Vector Database
Published on: 07 October 2025
Tags: #vector-database #ai
High-Level Architecture of a Vector Database
graph TD
subgraph "Vector Database"
A[Vector Storage]
B[Indexing Layer - ANN Algorithms]
C[Query Processing Engine]
D[Metadata Store]
end
E["Unstructured Data
(Text, Images, Audio)"] --> F{Embedding Model};
F --> G[Vector Embeddings];
G --> A;
A <--> B;
B <--> C;
A <--> D;
H[User Query] --> I{Query Vectorization};
I --> C;
C --> J[Ranked Search Results];
subgraph "Query Options"
K[Similarity Search]
L[Metadata Filtering]
end
K --> C;
L --> C;
style F fill:#f9f,stroke:#333,stroke-width:2px
style G fill:#ccf,stroke:#333,stroke-width:2px
style I fill:#f9f,stroke:#333,stroke-width:2px
The Data Ingestion and Indexing Pipeline
graph LR
A[Start] --> B["Ingest Raw Data
e.g., documents, images"];
B --> C["Apply Embedding Model
(e.g., BERT, ResNet)"];
C --> D[Generate Vector Embeddings];
D --> E["Store Vectors and
Associated Metadata"];
E --> F[Update or Build ANN Index];
F --> G[End - Ready for Queries];
style C fill:#f9f,stroke:#333,stroke-width:2px
style F fill:#9f9,stroke:#333,stroke-width:2px
Similarity Search Query Process
sequenceDiagram
participant Client as Client/User
participant EmbeddingModel as Embedding Model
participant VectorDB as Vector Database
Client->>EmbeddingModel: 1. Send query data (e.g., "What are vector databases?")
EmbeddingModel-->>Client: 2. Return query vector [0.1, 0.9, ...]
Client->>VectorDB: 3. Execute similarity search with query vector
activate VectorDB
VectorDB->>VectorDB: 4. Use ANN Index to find nearest neighbors
VectorDB-->>Client: 5. Return ranked list of similar vectors (and their IDs)
deactivate VectorDB
Client->>VectorDB: 6. (Optional) Fetch metadata for the returned IDs
activate VectorDB
VectorDB-->>Client: 7. Return metadata (e.g., original text, image URL)
deactivate VectorDB
ANN: Approximate Nearest Neighbor