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.

LongCite is an open-source model that trains large language models (LLMs) to produce accurate responses and precise sentence-level citations in long text question-answering scenarios. Its significance lies in enhancing the accuracy and credibility of question-answering systems, allowing users to verify the sources of the output information. LongCite supports context lengths of up to 128K and provides two models: LongCite-glm4-9b and LongCite-llama3.1-8b, trained respectively on GLM-4-9B and Meta-Llama-3.1-8B.

Fact Finder is an open-source intelligent question-answering system that utilizes language models and knowledge graphs to generate natural language answers and provide evidence. The system generates Cypher queries by invoking language models, queries the knowledge graph for answers, and uses another language model to produce the final natural language response. The main advantages of Fact Finder include the ability to provide transparency, allowing users to see both queries and evidence, as well as offering intuitive evidence through visual subgraphs.

AI Timeline is an open-source project that records significant technological milestones in the field of artificial intelligence. It meticulously chronicles key developments in AI technologies, including text-to-image, text-to-video, and large language models. The project is built with Vue and TypeScript, providing a platform for AI enthusiasts and researchers to explore the history and trends of AI.

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.

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.

ChatQA is a series of conversational question answering (QA) models that can achieve GPT-4 level accuracy. We propose a two-stage instruction tuning method that can significantly improve the zero-shot conversational QA results of large language models (LLMs). To handle retrieval in dialogue-based QA, we fine-tuned dense retrievers on multi-turn QA datasets, which provides comparable results to using state-of-the-art query rewrite models while significantly reducing deployment costs. Notably, our ChatQA-70B outperforms GPT-4 (54.14 vs. 53.90) on the average score across 10 conversational QA datasets without relying on any synthetic data from the OpenAI GPT model.

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.

LongCite is an open-source model that trains large language models (LLMs) to produce accurate responses and precise sentence-level citations in long text question-answering scenarios. Its significance lies in enhancing the accuracy and credibility of question-answering systems, allowing users to verify the sources of the output information. LongCite supports context lengths of up to 128K and provides two models: LongCite-glm4-9b and LongCite-llama3.1-8b, trained respectively on GLM-4-9B and Meta-Llama-3.1-8B.

Fact Finder is an open-source intelligent question-answering system that utilizes language models and knowledge graphs to generate natural language answers and provide evidence. The system generates Cypher queries by invoking language models, queries the knowledge graph for answers, and uses another language model to produce the final natural language response. The main advantages of Fact Finder include the ability to provide transparency, allowing users to see both queries and evidence, as well as offering intuitive evidence through visual subgraphs.

AI Timeline is an open-source project that records significant technological milestones in the field of artificial intelligence. It meticulously chronicles key developments in AI technologies, including text-to-image, text-to-video, and large language models. The project is built with Vue and TypeScript, providing a platform for AI enthusiasts and researchers to explore the history and trends of AI.

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.

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.

ChatQA is a series of conversational question answering (QA) models that can achieve GPT-4 level accuracy. We propose a two-stage instruction tuning method that can significantly improve the zero-shot conversational QA results of large language models (LLMs). To handle retrieval in dialogue-based QA, we fine-tuned dense retrievers on multi-turn QA datasets, which provides comparable results to using state-of-the-art query rewrite models while significantly reducing deployment costs. Notably, our ChatQA-70B outperforms GPT-4 (54.14 vs. 53.90) on the average score across 10 conversational QA datasets without relying on any synthetic data from the OpenAI GPT model.