The project is in a healthy, maintained state
Adds Qiniu (Modelink) provider support to RubyLLM.
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 Dependencies

Runtime

>= 2.0.0.rc1
 Project Readme

ruby_llm-providers-qiniu (modelink)

RubyLLM provider gem for Qiniu's model gateway (documented at docs.modelink.ai — Modelink is the overseas brand of the same service).

Installation

Add this line to your application's Gemfile:

gem 'ruby_llm-providers-qiniu', require: 'ruby_llm/providers/qiniu'

Then configure the provider:

require 'ruby_llm/providers/qiniu'

RubyLLM.configure do |config|
  config.qiniu_api_key = ENV['QINIU_API_KEY']
  config.qiniu_api_base = ENV.fetch('QINIU_BASE_URL', 'https://api.qnaigc.com')
end

Endpoints

Pick one regional entry point per deployment; all of them serve the same protocols:

Region Base URL Use when
Mainland China https://api.qnaigc.com default; lowest latency for China deployments
Overseas https://api.modelink.ai overseas deployment or cross-border access
Hybrid https://xapi.qnaigc.com sharing one entry across protocol-different clients

qiniu_api_base is used verbatim — pass the bare host, since each protocol path already carries its own version segment (/v1, /bypass/…/v1, …).

Protocols

One provider, several wire formats. For chat, RubyLLM prefers each vendor's native bypass channel — Claude models use Anthropic Messages, Gemini models use GenerateContent, GPT models use Responses — while every other model and all auxiliary operations use the OpenAI-compatible endpoint. Override globally with config.qiniu_protocol = :anthropic or per call:

RubyLLM.chat(model: 'deepseek-v3', provider: :qiniu, protocol: :chat_completions)
Protocol Path Notes
:chat_completions (default) POST /v1/chat/completions All models; also serves model listing at GET /v1/models
:anthropic POST /v1/messages Anthropic Messages compat; accepts every model, gateway adapts per channel
:anthropic_bypass POST /bypass/anthropic/v1/messages Claude only; vendor-native Messages semantics
:gemini_bypass POST /bypass/vertex/v1/models/{model}:{func} Gemini only; native GenerateContent (generateContent / streamGenerateContent)
:responses POST /bypass/openai/v1/responses GPT family; OpenAI Responses API

Only chat is wired up for now — embeddings, images, audio, video, rerank and other capabilities raise RubyLLM::Error until the corresponding endpoints are added.

Prefer the compatible dialects for unified multi-model access. Reach for a bypass protocol when you depend on vendor-native fields, streaming events, or extended capabilities (Extended Thinking, Prompt Caching, Gemini-native tools, Responses). Bypass requests still pass through the gateway's auth, routing, rate limiting, and billing — "bypass" only skips the unified protocol conversion.

Authentication is Authorization: Bearer <api_key> everywhere (the Anthropic paths also accept X-Api-Key; Bearer alone is sufficient).

Usage

model = RubyLLM.models.by_provider(:qiniu).chat_models.first
chat = RubyLLM.chat(model: model.id)
response = chat.ask('Hello')
puts response.content

Notes

  • GET /v1/models may be called anonymously; the provider still sends credentials when configured. The listing is only a partial catalog — it omits whole vendors such as Claude, Gemini and GPT and exposes no capabilities or pricing — which is why models.json is maintained by hand from the official docs.
  • Batch inference (/v1/batchjob/inference), FAL-format async media (/queue/...), AK/SK management endpoints, and Volcengine asset signing are service-specific APIs this provider does not wrap.
  • The hybrid endpoint converts between protocols but may not preserve every vendor-native field or streaming event; verify tool calls, thinking, caching, structured output, and multimodal inputs with real requests before production use.

Development

The generator installs the bundle and creates an ignored .env. Edit the generated op read reference so it points to your 1Password credential. If you do not use the 1Password CLI, replace the expression with the provider key.

bundle exec rake

The packaged models.json at the gem root is maintained by hand from the official catalog at modelink.ai/models — GET /v1/models is too thin to generate it, since the listing omits whole vendors (no Claude, Gemini or GPT entries) and carries no context windows, capabilities or pricing. RubyLLM loads that catalog as a fallback when this provider is registered, so applications do not need to combine registry files. The main RubyLLM registry always wins when both catalogs carry the same model. If the API grows a richer listing path, update models_url in lib/ruby_llm/providers/qiniu.rb.

Edit models.json directly when you want to update the packaged catalog. RubyLLM.models.refresh updates the application's main registry and does not refresh provider gem catalogs.

If the packaged catalog is ever dropped, uncomment assume_models_exist? in the provider so chats can start against unlisted models.

The suite always runs the provider integration specs. The first local run calls the API and records VCR cassettes; CI only replays committed cassettes. A failing example deletes its cassette so the next local run tests the live API again.

After updating the catalog, add real model IDs to spec/support/models.rb. Keep only the operation matrices the provider supports. The portable contract specs are adapted from RubyLLM's live specs; use those as the reference when your provider needs coverage for another feature or dialect.