Local Knowledge Graph is a Flask-based web application that uses a local Llama language model to process user queries, generate step-by-step reasoning, and visualize the thought process in an interactive knowledge graph format. It can also find and display relevant questions and answers based on semantic similarity. Key advantages of this application include real-time visualization of the reasoning process, dynamic knowledge graph representation, computation and display of the strongest reasoning paths, and related Q&A based on semantic similarity.

IncarnaMind is an open-source project aimed at enabling conversational interactions with personal documents (PDF, TXT) using large language models (LLMs) such as GPT, Claude, and local open-source LLMs. The project enhances query efficiency and improves the accuracy of LLMs through a sliding window chunking mechanism and integrated retrievers. It supports multi-document conversational Q&A, overcoming the limitations of single-document interaction while being compatible with various file formats and LLM models.

GraphRAG-Ollama-UI is a local adaptation version of Microsoft's GraphRAG, supporting local model support using Ollama. A interactive user interface is provided through the Gradio UI, making it more convenient for users to manage data, run queries and visualize results. The main advantages of this model include local model support, cost-effectiveness, interactive user interface, real-time graph visualization, file management, settings management, output exploration, and logging.

MaxKB is an enterprise-grade knowledge base question-answering system powered by LLMs. It aims to be the ultimate brain for businesses. Features include document upload, automatic online document crawling, and a smart question-answering experience. It also supports quick embedding into third-party business systems. The tech stack includes Vue.js, Python/Django, Langchain, and PostgreSQL/pgvector.

This GitHub repository serves as a centralized hub for resources related to generative artificial intelligence, including the latest research papers, interview questions, course materials, and code notebooks. The content is updated regularly to ensure developers and professionals can stay up-to-date with the latest advancements and boost productivity. Key resources include abstracts of papers, categorized interview questions, lists of free courses, and open-source notebooks, as well as usage scenarios and examples.

MathPile is a mathematics-centric corpus containing approximately 9.5 billion tokens. It draws mathematical content from textbooks (including lecture notes), arXiv, Wikipedia, ProofWiki, StackExchange, and web pages. It is suitable for K-12, university, graduate-level, and math competition applications. MathPile boasts high data quality and comprehensive data documentation to enhance transparency and provide users with flexible data utilization capabilities. MathPile adheres to the BY-NC-SA 4.0 license and plans to release a commercially available version soon.

DocGraphLM is a document graph language model for information extraction and question answering. It employs advanced vision-rich document understanding techniques, combining pre-trained language models and graph semantics. Its uniqueness lies in proposing a joint encoder architecture to represent documents and a novel link prediction method to reconstruct the document graph. DocGraphLM predicts the direction and distance between nodes through a convergent joint loss function, prioritizing neighborhood restoration and minimizing the weight of remote node detection. Experiments on three SotA datasets demonstrate that incorporating graph features consistently improves performance in information extraction and question-answering tasks. Furthermore, we report that employing graph features accelerates convergence during training, despite these features being constructed solely through link prediction.

QAnything is a local knowledge question-and-answer system that supports any file format and database. It can easily import various format files stored locally and provides accurate, fast, and reliable answers. Currently supported formats include: PDF, Word (doc/docx), PPT, Markdown, Eml, TXT, images (jpg, png, etc.), webpage links, and more formats will be added in the future. QAnything has data security, supports offline installation and usage; supports cross-language questioning in Chinese and English; supports massive data questioning, solving the problem of data retrieval degradation for large-scale data; it is a high-performance production-grade system that can be directly used in enterprise applications; one-click installation and deployment, user-friendly experience out of the box; supports multi-knowledge base questioning and other functions.

Local Knowledge Graph is a Flask-based web application that uses a local Llama language model to process user queries, generate step-by-step reasoning, and visualize the thought process in an interactive knowledge graph format. It can also find and display relevant questions and answers based on semantic similarity. Key advantages of this application include real-time visualization of the reasoning process, dynamic knowledge graph representation, computation and display of the strongest reasoning paths, and related Q&A based on semantic similarity.

IncarnaMind is an open-source project aimed at enabling conversational interactions with personal documents (PDF, TXT) using large language models (LLMs) such as GPT, Claude, and local open-source LLMs. The project enhances query efficiency and improves the accuracy of LLMs through a sliding window chunking mechanism and integrated retrievers. It supports multi-document conversational Q&A, overcoming the limitations of single-document interaction while being compatible with various file formats and LLM models.

GraphRAG-Ollama-UI is a local adaptation version of Microsoft's GraphRAG, supporting local model support using Ollama. A interactive user interface is provided through the Gradio UI, making it more convenient for users to manage data, run queries and visualize results. The main advantages of this model include local model support, cost-effectiveness, interactive user interface, real-time graph visualization, file management, settings management, output exploration, and logging.

MaxKB is an enterprise-grade knowledge base question-answering system powered by LLMs. It aims to be the ultimate brain for businesses. Features include document upload, automatic online document crawling, and a smart question-answering experience. It also supports quick embedding into third-party business systems. The tech stack includes Vue.js, Python/Django, Langchain, and PostgreSQL/pgvector.

This GitHub repository serves as a centralized hub for resources related to generative artificial intelligence, including the latest research papers, interview questions, course materials, and code notebooks. The content is updated regularly to ensure developers and professionals can stay up-to-date with the latest advancements and boost productivity. Key resources include abstracts of papers, categorized interview questions, lists of free courses, and open-source notebooks, as well as usage scenarios and examples.

MathPile is a mathematics-centric corpus containing approximately 9.5 billion tokens. It draws mathematical content from textbooks (including lecture notes), arXiv, Wikipedia, ProofWiki, StackExchange, and web pages. It is suitable for K-12, university, graduate-level, and math competition applications. MathPile boasts high data quality and comprehensive data documentation to enhance transparency and provide users with flexible data utilization capabilities. MathPile adheres to the BY-NC-SA 4.0 license and plans to release a commercially available version soon.

DocGraphLM is a document graph language model for information extraction and question answering. It employs advanced vision-rich document understanding techniques, combining pre-trained language models and graph semantics. Its uniqueness lies in proposing a joint encoder architecture to represent documents and a novel link prediction method to reconstruct the document graph. DocGraphLM predicts the direction and distance between nodes through a convergent joint loss function, prioritizing neighborhood restoration and minimizing the weight of remote node detection. Experiments on three SotA datasets demonstrate that incorporating graph features consistently improves performance in information extraction and question-answering tasks. Furthermore, we report that employing graph features accelerates convergence during training, despite these features being constructed solely through link prediction.

QAnything is a local knowledge question-and-answer system that supports any file format and database. It can easily import various format files stored locally and provides accurate, fast, and reliable answers. Currently supported formats include: PDF, Word (doc/docx), PPT, Markdown, Eml, TXT, images (jpg, png, etc.), webpage links, and more formats will be added in the future. QAnything has data security, supports offline installation and usage; supports cross-language questioning in Chinese and English; supports massive data questioning, solving the problem of data retrieval degradation for large-scale data; it is a high-performance production-grade system that can be directly used in enterprise applications; one-click installation and deployment, user-friendly experience out of the box; supports multi-knowledge base questioning and other functions.