💼 productive forces

kg-gen

Artificial intelligence tools to extract knowledge graphs from any text.

#Artificial Intelligence
#natural language processing
#Knowledge graph
#Model training
#text analysis
kg-gen

Product Details

kg-gen is an artificial intelligence-based tool capable of extracting knowledge graphs from ordinary text. It supports processing text input ranging from single sentences to lengthy documents, and can process messages in conversational format. This tool uses advanced language models and structured output technology to help users quickly build knowledge graphs, and is suitable for fields such as natural language processing, knowledge management, and model training. kg-gen provides a flexible interface and multiple functions, aiming to simplify the knowledge graph generation process and improve efficiency.

Main Features

1
Supports knowledge graph extraction from plain text and conversation messages.
2
Able to process large texts, optimizing processing speed through chunking and clustering techniques.
3
Provides entity and relationship clustering functions to reduce duplicate and redundant information.
4
Supports multiple language models and API providers, such as OpenAI, Ollama, etc.
5
Multiple knowledge graphs can be aggregated to generate a more comprehensive knowledge structure.

How to Use

1
1. Install the kg-gen module: Use the pip install kg-gen command to install.
2
2. Import and initialize the KGGen class, optionally configuring the model and API key.
3
3. Provide text input and call the generate method to generate the knowledge graph.
4
4. You can optionally set the chunk_size parameter to process large text into chunks.
5
5. Use the cluster method to perform clustering optimization on the generated map.
6
6. Aggregate multiple maps to generate a more comprehensive knowledge structure.
7
7. Save or further process the generated knowledge graph as needed.

Target Users

kg-gen is suitable for developers, researchers, and data scientists who need to extract structured knowledge from text. It can help them quickly generate knowledge graphs for model training, knowledge management, and natural language processing tasks. In addition, it is also suitable for professionals who need to perform in-depth analysis and relationship mining of text.

Examples

Extracting character relationship graphs from family relationship texts.

Extract concepts and relationships from technical documents for knowledge management.

Analyze the conversation content and generate a relationship diagram of conversation topics and entities.

Quick Access

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Categories

💼 productive forces
› knowledge management
› research tools

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