#Knowledge Graph

The application efficiently transforms various formats of unstructured data into a structured Neo4j knowledge graph. It utilizes Large Language Models such as OpenAI and Gemini to extract nodes, relationships, and properties via the Langchain framework. Files can be uploaded from local devices, Google Cloud Storage (GCS), or Amazon S3, with the flexibility to select a preferred LLM model. Key features include supporting custom schemas, interactive graph visualization in Bloom, and conducting data interactions using conversational queries within Neo4j. Ensure a Neo4j Database V5.15+ with APOC support. Available for deployment via Docker locally or Google Cloud Platform.

KG-RAG combines knowledge graphs with large language models to optimize prompt generation using a comprehensive biomedical knowledge base. The system facilitates efficient retrieval-augmented generation, crucial in tasks such as summarizing FDA drug information for conditions like Bardet-Biedl Syndrome. KG-RAG can be run through a straightforward setup, offering smooth interaction with models like GPT and Llama. It is benchmarked with the BiomixQA dataset to demonstrate its potential in advancing biomedical natural language processing.

Chat is a versatile library for semantic understanding, enabling quick and custom chatbot development leveraging natural language understanding and machine learning. It offers advanced semantic analysis and knowledge graph tools, streamlining the creation process and minimizing repetitive tasks. Chat efficiently supports multiple users and custom multi-turn dialogues, providing an optimal option for exploring intelligent systems, utilizing NLP and ML in Q&A, or developing knowledge graphs.

Discover Nucleoid's approach to advancing Neuro-Symbolic AI, integrating symbolic logic and knowledge graphs for improved reasoning. The system adapts with new information, promoting dynamic decision-making and offering clear explanations via a logic graph. Supporting various programming languages, Nucleoid aims to enhance knowledge graphs with its declarative, logic-based runtime, addressing AI gaps with a holistic and rule-based reasoning framework.

Zep's Knowledge Graph enables continuous learning and personalized AI experiences. Its technology offers fast and relevant data retrieval, supporting diverse data types including chat messages and JSON. With APIs in Python, TypeScript, and Go, it integrates smoothly with frameworks like LangChain. Zep Community Edition is open-source, while Zep Cloud boosts scalability and low-latency memory retrieval with extra features like dialog classification.

Discover methods for detecting and mitigating hallucinations in large language models. This resource covers uncertainty estimation, graph evaluations, and multimodal detections, providing insights into model reliability with vast datasets and diverse metrics. It includes solutions such as context-aware decoding and interactive alignment to address factual inconsistencies, vital for developers optimizing AI's factual accuracy and trustworthiness.

GraphRAG is a data pipeline designed to convert unstructured text into structured data using LLMs. It enhances data interpretation through knowledge graph structures, offering a methodology for analyzing private narrative data. The GraphRAG Accelerator allows integration with Azure, simplifying deployment. While effective, managing GraphRAG's indexing is crucial as it can be costly. For optimal performance, it is advisable to use prompt tuning and follow given guidelines. Microsoft Research's blog and GitHub discussions provide further insights.

This project compiles extensive Chinese medical NLP resources, such as terminologies, corpora, word vectors, pre-trained models, and knowledge graphs. It includes tools for named entity recognition, QA systems, and information extraction. Highlighting resources like the CBLUE dataset, the project supports the growth of Chinese medical NLP technology and community. It is an essential source for researchers and practitioners focusing on Chinese medical texts, offering comprehensive tools from basic terminologies to advanced language models.

The Think-on-Graph project, presented at ICLR 2024, describes a method for integrating deep reasoning in large language models using knowledge graphs. Now available in a new repository, this project includes resources like datasets, evaluation scripts, and setup guides for Freebase and Wikidata. Users should install Freebase or Wikidata for best results, with complete guidance in the README file. Discover its applications and experiment outcomes through visual demonstrations to maximize utilization of ToG.

Explore a versatile platform for building scalable Retrieval-Augmented Generation (RAG) applications, featuring multimodal ingestion and hybrid search capabilities. With a robust containerized RESTful API, comprehensive app management, and observability features, R2R simplifies the deployment of advanced RAG solutions. Key functionalities include automatic relationship extraction and knowledge graph construction, enhanced by the latest updates such as Hatchet orchestration and Unstructured.io for improved ingestion. Refer to the documentation for in-depth insights into streamlined RAG development.

Discover tools and resources for prompt learning and KG applications, including tutorials and research models. This project supports the exploration and application of NLP and KG technologies.

This comprehensive project provides resources and experiences in NLP, encompassing text classification, information extraction, knowledge graphs, machine translation, QA systems, text generation, and more, making it an ideal starting point for newcomers aiming to understand and implement NLP effectively.