#python #agent #agents #ai_search #chatbot #chatgpt #data_pipelines #deep_learning #document_parser #document_understanding #genai #graph #graphrag #llm #nlp #pdf_to_text #preprocessing #rag #retrieval_augmented_generation #table_structure_recognition #text2sql

RAGFlow is an open-source tool that helps businesses answer questions accurately using large language models and deep document understanding. It extracts information from various complex data formats, such as Word documents, Excel files, and web pages, and provides grounded citations to support its answers. You can try a demo online or set it up on your own server using Docker. The setup is relatively straightforward, requiring a few steps like cloning the repository, building the Docker image, and configuring the system settings. RAGFlow offers key features like template-based chunking, reduced hallucinations, and compatibility with multiple data sources, making it a powerful tool for truthful question-answering capabilities. This benefits users by providing reliable and explainable answers, streamlining their workflow, and supporting integration with their business systems.

https://github.com/infiniflow/ragflow

#clojure #clojure #clojurescript #git #graph #knowledge_base #knowledge_graph #local_first #markdown #note_taking #org_mode #pkm

Logseq is a tool that helps you manage your knowledge and collaborate with others while keeping your data private. It offers powerful tools for organizing notes, collaborating, annotating PDFs, and managing tasks. You can use it to visually group and link your notes, videos, and images on a canvas. Logseq supports multiple file formats like Markdown and Org-mode and has a growing ecosystem of plugins and themes to customize your experience. It also has mobile apps, making it accessible anywhere. By using Logseq, you can improve your productivity and streamline your workflow easily.

https://github.com/logseq/logseq

#cplusplus #caffe #convolution #deep_learning #deep_neural_networks #diy #graph_algorithms #inference #inference_engine #maxpooling #ncnn #pnnx #pytorch #relu #resnet #sigmoid #yolo #yolov5

This course, "_动手自制大模型推理框架_" (Handcrafting Large Model Inference Framework), is a valuable resource for those interested in deep learning and model inference. It teaches you how to build a modern C++ project from scratch, focusing on designing and implementing a deep learning inference framework. The course supports latest models like LLama3.2 and Qwen2.5, and uses CUDA acceleration and Int8 quantization for better performance.

By taking this course, you will learn how to write efficient C++ code, manage projects with CMake and Git, design computational graphs, implement common operators like convolution and pooling, and optimize them for speed. This knowledge will be highly beneficial for job interviews and advancing your skills in deep learning. The course also includes practical demos on models like Unet and YoloV5, making it a hands-on learning experience.

https://github.com/zjhellofss/KuiperInfer

#go #cloud #cloud_management #graph #infrastructure_as_code #terraform

Terraform is a tool that helps you build, change, and manage your computer infrastructure safely and efficiently. It uses a simple code-like language to describe your infrastructure, so you can version and share it like any other code. Terraform shows you a plan of what it will do before making changes, avoids surprises, and works efficiently by managing resources in parallel. This reduces human errors and makes complex changes easier. You can learn more through tutorials and guides on the Terraform website, and even get certified to show off your skills. This helps you manage your infrastructure reliably and efficiently.

https://github.com/hashicorp/terraform

#python #deep_learning #geometric_deep_learning #graph_convolutional_networks #graph_neural_networks #pytorch

PyG (PyTorch Geometric) is a library that makes it easy to work with Graph Neural Networks (GNNs) using PyTorch. Here’s why it’s beneficial You can start training a GNN model with just 10-20 lines of code, especially if you're already familiar with PyTorch.
- **Comprehensive Models** The library supports large-scale graphs, dynamic graphs, and heterogeneous graphs, making it versatile for various applications.
- **Scalability** It provides extensive documentation, tutorials, and examples to help you get started quickly.

Overall, PyG simplifies the process of working with GNNs, making it a powerful tool for machine learning on graph-structured data.

https://github.com/pyg-team/pytorch_geometric

#cplusplus #cypher #database #embeddable #embedded #graph #graph_database #graphdb #neo4j #nosql #olap #wasm

Kuzu is a fast and scalable embedded graph database. It helps users handle complex data queries on large databases efficiently. Kuzu offers features like a flexible data model, fast join algorithms, and multi-core processing, making it ideal for analytical tasks. It integrates easily into applications without needing a separate server, reducing latency and complexity. This makes it beneficial for users who need to analyze large amounts of graph data quickly and effectively.

https://github.com/kuzudb/kuzu

#python #agents #graph #llms #rag

Graphiti helps AI systems handle constantly changing information by building real-time knowledge graphs that track relationships and historical data, allowing them to integrate user interactions, business data, and external sources seamlessly. Unlike traditional methods, it updates information instantly without needing full recomputations, enabling precise historical queries and efficient hybrid searches. This helps AI applications stay context-aware, automate tasks effectively, and manage complex, evolving data with minimal delay.

https://github.com/getzep/graphiti

#typescript #csv #diagrams #graph #json #nextjs #react #tool #visualization #yaml

JSON Crack is a free, open-source tool that instantly turns complex JSON, YAML, CSV, XML, or TOML data into clear, interactive graphs, making it easier to explore and understand your information. It lets you convert between formats, validate data, generate code (like TypeScript interfaces), run queries, and export visuals as images—all while keeping your data private since everything processes locally on your device[1][2][5].

https://github.com/AykutSarac/jsoncrack.com

#python #ai #ai_agents #ai_memory #cognitive_architecture #cognitive_memory #contributions_welcome #good_first_issue #good_first_pr #graph_database #graph_rag #graphrag #help_wanted #knowledge #knowledge_graph #neo4j #open_source #openai #rag #vector_database

Cognee is an open-source AI memory engine that helps improve how AI systems understand and process data. It mimics human cognitive processes, creating "memories" from various data types like text and images. This enhances the accuracy of large language models (LLMs) and allows them to recall past interactions and documents. Cognee is scalable, cost-effective, and integrates easily with existing systems, making it a valuable tool for developers seeking to boost AI performance without relying on expensive APIs.

https://github.com/topoteretes/cognee

#cplusplus #arduino #cansat #csv #embedded #graph #ground_station #iot #microcontroller #network #projects #qt #serial #serial_studio

Serial Studio is a free, easy-to-use tool that lets you visualize real-time data from devices like microcontrollers via serial ports, Bluetooth, or network connections. It works on Windows, macOS, and Linux, and offers customizable dashboards with various widgets to monitor sensor data, debug info, or telemetry. You can quickly plot data, export it as CSV for analysis, and even use advanced features like checksum validation and JavaScript data processing. It supports hobbyists, educators, and professionals by simplifying data monitoring and debugging, saving you time and effort in understanding your device’s output. Pro versions add commercial use and extra features[1][4][5].

https://github.com/Serial-Studio/Serial-Studio