We're whyhow, and we're working on simplifying how LLMs understand data extraction and conceptual relationships with knowledge graphs. We empower developers with essential tools to manage unstructured data for both complex and simple RAG systems.
WhyHow.ai
Elevate RAG Accuracy & Explainability with Knowledge Graphs
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- https://whyhow.ai
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knowledge-graph-studio
knowledge-graph-studio PublicOpen source repo for the WhyHow Knowledge Graph Studio
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knowledge-table
knowledge-table PublicKnowledge Table is an open-source package designed to simplify extracting and exploring structured data from unstructured documents.
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rule-based-retrieval
rule-based-retrieval PublicThe Rule-based Retrieval package is a Python package that enables you to create and manage Retrieval Augmented Generation (RAG) applications with advanced filtering capabilities. It seamlessly inte…
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Repositories
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- whyhow-sdk-docs Public
whyhow-ai/whyhow-sdk-docs’s past year of commit activity - knowledge-table Public
Knowledge Table is an open-source package designed to simplify extracting and exploring structured data from unstructured documents.
whyhow-ai/knowledge-table’s past year of commit activity - knowledge-table-internal Public Forked from whyhow-ai/knowledge-table
Knowledge Table is an open-source package designed to simplify extracting and exploring structured data from unstructured documents.
whyhow-ai/knowledge-table-internal’s past year of commit activity - rule-based-retrieval Public
The Rule-based Retrieval package is a Python package that enables you to create and manage Retrieval Augmented Generation (RAG) applications with advanced filtering capabilities. It seamlessly integrates with OpenAI for text generation and Pinecone or Milvus for efficient vector database management.
whyhow-ai/rule-based-retrieval’s past year of commit activity - recursive-retrieval Public
whyhow-ai/recursive-retrieval’s past year of commit activity
