langchain-expression-language

An AI developer that writes LangChain Expression Language (LCEL).

You’ll explore new advancements like ChatGPT’s function calling capability, and build a conversational agent using a new syntax called LangChain Expression Language (LCEL) for tasks like tagging, extraction, tool selection, and routing.

Langchain RAG model, with output streaming on Streamlit and using persistent VectorStore in disk

The "lcel-tutorial" repo is designed for mastering LangChain Expression Language (LCEL), offering exercises to build stateful, multi-actor LLM applications. It's a hands-on guide to leveraging LCEL for complex workflows and agent-like behaviors. Perfect for enthusiasts eager to explore LLM's potential.

A personal learning space for LangChain, featuring code snippets, notes, and mini-projects to explore LLM integrations. Ideal for beginners aiming to learn quickly through structured examples.

A lightweight implementation of a LangChain Expression Language (LCEL)-inspired pipeline execution framework built from scratch in Python using Object-Oriented Programming, operator overloading, and composable runnable components.

A collection of simple, practical examples to learn the basics of LangChain. This repo covers setup, memory management, chaining operations, structured output parsing, and retrieval-based question answering. Perfect for developers starting with LangChain who want to grasp its core concepts and functionalities.

This project combines the power of vector databases, large language models, and chat history management to create an interactive PDF chatbot

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

A text translation application built with LangChain Expression Language (LCEL) that translates English text into other languages. It uses the Groq API with the Llama 3.1 8B Instant model for fast inference and includes a FastAPI backend with a Streamlit frontend.

An AI-powered Multi-Agent Research System built with LangChain, Groq, Tavily Search, and Streamlit. The system uses specialized agents to search the web, extract relevant content, generate structured research reports, and critique the results, providing high-quality research insights on any topic.