Building blocks for rapid development of GenAI applications

Explore LangChain and build powerful chatbots that interact with your own data. Gain insights into document loading, splitting, retrieval, question answering, and more.

阶跃星辰知识库管理系统

A Python package to access different LLMs, embeddings, vector stores etc.

Course LangChain Chat with Your Data

Examples of integrating LangChain with vector stores for semantic search and RAG. Covers Chroma and FAISS. Ideal for learning how to store, index, and query embeddings with LLMs.

Developed A LLM Powered Recommendation System, Based on Instructor-XL, Google Flan / GPT3.5 and FAISS. Conducted a consumer survey to understand the problems of a consumer, created the problem statement from the insights derived from the survey.

A proof of concept for Question-Answering API on a specific topic using RAG from pre-defined document(s) on the topic

A simple demo of a Flutter application that implements Retrieval-Augmented Generation (RAG) using OpenAI's APIs.

Examples of using different retrievers in LangChain, including Wikipedia, Vector Store, MMR, MultiQuery, and Contextual Compression retrievers. Demonstrates how to fetch relevant context for semantic search, Q&A, summarization, and retrieval-augmented generation (RAG).

This repository showcases Python scripts demonstrating interactions with various models using the LangChain library. From fine-tuning to custom runnables, explore examples with Gemini, Hugging Face, and Mistral AI models.

Demonstrate two types of chat interactions with a Mattermost instance leveraging Mattermost OpenAPI v3 spec and Spring AI.