Accelerate your Gen AI with NVIDIA NIM and NVIDIA AI Workbench

This code is an implementation of a chatbot using LLM chat model API and Langchain.

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.

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.

The easiest way to supercharge your apps with LLM!

Explore Functions, Tools and Agents with LangChain along with LangChain Expression Language

A collection of Jupyter notebook experiments and applications centered around Generative AI with LLMs.

Step-by-step LangChain-based LLM implementations and experiments

向量化、RAG增强、智能体Agent、LangChain LCEL 管道组合等等。持续更新...

This is a simple application of the LLM (Lattice Conditional Execution Language) with the LCEL (Lattice Conditional Execution Language) library. The application is a simple calculator that can perform addition, subtraction, multiplication, and division.

This Conversational Q&A Chatbot is a Generative AI project built using LangChain. It enhances user interactions by maintaining memory of past exchanges, allowing for more context-aware responses.

RAGulator is a dynamic framework that enables manual evaluation of RAG pipelines created with LangChain Expression Language (LCEL) in real-time

Repo for Gen AI projects and tasks using LangChain and other Libraries

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.

Hands on experience with LangChain - Prompts, Memory Buffers, Agents

Simple RAG app to demonstrate functionalities using PL md files