Glossary

API Versioning

API versioning is the practice of managing changes to a vector database's public interface by assigning version identifiers (e.g., v1, v2) to prevent breaking existing client applications.

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VECTOR DATABASE APIS AND SDKS

What is API Versioning?

API Versioning is the systematic practice of managing changes to a software interface by assigning distinct identifiers to different iterations of its contract.

API versioning is the practice of managing changes to a software interface by assigning unique identifiers (e.g., v1, v2) to different iterations of its contract. For a vector database, this ensures that updates to the Query API or Index API do not break existing client applications. Changes requiring a new version include modifications to the request payload structure, API endpoint paths, or the behavior of core operations like a nearest neighbor query. This discipline is fundamental to maintaining backward compatibility and a stable developer experience.

Common strategies include URI path versioning (/v1/collections), query parameter versioning, and header-based versioning. A clear versioning policy, coupled with API deprecation notices and sunset timelines, allows consumers to migrate at their own pace. For mission-critical systems, versioning works in tandem with Service Level Agreements (SLAs) and resilience patterns like circuit breakers in Client SDKs to ensure reliable, long-term integration with the database's semantic search capabilities.

VECTOR DATABASE INFRASTRUCTURE

Common API Versioning Strategies

API versioning is a critical discipline for managing changes to a vector database's public interface without breaking existing client applications. These strategies define how version identifiers are communicated and enforced.

URI Path Versioning

The version is embedded directly in the API endpoint's URL path (e.g., /api/v1/collections, /api/v2/query). This is the most explicit and common strategy.

Explicit and Discoverable: The version is clear in every request.
Easy to Proxy and Cache: Standard HTTP tools can route and cache based on the path.
Client Lock-in: Clients must update their code to change the path segment to migrate to a new version.
Example: POST https://api.vectordb.com/v1/collections vs. POST https://api.vectordb.com/v2/collections.

EXPLORE

Query Parameter Versioning

The version is specified as a query parameter in the request URL (e.g., /api/collections?version=1).

Non-Intrusive to URI Structure: The base path remains clean and constant.
Flexible for Testing: Clients can easily test different versions by changing a parameter.
Caching Complexity: Caches may treat ?version=1 and ?version=2 as different resources, potentially reducing cache efficiency.
Less Explicit: The versioning contract is not immediately visible in the resource path.

Header Versioning

The version is communicated via a custom HTTP request header, such as API-Version: 1 or Accept: application/vnd.vectordb.v1+json.

Clean URLs: The resource URI remains completely unchanged and semantic.
Requires Custom Logic: Clients and servers must implement header parsing; not visible in browser address bars.
Media Type Versioning: Using the Accept header with custom MIME types is a formal variant of this strategy, promoting content negotiation.
Example Header: Accept: application/vnd.vectordb.v2+json.

Semantic Versioning (SemVer) for APIs

Applying Semantic Versioning principles (MAJOR.MINOR.PATCH) to the API contract itself, often communicated via the URI path or documentation.

MAJOR: Incremented for backward-incompatible changes (e.g., /v1 -> /v2).
MINOR: Incremented for backward-compatible new functionality added within the same major version.
PATCH: Incremented for backward-compatible bug fixes.
Manifested in Docs/Specs: The version number in the OpenAPI spec or SDK package (e.g., SDK v1.5.2) signals compatibility, even if the URI path only shows the major version.

Sunset Policy & Deprecation

The process and communication plan for retiring old API versions. This is not a versioning strategy but a critical complementary practice.

Deprecation Headers: Servers can return headers like Deprecation: true and Sunset: <date> (RFC 8594) to warn clients.
Grace Periods: Providing a multi-month or year-long period between deprecation announcement and version shutdown.
Clear Communication: Using changelogs, dedicated status pages, and developer portal announcements.
Essential for Trust: A predictable sunset policy is crucial for enterprise adoption of a vector database API.

Versioning Best Practices

Core principles for implementing any versioning strategy effectively in a vector database context.

Never Break Backwards Compatibility in-Place: Changes that break existing clients must trigger a new major version.
Version from Day One: Start with v1; it sets the expectation for future evolution.
Maintain Multiple Active Versions: Support at least N-1 major versions to give clients time to migrate.
Use OpenAPI/Swagger: Keep machine-readable specs versioned alongside the code.
Log Version Usage: Instrument your API to track which clients use which versions, informing your sunset strategy.

GLOSSARY

How API Versioning Works in Vector Databases

API versioning is a critical engineering practice for managing the evolution of a vector database's public interface, ensuring backward compatibility and stable integrations.

API versioning is the systematic practice of assigning unique identifiers (e.g., v1, v2) to distinct iterations of a vector database's public interface to manage changes without breaking existing client applications. This is typically implemented via a path prefix (/v1/query) or a request header, allowing multiple interface versions to coexist. The primary goal is to provide backward compatibility, giving developers a predictable migration path for new features, security patches, and performance optimizations while their existing integrations remain functional.

A robust versioning strategy includes a clear deprecation policy that communicates timelines for sunsetting old versions. Changes that necessitate a new major version include modifications to the request/response schema, removal of endpoints, or alterations to core query semantics. Minor or patch versions typically introduce additive changes, like new optional parameters, that maintain compatibility. This discipline is essential for production systems where client SDKs, automated pipelines, and multi-agent systems depend on a stable API contract for reliable vector storage and retrieval operations.

API VERSIONING

Frequently Asked Questions

API versioning is a critical practice for managing the evolution of a vector database's public interface. This FAQ addresses common questions about implementing and managing versions to ensure backward compatibility and stable integrations.

API versioning is the practice of assigning unique identifiers (e.g., v1, v2) to distinct iterations of a vector database's public programming interface to manage changes without breaking existing client applications. It is necessary because vector databases, as core infrastructure, evolve with new features (e.g., new distance metrics, filter operators, or index types), performance optimizations, and security updates. Without versioning, a change to an endpoint's request format or response structure could cause immediate, widespread failures for all integrated applications. Versioning provides a controlled mechanism for introducing breaking changes while maintaining stability for clients on older versions, allowing for phased migration and long-term support cycles.

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API VERSIONING

Related Terms

Managing changes to a public interface requires a suite of supporting protocols and patterns. These related terms define the ecosystem around versioning, stability, and client-server communication.

API Endpoint

A specific URL where a client sends requests to interact with a particular function of a vector database. Versioning is often reflected directly in the endpoint path (e.g., /v1/collections vs. /v2/collections).

Base URL: The root address of the API (e.g., https://api.vectordb.com).
Resource Path: Specifies the object and operation (e.g., /collections/{id}/query).
Version Identifier: Typically embedded in the path or as a header to isolate changes.

OpenAPI Specification

A standard, language-agnostic format for describing RESTful APIs. It is the foundational document for generating versioned client SDKs, interactive documentation, and automated tests.

Machine-Readable Contract: Defines all endpoints, request/response schemas, and authentication methods for a specific API version.
Toolchain Integration: Used by tools like Swagger UI for docs and code generators for SDKs.
Change Tracking: Differences between OpenAPI specs for v1 and v2 explicitly document breaking changes.

API Deprecation

The formal process of marking a version or feature of an API as obsolete and scheduled for removal. A critical component of a responsible versioning strategy.

Sunset Policy: A communicated timeline (e.g., 6 months warning) before an old version is disabled.
Deprecation Headers: APIs often return headers like Deprecation: true or Sunset: <date> in responses.
Migration Guidance: Provides clients with a clear path forward to the new, supported version.

Idempotency

A property of certain API operations where making the same request multiple times produces the same result as making it once. This is crucial for building reliable clients that handle retries, especially during version migrations.

Safe Retries: Clients can safely retry failed PUT or idempotent POST requests without causing duplicate side effects.
Idempotency Keys: Often implemented via a client-supplied key header to identify duplicate requests.
Version Stability: Ensures predictable behavior across API versions for state-mutating operations.

Request Payload

The data structure sent by a client in the body of an API request. Changes to the expected payload schema (adding required fields, changing types) are a primary reason for creating a new API version.

Schema Evolution: New versions may introduce new fields or change validation rules.
Backward Compatibility: A v2 API might accept v1 payload formats but not vice-versa.
Structured Data: For vector databases, payloads contain vectors, metadata, and query parameters.

SLA (Service Level Agreement)

A formal commitment defining measurable performance and availability targets for an API. Different API versions may have distinct SLAs, especially if a newer version uses a more scalable backend architecture.

Quantitative Metrics: Includes uptime percentage (e.g., 99.9%), latency percentiles (p95, p99), and throughput limits.
Version-Specific Guarantees: A provider may offer a higher SLA for the current stable version (v2) versus a deprecated version (v1).
Compliance & Trust: Provides clients with contractual assurance for production systems.

About the author

Prasad Kumkar

CEO & MD, Inference Systems

Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.

His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.

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