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zeekay 6f13ef421a pkg/miner: pluggable inference-engine backend interface

Close three `// TODO: Integrate with actual ...` stubs in
pkg/miner/miner.go (runInference, runChat, runEmbedding) by introducing
a small pluggable backend interface. The miner now dispatches through
backend.InferenceBackend rather than inlining placeholder output.

Two reference backends ship in-tree:

- pkg/miner/backend/noop  — deterministic mock, byte-for-byte identical
  to the pre-refactor inline stubs. Default. Zero config, zero deps.
- pkg/miner/backend/openai — OpenAI-compatible HTTP adapter built on
  net/http. Covers llama.cpp, vllm, ollama, LocalAI, and api.openai.com
  via OPENAI_API_BASE. No new go.mod dependencies.

Config gains Backend / OpenAIBase / OpenAIAPIKey / OpenAIModel /
OpenAIEmbeddingModel fields. Unknown backend values fall back to noop so
operator typos don't crash-loop the miner. Existing miner_test.go passes
unchanged; DefaultConfig() behaviour is preserved.

Also wire a GPUStatsProvider hook on Miner so GetStats can surface
best-effort GPU telemetry (GPUUtilization, MemoryUsed) without pulling
NVML/MLX bindings into the main binary. Default is nil = no-op, matching
current behaviour; a follow-up PR will wire a real provider.

Closes luxfi/ai#1.

Contributed by kcolbchain (https://kcolbchain.com) · https://abhishekkrishna.com

2026-04-21 06:55:21 +05:30

noop

pkg/miner: pluggable inference-engine backend interface

2026-04-21 06:55:21 +05:30

openai

pkg/miner: pluggable inference-engine backend interface

2026-04-21 06:55:21 +05:30

backend_test.go

pkg/miner: pluggable inference-engine backend interface

2026-04-21 06:55:21 +05:30

backend.go

pkg/miner: pluggable inference-engine backend interface

2026-04-21 06:55:21 +05:30

README.md

pkg/miner: pluggable inference-engine backend interface

2026-04-21 06:55:21 +05:30

README.md

pkg/miner/backend

Pluggable inference-engine seam for the Lux AI miner.

The miner used to inline three // TODO: Integrate with actual ... stubs for chat, inference, and embedding. Those stubs are now hidden behind a small interface so operators can point the miner at whichever engine they run without touching the mining binary.

type InferenceBackend interface {
    Name() string
    Capabilities() Capabilities
    Chat(ctx context.Context, req ChatRequest)          (ChatResponse, error)
    Inference(ctx context.Context, req InferenceRequest) (InferenceResponse, error)
    Embed(ctx context.Context, req EmbedRequest)         (EmbedResponse, error)
}

Backends shipped in-tree

Package	Name	Use case
`pkg/miner/backend/noop`	`noop`	Deterministic mock. Default. Zero config, zero deps.
`pkg/miner/backend/openai`	`openai`	OpenAI-compatible HTTP adapter (stdlib `net/http`).

noop preserves the pre-refactor placeholder output ("Response to: <prompt>", "I'm an AI assistant running on the Lux network.", 384-dim zero-vector embeddings) so existing tests and downstream consumers see no behaviour change.

openai works against any server that speaks the OpenAI HTTP dialect — which happens to cover all the local engines operators actually run:

Engine	`OPENAI_API_BASE` target	Notes
llama.cpp	`http://localhost:8080/v1`	`./server --port 8080`
vllm	`http://localhost:8000/v1`	`vllm serve <model>`
ollama	`http://localhost:11434/v1`	Native OpenAI compat endpoint
LocalAI	`http://localhost:8080/v1`	Drop-in OpenAI replacement
OpenAI	`https://api.openai.com/v1`	The real thing

One adapter, five engines. No new Go deps.

Wiring

Via Config:

cfg := miner.DefaultConfig()
cfg.Backend = "openai"
cfg.OpenAIBase = "http://localhost:11434/v1"  // ollama
cfg.OpenAIModel = "llama3.1"
m := miner.New(cfg)

Or via WithBackend for fully custom plumbing (e.g. a direct MLX/CUDA binding from your own main):

m := miner.New(cfg).WithBackend(myBackend)

Unknown Backend values fall back to noop instead of failing — operator typos show up in logs as name=noop without crash-looping the miner.

Writing a new backend

Implement InferenceBackend in your own module and pass it via WithBackend. Contract:

All methods must be safe for concurrent use.
Capabilities() should be cheap and pure.
Name() should be a short, stable identifier ("noop", "openai", etc.).
Return errors rather than panicking on upstream failures; the miner marks tasks failed and bumps Stats.TasksFailed.

Why OpenAI-compatible instead of direct bindings

llama.cpp bindings pull in ~20 MB of C source. vllm is Python-only. MLX bindings require CGo. The OpenAI HTTP contract lets one small Go adapter cover every engine an operator would reasonably run, with zero new dependencies in go.mod. A direct-binding backend can still be added in a future PR for latency-sensitive deployments — the interface supports it.