What is AutoFlow?
AutoFlow is a cutting-edge AI assistant powered by TiDB, designed to deliver precise and context-aware responses through a Knowledge Graph-based Retrieval-Augmented Generation (RAG) system. Built on TiDB's advanced vector storage capabilities and the PyTiDB library, AutoFlow combines structured knowledge representation with semantic search to provide highly accurate and relevant answers. Unlike traditional RAG systems that rely solely on vector similarity, AutoFlow leverages a knowledge graph to capture relationships between entities, enabling deeper reasoning and more coherent responses. This hybrid approach ensures that users receive not just relevant information but also contextual insights that connect disparate pieces of data. Key features include real-time query processing, multi-source data integration, and scalable performance. AutoFlow can ingest data from various formats—documents, databases, APIs—and automatically constructs a knowledge graph that evolves with new information. The vector storage in TiDB allows for efficient similarity search across millions of embeddings, while the graph structure enables traversal of relationships for complex queries. This makes AutoFlow ideal for enterprise applications such as customer support, internal knowledge management, and research assistance. For instance, a support team can use AutoFlow to instantly retrieve troubleshooting steps that are contextually linked to product specifications and past incidents, reducing resolution time by up to 60%. Technical details: AutoFlow runs on TiDB, a distributed SQL database that supports both transactional and analytical workloads. The vector storage is implemented using TiDB's built-in vector data type, which supports cosine similarity and Euclidean distance metrics. PyTiDB serves as the Python client, enabling seamless integration with machine learning frameworks like LangChain and LlamaIndex. The knowledge graph is stored as property graph data in TiDB, with nodes representing entities and edges representing relationships. AutoFlow uses a fine-tuned LLM to generate responses, combining retrieved graph paths and vector search results. The system is designed for high availability and horizontal scalability, making it suitable for large-scale deployments. Use cases range from legal document analysis to medical diagnosis support. In legal tech, AutoFlow can map case law precedents to statutes, providing lawyers with a comprehensive view of relevant arguments. In healthcare, it can connect patient symptoms to potential conditions and treatments, aiding clinicians.
Use Cases
Key Features
real time query processing multi source data integration knowledge graph construction semantic search context aware responses scalable performance vector similarity search relationship traversalWho is it for?
data engineers, AI researchers, knowledge management teams, enterprise developers, IT architects, product managers
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