Files
SillyTavern/public/scripts/custom-request.js
T
Octopus aecbb9a2ee feat: add MiniMax as a chat completion provider (#5452)
* feat: add MiniMax as a chat completion provider

Add MiniMax (https://www.minimax.io) as a first-class chat completion
provider. MiniMax already has TTS integration in SillyTavern; this
extends support to LLM chat completions via their OpenAI-compatible API.

Supported models:
- MiniMax-M2.5 (default) — 204K context
- MiniMax-M2.5-highspeed — same capability, faster inference

Key implementation details:
- Reuses existing SECRET_KEYS.MINIMAX (shared with TTS)
- API endpoint: https://api.minimax.io/v1
- Temperature clamped to (0.0, 1.0] as required by MiniMax API
- Returns hardcoded model list since MiniMax doesn't expose /v1/models
- Full UI integration: model selector, sampler parameters, streaming

Co-Authored-By: octo-patch <octo-patch@users.noreply.github.com>

* feat: upgrade MiniMax default model to M2.7

- Add MiniMax-M2.7 and MiniMax-M2.7-highspeed to model list
- Set MiniMax-M2.7 as default model
- Keep all previous models as alternatives

* feat: independent request function, vision support, temp clamping for MiniMax

- Extract sendMinimaxRequest() following Chutes pattern (PR #4844)
  with function calling and JSON Schema structured output support
- Clamp temperature to (0.01, 1.0] on backend; limit frontend UI max to 1.0
- Enable image inlining for MiniMax M2.7 model
- Add MiniMax to slash-commands model selector and tokenizer mapping
- Add minimax_model to default preset

* feat: add VLM-based vision support for MiniMax M2.7

M2.7 does not natively accept image input. When images are detected
in messages, pre-process them via the MiniMax VLM endpoint
(/v1/coding_plan/vlm) to convert images to text descriptions before
sending to the chat completions API. Uses the same API key.

* feat: add M2-her model to MiniMax provider

M2-her is MiniMax's dialogue/roleplay-optimized model with 64K context
and 2048 max completion tokens. Text-only (no vision).

* feat: add MiniMax China endpoint (minimaxi.com) support

Add endpoint selector (Global/China) for MiniMax, mirroring the
SiliconFlow pattern. Users can now choose between api.minimax.io
(international) and api.minimaxi.com (China domestic).

* fix: merge consecutive same-role messages for MiniMax

MiniMax API rejects consecutive messages with the same role with
error 'invalid chat setting (2013)'. Merge them before sending.

* review: address PR feedback on MiniMax provider

Backend (src/endpoints/backends/chat-completions.js):
- Drop the entire MiniMax VLM image-preprocessing path; vision is no
  longer advertised for this provider, so M2.7 messages now go straight
  to /chat/completions without a separate VLM round-trip.
- Drop the json_schema -> response_format mapping (MiniMax does not
  document structured-output support; relying on it was speculative).
- Drop the backend temperature clamp; the same clamp now lives in the
  frontend so the wire payload matches what the user sees.
- Drop the MINIMAX branch in /status that returned a hard-coded model
  list; the frontend hardcodes the same list and bypasses /status via
  noValidateSources, so the round-trip was wasted.
- Add a streaming Transform + non-streaming helper that move
  <think>...</think> blocks from delta.content / message.content to
  reasoning_content. MiniMax M2.x emit chain-of-thought inline in
  content; without this transform the raw <think> tags leak into the
  rendered chat. Includes a state machine that holds back partial
  marker bytes so a marker split across SSE chunks is still detected.

Frontend:
- public/scripts/openai.js: add MINIMAX to noValidateSources so the key
  is accepted without a /models call; remove the dead saveModelList
  branch; clamp temperature to (0.0, 1.0] in createGenerationParameters.
- public/scripts/reasoning.js: add MINIMAX to the non-streaming
  reasoning_content extraction case (the backend transform now produces
  this field for MiniMax responses).
- public/scripts/slash-commands.js: add MINIMAX to the /api enum and
  add a MiniMax case to /api-url so users can switch endpoint by
  command.
- public/scripts/custom-request.js: pass minimax_endpoint through the
  override-payload merge alongside the other per-source endpoint fields.
- public/scripts/tokenizers.js: stop returning openai_model (which was
  always a MiniMax model id and thus an unknown tokenizer); fall back
  to gpt-3.5-turbo for a coarse but functional estimate.
- public/scripts/tool-calling.js: add MINIMAX to supportedSources so
  function-calling settings are exposed.
- public/index.html: drop the "-- Connect to the API --" placeholder
  option from the model select (the model list is hardcoded and always
  populated); remove minimax from the vision data-source attributes
  on the inline-media controls.
- public/img/minimax.svg: replace the multicolor brand SVG with a
  single-color currentColor version that matches the other provider
  icons in the connect panel.

* review: drop backend <think> parsing, defer to frontend

Per reviewer feedback: SillyTavern's reasoningHandler / reasoning_auto_parse
setting already extracts <think>...</think> blocks on the client side, so the
backend doesn't need to rewrite MiniMax responses. Removes the SSE Transform,
the non-streaming helper, and the corresponding case in reasoning.js.

* fix: remove isImageInliningSupported declaration for MINIMAX

* fix: remove MINIMAX from stream reasoning parsing

* fix: add to autoconnect logic

* fix: add missing MINIMAX models from docs

* fix: freq. and pres. pen aren't supported for MINIMAX

* fix: use clamp function for adjusting temperature

* fix: pass minimax_endpoint from connection profile to ChatCompletionService

* fix: update supported APIs in slash command documentation

* fix: replace bespoke merge with standard MERGE_TOOLS processing

* fix: add data-i18n attributes for headers

---------

Co-authored-by: octo-patch <octo-patch@users.noreply.github.com>
Co-authored-by: octo-patch <octo-patch@github.com>
Co-authored-by: Cohee <18619528+Cohee1207@users.noreply.github.com>
2026-04-24 00:43:05 +03:00

607 lines
25 KiB
JavaScript

import { getPresetManager } from './preset-manager.js';
import { extractJsonFromData, extractMessageFromData, getGenerateUrl, getRequestHeaders, name1, name2 } from '../script.js';
import { getTextGenServer, createTextGenGenerationData, setting_names, textgenerationwebui_settings } from './textgen-settings.js';
import { extractReasoningFromData } from './reasoning.js';
import { formatInstructModeChat, formatInstructModePrompt, getInstructStoppingSequences } from './instruct-mode.js';
import { getStreamingReply, tryParseStreamingError, createGenerationParameters, settingsToUpdate, oai_settings } from './openai.js';
import EventSourceStream from './sse-stream.js';
// #region Type Definitions
/**
* @typedef {Object} TextCompletionRequestBase
* @property {boolean?} [stream=false] - Whether to stream the response
* @property {number} max_tokens - Maximum number of tokens to generate
* @property {string} [model] - Optional model name
* @property {string} api_type - Type of API to use
* @property {string} [api_server] - Optional API server URL
* @property {number} [temperature] - Optional temperature parameter
* @property {number} [min_p] - Optional min_p parameter
*/
/**
* @typedef {Object} TextCompletionPayloadBase
* @property {boolean?} [stream=false] - Whether to stream the response
* @property {string} prompt - The text prompt for completion
* @property {number} max_tokens - Maximum number of tokens to generate
* @property {number} max_new_tokens - Alias for max_tokens
* @property {string} [model] - Optional model name
* @property {string} api_type - Type of API to use
* @property {string} api_server - API server URL
* @property {number} [temperature] - Optional temperature parameter
*/
/** @typedef {Record<string, any> & TextCompletionPayloadBase} TextCompletionPayload */
/**
* @typedef {Object} ChatCompletionMessage
* @property {string} [name] - The name of the message author (optional)
* @property {string} role - The role of the message author (e.g., "user", "assistant", "system")
* @property {string} content - The content of the message
*/
/**
* @typedef {Object} ChatCompletionPayloadBase
* @property {boolean?} [stream=false] - Whether to stream the response
* @property {ChatCompletionMessage[]} messages - Array of chat messages
* @property {string} [model] - Optional model name to use for completion
* @property {string} chat_completion_source - Source provider
* @property {number} max_tokens - Maximum number of tokens to generate
* @property {number} [temperature] - Optional temperature parameter for response randomness
* @property {string} [custom_url] - Optional custom URL
* @property {string} [reverse_proxy] - Optional reverse proxy URL
* @property {string} [proxy_password] - Optional proxy password
* @property {string} [custom_prompt_post_processing] - Optional custom prompt post-processing
*/
/** @typedef {Record<string, any> & ChatCompletionPayloadBase} ChatCompletionPayload */
/**
* @typedef {Object} ExtractedData
* @property {string} content - Extracted content.
* @property {string} reasoning - Extracted reasoning.
*/
/**
* @typedef {Object} StreamResponse
* @property {string} text - Generated text.
* @property {string[]} swipes - Generated swipes
* @property {Object} state - Generated state
* @property {string?} [state.reasoning] - Generated reasoning
* @property {string?} [state.image] - Generated image
*/
// #endregion
/**
* Creates & sends a text completion request.
*/
export class TextCompletionService {
static TYPE = 'textgenerationwebui';
/**
* @param {Record<string, any> & TextCompletionRequestBase & {prompt: string}} custom
* @returns {TextCompletionPayload}
*/
static createRequestData({ stream = false, prompt, max_tokens, model, api_type, api_server, temperature, min_p, ...props }) {
const payload = {
stream,
prompt,
max_tokens,
max_new_tokens: max_tokens,
model,
api_type,
api_server: api_server ?? getTextGenServer(api_type),
temperature,
min_p,
...props,
};
// Remove undefined values to avoid API errors
Object.keys(payload).forEach(key => {
if (payload[key] === undefined) {
delete payload[key];
}
});
return payload;
}
/**
* Sends a text completion request to the specified server
* @param {TextCompletionPayload} data Request data
* @param {boolean?} extractData Extract message from the response. Default true
* @param {AbortSignal?} signal
* @returns {Promise<ExtractedData | (() => AsyncGenerator<StreamResponse>)>} If not streaming, returns extracted data; if streaming, returns a function that creates an AsyncGenerator
* @throws {Error}
*/
static async sendRequest(data, extractData = true, signal = null) {
if (!data.stream) {
const response = await fetch(getGenerateUrl(this.TYPE), {
method: 'POST',
headers: getRequestHeaders(),
cache: 'no-cache',
body: JSON.stringify(data),
signal: signal ?? new AbortController().signal,
});
const json = await response.json();
if (!response.ok || json.error) {
throw new Error(String(json.error?.message || 'Response not OK'));
}
if (!extractData) {
return json;
}
return {
content: extractMessageFromData(json, this.TYPE),
reasoning: extractReasoningFromData(json, {
mainApi: this.TYPE,
textGenType: data.api_type,
ignoreShowThoughts: true,
}),
};
}
const response = await fetch('/api/backends/text-completions/generate', {
method: 'POST',
headers: getRequestHeaders(),
cache: 'no-cache',
body: JSON.stringify(data),
signal: signal ?? new AbortController().signal,
});
if (!response.ok) {
const text = await response.text();
tryParseStreamingError(response, text, { quiet: true });
throw new Error(`Got response status ${response.status}`);
}
const eventStream = new EventSourceStream();
response.body.pipeThrough(eventStream);
const reader = eventStream.readable.getReader();
return async function* streamData() {
let text = '';
const swipes = [];
const state = { reasoning: '' };
while (true) {
const { done, value } = await reader.read();
if (done) return;
if (value.data === '[DONE]') return;
tryParseStreamingError(response, value.data, { quiet: true });
let data = JSON.parse(value.data);
if (data?.choices?.[0]?.index > 0) {
const swipeIndex = data.choices[0].index - 1;
swipes[swipeIndex] = (swipes[swipeIndex] || '') + data.choices[0].text;
} else {
const newText = data?.choices?.[0]?.text || data?.content || '';
text += newText;
state.reasoning += data?.choices?.[0]?.reasoning ?? '';
}
yield { text, swipes, state };
}
};
}
/**
* Return a formatted prompt string given an array of messages, a chosen instruct preset, and instruct settings.
* @param {(ChatCompletionMessage & {ignoreInstruct?: boolean})[]} prompt An array of messages
* @param {InstructSettings|string} instructPreset Either the name of an instruct preset or the instruct preset object itself.
* @param {Partial<InstructSettings>} instructSettings Optional instruct settings
*/
static constructPrompt(prompt, instructPreset, instructSettings) {
// InstructPreset may either be a name or itself a preset
if (typeof instructPreset === 'string') {
const instructPresetManager = getPresetManager('instruct');
instructPreset = instructPresetManager?.getCompletionPresetByName(instructPreset);
}
// Clone the preset to avoid modifying the original
instructPreset = structuredClone(instructPreset);
if (instructSettings) { // apply any additional settings
Object.assign(instructPreset, instructSettings);
}
// Make the type check shut up. We 100% don't have a string here.
if (typeof instructPreset === 'string') {
return;
}
// Format messages using instruct formatting
const formattedMessages = [];
const prefillActive = prompt.length > 0 ? prompt[prompt.length - 1].role === 'assistant' : false;
for (const message of prompt) {
let messageContent = message.content;
if (!message.ignoreInstruct) {
const isLastMessage = message === prompt[prompt.length - 1];
// This complicated logic means:
// 1. If prefill is not active, format all messages
// 2. If prefill is active, format all messages except the last one
if (!isLastMessage || !prefillActive) {
messageContent = formatInstructModeChat(
message.name ?? message.role,
message.content,
message.role === 'user',
message.role === 'system',
undefined,
name1, // for macros
name2, // for macros
undefined,
instructPreset,
);
}
// Add prompt formatting for the last message.
// e.g. "<|im_start|>assistant"
if (isLastMessage) {
let last_line = formatInstructModePrompt(
'assistant', // for sequences using {{name}}
false, // not an impersonation
prefillActive ? message.content : undefined, // if using prefill, last message is the prefill
name1, // for macros
name2, // for macros
true, // quiet
false,
instructPreset,
);
if (prefillActive) { // content is the prefilled message
if (last_line.endsWith('\n') && !message.content.endsWith('\n')) {
last_line = last_line.slice(0, -1); // remove newline after prefill if it's not in the prefill itself
}
messageContent = last_line;
} else { // append last line to content (e.g. "<|im_start|>assistant:")
messageContent += last_line;
}
}
}
formattedMessages.push(messageContent);
}
return formattedMessages.join('');
}
/**
* Process and send a text completion request with optional preset & instruct
* @param {TextCompletionPayload} requestData
* @param {Object} options - Configuration options
* @param {string?} [options.presetName] - Name of the preset to use for generation settings
* @param {string?} [options.instructName] - Name of instruct preset for message formatting
* @param {Partial<InstructSettings>?} [options.instructSettings] - Override instruct settings
* @param {boolean} extractData - Whether to extract structured data from response
* @param {AbortSignal?} [signal]
* @returns {Promise<ExtractedData | (() => AsyncGenerator<StreamResponse>)>} If not streaming, returns extracted data; if streaming, returns a function that creates an AsyncGenerator
* @throws {Error}
*/
static async processRequest(requestData, options = {}, extractData = true, signal = null) {
const { presetName, instructName } = options;
// remove any undefined params in given request data
requestData = this.createRequestData(requestData);
/** @type {InstructSettings | undefined} */
let instructPreset;
const prompt = requestData.prompt;
// Handle instruct formatting if requested
if (Array.isArray(prompt)) {
if (instructName) {
const instructPresetManager = getPresetManager('instruct');
instructPreset = instructPresetManager?.getCompletionPresetByName(instructName);
if (instructPreset) {
requestData.prompt = this.constructPrompt(prompt, instructPreset, options.instructSettings);
const stoppingStrings = getInstructStoppingSequences({ customInstruct: instructPreset, useStopStrings: false });
requestData.stop = stoppingStrings;
requestData.stopping_strings = stoppingStrings;
} else {
console.warn(`Instruct preset "${instructName}" not found, using basic formatting`);
requestData.prompt = prompt.map(x => x.content).join('\n\n');
}
} else {
requestData.prompt = prompt.map(x => x.content).join('\n\n');
}
} else if (typeof prompt === 'string') {
requestData.prompt = prompt;
}
// Apply generation preset if specified
if (presetName) {
const presetManager = getPresetManager(this.TYPE);
if (presetManager) {
const preset = presetManager.getCompletionPresetByName(presetName);
if (preset) {
// Convert preset to payload and merge with custom data
requestData = this.presetToGeneratePayload(preset, {}, requestData);
} else {
console.warn(`Preset "${presetName}" not found, continuing with default settings`);
}
} else {
console.warn('Preset manager not found, continuing with default settings');
}
}
const response = await this.sendRequest(requestData, extractData, signal);
// Remove stopping strings from the end
if (!requestData.stream && extractData) {
/** @type {ExtractedData} */
// @ts-ignore
const extractedData = response;
let message = extractedData.content;
message = message.replace(/[^\S\r\n]+$/gm, '');
if (requestData.stopping_strings) {
for (const stoppingString of requestData.stopping_strings) {
if (stoppingString.length) {
for (let j = stoppingString.length; j > 0; j--) {
if (message.slice(-j) === stoppingString.slice(0, j)) {
message = message.slice(0, -j);
break;
}
}
}
}
}
if (instructPreset) {
[
instructPreset.stop_sequence,
instructPreset.input_sequence,
].forEach(sequence => {
if (sequence?.trim()) {
const index = message.indexOf(sequence);
if (index !== -1) {
message = message.substring(0, index);
}
}
});
[
instructPreset.output_sequence,
instructPreset.last_output_sequence,
].forEach(sequences => {
if (sequences) {
sequences.split('\n')
.filter(line => line.trim() !== '')
.forEach(line => {
message = message.replaceAll(line, '');
});
}
});
}
extractedData.content = message;
}
return response;
}
/**
* Converts a preset to a valid text completion payload.
* Only supports temperature.
* @param {Object} preset - The preset configuration
* @param {Object} overridePreset - Additional parameters to override preset values
* @param {Object} overridePayload - Additional parameters to override payload values
* @returns {Object} - Formatted payload for text completion API
*/
static presetToGeneratePayload(preset, overridePreset = {}, overridePayload = {}) {
if (!preset || typeof preset !== 'object') {
throw new Error('Invalid preset: must be an object');
}
// apply preset overrides
preset = { ...preset, ...overridePreset };
// Only take fields from the preset specified in setting_names to use as TextCompletionSettings
const settings = structuredClone(textgenerationwebui_settings);
for (const [key, value] of Object.entries(preset)) {
if (!setting_names.includes(key)) continue;
settings[key] = value;
}
// convert to a generation payload
const payload = createTextGenGenerationData(settings, overridePayload.model, overridePayload.prompt, preset.genamt);
// apply overrides
return this.createRequestData({ ...payload, ...overridePayload });
}
}
/**
* Creates & sends a chat completion request.
*/
export class ChatCompletionService {
static TYPE = 'openai';
/**
* @param {ChatCompletionPayload} custom
* @returns {ChatCompletionPayload}
*/
static createRequestData({ stream = false, messages, model, chat_completion_source, max_tokens, temperature, custom_url, reverse_proxy, proxy_password, custom_prompt_post_processing, ...props }) {
const payload = {
stream,
messages,
model,
chat_completion_source,
max_tokens,
temperature,
custom_url,
reverse_proxy,
proxy_password,
custom_prompt_post_processing,
use_sysprompt: true,
...props,
};
// Remove undefined values to avoid API errors
Object.keys(payload).forEach(key => {
if (payload[key] === undefined) {
delete payload[key];
}
});
return payload;
}
/**
* Sends a chat completion request
* @param {ChatCompletionPayload} data Request data
* @param {boolean?} extractData Extract message from the response. Default true
* @param {AbortSignal?} signal Abort signal
* @returns {Promise<ExtractedData | (() => AsyncGenerator<StreamResponse>)>} If not streaming, returns extracted data; if streaming, returns a function that creates an AsyncGenerator
* @throws {Error}
*/
static async sendRequest(data, extractData = true, signal = null) {
const response = await fetch('/api/backends/chat-completions/generate', {
method: 'POST',
headers: getRequestHeaders(),
cache: 'no-cache',
body: JSON.stringify(data),
signal: signal ?? new AbortController().signal,
});
if (!data.stream) {
const json = await response.json();
if (!response.ok || json.error) {
throw new Error(String(json.error?.message || 'Response not OK'));
}
if (!extractData) {
return json;
}
const result = {
content: extractMessageFromData(json, this.TYPE),
reasoning: extractReasoningFromData(json, {
mainApi: this.TYPE,
textGenType: data.chat_completion_source,
ignoreShowThoughts: true,
}),
};
// Try parse JSON
if (data.json_schema) {
result.content = JSON.parse(extractJsonFromData(json, { mainApi: this.TYPE, chatCompletionSource: data.chat_completion_source }));
}
return result;
}
if (!response.ok) {
const text = await response.text();
tryParseStreamingError(response, text, { quiet: true });
throw new Error(`Got response status ${response.status}`);
}
const eventStream = new EventSourceStream();
response.body.pipeThrough(eventStream);
const reader = eventStream.readable.getReader();
return async function* streamData() {
let text = '';
const swipes = [];
const state = { reasoning: '', images: [], signature: '', toolSignatures: {} };
while (true) {
const { done, value } = await reader.read();
if (done) return;
const rawData = value.data;
if (rawData === '[DONE]') return;
tryParseStreamingError(response, rawData, { quiet: true });
const parsed = JSON.parse(rawData);
const reply = getStreamingReply(parsed, state, {
chatCompletionSource: data.chat_completion_source,
overrideShowThoughts: true,
});
if (Array.isArray(parsed?.choices) && parsed?.choices?.[0]?.index > 0) {
const swipeIndex = parsed.choices[0].index - 1;
swipes[swipeIndex] = (swipes[swipeIndex] || '') + reply;
} else {
text += reply;
}
yield { text, swipes: swipes, state };
}
};
}
/**
* Process and send a chat completion request with optional preset
* @param {ChatCompletionPayload} requestData - payload data, overriding preset if given
* @param {Object} options - Configuration options
* @param {string?} [options.presetName] - Name of the preset to use for generation settings
* @param {boolean} [extractData=true] - Whether to extract structured data from response
* @param {AbortSignal?} [signal] - Abort signal
* @returns {Promise<ExtractedData | (() => AsyncGenerator<StreamResponse>)>} If not streaming, returns extracted data; if streaming, returns a function that creates an AsyncGenerator
* @throws {Error}
*/
static async processRequest(requestData, options, extractData = true, signal = null) {
const { presetName } = options;
requestData = this.createRequestData(requestData);
// Apply generation preset if specified
if (presetName) {
const presetManager = getPresetManager(this.TYPE);
if (presetManager) {
const preset = presetManager.getCompletionPresetByName(presetName);
if (preset) {
// Convert preset to payload and merge with custom parameters
requestData = await this.presetToGeneratePayload(preset, {}, requestData);
} else {
console.warn(`Preset "${presetName}" not found, continuing with default settings`);
}
} else {
console.warn('Preset manager not found, continuing with default settings');
}
}
return await this.sendRequest(requestData, extractData, signal);
}
/**
* Converts a preset to a valid chat completion payload
* Only supports temperature.
* @param {Object} preset - The preset configuration
* @param {Object} overridePreset - Additional parameters to override preset values
* @param {Object} overridePayload - Additional parameters to override payload values
* @returns {Promise<any>} - Formatted payload for chat completion API
*/
static async presetToGeneratePayload(preset, overridePreset = {}, overridePayload = {}) {
if (!preset || typeof preset !== 'object') {
throw new Error('Invalid preset: must be an object');
}
// apply preset overrides
preset = { ...preset, ...overridePreset };
// Fix any fields before converting to settings
preset.bias_preset_selected = preset.bias_presets !== undefined ? preset.bias_preset_selected : undefined; // presets might have bias_preset_selected but not bias_presets, but settings need both or neither.
// Convert from preset to ChatCompletionSettings
const settings = structuredClone(oai_settings);
for (const [key, value] of Object.entries(preset)) {
const settingToUpdate = settingsToUpdate[key];
if (!settingToUpdate) continue;
settings[settingToUpdate[1]] = value;
}
// Ensure api-url is properly applied for all sources that accept it
['custom_url', 'vertexai_region', 'zai_endpoint', 'siliconflow_endpoint', 'minimax_endpoint'].forEach(field => {
// The order is: connection profile => CC preset => CC settings
overridePayload[field] = overridePayload[field] || settings[field] || oai_settings[field];
});
// Convert from settings to generation payload
const data = await createGenerationParameters(settings, overridePayload.model, 'quiet', overridePayload.messages);
const payload = data.generate_data;
// apply overrides
return this.createRequestData({ ...payload, ...overridePayload });
}
}