Knowledge_graph

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knowledge_graph is an Open Source Data & Analytics tool that converts any text into a graph of knowledge. It can be used for Graph Augmented Generation or Knowledge Graph based QnA, representing relationships between real-world entities.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is knowledge_graph?

knowledge_graph is an open-source tool designed to transform any text corpus into a structured knowledge graph. This process involves cleaning the text, extracting concepts and their relationships using a Large Language Model (LLM) like Mistral 7B OpenOrca, and then populating nodes and edges to visualize the graph. The tool supports Graph Retrieval Augmented Generation (GRAG) for enhanced Q&A capabilities, offering a more profound way to interact with documents compared to traditional RAG methods. It operates locally, utilizing Ollama for model setup, ensuring an economical and private solution. The project emphasizes the importance of concepts over entities for more meaningful graphs and provides a clear methodology for graph creation, including chunking text, assigning chunk IDs, and weighting relationships based on contextual proximity.

Best used for

Ideal for data scientists who need to transform unstructured text into actionable knowledge graphs, extract meaningful concepts and relationships, and implement advanced Graph Retrieval Augmented Generation (GRAG). Especially valuable for those seeking an economical, locally runnable solution for text analysis and semantic network creation.

Common actions

convert text to graph

extract knowledge

analyze text data

build semantic networks

enhance Q&A

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Capabilities

Key features

Convert text to knowledge graph
Extract concepts and relations
Local LLM integration (Mistral 7B)
Graph visualization with Pyvis
Graph Retrieval Augmented Generation
Contextual proximity weighting

Target Audience

data scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What LLM does knowledge_graph use for concept extraction?

The knowledge_graph project primarily uses the Mistral 7B OpenOrca instruct model for extracting concepts from text chunks. This model is chosen for its effectiveness in following system prompt instructions and its ability to be set up and run locally using Ollama, making the process economical.

How does knowledge_graph handle relationships between concepts?

knowledge_graph extracts semantic relationships between concepts using an LLM and also considers concepts mentioned in the same text chunk as related by contextual proximity. It assigns weightages to these relations and groups similar pairs, summing their weights to create a single, weighted edge between distinct concepts.

Can knowledge_graph be run locally?

Yes, knowledge_graph is designed to be run entirely locally. It leverages Ollama to host models like Mistral 7B OpenOrca on a personal machine, ensuring that the generation of knowledge graphs is free and does not require external API calls.

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