AI orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.

Easy-to-use and powerful LLM and SLM library with awesome model zoo.

大规模中文自然语言处理语料 Large Scale Chinese Corpus for NLP

SoTA production-ready AI retrieval system. Agentic Retrieval-Augmented Generation (RAG) with a RESTful API.

An open source library for deep learning end-to-end dialog systems and chatbots.

LLM-powered framework for deep document understanding, semantic retrieval, and context-aware answers using RAG paradigm.

农业知识图谱(AgriKG)：农业领域的信息检索，命名实体识别，关系抽取，智能问答，辅助决策

Transformers for Information Retrieval, Text Classification, NER, QA, Language Modelling, Language Generation, T5, Multi-Modal, and Conversational AI

State of the Art Natural Language Processing

AdalFlow: The library to build & auto-optimize LLM applications.

OP Vault ChatGPT: Give ChatGPT long-term memory using the OP Stack (OpenAI + Pinecone Vector Database). Upload your own custom knowledge base files (PDF, txt, epub, etc) using a simple React frontend.

Rust native ready-to-use NLP pipelines and transformer-based models (BERT, DistilBERT, GPT2,...)

FAQ-based Question Answering System

The Self-Coding System for Your App — Alan AI SDK for Web

Dealing with all unstructured data, such as reverse image search, audio search, molecular search, video analysis, question and answer systems, NLP, etc.

Self-contained Machine Learning and Natural Language Processing library in Go

Datasets, SOTA results of every fields of Chinese NLP

北京航空航天大学大数据高精尖中心自然语言处理研究团队开展了智能问答的研究与应用总结。包括基于知识图谱的问答（KBQA），基于文本的问答系统（TextQA），基于表格的问答系统（TableQA）、基于视觉的问答系统（VisualQA）和机器阅读理解（MRC）等，每类任务分别对学术界和工业界进行了相关总结。

A collection of research on knowledge graphs

🏡 Fast & easy transfer learning for NLP. Harvesting language models for the industry. Focus on Question Answering.