Merge remote-tracking branch 'upstream/main'
This commit is contained in:
LightningHyperBlaze45654
@@ -23,6 +23,7 @@
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@@ -37,7 +38,6 @@
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386
pnpm-lock.yaml
generated
386
pnpm-lock.yaml
generated
@@ -29,6 +29,9 @@ importers:
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|
||||
'@img/sharp-libvips-linuxmusl-arm64': 1.0.4
|
||||
'@img/sharp-libvips-linuxmusl-x64': 1.0.4
|
||||
'@img/sharp-linux-arm': 0.33.5
|
||||
'@img/sharp-linux-arm64': 0.33.5
|
||||
'@img/sharp-linux-s390x': 0.33.5
|
||||
'@img/sharp-linux-x64': 0.33.5
|
||||
'@img/sharp-linuxmusl-arm64': 0.33.5
|
||||
'@img/sharp-linuxmusl-x64': 0.33.5
|
||||
'@img/sharp-wasm32': 0.33.5
|
||||
'@img/sharp-win32-ia32': 0.33.5
|
||||
'@img/sharp-win32-x64': 0.33.5
|
||||
|
||||
shebang-command@2.0.0:
|
||||
dependencies:
|
||||
shebang-regex: 3.0.0
|
||||
@@ -7126,6 +7401,15 @@ snapshots:
|
||||
mkdirp: 1.0.4
|
||||
yallist: 4.0.0
|
||||
|
||||
tar@7.4.3:
|
||||
dependencies:
|
||||
'@isaacs/fs-minipass': 4.0.1
|
||||
chownr: 3.0.0
|
||||
minipass: 7.1.2
|
||||
minizlib: 3.0.1
|
||||
mkdirp: 3.0.1
|
||||
yallist: 5.0.0
|
||||
|
||||
temp-dir@2.0.0: {}
|
||||
|
||||
tempy@1.0.1:
|
||||
@@ -7422,6 +7706,8 @@ snapshots:
|
||||
|
||||
yallist@4.0.0: {}
|
||||
|
||||
yallist@5.0.0: {}
|
||||
|
||||
yaml@2.4.2: {}
|
||||
|
||||
yargs-parser@18.1.3:
|
||||
|
||||
@@ -29,7 +29,7 @@
|
||||
},
|
||||
"productName": "RisuAI",
|
||||
"mainBinaryName": "RisuAI",
|
||||
"version": "141.2.0",
|
||||
"version": "143.0.1",
|
||||
"identifier": "co.aiclient.risu",
|
||||
"plugins": {
|
||||
"updater": {
|
||||
|
||||
@@ -171,6 +171,8 @@ export const languageEnglish = {
|
||||
translatorPrompt: "The prompt that is used for translation. if it is blank, it will use the default prompt. you can also use ChatML formating with {{slot}} for the dest language, {{solt::content}} for the content, and {{slot::tnote}} for the translator note.",
|
||||
translateBeforeHTMLFormatting: "If enabled, it will translate the text before Regex scripts and HTML formatting. this could make the token lesser but could break the formatting.",
|
||||
autoTranslateCachedOnly: "If enabled, it will automatically translate only the text that the user has translated previously.",
|
||||
presetChain: "If it is not blank, the preset will be changed and applied randomly every time when user sends a message in the preset list in this input. preset list should be seperated by comma, for example, `preset1,preset2`.",
|
||||
legacyMediaFindings: "If enabled, it will use the old method to find media assets, without using the additional search algorithm.",
|
||||
},
|
||||
setup: {
|
||||
chooseProvider: "Choose AI Provider",
|
||||
@@ -817,4 +819,7 @@ export const languageEnglish = {
|
||||
permissionDenied: "Permission Denied by Your Browser or OS",
|
||||
customFlags: "Custom Flags",
|
||||
enableCustomFlags: "Enable Custom Flags",
|
||||
googleCloudTokenization: "Google Cloud Tokenization",
|
||||
presetChain: "Preset Chain",
|
||||
legacyMediaFindings: "Legacy Media Findings",
|
||||
}
|
||||
@@ -20,7 +20,7 @@ export const languageChineseTraditional = {
|
||||
"noData": "無法找到檔案中的資料，或者檔案已經損毀",
|
||||
"onlyOneChat": "至少需要一個聊天室",
|
||||
"alreadyCharInGroup": "該群組中已經有一個同名角色。",
|
||||
"noUserIcon": "請先設定你的個人頭像。",
|
||||
"noUserIcon": "請先設定您的個人頭像。",
|
||||
"emptyText": "文字內容為空。",
|
||||
"wrongPassword": "密碼錯誤",
|
||||
"networkFetch": "這通常是由於網路連線不穩定或伺服器故障引起的。",
|
||||
@@ -32,18 +32,18 @@ export const languageChineseTraditional = {
|
||||
"showHelp": "顯示幫助",
|
||||
"help": {
|
||||
"model": "此模型是指聊天中使用的主要模型。",
|
||||
"submodel": "輔助模型是一個用於分析情感圖像、產生自動建議等的模型，推薦使用 GPT-3.5。",
|
||||
"submodel": "輔助模型是一個用於分析情感圖片、產生自動建議等的模型，推薦使用 GPT-3.5。",
|
||||
"oaiapikey": "OpenAI 的 API 金鑰（Key），可在 https://platform.openai.com/account/api-keys 取得。",
|
||||
"mainprompt": "主要提示詞設定用於決定模型的預設行為。",
|
||||
"jailbreak": "當角色中的越獄開關被啟動後，越獄提示詞將被使用。",
|
||||
"globalNote": "一個對模型行為有強烈影響的備註（也稱為 UJB），適用於所有角色。",
|
||||
"autoSuggest": "用於自動建議使用者回應時生成選項的提示詞。",
|
||||
"formatOrder": "提示詞的排列順序：越靠下的區塊對模型的影響越大。",
|
||||
"forceUrl": "此欄位不為空時，請求將被發送到你所輸入的網址。",
|
||||
"forceUrl": "此欄位不為空時，請求將被發送到您所輸入的網址。",
|
||||
"tempature": "較低的數值會使角色更緊密地遵循提示詞，但會使回應更制式與機械化。\n較高的數值則會增強角色的創意表現，但回應可能會變得不穩定。",
|
||||
"frequencyPenalty": "較高的數值可以避免角色在個別回應中重複使用相同的詞彙，但回應也更容易出現語意混亂。",
|
||||
"presensePenalty": "較高的數值可以避免角色在整體對話中重複使用相同的詞彙，但這也可能導致回答失去一致性和穩定性。",
|
||||
"sdProvider": "圖像生成提供者。",
|
||||
"sdProvider": "圖片生成提供者。",
|
||||
"msgSound": "當角色回應時，播放 *叮* 的提示音",
|
||||
"charDesc": "角色的簡要描述。這會影響角色的回應方式。",
|
||||
"charFirstMessage": "角色的初始訊息，這會極大地影響角色的回應方式。",
|
||||
@@ -54,11 +54,11 @@ export const languageChineseTraditional = {
|
||||
"loreActivationKey": "當上下文中包含任一關鍵字時，該條目將被啟用，並啟動相應的提示詞。使用逗號分隔。",
|
||||
"loreorder": "插入順序越高，對模型的影響力越大。在啟動大量條目時，也更不容易被截斷。",
|
||||
"bias": "Bias 是一組鍵值數據，用於修改某些字串出現的機率。\n其數值範圍可以是 -100 到 100。較高的數值會使該字串更可能出現，較低的數值則降低出現機率。\n另外，在某些模型中，若將數值設為 -101，該字串將被標記為「強制禁止詞」。\n警告：若 Tokenizer 設定有誤，可能無法正常運作。",
|
||||
"emotion": "「表情立繪會根據角色情緒顯示對應的圖片。具體的情緒由角色的回應進行分析。你必須輸入情緒名稱作為詞彙*（如：joy, happy, fear 等）*。若存在名為 **neutral** 的情緒，將作為默認情緒。至少需要三張圖片才能正常運作。」",
|
||||
"emotion": "「表情立繪會根據角色情緒顯示對應的圖片。具體的情緒由角色的回應進行分析。您必須輸入情緒名稱作為詞彙*（如：joy, happy, fear 等）*。若存在名為 **neutral** 的情緒，將作為默認情緒。至少需要三張圖片才能正常運作。」",
|
||||
"imggen": "分析聊天內容後，將提示套用至 {{slot}}。",
|
||||
"regexScript": "正規表達式（Regex Script）是一個自定義工具，用於將符合條件的字串由「IN」替換為「OUT」。\n\n有四種類型選項：\n\n- **修改輸入（Modify Input）**：修改使用者的輸入內容\n\n- **修改輸出（Modify Output）**：修改角色的輸出內容\n\n- **修改請求資料（Modify Request Data）**：在當前聊天資料發送時進行修改\n\n- **修改顯示（Modify Display）**：僅修改顯示的文字，不更改聊天資料\n\n「IN」必須是一個不帶標誌（flags）的正規表達式，且開頭和結尾不包含斜線。\n\n「OUT」是一個可以包含替換模式的字串。這些替換模式如下：\n\n- $$\n\n - 插入符號「$」\n\n- $&\n\n - 插入匹配到的子字串\n\n- $`\n\n - 插入匹配子字串前的部分\n\n- $1\n\n - 插入第一個匹配群組，可以替換為其他數字（例如 2、3...）\n\n- $(name)\n\n - 插入命名群組\n\n針對標誌（flags），除了原生支援的標誌之外，還可以使用下列專為進階使用者設計的標誌：\n\n- `<inject>`：將結果注入當前字串中\n- `<move_top>`：將結果移到字串的頂部\n- `<move_bottom>`：將結果移到字串的底部\n- `<repeat_back>`：如果找不到匹配，則使用上一個匹配的結果\n- `<order n>`：設定結果的順序，數值越高顯示越靠前。「n」代表一個數字（例如 `<order 1>`）。如果未設定，默認為 0。\n- `<cbs>`：解析「IN」中的大括號語法\n\n若要與原生標誌結合使用，可以像這樣使用：`gi<cbs><move_top>`。",
|
||||
"experimental": "此為實驗性功能，可能不穩定。",
|
||||
"oogaboogaURL": "如果你的 WebUI 支援舊版 API，你的 URL 應類似於 *https://.../run/textgen*。\n\n如果你的 WebUI 支援新版 API，你的 URL 應類似於 *https://.../api/v1/generate*，且將使用 API 伺服器作為主機，並在參數中添加 —api。",
|
||||
"oogaboogaURL": "如果您的 WebUI 支援舊版 API，您的 URL 應類似於 *https://.../run/textgen*。\n\n如果您的 WebUI 支援新版 API，您的 URL 應類似於 *https://.../api/v1/generate*，且將使用 API 伺服器作為主機，並在參數中添加 —api。",
|
||||
"exampleMessage": "示範對話會影響角色的回應，但不會永久佔用 Token。\n\n對話格式示例：\n\n```\n<START>\n{{user}}: hi\n{{char}}: hello\n<START>\n{{user}}: hi\nHaruhi: hello\n```\n\n```<START>``` 標記了一段新對話的開始。",
|
||||
"creatorQuotes": "說明將顯示在初始訊息之上，用於向使用者提供角色說明。此內容不會進入提示詞中。",
|
||||
"systemPrompt": "此欄位不為空時，將替換設定中的主要提示詞為此內容。",
|
||||
@@ -69,10 +69,10 @@ export const languageChineseTraditional = {
|
||||
"loreSelective": "啟用選擇性模式後，需同時匹配關鍵字與次要關鍵字，方可啟用該條目。",
|
||||
"loreRandomActivation": "啟用「使用機率條件」後，若同時符合啟用條目的其他條件，則在每次發送聊天時，該條目將依照設定的機率被使用。",
|
||||
"additionalAssets": "在聊天中顯示的額外資源。\n\n - 使用 `{{raw::<資源名稱>}}` 作為路徑。\n - 使用 `{{image::<資源名稱>}}` 作為圖片。\n - 使用 `{{video::<資源名稱>}}` 作為影片。\n - 使用 `{{audio::<資源名稱>}}` 作為音訊。\n - 建議放置在背景 HTML 中。",
|
||||
"superMemory": "SuperMemory 通過向 AI 提供摘要資料來增強角色的記憶能力。\n\nSuperMemory 是一個文本摘要功能，推薦使用 davinci 模型。不建議使用輔助模型，除非它是未經過濾、最大上下文長度超過 2000 Tokens，且具有良好摘要能力的模型。\n\nSuperMemory 提示詞決定了模型如何撰寫摘要。留空將使用預設提示詞，建議保持留空。\n\n完成所有設定後，你可以在角色的設定中啟用此功能。",
|
||||
"superMemory": "SuperMemory 通過向 AI 提供摘要資料來增強角色的記憶能力。\n\nSuperMemory 是一個文本摘要功能，推薦使用 davinci 模型。不建議使用輔助模型，除非它是未經過濾、最大上下文長度超過 2000 Tokens，且具有良好摘要能力的模型。\n\nSuperMemory 提示詞決定了模型如何撰寫摘要。留空將使用預設提示詞，建議保持留空。\n\n完成所有設定後，您可以在角色的設定中啟用此功能。",
|
||||
"replaceGlobalNote": "此欄位不為空時，將替換當前的全域備註為此內容。",
|
||||
"backgroundHTML": "將 Markdown/HTML 注入到聊天畫面的背景中。\n\n你也可以使用額外資源。例如，你可以使用 {{audio::<資源名稱}} 作為背景音樂。\n\n此外，你還可以與額外資源搭配使用以下格式：\n - {{bg::<資源名稱>}}：將資源設為背景。",
|
||||
"additionalText": "只有當 AI 認為有必要時，才會將該段文本添加到角色描述中。你可以在此處放置較長的文本。使用雙換行進行內容分隔。",
|
||||
"backgroundHTML": "將 Markdown/HTML 注入到聊天畫面的背景中。\n\n您也可以使用額外資源。例如，您可以使用 {{audio::<資源名稱}} 作為背景音樂。\n\n此外，您還可以與額外資源搭配使用以下格式：\n - {{bg::<資源名稱>}}：將資源設為背景。",
|
||||
"additionalText": "只有當 AI 認為有必要時，才會將該段文本添加到角色描述中。您可以在此處放置較長的文本。使用雙換行進行內容分隔。",
|
||||
"charjs": "這是一段會與角色一同運行的 JavaScript。詳情請查看：https://github.com/kwaroran/RisuAI/blob/main/src/etc/example-char.js\n**出於安全原因，目前不建議使用。這些代碼不會被包含在匯出中。**",
|
||||
"romanizer": "Romanizer 是一個將非羅馬字母轉換為羅馬字母的外掛程式，用於減少請求資料時的 Token。這可能會導致輸出結果與原始模型不同。如果已在聊天中使用羅馬字母，不建議啟用。",
|
||||
"oaiRandomUser": "啟用後，請求中的使用者參數將被隨機 UUID 替代，並在重新整理時修改。這可以用來防止 AI 識別使用者。",
|
||||
@@ -89,7 +89,7 @@ export const languageChineseTraditional = {
|
||||
"forcePlainFetch": "啟用後，將使用瀏覽器的 Fetch API 來替代原生 HTTP 請求。這可能會導致 CORS 錯誤。",
|
||||
"autoFillRequestURL": "啟用後，將自動填入請求 URL 以匹配當前模型。",
|
||||
"chainOfThought": "啟用後，將在提示詞中添加思維鏈（CoT, Chain-of-Thought）提示。",
|
||||
"gptVisionQuality": "此選項用於設定圖像檢測模型的品質。品質越高，檢測越準確，但會使用更多的 Token。",
|
||||
"gptVisionQuality": "此選項用於設定圖片檢測模型的品質。品質越高，檢測越準確，但會使用更多的 Token。",
|
||||
"genTimes": "此設定支援模型上的重滾（reroll）回應數量。除第一則回應外，其他回應將作為快取使用，以降低成本。但若未多次重滾回應，可能增加成本。",
|
||||
"requestretrys": "此選項用於設定請求失敗時的重試次數。",
|
||||
"emotionPrompt": "此選項用於設定情緒檢測的提示詞。留空將使用預設提示詞。",
|
||||
@@ -104,10 +104,10 @@ export const languageChineseTraditional = {
|
||||
"nickname": "設定後，將在聊天中以此暱稱取代角色名稱，並顯示於 {{char}} 和 <char>。",
|
||||
"useRegexLorebook": "啟用後，Lorebook 將改用正規表達式（Regex）搜尋，而不再使用字串匹配。格式為 /regex/flags。",
|
||||
"customChainOfThought": "警告：不再建議使用思維鏈（CoT, Chain-of-Thought）切換功能。請將相關提示詞移至其他提示詞欄位。",
|
||||
"customPromptTemplateToggle": "可在此處設定自定義提示詞切換功能。使用 `<toggle variable>=<toggle name>` 格式，每行一個，例如：`cot=Toggle COT`。你可以在提示詞中透過 `{{getglobalvar::toggle_<toggle variable>}}` 語法來使用這些切換功能，如：`{{getglobalvar::toggle_cot}}`。",
|
||||
"customPromptTemplateToggle": "可在此處設定自定義提示詞切換功能。使用 `<toggle variable>=<toggle name>` 格式，每行一個，例如：`cot=Toggle COT`。您可以在提示詞中透過 `{{getglobalvar::toggle_<toggle variable>}}` 語法來使用這些切換功能，如：`{{getglobalvar::toggle_cot}}`。",
|
||||
"defaultVariables": "可在此處設定自訂預設變數。使用 `<variable name>=<variable value>` 格式，每行一個。例如：`name=叡甦`，可在觸發式和 CBS 變數中使用，如：`{{getvar::A}}`、`{{setvar::A::B}}` 或 `{{? $A + 1}}`。若提示詞範本的預設變數與角色的預設變數名稱相同，系統將使用角色的預設變數。",
|
||||
"lowLevelAccess": "啟用後，將開放需要高計算能力的功能，並允許通過角色中的觸發式執行 AI 模型。除非確實需要這些功能，否則不要啟用此選項。",
|
||||
"triggerLLMPrompt": "這是將發送到模型的提示詞。你可以使用 `@@role user`、`@@role system`、`@@role assistant` 來設定多輪對話及角色。例如：\n```\n@@role system\nrespond as hello\n@@role assistant\nhello\n@@role user\nhi\n```",
|
||||
"triggerLLMPrompt": "這是將發送到模型的提示詞。您可以使用 `@@role user`、`@@role system`、`@@role assistant` 來設定多輪對話及角色。例如：\n```\n@@role system\nrespond as hello\n@@role assistant\nhello\n@@role user\nhi\n```",
|
||||
"legacyTranslation": "啟用後，將使用舊版翻譯方法，在翻譯前對 Markdown 和引號進行預處理，而非在翻譯後處理。",
|
||||
"luaHelp": "可使用 Lua 作為觸發式，並可定義 onInput、onOutput 和 onStart 函數。當使用者發送消息時，調用 onInput；當角色發送消息時，調用 onOutput；當對話開始時，調用 onStart。詳情請參閱說明文檔。",
|
||||
"claudeCachingExperimental": "Claude 快取是實驗性功能，可減少模型成本。但若在不使用重滾（reroll）回應的情況下啟用，則可能增加成本。實驗性功能可能不穩定，且未來可能會有所變動。",
|
||||
@@ -117,7 +117,7 @@ export const languageChineseTraditional = {
|
||||
"customCSS": "自訂 CSS 樣式。若出現問題，可使用 (Ctrl + .) 啟用或禁用。",
|
||||
"betaMobileGUI": "啟用後，將在小於 800px 的螢幕上使用測試版行動介面，需重新整理頁面。",
|
||||
"unrecommended": "這是一個不建議使用的設定。建議關閉。",
|
||||
"jsonSchema": "JSON Schema 將在 AI 模型支援時發送給模型。\n\n然而，由於 JSON Schema 學習難度較高，在叡甦中，你可以使用 TypeScript 接口的子集來代替 JSON Schema。叡甦將在運行時進行轉換。例如，如果你想發送如下的JSON：\n\n```js\n{\n \"name\": \"叡甦\", // name 必須是叡甦，\n \"age\": 1, // age 必須是數字，\n \"icon\": \"slim\", // icon 必須是 ’slim‘ 或 ’rounded‘\n \"thoughts\": [\"Good View!\", \"Lorem\"] // thoughts 必須是字符串數組\n}\n```\n\n你可以使用以下 TypeScript 接口：\n\n```typescript\ninterface Schema {\n name: string;\n age: number;\n icon: ’slim‘|’rounded‘\n thoughts: string[]\n}\n```\n\n接口名稱不重要。欲了解更多資訊，請參閱 TypeScript 說明文件：https://www.typescriptlang.org/docs/handbook/interfaces.html 。要檢查支持的 TypeScript 子集，請查看以下內容。<details><summary>支持的 TypeScript 子集</summary>\n\n支援的類型包括 `boolean`、`number`、`string` 和 `Array`。高級類型不被支援（如：單元類型、交集類型、聯合類型、可選類型、字面量類型等），除了以下幾種情況：\n\n - 原始資料型別（Primitive Type）的陣列（Array）：（如 `string[]`、`Array<boolean>`）\n - 字符串之間的單值類型（Unit Types）：（例如 `’slim‘|’rounded‘`）\n\n 屬性必須在同一行內定義。若一行中有多個屬性，將會產生錯誤。屬性和接口名稱僅可使用拉丁字符，並在 ASCII 範圍內。屬性名稱不得以單引號或雙引號包裹。接口內部不支持嵌套。在定義屬性的行中，不能包含 `{` 或 `}`。如果想使用更高級的類型，請使用 JSON Schema。\n </details>",
|
||||
"jsonSchema": "JSON Schema 將在 AI 模型支援時發送給模型。\n\n然而，由於 JSON Schema 學習難度較高，在叡甦中，您可以使用 TypeScript 接口的子集來代替 JSON Schema。叡甦將在運行時進行轉換。例如，如果您想發送如下的JSON：\n\n```js\n{\n \"name\": \"叡甦\", // name 必須是叡甦，\n \"age\": 1, // age 必須是數字，\n \"icon\": \"slim\", // icon 必須是 ’slim‘ 或 ’rounded‘\n \"thoughts\": [\"Good View!\", \"Lorem\"] // thoughts 必須是字符串數組\n}\n```\n\n您可以使用以下 TypeScript 接口：\n\n```typescript\ninterface Schema {\n name: string;\n age: number;\n icon: ’slim‘|’rounded‘\n thoughts: string[]\n}\n```\n\n接口名稱不重要。欲了解更多資訊，請參閱 TypeScript 說明文件：https://www.typescriptlang.org/docs/handbook/interfaces.html 。要檢查支持的 TypeScript 子集，請查看以下內容。<details><summary>支持的 TypeScript 子集</summary>\n\n支援的類型包括 `boolean`、`number`、`string` 和 `Array`。高級類型不被支援（如：單元類型、交集類型、聯合類型、可選類型、字面量類型等），除了以下幾種情況：\n\n - 原始資料型別（Primitive Type）的陣列（Array）：（如 `string[]`、`Array<boolean>`）\n - 字符串之間的單值類型（Unit Types）：（例如 `’slim‘|’rounded‘`）\n\n 屬性必須在同一行內定義。若一行中有多個屬性，將會產生錯誤。屬性和接口名稱僅可使用拉丁字符，並在 ASCII 範圍內。屬性名稱不得以單引號或雙引號包裹。接口內部不支持嵌套。在定義屬性的行中，不能包含 `{` 或 `}`。如果想使用更高級的類型，請使用 JSON Schema。\n </details>",
|
||||
"strictJsonSchema": "啟用後，某些模型將嚴格遵循提供的 JSON Schema。若禁用，可能會忽略 JSON Schema。",
|
||||
"extractJson": "此欄位不為空時，將從回應中提取特定的 JSON 資料。例如：想從回應 `{\"response\": {\"text\": [\"hello\"]}}` 中提取 `response.text[0]`，可以填寫 `response.text.0`。",
|
||||
"translatorNote": "可在此處為每個角色添加獨特的翻譯提示，但僅適用於使用 Ax. 模型進行翻譯。要啟用此功能，請在語言設定中包含 `{{slot::tnote}}`。此功能不適用群組聊天。",
|
||||
@@ -126,9 +126,11 @@ export const languageChineseTraditional = {
|
||||
"chatHTML": "每個聊天插入的 HTML。\n\n可以使用CBS和特殊標籤。\n- `<risutextbox>`：用於呈現文字的文本框\n- `<risuicon>`：用於顯示使用者或助理的頭像\n- `<risubuttons>`：用於聊天編輯、翻譯等圖示按鈕\n- `<risugeninfo>`：生成訊息按鈕。",
|
||||
"systemContentReplacement": "若模型不支援系統提示詞，則使用此格式取代系統提示詞內容。",
|
||||
"systemRoleReplacement": "若模型不支援系統角色，將使用此角色取代系統角色。",
|
||||
"summarizationPrompt": "用於摘要的提示詞。留空將使用預設提示詞。你也可以使用包含 {{slot}} 的 ChatML 格式來處理聊天數據。",
|
||||
"translatorPrompt": "用於翻譯的提示詞。留空將使用默認提示。你還可以使用帶有 {{slot}} 的 ChatML 格式表示目標語言：用 {{slot::content}} 表示內容，用 {{slot::tnote}} 表示翻譯註釋。",
|
||||
"translateBeforeHTMLFormatting": "啟用後，將在正規表達式和 HTML 格式化之前翻譯文本。這可能減少 Token 數，但可能破壞格式。"
|
||||
"summarizationPrompt": "用於摘要的提示詞。留空將使用預設提示詞。您也可以使用包含 {{slot}} 的 ChatML 格式來處理聊天數據。",
|
||||
"translatorPrompt": "用於翻譯的提示詞。留空將使用默認提示。您還可以使用帶有 {{slot}} 的 ChatML 格式表示目標語言：用 {{slot::content}} 表示內容，用 {{slot::tnote}} 表示翻譯註釋。",
|
||||
"translateBeforeHTMLFormatting": "啟用後，將在正規表達式和 HTML 格式化之前翻譯文本。這可能減少 Token 數，但可能破壞格式。",
|
||||
"autoTranslateCachedOnly": "啟用後，僅會自動翻譯使用者之前已翻譯的內容。",
|
||||
"APIPool": "啟用後，系統將連接到 RisuAI 的 API 資源池。已啟用的使用者可共享免費、速率受限模型的 API 金鑰，從而利用其他使用者未充分使用的金鑰，增加對速率受限模型的請求次數。"
|
||||
},
|
||||
"setup": {
|
||||
"chooseProvider": "選擇 AI 提供者",
|
||||
@@ -143,17 +145,17 @@ export const languageChineseTraditional = {
|
||||
"themeDescWifuCut": "適合在行動裝置上使用",
|
||||
"themeDescClassic": "適用於所有裝置",
|
||||
"texttheme": "設定文字顏色",
|
||||
"inputName": "最後，請輸入你的暱稱。",
|
||||
"welcome": "歡迎使用 Risu（叡甦）！我將引導你進行設定。請問我該如何稱呼你？",
|
||||
"welcome2": "你好，{username}！在開始之前，我會問你一些問題，稍後可在設定中進行修改。\n\n首先，請選擇 AI 提供者。",
|
||||
"inputName": "最後，請輸入您的暱稱。",
|
||||
"welcome": "歡迎使用 Risu（叡甦）！我將引導您進行設定。請問我該如何稱呼您？",
|
||||
"welcome2": "您好，{username}！在開始之前，我會問您一些問題，稍後可在設定中進行修改。\n\n首先，請選擇 AI 提供者。",
|
||||
"openrouterProvider": "Openrouter 提供許多模型，部分免費且未經內容過濾，但品質不如 OpenAI。",
|
||||
"hordeProvider": "Horde 提供免費服務，但回應時間較長且品質較低。",
|
||||
"setProviderLater": "還有其他提供者，你可以稍後在設定中配置。如想稍後設定，請選擇此選項。",
|
||||
"setProviderLater": "還有其他提供者，您可以稍後在設定中配置。如想稍後設定，請選擇此選項。",
|
||||
"setupOpenAI": "使用 OpenAI，需要取得 API 金鑰（Key）。\n1. 前往 https://beta.openai.com/ \n2. 使用帳號登入 \n3. 前往 https://beta.openai.com/account/api-keys \n4. 點擊「Create New API Key」，並命名金鑰。 \n5. 複製該金鑰。 \n6. 返回叡甦\n7. 貼上金鑰並點擊「發送」。",
|
||||
"setupClaude": "使用 Claude，需要取得一個 API 金鑰（Key）。",
|
||||
"setupClaudeSteps": [
|
||||
"點擊此連結並使用 Google 帳號登錄",
|
||||
"輸入你的資料並點擊「繼續」（Continue）",
|
||||
"輸入您的資料並點擊「繼續」（Continue）",
|
||||
"僅在第一個框中輸入任意名稱，然後點擊「建立帳號」（Create Account）",
|
||||
"點擊「購買點數」（Buy Credits）",
|
||||
"點擊「完成設定」（Complete Setup）",
|
||||
@@ -167,20 +169,20 @@ export const languageChineseTraditional = {
|
||||
],
|
||||
"setupOpenrouter": "使用 Openrouter 需要獲取 API 金鑰（Key）。 \n1. 前往 https://openrouter.ai/keys\n2. 點擊「Create Key」\n3. 任意命名金鑰名稱。\n4. 複製該金鑰。\n5. 返回叡甦\n6. 貼上金鑰並點擊「發送」。",
|
||||
"allDone": "完成所有設定！請稍待片刻。",
|
||||
"setupLaterMessage": "歡迎，{username}！你希望我引導你完成設定還是自行設定？",
|
||||
"setupLaterMessage": "歡迎，{username}！您希望我引導您完成設定還是自行設定？",
|
||||
"setupMessageOption1": "引導我完成設定",
|
||||
"setupMessageOption1Desc": "推薦新使用者使用",
|
||||
"setupMessageOption2": "由我自己完成設定",
|
||||
"claudeDesc": "Claude 是由 Antropic 開發的 AI 模型，是 OpenAI 的競爭對手。若你希望使用非英語語言，它優於 GPT。",
|
||||
"claudeDesc": "Claude 是由 Antropic 開發的 AI 模型，是 OpenAI 的競爭對手。若您希望使用非英語語言，它優於 GPT。",
|
||||
"openAIDesc": "OpenAI GPT 是高品質的 AI 模型，但它付費且存在內容審核，在非英語環境下表現較弱。",
|
||||
"chooseChatType": "很好！現在請選擇聊天語言。",
|
||||
"chooseChatTypeOption1": "全英語",
|
||||
"chooseChatTypeOption1Desc": "推薦英語使用者。AI 將使用英語進行輸入和輸出。",
|
||||
"chooseChatTypeOption2": "英語處理",
|
||||
"chooseChatTypeOption2Desc": "推薦非英語使用者。AI 內部使用英語處理，但輸入輸出為你的語言。",
|
||||
"chooseChatTypeOption2Desc": "推薦非英語使用者。AI 內部使用英語處理，但輸入輸出為您的語言。",
|
||||
"chooseChatTypeOption3": "無語言側重",
|
||||
"chooseChatTypeOption3Desc": "AI 將使用你的語言處理，但可能會降低回應品質。",
|
||||
"chooseCheapOrMemory": "除此之外，你更傾向於記憶功能還是節省成本？",
|
||||
"chooseChatTypeOption3Desc": "AI 將使用您的語言處理，但可能會降低回應品質。",
|
||||
"chooseCheapOrMemory": "除此之外，您更傾向於記憶功能還是節省成本？",
|
||||
"chooseCheapOrMemoryOption1": "記憶功能",
|
||||
"chooseCheapOrMemoryOption1Desc": "AI 會記住更多內容，但費用較高。",
|
||||
"chooseCheapOrMemoryOption2": "省錢模式",
|
||||
@@ -189,7 +191,7 @@ export const languageChineseTraditional = {
|
||||
"chooseCheapOrMemoryOption3Desc": "AI 記住的內容多於省錢模式，但不及使用記憶功能。",
|
||||
"chooseCheapOrMemoryOption4": "無限制",
|
||||
"chooseCheapOrMemoryOption4Desc": "AI 會記住幾乎所有內容，但費用極高。",
|
||||
"finally": "最後，你是否希望使用進階工具？",
|
||||
"finally": "最後，您是否希望使用進階工具？",
|
||||
"finallyOption1": "是",
|
||||
"finallyOption1Desc": "使用進階工具會使界面變得更複雜。推薦進階使用者使用。",
|
||||
"finallyOption2": "否",
|
||||
@@ -241,9 +243,9 @@ export const languageChineseTraditional = {
|
||||
"group": "群組",
|
||||
"groupLoreInfo": "群組 Lorebook 適用於該群組的所有對話。",
|
||||
"localLoreInfo": "聊天 Lorebook 僅用於此對話。",
|
||||
"removeConfirm": "你確定要刪除：",
|
||||
"removeConfirm2": "你**真的**確定要刪除：",
|
||||
"exportConfirm": "你想要匯出此資料嗎？",
|
||||
"removeConfirm": "您確定要刪除：",
|
||||
"removeConfirm2": "您**真的**確定要刪除：",
|
||||
"exportConfirm": "您想要匯出此資料嗎？",
|
||||
"insertOrder": "插入順序",
|
||||
"activationKeys": "關鍵字",
|
||||
"activationKeysInfo": "使用逗號分隔",
|
||||
@@ -254,9 +256,9 @@ export const languageChineseTraditional = {
|
||||
"removeGroup": "刪除群組",
|
||||
"exportCharacter": "匯出角色",
|
||||
"userSetting": "使用者設定",
|
||||
"username": "你的名稱",
|
||||
"userIcon": "你的頭像",
|
||||
"successExport": "已成功匯出並保存至你的下載資料夾",
|
||||
"username": "您的名稱",
|
||||
"userIcon": "您的頭像",
|
||||
"successExport": "已成功匯出並保存至您的下載資料夾",
|
||||
"successImport": "成功匯入",
|
||||
"importedCharacter": "匯入角色",
|
||||
"alwaysActive": "始終啟用",
|
||||
@@ -304,21 +306,21 @@ export const languageChineseTraditional = {
|
||||
"savebackup": "備份至 Google",
|
||||
"loadbackup": "從 Google 讀取備份",
|
||||
"files": "檔案",
|
||||
"backupConfirm": "你確定要保存備份嗎？",
|
||||
"backupLoadConfirm": "你確定要讀取備份嗎？所有資料將被覆蓋！",
|
||||
"backupLoadConfirm2": "你**真的、真的**確定要加載備份嗎？這將會清除所有資料！",
|
||||
"backupConfirm": "您確定要保存備份嗎？",
|
||||
"backupLoadConfirm": "您確定要讀取備份嗎？所有資料將被覆蓋！",
|
||||
"backupLoadConfirm2": "您**真的、真的**確定要加載備份嗎？這將會清除所有資料！",
|
||||
"pasteAuthCode": "請從彈出窗口複製驗證碼並貼入：",
|
||||
"others": "其他",
|
||||
"presets": "預設設定",
|
||||
"imageGeneration": "圖像生成",
|
||||
"imageGeneration": "圖片生成",
|
||||
"provider": "提供者",
|
||||
"key": "金鑰（Key）",
|
||||
"noData": "沒有資料",
|
||||
"currentImageGeneration": "當前圖像生成數據",
|
||||
"currentImageGeneration": "當前圖片生成數據",
|
||||
"promptPreprocess": "使用提示詞預處理",
|
||||
"SwipeRegenerate": "使用滑動箭頭重新產生訊息",
|
||||
"instantRemove": "刪除訊息時連帶刪除後續訊息",
|
||||
"instantRemoveConfirm": "你想只刪除一條訊息嗎？若選擇「否」，後續訊息也將被刪除。",
|
||||
"instantRemoveConfirm": "您想只刪除一條訊息嗎？若選擇「否」，後續訊息也將被刪除。",
|
||||
"textColor": "文字顏色",
|
||||
"classicRisu": "經典 Risu",
|
||||
"highcontrast": "高對比度",
|
||||
@@ -506,7 +508,7 @@ export const languageChineseTraditional = {
|
||||
"innerFormat": "內部格式",
|
||||
"HypaMemory": "HypaMemory",
|
||||
"ToggleHypaMemory": "啟動 HypaMemory",
|
||||
"resetPromptTemplateConfirm": "你真的確定要重置提示詞模板嗎？",
|
||||
"resetPromptTemplateConfirm": "您真的確定要重置提示詞模板嗎？",
|
||||
"emotionMethod": "情緒檢測方式",
|
||||
"continueResponse": "繼續回應",
|
||||
"showMenuChatList": "在選單中顯示聊天列表",
|
||||
@@ -520,7 +522,7 @@ export const languageChineseTraditional = {
|
||||
"importPersona": "匯入使用者設定",
|
||||
"export": "匯出",
|
||||
"import": "匯入",
|
||||
"supporterThanks": "支持者感謝",
|
||||
"supporterThanks": "支持者鳴謝",
|
||||
"supporterThanksDesc": "感謝您的支持！",
|
||||
"donatorPatreonDesc": "為保護隱私，默認不會顯示在名單中。若想顯示您的暱稱，請前往叡甦的 Patreon 頁面並點擊連結按鈕。",
|
||||
"useNamePrefix": "使用名稱前綴",
|
||||
@@ -539,8 +541,8 @@ export const languageChineseTraditional = {
|
||||
"exactTokens": "精確 Tokens",
|
||||
"fixedTokens": "估算 Tokens",
|
||||
"inlayViewScreen": "內嵌視窗",
|
||||
"imgGenPrompt": "圖像生成提示詞",
|
||||
"imgGenNegatives": "圖像生成負面提示詞",
|
||||
"imgGenPrompt": "圖片生成提示詞",
|
||||
"imgGenNegatives": "圖片生成負面提示詞",
|
||||
"imgGenInstructions": "系統提示詞",
|
||||
"usePlainFetchWarn": "使用 NovelAI 時請關閉此選項，避免出現 CORS 錯誤。",
|
||||
"translationPrompt": "翻譯提示詞",
|
||||
@@ -578,12 +580,12 @@ export const languageChineseTraditional = {
|
||||
"inputCardPassword": "輸入角色卡密碼",
|
||||
"ccv2Desc": "V2 角色卡是廣泛用於聊天機器人前端的格式。",
|
||||
"ccv3Desc": "V3 角色卡是用於聊天機器人前端的新型格式。",
|
||||
"realmDesc": "RisuRealm 是叡甦的內容分享平台，你可以將角色分享給其他使用者。",
|
||||
"realmDesc": "RisuRealm 是叡甦的內容分享平台，您可以將角色分享給其他使用者。",
|
||||
"rccDesc": "Risu Refined 角色卡具有密碼保護、完整性驗證等附加功能。",
|
||||
"password": "密碼",
|
||||
"license": "授權",
|
||||
"licenseDesc": "你可以設定下載授權，限制角色卡對提示詞的使用。",
|
||||
"passwordDesc": "你可以為角色卡設置密碼，防止未經授權的訪問。",
|
||||
"licenseDesc": "您可以設定下載授權，限制角色卡對提示詞的使用。",
|
||||
"passwordDesc": "您可以為角色卡設置密碼，防止未經授權的訪問。",
|
||||
"largePersonaPortrait": "使用者肖像",
|
||||
"module": "模組",
|
||||
"modules": "模組",
|
||||
@@ -601,9 +603,9 @@ export const languageChineseTraditional = {
|
||||
"sideMenuRerollButton": "側欄選單重新載入",
|
||||
"persistentStorage": "永久儲存",
|
||||
"persistentStorageSuccess": "儲存已成功永久化",
|
||||
"persistentStorageFail": "儲存未能永久化。你是否拒絕了請求，或瀏覽器不支持？",
|
||||
"persistentStorageFail": "儲存未能永久化。您是否拒絕了請求，或瀏覽器不支持？",
|
||||
"persistentStorageRecommended": "建議使用永久儲存",
|
||||
"persistentStorageDesc": "你的瀏覽器支持永久儲存，建議啟用以提升效能和使用者體驗。",
|
||||
"persistentStorageDesc": "您的瀏覽器支持永久儲存，建議啟用以提升效能和使用者體驗。",
|
||||
"enable": "啟用",
|
||||
"postFile": "上傳檔案",
|
||||
"requestInfoInsideChat": "在聊天中顯示請求資料",
|
||||
@@ -617,7 +619,7 @@ export const languageChineseTraditional = {
|
||||
"useAdvancedEditor": "使用進階編輯器",
|
||||
"noWaitForTranslate": "不等待翻譯",
|
||||
"updateRealm": "更新至 RisuRealm",
|
||||
"updateRealmDesc": "你正試圖將角色更新至 RisuRealm。此操作將使角色更新至 RisuRealm，且無法還原。",
|
||||
"updateRealmDesc": "您正試圖將角色更新至 RisuRealm。此操作將使角色更新至 RisuRealm，且無法還原。",
|
||||
"antiClaudeOverload": "防止 Claude 超載",
|
||||
"activeTabChange": "目前的標籤已停用，因其他標籤處於活動中。若要啟動此標籤，請按「確定」。",
|
||||
"maxSupaChunkSize": "最大 SupaMemory Chunk 大小",
|
||||
@@ -672,11 +674,11 @@ export const languageChineseTraditional = {
|
||||
"error": "錯誤",
|
||||
"input": "輸入",
|
||||
"select": "選擇",
|
||||
"lowLevelAccessConfirm": "此內容使用低層級訪問，可直接存取 AI 模型和你的儲存資料。你確定要匯入嗎？",
|
||||
"lowLevelAccessConfirm": "此內容使用低層級訪問，可直接存取 AI 模型和您的儲存資料。您確定要匯入嗎？",
|
||||
"triggerLowLevelOnly": "此觸發僅適用於低層級訪問，需在角色或模組的進階設定中啟用低層級訪問。",
|
||||
"truthy": "真值",
|
||||
"extractRegex": "使用正規表達式提取文字",
|
||||
"runImgGen": "執行圖像生成",
|
||||
"runImgGen": "執行圖片生成",
|
||||
"cutChat": "分割聊天",
|
||||
"modifyChat": "修改聊天",
|
||||
"regex": "正規表達式",
|
||||
@@ -703,7 +705,7 @@ export const languageChineseTraditional = {
|
||||
"doNotTranslate": "不進行翻譯",
|
||||
"includePersonaName": "包含使用者名稱",
|
||||
"hidePersonaName": "隱藏使用者名稱",
|
||||
"triggerSwitchWarn": "更改觸發類型後，現有觸發將被清除。你確定要繼續嗎？",
|
||||
"triggerSwitchWarn": "更改觸發類型後，現有觸發將被清除。您確定要繼續嗎？",
|
||||
"codeMode": "程式碼",
|
||||
"blockMode": "區塊",
|
||||
"helpBlock": "幫助",
|
||||
@@ -735,10 +737,10 @@ export const languageChineseTraditional = {
|
||||
"betaMobileGUI": "測試版行動介面",
|
||||
"menu": "選單",
|
||||
"connectionOpen": "已開啟連線",
|
||||
"connectionOpenInfo": "多人聊天室已開啟，你可以將聊天室代碼分享給其他使用者。其他使用者可在 Playground > 加入多人聊天室 > 使用代碼加入。",
|
||||
"connectionOpenInfo": "多人聊天室已開啟，您可以將聊天室代碼分享給其他使用者。其他使用者可在 Playground > 加入多人聊天室 > 使用代碼加入。",
|
||||
"createMultiuserRoom": "新建多人聊天室",
|
||||
"connectionHost": "你是聊天室的主持人。",
|
||||
"connectionGuest": "你是聊天室的訪客。",
|
||||
"connectionHost": "您是聊天室的主持人。",
|
||||
"connectionGuest": "您是聊天室的訪客。",
|
||||
"otherUserRequesting": "其他使用者正在請求中，請稍後重試。",
|
||||
"jsonSchema": "JSON Schema",
|
||||
"enableJsonSchema": "Enable Schema",
|
||||
@@ -770,5 +772,12 @@ export const languageChineseTraditional = {
|
||||
"translatorPrompt": "翻譯提示詞",
|
||||
"translateBeforeHTMLFormatting": "於 HTML 格式化前翻譯",
|
||||
"retranslate": "重新翻譯",
|
||||
"loading": "載入中"
|
||||
"loading": "載入中",
|
||||
"autoTranslateCachedOnly": "僅自動翻譯已快取的內容",
|
||||
"notification": "使用系統通知",
|
||||
"permissionDenied": "權限被您的瀏覽器或操作系統拒絕",
|
||||
"customFlags": "自定義修飾詞（Flags）",
|
||||
"enableCustomFlags": "啟用自定義修飾詞（Flags）",
|
||||
"googleCloudTokenization": "Google Cloud Tokenization",
|
||||
"APIPool": "API 工具"
|
||||
}
|
||||
|
||||
@@ -57,7 +57,7 @@
|
||||
}: Props = $props();
|
||||
|
||||
let msgDisplay = $state('')
|
||||
let translated = $state(DBState.db.autoTranslate)
|
||||
let translated = $state(false)
|
||||
let role = $derived(DBState.db.characters[selIdState.selId].chats[DBState.db.characters[selIdState.selId].chatPage].message[idx]?.role)
|
||||
async function rm(e:MouseEvent, rec?:boolean){
|
||||
if(e.shiftKey){
|
||||
|
||||
@@ -28,8 +28,15 @@
|
||||
|
||||
<span class="text-textcolor text-lg">Model</span>
|
||||
<SelectInput bind:value={model}>
|
||||
<OptionInput value="MiniLM">MiniLM L6 v2</OptionInput>
|
||||
<OptionInput value="nomic">Nomic Embed Text v1.5</OptionInput>
|
||||
<OptionInput value="MiniLM">MiniLM L6 v2 (CPU)</OptionInput>
|
||||
<OptionInput value="nomic">Nomic Embed Text v1.5 (CPU)</OptionInput>
|
||||
<OptionInput value="nomicGPU">Nomic Embed Text v1.5 (GPU)</OptionInput>
|
||||
<OptionInput value="bgeSmallEn">BGE Small English (CPU)</OptionInput>
|
||||
<OptionInput value="bgeSmallEnGPU">BGE Small English (GPU)</OptionInput>
|
||||
<OptionInput value="bgem3">BGE Medium 3 (CPU)</OptionInput>
|
||||
<OptionInput value="bgem3GPU">BGE Medium 3 (GPU)</OptionInput>
|
||||
<OptionInput value="openai3small">OpenAI text-embedding-3-small</OptionInput>
|
||||
<OptionInput value="openai3large">OpenAI text-embedding-3-large</OptionInput>
|
||||
<OptionInput value="custom">Custom (OpenAI-compatible)</OptionInput>
|
||||
</SelectInput>
|
||||
|
||||
|
||||
@@ -6,9 +6,12 @@
|
||||
let input = $state("");
|
||||
let output = $state("");
|
||||
let outputLength = $state(0);
|
||||
let time = $state(0)
|
||||
const onInput = async () => {
|
||||
try {
|
||||
const start = performance.now();
|
||||
const tokenized = await encode(input);
|
||||
time = performance.now() - start;
|
||||
const tokenizedNumArray = Array.from(tokenized)
|
||||
outputLength = tokenizedNumArray.length;
|
||||
output = JSON.stringify(tokenizedNumArray);
|
||||
@@ -29,3 +32,4 @@
|
||||
<TextAreaInput value={output} />
|
||||
|
||||
<span class="text-textcolor2 text-lg">{outputLength} {language.tokens}</span>
|
||||
<span class="text-textcolor2 text-lg">{time} ms</span>
|
||||
|
||||
@@ -41,6 +41,10 @@
|
||||
<span class="text-textcolor">Kei Server URL</span>
|
||||
<TextInput marginBottom={true} size={"sm"} bind:value={DBState.db.keiServerURL} placeholder="Leave it blank to use default"/>
|
||||
|
||||
<span class="text-textcolor">{language.presetChain} <Help key="presetChain"/></span>
|
||||
<TextInput marginBottom={true} size={"sm"} bind:value={DBState.db.presetChain} placeholder="Leave it blank to not use">
|
||||
</TextInput>
|
||||
|
||||
<span class="text-textcolor">{language.requestretrys} <Help key="requestretrys"/></span>
|
||||
<NumberInput marginBottom={true} size={"sm"} min={0} max={20} bind:value={DBState.db.requestRetrys}/>
|
||||
|
||||
@@ -78,6 +82,9 @@
|
||||
<div class="flex items-center mt-4">
|
||||
<Check bind:check={DBState.db.forceProxyAsOpenAI} name={language.forceProxyAsOpenAI}> <Help key="forceProxyAsOpenAI"/></Check>
|
||||
</div>
|
||||
<div class="flex items-center mt-4">
|
||||
<Check bind:check={DBState.db.legacyMediaFindings} name={language.legacyMediaFindings}> <Help key="legacyMediaFindings"/></Check>
|
||||
</div>
|
||||
<div class="flex items-center mt-4">
|
||||
<Check bind:check={DBState.db.autofillRequestUrl} name={language.autoFillRequestURL}> <Help key="autoFillRequestURL"/></Check>
|
||||
</div>
|
||||
@@ -112,6 +119,11 @@
|
||||
<Help key="experimental"/><Help key="oaiRandomUser"/>
|
||||
</Check>
|
||||
</div>
|
||||
<div class="flex items-center mt-4">
|
||||
<Check bind:check={DBState.db.googleClaudeTokenizing} name={language.googleCloudTokenization}>
|
||||
<Help key="experimental"/>
|
||||
</Check>
|
||||
</div>
|
||||
{/if}
|
||||
{#if DBState.db.showUnrecommended}
|
||||
<div class="flex items-center mt-4">
|
||||
|
||||
@@ -426,12 +426,6 @@
|
||||
{/if}
|
||||
<span class="text-textcolor">{language.summarizationPrompt} <Help key="summarizationPrompt" /></span>
|
||||
<TextAreaInput size="sm" bind:value={DBState.db.supaMemoryPrompt} placeholder="Leave it blank to use default"/>
|
||||
<span class="text-textcolor">{language.HypaMemory} Model</span>
|
||||
<SelectInput className="mt-2 mb-2" bind:value={DBState.db.hypaModel}>
|
||||
<OptionInput value="MiniLM">MiniLM-L6-v2 (Free / Local)</OptionInput>
|
||||
<OptionInput value="nomic">Nomic (Free / Local)</OptionInput>
|
||||
<OptionInput value="ada">OpenAI Ada (Davinci / Curie Only)</OptionInput>
|
||||
</SelectInput>
|
||||
<span class="text-textcolor">{language.hypaChunkSize}</span>
|
||||
<NumberInput size="sm" marginBottom bind:value={DBState.db.hypaChunkSize} min={100} />
|
||||
<span class="text-textcolor">{language.hypaAllocatedTokens}</span>
|
||||
@@ -454,17 +448,31 @@
|
||||
<span class="text-textcolor">{language.SuperMemory} Prompt</span>
|
||||
<TextInput size="sm" marginBottom bind:value={DBState.db.supaMemoryPrompt} placeholder="Leave it blank to use default"/>
|
||||
{/if}
|
||||
{#if DBState.db.hypaMemory}
|
||||
<span class="text-textcolor">{language.HypaMemory} Model</span>
|
||||
<SelectInput className="mt-2 mb-2" bind:value={DBState.db.hypaModel}>
|
||||
<OptionInput value="MiniLM" >MiniLM-L6-v2 (Free / Local)</OptionInput>
|
||||
<OptionInput value="ada" >OpenAI Ada (Davinci / Curie Only)</OptionInput>
|
||||
</SelectInput>
|
||||
{/if}
|
||||
<div class="flex">
|
||||
<Check bind:check={DBState.db.hypaMemory} name={language.enable + ' ' + language.HypaMemory}/>
|
||||
</div>
|
||||
{/if}
|
||||
|
||||
<span class="text-textcolor">{language.embedding}</span>
|
||||
<SelectInput className="mt-2 mb-2" bind:value={DBState.db.hypaModel}>
|
||||
{#if 'gpu' in navigator}
|
||||
<OptionInput value="nomicGPU">Nomic Embed Text v1.5 (GPU)</OptionInput>
|
||||
<OptionInput value="bgeSmallEnGPU">BGE Small English (GPU)</OptionInput>
|
||||
<OptionInput value="bgem3GPU">BGE Medium 3 (GPU)</OptionInput>
|
||||
{/if}
|
||||
<OptionInput value="MiniLM">MiniLM L6 v2 (CPU)</OptionInput>
|
||||
<OptionInput value="nomic">Nomic Embed Text v1.5 (CPU)</OptionInput>
|
||||
<OptionInput value="bgeSmallEn">BGE Small English (CPU)</OptionInput>
|
||||
<OptionInput value="bgem3">BGE Medium 3 (CPU)</OptionInput>
|
||||
<OptionInput value="openai3small">OpenAI text-embedding-3-small</OptionInput>
|
||||
<OptionInput value="openai3large">OpenAI text-embedding-3-large</OptionInput>
|
||||
<OptionInput value="ada">OpenAI Ada</OptionInput>
|
||||
</SelectInput>
|
||||
|
||||
{#if DBState.db.hypaModel === 'openai3small' || DBState.db.hypaModel === 'openai3large' || DBState.db.hypaModel === 'ada'}
|
||||
<span class="text-textcolor">OpenAI API Key</span>
|
||||
<TextInput size="sm" marginBottom bind:value={DBState.db.supaMemoryKey}/>
|
||||
{/if}
|
||||
|
||||
</Arcodion>
|
||||
{/if}
|
||||
@@ -72,9 +72,6 @@
|
||||
{/each}
|
||||
{/await}
|
||||
</Arcodion>
|
||||
{#if DBState.db.plugins.length > 0}
|
||||
<button onclick={() => {changeModel('custom')}} class="hover:bg-selected px-6 py-2 text-lg" >Plugin</button>
|
||||
{/if}
|
||||
<div class="text-textcolor2 text-xs">
|
||||
<CheckInput name={language.showUnrecommended} grayText bind:check={showUnrec}/>
|
||||
</div>
|
||||
|
||||
@@ -88,7 +88,7 @@ export async function importCharacterProcess(f:{
|
||||
return
|
||||
}
|
||||
const card:CharacterCardV3 = JSON.parse(cardData)
|
||||
if(CCardLib.character.check(card) !== 'v3'){
|
||||
if(card.spec !== 'chara_card_v3'){
|
||||
alertError(language.errors.noData)
|
||||
return
|
||||
}
|
||||
@@ -1316,7 +1316,7 @@ export async function exportCharacterCard(char:character, type:'png'|'json'|'cha
|
||||
path = `assets/${type}/${itype}/${name}.${card.data.assets[i].ext}`
|
||||
}
|
||||
card.data.assets[i].uri = 'embeded://' + path
|
||||
await writer.write(path, rData)
|
||||
await writer.write(path, Buffer.from(await convertImage(rData)))
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -13,6 +13,7 @@ export enum LLMFlags{
|
||||
hasStreaming,
|
||||
requiresAlternateRole,
|
||||
mustStartWithUserInput,
|
||||
poolSupported
|
||||
}
|
||||
|
||||
export enum LLMProvider{
|
||||
@@ -52,6 +53,22 @@ export enum LLMFormat{
|
||||
AWSBedrockClaude
|
||||
}
|
||||
|
||||
export enum LLMTokenizer{
|
||||
Unknown,
|
||||
tiktokenCl100kBase,
|
||||
tiktokenO200Base,
|
||||
Mistral,
|
||||
Llama,
|
||||
NovelAI,
|
||||
Claude,
|
||||
NovelList,
|
||||
Llama3,
|
||||
Gemma,
|
||||
GoogleCloud,
|
||||
Cohere,
|
||||
Local
|
||||
}
|
||||
|
||||
export interface LLMModel{
|
||||
id: string
|
||||
name: string
|
||||
@@ -61,7 +78,8 @@ export interface LLMModel{
|
||||
provider: LLMProvider
|
||||
flags: LLMFlags[]
|
||||
format: LLMFormat
|
||||
parameters: Parameter[]
|
||||
parameters: Parameter[],
|
||||
tokenizer: LLMTokenizer
|
||||
recommended?: boolean
|
||||
}
|
||||
|
||||
@@ -92,6 +110,7 @@ export const LLMModels: LLMModel[] = [
|
||||
format: LLMFormat.OpenAICompatible,
|
||||
flags: [LLMFlags.hasFullSystemPrompt, LLMFlags.hasStreaming],
|
||||
parameters: OpenAIParameters,
|
||||
tokenizer: LLMTokenizer.tiktokenCl100kBase
|
||||
},
|
||||
{
|
||||
id: 'instructgpt35',
|
||||
@@ -101,6 +120,7 @@ export const LLMModels: LLMModel[] = [
|
||||
format: LLMFormat.OpenAILegacyInstruct,
|
||||
flags: [LLMFlags.hasFullSystemPrompt, LLMFlags.hasStreaming],
|
||||
parameters: OpenAIParameters,
|
||||
tokenizer: LLMTokenizer.tiktokenCl100kBase
|
||||
},
|
||||
{
|
||||
id: 'gpt4_turbo',
|
||||
@@ -110,6 +130,7 @@ export const LLMModels: LLMModel[] = [
|
||||
format: LLMFormat.OpenAICompatible,
|
||||
flags: [LLMFlags.hasFullSystemPrompt, LLMFlags.hasStreaming],
|
||||
parameters: OpenAIParameters,
|
||||
tokenizer: LLMTokenizer.tiktokenCl100kBase
|
||||
},
|
||||
{
|
||||
id: 'gpt4o',
|
||||
@@ -124,6 +145,7 @@ export const LLMModels: LLMModel[] = [
|
||||
],
|
||||
recommended: true,
|
||||
parameters: OpenAIParameters,
|
||||
tokenizer: LLMTokenizer.tiktokenO200Base
|
||||
},
|
||||
{
|
||||
id: 'gpt4om',
|
||||
@@ -138,6 +160,7 @@ export const LLMModels: LLMModel[] = [
|
||||
],
|
||||
recommended: true,
|
||||
parameters: OpenAIParameters,
|
||||
tokenizer: LLMTokenizer.tiktokenO200Base
|
||||
},
|
||||
{
|
||||
id: 'gpt4',
|
||||
@@ -150,6 +173,7 @@ export const LLMModels: LLMModel[] = [
|
||||
LLMFlags.hasStreaming
|
||||
],
|
||||
parameters: OpenAIParameters,
|
||||
tokenizer: LLMTokenizer.tiktokenCl100kBase
|
||||
},
|
||||
{
|
||||
id: 'gpt4_32k',
|
||||
@@ -162,6 +186,7 @@ export const LLMModels: LLMModel[] = [
|
||||
LLMFlags.hasStreaming
|
||||
],
|
||||
parameters: OpenAIParameters,
|
||||
tokenizer: LLMTokenizer.tiktokenCl100kBase
|
||||
},
|
||||
{
|
||||
id: 'gpt35_16k',
|
||||
@@ -174,6 +199,7 @@ export const LLMModels: LLMModel[] = [
|
||||
LLMFlags.hasStreaming
|
||||
],
|
||||
parameters: OpenAIParameters,
|
||||
tokenizer: LLMTokenizer.tiktokenCl100kBase
|
||||
},
|
||||
{
|
||||
id: 'gpt4_0314',
|
||||
@@ -186,6 +212,7 @@ export const LLMModels: LLMModel[] = [
|
||||
LLMFlags.hasStreaming
|
||||
],
|
||||
parameters: OpenAIParameters,
|
||||
tokenizer: LLMTokenizer.tiktokenCl100kBase
|
||||
},
|
||||
{
|
||||
id: 'gpt4_0613',
|
||||
@@ -198,6 +225,7 @@ export const LLMModels: LLMModel[] = [
|
||||
LLMFlags.hasStreaming
|
||||
],
|
||||
parameters: OpenAIParameters,
|
||||
tokenizer: LLMTokenizer.tiktokenCl100kBase
|
||||
},
|
||||
{
|
||||
id: 'gpt4_32k_0613',
|
||||
@@ -210,6 +238,7 @@ export const LLMModels: LLMModel[] = [
|
||||
LLMFlags.hasStreaming
|
||||
],
|
||||
parameters: OpenAIParameters,
|
||||
tokenizer: LLMTokenizer.tiktokenCl100kBase
|
||||
},
|
||||
{
|
||||
id: 'gpt4_1106',
|
||||
@@ -222,6 +251,7 @@ export const LLMModels: LLMModel[] = [
|
||||
LLMFlags.hasStreaming
|
||||
],
|
||||
parameters: OpenAIParameters,
|
||||
tokenizer: LLMTokenizer.tiktokenCl100kBase
|
||||
},
|
||||
{
|
||||
id: 'gpt35_0125',
|
||||
@@ -234,6 +264,7 @@ export const LLMModels: LLMModel[] = [
|
||||
LLMFlags.hasStreaming
|
||||
],
|
||||
parameters: OpenAIParameters,
|
||||
tokenizer: LLMTokenizer.tiktokenCl100kBase
|
||||
},
|
||||
{
|
||||
id: 'gpt35_1106',
|
||||
@@ -246,6 +277,7 @@ export const LLMModels: LLMModel[] = [
|
||||
LLMFlags.hasStreaming
|
||||
],
|
||||
parameters: OpenAIParameters,
|
||||
tokenizer: LLMTokenizer.tiktokenCl100kBase
|
||||
},
|
||||
{
|
||||
id: 'gpt35_0613',
|
||||
@@ -258,6 +290,7 @@ export const LLMModels: LLMModel[] = [
|
||||
LLMFlags.hasStreaming
|
||||
],
|
||||
parameters: OpenAIParameters,
|
||||
tokenizer: LLMTokenizer.tiktokenCl100kBase
|
||||
},
|
||||
{
|
||||
id: 'gpt35_16k_0613',
|
||||
@@ -270,6 +303,7 @@ export const LLMModels: LLMModel[] = [
|
||||
LLMFlags.hasStreaming
|
||||
],
|
||||
parameters: OpenAIParameters,
|
||||
tokenizer: LLMTokenizer.tiktokenCl100kBase
|
||||
},
|
||||
{
|
||||
id: 'gpt35_0301',
|
||||
@@ -282,6 +316,7 @@ export const LLMModels: LLMModel[] = [
|
||||
LLMFlags.hasStreaming
|
||||
],
|
||||
parameters: OpenAIParameters,
|
||||
tokenizer: LLMTokenizer.tiktokenCl100kBase
|
||||
},
|
||||
{
|
||||
id: 'gpt4_0125',
|
||||
@@ -294,6 +329,7 @@ export const LLMModels: LLMModel[] = [
|
||||
LLMFlags.hasStreaming
|
||||
],
|
||||
parameters: OpenAIParameters,
|
||||
tokenizer: LLMTokenizer.tiktokenCl100kBase
|
||||
},
|
||||
{
|
||||
id: 'gptvi4_1106',
|
||||
@@ -306,6 +342,7 @@ export const LLMModels: LLMModel[] = [
|
||||
LLMFlags.hasStreaming
|
||||
],
|
||||
parameters: OpenAIParameters,
|
||||
tokenizer: LLMTokenizer.tiktokenCl100kBase
|
||||
},
|
||||
{
|
||||
id: 'gpt4_turbo_20240409',
|
||||
@@ -318,6 +355,7 @@ export const LLMModels: LLMModel[] = [
|
||||
LLMFlags.hasStreaming
|
||||
],
|
||||
parameters: OpenAIParameters,
|
||||
tokenizer: LLMTokenizer.tiktokenCl100kBase
|
||||
},
|
||||
{
|
||||
id: 'gpt4o-2024-05-13',
|
||||
@@ -331,6 +369,7 @@ export const LLMModels: LLMModel[] = [
|
||||
LLMFlags.hasStreaming
|
||||
],
|
||||
parameters: OpenAIParameters,
|
||||
tokenizer: LLMTokenizer.tiktokenO200Base
|
||||
},
|
||||
{
|
||||
id: 'gpt4o-2024-08-06',
|
||||
@@ -344,6 +383,7 @@ export const LLMModels: LLMModel[] = [
|
||||
LLMFlags.hasStreaming
|
||||
],
|
||||
parameters: OpenAIParameters,
|
||||
tokenizer: LLMTokenizer.tiktokenO200Base
|
||||
},
|
||||
{
|
||||
id: 'gpt4o-2024-11-20',
|
||||
@@ -357,6 +397,7 @@ export const LLMModels: LLMModel[] = [
|
||||
LLMFlags.hasStreaming
|
||||
],
|
||||
parameters: OpenAIParameters,
|
||||
tokenizer: LLMTokenizer.tiktokenO200Base
|
||||
},
|
||||
{
|
||||
id: 'gpt4o-chatgpt',
|
||||
@@ -370,6 +411,7 @@ export const LLMModels: LLMModel[] = [
|
||||
LLMFlags.hasStreaming
|
||||
],
|
||||
parameters: OpenAIParameters,
|
||||
tokenizer: LLMTokenizer.tiktokenO200Base
|
||||
},
|
||||
{
|
||||
id: 'gpt4o1-preview',
|
||||
@@ -382,6 +424,7 @@ export const LLMModels: LLMModel[] = [
|
||||
LLMFlags.hasStreaming
|
||||
],
|
||||
parameters: OpenAIParameters,
|
||||
tokenizer: LLMTokenizer.tiktokenO200Base
|
||||
},
|
||||
{
|
||||
id: 'gpt4o1-mini',
|
||||
@@ -394,6 +437,7 @@ export const LLMModels: LLMModel[] = [
|
||||
LLMFlags.hasStreaming
|
||||
],
|
||||
parameters: OpenAIParameters,
|
||||
tokenizer: LLMTokenizer.tiktokenO200Base
|
||||
},
|
||||
{
|
||||
name: "Claude 3.5 Sonnet",
|
||||
@@ -409,6 +453,7 @@ export const LLMModels: LLMModel[] = [
|
||||
],
|
||||
recommended: true,
|
||||
parameters: ClaudeParameters,
|
||||
tokenizer: LLMTokenizer.Claude
|
||||
},
|
||||
{
|
||||
name: "Claude 3.5 Haiku",
|
||||
@@ -424,6 +469,7 @@ export const LLMModels: LLMModel[] = [
|
||||
],
|
||||
recommended: true,
|
||||
parameters: ClaudeParameters,
|
||||
tokenizer: LLMTokenizer.Claude
|
||||
},
|
||||
{
|
||||
name: 'Claude 3.5 Sonnet (20241022)',
|
||||
@@ -438,6 +484,7 @@ export const LLMModels: LLMModel[] = [
|
||||
LLMFlags.hasStreaming
|
||||
],
|
||||
parameters: ClaudeParameters,
|
||||
tokenizer: LLMTokenizer.Claude
|
||||
},
|
||||
{
|
||||
name: "Claude 3.5 Haiku (20241022)",
|
||||
@@ -452,6 +499,7 @@ export const LLMModels: LLMModel[] = [
|
||||
LLMFlags.hasStreaming
|
||||
],
|
||||
parameters: ClaudeParameters,
|
||||
tokenizer: LLMTokenizer.Claude
|
||||
},
|
||||
{
|
||||
name: 'Claude 3 Haiku (20240307)',
|
||||
@@ -466,6 +514,7 @@ export const LLMModels: LLMModel[] = [
|
||||
LLMFlags.hasStreaming
|
||||
],
|
||||
parameters: ClaudeParameters,
|
||||
tokenizer: LLMTokenizer.Claude
|
||||
},
|
||||
{
|
||||
name: 'Claude 3.5 Sonnet (20240620)',
|
||||
@@ -480,6 +529,7 @@ export const LLMModels: LLMModel[] = [
|
||||
LLMFlags.hasStreaming
|
||||
],
|
||||
parameters: ClaudeParameters,
|
||||
tokenizer: LLMTokenizer.Claude
|
||||
},
|
||||
{
|
||||
name: 'Claude 3 Opus (20240229)',
|
||||
@@ -494,6 +544,7 @@ export const LLMModels: LLMModel[] = [
|
||||
LLMFlags.hasStreaming
|
||||
],
|
||||
parameters: ClaudeParameters,
|
||||
tokenizer: LLMTokenizer.Claude
|
||||
},
|
||||
{
|
||||
name: 'Claude 3 Sonnet (20240229)',
|
||||
@@ -508,6 +559,7 @@ export const LLMModels: LLMModel[] = [
|
||||
LLMFlags.hasStreaming
|
||||
],
|
||||
parameters: ClaudeParameters,
|
||||
tokenizer: LLMTokenizer.Claude
|
||||
},
|
||||
{
|
||||
name: 'Claude 2.1',
|
||||
@@ -518,6 +570,7 @@ export const LLMModels: LLMModel[] = [
|
||||
LLMFlags.hasPrefill,
|
||||
],
|
||||
parameters: ClaudeParameters,
|
||||
tokenizer: LLMTokenizer.Claude
|
||||
},
|
||||
{
|
||||
name: 'Claude 2',
|
||||
@@ -526,6 +579,7 @@ export const LLMModels: LLMModel[] = [
|
||||
format: LLMFormat.AnthropicLegacy,
|
||||
flags: [LLMFlags.hasPrefill],
|
||||
parameters: ClaudeParameters,
|
||||
tokenizer: LLMTokenizer.Claude
|
||||
},
|
||||
{
|
||||
name: 'Claude 2 100k',
|
||||
@@ -534,6 +588,7 @@ export const LLMModels: LLMModel[] = [
|
||||
format: LLMFormat.AnthropicLegacy,
|
||||
flags: [LLMFlags.hasPrefill],
|
||||
parameters: ClaudeParameters,
|
||||
tokenizer: LLMTokenizer.Claude
|
||||
},
|
||||
{
|
||||
name: 'Claude v1',
|
||||
@@ -542,6 +597,7 @@ export const LLMModels: LLMModel[] = [
|
||||
format: LLMFormat.AnthropicLegacy,
|
||||
flags: [LLMFlags.hasPrefill],
|
||||
parameters: ClaudeParameters,
|
||||
tokenizer: LLMTokenizer.Claude
|
||||
},
|
||||
{
|
||||
name: 'Claude v1 100k',
|
||||
@@ -550,6 +606,7 @@ export const LLMModels: LLMModel[] = [
|
||||
format: LLMFormat.AnthropicLegacy,
|
||||
flags: [LLMFlags.hasPrefill],
|
||||
parameters: ClaudeParameters,
|
||||
tokenizer: LLMTokenizer.Claude
|
||||
},
|
||||
{
|
||||
name: 'Claude Instant v1',
|
||||
@@ -558,6 +615,7 @@ export const LLMModels: LLMModel[] = [
|
||||
format: LLMFormat.AnthropicLegacy,
|
||||
flags: [LLMFlags.hasPrefill],
|
||||
parameters: ClaudeParameters,
|
||||
tokenizer: LLMTokenizer.Claude
|
||||
},
|
||||
{
|
||||
name: 'Claude Instant v1 100k',
|
||||
@@ -566,6 +624,7 @@ export const LLMModels: LLMModel[] = [
|
||||
format: LLMFormat.AnthropicLegacy,
|
||||
flags: [LLMFlags.hasPrefill],
|
||||
parameters: ClaudeParameters,
|
||||
tokenizer: LLMTokenizer.Claude
|
||||
},
|
||||
{
|
||||
name: 'Claude v1.2',
|
||||
@@ -574,6 +633,7 @@ export const LLMModels: LLMModel[] = [
|
||||
format: LLMFormat.AnthropicLegacy,
|
||||
flags: [LLMFlags.hasPrefill],
|
||||
parameters: ClaudeParameters,
|
||||
tokenizer: LLMTokenizer.Claude
|
||||
},
|
||||
{
|
||||
name: 'Claude v1.0',
|
||||
@@ -582,6 +642,7 @@ export const LLMModels: LLMModel[] = [
|
||||
format: LLMFormat.AnthropicLegacy,
|
||||
flags: [LLMFlags.hasPrefill],
|
||||
parameters: ClaudeParameters,
|
||||
tokenizer: LLMTokenizer.Claude
|
||||
},
|
||||
{
|
||||
name: 'Claude 3.5 Sonnet (20241022) v2',
|
||||
@@ -590,6 +651,7 @@ export const LLMModels: LLMModel[] = [
|
||||
format: LLMFormat.AWSBedrockClaude,
|
||||
flags: [LLMFlags.hasPrefill, LLMFlags.hasImageInput, LLMFlags.hasFirstSystemPrompt],
|
||||
parameters: ClaudeParameters,
|
||||
tokenizer: LLMTokenizer.Claude
|
||||
},
|
||||
{
|
||||
name: 'Claude 3.5 Sonnet (20240620) v1',
|
||||
@@ -598,6 +660,7 @@ export const LLMModels: LLMModel[] = [
|
||||
format: LLMFormat.AWSBedrockClaude,
|
||||
flags: [LLMFlags.hasPrefill, LLMFlags.hasImageInput, LLMFlags.hasFirstSystemPrompt],
|
||||
parameters: ClaudeParameters,
|
||||
tokenizer: LLMTokenizer.Claude
|
||||
},
|
||||
{
|
||||
name: 'Claude 3 Opus (20240229) v1',
|
||||
@@ -606,6 +669,7 @@ export const LLMModels: LLMModel[] = [
|
||||
format: LLMFormat.AWSBedrockClaude,
|
||||
flags: [LLMFlags.hasPrefill, LLMFlags.hasImageInput, LLMFlags.hasFirstSystemPrompt],
|
||||
parameters: ClaudeParameters,
|
||||
tokenizer: LLMTokenizer.Claude
|
||||
},
|
||||
{
|
||||
name: 'Ooba',
|
||||
@@ -614,7 +678,8 @@ export const LLMModels: LLMModel[] = [
|
||||
format: LLMFormat.Ooba,
|
||||
flags: [LLMFlags.hasFirstSystemPrompt],
|
||||
recommended: true,
|
||||
parameters: []
|
||||
parameters: [],
|
||||
tokenizer: LLMTokenizer.Llama
|
||||
},
|
||||
{
|
||||
name: 'Mancer',
|
||||
@@ -622,7 +687,8 @@ export const LLMModels: LLMModel[] = [
|
||||
provider: LLMProvider.AsIs,
|
||||
format: LLMFormat.OobaLegacy,
|
||||
flags: [LLMFlags.hasFirstSystemPrompt],
|
||||
parameters: []
|
||||
parameters: [],
|
||||
tokenizer: LLMTokenizer.Llama
|
||||
},
|
||||
{
|
||||
name: 'OpenRouter',
|
||||
@@ -631,7 +697,8 @@ export const LLMModels: LLMModel[] = [
|
||||
format: LLMFormat.OpenAICompatible,
|
||||
flags: [LLMFlags.hasFullSystemPrompt, LLMFlags.hasImageInput, LLMFlags.hasStreaming],
|
||||
parameters: ['temperature', 'top_p', 'frequency_penalty', 'presence_penalty', 'repetition_penalty', 'min_p', 'top_a', 'top_k'],
|
||||
recommended: true
|
||||
recommended: true,
|
||||
tokenizer: LLMTokenizer.Unknown
|
||||
},
|
||||
{
|
||||
name: 'Mistral Small Latest',
|
||||
@@ -641,7 +708,8 @@ export const LLMModels: LLMModel[] = [
|
||||
format: LLMFormat.Mistral,
|
||||
flags: [LLMFlags.hasFirstSystemPrompt, LLMFlags.mustStartWithUserInput, LLMFlags.requiresAlternateRole],
|
||||
recommended: true,
|
||||
parameters: ['temperature', 'presence_penalty', 'frequency_penalty']
|
||||
parameters: ['temperature', 'presence_penalty', 'frequency_penalty'],
|
||||
tokenizer: LLMTokenizer.Mistral
|
||||
},
|
||||
{
|
||||
name: 'Mistral Medium Latest',
|
||||
@@ -651,7 +719,8 @@ export const LLMModels: LLMModel[] = [
|
||||
format: LLMFormat.Mistral,
|
||||
flags: [LLMFlags.hasFirstSystemPrompt, LLMFlags.mustStartWithUserInput, LLMFlags.requiresAlternateRole],
|
||||
recommended: true,
|
||||
parameters: ['temperature', 'presence_penalty', 'frequency_penalty']
|
||||
parameters: ['temperature', 'presence_penalty', 'frequency_penalty'],
|
||||
tokenizer: LLMTokenizer.Mistral
|
||||
},
|
||||
{
|
||||
name: 'Mistral Large 2411',
|
||||
@@ -660,7 +729,8 @@ export const LLMModels: LLMModel[] = [
|
||||
provider: LLMProvider.Mistral,
|
||||
format: LLMFormat.Mistral,
|
||||
flags: [LLMFlags.hasFirstSystemPrompt, LLMFlags.mustStartWithUserInput, LLMFlags.requiresAlternateRole],
|
||||
parameters: ['temperature', 'presence_penalty', 'frequency_penalty']
|
||||
parameters: ['temperature', 'presence_penalty', 'frequency_penalty'],
|
||||
tokenizer: LLMTokenizer.Mistral
|
||||
},
|
||||
{
|
||||
name: 'Mistral Nemo',
|
||||
@@ -669,7 +739,8 @@ export const LLMModels: LLMModel[] = [
|
||||
provider: LLMProvider.Mistral,
|
||||
format: LLMFormat.Mistral,
|
||||
flags: [LLMFlags.hasFirstSystemPrompt, LLMFlags.mustStartWithUserInput, LLMFlags.requiresAlternateRole],
|
||||
parameters: ['temperature', 'presence_penalty', 'frequency_penalty']
|
||||
parameters: ['temperature', 'presence_penalty', 'frequency_penalty'],
|
||||
tokenizer: LLMTokenizer.Mistral
|
||||
},
|
||||
{
|
||||
name: 'Mistral Large Latest',
|
||||
@@ -679,7 +750,8 @@ export const LLMModels: LLMModel[] = [
|
||||
format: LLMFormat.Mistral,
|
||||
flags: [LLMFlags.hasFirstSystemPrompt, LLMFlags.mustStartWithUserInput, LLMFlags.requiresAlternateRole],
|
||||
parameters: ['temperature', 'presence_penalty', 'frequency_penalty'],
|
||||
recommended: true
|
||||
recommended: true,
|
||||
tokenizer: LLMTokenizer.Mistral
|
||||
},
|
||||
{
|
||||
name: "Gemini Pro 1.5 0827",
|
||||
@@ -687,16 +759,27 @@ export const LLMModels: LLMModel[] = [
|
||||
provider: LLMProvider.GoogleCloud,
|
||||
format: LLMFormat.GoogleCloud,
|
||||
flags: [LLMFlags.hasImageInput, LLMFlags.hasFirstSystemPrompt],
|
||||
parameters: ['temperature', 'top_k', 'top_p']
|
||||
parameters: ['temperature', 'top_k', 'top_p'],
|
||||
tokenizer: LLMTokenizer.GoogleCloud
|
||||
},
|
||||
{
|
||||
name: "Gemini Exp 1121",
|
||||
id: 'gemini-exp-1121',
|
||||
provider: LLMProvider.GoogleCloud,
|
||||
format: LLMFormat.GoogleCloud,
|
||||
flags: [LLMFlags.hasImageInput, LLMFlags.hasFirstSystemPrompt],
|
||||
flags: [LLMFlags.hasImageInput, LLMFlags.hasFirstSystemPrompt, LLMFlags.poolSupported],
|
||||
parameters: ['temperature', 'top_k', 'top_p'],
|
||||
tokenizer: LLMTokenizer.GoogleCloud,
|
||||
},
|
||||
{
|
||||
name: "Gemini Exp 1206",
|
||||
id: 'gemini-exp-1206',
|
||||
provider: LLMProvider.GoogleCloud,
|
||||
format: LLMFormat.GoogleCloud,
|
||||
flags: [LLMFlags.hasImageInput, LLMFlags.hasFirstSystemPrompt, LLMFlags.poolSupported],
|
||||
recommended: true,
|
||||
parameters: ['temperature', 'top_k', 'top_p']
|
||||
parameters: ['temperature', 'top_k', 'top_p'],
|
||||
tokenizer: LLMTokenizer.GoogleCloud
|
||||
},
|
||||
{
|
||||
name: "Gemini Pro 1.5",
|
||||
@@ -705,7 +788,8 @@ export const LLMModels: LLMModel[] = [
|
||||
format: LLMFormat.GoogleCloud,
|
||||
flags: [LLMFlags.hasImageInput, LLMFlags.hasFirstSystemPrompt],
|
||||
recommended: true,
|
||||
parameters: ['temperature', 'top_k', 'top_p']
|
||||
parameters: ['temperature', 'top_k', 'top_p'],
|
||||
tokenizer: LLMTokenizer.GoogleCloud
|
||||
},
|
||||
{
|
||||
name: "Gemini Flash 1.5",
|
||||
@@ -714,7 +798,8 @@ export const LLMModels: LLMModel[] = [
|
||||
format: LLMFormat.GoogleCloud,
|
||||
flags: [LLMFlags.hasImageInput, LLMFlags.hasFirstSystemPrompt],
|
||||
recommended: true,
|
||||
parameters: ['temperature', 'top_k', 'top_p']
|
||||
parameters: ['temperature', 'top_k', 'top_p'],
|
||||
tokenizer: LLMTokenizer.GoogleCloud
|
||||
},
|
||||
{
|
||||
name: "Gemini Exp 1121",
|
||||
@@ -723,7 +808,8 @@ export const LLMModels: LLMModel[] = [
|
||||
provider: LLMProvider.VertexAI,
|
||||
format: LLMFormat.VertexAIGemini,
|
||||
flags: [LLMFlags.hasImageInput, LLMFlags.hasFirstSystemPrompt],
|
||||
parameters: ['temperature', 'top_k', 'top_p']
|
||||
parameters: ['temperature', 'top_k', 'top_p'],
|
||||
tokenizer: LLMTokenizer.Gemma
|
||||
},
|
||||
{
|
||||
name: "Gemini Pro 1.5",
|
||||
@@ -732,7 +818,8 @@ export const LLMModels: LLMModel[] = [
|
||||
provider: LLMProvider.VertexAI,
|
||||
format: LLMFormat.VertexAIGemini,
|
||||
flags: [LLMFlags.hasImageInput, LLMFlags.hasFirstSystemPrompt],
|
||||
parameters: ['temperature', 'top_k', 'top_p']
|
||||
parameters: ['temperature', 'top_k', 'top_p'],
|
||||
tokenizer: LLMTokenizer.Gemma
|
||||
},
|
||||
{
|
||||
name: "Gemini Flash 1.5",
|
||||
@@ -741,7 +828,8 @@ export const LLMModels: LLMModel[] = [
|
||||
provider: LLMProvider.VertexAI,
|
||||
format: LLMFormat.VertexAIGemini,
|
||||
flags: [LLMFlags.hasImageInput, LLMFlags.hasFirstSystemPrompt],
|
||||
parameters: ['temperature', 'top_k', 'top_p']
|
||||
parameters: ['temperature', 'top_k', 'top_p'],
|
||||
tokenizer: LLMTokenizer.Gemma
|
||||
},
|
||||
{
|
||||
name: "Gemini Exp 1114",
|
||||
@@ -749,7 +837,8 @@ export const LLMModels: LLMModel[] = [
|
||||
provider: LLMProvider.GoogleCloud,
|
||||
format: LLMFormat.GoogleCloud,
|
||||
flags: [LLMFlags.hasImageInput, LLMFlags.hasFirstSystemPrompt],
|
||||
parameters: ['temperature', 'top_k', 'top_p']
|
||||
parameters: ['temperature', 'top_k', 'top_p'],
|
||||
tokenizer: LLMTokenizer.GoogleCloud
|
||||
},
|
||||
{
|
||||
name: "Gemini Pro 1.5 002",
|
||||
@@ -757,7 +846,8 @@ export const LLMModels: LLMModel[] = [
|
||||
provider: LLMProvider.GoogleCloud,
|
||||
format: LLMFormat.GoogleCloud,
|
||||
flags: [LLMFlags.hasImageInput, LLMFlags.hasFirstSystemPrompt],
|
||||
parameters: ['temperature', 'top_k', 'top_p']
|
||||
parameters: ['temperature', 'top_k', 'top_p'],
|
||||
tokenizer: LLMTokenizer.GoogleCloud
|
||||
},
|
||||
{
|
||||
name: "Gemini Flash 1.5 002",
|
||||
@@ -765,7 +855,8 @@ export const LLMModels: LLMModel[] = [
|
||||
provider: LLMProvider.GoogleCloud,
|
||||
format: LLMFormat.GoogleCloud,
|
||||
flags: [LLMFlags.hasImageInput, LLMFlags.hasFirstSystemPrompt],
|
||||
parameters: ['temperature', 'top_k', 'top_p']
|
||||
parameters: ['temperature', 'top_k', 'top_p'],
|
||||
tokenizer: LLMTokenizer.GoogleCloud
|
||||
},
|
||||
{
|
||||
name: "Gemini Pro",
|
||||
@@ -773,7 +864,8 @@ export const LLMModels: LLMModel[] = [
|
||||
provider: LLMProvider.GoogleCloud,
|
||||
format: LLMFormat.GoogleCloud,
|
||||
flags: [LLMFlags.hasImageInput, LLMFlags.hasFirstSystemPrompt],
|
||||
parameters: ['temperature', 'top_k', 'top_p']
|
||||
parameters: ['temperature', 'top_k', 'top_p'],
|
||||
tokenizer: LLMTokenizer.GoogleCloud
|
||||
},
|
||||
{
|
||||
name: "Gemini Pro Vision",
|
||||
@@ -781,7 +873,8 @@ export const LLMModels: LLMModel[] = [
|
||||
provider: LLMProvider.GoogleCloud,
|
||||
format: LLMFormat.GoogleCloud,
|
||||
flags: [LLMFlags.hasImageInput, LLMFlags.hasFirstSystemPrompt],
|
||||
parameters: ['temperature', 'top_k', 'top_p']
|
||||
parameters: ['temperature', 'top_k', 'top_p'],
|
||||
tokenizer: LLMTokenizer.GoogleCloud
|
||||
},
|
||||
{
|
||||
name: "Gemini Ultra",
|
||||
@@ -789,7 +882,8 @@ export const LLMModels: LLMModel[] = [
|
||||
provider: LLMProvider.GoogleCloud,
|
||||
format: LLMFormat.GoogleCloud,
|
||||
flags: [LLMFlags.hasImageInput, LLMFlags.hasFirstSystemPrompt],
|
||||
parameters: ['temperature', 'top_k', 'top_p']
|
||||
parameters: ['temperature', 'top_k', 'top_p'],
|
||||
tokenizer: LLMTokenizer.GoogleCloud
|
||||
},
|
||||
{
|
||||
name: "Gemini Ultra Vision",
|
||||
@@ -797,7 +891,8 @@ export const LLMModels: LLMModel[] = [
|
||||
provider: LLMProvider.GoogleCloud,
|
||||
format: LLMFormat.GoogleCloud,
|
||||
flags: [LLMFlags.hasImageInput, LLMFlags.hasFirstSystemPrompt],
|
||||
parameters: ['temperature', 'top_k', 'top_p']
|
||||
parameters: ['temperature', 'top_k', 'top_p'],
|
||||
tokenizer: LLMTokenizer.GoogleCloud
|
||||
},
|
||||
{
|
||||
name: 'Kobold',
|
||||
@@ -812,7 +907,8 @@ export const LLMModels: LLMModel[] = [
|
||||
'repetition_penalty',
|
||||
'top_k',
|
||||
'top_a'
|
||||
]
|
||||
],
|
||||
tokenizer: LLMTokenizer.Unknown
|
||||
},
|
||||
{
|
||||
name: "SuperTrin",
|
||||
@@ -820,7 +916,8 @@ export const LLMModels: LLMModel[] = [
|
||||
provider: LLMProvider.NovelList,
|
||||
format: LLMFormat.NovelList,
|
||||
flags: [],
|
||||
parameters: []
|
||||
parameters: [],
|
||||
tokenizer: LLMTokenizer.NovelList
|
||||
},
|
||||
{
|
||||
name: "Damsel",
|
||||
@@ -828,7 +925,8 @@ export const LLMModels: LLMModel[] = [
|
||||
provider: LLMProvider.NovelList,
|
||||
format: LLMFormat.NovelList,
|
||||
flags: [],
|
||||
parameters: []
|
||||
parameters: [],
|
||||
tokenizer: LLMTokenizer.NovelList
|
||||
},
|
||||
{
|
||||
name: "Command R",
|
||||
@@ -840,7 +938,8 @@ export const LLMModels: LLMModel[] = [
|
||||
recommended: true,
|
||||
parameters: [
|
||||
'temperature', 'top_k', 'top_p', 'presence_penalty', 'frequency_penalty'
|
||||
]
|
||||
],
|
||||
tokenizer: LLMTokenizer.Cohere
|
||||
},
|
||||
{
|
||||
name: "Command R Plus",
|
||||
@@ -852,7 +951,8 @@ export const LLMModels: LLMModel[] = [
|
||||
recommended: true,
|
||||
parameters: [
|
||||
'temperature', 'top_k', 'top_p', 'presence_penalty', 'frequency_penalty'
|
||||
]
|
||||
],
|
||||
tokenizer: LLMTokenizer.Cohere
|
||||
},
|
||||
{
|
||||
name: "Command R 08-2024",
|
||||
@@ -863,7 +963,8 @@ export const LLMModels: LLMModel[] = [
|
||||
flags: [LLMFlags.hasFirstSystemPrompt, LLMFlags.requiresAlternateRole, LLMFlags.mustStartWithUserInput],
|
||||
parameters: [
|
||||
'temperature', 'top_k', 'top_p', 'presence_penalty', 'frequency_penalty'
|
||||
]
|
||||
],
|
||||
tokenizer: LLMTokenizer.Cohere
|
||||
},
|
||||
{
|
||||
name: "Command R 03-2024",
|
||||
@@ -874,7 +975,8 @@ export const LLMModels: LLMModel[] = [
|
||||
flags: [LLMFlags.hasFirstSystemPrompt, LLMFlags.requiresAlternateRole, LLMFlags.mustStartWithUserInput],
|
||||
parameters: [
|
||||
'temperature', 'top_k', 'top_p', 'presence_penalty', 'frequency_penalty'
|
||||
]
|
||||
],
|
||||
tokenizer: LLMTokenizer.Cohere
|
||||
},
|
||||
{
|
||||
name: "Command R Plus 08-2024",
|
||||
@@ -885,7 +987,8 @@ export const LLMModels: LLMModel[] = [
|
||||
flags: [LLMFlags.hasFirstSystemPrompt, LLMFlags.requiresAlternateRole, LLMFlags.mustStartWithUserInput],
|
||||
parameters: [
|
||||
'temperature', 'top_k', 'top_p', 'presence_penalty', 'frequency_penalty'
|
||||
]
|
||||
],
|
||||
tokenizer: LLMTokenizer.Cohere
|
||||
},
|
||||
{
|
||||
name: "Command R Plus 04-2024",
|
||||
@@ -896,7 +999,8 @@ export const LLMModels: LLMModel[] = [
|
||||
flags: [LLMFlags.hasFirstSystemPrompt, LLMFlags.requiresAlternateRole, LLMFlags.mustStartWithUserInput],
|
||||
parameters: [
|
||||
'temperature', 'top_k', 'top_p', 'presence_penalty', 'frequency_penalty'
|
||||
]
|
||||
],
|
||||
tokenizer: LLMTokenizer.Cohere
|
||||
},
|
||||
{
|
||||
name: "Clio",
|
||||
@@ -907,7 +1011,8 @@ export const LLMModels: LLMModel[] = [
|
||||
recommended: true,
|
||||
parameters: [
|
||||
'temperature', 'top_k', 'top_p', 'presence_penalty', 'frequency_penalty'
|
||||
]
|
||||
],
|
||||
tokenizer: LLMTokenizer.NovelAI
|
||||
},
|
||||
{
|
||||
name: "Kayra",
|
||||
@@ -918,7 +1023,8 @@ export const LLMModels: LLMModel[] = [
|
||||
recommended: true,
|
||||
parameters: [
|
||||
'temperature', 'top_k', 'top_p', 'presence_penalty', 'frequency_penalty'
|
||||
]
|
||||
],
|
||||
tokenizer: LLMTokenizer.NovelAI
|
||||
},
|
||||
{
|
||||
id: 'ollama-hosted',
|
||||
@@ -926,7 +1032,8 @@ export const LLMModels: LLMModel[] = [
|
||||
provider: LLMProvider.AsIs,
|
||||
format: LLMFormat.Ollama,
|
||||
flags: [LLMFlags.hasFullSystemPrompt],
|
||||
parameters: OpenAIParameters
|
||||
parameters: OpenAIParameters,
|
||||
tokenizer: LLMTokenizer.Unknown
|
||||
},
|
||||
{
|
||||
id: 'hf:::Xenova/opt-350m',
|
||||
@@ -934,7 +1041,8 @@ export const LLMModels: LLMModel[] = [
|
||||
provider: LLMProvider.WebLLM,
|
||||
format: LLMFormat.WebLLM,
|
||||
flags: [LLMFlags.hasFullSystemPrompt],
|
||||
parameters: OpenAIParameters
|
||||
parameters: OpenAIParameters,
|
||||
tokenizer: LLMTokenizer.Local
|
||||
},
|
||||
{
|
||||
id: 'hf:::Xenova/tiny-random-mistral',
|
||||
@@ -942,7 +1050,8 @@ export const LLMModels: LLMModel[] = [
|
||||
provider: LLMProvider.WebLLM,
|
||||
format: LLMFormat.WebLLM,
|
||||
flags: [LLMFlags.hasFullSystemPrompt],
|
||||
parameters: OpenAIParameters
|
||||
parameters: OpenAIParameters,
|
||||
tokenizer: LLMTokenizer.Local
|
||||
},
|
||||
{
|
||||
id: 'hf:::Xenova/gpt2-large-conversational',
|
||||
@@ -950,7 +1059,8 @@ export const LLMModels: LLMModel[] = [
|
||||
provider: LLMProvider.WebLLM,
|
||||
format: LLMFormat.WebLLM,
|
||||
flags: [LLMFlags.hasFullSystemPrompt],
|
||||
parameters: OpenAIParameters
|
||||
parameters: OpenAIParameters,
|
||||
tokenizer: LLMTokenizer.Local
|
||||
},
|
||||
{
|
||||
id: 'custom',
|
||||
@@ -959,7 +1069,8 @@ export const LLMModels: LLMModel[] = [
|
||||
format: LLMFormat.Plugin,
|
||||
flags: [LLMFlags.hasFullSystemPrompt],
|
||||
recommended: true,
|
||||
parameters: ['temperature', 'top_p', 'frequency_penalty', 'presence_penalty', 'repetition_penalty', 'min_p', 'top_a', 'top_k']
|
||||
parameters: ['temperature', 'top_p', 'frequency_penalty', 'presence_penalty', 'repetition_penalty', 'min_p', 'top_a', 'top_k'],
|
||||
tokenizer: LLMTokenizer.Unknown
|
||||
},
|
||||
{
|
||||
id: 'reverse_proxy',
|
||||
@@ -968,7 +1079,8 @@ export const LLMModels: LLMModel[] = [
|
||||
format: LLMFormat.OpenAICompatible,
|
||||
flags: [LLMFlags.hasFullSystemPrompt, LLMFlags.hasStreaming],
|
||||
recommended: true,
|
||||
parameters: ['temperature', 'top_p', 'frequency_penalty', 'presence_penalty', 'repetition_penalty', 'min_p', 'top_a', 'top_k']
|
||||
parameters: ['temperature', 'top_p', 'frequency_penalty', 'presence_penalty', 'repetition_penalty', 'min_p', 'top_a', 'top_k'],
|
||||
tokenizer: LLMTokenizer.Unknown
|
||||
}
|
||||
]
|
||||
|
||||
@@ -1002,7 +1114,8 @@ export function getModelInfo(id: string): LLMModel{
|
||||
provider: LLMProvider.WebLLM,
|
||||
format: LLMFormat.WebLLM,
|
||||
flags: [],
|
||||
parameters: OpenAIParameters
|
||||
parameters: OpenAIParameters,
|
||||
tokenizer: LLMTokenizer.Local
|
||||
}
|
||||
}
|
||||
if(id.startsWith('horde:::')){
|
||||
@@ -1016,7 +1129,8 @@ export function getModelInfo(id: string): LLMModel{
|
||||
provider: LLMProvider.Horde,
|
||||
format: LLMFormat.Horde,
|
||||
flags: [],
|
||||
parameters: OpenAIParameters
|
||||
parameters: OpenAIParameters,
|
||||
tokenizer: LLMTokenizer.Unknown
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1029,7 +1143,8 @@ export function getModelInfo(id: string): LLMModel{
|
||||
provider: LLMProvider.AsIs,
|
||||
format: LLMFormat.OpenAICompatible,
|
||||
flags: [],
|
||||
parameters: OpenAIParameters
|
||||
parameters: OpenAIParameters,
|
||||
tokenizer: LLMTokenizer.Unknown
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@@ -318,9 +318,17 @@ async function parseAdditionalAssets(data:string, char:simpleCharacterArgument|c
|
||||
}
|
||||
}
|
||||
}
|
||||
const path = assetPaths[name]
|
||||
let path = assetPaths[name]
|
||||
if(!path){
|
||||
return ''
|
||||
if(DBState.db.legacyMediaFindings){
|
||||
return ''
|
||||
}
|
||||
|
||||
path = getClosestMatch(name, assetPaths)
|
||||
|
||||
if(!path){
|
||||
return ''
|
||||
}
|
||||
}
|
||||
switch(type){
|
||||
case 'raw':
|
||||
@@ -366,6 +374,50 @@ async function parseAdditionalAssets(data:string, char:simpleCharacterArgument|c
|
||||
return data
|
||||
}
|
||||
|
||||
function getClosestMatch(name:string, assetPaths:{[key:string]:{path:string, ext?:string}}){
|
||||
if(Object.keys(assetPaths).length === 0){
|
||||
return null
|
||||
}
|
||||
|
||||
const dest = (a:string, b:string) => {
|
||||
let d:Int16Array[] = []
|
||||
|
||||
for(let i=0;i<a.length+1;i++){
|
||||
d.push(new Int16Array(b.length+1))
|
||||
}
|
||||
|
||||
for(let i=0;i<=a.length;i++){
|
||||
d[i][0] = i
|
||||
}
|
||||
|
||||
for(let i=0;i<=b.length;i++){
|
||||
d[0][i] = i
|
||||
}
|
||||
|
||||
for(let i=1; i<=a.length; i++){
|
||||
for(let j=1;j<=b.length;j++){
|
||||
d[i][j] = Math.min(
|
||||
d[i-1][j-1] + (a.charAt(i-1)===b.charAt(j-1) ? 0 : 1),
|
||||
d[i-1][j]+1, d[i][j-1]+1
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
return d[a.length][b.length];
|
||||
}
|
||||
|
||||
let closest = ''
|
||||
let closestDist = 999999
|
||||
for(const key in assetPaths){
|
||||
const dist = dest(name.trim().replace(/[_ ]/g, ''), key.trim().replace(/[_ ]/g, ''))
|
||||
if(dist < closestDist){
|
||||
closest = key
|
||||
closestDist = dist
|
||||
}
|
||||
}
|
||||
return assetPaths[closest]
|
||||
}
|
||||
|
||||
async function parseInlayImages(data:string){
|
||||
const inlayMatch = data.match(/{{inlay::(.+?)}}/g)
|
||||
if(inlayMatch){
|
||||
|
||||
@@ -3,7 +3,7 @@ import { HypaProcesser } from '../memory/hypamemory'
|
||||
import { getUserName } from "src/ts/util";
|
||||
|
||||
export async function additionalInformations(char: character,chats:Chat,){
|
||||
const processer = new HypaProcesser('MiniLM')
|
||||
const processer = new HypaProcesser()
|
||||
const db = getDatabase()
|
||||
|
||||
const info = char.additionalText
|
||||
|
||||
@@ -84,7 +84,7 @@ export async function getInlayImage(id: string){
|
||||
|
||||
export function supportsInlayImage(){
|
||||
const db = getDatabase()
|
||||
return db.aiModel.startsWith('gptv') || db.aiModel === 'gemini-pro-vision' || db.aiModel.startsWith('claude-3') || db.aiModel.startsWith('gpt4_turbo') || db.aiModel.startsWith('gpt5') || db.aiModel.startsWith('gpt4o') ||
|
||||
return db.aiModel.startsWith('gptv') || db.aiModel === 'gemini-pro-vision' || db.aiModel.startsWith('gemini-exp') || db.aiModel.startsWith('claude-3') || db.aiModel.startsWith('gpt4_turbo') || db.aiModel.startsWith('gpt5') || db.aiModel.startsWith('gpt4o') ||
|
||||
(db.aiModel === 'reverse_proxy' && (
|
||||
db.proxyRequestModel?.startsWith('gptv') || db.proxyRequestModel === 'gemini-pro-vision' || db.proxyRequestModel?.startsWith('claude-3') || db.proxyRequestModel.startsWith('gpt4_turbo') ||
|
||||
db.proxyRequestModel?.startsWith('gpt5') || db.proxyRequestModel?.startsWith('gpt4o') ||
|
||||
|
||||
@@ -124,7 +124,7 @@ async function sendPDFFile(arg:sendFileArg) {
|
||||
}
|
||||
}
|
||||
console.log(texts)
|
||||
const hypa = new HypaProcesser('MiniLM')
|
||||
const hypa = new HypaProcesser()
|
||||
hypa.addText(texts)
|
||||
const result = await hypa.similaritySearch(arg.query)
|
||||
let message = ''
|
||||
@@ -142,7 +142,7 @@ async function sendTxtFile(arg:sendFileArg) {
|
||||
const lines = arg.file.split('\n').filter((a) => {
|
||||
return a !== ''
|
||||
})
|
||||
const hypa = new HypaProcesser('MiniLM')
|
||||
const hypa = new HypaProcesser()
|
||||
hypa.addText(lines)
|
||||
const result = await hypa.similaritySearch(arg.query)
|
||||
let message = ''
|
||||
@@ -157,7 +157,7 @@ async function sendTxtFile(arg:sendFileArg) {
|
||||
}
|
||||
|
||||
async function sendXMLFile(arg:sendFileArg) {
|
||||
const hypa = new HypaProcesser('MiniLM')
|
||||
const hypa = new HypaProcesser()
|
||||
let nodeTexts:string[] = []
|
||||
const parser = new DOMParser();
|
||||
const xmlDoc = parser.parseFromString(arg.file, "text/xml");
|
||||
|
||||
@@ -1,10 +1,10 @@
|
||||
import { get, writable } from "svelte/store";
|
||||
import { type character, type MessageGenerationInfo, type Chat } from "../storage/database.svelte";
|
||||
import { type character, type MessageGenerationInfo, type Chat, changeToPreset } from "../storage/database.svelte";
|
||||
import { DBState } from '../stores.svelte';
|
||||
import { CharEmotion, selectedCharID } from "../stores.svelte";
|
||||
import { ChatTokenizer, tokenize, tokenizeNum } from "../tokenizer";
|
||||
import { language } from "../../lang";
|
||||
import { alertError } from "../alert";
|
||||
import { alertError, alertToast } from "../alert";
|
||||
import { loadLoreBookV3Prompt } from "./lorebook.svelte";
|
||||
import { findCharacterbyId, getAuthorNoteDefaultText, getPersonaPrompt, getUserName, isLastCharPunctuation, trimUntilPunctuation } from "../util";
|
||||
import { requestChatData } from "./request";
|
||||
@@ -109,6 +109,23 @@ export async function sendChat(chatProcessIndex = -1,arg:{
|
||||
}
|
||||
doingChat.set(true)
|
||||
|
||||
if(chatProcessIndex === -1 && DBState.db.presetChain){
|
||||
const names = DBState.db.presetChain.split(',').map((v) => v.trim())
|
||||
const randomSelect = Math.floor(Math.random() * names.length)
|
||||
const ele = names[randomSelect]
|
||||
|
||||
const findId = DBState.db.botPresets.findIndex((v) => {
|
||||
return v.name === ele
|
||||
})
|
||||
|
||||
if(findId === -1){
|
||||
alertToast(`Cannot find preset: ${ele}`)
|
||||
}
|
||||
else{
|
||||
changeToPreset(findId, true)
|
||||
}
|
||||
}
|
||||
|
||||
if(connectionOpen){
|
||||
chatProcessStage.set(4)
|
||||
const peerSafe = await peerSafeCheck()
|
||||
@@ -1396,7 +1413,7 @@ export async function sendChat(chatProcessIndex = -1,arg:{
|
||||
}
|
||||
|
||||
if(DBState.db.emotionProcesser === 'embedding'){
|
||||
const hypaProcesser = new HypaProcesser('MiniLM')
|
||||
const hypaProcesser = new HypaProcesser()
|
||||
await hypaProcesser.addText(emotionList.map((v) => 'emotion:' + v))
|
||||
let searched = (await hypaProcesser.similaritySearchScored(result)).map((v) => {
|
||||
v[0] = v[0].replace("emotion:",'')
|
||||
|
||||
@@ -213,7 +213,7 @@ export async function runLua(code:string, arg:{
|
||||
if(!LuaLowLevelIds.has(id)){
|
||||
return
|
||||
}
|
||||
const processer = new HypaProcesser('MiniLM')
|
||||
const processer = new HypaProcesser()
|
||||
await processer.addText(value)
|
||||
return await processer.similaritySearch(source)
|
||||
})
|
||||
|
||||
@@ -13,7 +13,7 @@ export async function hanuraiMemory(chats:OpenAIChat[],arg:{
|
||||
}){
|
||||
const db = getDatabase()
|
||||
const tokenizer = arg.tokenizer
|
||||
const processer = new HypaProcesser('MiniLM')
|
||||
const processer = new HypaProcesser()
|
||||
let addTexts:string[] = []
|
||||
const queryStartIndex=chats.length-maxRecentChatQuery
|
||||
console.log(chats.length,maxRecentChatQuery,queryStartIndex)
|
||||
|
||||
@@ -1,22 +1,49 @@
|
||||
import localforage from "localforage";
|
||||
import {globalFetch} from "src/ts/globalApi.svelte";
|
||||
import {runEmbedding} from "../transformers";
|
||||
import {appendLastPath} from "src/ts/util";
|
||||
import { globalFetch } from "src/ts/globalApi.svelte";
|
||||
import { runEmbedding } from "../transformers";
|
||||
import { alertError } from "src/ts/alert";
|
||||
import { appendLastPath } from "src/ts/util";
|
||||
import { getDatabase } from "src/ts/storage/database.svelte";
|
||||
|
||||
|
||||
export type HypaModel = 'ada'|'MiniLM'|'nomic'|'custom'|'nomicGPU'|'bgeSmallEn'|'bgeSmallEnGPU'|'bgem3'|'bgem3GPU'|'openai3small'|'openai3large'
|
||||
|
||||
const localModels = {
|
||||
models: {
|
||||
'MiniLM':'Xenova/all-MiniLM-L6-v2',
|
||||
'nomic':'nomic-ai/nomic-embed-text-v1.5',
|
||||
'nomicGPU':'nomic-ai/nomic-embed-text-v1.5',
|
||||
'bgeSmallEn': 'BAAI/bge-small-en-v1.5',
|
||||
'bgeSmallEnGPU': 'BAAI/bge-small-en-v1.5',
|
||||
'bgem3': 'BAAI/bge-m3',
|
||||
'bgem3GPU': 'BAAI/bge-m3',
|
||||
},
|
||||
gpuModels:[
|
||||
'nomicGPU',
|
||||
'bgeSmallEnGPU',
|
||||
'bgem3GPU'
|
||||
]
|
||||
}
|
||||
|
||||
export class HypaProcesser{
|
||||
oaikey:string
|
||||
vectors:memoryVector[]
|
||||
forage:LocalForage
|
||||
model:'ada'|'MiniLM'|'nomic'|'custom'
|
||||
model:HypaModel
|
||||
customEmbeddingUrl:string
|
||||
|
||||
constructor(model:'ada'|'MiniLM'|'nomic'|'custom',customEmbeddingUrl?:string){
|
||||
constructor(model:HypaModel|'auto' = 'auto',customEmbeddingUrl?:string){
|
||||
this.forage = localforage.createInstance({
|
||||
name: "hypaVector"
|
||||
})
|
||||
this.vectors = []
|
||||
this.model = model
|
||||
if(model === 'auto'){
|
||||
const db = getDatabase()
|
||||
this.model = db.hypaModel || 'MiniLM'
|
||||
}
|
||||
else{
|
||||
this.model = model
|
||||
}
|
||||
this.customEmbeddingUrl = customEmbeddingUrl
|
||||
}
|
||||
|
||||
@@ -38,9 +65,9 @@ export class HypaProcesser{
|
||||
|
||||
|
||||
async getEmbeds(input:string[]|string):Promise<VectorArray[]> {
|
||||
if(this.model === 'MiniLM' || this.model === 'nomic'){
|
||||
if(Object.keys(localModels.models).includes(this.model)){
|
||||
const inputs:string[] = Array.isArray(input) ? input : [input]
|
||||
let results:Float32Array[] = await runEmbedding(inputs, this.model === 'nomic' ? 'nomic-ai/nomic-embed-text-v1.5' : 'Xenova/all-MiniLM-L6-v2')
|
||||
let results:Float32Array[] = await runEmbedding(inputs, localModels.models[this.model], localModels.gpuModels.includes(this.model) ? 'webgpu' : 'wasm')
|
||||
return results
|
||||
}
|
||||
let gf = null;
|
||||
@@ -57,14 +84,21 @@ export class HypaProcesser{
|
||||
},
|
||||
})
|
||||
}
|
||||
if(this.model === 'ada'){
|
||||
if(this.model === 'ada' || this.model === 'openai3small' || this.model === 'openai3large'){
|
||||
const db = getDatabase()
|
||||
const models = {
|
||||
'ada':'text-embedding-ada-002',
|
||||
'openai3small':'text-embedding-3-small',
|
||||
'openai3large':'text-embedding-3-large'
|
||||
}
|
||||
|
||||
gf = await globalFetch("https://api.openai.com/v1/embeddings", {
|
||||
headers: {
|
||||
"Authorization": "Bearer " + this.oaikey
|
||||
"Authorization": "Bearer " + db.supaMemoryKey || this.oaikey
|
||||
},
|
||||
body: {
|
||||
"input": input,
|
||||
"model": "text-embedding-ada-002"
|
||||
"input": input,
|
||||
"model": models[this.model]
|
||||
}
|
||||
})
|
||||
}
|
||||
@@ -138,7 +172,7 @@ export class HypaProcesser{
|
||||
}
|
||||
|
||||
async similaritySearchScored(query: string) {
|
||||
return await this.similaritySearchVectorWithScore((await this.getEmbeds(query))[0],)
|
||||
return await this.similaritySearchVectorWithScore((await this.getEmbeds(query))[0],);
|
||||
}
|
||||
|
||||
private async similaritySearchVectorWithScore(
|
||||
|
||||
@@ -30,6 +30,7 @@ async function summary(
|
||||
stringlizedChat: string
|
||||
): Promise<{ success: boolean; data: string }> {
|
||||
const db = getDatabase();
|
||||
console.log("Summarizing");
|
||||
|
||||
if (db.supaModelType === "distilbart") {
|
||||
try {
|
||||
@@ -101,35 +102,27 @@ async function summary(
|
||||
supaPrompt.replaceAll("{{slot}}", stringlizedChat)
|
||||
);
|
||||
|
||||
const promptbody: OpenAIChat[] = parsedPrompt ?? [
|
||||
const promptbody: OpenAIChat[] = (parsedPrompt ?? [
|
||||
{
|
||||
role: "user",
|
||||
content: stringlizedChat,
|
||||
},
|
||||
{
|
||||
role: "system",
|
||||
content: supaPrompt,
|
||||
},
|
||||
];
|
||||
console.log(
|
||||
"Using submodel: ",
|
||||
db.subModel,
|
||||
"for supaMemory model"
|
||||
);
|
||||
const da = await requestChatData(
|
||||
{
|
||||
formated: promptbody,
|
||||
bias: {},
|
||||
useStreaming: false,
|
||||
noMultiGen: true,
|
||||
},
|
||||
"memory"
|
||||
);
|
||||
if (
|
||||
da.type === "fail" ||
|
||||
da.type === "streaming" ||
|
||||
da.type === "multiline"
|
||||
) {
|
||||
content: supaPrompt
|
||||
}
|
||||
]).map(message => ({
|
||||
...message,
|
||||
memo: "supaPrompt"
|
||||
}));
|
||||
console.log("Using submodel: ", db.subModel, "for supaMemory model");
|
||||
const da = await requestChatData({
|
||||
formated: promptbody,
|
||||
bias: {},
|
||||
useStreaming: false,
|
||||
noMultiGen: true
|
||||
}, 'memory');
|
||||
if (da.type === 'fail' || da.type === 'streaming' || da.type === 'multiline') {
|
||||
return {
|
||||
success: false,
|
||||
data: "SupaMemory: HTTP: " + da.result,
|
||||
@@ -179,7 +172,7 @@ export async function regenerateSummary(
|
||||
mainChunkIndex: number
|
||||
) : Promise<void> {
|
||||
const targetMainChunk = data.mainChunks[mainChunkIndex];
|
||||
|
||||
|
||||
}
|
||||
export async function hypaMemoryV2(
|
||||
chats: OpenAIChat[],
|
||||
@@ -209,6 +202,7 @@ export async function hypaMemoryV2(
|
||||
currentTokens += allocatedTokens + chats.length * 4; // ChatML token counting from official openai documentation
|
||||
let mainPrompt = "";
|
||||
const lastTwoChats = chats.slice(-2);
|
||||
// Error handling for infinite summarization attempts
|
||||
let summarizationFailures = 0;
|
||||
const maxSummarizationFailures = 3;
|
||||
|
||||
|
||||
@@ -4,7 +4,7 @@ export function getGenerationModelString(){
|
||||
const db = getDatabase()
|
||||
switch (db.aiModel){
|
||||
case 'reverse_proxy':
|
||||
return 'reverse_proxy-' + (db.reverseProxyOobaMode ? 'ooba' : db.proxyRequestModel)
|
||||
return 'custom-' + (db.reverseProxyOobaMode ? 'ooba' : db.customProxyRequestModel)
|
||||
case 'openrouter':
|
||||
return 'openrouter-' + db.openrouterRequestModel
|
||||
default:
|
||||
|
||||
@@ -1364,7 +1364,6 @@ async function requestGoogleCloudVertex(arg:RequestDataArgumentExtended):Promise
|
||||
|
||||
|
||||
let reformatedChat:GeminiChat[] = []
|
||||
let pendingImage = ''
|
||||
let systemPrompt = ''
|
||||
|
||||
if(formated[0].role === 'system'){
|
||||
@@ -1374,10 +1373,7 @@ async function requestGoogleCloudVertex(arg:RequestDataArgumentExtended):Promise
|
||||
|
||||
for(let i=0;i<formated.length;i++){
|
||||
const chat = formated[i]
|
||||
if(chat.memo && chat.memo.startsWith('inlayImage')){
|
||||
pendingImage = chat.content
|
||||
continue
|
||||
}
|
||||
|
||||
if(i === 0){
|
||||
if(chat.role === 'user' || chat.role === 'assistant'){
|
||||
reformatedChat.push({
|
||||
@@ -1403,7 +1399,34 @@ async function requestGoogleCloudVertex(arg:RequestDataArgumentExtended):Promise
|
||||
chat.role === 'assistant' ? 'MODEL' :
|
||||
chat.role
|
||||
|
||||
if(prevChat.role === qRole){
|
||||
if (chat.multimodals && chat.multimodals.length > 0 && chat.role === "user") {
|
||||
let geminiParts: GeminiPart[] = [];
|
||||
|
||||
geminiParts.push({
|
||||
text: chat.content,
|
||||
});
|
||||
|
||||
for (const modal of chat.multimodals) {
|
||||
if (modal.type === "image") {
|
||||
const dataurl = modal.base64;
|
||||
const base64 = dataurl.split(",")[1];
|
||||
const mediaType = dataurl.split(";")[0].split(":")[1];
|
||||
|
||||
geminiParts.push({
|
||||
inlineData: {
|
||||
mimeType: mediaType,
|
||||
data: base64,
|
||||
}
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
reformatedChat.push({
|
||||
role: "USER",
|
||||
parts: geminiParts,
|
||||
});
|
||||
|
||||
} else if (prevChat.role === qRole) {
|
||||
reformatedChat[reformatedChat.length-1].parts[0].text += '\n' + chat.content
|
||||
continue
|
||||
}
|
||||
@@ -1420,36 +1443,7 @@ async function requestGoogleCloudVertex(arg:RequestDataArgumentExtended):Promise
|
||||
})
|
||||
}
|
||||
}
|
||||
else if(chat.role === 'user' && pendingImage !== ''){
|
||||
//conver image to jpeg so it can be inlined
|
||||
const canv = document.createElement('canvas')
|
||||
const img = new Image()
|
||||
img.src = pendingImage
|
||||
await img.decode()
|
||||
canv.width = img.width
|
||||
canv.height = img.height
|
||||
const ctx = canv.getContext('2d')
|
||||
ctx.drawImage(img, 0, 0)
|
||||
const base64 = canv.toDataURL('image/jpeg').replace(/^data:image\/jpeg;base64,/, "")
|
||||
const mimeType = 'image/jpeg'
|
||||
pendingImage = ''
|
||||
canv.remove()
|
||||
img.remove()
|
||||
|
||||
reformatedChat.push({
|
||||
role: "USER",
|
||||
parts: [
|
||||
{
|
||||
text: chat.content,
|
||||
},
|
||||
{
|
||||
inlineData: {
|
||||
mimeType: mimeType,
|
||||
data: base64
|
||||
}
|
||||
}]
|
||||
})
|
||||
}
|
||||
else if(chat.role === 'assistant' || chat.role === 'user'){
|
||||
reformatedChat.push({
|
||||
role: chat.role === 'user' ? 'USER' : 'MODEL',
|
||||
@@ -1578,14 +1572,24 @@ async function requestGoogleCloudVertex(arg:RequestDataArgumentExtended):Promise
|
||||
}
|
||||
}
|
||||
|
||||
const url = arg.customURL ?? (arg.modelInfo.format === LLMFormat.VertexAIGemini ?
|
||||
`https://${REGION}-aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/us-central1/publishers/google/models/${arg.modelInfo.internalID}:streamGenerateContent`
|
||||
: `https://generativelanguage.googleapis.com/v1beta/models/${arg.modelInfo.internalID}:generateContent?key=${db.google.accessToken}`)
|
||||
let url = ''
|
||||
|
||||
if(arg.customURL){
|
||||
const u = new URL(arg.customURL)
|
||||
u.searchParams.set('key', db.proxyKey)
|
||||
url = u.toString()
|
||||
}
|
||||
else if(arg.modelInfo.format === LLMFormat.VertexAIGemini){
|
||||
url =`https://${REGION}-aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/us-central1/publishers/google/models/${arg.modelInfo.internalID}:streamGenerateContent`
|
||||
}
|
||||
else{
|
||||
url = `https://generativelanguage.googleapis.com/v1beta/models/${arg.modelInfo.internalID}:generateContent?key=${db.google.accessToken}`
|
||||
}
|
||||
const res = await globalFetch(url, {
|
||||
headers: headers,
|
||||
body: body,
|
||||
chatId: arg.chatId,
|
||||
abortSignal: arg.abortSignal
|
||||
abortSignal: arg.abortSignal,
|
||||
})
|
||||
|
||||
if(!res.ok){
|
||||
@@ -2533,7 +2537,7 @@ async function requestWebLLM(arg:RequestDataArgumentExtended):Promise<requestDat
|
||||
top_p: db.ooba.top_p,
|
||||
repetition_penalty: db.ooba.repetition_penalty,
|
||||
typical_p: db.ooba.typical_p,
|
||||
})
|
||||
} as any)
|
||||
return {
|
||||
type: 'success',
|
||||
result: unstringlizeChat(v.generated_text as string, formated, currentChar?.name ?? '')
|
||||
|
||||
@@ -321,7 +321,7 @@ export async function processScriptFull(char:character|groupChat|simpleCharacter
|
||||
}
|
||||
}
|
||||
|
||||
const processer = new HypaProcesser('MiniLM')
|
||||
const processer = new HypaProcesser()
|
||||
await processer.addText(assetNames)
|
||||
const matches = data.matchAll(assetRegex)
|
||||
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
import {env, AutoTokenizer, pipeline, type SummarizationOutput, type TextGenerationConfig, type TextGenerationOutput, FeatureExtractionPipeline, TextToAudioPipeline, type ImageToTextOutput } from '@xenova/transformers';
|
||||
import {env, AutoTokenizer, pipeline, type SummarizationOutput, type TextGenerationConfig, type TextGenerationOutput, FeatureExtractionPipeline, TextToAudioPipeline, type ImageToTextOutput } from '@huggingface/transformers';
|
||||
import { unzip } from 'fflate';
|
||||
import { globalFetch, loadAsset, saveAsset } from 'src/ts/globalApi.svelte';
|
||||
import { selectSingleFile } from 'src/ts/util';
|
||||
@@ -15,6 +15,7 @@ async function initTransformers(){
|
||||
env.useBrowserCache = false
|
||||
env.useFSCache = false
|
||||
env.useCustomCache = true
|
||||
env.allowLocalModels = true
|
||||
env.customCache = {
|
||||
put: async (url:URL|string, response:Response) => {
|
||||
await tfCache.put(url, response)
|
||||
@@ -33,10 +34,12 @@ async function initTransformers(){
|
||||
console.log('transformers loaded')
|
||||
}
|
||||
|
||||
export const runTransformers = async (baseText:string, model:string,config:TextGenerationConfig = {}) => {
|
||||
export const runTransformers = async (baseText:string, model:string,config:TextGenerationConfig, device:'webgpu'|'wasm' = 'wasm') => {
|
||||
await initTransformers()
|
||||
let text = baseText
|
||||
let generator = await pipeline('text-generation', model);
|
||||
let generator = await pipeline('text-generation', model, {
|
||||
device
|
||||
});
|
||||
let output = await generator(text, config) as TextGenerationOutput
|
||||
const outputOne = output[0]
|
||||
return outputOne
|
||||
@@ -50,16 +53,25 @@ export const runSummarizer = async (text: string) => {
|
||||
}
|
||||
|
||||
let extractor:FeatureExtractionPipeline = null
|
||||
let lastEmbeddingModelQuery:string = ''
|
||||
type EmbeddingModel = 'Xenova/all-MiniLM-L6-v2'|'nomic-ai/nomic-embed-text-v1.5'
|
||||
export const runEmbedding = async (texts: string[], model:EmbeddingModel = 'Xenova/all-MiniLM-L6-v2'):Promise<Float32Array[]> => {
|
||||
export const runEmbedding = async (texts: string[], model:EmbeddingModel = 'Xenova/all-MiniLM-L6-v2', device:'webgpu'|'wasm'):Promise<Float32Array[]> => {
|
||||
await initTransformers()
|
||||
if(!extractor){
|
||||
extractor = await pipeline('feature-extraction', model);
|
||||
console.log('running embedding')
|
||||
let embeddingModelQuery = model + device
|
||||
if(!extractor || embeddingModelQuery !== lastEmbeddingModelQuery){
|
||||
extractor = await pipeline('feature-extraction', model, {
|
||||
device: device,
|
||||
progress_callback: (progress) => {
|
||||
console.log(progress)
|
||||
}
|
||||
});
|
||||
console.log('extractor loaded')
|
||||
}
|
||||
let result = await extractor(texts, { pooling: 'mean', normalize: true });
|
||||
console.log(texts, result)
|
||||
const data = result.data as Float32Array
|
||||
|
||||
console.log(data)
|
||||
const lenPerText = data.length / texts.length
|
||||
let res:Float32Array[] = []
|
||||
for(let i = 0; i < texts.length; i++){
|
||||
|
||||
@@ -459,7 +459,7 @@ export async function runTrigger(char:character,mode:triggerMode, arg:{
|
||||
break
|
||||
}
|
||||
|
||||
const processer = new HypaProcesser('MiniLM')
|
||||
const processer = new HypaProcesser()
|
||||
const effectValue = risuChatParser(effect.value,{chara:char})
|
||||
const source = risuChatParser(effect.source,{chara:char})
|
||||
await processer.addText(effectValue.split('§'))
|
||||
|
||||
@@ -12,7 +12,7 @@ import { defaultColorScheme, type ColorScheme } from '../gui/colorscheme';
|
||||
import type { PromptItem, PromptSettings } from '../process/prompt';
|
||||
import type { OobaChatCompletionRequestParams } from '../model/ooba';
|
||||
|
||||
export let appVer = "141.2.0"
|
||||
export let appVer = "143.0.1"
|
||||
export let webAppSubVer = ''
|
||||
|
||||
|
||||
@@ -703,7 +703,7 @@ export interface Database{
|
||||
colorSchemeName:string
|
||||
promptTemplate?:PromptItem[]
|
||||
forceProxyAsOpenAI?:boolean
|
||||
hypaModel:'ada'|'MiniLM'
|
||||
hypaModel:HypaModel
|
||||
saveTime?:number
|
||||
mancerHeader:string
|
||||
emotionProcesser:'submodel'|'embedding',
|
||||
@@ -857,6 +857,9 @@ export interface Database{
|
||||
notification: boolean
|
||||
customFlags: LLMFlags[]
|
||||
enableCustomFlags: boolean
|
||||
googleClaudeTokenizing: boolean
|
||||
presetChain: string
|
||||
legacyMediaFindings?:boolean
|
||||
}
|
||||
|
||||
interface SeparateParameters{
|
||||
@@ -1597,6 +1600,7 @@ import { decodeRPack, encodeRPack } from '../rpack/rpack_bg';
|
||||
import { DBState, selectedCharID } from '../stores.svelte';
|
||||
import { LLMFlags, LLMFormat } from '../model/modellist';
|
||||
import type { Parameter } from '../process/request';
|
||||
import type { HypaModel } from '../process/memory/hypamemory';
|
||||
|
||||
export async function downloadPreset(id:number, type:'json'|'risupreset'|'return' = 'json'){
|
||||
saveCurrentPreset()
|
||||
|
||||
@@ -6,6 +6,7 @@ import { supportsInlayImage } from "./process/files/image";
|
||||
import { risuChatParser } from "./parser.svelte";
|
||||
import { tokenizeGGUFModel } from "./process/models/local";
|
||||
import { globalFetch } from "./globalApi.svelte";
|
||||
import { getModelInfo, LLMTokenizer } from "./model/modellist";
|
||||
|
||||
|
||||
export const tokenizerList = [
|
||||
@@ -44,58 +45,86 @@ export async function encode(data:string):Promise<(number[]|Uint32Array|Int32Arr
|
||||
return await tikJS(data, 'o200k_base')
|
||||
}
|
||||
}
|
||||
if(db.aiModel.startsWith('novellist')){
|
||||
const modelInfo = getModelInfo(db.aiModel)
|
||||
|
||||
if(modelInfo.tokenizer === LLMTokenizer.NovelList){
|
||||
const nv= await tokenizeWebTokenizers(data, 'novellist')
|
||||
return nv
|
||||
}
|
||||
if(db.aiModel.startsWith('claude')){
|
||||
if(modelInfo.tokenizer === LLMTokenizer.Claude){
|
||||
return await tokenizeWebTokenizers(data, 'claude')
|
||||
}
|
||||
if(db.aiModel.startsWith('novelai')){
|
||||
if(modelInfo.tokenizer === LLMTokenizer.NovelAI){
|
||||
return await tokenizeWebTokenizers(data, 'novelai')
|
||||
}
|
||||
if(db.aiModel.startsWith('mistral')){
|
||||
if(modelInfo.tokenizer === LLMTokenizer.Mistral){
|
||||
return await tokenizeWebTokenizers(data, 'mistral')
|
||||
}
|
||||
if(db.aiModel === 'mancer' ||
|
||||
db.aiModel === 'textgen_webui' ||
|
||||
(db.aiModel === 'reverse_proxy' && db.reverseProxyOobaMode)){
|
||||
if(modelInfo.tokenizer === LLMTokenizer.Llama){
|
||||
return await tokenizeWebTokenizers(data, 'llama')
|
||||
}
|
||||
if(db.aiModel.startsWith('local_')){
|
||||
if(modelInfo.tokenizer === LLMTokenizer.Local){
|
||||
return await tokenizeGGUFModel(data)
|
||||
}
|
||||
if(db.aiModel === 'ooba'){
|
||||
if(db.reverseProxyOobaArgs.tokenizer === 'mixtral' || db.reverseProxyOobaArgs.tokenizer === 'mistral'){
|
||||
return await tokenizeWebTokenizers(data, 'mistral')
|
||||
}
|
||||
else if(db.reverseProxyOobaArgs.tokenizer === 'llama'){
|
||||
return await tokenizeWebTokenizers(data, 'llama')
|
||||
}
|
||||
else{
|
||||
return await tokenizeWebTokenizers(data, 'llama')
|
||||
}
|
||||
}
|
||||
if(db.aiModel.startsWith('gpt4o')){
|
||||
if(modelInfo.tokenizer === LLMTokenizer.tiktokenO200Base){
|
||||
return await tikJS(data, 'o200k_base')
|
||||
}
|
||||
if(db.aiModel.startsWith('gemini')){
|
||||
if(modelInfo.tokenizer === LLMTokenizer.GoogleCloud && db.googleClaudeTokenizing){
|
||||
return await tokenizeGoogleCloud(data)
|
||||
}
|
||||
if(modelInfo.tokenizer === LLMTokenizer.Gemma || modelInfo.tokenizer === LLMTokenizer.GoogleCloud){
|
||||
return await tokenizeWebTokenizers(data, 'gemma')
|
||||
}
|
||||
if(db.aiModel.startsWith('cohere')){
|
||||
if(modelInfo.tokenizer === LLMTokenizer.Cohere){
|
||||
return await tokenizeWebTokenizers(data, 'cohere')
|
||||
}
|
||||
|
||||
return await tikJS(data)
|
||||
}
|
||||
|
||||
type tokenizerType = 'novellist'|'claude'|'novelai'|'llama'|'mistral'|'llama3'|'gemma'|'cohere'
|
||||
type tokenizerType = 'novellist'|'claude'|'novelai'|'llama'|'mistral'|'llama3'|'gemma'|'cohere'|'googleCloud'
|
||||
|
||||
let tikParser:Tiktoken = null
|
||||
let tokenizersTokenizer:Tokenizer = null
|
||||
let tokenizersType:tokenizerType = null
|
||||
let lastTikModel = 'cl100k_base'
|
||||
|
||||
let googleCloudTokenizedCache = new Map<string, number>()
|
||||
|
||||
async function tokenizeGoogleCloud(text:string) {
|
||||
const db = getDatabase()
|
||||
const model = getModelInfo(db.aiModel)
|
||||
|
||||
if(googleCloudTokenizedCache.has(text + model.internalID)){
|
||||
const count = googleCloudTokenizedCache.get(text)
|
||||
return new Uint32Array(count)
|
||||
}
|
||||
|
||||
const res = await fetch(`https://generativelanguage.googleapis.com/v1beta/models/${model.internalID}:countTokens?key=${db.google?.accessToken}`, {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
"Content-Type": "application/json",
|
||||
},
|
||||
body: JSON.stringify({
|
||||
contents: [{
|
||||
parts:[{
|
||||
text: text
|
||||
}]
|
||||
}]
|
||||
}),
|
||||
})
|
||||
|
||||
if(res.status !== 200){
|
||||
return await tokenizeWebTokenizers(text, 'gemma')
|
||||
}
|
||||
|
||||
const json = await res.json()
|
||||
googleCloudTokenizedCache.set(text + model.internalID, json.totalTokens as number)
|
||||
const count = json.totalTokens as number
|
||||
|
||||
return new Uint32Array(count)
|
||||
}
|
||||
|
||||
async function tikJS(text:string, model='cl100k_base') {
|
||||
if(!tikParser || lastTikModel !== model){
|
||||
if(model === 'cl100k_base'){
|
||||
|
||||
@@ -1 +1 @@
|
||||
{"version":"141.2.0"}
|
||||
{"version":"143.0.1"}
|
||||
Reference in New Issue
Block a user