🔍 LLM 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 NLP and LLM library with 🤗 Awesome model zoo, supporting wide-range of NLP tasks from research to industrial applications, including 🗂Text Classification, 🔍 Neural Search, ❓ Question Answering, ℹ️ Information Extraction, 📄 Document Intelligence, 💌 Sentiment Analysis etc.

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

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

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

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

State of the Art Natural Language Processing

LLM Chain for answering questions from documents with citations

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.

Providing enterprise-grade LLM-based development framework, tools, and fine-tuned models.

FAQ-based Question Answering System

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

Generative AI SDK for Web to build AI Assistants for apps built with JavaScript, React, Angular, Vue, Ember, Electron

Datasets, SOTA results of every fields of Chinese NLP

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

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

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

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

Harness LLMs with Multi-Agent Programming