain applications, whether they lie in LLM interactions, data retrieval, or custom processing steps. We will cover strategies for optimizing LLM calls, including caching, reducing consumption, and parallelization. Managing operational costs through effective monitoring and resource allocation is also addressed. Furthermore, we will examine how to scale retrieval systems for large datasets and high query volumes, design applications for high concurrency, and utilize batch processing for offline tasks. By the end of this chapter, you will have the practical knowledge to tune your LangChain applications for efficiency and scale.
6.1 Identifying Performance Bottlenecks
6.2 LLM Call Optimization Techniques
6.3 Cost Management and Token Usage Tracking
6.4 Scaling Data Retrieval Systems
6.5 Handling High Concurrency and Throughput
6.6 Batch Processing for Offline Tasks
6.7 Practice: Performance Tuning a LangChain Chain