Graph_RAG is a Python-based framework designed to build and query knowledge graphs for retrieval-augmented generation (RAG). It supports ingestion of unstructured documents, automated extraction of entities and relationships using LLMs or NLP tools, and storage in graph databases such as Neo4j. With Graph_RAG, developers can construct connected knowledge graphs, execute semantic graph queries to identify relevant nodes and paths, and feed the retrieved context into LLM prompts. The framework provides modular pipelines, configurable components, and integration examples to facilitate end-to-end RAG applications, improving answer accuracy and interpretability through structured knowledge representation.

NuMind is a powerful tool that allows users to develop tailored NLP models by teaching an AI to perform specific information extraction tasks. It automates several processes including classification, name entity recognition (NER), and data structuring, enabling users to extract meaningful insights from unstructured texts. The platform supports multilingual models and provides collaborative tools, GPU optimization, and extensive API access, designed especially for easy deployment in real-world applications.

GPT-Stores is a specialized marketplace for custom GPT versions, developed by experts and community contributors. Users can browse a wide array of GPTs tailored for specific tasks, ranging from business automation to personal assistance. Each GPT is designed to offer unique functionalities, helping users optimize their workflows and improve productivity.

PrivacyDoc leverages advanced Natural Language Processing (NLP) to provide in-depth analysis and querying capabilities for PDF and ebook documents. With PrivacyDoc, you can unearth insights and extract key information effortlessly. The intuitive interface and powerful backend allow users to work with large and complex documents seamlessly, making knowledge retrieval faster and more efficient, ultimately enhancing productivity and research capabilities.

Docsumo leverages advanced machine learning algorithms to automate the extraction of vital data from documents. By combining Natural Language Processing (NLP) and Optical Character Recognition (OCR), it efficiently scans various types of files such as invoices and contracts. This automation not only speeds up the data processing time but also minimizes errors associated with manual data entry, enabling businesses to focus on core activities rather than tedious paperwork.