Summary
In this chapter, we've taken our first steps with LangChain, laying the groundwork for building sophisticated LLM applications. We began by examining the limitations of raw LLMs and how LangChain addresses these challenges through its agent-based architecture. We compared LangChain with other frameworks in the ecosystem, understanding why it has emerged as a leading solution for production AI applications.After setting up our development environment and configuring necessary API keys, we explored the modern LangChain Expression Language (LCEL), which provides a powerful way to compose LLM applications. We then dove into LangChain's core components:
- Model interfaces for both traditional LLMs and chat models
- Prompt templates and management systems
- Chains and sequences for complex workflows
- Memory systems for maintaining context
We examined various model providers, from cloud services like OpenAI and Anthropic to local deployment options using Ollama and HuggingFace....
