Knowledge Graph RAG

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Introduce：

Knowledge Graph RAG is an open source Python library that enhances the performance of large language models (LLMS) by creating knowledge graphs and document networks. This library allows users to search and correlate information through graph structures to provide richer context for language models. It is mainly used in natural language processing, especially in document retrieval and information extraction tasks.

Stakeholders:
The target audience is primarily data scientists, natural language processing engineers, and researchers who need to process large amounts of text data and extract useful information from it. Knowledge Graph RAG can help them build structured text information network, so as to improve the efficiency of information retrieval and processing.
Usage Scenario Examples:

• In academic research, Knowledge Gralih RAG is used to construct domain knowledge map, assist literature review and information integration.
• Enterprises use this model to conduct market analysis, link competitor information through document network, and enhance business insight.
• In the medical field, the knowledge map of diseases and drugs is constructed to assist doctors in diagnosis and treatment decisions.

The features of the tool:

• Automatically create knowledge graphs and document networks
• Search for knowledge entities or connected documents by graph structure
• The tf-idf algorithm is used to create the document graph
• Supports search for neighbor nodes and similar documents
• Provides a Python interface for easy integration and extension
• Supports custom attributes of graph nodes and edges
• It is suitable for enhancing the context understanding of large language models

Steps for Use:

• 1. Install the Knowledge Gralih RAG library by using the liili command.
• 2. Create a knowledge graph or document graph: Define the graph structure and node attributes as required.
• 3. Search for entities or documents in the Knowledge graph: Use the search function of the graph to find relevant information.
• 4. Use graph structure to enhance language model: Integrate graph information into model input to improve model performance.
• 5. Customize nodes and edges of the graph: Adjust the graph structure according to specific requirements.
• 6. Integration into existing projects: Integrate Knowledge Gralih RAG into Python projects as a module.
• 7. Continuous optimization and update: Update maps and models based on feedback and the latest research results.

Tool’s Tabs: Knowledge graph, document network