runnable-parallel

A Production-Grade Streamlit Chatbot application built using LangChain Expression Language (LCEL), Pydantic Validation, and Groq LLM.

LangChainRunnables is a Python-based project showcasing and learing five distinct LangChain workflows—Branch, Lambda, Parallel, Passthrough, and Sequence—using OpenRouter’s free API. It demonstrates AIdriven text processing tasks generating facts, summarizing reports, creating notes and quizzes, responding to sentiment feedback, and crafting jokes

Production-ready AI Study Assistant built with LangChain, Google Gemini, RunnableParallel, RunnableBranch, and Streamlit. Generates notes, interview questions, answers, and sentiment-based feedback.

A collection of runnable examples using LangChain and Groq LLMs, demonstrating different AI workflow patterns like sequence, parallel, branch, passthrough, and lambda operations. Perfect for learning modular AI development with LLMs.

Examples of LangChain Runnables including Sequence, Parallel, Passthrough, Lambda, and Branch. Demonstrates how to build modular, scalable pipelines for tasks like text processing, summarization, and conditional workflows using reusable components.

A collection of practical examples demonstrating LangChain's Runnable architecture for building modular, scalable, and production-ready LLM applications.

demonstrates LangChain Runnables for building modular AI workflows. It covers sequential execution, parallel processing, conditional branching, lambda-based transformations, and direct passthroughs using Google Gemini and OpenAI models. Each file showcases a different runnable: generating reports, summarization, joke creation and content writing