feat: add MiniMax as first-class LLM provider with preset system

.env.example

@@ -35,9 +35,17 @@ ABOGEN_GID=1000
 # Optional: Seed the web UI with working defaults for the LLM-powered
 # text normalization features. Leave these blank to configure everything
 # from the Settings page.
+
+# --- Ollama (local) ---
 ABOGEN_LLM_BASE_URL=http://localhost:11434  # Supply the server root; /v1 is added automatically.
 ABOGEN_LLM_API_KEY=ollama
 ABOGEN_LLM_MODEL=llama3.1:8b
+
+# --- MiniMax Cloud ---
+# ABOGEN_LLM_BASE_URL=https://api.minimax.io/v1
+# ABOGEN_LLM_API_KEY=your-minimax-api-key
+# ABOGEN_LLM_MODEL=MiniMax-M2.7
+
 ABOGEN_LLM_TIMEOUT=45
 ABOGEN_LLM_CONTEXT_MODE=sentence
 # For custom prompts, keep the text on a single line or escape newlines.

README.md

@@ -385,11 +385,22 @@ docker run --rm \
 ## `LLM-assisted text normalization`
 Abogen can hand tricky apostrophes and contractions to an OpenAI-compatible large language model. Configure it from **Settings → LLM**:
 
-1. Enter the base URL for your endpoint (Ollama, OpenAI proxy, etc.) and an API key if required. Use the server root (for Ollama: `http://localhost:11434`)—Abogen appends `/v1/...` automatically, but it also accepts inputs that already end in `/v1`.
-2. Click **Refresh models** to load the catalog, pick a default model, and adjust the timeout or prompt template.
+1. Pick a **Provider** from the dropdown (MiniMax, OpenAI, DeepSeek, Ollama) to auto-fill the endpoint and available models, or choose *Custom endpoint* to enter any OpenAI-compatible URL manually.
+2. Enter an API key if required, then click **Refresh models** to load the catalog. Pick a default model and adjust the timeout or prompt template.
 3. Use the preview box to test the prompt, then save the settings. The Normalization panel can synthesize a short audio preview with the current configuration.
 
-When you are running inside Docker or a CI pipeline, seed the form automatically with `ABOGEN_LLM_*` variables in your `.env` file. The `.env.example` file includes sample values for a local Ollama server.
+### Supported providers
+
+| Provider | Base URL | Models |
+|----------|----------|--------|
+| **MiniMax** | `https://api.minimax.io/v1` | MiniMax-M2.7, MiniMax-M2.5-highspeed, … |
+| **OpenAI** | `https://api.openai.com/v1` | gpt-4o, gpt-4o-mini, … |
+| **DeepSeek** | `https://api.deepseek.com/v1` | deepseek-chat, deepseek-reasoner |
+| **Ollama** | `http://localhost:11434/v1` | *(local models)* |
+
+Any service that exposes `/v1/chat/completions` (e.g. LM Studio, vLLM, text-generation-webui) also works via *Custom endpoint*.
+
+When you are running inside Docker or a CI pipeline, seed the form automatically with `ABOGEN_LLM_*` variables in your `.env` file. The `.env.example` file includes sample values for a local Ollama server and MiniMax Cloud.
 
 ## `Audiobookshelf integration`
 Abogen can push finished audiobooks directly into Audiobookshelf. Configure this under **Settings → Integrations → Audiobookshelf** by providing:

abogen/llm_providers.py

@@ -0,0 +1,99 @@
+"""Built-in LLM provider presets for quick configuration.
+
+Each preset bundles the endpoint URL, a list of known models, and the
+environment variable that typically holds the API key.  The Web UI
+uses these presets so users can pick a provider from a dropdown instead
+of typing the URL manually.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Dict, List, Sequence, Tuple
+
+
+@dataclass(frozen=True)
+class LLMProviderPreset:
+    """A preconfigured cloud or local LLM endpoint."""
+
+    id: str
+    name: str
+    base_url: str
+    api_key_env: str = ""
+    api_key_hint: str = ""
+    models: Tuple[str, ...] = ()
+
+    def to_dict(self) -> Dict[str, object]:
+        return {
+            "id": self.id,
+            "name": self.name,
+            "base_url": self.base_url,
+            "api_key_env": self.api_key_env,
+            "api_key_hint": self.api_key_hint,
+            "models": list(self.models),
+        }
+
+
+_BUILTIN_PRESETS: Tuple[LLMProviderPreset, ...] = (
+    LLMProviderPreset(
+        id="minimax",
+        name="MiniMax",
+        base_url="https://api.minimax.io/v1",
+        api_key_env="MINIMAX_API_KEY",
+        api_key_hint="Get your key at https://platform.minimax.io",
+        models=(
+            "MiniMax-M1",
+            "MiniMax-Text-01",
+            "MiniMax-M2.5",
+            "MiniMax-M2.5-highspeed",
+            "MiniMax-M2.7",
+            "MiniMax-M2.7-highspeed",
+        ),
+    ),
+    LLMProviderPreset(
+        id="openai",
+        name="OpenAI",
+        base_url="https://api.openai.com/v1",
+        api_key_env="OPENAI_API_KEY",
+        api_key_hint="Get your key at https://platform.openai.com/api-keys",
+        models=(
+            "gpt-4o",
+            "gpt-4o-mini",
+            "gpt-4.1",
+            "gpt-4.1-mini",
+            "gpt-4.1-nano",
+        ),
+    ),
+    LLMProviderPreset(
+        id="deepseek",
+        name="DeepSeek",
+        base_url="https://api.deepseek.com/v1",
+        api_key_env="DEEPSEEK_API_KEY",
+        api_key_hint="Get your key at https://platform.deepseek.com",
+        models=(
+            "deepseek-chat",
+            "deepseek-reasoner",
+        ),
+    ),
+    LLMProviderPreset(
+        id="ollama",
+        name="Ollama (local)",
+        base_url="http://localhost:11434/v1",
+        api_key_env="",
+        api_key_hint='Use "ollama" or leave blank',
+        models=(),
+    ),
+)
+
+
+def get_provider_presets() -> Sequence[LLMProviderPreset]:
+    """Return all built-in provider presets."""
+    return _BUILTIN_PRESETS
+
+
+def get_provider_by_id(provider_id: str) -> LLMProviderPreset | None:
+    """Look up a single preset by its identifier."""
+    for preset in _BUILTIN_PRESETS:
+        if preset.id == provider_id:
+            return preset
+    return None

abogen/normalization_settings.py

@@ -27,6 +27,7 @@
 )
 
 _SETTINGS_DEFAULTS: Dict[str, Any] = {
+    "llm_provider": "",
     "llm_base_url": "",
     "llm_api_key": "",
     "llm_model": "",

abogen/webui/routes/settings.py

@@ -23,6 +23,7 @@
 from abogen.webui.debug_tts_runner import run_debug_tts_wavs
 from abogen.debug_tts_samples import DEBUG_TTS_SAMPLES
 from abogen.utils import get_user_output_path, load_config
+from abogen.llm_providers import get_provider_presets
 
 settings_bp = Blueprint("settings", __name__)
 
@@ -216,6 +217,7 @@ def settings_page() -> str | ResponseReturnValue:
         save_locations=save_locations,
         default_output_dir=default_output_dir,
         llm_ready=llm_ready(load_settings()),
+        llm_provider_presets=[p.to_dict() for p in get_provider_presets()],
         debug_samples=DEBUG_TTS_SAMPLES,
         debug_manifest=debug_manifest,
     )

abogen/webui/routes/utils/settings.py

@@ -196,6 +196,7 @@ def settings_defaults() -> Dict[str, Any]:
         "speaker_analysis_threshold": _DEFAULT_ANALYSIS_THRESHOLD,
         "speaker_pronunciation_sentence": "This is {{name}} speaking.",
         "speaker_random_languages": [],
+        "llm_provider": "",
         "llm_base_url": llm_env_defaults.get("llm_base_url", ""),
         "llm_api_key": llm_env_defaults.get("llm_api_key", ""),
         "llm_model": llm_env_defaults.get("llm_model", ""),
@@ -344,7 +345,7 @@ def normalize_setting_value(key: str, value: Any, defaults: Dict[str, Any]) -> A
     if key == "llm_prompt":
         candidate = str(value or "").strip()
         return candidate if candidate else defaults[key]
-    if key in {"llm_base_url", "llm_api_key", "llm_model"}:
+    if key in {"llm_provider", "llm_base_url", "llm_api_key", "llm_model"}:
         return str(value or "").strip()
     if key == "speaker_random_languages":
         if isinstance(value, (list, tuple, set)):

abogen/webui/static/settings.js

@@ -367,16 +367,67 @@ function collectLLMFields() {
   const prompt = form.querySelector('#llm_prompt');
   const timeout = form.querySelector('#llm_timeout');
   const context = form.querySelector('input[name="llm_context_mode"]:checked');
+  const provider = form.querySelector('#llm_provider');
   return {
     base_url: baseUrl ? baseUrl.value.trim() : '',
     api_key: apiKey ? apiKey.value.trim() : '',
     model: model ? model.value.trim() : '',
     prompt: prompt ? prompt.value : '',
     context_mode: context ? context.value : 'sentence',
     timeout: timeout ? parseNumber(timeout.value, 30) : 30,
+    provider: provider ? provider.value : '',
   };
 }
 
+function getProviderPresets() {
+  const select = form.querySelector('#llm_provider');
+  if (!select || !select.dataset.presets) {
+    return [];
+  }
+  try {
+    return JSON.parse(select.dataset.presets);
+  } catch (_) {
+    return [];
+  }
+}
+
+function applyProviderPreset(providerId) {
+  const presets = getProviderPresets();
+  const preset = presets.find((p) => p.id === providerId);
+  const baseUrlInput = form.querySelector('#llm_base_url');
+  const apiKeyInput = form.querySelector('#llm_api_key');
+  const apiKeyHint = document.querySelector('#llm_api_key_hint');
+
+  if (!preset) {
+    if (apiKeyHint) {
+      apiKeyHint.innerHTML = 'Leave blank or use <code>ollama</code> for local servers that do not require keys.';
+    }
+    return;
+  }
+
+  if (baseUrlInput) {
+    baseUrlInput.value = preset.base_url;
+  }
+  if (apiKeyHint && preset.api_key_hint) {
+    apiKeyHint.textContent = preset.api_key_hint;
+  }
+  if (preset.models && preset.models.length) {
+    const models = preset.models.map((id) => ({ id, label: id }));
+    updateModelOptions(models);
+  }
+  updateLLMNavState();
+}
+
+function initProviderDropdown() {
+  const providerSelect = form.querySelector('#llm_provider');
+  if (!providerSelect) {
+    return;
+  }
+  providerSelect.addEventListener('change', () => {
+    applyProviderPreset(providerSelect.value);
+  });
+}
+
 function updateModelOptions(models) {
   const select = form.querySelector('#llm_model');
   if (!select) {
@@ -879,4 +930,5 @@ if (form) {
   initFolderPicker();
   initContractionModal();
   initLLMStateWatchers();
+  initProviderDropdown();
 }

abogen/webui/templates/settings.html

@@ -276,6 +276,16 @@ <h1 class="card__title">Application Settings</h1>
         <section class="settings-panel" data-section="llm">
           <fieldset class="settings__section">
             <legend>Endpoint</legend>
+            <div class="field">
+              <label for="llm_provider">Provider</label>
+              <select id="llm_provider" name="llm_provider" data-presets='{{ llm_provider_presets | tojson }}'>
+                <option value="">Custom endpoint</option>
+                {% for preset in llm_provider_presets %}
+                <option value="{{ preset.id }}" {% if settings.llm_provider == preset.id %}selected{% endif %}>{{ preset.name }}</option>
+                {% endfor %}
+              </select>
+              <p class="hint">Pick a cloud provider to auto-fill the endpoint, or choose <em>Custom endpoint</em> to enter any OpenAI-compatible URL.</p>
+            </div>
             <div class="field">
               <label for="llm_base_url">Base URL</label>
               <input type="url" id="llm_base_url" name="llm_base_url" value="{{ settings.llm_base_url }}" placeholder="https://localhost:11434/v1">
@@ -284,7 +294,7 @@ <h1 class="card__title">Application Settings</h1>
             <div class="field">
               <label for="llm_api_key">API Key</label>
               <input type="text" id="llm_api_key" name="llm_api_key" value="{{ settings.llm_api_key }}" autocomplete="off" placeholder="ollama">
-              <p class="hint">Leave blank or use <code>ollama</code> for local servers that do not require keys.</p>
+              <p class="hint" id="llm_api_key_hint">Leave blank or use <code>ollama</code> for local servers that do not require keys.</p>
             </div>
             <div class="field field--inline">
               <div class="field__group">

tests/test_llm_providers.py

@@ -0,0 +1,76 @@
+"""Tests for the LLM provider presets module."""
+
+from __future__ import annotations
+
+import pytest
+
+from abogen.llm_providers import (
+    LLMProviderPreset,
+    get_provider_presets,
+    get_provider_by_id,
+)
+
+
+def test_get_provider_presets_returns_non_empty():
+    presets = get_provider_presets()
+    assert len(presets) >= 4
+
+
+def test_minimax_preset_exists():
+    preset = get_provider_by_id("minimax")
+    assert preset is not None
+    assert preset.name == "MiniMax"
+    assert preset.base_url == "https://api.minimax.io/v1"
+    assert preset.api_key_env == "MINIMAX_API_KEY"
+    assert len(preset.models) >= 1
+    assert "MiniMax-M2.7" in preset.models
+
+
+def test_openai_preset_exists():
+    preset = get_provider_by_id("openai")
+    assert preset is not None
+    assert preset.base_url == "https://api.openai.com/v1"
+
+
+def test_ollama_preset_has_no_models():
+    preset = get_provider_by_id("ollama")
+    assert preset is not None
+    assert preset.models == ()
+
+
+def test_get_provider_by_id_returns_none_for_unknown():
+    assert get_provider_by_id("nonexistent") is None
+    assert get_provider_by_id("") is None
+
+
+def test_preset_ids_are_unique():
+    presets = get_provider_presets()
+    ids = [p.id for p in presets]
+    assert len(ids) == len(set(ids))
+
+
+def test_to_dict_has_required_keys():
+    preset = get_provider_by_id("minimax")
+    d = preset.to_dict()
+    assert set(d.keys()) == {"id", "name", "base_url", "api_key_env", "api_key_hint", "models"}
+    assert isinstance(d["models"], list)
+    assert d["id"] == "minimax"
+
+
+def test_preset_is_frozen():
+    preset = get_provider_by_id("minimax")
+    with pytest.raises(AttributeError):
+        preset.name = "changed"
+
+
+def test_all_presets_have_base_url():
+    for preset in get_provider_presets():
+        assert preset.base_url, f"Preset {preset.id!r} missing base_url"
+
+
+def test_normalization_settings_includes_llm_provider():
+    """The llm_provider key must exist in the settings defaults."""
+    from abogen.normalization_settings import _SETTINGS_DEFAULTS
+
+    assert "llm_provider" in _SETTINGS_DEFAULTS
+    assert _SETTINGS_DEFAULTS["llm_provider"] == ""

tests/test_llm_providers_integration.py

@@ -0,0 +1,35 @@
+"""Integration tests for the LLM provider presets in the settings pipeline."""
+
+from __future__ import annotations
+
+from abogen.llm_providers import get_provider_presets, get_provider_by_id
+from abogen.normalization_settings import (
+    _extract_settings,
+    build_llm_configuration,
+)
+
+
+def test_extract_settings_preserves_llm_provider():
+    """When llm_provider is supplied it must survive _extract_settings()."""
+    extracted = _extract_settings({"llm_provider": "minimax"})
+    assert extracted["llm_provider"] == "minimax"
+
+
+def test_extract_settings_defaults_llm_provider_to_empty():
+    extracted = _extract_settings({})
+    assert extracted["llm_provider"] == ""
+
+
+def test_build_llm_configuration_with_minimax_preset():
+    """Simulate choosing the MiniMax preset and building the LLM config."""
+    preset = get_provider_by_id("minimax")
+    settings = _extract_settings({
+        "llm_provider": preset.id,
+        "llm_base_url": preset.base_url,
+        "llm_api_key": "test-key",
+        "llm_model": preset.models[0],
+    })
+    config = build_llm_configuration(settings)
+    assert config.base_url == "https://api.minimax.io/v1"
+    assert config.api_key == "test-key"
+    assert config.model == preset.models[0]