Yes, you can expose a vector-based product search through either a GraphQL or REST API. Both approaches are technically feasible and widely used, though they differ in implementation details and trade-offs. The choice depends on your specific needs, such as query flexibility, performance, or integration with existing systems. Vector search typically involves converting products into numerical embeddings (using models like BERT or ResNet) and querying them via similarity metrics like cosine distance. Exposing this through an API lets clients send search requests and receive ranked results without managing the underlying vector database.
For a REST API, you’d design endpoints like POST /search that accept a query vector (or raw text/image to be embedded) and return product matches. For example, a request body might include a vector array [0.2, -0.5, 0.7,...] and parameters like top_k=10 to limit results. The server would compute similarities against stored vectors and return product IDs, metadata, and scores. REST is straightforward for developers familiar with HTTP conventions and works well with caching, rate limiting, and tools like OpenAPI for documentation. However, it can become cumbersome for complex queries requiring multiple round trips or nested data.
GraphQL offers more flexibility by letting clients specify exactly which fields to return (e.g., product name, price, and image URL) in a single request. A query might look like:
query {
vectorSearch(queryVector: [0.2, -0.5, ...], topK: 10) {
id
name
price
}
This reduces over-fetching and allows combining vector results with other data (e.g., inventory status). However, GraphQL requires more upfront schema design and tooling (like Apollo Server) and can complicate caching or rate limiting since all operations use a single endpoint. For vector search, both APIs would need efficient handling of large payloads (e.g., vectors with thousands of dimensions) and optimizations like approximate nearest neighbor indexing (e.g., FAISS or HNSW) to ensure low latency.
Implementation-wise, you’d use frameworks like FastAPI (Python) or Express (Node.js) for REST, or libraries like Strawberry (Python) for GraphQL. The backend would connect to a vector database (Pinecone, Milvus) or use in-memory indexes. Performance considerations include payload size (compressing vectors), authentication (API keys/OAuth), and scaling for high request volumes. For example, a hybrid approach might use REST for simplicity but add a GraphQL layer for clients needing advanced query capabilities.