

Whyhow Knowledge Graph Studio
Overview :
WhyHow Knowledge Graph Studio is an open-source platform designed to streamline the creation and management of RAG-native knowledge graphs. The platform offers rule-based entity parsing, modular graph construction, flexible data ingestion, and an API-first design, along with SDK support. It is built on a NoSQL database, providing a flexible and scalable storage layer that simplifies the retrieval and traversal of complex relationships. The platform is suitable for handling both structured and unstructured data, enabling the construction of exploratory graphs or highly structured constraint graphs, aiming for scalability and flexibility for both experimental and large-scale use.
Target Users :
The target audience includes data scientists, AI developers, and enterprise data analysts. This product is well-suited for them as it offers a flexible and scalable platform for building and managing knowledge graphs, supporting complex data analysis and AI application development.
Use Cases
Build an enterprise knowledge base that integrates internal data resources.
Develop an intelligent Q&A system that provides graph-based semantic search.
Implement a personalized recommendation system that builds knowledge graphs based on user behavior and preferences.
Features
? Rule-based entity parsing: Provides rule-based entity recognition and parsing capabilities.
? Modular graph construction: Supports a modular approach to graph building, facilitating management and expansion.
? Flexible data ingestion: Allows data ingestion from various sources, including both structured and unstructured data.
? API-first design: Features RESTful API interfaces for quick integration and development by developers.
? SDK support: Offers a Python SDK to simplify interactions with the knowledge graph.
? Scalability: Built on top of a NoSQL database, easy to scale and allows for rapid data retrieval.
? Database agnosticism: Designed to support various types of databases, including relational and graph databases.
How to Use
1. Clone the code repository to your local environment.
2. Install Python 3.10 or higher.
3. Install project dependencies via pip.
4. Configure environment variables, including database connections and API keys.
5. Use CLI scripts to create database collections and indexes.
6. Create users and obtain API keys.
7. Start the API server and access Swagger UI for API testing.
8. Use the Python SDK to interact with the knowledge graph, creating and managing graph data.
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