This document introduces Flask, a Python micro web framework, covering its main features, installation process, built-in dependencies, popular community extensions, and key differences from Django.
This document describes how to build flexible, composable chains using LangChain Expression Language (LCEL), including prompt templates, pipe operators, runnable primitives, and type coercion mechanisms.
This document describes chains in LangChain for generating responses, memory storage mechanisms, and agents for dynamic action sequencing to build sophisticated LLM applications.
This document defines LangChain and explores its core components including language models, chat models, chat messages, prompt templates, example selectors, and output parsers for building LLM applications.
This document introduces LangChain Expression Language (LCEL), covering how to build flexible chains using the pipe operator, structure prompts with templates, and develop reusable patterns for AI applications.
This document introduces LangChain, an open-source Python framework for developing LLM applications, exploring its benefits, practical uses, and integration capabilities with various data types.