pingcap / autoflow

Knowledge Graph (KG) and Retrieval Augmented Generation (RAG)

KG: The Knowledge Graph (KG) is used to store and query relationships between different entities in the AutoFlow system. It is built on top of the TiDB database, which provides a scalable and reliable foundation for the KG. The KG is used to answer various questions related to AutoFlow, such as:

  • What are the dependencies between different components?
  • What are the available configurations for a particular component?
  • What are the best practices for using a specific feature?

RAG: Retrieval Augmented Generation (RAG) uses the KG to enhance the quality of responses generated by the AutoFlow system. It works by retrieving relevant information from the KG and using it to inform the generation process. This allows the system to provide more accurate, informative, and contextually relevant responses.

Benefits:

  • Improved Accuracy: RAG leverages the KG to provide more accurate responses by incorporating factual information from the KG.
  • Enhanced Contextuality: RAG considers the context of the user’s query and retrieves relevant information from the KG to provide more contextually relevant responses.
  • Increased Efficiency: RAG reduces the need for manual knowledge management by leveraging the KG to store and retrieve information.

Implementation:

  • KG Construction:
    • The KG is populated with data from various sources, including the AutoFlow codebase, documentation, and user feedback.
    • The KG is updated regularly to ensure that it remains up-to-date with the latest changes to the system.
  • RAG Integration:
    • The RAG module is integrated with the AutoFlow system’s response generation pipeline.
    • When a user submits a query, the RAG module first retrieves relevant information from the KG and then uses it to generate a response.

Examples:

  • Question: “What are the dependencies of the AutoFlow scheduler component?”
    • RAG Response: “The AutoFlow scheduler component depends on the following components: the Workflow Manager, the Task Executor, and the Resource Manager.”
  • Question: “What are the available configurations for the AutoFlow executor?”
    • RAG Response: “The AutoFlow executor supports the following configurations: concurrency, max_retries, and timeout.”

Data Sources:

Configuration Options:

  • KG Schema: The schema of the KG defines the types of entities and relationships that are stored in the KG.
  • RAG Model: The RAG module uses a language model to generate responses. Different language models can be used, each with its own strengths and weaknesses.

Future Directions:

  • Knowledge Graph Enhancement: The KG can be expanded to include more information, such as user preferences and historical data.
  • RAG Model Optimization: The RAG module can be further optimized to improve the quality and efficiency of response generation.

Note: This outline is based on the provided context and assumes the presence of a documentation page for the AutoFlow project. Please replace the “[Insert Link Here]” placeholder with the actual link to the AutoFlow documentation.