# Knowledge Graph

Yuxi Know
Yuxi-Know is a knowledge graph question-and-answer system based on a large model RAG knowledge base, built using Llamaindex + VueJS + Flask + Neo4j. It supports model calls from OpenAI, mainstream domestic large model platforms, and local vllm deployment, and can implement functions such as knowledge base Q&A, knowledge graph retrieval, and online search. The main advantages of this system are flexible adaptation to multiple models, support for multiple knowledge base formats, and strong knowledge graph integration capabilities. It is suitable for enterprises and research institutions that need efficient knowledge management and intelligent question-and-answer, and has high technological advancement and practicality.
Knowledge Management
56.0K

Google AI Mode
AI Mode is an experimental feature in Google Search, developed based on the Gemini 2.0 model. It leverages advanced reasoning and multimodal capabilities to provide users with deeper, more comprehensive search results. This feature aims to help users more efficiently handle complex, multi-part questions and deliver high-quality responses using real-time data and knowledge graphs. The launch of AI Mode reflects Google's ongoing innovation in enhancing the search experience and showcases the potential of generative AI in information retrieval.
AI Search
55.8K

Graphiti
Graphiti is a technology model focused on building dynamic temporal knowledge graphs, designed to handle constantly changing information and the evolution of complex relationships. By combining semantic search and graph algorithms, it supports extracting knowledge from unstructured text and structured JSON data and can perform point-in-time queries. Graphiti is the core technology of the Zep memory layer, supporting long-term memory and state-based reasoning. It is suitable for application scenarios requiring dynamic data processing and complex task automation, such as sales, customer service, healthcare, finance, and more.
Knowledge Management
65.1K

Kg Gen
kg-gen is an artificial intelligence-based tool that extracts knowledge graphs from plain text. It supports text inputs ranging from single sentences to lengthy documents and can handle conversational-style messages. Leveraging advanced language models and structured output techniques, this tool helps users quickly construct knowledge graphs, suitable for natural language processing, knowledge management, and model training, among other applications. kg-gen provides flexible interfaces and a variety of functionalities designed to streamline the knowledge graph generation process and enhance efficiency.
Knowledge Management
66.2K

KET RAG
KET-RAG (Knowledge-Enhanced Text Retrieval Augmented Generation) is a powerful retrieval-augmented generation framework enhanced with knowledge graph technology. It achieves efficient knowledge retrieval and generation through a multi-granularity indexing framework, such as a knowledge graph skeleton and a text-keyword bipartite graph. This framework significantly improves retrieval and generation quality while reducing indexing costs, making it well-suited for large-scale RAG applications. Developed in Python, KET-RAG supports flexible configuration and extension, catering to the needs of developers and researchers seeking efficient knowledge retrieval and generation.
Model Training and Deployment
67.9K

Videorag
VideoRAG is an innovative retrieval-augmented generation framework specifically developed for understanding and processing videos with very long contexts. It intelligently combines graph-driven textual knowledge anchoring with hierarchical multimodal context encoding, enabling comprehension of videos of unrestricted lengths. The framework dynamically builds knowledge graphs, maintains semantic coherence across multiple video contexts, and enhances retrieval efficiency through adaptive multimodal fusion mechanisms. Key advantages of VideoRAG include efficient processing of long-context videos, structured video knowledge indexing, and multimodal retrieval capabilities, allowing it to provide comprehensive answers to complex queries. This framework holds significant technical value and application prospects in the field of long video understanding.
Video Editing
52.7K
English Picks

Data Commons
Data Commons is a powerful public data platform designed to provide a unified knowledge graph by integrating global public data, helping users easily explore and analyze data. Initiated by Google, it supports the integration of multiple data sources and offers a variety of visualization tools and APIs, facilitating data exploration and research. The key advantage of Data Commons is the standardization and unification of data, allowing users to quickly access and analyze data without complex preprocessing. Additionally, it supports community contributions, enabling users to share their analyses and insights to advance the field of data science. Data Commons is suitable for researchers, data analysts, policymakers, and any group that needs public data to support decision-making. Its free access model lowers the barriers to data usage, promoting widespread dissemination and application of data.
Data Analysis
49.1K

Potpie
Potpie is a technology platform designed for developers, offering AI agents based on code repositories to aid in debugging, testing, system design, code review, and documentation generation tasks. This product leverages powerful knowledge graph technology to enable AI agents to deeply understand the context of code repositories, resulting in high-precision execution of engineering tasks. Potpie's main advantages include its high level of customization and ease of integration, significantly enhancing development efficiency and code quality. The product offers a free trial, and an open-source version is also available.
Development & Tools
61.8K

Graphagent
GraphAgent is an automated agent pipeline designed to manage explicit graphic dependencies and implicit semantic interdependencies to suit real-world data scenarios involving predictive tasks (e.g., node classification) and generative tasks (e.g., text generation). It consists of three key components: a graphical generation agent that constructs knowledge graphs reflecting complex semantic dependencies; a planning agent that interprets various user queries and formulates corresponding tasks; and an execution agent that efficiently performs planned tasks while automating tool matching and invocation. GraphAgent reveals intricate relational information and data semantic dependencies by integrating language and graphical language models.
Automated Workflow
53.3K

Depth AI
Depth AI is an artificial intelligence product built by engineers that constructs knowledge graphs of codebases to address complex technical questions and supports the deployment of customized AI assistants in various work scenarios. The product aims to assist engineers and development teams in understanding and utilizing codebases more efficiently by integrating into existing tools and workflows, such as Slack, GitHub Copilot, and Jira, thereby enhancing team productivity. Key advantages of Depth AI include answering deep technical inquiries, comprehensive understanding of code graphs, abstract reasoning capabilities, and potential space interactions. Depth AI offers enterprise-grade security and compliance features to ensure data safety and does not use client data for model training.
Coding Assistant
57.7K

Whyhow Knowledge Graph Studio
WhyHow Knowledge Graph Studio is an open-source platform designed to streamline the creation and management of RAG-native knowledge graphs. The platform offers rule-based entity parsing, modular graph construction, flexible data ingestion, and an API-first design, along with SDK support. It is built on a NoSQL database, providing a flexible and scalable storage layer that simplifies the retrieval and traversal of complex relationships. The platform is suitable for handling both structured and unstructured data, enabling the construction of exploratory graphs or highly structured constraint graphs, aiming for scalability and flexibility for both experimental and large-scale use.
Knowledge Management
70.9K

Graphusion
Graphusion is a pipeline tool designed for extracting knowledge graph triples from text. It builds knowledge graphs through a series of steps, including concept extraction, candidate triple extraction, and triple fusion. This tool is significant as it automates the extraction of structured information from large volumes of text data, supporting knowledge management and data science projects. The main advantages of Graphusion include its automation capabilities, adaptability to different datasets, and flexible configuration options. Developed by tdurieux, the related code and documentation can be found on GitHub. Currently, the tool is free, but the pricing strategy may change based on developer updates and maintenance.
Research Equipment
52.2K

Knowledge Table
Knowledge Table is an open-source toolkit designed to streamline the process of extracting and exploring structured data from unstructured documents. It allows users to create structured knowledge representations, such as tables and charts, through a natural language query interface. The toolkit features customizable extraction rules, finely-tuned formatting options, and data provenance displayed through the UI, adapting to a variety of use cases. Its goal is to provide business users with a familiar spreadsheet-like interface while offering developers a flexible and highly configurable backend, ensuring seamless integration with existing Retrieval-Augmented Generation (RAG) workflows.
AI Data Mining
62.7K

Graphreasoning
GraphReasoning is a project that utilizes generative AI techniques to transform 1,000 scientific papers into knowledge graphs. Through structured analysis, it computes node degrees, identifies communities and connectivity, evaluates clustering coefficients and betweenness centrality of key nodes, revealing a fascinating knowledge architecture. The graph exhibits scale-free properties and a high degree of interconnectivity, facilitating graph reasoning that leverages transitivity and isomorphism to uncover unprecedented interdisciplinary relationships for answering questions, identifying knowledge gaps, proposing innovative material designs, and predicting material behaviors.
AI Knowledge Graph
45.5K

Local Knowledge Graph
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.
AI Knowledge Base
51.6K

Fine AI Coding Workflows
Fine AI Coding Workflows is an AI-driven software development automation platform that accelerates development cycles through customized AI workflows. Built on the Atlas knowledge graph, this platform integrates the tools used by the team, providing rich contextual information for AI agents to execute tasks more accurately. It supports integration with various development tools such as OpenAI, Anthropic, Sentry, GitHub, etc., aiming to improve development efficiency, code quality, and problem-solving speed.
Development & Tools
53.3K

Muagent
muAgent is an innovative agent framework powered by a knowledge graph engine, supporting multi-agent orchestration and collaboration technology. It leverages LLM + EKG (Eventic Knowledge Graph for industry knowledge) technology, integrating FunctionCall, CodeInterpreter, and more, to automate complex SOP processes through drag-and-drop canvas design and lightweight text scripting. muAgent is compatible with various existing agent frameworks and features core functionalities such as complex reasoning, online collaboration, human interaction, and knowledge accessibility. This framework has been validated in multiple complex DevOps scenarios at Ant Group.
AI Agents
57.4K

Itext2kg
iText2KG is a Python package designed to leverage large language models for extracting entities and relationships from textual documents, incrementally constructing coherent knowledge graphs. It features zero-shot capabilities, enabling knowledge extraction across various domains without specific training. The package includes modules for document distillation, entity extraction, and relationship extraction, ensuring that entities and relationships are resolved and unique. It provides a visual representation of knowledge graphs through Neo4j, supporting interactive exploration and analysis of structured data.
AI knowledge map
60.4K

Fact Finder
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 Q&A
54.4K

Easy RAG
Easy-RAG is a Retrieval-Augmented Generation (RAG) system that is ideal for learners to understand and master RAG technology, while also being convenient for developers to use and expand independently. This system enhances retrieval efficiency and generation quality by integrating knowledge graph extraction tools, reranking mechanisms, and the FAISS vector database.
AI Model
94.1K
English Picks

Triplex
Triplex is an innovative open-source model that transforms large amounts of unstructured data into structured data. Its performance in knowledge graph construction surpasses that of GPT-4, and its cost is only one-tenth of it. By efficiently converting unstructured text into the foundational building blocks of knowledge graphs—semantic triples—it significantly reduces the cost of knowledge graph generation.
AI knowledge graph
84.2K

Memary
Memary is an open-source memory layer designed specifically for autonomous agents, enhancing their reasoning and learning abilities by mimicking human memory. It utilizes the Neo4j graph database to store knowledge and integrates with the Llama Index and Perplexity models to improve the querying capabilities of the knowledge graph. Key features of Memary include automatic memory generation, a memory module, system improvement, and memory recall, all aimed at seamlessly integrating with existing agents through minimal developer intervention, while offering visual data for memory analysis and system enhancement via a dashboard.
AI Model
55.2K

The Knowledge Graph Maker
knowledge_graph_maker is a Python library that converts any text into a knowledge graph based on a given ontology. A knowledge graph is a semantic network representing the connections between real-world entities and their relationships. This library assists users in analyzing textual content in depth through graph algorithms and centrality calculations, enabling connectivity analysis between concepts, and enhances communication with text through Graph Retrieval Augmentation (GRAG) techniques.
AI Knowledge Graph
58.2K
English Picks

Rdfox
RDFox is a rule-driven artificial intelligence technology developed by three professors from the University of Oxford's Department of Computer Science, drawing on decades of research in Knowledge Representation and Reasoning (KRR). Its uniqueness lies in: 1. Powerful AI reasoning capabilities: RDFox can create knowledge from data like humans, reason based on facts, ensuring result accuracy and interpretability. 2. High performance: As the only in-memory knowledge graph, RDFox significantly outperforms other graph technologies in benchmarks, capable of handling billions of triples of complex data storage. 3. Scalable deployment: RDFox offers high efficiency and optimized space usage and can be embedded in edge and mobile devices to operate independently as the brain of AI applications. 4. Enterprise-grade features: Including high performance, high availability, access control, interpretability, human-like reasoning capabilities, data import, and API support. 5. Incremental reasoning: RDFox's reasoning capabilities are updated instantaneously during data addition or deletion without performance degradation or the need for reloading.
Knowledge Management
50.5K

Llm Graph Builder
llm-graph-builder is an application that utilizes large language models (like OpenAI, Gemini, etc.) to extract nodes, relationships, and their attributes from unstructured data (PDFs, DOCS, TXTs, YouTube videos, webpages, etc.) and uses the Langchain framework to create structured knowledge graphs. It supports uploading files from local machines, GCS or S3 buckets, or network resources, selecting an LLM model, and generating knowledge graphs.
AI Knowledge Graph
148.8K
Fresh Picks

Meet Libai
Meet-libai is a knowledge graph and AI intelligent agent project centered around the Tang Dynasty poet Li Bai and his poetic works, leveraging artificial intelligence technology. This project innovates traditional cultural dissemination methods through digital means, allowing Li Bai's poetic culture to be more widely spread and deeply understood. The project utilizes natural language processing technology to construct a knowledge graph encompassing various dimensions of information about Li Bai, including his biography, poetic style, and artistic achievements. It also trains AI intelligent agents capable of high-quality interaction with users, providing a novel way to learn and experience traditional culture.
AI knowledge graph
56.3K

Knowledge Graph RAG
Knowledge Graph RAG is an open-source Python library that leverages knowledge graphs and document networks to improve the performance of large language models (LLMs). This library enables users to search and connect information through graph structures, providing richer context for language models. It is widely used in the natural language processing field, particularly in document retrieval and information extraction tasks.
AI Knowledge Graph
77.0K

Prettygraph
Prettygraph is a Python-based web application developed by @yoheinakajima, showcasing a new UI paradigm for dynamically converting text input into knowledge graphs. This project is a quick prototype aimed at providing a simple UI idea, generating knowledge graphs by highlighting the text in the UI in real-time.
AI knowledge graph
56.9K

Graphrag
GraphRAG (Graphs + Retrieval Augmented Generation) is a technology that enhances the understanding of textual datasets by combining text extraction, network analysis, and prompts and summaries from large language models (LLM). Set to be open-sourced on GitHub as part of Microsoft's research projects, this technology aims to enhance the processing and analysis capabilities of textual data through advanced algorithms.
AI Model
166.7K

Mygo
MyGO is a tool for multimodal knowledge graph completion. It processes discrete modal information as fine-grained labels to enhance completion accuracy. MyGO utilizes the transformers library to embed text labels and trains and evaluates on multimodal datasets. It supports custom datasets and provides training scripts for replicating experimental results.
AI Data Mining
67.1K
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