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WhyHow.ai

Elevate RAG Accuracy & Explainability with Knowledge Graphs

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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.

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  1. rule-based-retrieval 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 inte…

    Python 181 19

  2. whyhow whyhow Public

    Automated knowledge graph creation SDK

    Python 67 15

  3. schemas schemas Public

    Schemas for WhyHow's automated knowledge graph creation SDK

    44 6

  4. .github .github Public

Repositories

Showing 4 of 4 repositories
  • 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
    Python 181 MIT 19 2 2 Updated Jun 14, 2024
  • whyhow Public

    Automated knowledge graph creation SDK

    whyhow-ai/whyhow’s past year of commit activity
    Python 67 15 1 1 Updated Jun 13, 2024
  • schemas Public

    Schemas for WhyHow's automated knowledge graph creation SDK

    whyhow-ai/schemas’s past year of commit activity
    44 6 0 1 Updated Jun 3, 2024
  • .github Public
    whyhow-ai/.github’s past year of commit activity
    0 0 0 0 Updated Mar 19, 2024

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