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https://github.com/luxfi/ai.git
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zeekay
6f13ef421a
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
93 lines
2.6 KiB
Go
93 lines
2.6 KiB
Go
// Copyright (C) 2019-2025, Lux Industries Inc. All rights reserved.
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// See the file LICENSE for licensing terms.
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package noop
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import (
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"context"
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"testing"
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"github.com/luxfi/ai/pkg/miner/backend"
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)
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func TestName(t *testing.T) {
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if got := New().Name(); got != "noop" {
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t.Errorf("Name: got %q want %q", got, "noop")
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}
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}
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// TestChatMatchesLegacyStub pins the assistant content string so the
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// miner-level behaviour is indistinguishable from the pre-refactor inline
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// stub in miner.runChat.
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func TestChatMatchesLegacyStub(t *testing.T) {
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b := New()
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resp, err := b.Chat(context.Background(), backend.ChatRequest{Model: "m"})
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if err != nil {
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t.Fatalf("Chat: %v", err)
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}
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const want = "I'm an AI assistant running on the Lux network."
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if resp.Content != want {
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t.Errorf("Chat content: got %q want %q", resp.Content, want)
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}
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if resp.Role != "assistant" {
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t.Errorf("Chat role: got %q want %q", resp.Role, "assistant")
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}
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if resp.Model != "m" {
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t.Errorf("Chat model: got %q want %q", resp.Model, "m")
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}
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}
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// TestInferenceMatchesLegacyStub pins the echo-prompt format used by the
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// pre-refactor miner.runInference.
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func TestInferenceMatchesLegacyStub(t *testing.T) {
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b := New()
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resp, err := b.Inference(context.Background(), backend.InferenceRequest{
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Model: "m",
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Prompt: "what time is it?",
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})
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if err != nil {
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t.Fatalf("Inference: %v", err)
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}
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const want = "Response to: what time is it?"
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if resp.Text != want {
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t.Errorf("Inference text: got %q want %q", resp.Text, want)
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}
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if resp.Tokens != 10 {
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t.Errorf("Inference tokens: got %d want 10", resp.Tokens)
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}
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}
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// TestEmbedMatchesLegacyStub pins the zero-vector behaviour and the 384 dim.
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func TestEmbedMatchesLegacyStub(t *testing.T) {
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b := New()
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resp, err := b.Embed(context.Background(), backend.EmbedRequest{Model: "m", Text: "x"})
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if err != nil {
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t.Fatalf("Embed: %v", err)
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}
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if len(resp.Embedding) != DefaultEmbeddingDims {
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t.Errorf("Embed dims: got %d want %d", len(resp.Embedding), DefaultEmbeddingDims)
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}
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for i, v := range resp.Embedding {
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if v != 0.0 {
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t.Errorf("Embed[%d]: got %v want 0.0", i, v)
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break
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}
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}
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}
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// TestCustomEmbeddingDims checks the dim knob (used by callers that want to
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// mimic a real model's output shape in tests).
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func TestCustomEmbeddingDims(t *testing.T) {
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b := &Backend{EmbeddingDims: 16}
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resp, err := b.Embed(context.Background(), backend.EmbedRequest{Text: "x"})
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if err != nil {
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t.Fatalf("Embed: %v", err)
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}
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if len(resp.Embedding) != 16 {
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t.Errorf("custom dims: got %d want 16", len(resp.Embedding))
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}
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if caps := b.Capabilities(); caps.EmbeddingDims != 16 {
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t.Errorf("Capabilities.EmbeddingDims: got %d want 16", caps.EmbeddingDims)
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}
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}
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