Show HN: A Simple GraphRAG Implementation
nano-GraphRAG is a lightweight version of GraphRAG, offering efficient installation, asynchronous operations, customizable storage, and query capabilities, with future updates planned for improved functionality and community engagement.
Read original article
nano-GraphRAG is a simplified implementation of the GraphRAG model, designed to be smaller, faster, and cleaner than the official version while retaining core functionalities. The project consists of approximately 800 lines of code, excluding tests and prompts. Users can install nano-GraphRAG via PyPi or from the source by cloning the GitHub repository. To get started, users need to set their OpenAI API key, download a sample text, and utilize a provided Python snippet to perform queries. Key features include incremental data insertion, asynchronous methods, customizable storage options, integration with various language models, and support for custom embedding functions. Advanced usage allows for customization of prompts and storage, with benchmarks available for performance evaluation. However, some features from the original GraphRAG are not yet implemented, and future improvements are planned, focusing on better data source management and enhanced community reporting.
- nano-GraphRAG is a lightweight and efficient version of the GraphRAG model.
- Installation can be done via PyPi or by cloning the GitHub repository.
- The project supports asynchronous operations and customizable storage solutions.
- Users can perform both global and local queries on inserted data.
- Future updates aim to enhance functionality and community engagement.
Related
Show HN: R2R V2 – A open source RAG engine with prod features
The R2R GitHub repository offers an open-source RAG answer engine for scalable systems, featuring multimodal support, hybrid search, and a RESTful API. It includes installation guides, a dashboard, and community support. Developers benefit from configurable functionalities and resources for integration. Full documentation is available on the repository for exploration and contribution.
GraphRAG (from Microsoft) is now open-source!
GraphRAG, a GitHub tool, enhances question-answering over private datasets with structured retrieval and response generation. It outperforms naive RAG methods, offering semantic analysis and diverse, comprehensive data summaries efficiently.
Knowledge Graphs in RAG: Hype vs. Ragas Analysis
The analysis questions Microsoft's GraphRAG paper on knowledge graphs in RAG systems, revealing Neo4j's potential over FAISS in context retrieval. The study scrutinizes metrics and aims for precise quantification beyond vague claims.
Show HN: RAGBuilder – Hyperparameter tuning on various RAG parameters
RagBuilder is a toolkit for creating efficient Retrieval-Augmented Generation setups, focusing on hyperparameter tuning, offering pre-defined templates, and providing an intuitive interface for data processing and optimization.
3 commentsbut how does that affect the performance, theoretically?
Related
Show HN: R2R V2 – A open source RAG engine with prod features
The R2R GitHub repository offers an open-source RAG answer engine for scalable systems, featuring multimodal support, hybrid search, and a RESTful API. It includes installation guides, a dashboard, and community support. Developers benefit from configurable functionalities and resources for integration. Full documentation is available on the repository for exploration and contribution.
GraphRAG (from Microsoft) is now open-source!
GraphRAG, a GitHub tool, enhances question-answering over private datasets with structured retrieval and response generation. It outperforms naive RAG methods, offering semantic analysis and diverse, comprehensive data summaries efficiently.
Knowledge Graphs in RAG: Hype vs. Ragas Analysis
The analysis questions Microsoft's GraphRAG paper on knowledge graphs in RAG systems, revealing Neo4j's potential over FAISS in context retrieval. The study scrutinizes metrics and aims for precise quantification beyond vague claims.
Show HN: RAGBuilder – Hyperparameter tuning on various RAG parameters
RagBuilder is a toolkit for creating efficient Retrieval-Augmented Generation setups, focusing on hyperparameter tuning, offering pre-defined templates, and providing an intuitive interface for data processing and optimization.