Core LLM Workflow Libraries: LangChain Fundamentals
Having established how to set up your environment and interact directly with LLM APIs, we now turn to libraries designed to streamline the development of more complex LLM applications. This chapter introduces LangChain, a widely-used Python framework for orchestrating LLM workflows.
You will learn about LangChain's purpose and structure. We will cover its fundamental components:
- Models: Interfaces for interacting with different LLM providers.
- Prompts: Tools for constructing and managing the inputs sent to LLMs.
- Output Parsers: Methods for extracting structured information from LLM responses.
We will demonstrate how to integrate various LLM services, create reusable prompt templates, and parse the output generated by the models. The chapter concludes with a hands-on exercise where you will apply these concepts to build a basic LangChain application.
Sections
4.1 Introduction to LangChain
4.2 Core Concepts: Models, Prompts, Output Parsers
4.3 Working with Different LLM Providers in LangChain
4.4 Creating and Using Prompt Templates
4.5 Parsing LLM Output Structures
4.6 Hands-on Practical: Building a Simple LangChain Application
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