Knowledge Graph RAG

Enhances the performance of language models using knowledge graphs and document networks

CommonProductProgrammingKnowledge GraphDocument Network

Knowledge Graph RAG is an open-source Python library that leverages knowledge graphs and document networks to improve the performance of large language models (LLMs). This library enables users to search and connect information through graph structures, providing richer context for language models. It is widely used in the natural language processing field, particularly in document retrieval and information extraction tasks.

Automatically creates knowledge graphs and document networks
Searches for knowledge entities or interconnected documents through graph structures
Utilizes the tf-idf algorithm to create document graphs
Supports searching for neighboring nodes and similar documents
Provides a Python interface for easy integration and extension
Supports customizing graph node and edge attributes
Suitable for enhancing the contextual understanding capabilities of large language models

This tool is primarily designed for data scientists
natural language processing engineers
and researchers who work with large volumes of text data and need to extract valuable information. Knowledge Graph RAG helps them build a structured network of textual information
enhancing the efficiency of information retrieval and processing.

In academic research
Knowledge Graph RAG can be used to build domain-specific knowledge graphs
aiding literature reviews and information integration.
Businesses can leverage this model for market analysis
connecting competitor information through document networks to gain deeper business insights.

1. Install the Knowledge Graph RAG library: Use the pip command to install it.
2. Create a knowledge graph or document graph: Define the graph structure and node attributes based on your needs.
3. Search for entities or documents in the knowledge graph: Utilize the graph's search functionality to find relevant information.
4. Enhance language models using graph structures: Integrate graph information into the model input to improve performance.
5. Customize graph nodes and edges: Adjust the graph structure according to specific requirements.

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494758773

Bounce Rate

37.69%

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5.7

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Knowledge Graph RAG

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