A Comprehensive Guide to Vector Database Architecture and Application Scenarios

Presenter Wang Geng from Jiuzhang Cloud DataCanvas introduces the topic "A Step‑by‑Step Dissection of Vector Database Structure and Application Scenarios" and outlines eight main sections.

1. NewDataStack Era Data Architecture Map – A diagram from Andreessen Horowitz predicts future data architectures; the author adds current popular large‑model and vector‑database components. The data‑source layer gathers heterogeneous enterprise data, which is ingested and transformed (ETL, feature engineering, streaming). The storage‑compute layer leverages vector‑database engines to store and process diverse data types. The analysis‑prediction layer uses vector databases to support AI models, real‑time analytics, and vector search. The application layer includes BI dashboards, embedded analytics, and self‑service tools.

2. Evolution of Vector Databases – Three stages are described: (a) Exploration stage with file‑based storage (e.g., Lucene) lacking indexes; (b) Development stage with KD‑tree, Annoy, FAISS improving high‑dimensional search; (c) Application stage where modern vector databases (ElasticSearch, DingoDB, Weaviate) provide efficient high‑dimensional indexing, massive scalability, and integration with large models.

3. Enterprise Pain Points & Challenges – Four challenges are highlighted: (a) Adapting data architecture to the large‑model era, treating vectors as the universal data representation; (b) Storing, analyzing, and serving multimodal data (mixing structured and unstructured, scalar and vector queries); (c) Ensuring high performance and easy operations for massive indexes and low‑latency services; (d) Guaranteeing data security, high availability, multi‑tenant isolation, and compliance with domestic (国产化) requirements.

4. Overall Shape of Vector Databases – A diagram shows multimodal data (images, text, audio, video) being vectorized and stored alongside relational and key‑value data. The vector engine provides similarity search, while upper layers offer BI, streaming analytics, AI, data science, and large‑model support. The system exposes MySQL‑compatible protocols, serving APIs, native vector APIs, metadata management, optimizers, transaction managers, and supports relational, vector, HDFS, and lake storage backends.

5. Functional Features of Vector Data – An industry standard (from China Academy of Information and Communications Technology) defines core capabilities: functional completeness, operational management, security, compatibility, extensibility, and high availability.

6. Future Trends & Core Capabilities of Multimodal Vector Databases – Five trends are identified: (a) Hybrid scalar‑vector queries; (b) Diverse access interfaces (SQL, SDK, API) for different latency requirements; (c) Automatic elastic data sharding with dynamic split/merge; (d) Real‑time index construction and self‑optimizing indexes; (e) Built‑in high availability without external components.

7. Key Supporting Scenarios – In the large‑model era, vector databases underpin memory for large models, enterprise knowledge bases, unstructured data retrieval, real‑time decision metrics, fusion analysis of structured and unstructured data, and platforms like VectorOcean.

8. Vector Database for Large‑Model Knowledge Agent Applications – The vector store acts as the backend for a knowledge‑assistant: enterprise data is privately stored, large models generate queries, the vector DB returns top‑N similar knowledge fragments, and the model composes final answers, reducing hallucination and improving reliability.

The presentation concludes with a thank‑you note.