mirror of
https://github.com/enricoros/big-AGI.git
synced 2026-05-10 21:50:14 -07:00
Compare commits
1 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 Enrico Ros
|
f9d30114c5 |
@@ -11,30 +11,19 @@ Stay ahead of the curve with big-AGI. 🚀 Pros & Devs love big-AGI. 🤖
|
||||
|
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[](https://big-agi.com)
|
||||
|
||||
> 🚀 Big-AGI 2 is launching Q4 2024. Be the first to experience it before the public release.
|
||||
>
|
||||
> 👉 [Apply for Early Access](https://y2rjg0zillz.typeform.com/to/FDggwwZv?utm_source=github&utm_medium=readme&utm_campaign=ba20)
|
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|
||||
Or fork & run on Vercel
|
||||
|
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[](https://vercel.com/new/clone?repository-url=https%3A%2F%2Fgithub.com%2Fenricoros%2Fbig-AGI&env=OPENAI_API_KEY&envDescription=Backend%20API%20keys%2C%20optional%20and%20may%20be%20overridden%20by%20the%20UI.&envLink=https%3A%2F%2Fgithub.com%2Fenricoros%2Fbig-AGI%2Fblob%2Fmain%2Fdocs%2Fenvironment-variables.md&project-name=big-AGI)
|
||||
|
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### Quick links: 👉 [roadmap](https://github.com/users/enricoros/projects/4/views/2) 👉 [installation](docs/installation.md) 👉 [documentation](docs/README.md)
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## 👉 [roadmap](https://github.com/users/enricoros/projects/4/views/2) 👉 [installation](docs/installation.md) 👉 [documentation](docs/README.md)
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|
||||
### What's New in 1.16.1...1.16.8 · Sep 13, 2024 (patch releases)
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> Note: bigger better features (incl. Beam-2) are being cooked outside of `main`.
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- 1.16.8: OpenAI ChatGPT-4o Latest (o1-preview and o1-mini are supported in Big-AGI 2)
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||||
- 1.16.7: OpenAI support for GPT-4o 2024-08-06
|
||||
- 1.16.6: Groq support for Llama 3.1 models
|
||||
- 1.16.5: GPT-4o Mini support
|
||||
- 1.16.4: 8192 tokens support for Claude 3.5 Sonnet
|
||||
- 1.16.3: Anthropic Claude 3.5 Sonnet model support
|
||||
- 1.16.2: Improve web downloads, as text, markdwon, or HTML
|
||||
- 1.16.2: Proper support for Gemini models
|
||||
- 1.16.2: Added the latest Mistral model
|
||||
- 1.16.2: Tokenizer support for gpt-4o
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||||
- 1.16.2: Updates to Beam
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||||
- 1.16.1: Support for the new OpenAI GPT-4o 2024-05-13 model
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||||
[//]: # (big-AGI is an open book; see the **[ready-to-ship and future ideas](https://github.com/users/enricoros/projects/4/views/2)** in our open roadmap)
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||||
|
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### What's New in 1.16.1 · May 13, 2024 (minor release, models support)
|
||||
|
||||
- Support for the new OpenAI GPT-4o 2024-05-13 model
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### What's New in 1.16.0 · May 9, 2024 · Crystal Clear
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+2
-13
@@ -10,20 +10,9 @@ by release.
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- milestone: [1.17.0](https://github.com/enricoros/big-agi/milestone/17)
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- work in progress: [big-AGI open roadmap](https://github.com/users/enricoros/projects/4/views/2), [help here](https://github.com/users/enricoros/projects/4/views/4)
|
||||
|
||||
### What's New in 1.16.1...1.16.8 · Sep 13, 2024 (patch releases)
|
||||
### What's New in 1.16.1 · May 13, 2024 (minor release, models support)
|
||||
|
||||
- 1.16.8: OpenAI ChatGPT-4o Latest (o1-preview and o1-mini are supported in Big-AGI 2)
|
||||
- 1.16.7: OpenAI support for GPT-4o 2024-08-06
|
||||
- 1.16.6: Groq support for Llama 3.1 models
|
||||
- 1.16.5: GPT-4o Mini support
|
||||
- 1.16.4: 8192 tokens support for Claude 3.5 Sonnet
|
||||
- 1.16.3: Anthropic Claude 3.5 Sonnet model support
|
||||
- 1.16.2: Improve web downloads, as text, markdwon, or HTML
|
||||
- 1.16.2: Proper support for Gemini models
|
||||
- 1.16.2: Added the latest Mistral model
|
||||
- 1.16.2: Tokenizer support for gpt-4o
|
||||
- 1.16.2: Updates to Beam
|
||||
- 1.16.1: Support for the new OpenAI GPT-4o 2024-05-13 model
|
||||
- Support for the new OpenAI GPT-4o 2024-05-13 model
|
||||
|
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### What's New in 1.16.0 · May 9, 2024 · Crystal Clear
|
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|
||||
|
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@@ -0,0 +1,7 @@
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import { clerkMiddleware } from '@clerk/nextjs/server';
|
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|
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export default clerkMiddleware();
|
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|
||||
export const config = {
|
||||
matcher: ['/((?!.+.[w]+$|_next).*)', '/', '/(api|trpc)(.*)'],
|
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};
|
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Generated
+229
-182
@@ -9,6 +9,7 @@
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"version": "1.16.0",
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||||
"hasInstallScript": true,
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||||
"dependencies": {
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"@clerk/nextjs": "^5.0.8",
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"@emotion/cache": "^11.11.0",
|
||||
"@emotion/react": "^11.11.4",
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||||
"@emotion/server": "^11.11.0",
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||||
@@ -29,7 +30,6 @@
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||||
"@vercel/analytics": "^1.2.2",
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"@vercel/speed-insights": "^1.0.10",
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"browser-fs-access": "^0.35.0",
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"cheerio": "^1.0.0-rc.12",
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"eventsource-parser": "^1.1.2",
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"idb-keyval": "^6.2.1",
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"next": "~14.1.4",
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@@ -52,8 +52,7 @@
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"sharp": "^0.33.3",
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"superjson": "^2.2.1",
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"tesseract.js": "^5.1.0",
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"tiktoken": "^1.0.15",
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"turndown": "^7.2.0",
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"tiktoken": "^1.0.14",
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"uuid": "^9.0.1",
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"zod": "^3.23.8",
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"zustand": "^4.5.2"
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||||
@@ -70,7 +69,6 @@
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"@types/react-dom": "^18.3.0",
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"@types/react-katex": "^3.0.4",
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"@types/react-timeago": "^4.1.7",
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"@types/turndown": "^5.0.4",
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"@types/uuid": "^9.0.8",
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"eslint": "^8.57.0",
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"eslint-config-next": "^14.2.3",
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@@ -223,6 +221,116 @@
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"node": ">=6.9.0"
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}
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},
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"node_modules/@clerk/backend": {
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@@ -1027,11 +1135,6 @@
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"node-pre-gyp": "bin/node-pre-gyp"
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@@ -2090,12 +2193,6 @@
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@@ -2847,42 +2939,6 @@
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"url": "https://github.com/sponsors/wooorm"
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|
||||
"@mixmark-io/domino": "^2.2.0"
|
||||
}
|
||||
},
|
||||
"node_modules/type-check": {
|
||||
"version": "0.4.0",
|
||||
"resolved": "https://registry.npmjs.org/type-check/-/type-check-0.4.0.tgz",
|
||||
|
||||
+2
-4
@@ -18,6 +18,7 @@
|
||||
"schema": "src/server/prisma/schema.prisma"
|
||||
},
|
||||
"dependencies": {
|
||||
"@clerk/nextjs": "^5.0.8",
|
||||
"@emotion/cache": "^11.11.0",
|
||||
"@emotion/react": "^11.11.4",
|
||||
"@emotion/server": "^11.11.0",
|
||||
@@ -38,7 +39,6 @@
|
||||
"@vercel/analytics": "^1.2.2",
|
||||
"@vercel/speed-insights": "^1.0.10",
|
||||
"browser-fs-access": "^0.35.0",
|
||||
"cheerio": "^1.0.0-rc.12",
|
||||
"eventsource-parser": "^1.1.2",
|
||||
"idb-keyval": "^6.2.1",
|
||||
"next": "~14.1.4",
|
||||
@@ -61,8 +61,7 @@
|
||||
"sharp": "^0.33.3",
|
||||
"superjson": "^2.2.1",
|
||||
"tesseract.js": "^5.1.0",
|
||||
"tiktoken": "^1.0.15",
|
||||
"turndown": "^7.2.0",
|
||||
"tiktoken": "^1.0.14",
|
||||
"uuid": "^9.0.1",
|
||||
"zod": "^3.23.8",
|
||||
"zustand": "^4.5.2"
|
||||
@@ -79,7 +78,6 @@
|
||||
"@types/react-dom": "^18.3.0",
|
||||
"@types/react-katex": "^3.0.4",
|
||||
"@types/react-timeago": "^4.1.7",
|
||||
"@types/turndown": "^5.0.4",
|
||||
"@types/uuid": "^9.0.8",
|
||||
"eslint": "^8.57.0",
|
||||
"eslint-config-next": "^14.2.3",
|
||||
|
||||
+15
-12
@@ -13,6 +13,7 @@ import '~/common/styles/GithubMarkdown.css';
|
||||
import '~/common/styles/NProgress.css';
|
||||
import '~/common/styles/app.styles.css';
|
||||
|
||||
import { ProviderAuth } from '~/common/providers/ProviderAuth';
|
||||
import { ProviderBackendCapabilities } from '~/common/providers/ProviderBackendCapabilities';
|
||||
import { ProviderBootstrapLogic } from '~/common/providers/ProviderBootstrapLogic';
|
||||
import { ProviderSingleTab } from '~/common/providers/ProviderSingleTab';
|
||||
@@ -32,18 +33,20 @@ const MyApp = ({ Component, emotionCache, pageProps }: MyAppProps) =>
|
||||
</Head>
|
||||
|
||||
<ProviderTheming emotionCache={emotionCache}>
|
||||
<ProviderSingleTab>
|
||||
<ProviderTRPCQuerySettings>
|
||||
<ProviderBackendCapabilities>
|
||||
{/* ^ SSR boundary */}
|
||||
<ProviderBootstrapLogic>
|
||||
<ProviderSnacks>
|
||||
<Component {...pageProps} />
|
||||
</ProviderSnacks>
|
||||
</ProviderBootstrapLogic>
|
||||
</ProviderBackendCapabilities>
|
||||
</ProviderTRPCQuerySettings>
|
||||
</ProviderSingleTab>
|
||||
<ProviderAuth>
|
||||
<ProviderSingleTab>
|
||||
<ProviderTRPCQuerySettings>
|
||||
<ProviderBackendCapabilities>
|
||||
{/* ^ SSR boundary */}
|
||||
<ProviderBootstrapLogic>
|
||||
<ProviderSnacks>
|
||||
<Component {...pageProps} />
|
||||
</ProviderSnacks>
|
||||
</ProviderBootstrapLogic>
|
||||
</ProviderBackendCapabilities>
|
||||
</ProviderTRPCQuerySettings>
|
||||
</ProviderSingleTab>
|
||||
</ProviderAuth>
|
||||
</ProviderTheming>
|
||||
|
||||
{isVercelFromFrontend && <VercelAnalytics debug={false} />}
|
||||
|
||||
@@ -77,12 +77,9 @@ function AppShareTarget() {
|
||||
setIsDownloading(true);
|
||||
callBrowseFetchPage(intentURL)
|
||||
.then(page => {
|
||||
if (page.stopReason !== 'error') {
|
||||
let pageContent = page.content.markdown || page.content.text || page.content.html || '';
|
||||
if (pageContent)
|
||||
pageContent = '\n\n```' + intentURL + '\n' + pageContent + '\n```\n';
|
||||
queueComposerTextAndLaunchApp(pageContent);
|
||||
} else
|
||||
if (page.stopReason !== 'error')
|
||||
queueComposerTextAndLaunchApp('\n\n```' + intentURL + '\n' + page.content + '\n```\n');
|
||||
else
|
||||
setErrorMessage('Could not read any data' + page.error ? ': ' + page.error : '');
|
||||
})
|
||||
.catch(error => setErrorMessage(error?.message || error || 'Unknown error'))
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import * as React from 'react';
|
||||
import { useShallow } from 'zustand/react/shallow';
|
||||
import { shallow } from 'zustand/shallow';
|
||||
|
||||
import { Box, Card, ListDivider, ListItemDecorator, MenuItem, Switch, Typography } from '@mui/joy';
|
||||
import ArrowBackIcon from '@mui/icons-material/ArrowBack';
|
||||
@@ -99,7 +99,7 @@ export function Telephone(props: {
|
||||
|
||||
// external state
|
||||
const { chatLLMId, chatLLMDropdown } = useChatLLMDropdown();
|
||||
const { chatTitle, reMessages } = useChatStore(useShallow(state => {
|
||||
const { chatTitle, reMessages } = useChatStore(state => {
|
||||
const conversation = props.callIntent.conversationId
|
||||
? state.conversations.find(conversation => conversation.id === props.callIntent.conversationId) ?? null
|
||||
: null;
|
||||
@@ -107,7 +107,7 @@ export function Telephone(props: {
|
||||
chatTitle: conversation ? conversationTitle(conversation) : null,
|
||||
reMessages: conversation ? conversation.messages : null,
|
||||
};
|
||||
}));
|
||||
}, shallow);
|
||||
const persona = SystemPurposes[props.callIntent.personaId as SystemPurposeId] ?? undefined;
|
||||
const personaCallStarters = persona?.call?.starters ?? undefined;
|
||||
const personaVoiceId = overridePersonaVoice ? undefined : (persona?.voices?.elevenLabs?.voiceId ?? undefined);
|
||||
@@ -225,7 +225,7 @@ export function Telephone(props: {
|
||||
let finalText = '';
|
||||
let error: any | null = null;
|
||||
setPersonaTextInterim('💭...');
|
||||
llmStreamingChatGenerate(chatLLMId, callPrompt, 'call', callMessages[0].id, null, null, responseAbortController.current.signal, ({ textSoFar }) => {
|
||||
llmStreamingChatGenerate(chatLLMId, callPrompt, null, null, responseAbortController.current.signal, ({ textSoFar }) => {
|
||||
const text = textSoFar?.trim();
|
||||
if (text) {
|
||||
finalText = text;
|
||||
|
||||
@@ -277,7 +277,7 @@ export function AppChat() {
|
||||
const conversation = getConversation(conversationId);
|
||||
if (!conversation)
|
||||
return;
|
||||
const imaginedPrompt = await imaginePromptFromText(messageText, conversationId) || 'An error sign.';
|
||||
const imaginedPrompt = await imaginePromptFromText(messageText) || 'An error sign.';
|
||||
await handleExecuteAndOutcome('generate-image', conversationId, [
|
||||
...conversation.messages,
|
||||
createDMessage('user', imaginedPrompt),
|
||||
|
||||
@@ -310,7 +310,7 @@ function ChatDrawer(props: {
|
||||
bottomBarBasis={filteredChatsBarBasis}
|
||||
onConversationActivate={handleConversationActivate}
|
||||
onConversationBranch={onConversationBranch}
|
||||
onConversationDeleteNoConfirmation={handleConversationDeleteNoConfirmation}
|
||||
onConversationDelete={handleConversationDeleteNoConfirmation}
|
||||
onConversationExport={onConversationsExportDialog}
|
||||
onConversationFolderChange={handleConversationFolderChange}
|
||||
/>
|
||||
|
||||
@@ -42,7 +42,7 @@ export const ChatDrawerItemMemo = React.memo(ChatDrawerItem, (prev, next) =>
|
||||
prev.bottomBarBasis === next.bottomBarBasis &&
|
||||
prev.onConversationActivate === next.onConversationActivate &&
|
||||
prev.onConversationBranch === next.onConversationBranch &&
|
||||
prev.onConversationDeleteNoConfirmation === next.onConversationDeleteNoConfirmation &&
|
||||
prev.onConversationDelete === next.onConversationDelete &&
|
||||
prev.onConversationExport === next.onConversationExport &&
|
||||
prev.onConversationFolderChange === next.onConversationFolderChange,
|
||||
);
|
||||
@@ -76,7 +76,7 @@ function ChatDrawerItem(props: {
|
||||
bottomBarBasis: number,
|
||||
onConversationActivate: (conversationId: DConversationId, closeMenu: boolean) => void,
|
||||
onConversationBranch: (conversationId: DConversationId, messageId: string | null) => void,
|
||||
onConversationDeleteNoConfirmation: (conversationId: DConversationId) => void,
|
||||
onConversationDelete: (conversationId: DConversationId) => void,
|
||||
onConversationExport: (conversationId: DConversationId, exportAll: boolean) => void,
|
||||
onConversationFolderChange: (folderChangeRequest: FolderChangeRequest) => void,
|
||||
}) {
|
||||
@@ -155,16 +155,7 @@ function ChatDrawerItem(props: {
|
||||
|
||||
// Delete
|
||||
|
||||
const { onConversationDeleteNoConfirmation } = props;
|
||||
const handleDeleteButtonShow = React.useCallback((event: React.MouseEvent) => {
|
||||
// special case: if 'Shift' is pressed, delete immediately
|
||||
if (event.shiftKey) {
|
||||
event.stopPropagation();
|
||||
onConversationDeleteNoConfirmation(conversationId);
|
||||
return;
|
||||
}
|
||||
setDeleteArmed(true);
|
||||
}, [conversationId, onConversationDeleteNoConfirmation]);
|
||||
const handleDeleteButtonShow = React.useCallback(() => setDeleteArmed(true), []);
|
||||
|
||||
const handleDeleteButtonHide = React.useCallback(() => setDeleteArmed(false), []);
|
||||
|
||||
@@ -172,9 +163,9 @@ function ChatDrawerItem(props: {
|
||||
if (deleteArmed) {
|
||||
setDeleteArmed(false);
|
||||
event.stopPropagation();
|
||||
onConversationDeleteNoConfirmation(conversationId);
|
||||
props.onConversationDelete(conversationId);
|
||||
}
|
||||
}, [conversationId, deleteArmed, onConversationDeleteNoConfirmation]);
|
||||
}, [conversationId, deleteArmed, props]);
|
||||
|
||||
|
||||
const textSymbol = SystemPurposes[systemPurposeId]?.symbol || '❓';
|
||||
|
||||
@@ -58,12 +58,16 @@ export async function attachmentLoadInputAsync(source: Readonly<AttachmentSource
|
||||
edit({ label: source.refUrl, ref: source.refUrl });
|
||||
try {
|
||||
const page = await callBrowseFetchPage(source.url);
|
||||
edit(
|
||||
page.content.markdown ? { input: { mimeType: 'text/markdown', data: page.content.markdown, dataSize: page.content.markdown.length } }
|
||||
: page.content.text ? { input: { mimeType: 'text/plain', data: page.content.text, dataSize: page.content.text.length } }
|
||||
: page.content.html ? { input: { mimeType: 'text/html', data: page.content.html, dataSize: page.content.html.length } }
|
||||
: { inputError: 'No content found at this link' },
|
||||
);
|
||||
if (page.content) {
|
||||
edit({
|
||||
input: {
|
||||
mimeType: 'text/plain',
|
||||
data: page.content,
|
||||
dataSize: page.content.length,
|
||||
},
|
||||
});
|
||||
} else
|
||||
edit({ inputError: 'No content found at this link' });
|
||||
} catch (error: any) {
|
||||
edit({ inputError: `Issue downloading page: ${error?.message || (typeof error === 'string' ? error : JSON.stringify(error))}` });
|
||||
}
|
||||
|
||||
@@ -280,7 +280,6 @@ export function ChatMessage(props: {
|
||||
const wasEdited = !!messageUpdated;
|
||||
|
||||
const textSel = selText ? selText : messageText;
|
||||
// WARNING: if you get an issue here, you're downgrading from the new Big-AGI 2 data format to 1.x.
|
||||
const isSpecialT2I = textSel.startsWith('https://images.prodia.xyz/') || textSel.startsWith('/draw ') || textSel.startsWith('/imagine ') || textSel.startsWith('/img ');
|
||||
const couldDiagram = textSel.length >= 100 && !isSpecialT2I;
|
||||
const couldImagine = textSel.length >= 3 && !isSpecialT2I;
|
||||
|
||||
@@ -15,8 +15,7 @@ export const runBrowseGetPageUpdatingState = async (cHandler: ConversationHandle
|
||||
|
||||
try {
|
||||
const page = await callBrowseFetchPage(url);
|
||||
const pageContent = page.content.markdown || page.content.text || page.content.html || 'Issue: page load did not produce an answer: no text found';
|
||||
cHandler.messageEdit(assistantMessageId, { text: pageContent, typing: false }, true);
|
||||
cHandler.messageEdit(assistantMessageId, { text: page.content || 'Issue: page load did not produce an answer: no text found', typing: false }, true);
|
||||
return true;
|
||||
} catch (error: any) {
|
||||
console.error(error);
|
||||
|
||||
@@ -2,7 +2,7 @@ import type { DLLMId } from '~/modules/llms/store-llms';
|
||||
import type { StreamingClientUpdate } from '~/modules/llms/vendors/unifiedStreamingClient';
|
||||
import { autoSuggestions } from '~/modules/aifn/autosuggestions/autoSuggestions';
|
||||
import { conversationAutoTitle } from '~/modules/aifn/autotitle/autoTitle';
|
||||
import { llmStreamingChatGenerate, VChatContextRef, VChatMessageIn, VChatStreamContextName } from '~/modules/llms/llm.client';
|
||||
import { llmStreamingChatGenerate, VChatMessageIn } from '~/modules/llms/llm.client';
|
||||
import { speakText } from '~/modules/elevenlabs/elevenlabs.client';
|
||||
|
||||
import type { DMessage } from '~/common/state/store-chats';
|
||||
@@ -34,8 +34,6 @@ export async function runAssistantUpdatingState(conversationId: string, history:
|
||||
const messageStatus = await streamAssistantMessage(
|
||||
assistantLlmId,
|
||||
history.map((m): VChatMessageIn => ({ role: m.role, content: m.text })),
|
||||
'conversation',
|
||||
conversationId,
|
||||
parallelViewCount,
|
||||
autoSpeak,
|
||||
(update) => cHandler.messageEdit(assistantMessageId, update, false),
|
||||
@@ -63,8 +61,6 @@ type StreamMessageStatus = { outcome: StreamMessageOutcome, errorMessage?: strin
|
||||
export async function streamAssistantMessage(
|
||||
llmId: DLLMId,
|
||||
messagesHistory: VChatMessageIn[],
|
||||
contextName: VChatStreamContextName,
|
||||
contextRef: VChatContextRef,
|
||||
throttleUnits: number, // 0: disable, 1: default throttle (12Hz), 2+ reduce the message frequency with the square root
|
||||
autoSpeak: ChatAutoSpeakType,
|
||||
editMessage: (update: Partial<DMessage>) => void,
|
||||
@@ -96,7 +92,7 @@ export async function streamAssistantMessage(
|
||||
const incrementalAnswer: Partial<DMessage> = { text: '' };
|
||||
|
||||
try {
|
||||
await llmStreamingChatGenerate(llmId, messagesHistory, contextName, contextRef, null, null, abortSignal, (update: StreamingClientUpdate) => {
|
||||
await llmStreamingChatGenerate(llmId, messagesHistory, null, null, abortSignal, (update: StreamingClientUpdate) => {
|
||||
const textSoFar = update.textSoFar;
|
||||
|
||||
// grow the incremental message
|
||||
|
||||
@@ -14,7 +14,6 @@ import { capitalizeFirstLetter } from '~/common/util/textUtils';
|
||||
|
||||
import { NewsItems } from './news.data';
|
||||
import { beamNewsCallout } from './beam.data';
|
||||
import { bigAgi2NewsCallout } from './bigAgi2.data';
|
||||
|
||||
|
||||
// number of news items to show by default, before the expander
|
||||
@@ -111,13 +110,6 @@ export function AppNews() {
|
||||
const addPadding = false; //!firstCard; // || showExpander;
|
||||
return <React.Fragment key={idx}>
|
||||
|
||||
{/* Inject the Big-AGI 2.0 item here*/}
|
||||
{idx === 0 && (
|
||||
<Box sx={{ mb: 3 }}>
|
||||
{bigAgi2NewsCallout}
|
||||
</Box>
|
||||
)}
|
||||
|
||||
{/* Inject the Beam item here*/}
|
||||
{idx === 2 && (
|
||||
<Box sx={{ mb: 3 }}>
|
||||
|
||||
@@ -2,6 +2,7 @@ import * as React from 'react';
|
||||
|
||||
import { Button, Card, CardContent, Grid, Typography } from '@mui/joy';
|
||||
import LaunchIcon from '@mui/icons-material/Launch';
|
||||
import ThumbUpRoundedIcon from '@mui/icons-material/ThumbUpRounded';
|
||||
|
||||
import { Link } from '~/common/components/Link';
|
||||
|
||||
|
||||
@@ -1,40 +0,0 @@
|
||||
import * as React from 'react';
|
||||
|
||||
import { Button, Card, CardContent, Grid, Typography } from '@mui/joy';
|
||||
import AccessTimeIcon from '@mui/icons-material/AccessTime';
|
||||
import LaunchIcon from '@mui/icons-material/Launch';
|
||||
|
||||
import { Link } from '~/common/components/Link';
|
||||
|
||||
|
||||
const bigAgi2SurveyUrl = 'https://y2rjg0zillz.typeform.com/to/FDggwwZv?utm_source=github&utm_medium=callout&utm_campaign=ba20';
|
||||
|
||||
export const bigAgi2NewsCallout =
|
||||
<Card variant='solid' invertedColors>
|
||||
<CardContent sx={{ gap: 2 }}>
|
||||
<Typography level='title-lg'>
|
||||
Big-AGI 2.0 - In Development
|
||||
</Typography>
|
||||
<Typography level='body-sm'>
|
||||
We're building the next version of Big-AGI with your needs in mind. New features, better performance, enhanced AI interactions. Help us shape it.
|
||||
</Typography>
|
||||
<Grid container spacing={1}>
|
||||
<Grid xs={12} sm={7}>
|
||||
<Button
|
||||
fullWidth variant='soft' color='primary' endDecorator={<LaunchIcon />}
|
||||
component={Link} href={bigAgi2SurveyUrl} noLinkStyle target='_blank'
|
||||
>
|
||||
Apply for Early Access
|
||||
</Button>
|
||||
</Grid>
|
||||
<Grid xs={12} sm={5} sx={{ display: 'flex', flexAlign: 'center', justifyContent: 'center' }}>
|
||||
<Button
|
||||
fullWidth variant='outlined' color='primary' startDecorator={<AccessTimeIcon />}
|
||||
disabled
|
||||
>
|
||||
Coming Fall 2024
|
||||
</Button>
|
||||
</Grid>
|
||||
</Grid>
|
||||
</CardContent>
|
||||
</Card>;
|
||||
@@ -52,19 +52,17 @@ interface NewsItem {
|
||||
// news and feature surfaces
|
||||
export const NewsItems: NewsItem[] = [
|
||||
/*{
|
||||
versionCode: '1.17.0',
|
||||
versionCode: '1.16.0',
|
||||
items: [
|
||||
Screen Capture (when removed from labs)
|
||||
Auto-Merge
|
||||
Draw
|
||||
...
|
||||
Screen Capture (when removed from labs)
|
||||
]
|
||||
}*/
|
||||
{
|
||||
versionCode: '1.16.8',
|
||||
versionCode: '1.16.1',
|
||||
versionName: 'Crystal Clear',
|
||||
versionDate: new Date('2024-06-07T05:00:00Z'),
|
||||
// versionDate: new Date('2024-05-13T19:00:00Z'),
|
||||
versionDate: new Date('2024-05-13T19:00:00Z'),
|
||||
// versionDate: new Date('2024-05-09T00:00:00Z'),
|
||||
versionCoverImage: coverV116,
|
||||
items: [
|
||||
@@ -77,15 +75,7 @@ export const NewsItems: NewsItem[] = [
|
||||
{ text: <>More: <B issue={517}>code soft-wrap</B>, selection toolbar, <B issue={507}>3x faster</B> on Apple silicon</>, issue: 507 },
|
||||
{ text: <>Updated <B>Anthropic</B>*, <B>Groq</B>, <B>Ollama</B>, <B>OpenAI</B>*, <B>OpenRouter</B>*, and <B>Perplexity</B></> },
|
||||
{ text: <>Developers: update LLMs data structures</>, dev: true },
|
||||
{ text: <>1.16.1: Support for <B>OpenAI</B> <B href='https://openai.com/index/hello-gpt-4o/'>GPT-4o</B></> },
|
||||
{ text: <>1.16.2: Proper <B>Gemini</B> support, <B>HTML/Markdown</B> downloads, and latest <B>Mistral</B></> },
|
||||
{ text: <>1.16.3: Support for <B href='https://www.anthropic.com/news/claude-3-5-sonnet'>Claude 3.5 Sonnet</B> (refresh your <B>Anthropic</B> models)</> },
|
||||
{ text: <>1.16.4: <B>8192 tokens</B> support for Claude 3.5 Sonnet</> },
|
||||
{ text: <>1.16.5: OpenAI <B>GPT-4o Mini</B> support</> },
|
||||
{ text: <>1.16.6: Groq <B>Llama 3.1</B> support</> },
|
||||
{ text: <>1.16.7: Gpt-4o <B>2024-08-06</B></> },
|
||||
{ text: <>1.16.8: <B>ChatGPT-4o</B> latest</> },
|
||||
{ text: <>OpenAI <B>o1</B> and newer models require Big-AGI 2. <B href='https://y2rjg0zillz.typeform.com/to/FDggwwZv?utm_source=github&utm_medium=news116&utm_campaign=ba20'>Sign up here</B></> },
|
||||
{ text: <>1.16.1: Support for <B>OpenAI</B> <B href='https://openai.com/index/hello-gpt-4o/'>GPT-4o</B> (refresh your OpenAI models)</> },
|
||||
],
|
||||
},
|
||||
{
|
||||
|
||||
@@ -7,7 +7,7 @@ import { useAppStateStore } from '~/common/state/store-appstate';
|
||||
|
||||
|
||||
// update this variable every time you want to broadcast a new version to clients
|
||||
export const incrementalNewsVersion: number = 16.1; // not notifying for 1.16.8
|
||||
export const incrementalNewsVersion: number = 16.1;
|
||||
|
||||
|
||||
interface NewsState {
|
||||
|
||||
@@ -1,5 +1,4 @@
|
||||
import * as React from 'react';
|
||||
import { v4 as uuidv4 } from 'uuid';
|
||||
|
||||
import { Alert, Box, Button, Card, CardContent, CircularProgress, Divider, FormLabel, Grid, IconButton, LinearProgress, Tab, tabClasses, TabList, TabPanel, Tabs, Typography } from '@mui/joy';
|
||||
import AddIcon from '@mui/icons-material/Add';
|
||||
@@ -103,11 +102,8 @@ export function Creator(props: { display: boolean }) {
|
||||
strings: editedInstructions, stringEditors: instructionEditors,
|
||||
} = useFormEditTextArray(Prompts, PromptTitles);
|
||||
|
||||
const { steps: creationChainSteps, id: chainId } = React.useMemo(() => {
|
||||
return {
|
||||
steps: createChain(editedInstructions, PromptTitles),
|
||||
id: uuidv4(),
|
||||
};
|
||||
const creationChainSteps = React.useMemo(() => {
|
||||
return createChain(editedInstructions, PromptTitles);
|
||||
}, [editedInstructions]);
|
||||
|
||||
const llmLabel = personaLlm?.label || undefined;
|
||||
@@ -126,7 +122,7 @@ export function Creator(props: { display: boolean }) {
|
||||
chainError,
|
||||
userCancelChain,
|
||||
restartChain,
|
||||
} = useLLMChain(creationChainSteps, personaLlm?.id, chainInputText ?? undefined, savePersona, 'persona-extract', chainId);
|
||||
} = useLLMChain(creationChainSteps, personaLlm?.id, chainInputText ?? undefined, savePersona);
|
||||
|
||||
|
||||
// Reset the relevant state when the selected tab changes
|
||||
|
||||
@@ -200,7 +200,7 @@ export function SettingsModal(props: {
|
||||
|
||||
<TabPanel value={PreferencesTab.Tools} variant='outlined' sx={{ p: 'var(--Tabs-gap)', borderRadius: 'md' }}>
|
||||
<Topics>
|
||||
<Topic icon={<SearchIcon />} title='Browsing'>
|
||||
<Topic icon={<SearchIcon />} title='Browsing' startCollapsed>
|
||||
<BrowseSettings />
|
||||
</Topic>
|
||||
<Topic icon={<SearchIcon />} title='Google Search API' startCollapsed>
|
||||
|
||||
@@ -1,15 +0,0 @@
|
||||
import * as React from 'react';
|
||||
|
||||
import { Typography } from '@mui/joy';
|
||||
|
||||
import CheckRoundedIcon from '@mui/icons-material/CheckRounded';
|
||||
|
||||
|
||||
export function AlreadySet(props: { required?: boolean }) {
|
||||
return (
|
||||
<Typography level='body-sm' startDecorator={props.required ? undefined : <CheckRoundedIcon color='success' />}>
|
||||
{/*Installed Already*/}
|
||||
{props.required ? 'required' : 'Already set on server'}
|
||||
</Typography>
|
||||
);
|
||||
}
|
||||
@@ -9,6 +9,7 @@ import MoreHorizIcon from '@mui/icons-material/MoreHoriz';
|
||||
import { useModelsStore } from '~/modules/llms/store-llms';
|
||||
|
||||
import { AgiSquircleIcon } from '~/common/components/icons/AgiSquircleIcon';
|
||||
import { authUserButton } from '~/common/providers/ProviderAuth';
|
||||
import { checkDivider, checkVisibileIcon, NavItemApp, navItems } from '~/common/app.nav';
|
||||
import { themeZIndexDesktopNav } from '~/common/app.theme';
|
||||
|
||||
@@ -184,6 +185,7 @@ export function DesktopNav(props: { component: React.ElementType, currentApp?: N
|
||||
<DesktopNavGroupBox sx={{ mb: 'calc(2 * var(--GroupMarginY))' }}>
|
||||
{navExtLinkItems}
|
||||
{navModalItems}
|
||||
{authUserButton}
|
||||
</DesktopNavGroupBox>
|
||||
|
||||
</InvertedBar>
|
||||
|
||||
@@ -0,0 +1,36 @@
|
||||
import * as React from 'react';
|
||||
import dynamic from 'next/dynamic';
|
||||
import { RedirectToSignIn, SignedIn, SignedOut, UserButton } from '@clerk/nextjs';
|
||||
|
||||
|
||||
const enableClerk = !!process.env.NEXT_PUBLIC_CLERK_PUBLISHABLE_KEY;
|
||||
|
||||
// Dynamically import ClerkProvider only if ENABLE_CLERK is true
|
||||
const ClerkProviderDynamic = !enableClerk ? null : dynamic(() =>
|
||||
import('@clerk/nextjs')
|
||||
.then(({ ClerkProvider }) => ClerkProvider)
|
||||
.catch(err => {
|
||||
console.error('Failed to load ClerkProvider:', err);
|
||||
const AuthLoadIssue = () => <>Issue Loading Auth</>;
|
||||
AuthLoadIssue.displayName = 'AuthLoadIssue';
|
||||
return AuthLoadIssue;
|
||||
}),
|
||||
);
|
||||
|
||||
export const ProviderAuth = (props: { children: React.ReactNode }) => (enableClerk && ClerkProviderDynamic)
|
||||
? (
|
||||
<ClerkProviderDynamic>
|
||||
<SignedIn>
|
||||
{props.children}
|
||||
</SignedIn>
|
||||
<SignedOut>
|
||||
<RedirectToSignIn />
|
||||
</SignedOut>
|
||||
</ClerkProviderDynamic>
|
||||
) : props.children;
|
||||
|
||||
export const authUserButton = (enableClerk && ClerkProviderDynamic)
|
||||
? <>
|
||||
<UserButton />
|
||||
</>
|
||||
: null;
|
||||
@@ -5,7 +5,7 @@ import { v4 as uuidv4 } from 'uuid';
|
||||
|
||||
import { DLLMId, getChatLLMId } from '~/modules/llms/store-llms';
|
||||
|
||||
import { idbStateStorage } from '../util/idbUtils';
|
||||
import { IDB_MIGRATION_INITIAL, idbStateStorage } from '../util/idbUtils';
|
||||
import { countModelTokens } from '../util/token-counter';
|
||||
import { defaultSystemPurposeId, SystemPurposeId } from '../../data';
|
||||
|
||||
@@ -407,7 +407,10 @@ export const useChatStore = create<ConversationsStore>()(devtools(
|
||||
storage: createJSONStorage(() => idbStateStorage),
|
||||
|
||||
// Migrations
|
||||
migrate: (persistedState: unknown, _fromVersion: number): ConversationsStore => {
|
||||
migrate: (persistedState: unknown, fromVersion: number): ConversationsStore => {
|
||||
// -1 -> 3: migration loading from localStorage to IndexedDB
|
||||
if (fromVersion === IDB_MIGRATION_INITIAL)
|
||||
return _migrateLocalStorageData() as any;
|
||||
|
||||
// other: just proceed
|
||||
return persistedState as any;
|
||||
@@ -462,6 +465,32 @@ function getNextBranchTitle(currentTitle: string): string {
|
||||
return `(1) ${currentTitle}`;
|
||||
}
|
||||
|
||||
/**
|
||||
* Returns the chats stored in the localStorage, and rename the key for
|
||||
* backup/data loss prevention purposes
|
||||
*/
|
||||
function _migrateLocalStorageData(): ChatState | {} {
|
||||
const key = 'app-chats';
|
||||
const value = localStorage.getItem(key);
|
||||
if (!value) return {};
|
||||
try {
|
||||
// parse the localStorage state
|
||||
const localStorageState = JSON.parse(value)?.state;
|
||||
|
||||
// backup and delete the localStorage key
|
||||
const backupKey = `${key}-v2`;
|
||||
localStorage.setItem(backupKey, value);
|
||||
localStorage.removeItem(key);
|
||||
|
||||
// match the state from localstorage
|
||||
return {
|
||||
conversations: localStorageState?.conversations ?? [],
|
||||
};
|
||||
} catch (error) {
|
||||
console.error('LocalStorage migration error', error);
|
||||
return {};
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Convenience function to count the tokens in a DMessage object
|
||||
|
||||
@@ -1,6 +1,10 @@
|
||||
import type { StateStorage } from 'zustand/middleware';
|
||||
import { del as idbDel, get as idbGet, set as idbSet } from 'idb-keyval';
|
||||
|
||||
// used by the state storage middleware to detect data migration from the old state storage (localStorage)
|
||||
// NOTE: remove past 2024-03-19 (6 months past release of this utility conversion)
|
||||
export const IDB_MIGRATION_INITIAL = -1;
|
||||
|
||||
|
||||
// set to true to enable debugging
|
||||
const DEBUG_SCHEDULER = false;
|
||||
@@ -126,6 +130,17 @@ export const idbStateStorage: StateStorage = {
|
||||
if (DEBUG_SCHEDULER)
|
||||
console.warn(' (read bytes:', value?.length?.toLocaleString(), ')');
|
||||
|
||||
/* IMPORTANT!
|
||||
* We modify the default behavior of `getItem` to return a {version: -1} object if a key is not found.
|
||||
* This is to trigger the migration across state storage implementations, as Zustand would not call the
|
||||
* 'migrate' function otherwise.
|
||||
* See 'https://github.com/enricoros/big-agi/pull/158' for more details
|
||||
*/
|
||||
if (value === undefined) {
|
||||
return JSON.stringify({
|
||||
version: IDB_MIGRATION_INITIAL,
|
||||
});
|
||||
}
|
||||
return value || null;
|
||||
},
|
||||
setItem: (name: string, value: string): void => {
|
||||
|
||||
@@ -8,11 +8,8 @@ export function prettyBaseModel(model: string | undefined): string {
|
||||
if (!model) return '';
|
||||
if (model.includes('gpt-4-vision-preview')) return 'GPT-4 Vision';
|
||||
if (model.includes('gpt-4-1106-preview')) return 'GPT-4 Turbo';
|
||||
if (model.includes('gpt-4-32k')) return 'GPT-4-32k';
|
||||
if (model.includes('gpt-4o-mini')) return 'GPT-4o Mini';
|
||||
if (model.includes('gpt-4o')) return 'GPT-4o';
|
||||
if (model.includes('gpt-4-turbo')) return 'GPT-4 Turbo';
|
||||
if (model.includes('gpt-4')) return 'GPT-4';
|
||||
if (model.includes('gpt-4-32k')) return 'gpt-4-32k';
|
||||
if (model.includes('gpt-4')) return 'gpt-4';
|
||||
if (model.includes('gpt-3.5-turbo-instruct')) return '3.5 Turbo Instruct';
|
||||
if (model.includes('gpt-3.5-turbo-1106')) return '3.5 Turbo 16k';
|
||||
if (model.includes('gpt-3.5-turbo-16k')) return '3.5 Turbo 16k';
|
||||
|
||||
@@ -6,20 +6,14 @@ import { DLLMId, findLLMOrThrow } from '~/modules/llms/store-llms';
|
||||
// Do not set this to true in production, it's very verbose
|
||||
const DEBUG_TOKEN_COUNT = false;
|
||||
|
||||
// Globals
|
||||
// const tokenEncodings: string[] = ['gpt2', 'r50k_base', 'p50k_base', 'p50k_edit', 'cl100k_base', 'o200k_base'] satisfies TiktokenEncoding[];
|
||||
|
||||
// Global symbols to dynamically load the Tiktoken library
|
||||
// global symbols to dynamically load the Tiktoken library
|
||||
let get_encoding: ((encoding: TiktokenEncoding) => Tiktoken) | null = null;
|
||||
let encoding_for_model: ((model: TiktokenModel) => Tiktoken) | null = null;
|
||||
let preloadPromise: Promise<void> | null = null;
|
||||
let informTheUser = false;
|
||||
|
||||
/**
|
||||
* Preloads the Tiktoken library if not already loaded.
|
||||
* @returns {Promise<void>} A promise that resolves when the library is loaded.
|
||||
*/
|
||||
export function preloadTiktokenLibrary(): Promise<void> {
|
||||
export function preloadTiktokenLibrary() {
|
||||
if (!preloadPromise) {
|
||||
preloadPromise = import('tiktoken')
|
||||
.then(tiktoken => {
|
||||
@@ -39,21 +33,16 @@ export function preloadTiktokenLibrary(): Promise<void> {
|
||||
|
||||
|
||||
/**
|
||||
* Wrapper around the Tiktoken library to keep tokenizers for all models in a cache.
|
||||
* Also, preloads the tokenizer for the default model to avoid initial stall.
|
||||
* Wrapper around the Tiktoken library, to keep tokenizers for all models in a cache
|
||||
*
|
||||
* We also preload the tokenizer for the default model, so that the first time a user types
|
||||
* a message, it doesn't stall loading the tokenizer.
|
||||
*/
|
||||
export const countModelTokens: (text: string, llmId: DLLMId, debugFrom: string) => number | null = (() => {
|
||||
// return () => 0;
|
||||
const tokenEncoders: { [modelId: string]: Tiktoken } = {};
|
||||
let encodingDefault: Tiktoken | null = null;
|
||||
let encodingCL100K: Tiktoken | null = null;
|
||||
|
||||
/**
|
||||
* Counts the tokens in the given text for the specified model.
|
||||
* @param {string} text - The text to tokenize.
|
||||
* @param {DLLMId} llmId - The ID of the LLM.
|
||||
* @param {string} debugFrom - Debug information.
|
||||
* @returns {number | null} The token count or null if not ready.
|
||||
*/
|
||||
function _tokenCount(text: string, llmId: DLLMId, debugFrom: string): number | null {
|
||||
|
||||
// The library shall have been preloaded - if not, attempt to start its loading and return null to indicate we're not ready to count
|
||||
@@ -66,23 +55,21 @@ export const countModelTokens: (text: string, llmId: DLLMId, debugFrom: string)
|
||||
return null;
|
||||
}
|
||||
|
||||
const openaiModel = findLLMOrThrow(llmId)?.options?.llmRef;
|
||||
const { options: { llmRef: openaiModel } } = findLLMOrThrow(llmId);
|
||||
if (!openaiModel) throw new Error(`LLM ${llmId} has no LLM reference id`);
|
||||
|
||||
if (!(openaiModel in tokenEncoders)) {
|
||||
try {
|
||||
tokenEncoders[openaiModel] = encoding_for_model(openaiModel as TiktokenModel);
|
||||
} catch (e) {
|
||||
// fallback to the default encoding across all models (not just OpenAI - this will be used everywhere..)
|
||||
if (!encodingDefault)
|
||||
encodingDefault = get_encoding('cl100k_base');
|
||||
tokenEncoders[openaiModel] = encodingDefault;
|
||||
// make sure we recycle the default encoding across all models
|
||||
if (!encodingCL100K)
|
||||
encodingCL100K = get_encoding('cl100k_base');
|
||||
tokenEncoders[openaiModel] = encodingCL100K;
|
||||
}
|
||||
}
|
||||
|
||||
let count: number = 0;
|
||||
// Note: the try/catch shouldn't be necessary, but there could be corner cases where the tiktoken library throws
|
||||
// https://github.com/enricoros/big-agi/issues/182
|
||||
let count = 0;
|
||||
try {
|
||||
count = tokenEncoders[openaiModel]?.encode(text, 'all', [])?.length || 0;
|
||||
} catch (e) {
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
import { llmChatGenerateOrThrow, VChatFunctionIn, VChatMessageIn } from '~/modules/llms/llm.client';
|
||||
import { llmChatGenerateOrThrow, VChatFunctionIn } from '~/modules/llms/llm.client';
|
||||
import { useModelsStore } from '~/modules/llms/store-llms';
|
||||
|
||||
import { useChatStore } from '~/common/state/store-chats';
|
||||
@@ -83,18 +83,13 @@ export function autoSuggestions(conversationId: string, assistantMessageId: stri
|
||||
|
||||
// Follow-up: Auto-Diagrams
|
||||
if (suggestDiagrams) {
|
||||
const instructions: VChatMessageIn[] = [
|
||||
{ role: 'system', content: systemMessage.text },
|
||||
{ role: 'user', content: userMessage.text },
|
||||
{ role: 'assistant', content: assistantMessageText },
|
||||
];
|
||||
llmChatGenerateOrThrow(
|
||||
funcLLMId,
|
||||
instructions,
|
||||
'chat-followup-diagram', conversationId,
|
||||
[suggestPlantUMLFn], 'draw_plantuml_diagram',
|
||||
void llmChatGenerateOrThrow(funcLLMId, [
|
||||
{ role: 'system', content: systemMessage.text },
|
||||
{ role: 'user', content: userMessage.text },
|
||||
{ role: 'assistant', content: assistantMessageText },
|
||||
], [suggestPlantUMLFn], 'draw_plantuml_diagram',
|
||||
).then(chatResponse => {
|
||||
// cheap way to check if the function was supported
|
||||
|
||||
if (!('function_arguments' in chatResponse))
|
||||
return;
|
||||
|
||||
@@ -115,8 +110,7 @@ export function autoSuggestions(conversationId: string, assistantMessageId: stri
|
||||
}
|
||||
}
|
||||
}).catch(err => {
|
||||
// Likely the model did not support function calling
|
||||
// console.log('autoSuggestions: diagram error:', err);
|
||||
console.error('autoSuggestions::diagram:', err);
|
||||
});
|
||||
}
|
||||
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import { getFastLLMId } from '~/modules/llms/store-llms';
|
||||
import { llmChatGenerateOrThrow, VChatMessageIn } from '~/modules/llms/llm.client';
|
||||
import { llmChatGenerateOrThrow } from '~/modules/llms/llm.client';
|
||||
|
||||
import { useChatStore } from '~/common/state/store-chats';
|
||||
|
||||
@@ -34,23 +34,21 @@ export async function conversationAutoTitle(conversationId: string, forceReplace
|
||||
|
||||
try {
|
||||
// LLM chat-generate call
|
||||
const instructions: VChatMessageIn[] = [
|
||||
{ role: 'system', content: `You are an AI conversation titles assistant who specializes in creating expressive yet few-words chat titles.` },
|
||||
{
|
||||
role: 'user', content:
|
||||
'Analyze the given short conversation (every line is truncated) and extract a concise chat title that ' +
|
||||
'summarizes the conversation in as little as a couple of words.\n' +
|
||||
'Only respond with the lowercase short title and nothing else.\n' +
|
||||
'\n' +
|
||||
'```\n' +
|
||||
historyLines.join('\n') +
|
||||
'```\n',
|
||||
},
|
||||
];
|
||||
const chatResponse = await llmChatGenerateOrThrow(
|
||||
fastLLMId,
|
||||
instructions,
|
||||
'chat-ai-title', conversationId,
|
||||
[
|
||||
{ role: 'system', content: `You are an AI conversation titles assistant who specializes in creating expressive yet few-words chat titles.` },
|
||||
{
|
||||
role: 'user', content:
|
||||
'Analyze the given short conversation (every line is truncated) and extract a concise chat title that ' +
|
||||
'summarizes the conversation in as little as a couple of words.\n' +
|
||||
'Only respond with the lowercase short title and nothing else.\n' +
|
||||
'\n' +
|
||||
'```\n' +
|
||||
historyLines.join('\n') +
|
||||
'```\n',
|
||||
},
|
||||
],
|
||||
null, null,
|
||||
);
|
||||
|
||||
|
||||
@@ -68,7 +68,7 @@ export function DiagramsModal(props: { config: DiagramConfig, onClose: () => voi
|
||||
const [diagramLlm, llmComponent] = useFormRadioLlmType('Generator', 'chat');
|
||||
|
||||
// derived state
|
||||
const { conversationId, messageId, text: subject } = props.config;
|
||||
const { conversationId, text: subject } = props.config;
|
||||
const diagramLlmId = diagramLlm?.id;
|
||||
|
||||
|
||||
@@ -98,7 +98,7 @@ export function DiagramsModal(props: { config: DiagramConfig, onClose: () => voi
|
||||
const diagramPrompt = bigDiagramPrompt(diagramType, diagramLanguage, systemMessage.text, subject, customInstruction);
|
||||
|
||||
try {
|
||||
await llmStreamingChatGenerate(diagramLlm.id, diagramPrompt, 'ai-diagram', messageId, null, null, stepAbortController.signal,
|
||||
await llmStreamingChatGenerate(diagramLlm.id, diagramPrompt, null, null, stepAbortController.signal,
|
||||
({ textSoFar }) => textSoFar && setDiagramCode(diagramCode = textSoFar),
|
||||
);
|
||||
} catch (error: any) {
|
||||
@@ -109,7 +109,7 @@ export function DiagramsModal(props: { config: DiagramConfig, onClose: () => voi
|
||||
setAbortController(null);
|
||||
}
|
||||
|
||||
}, [abortController, conversationId, customInstruction, diagramLanguage, diagramLlm, diagramType, messageId, subject]);
|
||||
}, [abortController, conversationId, diagramLanguage, diagramLlm, diagramType, subject, customInstruction]);
|
||||
|
||||
|
||||
// [Effect] Auto-abort on unmount
|
||||
|
||||
@@ -117,7 +117,7 @@ export function FlattenerModal(props: {
|
||||
await startStreaming(llm.id, [
|
||||
{ role: 'system', content: flattenProfile.systemPrompt },
|
||||
{ role: 'user', content: encodeConversationAsUserMessage(flattenProfile.userPrompt, messages) },
|
||||
], 'ai-flattener', messages[0].id);
|
||||
]);
|
||||
|
||||
}, [llm, props.conversationId, startStreaming]);
|
||||
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import { getFastLLMId } from '~/modules/llms/store-llms';
|
||||
import { llmChatGenerateOrThrow, VChatMessageIn } from '~/modules/llms/llm.client';
|
||||
import { llmChatGenerateOrThrow } from '~/modules/llms/llm.client';
|
||||
|
||||
|
||||
const simpleImagineSystemPrompt =
|
||||
@@ -10,15 +10,14 @@ Provide output as a lowercase prompt and nothing else.`;
|
||||
/**
|
||||
* Creates a caption for a drawing or photo given some description - used to elevate the quality of the imaging
|
||||
*/
|
||||
export async function imaginePromptFromText(messageText: string, contextRef: string): Promise<string | null> {
|
||||
export async function imaginePromptFromText(messageText: string): Promise<string | null> {
|
||||
const fastLLMId = getFastLLMId();
|
||||
if (!fastLLMId) return null;
|
||||
try {
|
||||
const instructions: VChatMessageIn[] = [
|
||||
const chatResponse = await llmChatGenerateOrThrow(fastLLMId, [
|
||||
{ role: 'system', content: simpleImagineSystemPrompt },
|
||||
{ role: 'user', content: 'Write a prompt, based on the following input.\n\n```\n' + messageText.slice(0, 1000) + '\n```\n' },
|
||||
];
|
||||
const chatResponse = await llmChatGenerateOrThrow(fastLLMId, instructions, 'draw-expand-prompt', contextRef, null, null);
|
||||
], null, null);
|
||||
return chatResponse.content?.trim() ?? null;
|
||||
} catch (error: any) {
|
||||
console.error('imaginePromptFromText: fetch request error:', error);
|
||||
|
||||
@@ -132,7 +132,7 @@ export class Agent {
|
||||
S.messages.push({ role: 'user', content: prompt });
|
||||
let content: string;
|
||||
try {
|
||||
content = (await llmChatGenerateOrThrow(llmId, S.messages, 'chat-react-turn', null, null, null, 500)).content;
|
||||
content = (await llmChatGenerateOrThrow(llmId, S.messages, null, null, 500)).content;
|
||||
} catch (error: any) {
|
||||
content = `Error in llmChatGenerateOrThrow: ${error}`;
|
||||
}
|
||||
@@ -194,8 +194,7 @@ async function search(query: string): Promise<string> {
|
||||
async function browse(url: string): Promise<string> {
|
||||
try {
|
||||
const page = await callBrowseFetchPage(url);
|
||||
const pageContent = page.content.markdown || page.content.text || page.content.html || '';
|
||||
return JSON.stringify(pageContent ? { text: pageContent } : { error: 'Issue reading the page' });
|
||||
return JSON.stringify(page.content ? { text: page.content } : { error: 'Issue reading the page' });
|
||||
} catch (error) {
|
||||
console.error('Error browsing:', (error as Error).message);
|
||||
return 'An error occurred while browsing to the URL. Missing WSS Key?';
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import { DLLMId, findLLMOrThrow } from '~/modules/llms/store-llms';
|
||||
import { llmChatGenerateOrThrow, VChatMessageIn } from '~/modules/llms/llm.client';
|
||||
import { llmChatGenerateOrThrow } from '~/modules/llms/llm.client';
|
||||
|
||||
|
||||
// prompt to be tried when doing recursive summerization.
|
||||
@@ -80,11 +80,10 @@ async function cleanUpContent(chunk: string, llmId: DLLMId, _ignored_was_targetW
|
||||
const autoResponseTokensSize = contextTokens ? Math.floor(contextTokens * outputTokenShare) : null;
|
||||
|
||||
try {
|
||||
const instructions: VChatMessageIn[] = [
|
||||
const chatResponse = await llmChatGenerateOrThrow(llmId, [
|
||||
{ role: 'system', content: cleanupPrompt },
|
||||
{ role: 'user', content: chunk },
|
||||
];
|
||||
const chatResponse = await llmChatGenerateOrThrow(llmId, instructions, 'chat-ai-summarize', null, null, null, autoResponseTokensSize ?? undefined);
|
||||
], null, null, autoResponseTokensSize ?? undefined);
|
||||
return chatResponse?.content ?? '';
|
||||
} catch (error: any) {
|
||||
return '';
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
import * as React from 'react';
|
||||
|
||||
import { DLLMId, findLLMOrThrow } from '~/modules/llms/store-llms';
|
||||
import { llmStreamingChatGenerate, VChatContextRef, VChatMessageIn, VChatStreamContextName } from '~/modules/llms/llm.client';
|
||||
import { llmStreamingChatGenerate, VChatMessageIn } from '~/modules/llms/llm.client';
|
||||
|
||||
|
||||
// set to true to log to the console
|
||||
@@ -20,7 +20,7 @@ export interface LLMChainStep {
|
||||
/**
|
||||
* React hook to manage a chain of LLM transformations.
|
||||
*/
|
||||
export function useLLMChain(steps: LLMChainStep[], llmId: DLLMId | undefined, chainInput: string | undefined, onSuccess: (output: string, input: string) => void, contextName: VChatStreamContextName, contextRef: VChatContextRef) {
|
||||
export function useLLMChain(steps: LLMChainStep[], llmId: DLLMId | undefined, chainInput: string | undefined, onSuccess?: (output: string, input: string) => void) {
|
||||
|
||||
// state
|
||||
const [chain, setChain] = React.useState<ChainState | null>(null);
|
||||
@@ -114,7 +114,7 @@ export function useLLMChain(steps: LLMChainStep[], llmId: DLLMId | undefined, ch
|
||||
setChainStepInterimText(null);
|
||||
|
||||
// LLM call (streaming, cancelable)
|
||||
llmStreamingChatGenerate(llmId, llmChatInput, contextName, contextRef, null, null, stepAbortController.signal,
|
||||
llmStreamingChatGenerate(llmId, llmChatInput, null, null, stepAbortController.signal,
|
||||
({ textSoFar }) => {
|
||||
textSoFar && setChainStepInterimText(interimText = textSoFar);
|
||||
})
|
||||
@@ -141,7 +141,7 @@ export function useLLMChain(steps: LLMChainStep[], llmId: DLLMId | undefined, ch
|
||||
stepAbortController.abort('step aborted');
|
||||
_chainAbortController.signal.removeEventListener('abort', globalToStepListener);
|
||||
};
|
||||
}, [chain, contextRef, contextName, llmId, onSuccess]);
|
||||
}, [chain, llmId, onSuccess]);
|
||||
|
||||
|
||||
return {
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
import * as React from 'react';
|
||||
|
||||
import type { DLLMId } from '~/modules/llms/store-llms';
|
||||
import { llmStreamingChatGenerate, VChatContextRef, VChatMessageIn, VChatStreamContextName } from '~/modules/llms/llm.client';
|
||||
import { llmStreamingChatGenerate, VChatMessageIn } from '~/modules/llms/llm.client';
|
||||
|
||||
|
||||
export function useStreamChatText() {
|
||||
@@ -13,7 +13,7 @@ export function useStreamChatText() {
|
||||
const abortControllerRef = React.useRef<AbortController | null>(null);
|
||||
|
||||
|
||||
const startStreaming = React.useCallback(async (llmId: DLLMId, prompt: VChatMessageIn[], contextName: VChatStreamContextName, contextRef: VChatContextRef) => {
|
||||
const startStreaming = React.useCallback(async (llmId: DLLMId, prompt: VChatMessageIn[]) => {
|
||||
setStreamError(null);
|
||||
setPartialText(null);
|
||||
setText(null);
|
||||
@@ -24,7 +24,7 @@ export function useStreamChatText() {
|
||||
|
||||
try {
|
||||
let lastText = '';
|
||||
await llmStreamingChatGenerate(llmId, prompt, contextName, contextRef, null, null, abortControllerRef.current.signal, ({ textSoFar }) => {
|
||||
await llmStreamingChatGenerate(llmId, prompt, null, null, abortControllerRef.current.signal, ({ textSoFar }) => {
|
||||
if (textSoFar) {
|
||||
lastText = textSoFar;
|
||||
setPartialText(lastText);
|
||||
|
||||
@@ -30,30 +30,32 @@ export function BeamView(props: {
|
||||
|
||||
// external state
|
||||
const { novel: explainerUnseen, touch: explainerCompleted, forget: explainerShow } = useUICounter('beam-wizard');
|
||||
const gatherAutoStartAfterScatter = useModuleBeamStore(state => state.gatherAutoStartAfterScatter);
|
||||
const {
|
||||
/* root */ editInputHistoryMessage,
|
||||
/* scatter */ setRayCount, startScatteringAll, stopScatteringAll,
|
||||
} = props.beamStore.getState();
|
||||
const {
|
||||
/* root */ inputHistory, inputIssues, inputReady,
|
||||
/* scatter */ hadImportedRays, isScattering, raysReady,
|
||||
/* scatter */ isScattering, raysReady,
|
||||
/* gather (composite) */ canGather,
|
||||
/* IDs */ rayIds, fusionIds,
|
||||
} = useBeamStore(props.beamStore, useShallow(state => ({
|
||||
// input
|
||||
inputHistory: state.inputHistory,
|
||||
inputIssues: state.inputIssues,
|
||||
inputReady: state.inputReady,
|
||||
// scatter
|
||||
hadImportedRays: state.hadImportedRays,
|
||||
isScattering: state.isScattering,
|
||||
raysReady: state.raysReady,
|
||||
// gather (composite)
|
||||
canGather: state.raysReady >= 2 && state.currentFactoryId !== null && state.currentGatherLlmId !== null,
|
||||
// IDs
|
||||
rayIds: state.rays.map(ray => ray.rayId),
|
||||
fusionIds: state.fusions.map(fusion => fusion.fusionId),
|
||||
})));
|
||||
const { gatherAutoStartAfterScatter } = useModuleBeamStore(useShallow(state => ({
|
||||
gatherAutoStartAfterScatter: state.gatherAutoStartAfterScatter,
|
||||
})));
|
||||
// the following are independent because of useShallow, which would break in the above call
|
||||
const rayIds = useBeamStore(props.beamStore, useShallow(state => state.rays.map(ray => ray.rayId)));
|
||||
const fusionIds = useBeamStore(props.beamStore, useShallow(state => state.fusions.map(fusion => fusion.fusionId)));
|
||||
|
||||
// derived state
|
||||
const raysCount = rayIds.length;
|
||||
@@ -171,7 +173,6 @@ export function BeamView(props: {
|
||||
beamStore={props.beamStore}
|
||||
isMobile={props.isMobile}
|
||||
rayIds={rayIds}
|
||||
hadImportedRays={hadImportedRays}
|
||||
onIncreaseRayCount={handleRayIncreaseCount}
|
||||
// linkedLlmId={currentGatherLlmId}
|
||||
/>
|
||||
|
||||
@@ -13,7 +13,6 @@ import { BeamStoreApi, useBeamStore } from '../store-beam.hooks';
|
||||
import { FFactoryId, FUSION_FACTORIES } from './instructions/beam.gather.factories';
|
||||
import { GATHER_COLOR } from '../beam.config';
|
||||
import { beamPaneSx } from '../BeamCard';
|
||||
import { useModuleBeamStore } from '../store-module-beam';
|
||||
|
||||
|
||||
const gatherPaneClasses = {
|
||||
@@ -80,9 +79,8 @@ export function BeamGatherPane(props: {
|
||||
setCurrentFactoryId: state.setCurrentFactoryId,
|
||||
setCurrentGatherLlmId: state.setCurrentGatherLlmId,
|
||||
})));
|
||||
const gatherAutoStartAfterScatter = useModuleBeamStore(state => state.gatherAutoStartAfterScatter);
|
||||
const [_, gatherLlmComponent/*, gatherLlmIcon*/] = useLLMSelect(
|
||||
currentGatherLlmId, setCurrentGatherLlmId, props.isMobile ? '' : 'Merge Model', true, !props.canGather && !gatherAutoStartAfterScatter,
|
||||
currentGatherLlmId, setCurrentGatherLlmId, props.isMobile ? '' : 'Merge Model', true, !props.canGather,
|
||||
);
|
||||
|
||||
// derived state
|
||||
|
||||
@@ -170,24 +170,23 @@ export function Fusion(props: {
|
||||
<GoodTooltip title='Use this message'>
|
||||
<IconButton
|
||||
size='sm'
|
||||
// variant='plain'
|
||||
// variant='solid'
|
||||
color={GATHER_COLOR}
|
||||
disabled={isFusing}
|
||||
onClick={handleFusionUse}
|
||||
// endDecorator={<TelegramIcon />}
|
||||
sx={{
|
||||
// ...BEAM_BTN_SX,
|
||||
fontSize: 'xs',
|
||||
// '--Icon-fontSize': 'var(--joy-fontSize-xl)',
|
||||
// fontSize: 'xs',
|
||||
// backgroundColor: 'background.popup',
|
||||
// border: '1px solid',
|
||||
// borderColor: `${GATHER_COLOR}.outlinedBorder`,
|
||||
// boxShadow: `0 4px 16px -4px rgb(var(--joy-palette-${GATHER_COLOR}-mainChannel) / 20%)`,
|
||||
animation: `${animationEnterBelow} 0.1s ease-out`,
|
||||
whiteSpace: 'nowrap',
|
||||
// whiteSpace: 'nowrap',
|
||||
}}
|
||||
>
|
||||
{/*Use*/}
|
||||
{/*Ok*/}
|
||||
<TelegramIcon />
|
||||
</IconButton>
|
||||
</GoodTooltip>
|
||||
|
||||
@@ -96,7 +96,7 @@ export async function executeChatGenerate(_i: ChatGenerateInstruction, inputs: E
|
||||
};
|
||||
|
||||
// LLM Streaming generation
|
||||
return streamAssistantMessage(inputs.llmId, history, 'beam-gather', inputs.contextRef, getUXLabsHighPerformance() ? 0 : 1, 'off', onMessageUpdate, inputs.chainAbortController.signal)
|
||||
return streamAssistantMessage(inputs.llmId, history, getUXLabsHighPerformance() ? 0 : 1, 'off', onMessageUpdate, inputs.chainAbortController.signal)
|
||||
.then((status) => {
|
||||
// re-throw errors, as streamAssistantMessage catches internally
|
||||
if (status.outcome === 'aborted') {
|
||||
|
||||
@@ -23,7 +23,6 @@ export interface ExecutionInputState {
|
||||
readonly chatMessages: DMessage[];
|
||||
readonly rayMessages: DMessage[];
|
||||
readonly llmId: DLLMId;
|
||||
readonly contextRef: string; // not useful
|
||||
// interaction
|
||||
readonly chainAbortController: AbortController;
|
||||
readonly updateProgressComponent: (component: React.ReactNode) => void;
|
||||
@@ -68,7 +67,6 @@ export function gatherStartFusion(
|
||||
chatMessages: chatMessages,
|
||||
rayMessages: rayMessages,
|
||||
llmId: initialFusion.llmId,
|
||||
contextRef: initialFusion.fusionId,
|
||||
// interaction
|
||||
chainAbortController: new AbortController(),
|
||||
updateProgressComponent: (component: React.ReactNode) => onUpdateBFusion({ fusingProgressComponent: component }),
|
||||
|
||||
@@ -16,7 +16,6 @@ import type { DLLMId } from '~/modules/llms/store-llms';
|
||||
|
||||
import { GoodTooltip } from '~/common/components/GoodTooltip';
|
||||
import { InlineError } from '~/common/components/InlineError';
|
||||
import { animationEnterBelow } from '~/common/util/animUtils';
|
||||
import { copyToClipboard } from '~/common/util/clipboardUtils';
|
||||
import { useLLMSelect } from '~/common/components/forms/useLLMSelect';
|
||||
|
||||
@@ -110,8 +109,7 @@ function RayControls(props: {
|
||||
|
||||
export function BeamRay(props: {
|
||||
beamStore: BeamStoreApi,
|
||||
hadImportedRays: boolean
|
||||
isRemovable: boolean,
|
||||
isRemovable: boolean
|
||||
rayId: string,
|
||||
rayIndexWeak: number,
|
||||
// linkedLlmId: DLLMId | null,
|
||||
@@ -242,20 +240,16 @@ export function BeamRay(props: {
|
||||
<GoodTooltip title='Choose this message'>
|
||||
<IconButton
|
||||
size='sm'
|
||||
// variant='plain'
|
||||
color={GATHER_COLOR}
|
||||
disabled={isImported || isScattering}
|
||||
onClick={handleRayUse}
|
||||
// endDecorator={!isImported ? <TelegramIcon /> : null}
|
||||
sx={{
|
||||
fontSize: 'xs',
|
||||
// '--Icon-fontSize': 'var(--joy-fontSize-xl)',
|
||||
px: isImported ? 1 : undefined,
|
||||
animation: `${animationEnterBelow} 0.1s ease-out`,
|
||||
whiteSpace: 'nowrap',
|
||||
}}
|
||||
>
|
||||
{isImported ? 'From Chat' : /*props.hadImportedRays ? 'Replace' : 'Use'*/ <TelegramIcon />}
|
||||
{isImported ? 'From Chat' : /*'Use'*/ <TelegramIcon />}
|
||||
</IconButton>
|
||||
</GoodTooltip>
|
||||
|
||||
|
||||
@@ -27,7 +27,6 @@ const rayGridMobileSx: SxProps = {
|
||||
|
||||
export function BeamRayGrid(props: {
|
||||
beamStore: BeamStoreApi,
|
||||
hadImportedRays: boolean
|
||||
isMobile: boolean,
|
||||
onIncreaseRayCount: () => void,
|
||||
rayIds: string[],
|
||||
@@ -45,7 +44,6 @@ export function BeamRayGrid(props: {
|
||||
key={'ray-' + rayId}
|
||||
rayIndexWeak={index}
|
||||
beamStore={props.beamStore}
|
||||
hadImportedRays={props.hadImportedRays}
|
||||
isRemovable={raysCount > SCATTER_RAY_MIN}
|
||||
rayId={rayId}
|
||||
// linkedLlmId={props.linkedLlmId}
|
||||
|
||||
@@ -67,7 +67,7 @@ function rayScatterStart(ray: BRay, llmId: DLLMId | null, inputHistory: DMessage
|
||||
|
||||
// stream the assistant's messages
|
||||
const messagesHistory: VChatMessageIn[] = inputHistory.map(({ role, text }) => ({ role, content: text }));
|
||||
streamAssistantMessage(llmId, messagesHistory, 'beam-scatter', ray.rayId, getUXLabsHighPerformance() ? 0 : rays.length, 'off', updateMessage, abortController.signal)
|
||||
streamAssistantMessage(llmId, messagesHistory, getUXLabsHighPerformance() ? 0 : rays.length, 'off', updateMessage, abortController.signal)
|
||||
.then((status) => {
|
||||
_rayUpdate(ray.rayId, {
|
||||
status: (status.outcome === 'success') ? 'success'
|
||||
@@ -134,7 +134,6 @@ export function rayIsImported(ray: BRay | null): boolean {
|
||||
interface ScatterStateSlice {
|
||||
|
||||
rays: BRay[];
|
||||
hadImportedRays: boolean;
|
||||
|
||||
// derived state
|
||||
isScattering: boolean; // true if any ray is scattering at the moment
|
||||
@@ -149,7 +148,6 @@ export const reInitScatterStateSlice = (prevRays: BRay[]): ScatterStateSlice =>
|
||||
return {
|
||||
// (remember) keep the same quantity of rays and same llms
|
||||
rays: prevRays.map(prevRay => createBRay(prevRay.rayLlmId)),
|
||||
hadImportedRays: false,
|
||||
|
||||
isScattering: false,
|
||||
raysReady: 0,
|
||||
@@ -240,7 +238,6 @@ export const createScatterSlice: StateCreator<RootStoreSlice & ScatterStoreSlice
|
||||
// append the other rays (excluding the ones to remove)
|
||||
...rays.filter((ray) => !raysToRemove.includes(ray)),
|
||||
],
|
||||
hadImportedRays: messages.length > 0,
|
||||
});
|
||||
_storeLastScatterConfig();
|
||||
_syncRaysStateToScatter();
|
||||
|
||||
@@ -70,7 +70,7 @@ const createRootSlice: StateCreator<BeamStore, [], [], RootStoreSlice> = (_set,
|
||||
|
||||
|
||||
open: (chatHistory: Readonly<DMessage[]>, initialChatLlmId: DLLMId | null, callback: BeamSuccessCallback) => {
|
||||
const { isOpen: wasAlreadyOpen, terminateKeepingSettings, loadBeamConfig, hadImportedRays, setRayLlmIds, setCurrentGatherLlmId } = _get();
|
||||
const { isOpen: wasAlreadyOpen, terminateKeepingSettings, loadBeamConfig, setRayLlmIds, setCurrentGatherLlmId } = _get();
|
||||
|
||||
// reset pending operations
|
||||
terminateKeepingSettings();
|
||||
@@ -89,7 +89,6 @@ const createRootSlice: StateCreator<BeamStore, [], [], RootStoreSlice> = (_set,
|
||||
onSuccessCallback: callback,
|
||||
|
||||
// rays already reset
|
||||
hadImportedRays,
|
||||
|
||||
// update the model only if the dialog was not already open
|
||||
...(!wasAlreadyOpen && initialChatLlmId && {
|
||||
|
||||
@@ -1,12 +1,10 @@
|
||||
import * as React from 'react';
|
||||
import { useShallow } from 'zustand/react/shallow';
|
||||
import { shallow } from 'zustand/shallow';
|
||||
|
||||
import { Checkbox, FormControl, FormHelperText, Option, Select, Typography } from '@mui/joy';
|
||||
import { Checkbox, FormControl, FormHelperText } from '@mui/joy';
|
||||
|
||||
import { AlreadySet } from '~/common/components/AlreadySet';
|
||||
import { ExternalLink } from '~/common/components/ExternalLink';
|
||||
import { FormInputKey } from '~/common/components/forms/FormInputKey';
|
||||
import { FormLabelStart } from '~/common/components/forms/FormLabelStart';
|
||||
import { Link } from '~/common/components/Link';
|
||||
import { platformAwareKeystrokes } from '~/common/components/KeyStroke';
|
||||
|
||||
import { useBrowseCapability, useBrowseStore } from './store-module-browsing';
|
||||
@@ -15,82 +13,50 @@ import { useBrowseCapability, useBrowseStore } from './store-module-browsing';
|
||||
export function BrowseSettings() {
|
||||
|
||||
// external state
|
||||
const { mayWork, isServerConfig, isClientValid, inCommand, inComposer, inReact, inPersonas } = useBrowseCapability();
|
||||
const {
|
||||
wssEndpoint, setWssEndpoint,
|
||||
pageTransform, setPageTransform,
|
||||
setEnableCommandBrowse, setEnableComposerAttach, setEnableReactTool, setEnablePersonaTool,
|
||||
} = useBrowseStore(useShallow(state => ({
|
||||
const { mayWork, isServerConfig, isClientValid, inCommand, inComposer, inReact } = useBrowseCapability();
|
||||
const { wssEndpoint, setWssEndpoint, setEnableCommandBrowse, setEnableComposerAttach, setEnableReactTool } = useBrowseStore(state => ({
|
||||
wssEndpoint: state.wssEndpoint,
|
||||
pageTransform: state.pageTransform,
|
||||
setPageTransform: state.setPageTransform,
|
||||
setWssEndpoint: state.setWssEndpoint,
|
||||
setEnableCommandBrowse: state.setEnableCommandBrowse,
|
||||
setEnableComposerAttach: state.setEnableComposerAttach,
|
||||
setEnableReactTool: state.setEnableReactTool,
|
||||
setEnablePersonaTool: state.setEnablePersonaTool,
|
||||
})));
|
||||
|
||||
const handlePageTransformChange = (_event: any, value: typeof pageTransform | null) => value && setPageTransform(value);
|
||||
|
||||
}), shallow);
|
||||
|
||||
return <>
|
||||
|
||||
<Typography level='body-sm'>
|
||||
Configure Browsing to enable loading links and web pages. <ExternalLink
|
||||
href='https://github.com/enricoros/big-agi/blob/main/docs/config-feature-browse.md'>
|
||||
Learn more</ExternalLink>.
|
||||
</Typography>
|
||||
<FormHelperText sx={{ display: 'block' }}>
|
||||
Configure a browsing service to enable loading links and pages. See the <Link
|
||||
href='https://github.com/enricoros/big-agi/blob/main/docs/config-feature-browse.md' target='_blank' noLinkStyle>
|
||||
browse configuration guide</Link> for more information.
|
||||
</FormHelperText>
|
||||
|
||||
<FormInputKey
|
||||
autoCompleteId='browse-wss' label='Puppeteer Wss' noKey
|
||||
autoCompleteId='browse-wss' label='Puppeteer Endpoint' noKey
|
||||
value={wssEndpoint} onChange={setWssEndpoint}
|
||||
rightLabel={<AlreadySet required={!isServerConfig} />}
|
||||
rightLabel={!isServerConfig ? 'required' : '✔️ already set in server'}
|
||||
required={!isServerConfig} isError={!isClientValid && !isServerConfig}
|
||||
placeholder='wss://...'
|
||||
/>
|
||||
|
||||
|
||||
<FormControl orientation='horizontal' sx={{ justifyContent: 'space-between', alignItems: 'center' }}>
|
||||
<FormLabelStart title='Load pages as:' />
|
||||
<Select
|
||||
variant='outlined'
|
||||
value={pageTransform} onChange={handlePageTransformChange}
|
||||
slotProps={{
|
||||
root: { sx: { minWidth: '140px' } },
|
||||
indicator: { sx: { opacity: 0.5 } },
|
||||
button: { sx: { whiteSpace: 'inherit' } },
|
||||
}}
|
||||
>
|
||||
<Option value='text'>Text (default)</Option>
|
||||
<Option value='markdown'>Markdown</Option>
|
||||
<Option value='html'>HTML</Option>
|
||||
</Select>
|
||||
</FormControl>
|
||||
|
||||
|
||||
<Typography level='body-sm' sx={{ mt: 2 }}>Browsing enablement:</Typography>
|
||||
|
||||
<FormControl disabled={!mayWork}>
|
||||
<Checkbox size='sm' label='Paste URLs' checked={inComposer} onChange={(event) => setEnableComposerAttach(event.target.checked)} />
|
||||
<FormHelperText>{platformAwareKeystrokes('Load and attach when pasting a URL')}</FormHelperText>
|
||||
<Checkbox variant='outlined' label='Attach URLs' checked={inComposer} onChange={(event) => setEnableComposerAttach(event.target.checked)} />
|
||||
<FormHelperText>{platformAwareKeystrokes('Load and attach a page when pasting a URL')}</FormHelperText>
|
||||
</FormControl>
|
||||
|
||||
<FormControl disabled={!mayWork}>
|
||||
<Checkbox size='sm' label='/browse' checked={inCommand} onChange={(event) => setEnableCommandBrowse(event.target.checked)} />
|
||||
<Checkbox variant='outlined' label='/browse' checked={inCommand} onChange={(event) => setEnableCommandBrowse(event.target.checked)} />
|
||||
<FormHelperText>{platformAwareKeystrokes('Use /browse to load a web page')}</FormHelperText>
|
||||
</FormControl>
|
||||
|
||||
<FormControl disabled={!mayWork}>
|
||||
<Checkbox size='sm' label='ReAct' checked={inReact} onChange={(event) => setEnableReactTool(event.target.checked)} />
|
||||
<Checkbox variant='outlined' label='ReAct' checked={inReact} onChange={(event) => setEnableReactTool(event.target.checked)} />
|
||||
<FormHelperText>Enables loadURL() in ReAct</FormHelperText>
|
||||
</FormControl>
|
||||
|
||||
<FormControl disabled>
|
||||
<Checkbox size='sm' label='Chat with Personas' checked={false} onChange={(event) => setEnablePersonaTool(event.target.checked)} />
|
||||
<FormHelperText>Not yet available</FormHelperText>
|
||||
{/*<FormHelperText>Enable loading URLs by Personas</FormHelperText>*/}
|
||||
</FormControl>
|
||||
{/*<FormControl disabled>*/}
|
||||
{/* <Checkbox variant='outlined' label='Personas' checked={inPersonas} onChange={(event) => setEnablePersonaTool(event.target.checked)} />*/}
|
||||
{/* <FormHelperText>Enable loading URLs by Personas</FormHelperText>*/}
|
||||
{/*</FormControl>*/}
|
||||
|
||||
</>;
|
||||
}
|
||||
@@ -7,39 +7,31 @@ import { apiAsyncNode } from '~/common/util/trpc.client';
|
||||
const DEBUG_SHOW_SCREENSHOT = false;
|
||||
|
||||
|
||||
// export function
|
||||
export async function callBrowseFetchPage(url: string) {
|
||||
|
||||
export async function callBrowseFetchPage(
|
||||
url: string,
|
||||
// transforms?: BrowsePageTransform[],
|
||||
// screenshotOptions?: { width: number, height: number, quality?: number },
|
||||
) {
|
||||
|
||||
// validate url
|
||||
// thow if no URL is provided
|
||||
url = url?.trim() || '';
|
||||
if (!url)
|
||||
throw new Error('Browsing error: Invalid URL');
|
||||
|
||||
// noinspection HttpUrlsUsage: assume https if no protocol is provided
|
||||
// assume https if no protocol is provided
|
||||
// noinspection HttpUrlsUsage
|
||||
if (!url.startsWith('http://') && !url.startsWith('https://'))
|
||||
url = 'https://' + url;
|
||||
|
||||
const { wssEndpoint, pageTransform } = useBrowseStore.getState();
|
||||
const clientWssEndpoint = useBrowseStore.getState().wssEndpoint;
|
||||
|
||||
const { pages } = await apiAsyncNode.browse.fetchPages.mutate({
|
||||
access: {
|
||||
dialect: 'browse-wss',
|
||||
...(!!wssEndpoint && { wssEndpoint }),
|
||||
...(!!clientWssEndpoint && { wssEndpoint: clientWssEndpoint }),
|
||||
},
|
||||
requests: [{
|
||||
url,
|
||||
transforms: /*transforms ? transforms :*/ [pageTransform],
|
||||
screenshot: /*screenshotOptions ? screenshotOptions :*/ !DEBUG_SHOW_SCREENSHOT ? undefined : {
|
||||
width: 512,
|
||||
height: 512,
|
||||
// quality: 100,
|
||||
},
|
||||
}],
|
||||
subjects: [{ url }],
|
||||
screenshot: DEBUG_SHOW_SCREENSHOT ? {
|
||||
width: 512,
|
||||
height: 512,
|
||||
// quality: 100,
|
||||
} : undefined,
|
||||
});
|
||||
|
||||
if (pages.length !== 1)
|
||||
@@ -50,7 +42,7 @@ export async function callBrowseFetchPage(
|
||||
// DEBUG: if there's a screenshot, append it to the dom
|
||||
if (DEBUG_SHOW_SCREENSHOT && page.screenshot) {
|
||||
const img = document.createElement('img');
|
||||
img.src = page.screenshot.webpDataUrl;
|
||||
img.src = page.screenshot.imageDataUrl;
|
||||
img.style.width = `${page.screenshot.width}px`;
|
||||
img.style.height = `${page.screenshot.height}px`;
|
||||
document.body.appendChild(img);
|
||||
@@ -59,7 +51,7 @@ export async function callBrowseFetchPage(
|
||||
// throw if there's an error
|
||||
if (page.error) {
|
||||
console.warn('Browsing service error:', page.error);
|
||||
if (!Object.keys(page.content).length)
|
||||
if (!page.content)
|
||||
throw new Error(page.error);
|
||||
}
|
||||
|
||||
|
||||
@@ -1,9 +1,6 @@
|
||||
import { z } from 'zod';
|
||||
import { TRPCError } from '@trpc/server';
|
||||
|
||||
import { BrowserContext, connect, ScreenshotOptions } from '@cloudflare/puppeteer';
|
||||
import { default as TurndownService } from 'turndown';
|
||||
import { load as cheerioLoad } from 'cheerio';
|
||||
import { BrowserContext, connect, ScreenshotOptions, TimeoutError } from '@cloudflare/puppeteer';
|
||||
|
||||
import { createTRPCRouter, publicProcedure } from '~/server/api/trpc.server';
|
||||
import { env } from '~/server/env.mjs';
|
||||
@@ -19,22 +16,17 @@ const browseAccessSchema = z.object({
|
||||
dialect: z.enum(['browse-wss']),
|
||||
wssEndpoint: z.string().trim().optional(),
|
||||
});
|
||||
type BrowseAccessSchema = z.infer<typeof browseAccessSchema>;
|
||||
|
||||
const pageTransformSchema = z.enum(['html', 'text', 'markdown']);
|
||||
type PageTransformSchema = z.infer<typeof pageTransformSchema>;
|
||||
|
||||
const fetchPageInputSchema = z.object({
|
||||
access: browseAccessSchema,
|
||||
requests: z.array(z.object({
|
||||
subjects: z.array(z.object({
|
||||
url: z.string().url(),
|
||||
transforms: z.array(pageTransformSchema),
|
||||
screenshot: z.object({
|
||||
width: z.number(),
|
||||
height: z.number(),
|
||||
quality: z.number().optional(),
|
||||
}).optional(),
|
||||
})),
|
||||
screenshot: z.object({
|
||||
width: z.number(),
|
||||
height: z.number(),
|
||||
quality: z.number().optional(),
|
||||
}).optional(),
|
||||
});
|
||||
|
||||
|
||||
@@ -42,18 +34,16 @@ const fetchPageInputSchema = z.object({
|
||||
|
||||
const fetchPageWorkerOutputSchema = z.object({
|
||||
url: z.string(),
|
||||
content: z.record(pageTransformSchema, z.string()),
|
||||
content: z.string(),
|
||||
error: z.string().optional(),
|
||||
stopReason: z.enum(['end', 'timeout', 'error']),
|
||||
screenshot: z.object({
|
||||
webpDataUrl: z.string().startsWith('data:image/webp'),
|
||||
imageDataUrl: z.string().startsWith('data:image/'),
|
||||
mimeType: z.string().startsWith('image/'),
|
||||
width: z.number(),
|
||||
height: z.number(),
|
||||
}).optional(),
|
||||
});
|
||||
type FetchPageWorkerOutputSchema = z.infer<typeof fetchPageWorkerOutputSchema>;
|
||||
|
||||
|
||||
const fetchPagesOutputSchema = z.object({
|
||||
pages: z.array(fetchPageWorkerOutputSchema),
|
||||
@@ -65,23 +55,21 @@ export const browseRouter = createTRPCRouter({
|
||||
fetchPages: publicProcedure
|
||||
.input(fetchPageInputSchema)
|
||||
.output(fetchPagesOutputSchema)
|
||||
.mutation(async ({ input: { access, requests } }) => {
|
||||
.mutation(async ({ input: { access, subjects, screenshot } }) => {
|
||||
const pages: FetchPageWorkerOutputSchema[] = [];
|
||||
|
||||
const pagePromises = requests.map(request =>
|
||||
workerPuppeteer(access, request.url, request.transforms, request.screenshot));
|
||||
|
||||
const results = await Promise.allSettled(pagePromises);
|
||||
|
||||
const pages: FetchPageWorkerOutputSchema[] = results.map((result, index) =>
|
||||
result.status === 'fulfilled'
|
||||
? result.value
|
||||
: {
|
||||
url: requests[index].url,
|
||||
content: {},
|
||||
error: result.reason?.message || 'Unknown fetch error',
|
||||
for (const subject of subjects) {
|
||||
try {
|
||||
pages.push(await workerPuppeteer(access, subject.url, screenshot?.width, screenshot?.height, screenshot?.quality));
|
||||
} catch (error: any) {
|
||||
pages.push({
|
||||
url: subject.url,
|
||||
content: '',
|
||||
error: error?.message || JSON.stringify(error) || 'Unknown fetch error',
|
||||
stopReason: 'error',
|
||||
},
|
||||
);
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
return { pages };
|
||||
}),
|
||||
@@ -89,13 +77,12 @@ export const browseRouter = createTRPCRouter({
|
||||
});
|
||||
|
||||
|
||||
async function workerPuppeteer(
|
||||
access: BrowseAccessSchema,
|
||||
targetUrl: string,
|
||||
transforms: PageTransformSchema[],
|
||||
screenshotOptions?: { width: number, height: number, quality?: number },
|
||||
): Promise<FetchPageWorkerOutputSchema> {
|
||||
type BrowseAccessSchema = z.infer<typeof browseAccessSchema>;
|
||||
type FetchPageWorkerOutputSchema = z.infer<typeof fetchPageWorkerOutputSchema>;
|
||||
|
||||
async function workerPuppeteer(access: BrowseAccessSchema, targetUrl: string, ssWidth: number | undefined, ssHeight: number | undefined, ssQuality: number | undefined): Promise<FetchPageWorkerOutputSchema> {
|
||||
|
||||
// access
|
||||
const browserWSEndpoint = (access.wssEndpoint || env.PUPPETEER_WSS_ENDPOINT || '').trim();
|
||||
const isLocalBrowser = browserWSEndpoint.startsWith('ws://');
|
||||
if (!browserWSEndpoint || (!browserWSEndpoint.startsWith('wss://') && !isLocalBrowser))
|
||||
@@ -106,7 +93,7 @@ async function workerPuppeteer(
|
||||
|
||||
const result: FetchPageWorkerOutputSchema = {
|
||||
url: targetUrl,
|
||||
content: {},
|
||||
content: '',
|
||||
error: undefined,
|
||||
stopReason: 'error',
|
||||
screenshot: undefined,
|
||||
@@ -130,49 +117,35 @@ async function workerPuppeteer(
|
||||
if (!isWebPage) {
|
||||
// noinspection ExceptionCaughtLocallyJS
|
||||
throw new Error(`Invalid content-type: ${contentType}`);
|
||||
} else {
|
||||
} else
|
||||
result.stopReason = 'end';
|
||||
}
|
||||
} catch (error: any) {
|
||||
// This was "error instanceof TimeoutError;" but threw some type error - trying the below instead
|
||||
const isTimeout = error?.message?.includes('Navigation timeout') || false;
|
||||
const isTimeout: boolean = error instanceof TimeoutError;
|
||||
result.stopReason = isTimeout ? 'timeout' : 'error';
|
||||
if (!isTimeout) {
|
||||
result.error = '[Puppeteer] ' + (error?.message || error?.toString() || 'Unknown goto error');
|
||||
}
|
||||
if (!isTimeout)
|
||||
result.error = '[Puppeteer] ' + error?.message || error?.toString() || 'Unknown goto error';
|
||||
}
|
||||
|
||||
// transform the content of the page as text
|
||||
try {
|
||||
if (result.stopReason !== 'error') {
|
||||
for (const transform of transforms) {
|
||||
switch (transform) {
|
||||
case 'html':
|
||||
result.content.html = cleanHtml(await page.content());
|
||||
break;
|
||||
case 'text':
|
||||
result.content.text = await page.evaluate(() => document.body.innerText || document.textContent || '');
|
||||
break;
|
||||
case 'markdown':
|
||||
const html = await page.content();
|
||||
const cleanedHtml = cleanHtml(html);
|
||||
const turndownService = new TurndownService({ headingStyle: 'atx' });
|
||||
result.content.markdown = turndownService.turndown(cleanedHtml);
|
||||
break;
|
||||
}
|
||||
}
|
||||
if (!Object.keys(result.content).length)
|
||||
result.error = '[Puppeteer] Empty content';
|
||||
result.content = await page.evaluate(() => {
|
||||
const content = document.body.innerText || document.textContent;
|
||||
if (!content)
|
||||
throw new Error('No content');
|
||||
return content;
|
||||
});
|
||||
}
|
||||
} catch (error: any) {
|
||||
result.error = '[Puppeteer] ' + (error?.message || error?.toString() || 'Unknown content error');
|
||||
result.error = '[Puppeteer] ' + error?.message || error?.toString() || 'Unknown evaluate error';
|
||||
}
|
||||
|
||||
// get a screenshot of the page
|
||||
try {
|
||||
if (screenshotOptions?.width && screenshotOptions?.height) {
|
||||
const { width, height, quality } = screenshotOptions;
|
||||
const scale = Math.round(100 * width / 1024) / 100;
|
||||
if (ssWidth && ssHeight) {
|
||||
const width = ssWidth;
|
||||
const height = ssHeight;
|
||||
const scale = Math.round(100 * ssWidth / 1024) / 100;
|
||||
|
||||
await page.setViewport({ width: width / scale, height: height / scale, deviceScaleFactor: scale });
|
||||
|
||||
@@ -183,10 +156,10 @@ async function workerPuppeteer(
|
||||
type: imageType,
|
||||
encoding: 'base64',
|
||||
clip: { x: 0, y: 0, width: width / scale, height: height / scale },
|
||||
...(quality && { quality }),
|
||||
...(ssQuality && { quality: ssQuality }),
|
||||
}) as string;
|
||||
|
||||
result.screenshot = { webpDataUrl: `data:${mimeType};base64,${dataString}`, mimeType, width, height };
|
||||
result.screenshot = { imageDataUrl: `data:${mimeType};base64,${dataString}`, mimeType, width, height };
|
||||
}
|
||||
} catch (error: any) {
|
||||
console.error('workerPuppeteer: page.screenshot', error);
|
||||
@@ -219,35 +192,3 @@ async function workerPuppeteer(
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
|
||||
function cleanHtml(html: string) {
|
||||
const $ = cheerioLoad(html);
|
||||
|
||||
// Remove standard unwanted elements
|
||||
$('script, style, nav, aside, noscript, iframe, svg, canvas, .ads, .comments, link[rel="stylesheet"]').remove();
|
||||
|
||||
// Remove elements that might be specific to proxy services or injected by them
|
||||
$('[id^="brightdata-"], [class^="brightdata-"]').remove();
|
||||
|
||||
// Remove comments
|
||||
$('*').contents().filter(function() {
|
||||
return this.type === 'comment';
|
||||
}).remove();
|
||||
|
||||
// Remove empty elements
|
||||
$('p, div, span').each(function() {
|
||||
if ($(this).text().trim() === '' && $(this).children().length === 0) {
|
||||
$(this).remove();
|
||||
}
|
||||
});
|
||||
|
||||
// Merge consecutive paragraphs
|
||||
$('p + p').each(function() {
|
||||
$(this).prev().append(' ' + $(this).text());
|
||||
$(this).remove();
|
||||
});
|
||||
|
||||
// Return the cleaned HTML
|
||||
return $.html();
|
||||
}
|
||||
@@ -5,16 +5,11 @@ import { CapabilityBrowsing } from '~/common/components/useCapabilities';
|
||||
import { getBackendCapabilities } from '~/modules/backend/store-backend-capabilities';
|
||||
|
||||
|
||||
export type BrowsePageTransform = 'html' | 'text' | 'markdown';
|
||||
|
||||
interface BrowseState {
|
||||
|
||||
wssEndpoint: string;
|
||||
setWssEndpoint: (url: string) => void;
|
||||
|
||||
pageTransform: BrowsePageTransform;
|
||||
setPageTransform: (transform: BrowsePageTransform) => void;
|
||||
|
||||
enableCommandBrowse: boolean;
|
||||
setEnableCommandBrowse: (value: boolean) => void;
|
||||
|
||||
@@ -36,9 +31,6 @@ export const useBrowseStore = create<BrowseState>()(
|
||||
wssEndpoint: '', // default WSS endpoint
|
||||
setWssEndpoint: (wssEndpoint: string) => set(() => ({ wssEndpoint })),
|
||||
|
||||
pageTransform: 'text',
|
||||
setPageTransform: (pageTransform: BrowsePageTransform) => set(() => ({ pageTransform })),
|
||||
|
||||
enableCommandBrowse: true,
|
||||
setEnableCommandBrowse: (enableCommandBrowse: boolean) => set(() => ({ enableCommandBrowse })),
|
||||
|
||||
|
||||
@@ -2,7 +2,6 @@ import * as React from 'react';
|
||||
|
||||
import { FormControl } from '@mui/joy';
|
||||
|
||||
import { AlreadySet } from '~/common/components/AlreadySet';
|
||||
import { FormInputKey } from '~/common/components/forms/FormInputKey';
|
||||
import { FormLabelStart } from '~/common/components/forms/FormLabelStart';
|
||||
import { useCapabilityElevenLabs } from '~/common/components/useCapabilities';
|
||||
@@ -32,7 +31,7 @@ export function ElevenlabsSettings() {
|
||||
|
||||
{!isConfiguredServerSide && <FormInputKey
|
||||
autoCompleteId='elevenlabs-key' label='ElevenLabs API Key'
|
||||
rightLabel={<AlreadySet required={!isConfiguredServerSide} />}
|
||||
rightLabel={isConfiguredServerSide ? '✔️ already set in server' : 'required'}
|
||||
value={apiKey} onChange={setApiKey}
|
||||
required={!isConfiguredServerSide} isError={!isValidKey}
|
||||
/>}
|
||||
|
||||
@@ -2,10 +2,10 @@ import { sendGAEvent } from '@next/third-parties/google';
|
||||
|
||||
import { hasGoogleAnalytics } from '~/common/components/GoogleAnalytics';
|
||||
|
||||
import type { GenerateContextNameSchema, ModelDescriptionSchema, StreamingContextNameSchema } from './server/llm.server.types';
|
||||
import type { ModelDescriptionSchema } from './server/llm.server.types';
|
||||
import type { OpenAIWire } from './server/openai/openai.wiretypes';
|
||||
import type { StreamingClientUpdate } from './vendors/unifiedStreamingClient';
|
||||
import { DLLM, DLLMId, DModelSource, DModelSourceId, LLM_IF_OAI_Chat, LLM_IF_OAI_Fn, useModelsStore } from './store-llms';
|
||||
import { DLLM, DLLMId, DModelSource, DModelSourceId, LLM_IF_OAI_Chat, useModelsStore } from './store-llms';
|
||||
import { FALLBACK_LLM_TEMPERATURE } from './vendors/openai/openai.vendor';
|
||||
import { findAccessForSourceOrThrow, findVendorForLlmOrThrow } from './vendors/vendors.registry';
|
||||
|
||||
@@ -21,10 +21,6 @@ export interface VChatMessageIn {
|
||||
|
||||
export type VChatFunctionIn = OpenAIWire.ChatCompletion.RequestFunctionDef;
|
||||
|
||||
export type VChatStreamContextName = StreamingContextNameSchema;
|
||||
export type VChatGenerateContextName = GenerateContextNameSchema;
|
||||
export type VChatContextRef = string;
|
||||
|
||||
export interface VChatMessageOut {
|
||||
role: 'assistant' | 'system' | 'user';
|
||||
content: string;
|
||||
@@ -73,7 +69,7 @@ function modelDescriptionToDLLMOpenAIOptions<TSourceSetup, TLLMOptions>(model: M
|
||||
// null means unknown contenxt/output tokens
|
||||
const contextTokens = model.contextWindow || null;
|
||||
const maxOutputTokens = model.maxCompletionTokens || (contextTokens ? Math.round(contextTokens / 2) : null);
|
||||
const llmResponseTokensRatio = model.maxCompletionTokens ? 1 : 1 / 4;
|
||||
const llmResponseTokensRatio = model.maxCompletionTokens ? 1 / 2 : 1 / 4;
|
||||
const llmResponseTokens = maxOutputTokens ? Math.round(maxOutputTokens * llmResponseTokensRatio) : null;
|
||||
|
||||
return {
|
||||
@@ -116,20 +112,13 @@ function modelDescriptionToDLLMOpenAIOptions<TSourceSetup, TLLMOptions>(model: M
|
||||
export async function llmChatGenerateOrThrow<TSourceSetup = unknown, TAccess = unknown, TLLMOptions = unknown>(
|
||||
llmId: DLLMId,
|
||||
messages: VChatMessageIn[],
|
||||
contextName: VChatGenerateContextName,
|
||||
contextRef: VChatContextRef | null,
|
||||
functions: VChatFunctionIn[] | null,
|
||||
forceFunctionName: string | null,
|
||||
functions: VChatFunctionIn[] | null, forceFunctionName: string | null,
|
||||
maxTokens?: number,
|
||||
): Promise<VChatMessageOut | VChatMessageOrFunctionCallOut> {
|
||||
|
||||
// id to DLLM and vendor
|
||||
const { llm, vendor } = findVendorForLlmOrThrow<TSourceSetup, TAccess, TLLMOptions>(llmId);
|
||||
|
||||
// if the model does not support function calling and we're trying to force a function, throw
|
||||
if (forceFunctionName && !llm.interfaces.includes(LLM_IF_OAI_Fn))
|
||||
throw new Error(`Model ${llmId} does not support function calling`);
|
||||
|
||||
// FIXME: relax the forced cast
|
||||
const options = llm.options as TLLMOptions;
|
||||
|
||||
@@ -143,15 +132,13 @@ export async function llmChatGenerateOrThrow<TSourceSetup = unknown, TAccess = u
|
||||
await new Promise(resolve => setTimeout(resolve, delay));
|
||||
|
||||
// execute via the vendor
|
||||
return await vendor.rpcChatGenerateOrThrow(access, options, messages, contextName, contextRef, functions, forceFunctionName, maxTokens);
|
||||
return await vendor.rpcChatGenerateOrThrow(access, options, messages, functions, forceFunctionName, maxTokens);
|
||||
}
|
||||
|
||||
|
||||
export async function llmStreamingChatGenerate<TSourceSetup = unknown, TAccess = unknown, TLLMOptions = unknown>(
|
||||
llmId: DLLMId,
|
||||
messages: VChatMessageIn[],
|
||||
contextName: VChatStreamContextName,
|
||||
contextRef: VChatContextRef,
|
||||
functions: VChatFunctionIn[] | null,
|
||||
forceFunctionName: string | null,
|
||||
abortSignal: AbortSignal,
|
||||
@@ -174,5 +161,5 @@ export async function llmStreamingChatGenerate<TSourceSetup = unknown, TAccess =
|
||||
await new Promise(resolve => setTimeout(resolve, delay));
|
||||
|
||||
// execute via the vendor
|
||||
return await vendor.streamingChatGenerateOrThrow(access, llmId, llmOptions, messages, contextName, contextRef, functions, forceFunctionName, abortSignal, onUpdate);
|
||||
return await vendor.streamingChatGenerateOrThrow(access, llmId, llmOptions, messages, functions, forceFunctionName, abortSignal, onUpdate);
|
||||
}
|
||||
|
||||
@@ -1,5 +1,4 @@
|
||||
import * as React from 'react';
|
||||
import TimeAgo from 'react-timeago';
|
||||
import { shallow } from 'zustand/shallow';
|
||||
|
||||
import { Box, Button, ButtonGroup, Divider, FormControl, Input, Switch, Tooltip, Typography } from '@mui/joy';
|
||||
@@ -133,10 +132,10 @@ export function LLMOptionsModal(props: { id: DLLMId, onClose: () => void }) {
|
||||
llm id: {llm.id}<br />
|
||||
context tokens: <b>{llm.contextTokens ? llm.contextTokens.toLocaleString() : 'not provided'}</b>{` · `}
|
||||
max output tokens: <b>{llm.maxOutputTokens ? llm.maxOutputTokens.toLocaleString() : 'not provided'}</b><br />
|
||||
{!!llm.created && <>created: <TimeAgo date={new Date(llm.created * 1000)} /><br /></>}
|
||||
{!!llm.created && <>created: {(new Date(llm.created * 1000)).toLocaleString()}<br /></>}
|
||||
{/*· tags: {llm.tags.join(', ')}*/}
|
||||
{!!llm.pricing && <>pricing: $<b>{llm.pricing.chatIn || '(unk) '}</b>/M in, $<b>{llm.pricing.chatOut || '(unk) '}</b>/M out<br /></>}
|
||||
{/*{!!llm.benchmark && <>benchmark: <b>{llm.benchmark.cbaElo?.toLocaleString() || '(unk) '}</b> CBA Elo<br /></>}*/}
|
||||
{!!llm.benchmark && <>benchmark: <b>{llm.benchmark.cbaElo?.toLocaleString() || '(unk) '}</b> CBA Elo<br /></>}
|
||||
config: {JSON.stringify(llm.options)}
|
||||
</Typography>
|
||||
</Box>}
|
||||
|
||||
@@ -4,64 +4,14 @@ import { LLM_IF_OAI_Chat, LLM_IF_OAI_Vision } from '../../store-llms';
|
||||
|
||||
const roundTime = (date: string) => Math.round(new Date(date).getTime() / 1000);
|
||||
|
||||
export const hardcodedAnthropicModels: (ModelDescriptionSchema & { isLegacy?: boolean })[] = [
|
||||
// Claude 3.5 models - https://docs.anthropic.com/en/docs/about-claude/models
|
||||
// {
|
||||
// id: 'claude-3.5-opus', // ...
|
||||
// label: 'Claude 3.5 Opus',
|
||||
// created: roundTime(?),
|
||||
// description: ?,
|
||||
// contextWindow: 200000 ?, // Characters
|
||||
// maxCompletionTokens: 4096 ?,
|
||||
// trainingDataCutoff: ?,
|
||||
// interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Vision],
|
||||
// pricing: { chatIn: 15, chatOut: 75 },
|
||||
// benchmark: {
|
||||
// cbaElo: 1256, // Placeholder
|
||||
// cbaMmlu: 86.8, // Placeholder
|
||||
// },
|
||||
// },
|
||||
{
|
||||
id: 'claude-3-5-sonnet-20240620',
|
||||
label: 'Claude 3.5 Sonnet',
|
||||
created: roundTime('2024-06-20 06:00'),
|
||||
description: 'The most intelligent Claude model',
|
||||
contextWindow: 200000, // Characters
|
||||
maxCompletionTokens: 8192,
|
||||
trainingDataCutoff: 'Apr 2024',
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Vision],
|
||||
pricing: { chatIn: 3, chatOut: 15 },
|
||||
benchmark: {
|
||||
heCode: 92.0,
|
||||
vqaMmmu: 68.3,
|
||||
// TODO: Update with official benchmarks when available
|
||||
cbaElo: 1256 - 1, // Placeholder
|
||||
cbaMmlu: 86.8 - 1, // Placeholder
|
||||
},
|
||||
},
|
||||
// {
|
||||
// id: 'claude-3.5-haiku', // ...
|
||||
// label: 'Claude 3.5 Haiku',
|
||||
// created: roundTime(?),
|
||||
// description: ?,
|
||||
// contextWindow: 200000 ?, // Characters
|
||||
// maxCompletionTokens: 4096 ?,
|
||||
// trainingDataCutoff: ?,
|
||||
// interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Vision],
|
||||
// pricing: { chatIn: 0.25, chatOut: 1.25 },
|
||||
// benchmark: {
|
||||
// cbaElo: 1181, // Placeholder
|
||||
// cbaMmlu: 75.2, // Placeholder
|
||||
// },
|
||||
// },
|
||||
export const hardcodedAnthropicModels: ModelDescriptionSchema[] = [
|
||||
|
||||
|
||||
// Claude 3 models
|
||||
// Claude-3 models - https://docs.anthropic.com/claude/docs/models-overview#model-comparison
|
||||
{
|
||||
id: 'claude-3-opus-20240229',
|
||||
label: 'Claude 3 Opus',
|
||||
created: roundTime('2024-02-29'),
|
||||
description: 'Powerful model for complex tasks',
|
||||
description: 'Most powerful model for highly complex tasks',
|
||||
contextWindow: 200000,
|
||||
maxCompletionTokens: 4096,
|
||||
trainingDataCutoff: 'Aug 2023',
|
||||
@@ -73,20 +23,19 @@ export const hardcodedAnthropicModels: (ModelDescriptionSchema & { isLegacy?: bo
|
||||
id: 'claude-3-sonnet-20240229',
|
||||
label: 'Claude 3 Sonnet',
|
||||
created: roundTime('2024-02-29'),
|
||||
description: 'Balance of speed, cost, and performance',
|
||||
description: 'Ideal balance of intelligence and speed for enterprise workloads',
|
||||
contextWindow: 200000,
|
||||
maxCompletionTokens: 4096,
|
||||
trainingDataCutoff: 'Aug 2023',
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Vision],
|
||||
pricing: { chatIn: 3, chatOut: 15 },
|
||||
benchmark: { cbaElo: 1203, cbaMmlu: 79 },
|
||||
hidden: true,
|
||||
},
|
||||
{
|
||||
id: 'claude-3-haiku-20240307',
|
||||
label: 'Claude 3 Haiku',
|
||||
created: roundTime('2024-03-07'),
|
||||
description: 'Fastest, most cost-effective model',
|
||||
description: 'Fastest and most compact model for near-instant responsiveness',
|
||||
contextWindow: 200000,
|
||||
maxCompletionTokens: 4096,
|
||||
trainingDataCutoff: 'Aug 2023',
|
||||
@@ -106,7 +55,6 @@ export const hardcodedAnthropicModels: (ModelDescriptionSchema & { isLegacy?: bo
|
||||
interfaces: [LLM_IF_OAI_Chat],
|
||||
pricing: { chatIn: 8, chatOut: 24 },
|
||||
benchmark: { cbaElo: 1119 },
|
||||
hidden: true,
|
||||
},
|
||||
{
|
||||
id: 'claude-2.0',
|
||||
@@ -129,26 +77,25 @@ export const hardcodedAnthropicModels: (ModelDescriptionSchema & { isLegacy?: bo
|
||||
maxCompletionTokens: 4096,
|
||||
interfaces: [LLM_IF_OAI_Chat],
|
||||
pricing: { chatIn: 0.8, chatOut: 2.4 },
|
||||
},
|
||||
{
|
||||
id: 'claude-instant-1.1',
|
||||
label: 'Claude Instant 1.1',
|
||||
created: roundTime('2023-03-14'),
|
||||
description: 'Precise and fast',
|
||||
contextWindow: 100000,
|
||||
maxCompletionTokens: 2048,
|
||||
interfaces: [LLM_IF_OAI_Chat],
|
||||
hidden: true,
|
||||
},
|
||||
{
|
||||
id: 'claude-1.3',
|
||||
label: 'Claude 1.3',
|
||||
created: roundTime('2023-03-14'),
|
||||
description: 'Claude 1.3 is the latest version of Claude v1',
|
||||
contextWindow: 100000,
|
||||
maxCompletionTokens: 4096,
|
||||
interfaces: [LLM_IF_OAI_Chat],
|
||||
hidden: true,
|
||||
},
|
||||
// {
|
||||
// id: 'claude-instant-1.1',
|
||||
// label: 'Claude Instant 1.1',
|
||||
// created: roundTime('2023-03-14'),
|
||||
// description: 'Precise and fast',
|
||||
// contextWindow: 100000,
|
||||
// maxCompletionTokens: 2048,
|
||||
// interfaces: [LLM_IF_OAI_Chat],
|
||||
// hidden: true,
|
||||
// },
|
||||
// {
|
||||
// id: 'claude-1.3',
|
||||
// label: 'Claude 1.3',
|
||||
// created: roundTime('2023-03-14'),
|
||||
// description: 'Claude 1.3 is the latest version of Claude v1',
|
||||
// contextWindow: 100000,
|
||||
// maxCompletionTokens: 4096,
|
||||
// interfaces: [LLM_IF_OAI_Chat],
|
||||
// hidden: true,
|
||||
// },
|
||||
];
|
||||
@@ -8,7 +8,7 @@ import { fetchJsonOrTRPCError } from '~/server/api/trpc.router.fetchers';
|
||||
import { fixupHost } from '~/common/util/urlUtils';
|
||||
|
||||
import { OpenAIHistorySchema, openAIHistorySchema, OpenAIModelSchema, openAIModelSchema } from '../openai/openai.router';
|
||||
import { llmsChatGenerateOutputSchema, llmsGenerateContextSchema, llmsListModelsOutputSchema } from '../llm.server.types';
|
||||
import { llmsChatGenerateOutputSchema, llmsListModelsOutputSchema } from '../llm.server.types';
|
||||
|
||||
import { AnthropicWireMessagesRequest, anthropicWireMessagesRequestSchema, AnthropicWireMessagesResponse, anthropicWireMessagesResponseSchema } from './anthropic.wiretypes';
|
||||
import { hardcodedAnthropicModels } from './anthropic.models';
|
||||
@@ -17,9 +17,7 @@ import { hardcodedAnthropicModels } from './anthropic.models';
|
||||
// Default hosts
|
||||
const DEFAULT_API_VERSION_HEADERS = {
|
||||
'anthropic-version': '2023-06-01',
|
||||
// Former Betas:
|
||||
// - messages-2023-12-15: to use the Messages API
|
||||
'anthropic-beta': 'max-tokens-3-5-sonnet-2024-07-15',
|
||||
'anthropic-beta': 'messages-2023-12-15',
|
||||
};
|
||||
const DEFAULT_MAX_TOKENS = 2048;
|
||||
const DEFAULT_ANTHROPIC_HOST = 'api.anthropic.com';
|
||||
@@ -160,11 +158,7 @@ const listModelsInputSchema = z.object({
|
||||
|
||||
const chatGenerateInputSchema = z.object({
|
||||
access: anthropicAccessSchema,
|
||||
model: openAIModelSchema,
|
||||
history: openAIHistorySchema,
|
||||
// functions: openAIFunctionsSchema.optional(),
|
||||
// forceFunctionName: z.string().optional(),
|
||||
context: llmsGenerateContextSchema.optional(),
|
||||
model: openAIModelSchema, history: openAIHistorySchema,
|
||||
});
|
||||
|
||||
|
||||
|
||||
@@ -1,166 +1,16 @@
|
||||
import type { GeminiModelSchema } from './gemini.wiretypes';
|
||||
import type { ModelDescriptionSchema } from '../llm.server.types';
|
||||
import { LLM_IF_OAI_Chat, LLM_IF_OAI_Json, LLM_IF_OAI_Vision } from '../../store-llms';
|
||||
import { LLM_IF_OAI_Chat, LLM_IF_OAI_Vision } from '../../store-llms';
|
||||
|
||||
|
||||
// dev options
|
||||
const DEV_DEBUG_GEMINI_MODELS = false;
|
||||
|
||||
|
||||
// supported interfaces
|
||||
const geminiChatInterfaces: GeminiModelSchema['supportedGenerationMethods'] = ['generateContent'];
|
||||
|
||||
// unsupported interfaces
|
||||
const filterUnallowedNames = ['Legacy'];
|
||||
const filterUnallowedInterfaces: GeminiModelSchema['supportedGenerationMethods'] = ['generateAnswer', 'embedContent', 'embedText'];
|
||||
|
||||
const geminiLinkModels = ['models/gemini-pro', 'models/gemini-pro-vision'];
|
||||
|
||||
/* Manual models details
|
||||
Gemini Name Mapping example:
|
||||
- Latest version gemini-1.0-pro-latest <model>-<generation>-<variation>-latest
|
||||
- Latest stable version gemini-1.0-pro <model>-<generation>-<variation>
|
||||
- Stable versions gemini-1.0-pro-001 <model>-<generation>-<variation>-<version>
|
||||
*/
|
||||
const _knownGeminiModels: ({
|
||||
id: string,
|
||||
isNewest?: boolean,
|
||||
isPreview?: boolean
|
||||
symLink?: string
|
||||
} & Pick<ModelDescriptionSchema, 'interfaces' | 'pricing' | 'trainingDataCutoff' | 'hidden'>)[] = [
|
||||
|
||||
// Generation 1.5
|
||||
{
|
||||
id: 'models/gemini-1.5-flash-latest', // updated regularly and might be a preview version
|
||||
isNewest: true,
|
||||
isPreview: true,
|
||||
pricing: {
|
||||
chatIn: 0.70, // 0.35 up to 128k tokens, 0.70 prompts > 128k tokens
|
||||
chatOut: 2.10, // 1.05 up to 128k tokens, 2.10 prompts > 128k tokens
|
||||
},
|
||||
trainingDataCutoff: 'May 2024',
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Vision, LLM_IF_OAI_Json], // input: audio, images and text
|
||||
},
|
||||
{
|
||||
id: 'models/gemini-1.5-flash',
|
||||
// copied from above
|
||||
pricing: {
|
||||
chatIn: 0.70, // 0.35 up to 128k tokens, 0.70 prompts > 128k tokens
|
||||
chatOut: 2.10, // 1.05 up to 128k tokens, 2.10 prompts > 128k tokens
|
||||
},
|
||||
trainingDataCutoff: 'Apr 2024',
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Vision, LLM_IF_OAI_Json], // input: audio, images and text
|
||||
hidden: true,
|
||||
},
|
||||
{
|
||||
id: 'models/gemini-1.5-flash-001',
|
||||
// copied from above
|
||||
pricing: {
|
||||
chatIn: 0.70, // 0.35 up to 128k tokens, 0.70 prompts > 128k tokens
|
||||
chatOut: 2.10, // 1.05 up to 128k tokens, 2.10 prompts > 128k tokens
|
||||
},
|
||||
trainingDataCutoff: 'Apr 2024',
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Vision, LLM_IF_OAI_Json], // input: audio, images and text
|
||||
hidden: true,
|
||||
},
|
||||
|
||||
{
|
||||
id: 'models/gemini-1.5-pro-latest', // updated regularly and might be a preview version
|
||||
isNewest: true,
|
||||
isPreview: true,
|
||||
pricing: {
|
||||
chatIn: 7.00, // $3.50 / 1 million tokens (for prompts up to 128K tokens), $7.00 / 1 million tokens (for prompts longer than 128K)
|
||||
chatOut: 21.00, // $10.50 / 1 million tokens (128K or less), $21.00 / 1 million tokens (128K+)
|
||||
},
|
||||
trainingDataCutoff: 'May 2024',
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Vision, LLM_IF_OAI_Json], // input: audio, images and text
|
||||
},
|
||||
{
|
||||
id: 'models/gemini-1.5-pro', // latest stable -> 001
|
||||
// copied from above
|
||||
pricing: {
|
||||
chatIn: 7.00, // $3.50 / 1 million tokens (for prompts up to 128K tokens), $7.00 / 1 million tokens (for prompts longer than 128K)
|
||||
chatOut: 21.00, // $10.50 / 1 million tokens (128K or less), $21.00 / 1 million tokens (128K+)
|
||||
},
|
||||
trainingDataCutoff: 'Apr 2024',
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Vision, LLM_IF_OAI_Json],
|
||||
hidden: true,
|
||||
},
|
||||
{
|
||||
id: 'models/gemini-1.5-pro-001', // stable snapshot
|
||||
// copied from above
|
||||
pricing: {
|
||||
chatIn: 7.00, // $3.50 / 1 million tokens (for prompts up to 128K tokens), $7.00 / 1 million tokens (for prompts longer than 128K)
|
||||
chatOut: 21.00, // $10.50 / 1 million tokens (128K or less), $21.00 / 1 million tokens (128K+)
|
||||
},
|
||||
trainingDataCutoff: 'Apr 2024',
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Vision, LLM_IF_OAI_Json],
|
||||
hidden: true,
|
||||
},
|
||||
|
||||
|
||||
// Generation 1.0
|
||||
{
|
||||
id: 'models/gemini-1.0-pro-latest',
|
||||
pricing: {
|
||||
chatIn: 0.50,
|
||||
chatOut: 1.50,
|
||||
},
|
||||
interfaces: [LLM_IF_OAI_Chat],
|
||||
},
|
||||
{
|
||||
id: 'models/gemini-1.0-pro',
|
||||
pricing: {
|
||||
chatIn: 0.50,
|
||||
chatOut: 1.50,
|
||||
},
|
||||
interfaces: [LLM_IF_OAI_Chat],
|
||||
hidden: true,
|
||||
},
|
||||
{
|
||||
id: 'models/gemini-1.0-pro-001',
|
||||
pricing: {
|
||||
chatIn: 0.50,
|
||||
chatOut: 1.50,
|
||||
},
|
||||
interfaces: [LLM_IF_OAI_Chat],
|
||||
hidden: true,
|
||||
},
|
||||
|
||||
// Generation 1.0 + Vision
|
||||
{
|
||||
id: 'models/gemini-1.0-pro-vision-latest',
|
||||
pricing: {
|
||||
chatIn: 0.50,
|
||||
chatOut: 1.50,
|
||||
},
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Vision], // Text and Images
|
||||
hidden: true,
|
||||
},
|
||||
|
||||
// Older symlinks
|
||||
{
|
||||
id: 'models/gemini-pro',
|
||||
symLink: 'models/gemini-1.0-pro',
|
||||
// copied from symlinked
|
||||
pricing: {
|
||||
chatIn: 0.50,
|
||||
chatOut: 1.50,
|
||||
},
|
||||
interfaces: [LLM_IF_OAI_Chat],
|
||||
hidden: true,
|
||||
},
|
||||
{
|
||||
id: 'models/gemini-pro-vision',
|
||||
// copied from symlinked
|
||||
symLink: 'models/gemini-1.0-pro-vision',
|
||||
pricing: {
|
||||
chatIn: 0.50,
|
||||
chatOut: 1.50,
|
||||
},
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Vision], // Text and Images
|
||||
hidden: true,
|
||||
},
|
||||
];
|
||||
// interfaces mapping
|
||||
const geminiChatInterfaces: GeminiModelSchema['supportedGenerationMethods'] = ['generateContent'];
|
||||
const geminiVisionNames = ['-vision'];
|
||||
|
||||
|
||||
export function geminiFilterModels(geminiModel: GeminiModelSchema): boolean {
|
||||
@@ -176,23 +26,17 @@ export function geminiSortModels(a: ModelDescriptionSchema, b: ModelDescriptionS
|
||||
return b.label.localeCompare(a.label);
|
||||
}
|
||||
|
||||
export function geminiModelToModelDescription(geminiModel: GeminiModelSchema): ModelDescriptionSchema {
|
||||
export function geminiModelToModelDescription(geminiModel: GeminiModelSchema, allModels: GeminiModelSchema[]): ModelDescriptionSchema {
|
||||
const { description, displayName, name: modelId, supportedGenerationMethods } = geminiModel;
|
||||
|
||||
if (DEV_DEBUG_GEMINI_MODELS)
|
||||
console.log('geminiModelToModelDescription', geminiModel);
|
||||
|
||||
// find known manual mapping
|
||||
const knownModel = _knownGeminiModels.find(m => m.id === modelId);
|
||||
|
||||
// handle symlinks
|
||||
const label = knownModel?.symLink
|
||||
? `🔗 ${displayName.replace('1.0', '')} → ${knownModel.symLink}`
|
||||
: displayName;
|
||||
const isSymlink = geminiLinkModels.includes(modelId);
|
||||
const symlinked = isSymlink ? allModels.find(m => m.displayName === displayName && m.name !== modelId) : null;
|
||||
const label = isSymlink ? `🔗 ${displayName.replace('1.0', '')} → ${symlinked ? symlinked.name : '?'}` : displayName;
|
||||
|
||||
// handle hidden models
|
||||
const hasChatInterfaces = supportedGenerationMethods.some(iface => geminiChatInterfaces.includes(iface));
|
||||
const hidden = knownModel?.hidden || !!knownModel?.symLink || !hasChatInterfaces;
|
||||
const hidden = isSymlink || !hasChatInterfaces;
|
||||
|
||||
// context window
|
||||
const { inputTokenLimit, outputTokenLimit } = geminiModel;
|
||||
@@ -202,27 +46,26 @@ export function geminiModelToModelDescription(geminiModel: GeminiModelSchema): M
|
||||
const { version, topK, topP, temperature } = geminiModel;
|
||||
const descriptionLong = description + ` (Version: ${version}, Defaults: temperature=${temperature}, topP=${topP}, topK=${topK}, interfaces=[${supportedGenerationMethods.join(',')}])`;
|
||||
|
||||
// use known interfaces, or add chat if this is a generateContent model
|
||||
const interfaces: ModelDescriptionSchema['interfaces'] = knownModel?.interfaces || [];
|
||||
if (!interfaces.length && hasChatInterfaces) {
|
||||
const interfaces: ModelDescriptionSchema['interfaces'] = [];
|
||||
if (hasChatInterfaces) {
|
||||
interfaces.push(LLM_IF_OAI_Chat);
|
||||
// if (geminiVisionNames.some(name => modelId.includes(name)))
|
||||
// interfaces.push(LLM_IF_OAI_Vision);
|
||||
if (geminiVisionNames.some(name => modelId.includes(name)))
|
||||
interfaces.push(LLM_IF_OAI_Vision);
|
||||
}
|
||||
|
||||
return {
|
||||
id: modelId,
|
||||
label: label, // + (knownModel?.isNewest ? ' 🌟' : ''),
|
||||
label,
|
||||
// created: ...
|
||||
// updated: ...
|
||||
description: descriptionLong,
|
||||
contextWindow: contextWindow,
|
||||
maxCompletionTokens: outputTokenLimit,
|
||||
trainingDataCutoff: knownModel?.trainingDataCutoff,
|
||||
// trainingDataCutoff: '...',
|
||||
interfaces,
|
||||
// rateLimits: isGeminiPro ? { reqPerMinute: 60 } : undefined,
|
||||
// benchmarks: ...
|
||||
pricing: knownModel?.pricing, // TODO: needs <>128k, and per-character and per-image pricing
|
||||
// pricing: isGeminiPro ? { needs per-character and per-image pricing } : undefined,
|
||||
hidden,
|
||||
};
|
||||
}
|
||||
|
||||
@@ -8,7 +8,7 @@ import { createTRPCRouter, publicProcedure } from '~/server/api/trpc.server';
|
||||
import { fetchJsonOrTRPCError } from '~/server/api/trpc.router.fetchers';
|
||||
|
||||
import { fixupHost } from '~/common/util/urlUtils';
|
||||
import { llmsChatGenerateOutputSchema, llmsGenerateContextSchema, llmsListModelsOutputSchema } from '../llm.server.types';
|
||||
import { llmsChatGenerateOutputSchema, llmsListModelsOutputSchema } from '../llm.server.types';
|
||||
|
||||
import { OpenAIHistorySchema, openAIHistorySchema, OpenAIModelSchema, openAIModelSchema } from '../openai/openai.router';
|
||||
|
||||
@@ -120,11 +120,8 @@ const accessOnlySchema = z.object({
|
||||
|
||||
const chatGenerateInputSchema = z.object({
|
||||
access: geminiAccessSchema,
|
||||
model: openAIModelSchema,
|
||||
history: openAIHistorySchema,
|
||||
// functions: openAIFunctionsSchema.optional(),
|
||||
// forceFunctionName: z.string().optional(),
|
||||
context: llmsGenerateContextSchema.optional(),
|
||||
model: openAIModelSchema, history: openAIHistorySchema,
|
||||
// functions: openAIFunctionsSchema.optional(), forceFunctionName: z.string().optional(),
|
||||
});
|
||||
|
||||
|
||||
@@ -150,7 +147,7 @@ export const llmGeminiRouter = createTRPCRouter({
|
||||
// map to our output schema
|
||||
const models = detailedModels
|
||||
.filter(geminiFilterModels)
|
||||
.map(geminiModel => geminiModelToModelDescription(geminiModel))
|
||||
.map(geminiModel => geminiModelToModelDescription(geminiModel, detailedModels))
|
||||
.sort(geminiSortModels);
|
||||
|
||||
return {
|
||||
|
||||
@@ -18,7 +18,6 @@ const geminiModelSchema = z.object({
|
||||
inputTokenLimit: z.number().int().min(1),
|
||||
outputTokenLimit: z.number().int().min(1),
|
||||
supportedGenerationMethods: z.array(z.enum([
|
||||
'createCachedContent', // appeared on 2024-06-10, see https://github.com/enricoros/big-AGI/issues/565
|
||||
'countMessageTokens',
|
||||
'countTextTokens',
|
||||
'countTokens',
|
||||
|
||||
@@ -19,10 +19,7 @@ import { OLLAMA_PATH_CHAT, ollamaAccess, ollamaAccessSchema, ollamaChatCompletio
|
||||
|
||||
// OpenAI server imports
|
||||
import type { OpenAIWire } from './openai/openai.wiretypes';
|
||||
import { openAIAccess, openAIAccessSchema, openAIChatCompletionPayload, openAIHistorySchema, openAIModelSchema } from './openai/openai.router';
|
||||
|
||||
|
||||
import { llmsStreamingContextSchema } from './llm.server.types';
|
||||
import { openAIAccess, openAIAccessSchema, openAIChatCompletionPayload, OpenAIHistorySchema, openAIHistorySchema, OpenAIModelSchema, openAIModelSchema } from './openai/openai.router';
|
||||
|
||||
|
||||
// configuration
|
||||
@@ -54,9 +51,6 @@ const chatStreamingInputSchema = z.object({
|
||||
access: z.union([anthropicAccessSchema, geminiAccessSchema, ollamaAccessSchema, openAIAccessSchema]),
|
||||
model: openAIModelSchema,
|
||||
history: openAIHistorySchema,
|
||||
// NOTE: made it optional for now as we have some old requests without it
|
||||
// 2024-07-07: remove .optional()
|
||||
context: llmsStreamingContextSchema.optional(),
|
||||
});
|
||||
export type ChatStreamingInputSchema = z.infer<typeof chatStreamingInputSchema>;
|
||||
|
||||
@@ -78,15 +72,14 @@ export async function llmStreamingRelayHandler(req: NextRequest): Promise<Respon
|
||||
|
||||
// Parse the request
|
||||
const body = await req.json();
|
||||
const _chatStreamingInput: ChatStreamingInputSchema = chatStreamingInputSchema.parse(body);
|
||||
const { dialect: accessDialect } = _chatStreamingInput.access;
|
||||
const prettyDialect = serverCapitalizeFirstLetter(accessDialect);
|
||||
const { access, model, history } = chatStreamingInputSchema.parse(body);
|
||||
const prettyDialect = serverCapitalizeFirstLetter(access.dialect);
|
||||
|
||||
|
||||
// Prepare the upstream API request and demuxer/parser
|
||||
let requestData: ReturnType<typeof _prepareRequestData>;
|
||||
try {
|
||||
requestData = _prepareRequestData(_chatStreamingInput);
|
||||
requestData = _prepareRequestData(access, model, history);
|
||||
} catch (error: any) {
|
||||
console.error(`[POST] /api/llms/stream: ${prettyDialect}: prepareRequestData issue:`, safeErrorString(error));
|
||||
return new NextResponse(`**[Service Issue] ${prettyDialect}**: ${safeErrorString(error) || 'Unknown streaming error'}`, {
|
||||
@@ -110,7 +103,7 @@ export async function llmStreamingRelayHandler(req: NextRequest): Promise<Respon
|
||||
} catch (error: any) {
|
||||
|
||||
// server-side admins message
|
||||
const capDialect = serverCapitalizeFirstLetter(accessDialect);
|
||||
const capDialect = serverCapitalizeFirstLetter(access.dialect);
|
||||
const fetchOrVendorError = safeErrorString(error) + (error?.cause ? ' · ' + JSON.stringify(error.cause) : '');
|
||||
console.error(`[POST] /api/llms/stream: ${capDialect}: fetch issue:`, fetchOrVendorError, requestData?.url);
|
||||
|
||||
@@ -132,7 +125,7 @@ export async function llmStreamingRelayHandler(req: NextRequest): Promise<Respon
|
||||
* a 'healthy' level of inventory (i.e., pre-buffering) on the pipe to the client.
|
||||
*/
|
||||
const transformUpstreamToBigAgiClient = createUpstreamTransformer(
|
||||
requestData.vendorMuxingFormat, requestData.vendorStreamParser, accessDialect,
|
||||
requestData.vendorMuxingFormat, requestData.vendorStreamParser, access.dialect,
|
||||
);
|
||||
|
||||
const chatResponseStream =
|
||||
@@ -493,7 +486,7 @@ function createStreamParserOpenAI(): AIStreamParser {
|
||||
}
|
||||
|
||||
|
||||
function _prepareRequestData({ access, model, history, context: _context }: ChatStreamingInputSchema): {
|
||||
function _prepareRequestData(access: ChatStreamingInputSchema['access'], model: OpenAIModelSchema, history: OpenAIHistorySchema): {
|
||||
headers: HeadersInit;
|
||||
url: string;
|
||||
body: object;
|
||||
|
||||
@@ -12,8 +12,6 @@ const pricingSchema = z.object({
|
||||
const benchmarkSchema = z.object({
|
||||
cbaElo: z.number().optional(),
|
||||
cbaMmlu: z.number().optional(),
|
||||
heCode: z.number().optional(), // HumanEval, code, 0-shot
|
||||
vqaMmmu: z.number().optional(), // Visual Question Answering, MMMU, 0-shot
|
||||
});
|
||||
|
||||
// const rateLimitsSchema = z.object({
|
||||
@@ -48,25 +46,6 @@ export const llmsListModelsOutputSchema = z.object({
|
||||
});
|
||||
|
||||
|
||||
// Chat Generation Input (some parts of)
|
||||
|
||||
const generateContextNameSchema = z.enum(['chat-ai-title', 'chat-ai-summarize', 'chat-followup-diagram', 'chat-react-turn', 'draw-expand-prompt']);
|
||||
export type GenerateContextNameSchema = z.infer<typeof generateContextNameSchema>;
|
||||
export const llmsGenerateContextSchema = z.object({
|
||||
method: z.literal('chat-generate'),
|
||||
name: generateContextNameSchema,
|
||||
ref: z.string(),
|
||||
});
|
||||
|
||||
const streamingContextNameSchema = z.enum(['conversation', 'ai-diagram', 'ai-flattener', 'call', 'beam-scatter', 'beam-gather', 'persona-extract']);
|
||||
export type StreamingContextNameSchema = z.infer<typeof streamingContextNameSchema>;
|
||||
export const llmsStreamingContextSchema = z.object({
|
||||
method: z.literal('chat-stream'),
|
||||
name: streamingContextNameSchema,
|
||||
ref: z.string(),
|
||||
});
|
||||
|
||||
|
||||
// (non-streaming) Chat Generation Output
|
||||
|
||||
export const llmsChatGenerateOutputSchema = z.object({
|
||||
|
||||
@@ -11,7 +11,7 @@ import { capitalizeFirstLetter } from '~/common/util/textUtils';
|
||||
import { fixupHost } from '~/common/util/urlUtils';
|
||||
|
||||
import { OpenAIHistorySchema, openAIHistorySchema, OpenAIModelSchema, openAIModelSchema } from '../openai/openai.router';
|
||||
import { llmsChatGenerateOutputSchema, llmsGenerateContextSchema, llmsListModelsOutputSchema, ModelDescriptionSchema } from '../llm.server.types';
|
||||
import { llmsChatGenerateOutputSchema, llmsListModelsOutputSchema, ModelDescriptionSchema } from '../llm.server.types';
|
||||
|
||||
import { OLLAMA_BASE_MODELS, OLLAMA_PREV_UPDATE } from './ollama.models';
|
||||
import { WireOllamaChatCompletionInput, wireOllamaChunkedOutputSchema, wireOllamaListModelsSchema, wireOllamaModelInfoSchema } from './ollama.wiretypes';
|
||||
@@ -117,11 +117,8 @@ const adminPullModelSchema = z.object({
|
||||
|
||||
const chatGenerateInputSchema = z.object({
|
||||
access: ollamaAccessSchema,
|
||||
model: openAIModelSchema,
|
||||
history: openAIHistorySchema,
|
||||
// functions: openAIFunctionsSchema.optional(),
|
||||
// forceFunctionName: z.string().optional(),
|
||||
context: llmsGenerateContextSchema.optional(),
|
||||
model: openAIModelSchema, history: openAIHistorySchema,
|
||||
// functions: openAIFunctionsSchema.optional(), forceFunctionName: z.string().optional(),
|
||||
});
|
||||
|
||||
const listPullableOutputSchema = z.object({
|
||||
|
||||
@@ -1,84 +0,0 @@
|
||||
// here for reference only - for future mapping of CBA scores to the model IDs
|
||||
// const modelIdToPrefixMap: { [key: string]: string } = {
|
||||
// // Anthropic models
|
||||
// 'Claude 3.5 Sonnet': 'claude-3-5-sonnet-20240620',
|
||||
// 'Claude 3 Opus': 'claude-3-opus-20240229',
|
||||
// 'Claude 3 Sonnet': 'claude-3-sonnet-20240229',
|
||||
// 'Claude 3 Haiku': 'claude-3-haiku-20240307',
|
||||
// 'Claude-2.1': 'claude-2.1',
|
||||
// 'Claude-2.0': 'claude-2.0',
|
||||
// 'Claude-1': '', // No exact match
|
||||
// 'Claude-Instant-1': 'claude-instant-1.2', // Closest match
|
||||
//
|
||||
// // Gemini models
|
||||
// 'Gemini-1.5-Pro-Exp-0801': 'models/gemini-1.5-pro-latest', // Closest match
|
||||
// 'Gemini Advanced App (2024-05-14)': '', // No exact match
|
||||
// 'Gemini-1.5-Pro-001': 'models/gemini-1.5-pro-001',
|
||||
// 'Gemini-1.5-Pro-Preview-0409': 'models/gemini-1.5-pro-latest', // Closest match
|
||||
// 'Gemini-1.5-Flash-001': 'models/gemini-1.5-flash-001',
|
||||
// 'Gemini App (2024-01-24)': '', // No exact match
|
||||
// 'Gemini-1.0-Pro-001': 'models/gemini-1.0-pro-001',
|
||||
// 'Gemini Pro': 'models/gemini-pro',
|
||||
//
|
||||
// // OpenAI models (from the previous file)
|
||||
// 'GPT-4o-2024-05-13': 'gpt-4o-2024-05-13',
|
||||
// 'GPT-4o-mini-2024-07-18': 'gpt-4o-mini-2024-07-18',
|
||||
// 'GPT-4-Turbo-2024-04-09': 'gpt-4-turbo-2024-04-09',
|
||||
// 'GPT-4-1106-preview': 'gpt-4-1106-preview',
|
||||
// 'GPT-4-0125-preview': 'gpt-4-0125-preview',
|
||||
// 'GPT-4-0314': 'gpt-4-0314',
|
||||
// 'GPT-4-0613': 'gpt-4-0613',
|
||||
// 'GPT-3.5-Turbo-0613': 'gpt-3.5-turbo-0613',
|
||||
// 'GPT-3.5-Turbo-0314': 'gpt-3.5-turbo-0314',
|
||||
// 'GPT-3.5-Turbo-0125': 'gpt-3.5-turbo-0125',
|
||||
//
|
||||
// // Mistral models (from the previous file)
|
||||
// 'Mistral-Large-2402': 'mistral-large-2402',
|
||||
// 'Mixtral-8x7b-Instruct-v0.1': 'mistralai/Mixtral-8x7B-Instruct-v0.1',
|
||||
//
|
||||
// // Other models without matches
|
||||
// 'Gemini-1.5-Pro-Exp-0801': '',
|
||||
// 'Meta-Llama-3.1-405b-Instruct': '',
|
||||
// 'Gemini-1.5-Pro-001': '',
|
||||
// 'Meta-Llama-3.1-70b-Instruct': '',
|
||||
// 'Yi-Large-preview': '',
|
||||
// 'Deepseek-v2-API-0628': '',
|
||||
// 'Gemma-2-27b-it': '',
|
||||
// 'Yi-Large': '',
|
||||
// 'Nemotron-4-340B-Instruct': '',
|
||||
// 'GLM-4-0520': '',
|
||||
// 'Llama-3-70b-Instruct': '',
|
||||
// 'Reka-Core-20240501': '',
|
||||
// 'Command R+': '',
|
||||
// 'Gemma-2-9b-it': '',
|
||||
// 'Qwen2-72B-Instruct': '',
|
||||
// 'GLM-4-0116': '',
|
||||
// 'Qwen-Max-0428': '',
|
||||
// 'DeepSeek-Coder-V2-Instruct': '',
|
||||
// 'Reka-Flash-Preview-20240611': '',
|
||||
// 'Meta-Llama-3.1-8b-Instruct': '',
|
||||
// 'Qwen1.5-110B-Chat': '',
|
||||
// 'Yi-1.5-34B-Chat': '',
|
||||
// 'Reka-Flash-21B-online': '',
|
||||
// 'Llama-3-8b-Instruct': '',
|
||||
// 'Command R': '',
|
||||
// 'Reka-Flash-21B': '',
|
||||
// 'Qwen1.5-72B-Chat': '',
|
||||
// 'Mixtral-8x22b-Instruct-v0.1': '',
|
||||
// 'Zephyr-ORPO-141b-A35b-v0.1': '',
|
||||
// 'Qwen1.5-32B-Chat': '',
|
||||
// 'Mistral-Next': '',
|
||||
// 'Phi-3-Medium-4k-Instruct': '',
|
||||
// 'Starling-LM-7B-beta': '',
|
||||
// 'Yi-34B-Chat': '',
|
||||
// 'Qwen1.5-14B-Chat': '',
|
||||
// 'WizardLM-70B-v1.0': '',
|
||||
// 'Tulu-2-DPO-70B': '',
|
||||
// 'DBRX-Instruct-Preview': '',
|
||||
// 'Phi-3-Small-8k-Instruct': '',
|
||||
// 'Llama-2-70b-chat': '',
|
||||
// 'OpenChat-3.5-0106': '',
|
||||
// 'Vicuna-33B': '',
|
||||
// 'Snowflake Arctic Instruct': '',
|
||||
// 'Starling-LM-7B-alpha': '',
|
||||
// };
|
||||
@@ -9,10 +9,9 @@ import { wireTogetherAIListOutputSchema } from './togetherai.wiretypes';
|
||||
|
||||
|
||||
// [Azure] / [OpenAI]
|
||||
// https://platform.openai.com/docs/models
|
||||
const _knownOpenAIChatModels: ManualMappings = [
|
||||
|
||||
// GPT-4o -> 2024-05-13 (Starting October 2nd, 2024, gpt-4o will point to the gpt-4o-2024-08-06 snapshot)
|
||||
// GPT-4o -> 2024-05-13
|
||||
{
|
||||
idPrefix: 'gpt-4o',
|
||||
label: 'GPT-4o',
|
||||
@@ -25,123 +24,19 @@ const _knownOpenAIChatModels: ManualMappings = [
|
||||
trainingDataCutoff: 'Oct 2023',
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Vision, LLM_IF_OAI_Fn, LLM_IF_OAI_Json],
|
||||
pricing: { chatIn: 5, chatOut: 15 },
|
||||
benchmark: { cbaElo: 1286 },
|
||||
benchmark: { cbaElo: 1310 },
|
||||
},
|
||||
{
|
||||
isLatest: true,
|
||||
idPrefix: 'gpt-4o-2024-08-06',
|
||||
label: 'GPT-4o (2024-08-06)',
|
||||
description: 'Latest snapshot that supports Structured Outputs',
|
||||
contextWindow: 128000,
|
||||
maxCompletionTokens: 16384,
|
||||
trainingDataCutoff: 'Oct 2023',
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Vision, LLM_IF_OAI_Fn, LLM_IF_OAI_Json], // + Structured Outputs?
|
||||
pricing: { chatIn: 2.5, chatOut: 10 },
|
||||
benchmark: { cbaElo: 1286 + 1 },
|
||||
},
|
||||
{
|
||||
idPrefix: 'gpt-4o-2024-05-13',
|
||||
label: 'GPT-4o (2024-05-13)',
|
||||
description: 'Advanced, multimodal flagship model that\'s cheaper and faster than GPT-4 Turbo.',
|
||||
description: 'Advanced, multimodal flagship model that’s cheaper and faster than GPT-4 Turbo.',
|
||||
contextWindow: 128000,
|
||||
maxCompletionTokens: 4096,
|
||||
trainingDataCutoff: 'Oct 2023',
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Vision, LLM_IF_OAI_Fn, LLM_IF_OAI_Json],
|
||||
pricing: { chatIn: 5, chatOut: 15 },
|
||||
benchmark: { cbaElo: 1286 },
|
||||
hidden: true,
|
||||
},
|
||||
{
|
||||
idPrefix: 'chatgpt-4o-latest',
|
||||
label: 'ChatGPT-4o Latest',
|
||||
description: 'Intended for research and evaluation. Dynamic model continuously updated to the current version of GPT-4o in ChatGPT.',
|
||||
contextWindow: 128000,
|
||||
maxCompletionTokens: 16384,
|
||||
trainingDataCutoff: 'Oct 2023',
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Vision, LLM_IF_OAI_Fn, LLM_IF_OAI_Json],
|
||||
pricing: { chatIn: 5, chatOut: 15 },
|
||||
},
|
||||
|
||||
// GPT-4o mini
|
||||
{
|
||||
idPrefix: 'gpt-4o-mini',
|
||||
label: 'GPT-4o mini',
|
||||
description: 'Currently points to gpt-4o-mini-2024-07-18.',
|
||||
symLink: 'gpt-4o-mini-2024-07-18',
|
||||
hidden: true,
|
||||
// copied from symlinked
|
||||
contextWindow: 128000,
|
||||
maxCompletionTokens: 16384,
|
||||
trainingDataCutoff: 'Oct 2023',
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Vision, LLM_IF_OAI_Fn, LLM_IF_OAI_Json],
|
||||
pricing: { chatIn: 0.15, chatOut: 0.60 },
|
||||
benchmark: { cbaElo: 1277, cbaMmlu: 82.0 },
|
||||
},
|
||||
{
|
||||
idPrefix: 'gpt-4o-mini-2024-07-18',
|
||||
label: 'GPT-4o Mini (2024-07-18)',
|
||||
description: 'Affordable model for fast, lightweight tasks. GPT-4o mini is cheaper and more capable than GPT-3.5 Turbo.',
|
||||
contextWindow: 128000,
|
||||
maxCompletionTokens: 16384,
|
||||
trainingDataCutoff: 'Oct 2023',
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Vision, LLM_IF_OAI_Fn, LLM_IF_OAI_Json],
|
||||
pricing: { chatIn: 0.15, chatOut: 0.60 },
|
||||
},
|
||||
|
||||
// o1-preview
|
||||
{
|
||||
idPrefix: 'o1-preview',
|
||||
label: 'o1 Preview',
|
||||
description: 'Supported in Big-AGI 2. Points to the most recent snapshot of the o1 model: o1-preview-2024-09-12',
|
||||
symLink: 'o1-preview-2024-09-12',
|
||||
hidden: true,
|
||||
// copied from symlinked
|
||||
contextWindow: 128000,
|
||||
maxCompletionTokens: 32768,
|
||||
trainingDataCutoff: 'Oct 2023',
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Vision],
|
||||
pricing: { chatIn: 15, chatOut: 60 },
|
||||
isPreview: true,
|
||||
},
|
||||
{
|
||||
hidden: true, // we can't support it in Big-AGI 1
|
||||
idPrefix: 'o1-preview-2024-09-12',
|
||||
label: 'o1 Preview (2024-09-12)',
|
||||
description: 'Supported in Big-AGI 2. New reasoning model for complex tasks that require broad general knowledge.',
|
||||
contextWindow: 128000,
|
||||
maxCompletionTokens: 32768,
|
||||
trainingDataCutoff: 'Oct 2023',
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Vision],
|
||||
pricing: { chatIn: 15, chatOut: 60 },
|
||||
isPreview: true,
|
||||
},
|
||||
|
||||
// o1-mini
|
||||
{
|
||||
idPrefix: 'o1-mini',
|
||||
label: 'o1 Mini',
|
||||
description: 'Supported in Big-AGI 2. Points to the most recent o1-mini snapshot: o1-mini-2024-09-12',
|
||||
symLink: 'o1-mini-2024-09-12',
|
||||
hidden: true,
|
||||
// copied from symlinked
|
||||
contextWindow: 128000,
|
||||
maxCompletionTokens: 65536,
|
||||
trainingDataCutoff: 'Oct 2023',
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Vision],
|
||||
pricing: { chatIn: 3, chatOut: 12 },
|
||||
isPreview: true,
|
||||
},
|
||||
{
|
||||
hidden: true, // we can't support it in Big-AGI 1
|
||||
idPrefix: 'o1-mini-2024-09-12',
|
||||
label: 'o1 Mini (2024-09-12)',
|
||||
description: 'Supported in Big-AGI 2. Fast, cost-efficient reasoning model tailored to coding, math, and science use cases.',
|
||||
contextWindow: 128000,
|
||||
maxCompletionTokens: 65536,
|
||||
trainingDataCutoff: 'Oct 2023',
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Vision],
|
||||
pricing: { chatIn: 3, chatOut: 12 },
|
||||
isPreview: true,
|
||||
benchmark: { cbaElo: 1310 },
|
||||
},
|
||||
|
||||
// GPT4 Turbo with Vision -> 2024-04-09
|
||||
@@ -157,7 +52,7 @@ const _knownOpenAIChatModels: ManualMappings = [
|
||||
trainingDataCutoff: 'Dec 2023',
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Vision, LLM_IF_OAI_Fn, LLM_IF_OAI_Json],
|
||||
pricing: { chatIn: 10, chatOut: 30 },
|
||||
benchmark: { cbaElo: 1257 },
|
||||
benchmark: { cbaElo: 1261 },
|
||||
},
|
||||
{
|
||||
idPrefix: 'gpt-4-turbo-2024-04-09',
|
||||
@@ -168,12 +63,12 @@ const _knownOpenAIChatModels: ManualMappings = [
|
||||
trainingDataCutoff: 'Dec 2023',
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Vision, LLM_IF_OAI_Fn, LLM_IF_OAI_Json],
|
||||
pricing: { chatIn: 10, chatOut: 30 },
|
||||
benchmark: { cbaElo: 1257 },
|
||||
benchmark: { cbaElo: 1261 },
|
||||
},
|
||||
|
||||
// GPT4 Turbo Previews
|
||||
{
|
||||
idPrefix: 'gpt-4-turbo-preview',
|
||||
idPrefix: 'gpt-4-turbo-preview', // GPT-4 Turbo preview model -> 0125
|
||||
label: 'GPT-4 Preview Turbo',
|
||||
description: 'GPT-4 Turbo preview model. Currently points to gpt-4-0125-preview.',
|
||||
symLink: 'gpt-4-0125-preview',
|
||||
@@ -185,33 +80,63 @@ const _knownOpenAIChatModels: ManualMappings = [
|
||||
trainingDataCutoff: 'Dec 2023',
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Fn, LLM_IF_OAI_Json],
|
||||
pricing: { chatIn: 10, chatOut: 30 },
|
||||
benchmark: { cbaElo: 1245 },
|
||||
benchmark: { cbaElo: 1251 },
|
||||
},
|
||||
{
|
||||
idPrefix: 'gpt-4-0125-preview',
|
||||
idPrefix: 'gpt-4-0125-preview', // GPT-4 Turbo preview model
|
||||
label: 'GPT-4 Turbo (0125)',
|
||||
description: 'GPT-4 Turbo preview model intended to reduce cases of "laziness" where the model doesn\'t complete a task.',
|
||||
description: 'GPT-4 Turbo preview model intended to reduce cases of "laziness" where the model doesn\'t complete a task. Returns a maximum of 4,096 output tokens.',
|
||||
isPreview: true,
|
||||
contextWindow: 128000,
|
||||
maxCompletionTokens: 4096,
|
||||
trainingDataCutoff: 'Dec 2023',
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Fn, LLM_IF_OAI_Json],
|
||||
pricing: { chatIn: 10, chatOut: 30 },
|
||||
benchmark: { cbaElo: 1245 },
|
||||
benchmark: { cbaElo: 1251 },
|
||||
hidden: true,
|
||||
},
|
||||
{
|
||||
idPrefix: 'gpt-4-1106-preview', // GPT-4 Turbo preview model
|
||||
label: 'GPT-4 Turbo (1106)',
|
||||
description: 'GPT-4 Turbo preview model featuring improved instruction following, JSON mode, reproducible outputs, parallel function calling, and more.',
|
||||
description: 'GPT-4 Turbo preview model featuring improved instruction following, JSON mode, reproducible outputs, parallel function calling, and more. Returns a maximum of 4,096 output tokens.',
|
||||
isPreview: true,
|
||||
contextWindow: 128000,
|
||||
maxCompletionTokens: 4096,
|
||||
trainingDataCutoff: 'Apr 2023',
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Fn, LLM_IF_OAI_Json],
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Fn],
|
||||
pricing: { chatIn: 10, chatOut: 30 },
|
||||
benchmark: { cbaElo: 1251 },
|
||||
benchmark: { cbaElo: 1255 },
|
||||
hidden: true,
|
||||
},
|
||||
|
||||
// GPT4 Vision Previews
|
||||
{
|
||||
idPrefix: 'gpt-4-vision-preview', // GPT-4 Turbo vision preview
|
||||
label: 'GPT-4 Preview Vision',
|
||||
description: 'GPT-4 model with the ability to understand images, in addition to all other GPT-4 Turbo capabilities. This is a preview model, we recommend developers to now use gpt-4-turbo which includes vision capabilities. Currently points to gpt-4-1106-vision-preview.',
|
||||
symLink: 'gpt-4-1106-vision-preview',
|
||||
// copied from symlinked
|
||||
isPreview: true,
|
||||
contextWindow: 128000,
|
||||
maxCompletionTokens: 4096,
|
||||
trainingDataCutoff: 'Apr 2023',
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Vision, LLM_IF_OAI_Fn],
|
||||
pricing: { chatIn: 10, chatOut: 30 },
|
||||
hidden: true, // Deprecated in favor of gpt-4-turbo
|
||||
},
|
||||
{
|
||||
idPrefix: 'gpt-4-1106-vision-preview',
|
||||
label: 'GPT-4 Preview Vision (1106)',
|
||||
description: 'GPT-4 model with the ability to understand images, in addition to all other GPT-4 Turbo capabilities. This is a preview model, we recommend developers to now use gpt-4-turbo which includes vision capabilities. Returns a maximum of 4,096 output tokens.',
|
||||
isPreview: true,
|
||||
contextWindow: 128000,
|
||||
maxCompletionTokens: 4096,
|
||||
trainingDataCutoff: 'Apr 2023',
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Vision, LLM_IF_OAI_Fn],
|
||||
pricing: { chatIn: 10, chatOut: 30 },
|
||||
hidden: true, // Deprecated in favor of gpt-4-turbo
|
||||
},
|
||||
|
||||
|
||||
// GPT4-32k's
|
||||
{
|
||||
@@ -257,7 +182,7 @@ const _knownOpenAIChatModels: ManualMappings = [
|
||||
trainingDataCutoff: 'Sep 2021',
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Fn],
|
||||
pricing: { chatIn: 30, chatOut: 60 },
|
||||
benchmark: { cbaElo: 1161 },
|
||||
benchmark: { cbaElo: 1164 },
|
||||
},
|
||||
{
|
||||
idPrefix: 'gpt-4-0314',
|
||||
@@ -267,7 +192,7 @@ const _knownOpenAIChatModels: ManualMappings = [
|
||||
trainingDataCutoff: 'Sep 2021',
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Fn],
|
||||
pricing: { chatIn: 30, chatOut: 60 },
|
||||
benchmark: { cbaElo: 1186 },
|
||||
benchmark: { cbaElo: 1189 },
|
||||
hidden: true,
|
||||
},
|
||||
{
|
||||
@@ -281,49 +206,9 @@ const _knownOpenAIChatModels: ManualMappings = [
|
||||
trainingDataCutoff: 'Sep 2021',
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Fn],
|
||||
pricing: { chatIn: 30, chatOut: 60 },
|
||||
benchmark: { cbaElo: 1161 },
|
||||
isLegacy: true,
|
||||
benchmark: { cbaElo: 1164 },
|
||||
},
|
||||
|
||||
// 3.5-Turbo
|
||||
// As of July 2024, gpt-4o-mini should be used in place of gpt-3.5-turbo, as it is cheaper, more capable, multimodal, and just as fast.
|
||||
{
|
||||
idPrefix: 'gpt-3.5-turbo-0125',
|
||||
label: '3.5-Turbo (0125)',
|
||||
description: 'The latest GPT-3.5 Turbo model with higher accuracy at responding in requested formats and a fix for a bug which caused a text encoding issue for non-English language function calls.',
|
||||
contextWindow: 16385,
|
||||
maxCompletionTokens: 4096,
|
||||
trainingDataCutoff: 'Sep 2021',
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Fn],
|
||||
pricing: { chatIn: 0.5, chatOut: 1.5 },
|
||||
benchmark: { cbaElo: 1105 },
|
||||
},
|
||||
{
|
||||
idPrefix: 'gpt-3.5-turbo-1106',
|
||||
label: '3.5-Turbo (1106)',
|
||||
description: 'GPT-3.5 Turbo model with improved instruction following, JSON mode, reproducible outputs, parallel function calling, and more.',
|
||||
contextWindow: 16385,
|
||||
maxCompletionTokens: 4096,
|
||||
trainingDataCutoff: 'Sep 2021',
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Fn],
|
||||
pricing: { chatIn: 1, chatOut: 2 },
|
||||
benchmark: { cbaElo: 1072 },
|
||||
hidden: true,
|
||||
},
|
||||
{
|
||||
idPrefix: 'gpt-3.5-turbo',
|
||||
label: '3.5-Turbo',
|
||||
description: 'Currently points to gpt-3.5-turbo-0125. As of July 2024, gpt-4o-mini should be used in place of gpt-3.5-turbo, as it is cheaper, more capable, multimodal, and just as fast.',
|
||||
symLink: 'gpt-3.5-turbo-0125',
|
||||
hidden: true,
|
||||
// copied
|
||||
contextWindow: 16385,
|
||||
maxCompletionTokens: 4096,
|
||||
trainingDataCutoff: 'Sep 2021',
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Fn],
|
||||
pricing: { chatIn: 0.5, chatOut: 1.5 },
|
||||
benchmark: { cbaElo: 1105 },
|
||||
},
|
||||
|
||||
// 3.5-Turbo-Instruct (Not for Chat)
|
||||
{
|
||||
@@ -338,6 +223,46 @@ const _knownOpenAIChatModels: ManualMappings = [
|
||||
},
|
||||
|
||||
|
||||
// 3.5-Turbo-16k's
|
||||
{
|
||||
idPrefix: 'gpt-3.5-turbo-0125',
|
||||
label: '3.5-Turbo (0125)',
|
||||
description: 'The latest GPT-3.5 Turbo model with higher accuracy at responding in requested formats and a fix for a bug which caused a text encoding issue for non-English language function calls. Returns a maximum of 4,096 output tokens.',
|
||||
contextWindow: 16385,
|
||||
maxCompletionTokens: 4096,
|
||||
trainingDataCutoff: 'Sep 2021',
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Fn],
|
||||
pricing: { chatIn: 0.5, chatOut: 1.5 },
|
||||
benchmark: { cbaElo: 1104 },
|
||||
},
|
||||
{
|
||||
idPrefix: 'gpt-3.5-turbo-1106',
|
||||
label: '3.5-Turbo (1106)',
|
||||
description: 'The latest GPT-3.5 Turbo model with improved instruction following, JSON mode, reproducible outputs, parallel function calling, and more.',
|
||||
contextWindow: 16385,
|
||||
maxCompletionTokens: 4096,
|
||||
trainingDataCutoff: 'Sep 2021',
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Fn],
|
||||
pricing: { chatIn: 1, chatOut: 2 },
|
||||
benchmark: { cbaElo: 1072 },
|
||||
hidden: true,
|
||||
},
|
||||
{
|
||||
idPrefix: 'gpt-3.5-turbo',
|
||||
label: '3.5-Turbo',
|
||||
description: 'Currently points to gpt-3.5-turbo-0125.',
|
||||
symLink: 'gpt-3.5-turbo-0125',
|
||||
hidden: true,
|
||||
// copied
|
||||
contextWindow: 16385,
|
||||
maxCompletionTokens: 4096,
|
||||
trainingDataCutoff: 'Sep 2021',
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Fn],
|
||||
pricing: { chatIn: 0.5, chatOut: 1.5 },
|
||||
benchmark: { cbaElo: 1104 },
|
||||
},
|
||||
|
||||
|
||||
// Azure variants - because someone forgot the dot
|
||||
{
|
||||
idPrefix: 'gpt-35-turbo-16k',
|
||||
@@ -451,31 +376,8 @@ export function localAIModelToModelDescription(modelId: string): ModelDescriptio
|
||||
|
||||
|
||||
// [Mistral]
|
||||
// updated from the models on: https://docs.mistral.ai/getting-started/models/
|
||||
// and the pricing available on: https://mistral.ai/technology/#pricing
|
||||
|
||||
const _knownMistralChatModels: ManualMappings = [
|
||||
// Codestral
|
||||
{
|
||||
idPrefix: 'codestral-2405',
|
||||
label: 'Codestral (2405)',
|
||||
description: 'Designed and optimized for code generation tasks.',
|
||||
contextWindow: 32768,
|
||||
interfaces: [LLM_IF_OAI_Chat],
|
||||
pricing: { chatIn: 1, chatOut: 3 },
|
||||
},
|
||||
{
|
||||
idPrefix: 'codestral-latest',
|
||||
label: 'Mistral Large (latest)',
|
||||
symLink: 'mistral-codestral-2405',
|
||||
hidden: true,
|
||||
// copied
|
||||
description: 'Designed and optimized for code generation tasks.',
|
||||
contextWindow: 32768,
|
||||
interfaces: [LLM_IF_OAI_Chat],
|
||||
pricing: { chatIn: 1, chatOut: 3 },
|
||||
},
|
||||
|
||||
// Large
|
||||
{
|
||||
idPrefix: 'mistral-large-2402',
|
||||
@@ -483,7 +385,7 @@ const _knownMistralChatModels: ManualMappings = [
|
||||
description: 'Top-tier reasoning for high-complexity tasks.',
|
||||
contextWindow: 32768,
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Fn],
|
||||
pricing: { chatIn: 4, chatOut: 12 },
|
||||
pricing: { chatIn: 8, chatOut: 24 },
|
||||
benchmark: { cbaElo: 1159 },
|
||||
},
|
||||
{
|
||||
@@ -495,135 +397,103 @@ const _knownMistralChatModels: ManualMappings = [
|
||||
description: 'Top-tier reasoning for high-complexity tasks.',
|
||||
contextWindow: 32768,
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Fn],
|
||||
pricing: { chatIn: 4, chatOut: 12 },
|
||||
pricing: { chatIn: 8, chatOut: 24 },
|
||||
benchmark: { cbaElo: 1159 },
|
||||
},
|
||||
|
||||
// Open Mixtral (8x22B)
|
||||
{
|
||||
idPrefix: 'open-mixtral-8x22b-2404',
|
||||
label: 'Open Mixtral 8x22B (2404)',
|
||||
description: 'Mixtral 8x22B model',
|
||||
contextWindow: 65536,
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Fn],
|
||||
pricing: { chatIn: 2, chatOut: 6 },
|
||||
},
|
||||
{
|
||||
idPrefix: 'open-mixtral-8x22b',
|
||||
label: 'Open Mixtral 8x22B',
|
||||
symLink: 'open-mixtral-8x22b-2404',
|
||||
idPrefix: 'mistral-large',
|
||||
label: 'Mistral Large (?)',
|
||||
description: 'Flagship model, with top-tier reasoning capabilities and language support (English, French, German, Italian, Spanish, and Code)',
|
||||
contextWindow: 32768,
|
||||
interfaces: [LLM_IF_OAI_Chat],
|
||||
hidden: true,
|
||||
// copied
|
||||
description: 'Mixtral 8x22B model',
|
||||
contextWindow: 65536,
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Fn],
|
||||
pricing: { chatIn: 2, chatOut: 6 },
|
||||
},
|
||||
// Medium (Deprecated)
|
||||
|
||||
// Medium - not updated on 2024-02-26
|
||||
{
|
||||
idPrefix: 'mistral-medium-2312',
|
||||
label: 'Mistral Medium (2312)',
|
||||
description: 'Ideal for intermediate tasks that require moderate reasoning (Data extraction, Summarizing a Document, Writing emails, Writing a Job Description, or Writing Product Descriptions)',
|
||||
description: 'Mistral internal prototype model.',
|
||||
contextWindow: 32768,
|
||||
interfaces: [LLM_IF_OAI_Chat],
|
||||
pricing: { chatIn: 2.7, chatOut: 8.1 },
|
||||
benchmark: { cbaElo: 1148 },
|
||||
isLegacy: true,
|
||||
hidden: true,
|
||||
},
|
||||
{
|
||||
idPrefix: 'mistral-medium-latest',
|
||||
label: 'Mistral Medium (latest)',
|
||||
symLink: 'mistral-medium-2312',
|
||||
hidden: true,
|
||||
// copied
|
||||
description: 'Ideal for intermediate tasks that require moderate reasoning (Data extraction, Summarizing a Document, Writing emails, Writing a Job Description, or Writing Product Descriptions)',
|
||||
description: 'Mistral internal prototype model.',
|
||||
contextWindow: 32768,
|
||||
interfaces: [LLM_IF_OAI_Chat],
|
||||
pricing: { chatIn: 2.7, chatOut: 8.1 },
|
||||
benchmark: { cbaElo: 1148 },
|
||||
isLegacy: true,
|
||||
hidden: true,
|
||||
},
|
||||
{
|
||||
idPrefix: 'mistral-medium',
|
||||
label: 'Mistral Medium',
|
||||
symLink: 'mistral-medium-2312',
|
||||
// copied
|
||||
description: 'Ideal for intermediate tasks that require moderate reasoning (Data extraction, Summarizing a Document, Writing emails, Writing a Job Description, or Writing Product Descriptions)',
|
||||
description: 'Mistral internal prototype model.',
|
||||
contextWindow: 32768,
|
||||
interfaces: [LLM_IF_OAI_Chat],
|
||||
pricing: { chatIn: 2.7, chatOut: 8.1 },
|
||||
benchmark: { cbaElo: 1148 },
|
||||
isLegacy: true,
|
||||
hidden: true,
|
||||
},
|
||||
|
||||
// Open Mixtral (8x7B) -> currently points to `mistral-small-2312` (as per the docs)
|
||||
{
|
||||
idPrefix: 'open-mixtral-8x7b',
|
||||
label: 'Open Mixtral (8x7B)',
|
||||
description: 'A sparse mixture of experts model. As such, it leverages up to 45B parameters but only uses about 12B during inference, leading to better inference throughput at the cost of more vRAM.',
|
||||
contextWindow: 32768,
|
||||
interfaces: [LLM_IF_OAI_Chat],
|
||||
pricing: { chatIn: 0.7, chatOut: 0.7 },
|
||||
},
|
||||
// Small (deprecated)
|
||||
// Small (8x7B)
|
||||
{
|
||||
idPrefix: 'mistral-small-2402',
|
||||
label: 'Mistral Small (2402)',
|
||||
description: 'Suitable for simple tasks that one can do in bulk (Classification, Customer Support, or Text Generation)',
|
||||
description: 'Optimized endpoint. Cost-efficient reasoning for low-latency workloads. Mistral Small outperforms Mixtral 8x7B and has lower latency',
|
||||
contextWindow: 32768,
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Fn],
|
||||
pricing: { chatIn: 1, chatOut: 3 },
|
||||
pricing: { chatIn: 2, chatOut: 6 },
|
||||
},
|
||||
{
|
||||
idPrefix: 'mistral-small-2312',
|
||||
label: 'Mistral Small (2312)',
|
||||
description: 'Aka open-mixtral-8x7b. Cost-efficient reasoning for low-latency workloads. Mistral Small outperforms Mixtral 8x7B and has lower latency',
|
||||
contextWindow: 32768,
|
||||
interfaces: [LLM_IF_OAI_Chat],
|
||||
pricing: { chatIn: 2, chatOut: 6 },
|
||||
hidden: true,
|
||||
isLegacy: true,
|
||||
},
|
||||
{
|
||||
idPrefix: 'mistral-small-latest',
|
||||
label: 'Mistral Small (latest)',
|
||||
symLink: 'mistral-small-2402',
|
||||
hidden: true,
|
||||
// copied
|
||||
description: 'Suitable for simple tasks that one can do in bulk (Classification, Customer Support, or Text Generation)',
|
||||
description: 'Cost-efficient reasoning for low-latency workloads. Mistral Small outperforms Mixtral 8x7B and has lower latency',
|
||||
contextWindow: 32768,
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Fn],
|
||||
pricing: { chatIn: 1, chatOut: 3 },
|
||||
hidden: true,
|
||||
isLegacy: true,
|
||||
},
|
||||
{
|
||||
idPrefix: 'mistral-small-2312',
|
||||
label: 'Mistral Small (2312)',
|
||||
description: 'Aka open-mixtral-8x7b. Suitable for simple tasks that one can do in bulk (Classification, Customer Support, or Text Generation)',
|
||||
contextWindow: 32768,
|
||||
interfaces: [LLM_IF_OAI_Chat],
|
||||
pricing: { chatIn: 1, chatOut: 3 },
|
||||
hidden: true,
|
||||
isLegacy: true,
|
||||
pricing: { chatIn: 2, chatOut: 6 },
|
||||
},
|
||||
{
|
||||
idPrefix: 'mistral-small',
|
||||
label: 'Mistral Small',
|
||||
symLink: 'mistral-small-2312',
|
||||
// copied
|
||||
description: 'Aka open-mixtral-8x7b. Suitable for simple tasks that one can do in bulk (Classification, Customer Support, or Text Generation)',
|
||||
description: 'Cost-efficient reasoning for low-latency workloads.',
|
||||
contextWindow: 32768,
|
||||
interfaces: [LLM_IF_OAI_Chat],
|
||||
pricing: { chatIn: 1, chatOut: 3 },
|
||||
pricing: { chatIn: 2, chatOut: 6 },
|
||||
hidden: true,
|
||||
isLegacy: true,
|
||||
},
|
||||
|
||||
|
||||
// Open Mistral (7B) -> currently points to mistral-tiny-2312 (as per the docs)
|
||||
// Open Mixtral (8x7B)
|
||||
{
|
||||
idPrefix: 'open-mistral-7b',
|
||||
label: 'Open Mistral (7B)',
|
||||
description: 'The first dense model released by Mistral AI, perfect for experimentation, customization, and quick iteration. At the time of the release, it matched the capabilities of models up to 30B parameters.',
|
||||
idPrefix: 'open-mixtral-8x7b',
|
||||
label: 'Open Mixtral (8x7B)',
|
||||
description: 'Mixtral 8x7B model, aka mistral-small-2312',
|
||||
// symLink: 'mistral-small-2312',
|
||||
// copied
|
||||
contextWindow: 32768,
|
||||
interfaces: [LLM_IF_OAI_Chat],
|
||||
pricing: { chatIn: 0.25, chatOut: 0.25 },
|
||||
pricing: { chatIn: 0.7, chatOut: 0.7 },
|
||||
},
|
||||
// Tiny (deprecated)
|
||||
|
||||
// Tiny (7B)
|
||||
{
|
||||
idPrefix: 'mistral-tiny-2312',
|
||||
label: 'Mistral Tiny (2312)',
|
||||
@@ -631,34 +501,43 @@ const _knownMistralChatModels: ManualMappings = [
|
||||
contextWindow: 32768,
|
||||
interfaces: [LLM_IF_OAI_Chat],
|
||||
hidden: true,
|
||||
isLegacy: true,
|
||||
},
|
||||
{
|
||||
idPrefix: 'mistral-tiny',
|
||||
label: 'Mistral Tiny',
|
||||
symLink: 'mistral-tiny-2312',
|
||||
// copied
|
||||
description: 'Aka open-mistral-7b. Used for large batch processing tasks where cost is a significant factor but reasoning capabilities are not crucial',
|
||||
description: 'Used for large batch processing tasks where cost is a significant factor but reasoning capabilities are not crucial',
|
||||
contextWindow: 32768,
|
||||
interfaces: [LLM_IF_OAI_Chat],
|
||||
hidden: true,
|
||||
isLegacy: true,
|
||||
},
|
||||
// Open Mistral (7B)
|
||||
{
|
||||
idPrefix: 'open-mistral-7b',
|
||||
label: 'Open Mistral (7B)',
|
||||
description: 'Mistral 7B model, aka mistral-tiny-2312',
|
||||
// symLink: 'mistral-tiny-2312',
|
||||
// copied
|
||||
contextWindow: 32768,
|
||||
interfaces: [LLM_IF_OAI_Chat],
|
||||
pricing: { chatIn: 0.25, chatOut: 0.25 },
|
||||
},
|
||||
|
||||
|
||||
{
|
||||
idPrefix: 'mistral-embed',
|
||||
label: 'Mistral Embed',
|
||||
description: 'A model that converts text into numerical vectors of embeddings in 1024 dimensions. Embedding models enable retrieval and retrieval-augmented generation applications.',
|
||||
description: 'State-of-the-art semantic for extracting representation of text extracts.',
|
||||
// output: 1024 dimensions
|
||||
maxCompletionTokens: 1024, // HACK - it's 1024 dimensions, but those are not 'completion tokens'
|
||||
contextWindow: 8192, // Updated context window
|
||||
contextWindow: 32768, // actually unknown, assumed from the other models
|
||||
interfaces: [],
|
||||
pricing: { chatIn: 0.1, chatOut: 0.1 },
|
||||
hidden: true,
|
||||
},
|
||||
];
|
||||
|
||||
|
||||
const mistralModelFamilyOrder = [
|
||||
'codestral', 'mistral-large', 'open-mixtral-8x22b', 'mistral-medium', 'open-mixtral-8x7b', 'mistral-small', 'open-mistral-7b', 'mistral-tiny', 'mistral-embed', '🔗',
|
||||
'mistral-large', 'mistral-medium', 'mistral-small', 'open-mixtral-8x7b', 'mistral-tiny', 'open-mistral-7b', 'mistral-embed', '🔗',
|
||||
];
|
||||
|
||||
export function mistralModelToModelDescription(_model: unknown): ModelDescriptionSchema {
|
||||
@@ -674,13 +553,13 @@ export function mistralModelToModelDescription(_model: unknown): ModelDescriptio
|
||||
}
|
||||
|
||||
export function mistralModelsSort(a: ModelDescriptionSchema, b: ModelDescriptionSchema): number {
|
||||
if (a.label.startsWith('🔗') && !b.label.startsWith('🔗')) return 1;
|
||||
if (!a.label.startsWith('🔗') && b.label.startsWith('🔗')) return -1;
|
||||
const aPrefixIndex = mistralModelFamilyOrder.findIndex(prefix => a.id.startsWith(prefix));
|
||||
const bPrefixIndex = mistralModelFamilyOrder.findIndex(prefix => b.id.startsWith(prefix));
|
||||
if (aPrefixIndex !== -1 && bPrefixIndex !== -1) {
|
||||
if (aPrefixIndex !== bPrefixIndex)
|
||||
return aPrefixIndex - bPrefixIndex;
|
||||
if (a.label.startsWith('🔗') && !b.label.startsWith('🔗')) return 1;
|
||||
if (!a.label.startsWith('🔗') && b.label.startsWith('🔗')) return -1;
|
||||
return b.label.localeCompare(a.label);
|
||||
}
|
||||
return aPrefixIndex !== -1 ? 1 : -1;
|
||||
@@ -934,84 +813,41 @@ export function perplexityAIModelSort(a: ModelDescriptionSchema, b: ModelDescrip
|
||||
const _knownGroqModels: ManualMappings = [
|
||||
{
|
||||
isLatest: true,
|
||||
idPrefix: 'llama-3.1-405b-reasoning',
|
||||
label: 'Llama 3.1 · 405B',
|
||||
description: 'LLaMA 3.1 405B developed by Meta with a context window of 131,072 tokens. Supports tool use.',
|
||||
contextWindow: 131072,
|
||||
maxCompletionTokens: 8000,
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Fn],
|
||||
},
|
||||
{
|
||||
isLatest: true,
|
||||
idPrefix: 'llama-3.1-70b-versatile',
|
||||
label: 'Llama 3.1 · 70B',
|
||||
description: 'LLaMA 3.1 70B developed by Meta with a context window of 131,072 tokens. Supports tool use.',
|
||||
contextWindow: 131072,
|
||||
maxCompletionTokens: 8000,
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Fn],
|
||||
},
|
||||
{
|
||||
isLatest: true,
|
||||
idPrefix: 'llama-3.1-8b-instant',
|
||||
label: 'Llama 3.1 · 8B',
|
||||
description: 'LLaMA 3.1 8B developed by Meta with a context window of 131,072 tokens. Supports tool use.',
|
||||
contextWindow: 131072,
|
||||
maxCompletionTokens: 8000,
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Fn],
|
||||
},
|
||||
{
|
||||
idPrefix: 'llama3-groq-70b-8192-tool-use-preview',
|
||||
label: 'Llama 3 Groq · 70B Tool Use',
|
||||
description: 'LLaMA 3 70B Tool Use developed by Groq with a context window of 8,192 tokens. Optimized for tool use.',
|
||||
contextWindow: 8192,
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Fn],
|
||||
},
|
||||
{
|
||||
idPrefix: 'llama3-groq-8b-8192-tool-use-preview',
|
||||
label: 'Llama 3 Groq · 8B Tool Use',
|
||||
description: 'LLaMA 3 8B Tool Use developed by Groq with a context window of 8,192 tokens. Optimized for tool use.',
|
||||
contextWindow: 8192,
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Fn],
|
||||
},
|
||||
{
|
||||
idPrefix: 'llama3-70b-8192',
|
||||
label: 'Llama 3 · 70B',
|
||||
description: 'LLaMA3 70B developed by Meta with a context window of 8,192 tokens. Supports tool use.',
|
||||
description: 'LLaMA3 70b developed by Meta with a context window of 8,192 tokens.',
|
||||
contextWindow: 8192,
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Fn],
|
||||
// isLegacy: true,
|
||||
hidden: true,
|
||||
interfaces: [LLM_IF_OAI_Chat],
|
||||
},
|
||||
{
|
||||
// isLatest: true,
|
||||
idPrefix: 'llama3-8b-8192',
|
||||
label: 'Llama 3 · 8B',
|
||||
description: 'LLaMA3 8B developed by Meta with a context window of 8,192 tokens. Supports tool use.',
|
||||
description: 'LLaMA3 8b developed by Meta with a context window of 8,192 tokens.',
|
||||
contextWindow: 8192,
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Fn],
|
||||
// isLegacy: true,
|
||||
interfaces: [LLM_IF_OAI_Chat],
|
||||
},
|
||||
{
|
||||
idPrefix: 'llama2-70b-4096',
|
||||
label: 'Llama 2 · 70B',
|
||||
description: 'LLaMA2 70b developed by Meta with a context window of 4,096 tokens.',
|
||||
contextWindow: 4096,
|
||||
interfaces: [LLM_IF_OAI_Chat],
|
||||
hidden: true,
|
||||
},
|
||||
{
|
||||
idPrefix: 'mixtral-8x7b-32768',
|
||||
label: 'Mixtral 8x7B',
|
||||
description: 'Mixtral 8x7B developed by Mistral with a context window of 32,768 tokens. Supports tool use.',
|
||||
description: 'Mixtral 8x7b developed by Mistral with a context window of 32,768 tokens.',
|
||||
contextWindow: 32768,
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Fn],
|
||||
},
|
||||
{
|
||||
idPrefix: 'gemma2-9b-it',
|
||||
label: 'Gemma 2 · 9B Instruct',
|
||||
description: 'Gemma 2 9B developed by Google with a context window of 8,192 tokens. Supports tool use.',
|
||||
contextWindow: 8192,
|
||||
interfaces: [LLM_IF_OAI_Chat, LLM_IF_OAI_Fn],
|
||||
interfaces: [LLM_IF_OAI_Chat],
|
||||
},
|
||||
{
|
||||
idPrefix: 'gemma-7b-it',
|
||||
label: 'Gemma 1.1 · 7B Instruct',
|
||||
description: 'Gemma 7B developed by Google with a context window of 8,192 tokens. Supports tool use.',
|
||||
description: 'Gemma 7b developed by Google with a context window of 8,192 tokens.',
|
||||
contextWindow: 8192,
|
||||
interfaces: [LLM_IF_OAI_Chat],
|
||||
hidden: true,
|
||||
},
|
||||
];
|
||||
|
||||
@@ -1028,11 +864,6 @@ export function groqModelToModelDescription(_model: unknown): ModelDescriptionSc
|
||||
}
|
||||
|
||||
export function groqModelSortFn(a: ModelDescriptionSchema, b: ModelDescriptionSchema): number {
|
||||
// sort hidden at the end
|
||||
if (a.hidden && !b.hidden)
|
||||
return 1;
|
||||
if (!a.hidden && b.hidden)
|
||||
return -1;
|
||||
// sort as per their order in the known models
|
||||
const aIndex = _knownGroqModels.findIndex(base => a.id.startsWith(base.idPrefix));
|
||||
const bIndex = _knownGroqModels.findIndex(base => b.id.startsWith(base.idPrefix));
|
||||
@@ -1044,13 +875,7 @@ export function groqModelSortFn(a: ModelDescriptionSchema, b: ModelDescriptionSc
|
||||
|
||||
// Helpers
|
||||
|
||||
type ManualMapping = ({
|
||||
idPrefix: string,
|
||||
isLatest?: boolean,
|
||||
isPreview?: boolean,
|
||||
isLegacy?: boolean,
|
||||
symLink?: string
|
||||
} & Omit<ModelDescriptionSchema, 'id' | 'created' | 'updated'>);
|
||||
type ManualMapping = ({ idPrefix: string, isLatest?: boolean, isPreview?: boolean, isLegacy?: boolean, symLink?: string } & Omit<ModelDescriptionSchema, 'id' | 'created' | 'updated'>);
|
||||
type ManualMappings = ManualMapping[];
|
||||
|
||||
function fromManualMapping(mappings: ManualMappings, id: string, created?: number, updated?: number, fallback?: ManualMapping): ModelDescriptionSchema {
|
||||
|
||||
@@ -12,7 +12,7 @@ import { fixupHost } from '~/common/util/urlUtils';
|
||||
|
||||
import { OpenAIWire, WireOpenAICreateImageOutput, wireOpenAICreateImageOutputSchema, WireOpenAICreateImageRequest } from './openai.wiretypes';
|
||||
import { azureModelToModelDescription, groqModelSortFn, groqModelToModelDescription, lmStudioModelToModelDescription, localAIModelToModelDescription, mistralModelsSort, mistralModelToModelDescription, oobaboogaModelToModelDescription, openAIModelFilter, openAIModelToModelDescription, openRouterModelFamilySortFn, openRouterModelToModelDescription, perplexityAIModelDescriptions, perplexityAIModelSort, togetherAIModelsToModelDescriptions } from './models.data';
|
||||
import { llmsChatGenerateWithFunctionsOutputSchema, llmsGenerateContextSchema, llmsListModelsOutputSchema, ModelDescriptionSchema } from '../llm.server.types';
|
||||
import { llmsChatGenerateWithFunctionsOutputSchema, llmsListModelsOutputSchema, ModelDescriptionSchema } from '../llm.server.types';
|
||||
import { wilreLocalAIModelsApplyOutputSchema, wireLocalAIModelsAvailableOutputSchema, wireLocalAIModelsListOutputSchema } from './localai.wiretypes';
|
||||
|
||||
|
||||
@@ -72,11 +72,8 @@ const listModelsInputSchema = z.object({
|
||||
|
||||
const chatGenerateWithFunctionsInputSchema = z.object({
|
||||
access: openAIAccessSchema,
|
||||
model: openAIModelSchema,
|
||||
history: openAIHistorySchema,
|
||||
functions: openAIFunctionsSchema.optional(),
|
||||
forceFunctionName: z.string().optional(),
|
||||
context: llmsGenerateContextSchema.optional(),
|
||||
model: openAIModelSchema, history: openAIHistorySchema,
|
||||
functions: openAIFunctionsSchema.optional(), forceFunctionName: z.string().optional(),
|
||||
});
|
||||
|
||||
const createImagesInputSchema = z.object({
|
||||
@@ -111,7 +108,7 @@ export const llmOpenAIRouter = createTRPCRouter({
|
||||
|
||||
// [Azure]: use an older 'deployments' API to enumerate the models, and a modified OpenAI id to description mapping
|
||||
if (access.dialect === 'azure') {
|
||||
const azureModels = await openaiGETOrThrow(access, `/openai/deployments?api-version=2023-03-15-preview`);
|
||||
const azureModels = await openaiGET(access, `/openai/deployments?api-version=2023-03-15-preview`);
|
||||
|
||||
const wireAzureListDeploymentsSchema = z.object({
|
||||
data: z.array(z.object({
|
||||
@@ -149,7 +146,7 @@ export const llmOpenAIRouter = createTRPCRouter({
|
||||
|
||||
|
||||
// [non-Azure]: fetch openAI-style for all but Azure (will be then used in each dialect)
|
||||
const openAIWireModelsResponse = await openaiGETOrThrow<OpenAIWire.Models.Response>(access, '/v1/models');
|
||||
const openAIWireModelsResponse = await openaiGET<OpenAIWire.Models.Response>(access, '/v1/models');
|
||||
|
||||
// [Together] missing the .data property
|
||||
if (access.dialect === 'togetherai')
|
||||
@@ -270,22 +267,17 @@ export const llmOpenAIRouter = createTRPCRouter({
|
||||
.output(llmsChatGenerateWithFunctionsOutputSchema)
|
||||
.mutation(async ({ input }) => {
|
||||
|
||||
const { access, model, history, functions, forceFunctionName, context } = input;
|
||||
const { access, model, history, functions, forceFunctionName } = input;
|
||||
const isFunctionsCall = !!functions && functions.length > 0;
|
||||
|
||||
const completionsBody = openAIChatCompletionPayload(access.dialect, model, history, isFunctionsCall ? functions : null, forceFunctionName ?? null, 1, false);
|
||||
const wireCompletions = await openaiPOSTOrThrow<OpenAIWire.ChatCompletion.Response, OpenAIWire.ChatCompletion.Request>(
|
||||
const wireCompletions = await openaiPOST<OpenAIWire.ChatCompletion.Response, OpenAIWire.ChatCompletion.Request>(
|
||||
access, model.id, completionsBody, '/v1/chat/completions',
|
||||
);
|
||||
|
||||
// expect a single output
|
||||
if (wireCompletions?.choices?.length !== 1) {
|
||||
console.error(`[POST] llmOpenAI.chatGenerateWithFunctions: ${access.dialect}: ${context?.name || 'no context'}: unexpected output${forceFunctionName ? ` (fn: ${forceFunctionName})` : ''}:`, model.id, wireCompletions?.choices);
|
||||
throw new TRPCError({
|
||||
code: 'UNPROCESSABLE_CONTENT',
|
||||
message: `[OpenAI Issue] Expected 1 completion, got ${wireCompletions?.choices?.length}`,
|
||||
});
|
||||
}
|
||||
if (wireCompletions?.choices?.length !== 1)
|
||||
throw new TRPCError({ code: 'INTERNAL_SERVER_ERROR', message: `[OpenAI Issue] Expected 1 completion, got ${wireCompletions?.choices?.length}` });
|
||||
let { message, finish_reason } = wireCompletions.choices[0];
|
||||
|
||||
// LocalAI hack/workaround, until https://github.com/go-skynet/LocalAI/issues/788 is fixed
|
||||
@@ -326,7 +318,7 @@ export const llmOpenAIRouter = createTRPCRouter({
|
||||
delete requestBody.response_format;
|
||||
|
||||
// create 1 image (dall-e-3 won't support more than 1, so better transfer the burden to the client)
|
||||
const wireOpenAICreateImageOutput = await openaiPOSTOrThrow<WireOpenAICreateImageOutput, WireOpenAICreateImageRequest>(
|
||||
const wireOpenAICreateImageOutput = await openaiPOST<WireOpenAICreateImageOutput, WireOpenAICreateImageRequest>(
|
||||
access, null, requestBody, '/v1/images/generations',
|
||||
);
|
||||
|
||||
@@ -348,7 +340,7 @@ export const llmOpenAIRouter = createTRPCRouter({
|
||||
.mutation(async ({ input: { access, text } }): Promise<OpenAIWire.Moderation.Response> => {
|
||||
try {
|
||||
|
||||
return await openaiPOSTOrThrow<OpenAIWire.Moderation.Response, OpenAIWire.Moderation.Request>(access, null, {
|
||||
return await openaiPOST<OpenAIWire.Moderation.Response, OpenAIWire.Moderation.Request>(access, null, {
|
||||
input: text,
|
||||
model: 'text-moderation-latest',
|
||||
}, '/v1/moderations');
|
||||
@@ -369,7 +361,7 @@ export const llmOpenAIRouter = createTRPCRouter({
|
||||
dialectLocalAI_galleryModelsAvailable: publicProcedure
|
||||
.input(listModelsInputSchema)
|
||||
.query(async ({ input: { access } }) => {
|
||||
const wireLocalAIModelsAvailable = await openaiGETOrThrow(access, '/models/available');
|
||||
const wireLocalAIModelsAvailable = await openaiGET(access, '/models/available');
|
||||
return wireLocalAIModelsAvailableOutputSchema.parse(wireLocalAIModelsAvailable);
|
||||
}),
|
||||
|
||||
@@ -382,7 +374,7 @@ export const llmOpenAIRouter = createTRPCRouter({
|
||||
}))
|
||||
.mutation(async ({ input: { access, galleryName, modelName } }) => {
|
||||
const galleryModelId = `${galleryName}@${modelName}`;
|
||||
const wireLocalAIModelApply = await openaiPOSTOrThrow(access, null, { id: galleryModelId }, '/models/apply');
|
||||
const wireLocalAIModelApply = await openaiPOST(access, null, { id: galleryModelId }, '/models/apply');
|
||||
return wilreLocalAIModelsApplyOutputSchema.parse(wireLocalAIModelApply);
|
||||
}),
|
||||
|
||||
@@ -393,7 +385,7 @@ export const llmOpenAIRouter = createTRPCRouter({
|
||||
jobId: z.string(),
|
||||
}))
|
||||
.query(async ({ input: { access, jobId } }) => {
|
||||
const wireLocalAIModelsJobs = await openaiGETOrThrow(access, `/models/jobs/${jobId}`);
|
||||
const wireLocalAIModelsJobs = await openaiGET(access, `/models/jobs/${jobId}`);
|
||||
return wireLocalAIModelsListOutputSchema.parse(wireLocalAIModelsJobs);
|
||||
}),
|
||||
|
||||
@@ -631,12 +623,12 @@ export function openAIChatCompletionPayload(dialect: OpenAIDialects, model: Open
|
||||
};
|
||||
}
|
||||
|
||||
async function openaiGETOrThrow<TOut extends object>(access: OpenAIAccessSchema, apiPath: string /*, signal?: AbortSignal*/): Promise<TOut> {
|
||||
async function openaiGET<TOut extends object>(access: OpenAIAccessSchema, apiPath: string /*, signal?: AbortSignal*/): Promise<TOut> {
|
||||
const { headers, url } = openAIAccess(access, null, apiPath);
|
||||
return await fetchJsonOrTRPCError<TOut>(url, 'GET', headers, undefined, `OpenAI/${access.dialect}`);
|
||||
}
|
||||
|
||||
async function openaiPOSTOrThrow<TOut extends object, TPostBody extends object>(access: OpenAIAccessSchema, modelRefId: string | null, body: TPostBody, apiPath: string /*, signal?: AbortSignal*/): Promise<TOut> {
|
||||
async function openaiPOST<TOut extends object, TPostBody extends object>(access: OpenAIAccessSchema, modelRefId: string | null, body: TPostBody, apiPath: string /*, signal?: AbortSignal*/): Promise<TOut> {
|
||||
const { headers, url } = openAIAccess(access, modelRefId, apiPath);
|
||||
return await fetchJsonOrTRPCError<TOut, TPostBody>(url, 'POST', headers, body, `OpenAI/${access.dialect}`);
|
||||
}
|
||||
|
||||
+1
-3
@@ -8,7 +8,7 @@ import type { DLLM, DLLMId, DModelSourceId } from '../store-llms';
|
||||
import type { ModelDescriptionSchema } from '../server/llm.server.types';
|
||||
import type { ModelVendorId } from './vendors.registry';
|
||||
import type { StreamingClientUpdate } from './unifiedStreamingClient';
|
||||
import type { VChatContextRef, VChatFunctionIn, VChatGenerateContextName, VChatMessageIn, VChatMessageOrFunctionCallOut, VChatMessageOut, VChatStreamContextName } from '../llm.client';
|
||||
import type { VChatFunctionIn, VChatMessageIn, VChatMessageOrFunctionCallOut, VChatMessageOut } from '../llm.client';
|
||||
|
||||
|
||||
export interface IModelVendor<TSourceSetup = unknown, TAccess = unknown, TLLMOptions = unknown, TDLLM = DLLM<TSourceSetup, TLLMOptions>> {
|
||||
@@ -44,7 +44,6 @@ export interface IModelVendor<TSourceSetup = unknown, TAccess = unknown, TLLMOpt
|
||||
access: TAccess,
|
||||
llmOptions: TLLMOptions,
|
||||
messages: VChatMessageIn[],
|
||||
contextName: VChatGenerateContextName, contextRef: VChatContextRef | null,
|
||||
functions: VChatFunctionIn[] | null, forceFunctionName: string | null,
|
||||
maxTokens?: number,
|
||||
) => Promise<VChatMessageOut | VChatMessageOrFunctionCallOut>;
|
||||
@@ -54,7 +53,6 @@ export interface IModelVendor<TSourceSetup = unknown, TAccess = unknown, TLLMOpt
|
||||
llmId: DLLMId,
|
||||
llmOptions: TLLMOptions,
|
||||
messages: VChatMessageIn[],
|
||||
contextName: VChatStreamContextName, contextRef: VChatContextRef,
|
||||
functions: VChatFunctionIn[] | null, forceFunctionName: string | null,
|
||||
abortSignal: AbortSignal,
|
||||
onUpdate: (update: StreamingClientUpdate, done: boolean) => void,
|
||||
|
||||
@@ -2,7 +2,6 @@ import * as React from 'react';
|
||||
|
||||
import { Alert } from '@mui/joy';
|
||||
|
||||
import { AlreadySet } from '~/common/components/AlreadySet';
|
||||
import { ExternalLink } from '~/common/components/ExternalLink';
|
||||
import { FormInputKey } from '~/common/components/forms/FormInputKey';
|
||||
import { FormTextField } from '~/common/components/forms/FormTextField';
|
||||
@@ -50,7 +49,7 @@ export function AnthropicSourceSetup(props: { sourceId: DModelSourceId }) {
|
||||
autoCompleteId='anthropic-key' label={!!anthropicHost ? 'API Key' : 'Anthropic API Key'}
|
||||
rightLabel={<>{needsUserKey
|
||||
? !anthropicKey && <Link level='body-sm' href='https://www.anthropic.com/earlyaccess' target='_blank'>request Key</Link>
|
||||
: <AlreadySet />
|
||||
: '✔️ already set in server'
|
||||
} {anthropicKey && keyValid && <Link level='body-sm' href='https://console.anthropic.com/settings/usage' target='_blank'>show tokens usage</Link>}
|
||||
</>}
|
||||
value={anthropicKey} onChange={value => updateSetup({ anthropicKey: value })}
|
||||
|
||||
+2
-7
@@ -3,7 +3,7 @@ import { apiAsync } from '~/common/util/trpc.client';
|
||||
|
||||
import type { AnthropicAccessSchema } from '../../server/anthropic/anthropic.router';
|
||||
import type { IModelVendor } from '../IModelVendor';
|
||||
import type { VChatContextRef, VChatGenerateContextName, VChatMessageOut } from '../../llm.client';
|
||||
import type { VChatMessageOut } from '../../llm.client';
|
||||
import { unifiedStreamingClient } from '../unifiedStreamingClient';
|
||||
|
||||
import { FALLBACK_LLM_RESPONSE_TOKENS, FALLBACK_LLM_TEMPERATURE, LLMOptionsOpenAI } from '../openai/openai.vendor';
|
||||
@@ -47,7 +47,7 @@ export const ModelVendorAnthropic: IModelVendor<SourceSetupAnthropic, AnthropicA
|
||||
rpcUpdateModelsOrThrow: async (access) => await apiAsync.llmAnthropic.listModels.query({ access }),
|
||||
|
||||
// Chat Generate (non-streaming) with Functions
|
||||
rpcChatGenerateOrThrow: async (access, llmOptions, messages, contextName: VChatGenerateContextName, contextRef: VChatContextRef | null, functions, forceFunctionName, maxTokens) => {
|
||||
rpcChatGenerateOrThrow: async (access, llmOptions, messages, functions, forceFunctionName, maxTokens) => {
|
||||
if (functions?.length || forceFunctionName)
|
||||
throw new Error('Anthropic does not support functions');
|
||||
|
||||
@@ -61,11 +61,6 @@ export const ModelVendorAnthropic: IModelVendor<SourceSetupAnthropic, AnthropicA
|
||||
maxTokens: maxTokens || llmResponseTokens || FALLBACK_LLM_RESPONSE_TOKENS,
|
||||
},
|
||||
history: messages,
|
||||
context: contextRef ? {
|
||||
method: 'chat-generate',
|
||||
name: contextName,
|
||||
ref: contextRef,
|
||||
} : undefined,
|
||||
}) as VChatMessageOut;
|
||||
} catch (error: any) {
|
||||
const errorMessage = error?.message || error?.toString() || 'Anthropic Chat Generate Error';
|
||||
|
||||
+1
-2
@@ -1,6 +1,5 @@
|
||||
import * as React from 'react';
|
||||
|
||||
import { AlreadySet } from '~/common/components/AlreadySet';
|
||||
import { FormInputKey } from '~/common/components/forms/FormInputKey';
|
||||
import { FormTextField } from '~/common/components/forms/FormTextField';
|
||||
import { InlineError } from '~/common/components/InlineError';
|
||||
@@ -50,7 +49,7 @@ export function AzureSourceSetup(props: { sourceId: DModelSourceId }) {
|
||||
autoCompleteId='azure-key' label='Azure Key'
|
||||
rightLabel={<>{needsUserKey
|
||||
? !azureKey && <Link level='body-sm' href='https://azure.microsoft.com/en-us/products/ai-services/openai-service' target='_blank'>request Key</Link>
|
||||
: <AlreadySet />}
|
||||
: '✔️ already set in server'}
|
||||
</>}
|
||||
value={azureKey} onChange={value => updateSetup({ azureKey: value })}
|
||||
required={needsUserKey} isError={keyError}
|
||||
|
||||
+1
-2
@@ -3,7 +3,6 @@ import * as React from 'react';
|
||||
import { FormControl, FormHelperText, Option, Select } from '@mui/joy';
|
||||
import HealthAndSafetyIcon from '@mui/icons-material/HealthAndSafety';
|
||||
|
||||
import { AlreadySet } from '~/common/components/AlreadySet';
|
||||
import { FormInputKey } from '~/common/components/forms/FormInputKey';
|
||||
import { FormLabelStart } from '~/common/components/forms/FormLabelStart';
|
||||
import { InlineError } from '~/common/components/InlineError';
|
||||
@@ -51,7 +50,7 @@ export function GeminiSourceSetup(props: { sourceId: DModelSourceId }) {
|
||||
autoCompleteId='gemini-key' label='Gemini API Key'
|
||||
rightLabel={<>{needsUserKey
|
||||
? !geminiKey && <Link level='body-sm' href={GEMINI_API_KEY_LINK} target='_blank'>request Key</Link>
|
||||
: <AlreadySet />}
|
||||
: '✔️ already set in server'}
|
||||
</>}
|
||||
value={geminiKey} onChange={value => updateSetup({ geminiKey: value.trim() })}
|
||||
required={needsUserKey} isError={showKeyError}
|
||||
|
||||
+3
-8
@@ -1,10 +1,10 @@
|
||||
import { GeminiIcon } from '~/common/components/icons/vendors/GeminiIcon';
|
||||
import { apiAsync } from '~/common/util/trpc.client';
|
||||
import { apiAsync } from '~/common/util/trpc.client';
|
||||
|
||||
import type { GeminiAccessSchema } from '../../server/gemini/gemini.router';
|
||||
import type { GeminiBlockSafetyLevel } from '../../server/gemini/gemini.wiretypes';
|
||||
import type { IModelVendor } from '../IModelVendor';
|
||||
import type { VChatContextRef, VChatGenerateContextName, VChatMessageOut } from '../../llm.client';
|
||||
import type { VChatMessageOut } from '../../llm.client';
|
||||
import { unifiedStreamingClient } from '../unifiedStreamingClient';
|
||||
|
||||
import { FALLBACK_LLM_RESPONSE_TOKENS, FALLBACK_LLM_TEMPERATURE } from '../openai/openai.vendor';
|
||||
@@ -60,7 +60,7 @@ export const ModelVendorGemini: IModelVendor<SourceSetupGemini, GeminiAccessSche
|
||||
rpcUpdateModelsOrThrow: async (access) => await apiAsync.llmGemini.listModels.query({ access }),
|
||||
|
||||
// Chat Generate (non-streaming) with Functions
|
||||
rpcChatGenerateOrThrow: async (access, llmOptions, messages, contextName: VChatGenerateContextName, contextRef: VChatContextRef | null, functions, forceFunctionName, maxTokens) => {
|
||||
rpcChatGenerateOrThrow: async (access, llmOptions, messages, functions, forceFunctionName, maxTokens) => {
|
||||
if (functions?.length || forceFunctionName)
|
||||
throw new Error('Gemini does not support functions');
|
||||
|
||||
@@ -74,11 +74,6 @@ export const ModelVendorGemini: IModelVendor<SourceSetupGemini, GeminiAccessSche
|
||||
maxTokens: maxTokens || maxOutputTokens || FALLBACK_LLM_RESPONSE_TOKENS,
|
||||
},
|
||||
history: messages,
|
||||
context: contextRef ? {
|
||||
method: 'chat-generate',
|
||||
name: contextName,
|
||||
ref: contextRef,
|
||||
} : undefined,
|
||||
}) as VChatMessageOut;
|
||||
} catch (error: any) {
|
||||
const errorMessage = error?.message || error?.toString() || 'Gemini Chat Generate Error';
|
||||
|
||||
+1
-2
@@ -2,7 +2,6 @@ import * as React from 'react';
|
||||
|
||||
import { Typography } from '@mui/joy';
|
||||
|
||||
import { AlreadySet } from '~/common/components/AlreadySet';
|
||||
import { FormInputKey } from '~/common/components/forms/FormInputKey';
|
||||
import { InlineError } from '~/common/components/InlineError';
|
||||
import { Link } from '~/common/components/Link';
|
||||
@@ -43,7 +42,7 @@ export function GroqSourceSetup(props: { sourceId: DModelSourceId }) {
|
||||
autoCompleteId='groq-key' label='Groq API Key'
|
||||
rightLabel={<>{needsUserKey
|
||||
? !groqKey && <Link level='body-sm' href={GROQ_REG_LINK} target='_blank'>API keys</Link>
|
||||
: <AlreadySet />}
|
||||
: '✔️ already set in server'}
|
||||
</>}
|
||||
value={groqKey} onChange={value => updateSetup({ groqKey: value })}
|
||||
required={needsUserKey} isError={showKeyError}
|
||||
|
||||
+1
-2
@@ -6,7 +6,6 @@ import CheckBoxOutlinedIcon from '@mui/icons-material/CheckBoxOutlined';
|
||||
|
||||
import { getBackendCapabilities } from '~/modules/backend/store-backend-capabilities';
|
||||
|
||||
import { AlreadySet } from '~/common/components/AlreadySet';
|
||||
import { ExpanderAccordion } from '~/common/components/ExpanderAccordion';
|
||||
import { FormInputKey } from '~/common/components/forms/FormInputKey';
|
||||
import { InlineError } from '~/common/components/InlineError';
|
||||
@@ -82,7 +81,7 @@ export function LocalAISourceSetup(props: { sourceId: DModelSourceId }) {
|
||||
noKey
|
||||
required={userHostRequired}
|
||||
isError={userHostError}
|
||||
rightLabel={backendHasHost ? <AlreadySet /> : <Link level='body-sm' href='https://localai.io' target='_blank'>Learn more</Link>}
|
||||
rightLabel={backendHasHost ? '✔️ already set in server' : <Link level='body-sm' href='https://localai.io' target='_blank'>Learn more</Link>}
|
||||
value={localAIHost} onChange={value => updateSetup({ localAIHost: value })}
|
||||
/>
|
||||
|
||||
|
||||
+1
-2
@@ -2,7 +2,6 @@ import * as React from 'react';
|
||||
|
||||
import { Typography } from '@mui/joy';
|
||||
|
||||
import { AlreadySet } from '~/common/components/AlreadySet';
|
||||
import { FormInputKey } from '~/common/components/forms/FormInputKey';
|
||||
import { InlineError } from '~/common/components/InlineError';
|
||||
import { Link } from '~/common/components/Link';
|
||||
@@ -40,7 +39,7 @@ export function MistralSourceSetup(props: { sourceId: DModelSourceId }) {
|
||||
autoCompleteId='mistral-key' label='Mistral Key'
|
||||
rightLabel={<>{needsUserKey
|
||||
? !mistralKey && <Link level='body-sm' href={MISTRAL_REG_LINK} target='_blank'>request Key</Link>
|
||||
: <AlreadySet />}
|
||||
: '✔️ already set in server'}
|
||||
</>}
|
||||
value={mistralKey} onChange={value => updateSetup({ oaiKey: value })}
|
||||
required={needsUserKey} isError={showKeyError}
|
||||
|
||||
+2
-7
@@ -3,7 +3,7 @@ import { apiAsync } from '~/common/util/trpc.client';
|
||||
|
||||
import type { IModelVendor } from '../IModelVendor';
|
||||
import type { OllamaAccessSchema } from '../../server/ollama/ollama.router';
|
||||
import type { VChatContextRef, VChatGenerateContextName, VChatMessageOut } from '../../llm.client';
|
||||
import type { VChatMessageOut } from '../../llm.client';
|
||||
import { unifiedStreamingClient } from '../unifiedStreamingClient';
|
||||
|
||||
import { FALLBACK_LLM_RESPONSE_TOKENS, FALLBACK_LLM_TEMPERATURE, LLMOptionsOpenAI } from '../openai/openai.vendor';
|
||||
@@ -42,7 +42,7 @@ export const ModelVendorOllama: IModelVendor<SourceSetupOllama, OllamaAccessSche
|
||||
rpcUpdateModelsOrThrow: async (access) => await apiAsync.llmOllama.listModels.query({ access }),
|
||||
|
||||
// Chat Generate (non-streaming) with Functions
|
||||
rpcChatGenerateOrThrow: async (access, llmOptions, messages, contextName: VChatGenerateContextName, contextRef: VChatContextRef | null, functions, forceFunctionName, maxTokens) => {
|
||||
rpcChatGenerateOrThrow: async (access, llmOptions, messages, functions, forceFunctionName, maxTokens) => {
|
||||
if (functions?.length || forceFunctionName)
|
||||
throw new Error('Ollama does not support functions');
|
||||
|
||||
@@ -56,11 +56,6 @@ export const ModelVendorOllama: IModelVendor<SourceSetupOllama, OllamaAccessSche
|
||||
maxTokens: maxTokens || llmResponseTokens || FALLBACK_LLM_RESPONSE_TOKENS,
|
||||
},
|
||||
history: messages,
|
||||
context: contextRef ? {
|
||||
method: 'chat-generate',
|
||||
name: contextName,
|
||||
ref: contextRef,
|
||||
} : undefined,
|
||||
}) as VChatMessageOut;
|
||||
} catch (error: any) {
|
||||
const errorMessage = error?.message || error?.toString() || 'Ollama Chat Generate Error';
|
||||
|
||||
+1
-2
@@ -2,7 +2,6 @@ import * as React from 'react';
|
||||
|
||||
import { Alert } from '@mui/joy';
|
||||
|
||||
import { AlreadySet } from '~/common/components/AlreadySet';
|
||||
import { Brand } from '~/common/app.config';
|
||||
import { FormInputKey } from '~/common/components/forms/FormInputKey';
|
||||
import { FormSwitchControl } from '~/common/components/forms/FormSwitchControl';
|
||||
@@ -49,7 +48,7 @@ export function OpenAISourceSetup(props: { sourceId: DModelSourceId }) {
|
||||
autoCompleteId='openai-key' label='API Key'
|
||||
rightLabel={<>{needsUserKey
|
||||
? !oaiKey && <><Link level='body-sm' href='https://platform.openai.com/account/api-keys' target='_blank'>create key</Link> and <Link level='body-sm' href='https://openai.com/waitlist/gpt-4-api' target='_blank'>request access to GPT-4</Link></>
|
||||
: <AlreadySet />
|
||||
: '✔️ already set in server'
|
||||
} {oaiKey && keyValid && <Link level='body-sm' href='https://platform.openai.com/account/usage' target='_blank'>check usage</Link>}
|
||||
</>}
|
||||
value={oaiKey} onChange={value => updateSetup({ oaiKey: value })}
|
||||
|
||||
+2
-7
@@ -3,7 +3,7 @@ import { apiAsync } from '~/common/util/trpc.client';
|
||||
|
||||
import type { IModelVendor } from '../IModelVendor';
|
||||
import type { OpenAIAccessSchema } from '../../server/openai/openai.router';
|
||||
import type { VChatContextRef, VChatGenerateContextName, VChatMessageOrFunctionCallOut } from '../../llm.client';
|
||||
import type { VChatMessageOrFunctionCallOut } from '../../llm.client';
|
||||
import { unifiedStreamingClient } from '../unifiedStreamingClient';
|
||||
|
||||
import { OpenAILLMOptions } from './OpenAILLMOptions';
|
||||
@@ -60,7 +60,7 @@ export const ModelVendorOpenAI: IModelVendor<SourceSetupOpenAI, OpenAIAccessSche
|
||||
rpcUpdateModelsOrThrow: async (access) => await apiAsync.llmOpenAI.listModels.query({ access }),
|
||||
|
||||
// Chat Generate (non-streaming) with Functions
|
||||
rpcChatGenerateOrThrow: async (access, llmOptions, messages, contextName: VChatGenerateContextName, contextRef: VChatContextRef | null, functions, forceFunctionName, maxTokens) => {
|
||||
rpcChatGenerateOrThrow: async (access, llmOptions, messages, functions, forceFunctionName, maxTokens) => {
|
||||
const { llmRef, llmTemperature, llmResponseTokens } = llmOptions;
|
||||
try {
|
||||
return await apiAsync.llmOpenAI.chatGenerateWithFunctions.mutate({
|
||||
@@ -73,11 +73,6 @@ export const ModelVendorOpenAI: IModelVendor<SourceSetupOpenAI, OpenAIAccessSche
|
||||
functions: functions ?? undefined,
|
||||
forceFunctionName: forceFunctionName ?? undefined,
|
||||
history: messages,
|
||||
context: contextRef ? {
|
||||
method: 'chat-generate',
|
||||
name: contextName,
|
||||
ref: contextRef,
|
||||
} : undefined,
|
||||
}) as VChatMessageOrFunctionCallOut;
|
||||
} catch (error: any) {
|
||||
const errorMessage = error?.message || error?.toString() || 'OpenAI Chat Generate Error';
|
||||
|
||||
@@ -2,7 +2,6 @@ import * as React from 'react';
|
||||
|
||||
import { Button, Typography } from '@mui/joy';
|
||||
|
||||
import { AlreadySet } from '~/common/components/AlreadySet';
|
||||
import { FormInputKey } from '~/common/components/forms/FormInputKey';
|
||||
import { InlineError } from '~/common/components/InlineError';
|
||||
import { Link } from '~/common/components/Link';
|
||||
@@ -57,7 +56,7 @@ export function OpenRouterSourceSetup(props: { sourceId: DModelSourceId }) {
|
||||
autoCompleteId='openrouter-key' label='OpenRouter API Key'
|
||||
rightLabel={<>{needsUserKey
|
||||
? !oaiKey && <Link level='body-sm' href='https://openrouter.ai/keys' target='_blank'>your keys</Link>
|
||||
: <AlreadySet />
|
||||
: '✔️ already set in server'
|
||||
} {oaiKey && keyValid && <Link level='body-sm' href='https://openrouter.ai/activity' target='_blank'>check usage</Link>}
|
||||
</>}
|
||||
value={oaiKey} onChange={value => updateSetup({ oaiKey: value })}
|
||||
|
||||
@@ -2,7 +2,6 @@ import * as React from 'react';
|
||||
|
||||
import { Typography } from '@mui/joy';
|
||||
|
||||
import { AlreadySet } from '~/common/components/AlreadySet';
|
||||
import { FormInputKey } from '~/common/components/forms/FormInputKey';
|
||||
import { InlineError } from '~/common/components/InlineError';
|
||||
import { Link } from '~/common/components/Link';
|
||||
@@ -43,7 +42,7 @@ export function PerplexitySourceSetup(props: { sourceId: DModelSourceId }) {
|
||||
autoCompleteId='perplexity-key' label='Perplexity API Key'
|
||||
rightLabel={<>{needsUserKey
|
||||
? !perplexityKey && <Link level='body-sm' href={PERPLEXITY_REG_LINK} target='_blank'>API keys</Link>
|
||||
: <AlreadySet />}
|
||||
: '✔️ already set in server'}
|
||||
</>}
|
||||
value={perplexityKey} onChange={value => updateSetup({ perplexityKey: value })}
|
||||
required={needsUserKey} isError={showKeyError}
|
||||
|
||||
@@ -2,7 +2,6 @@ import * as React from 'react';
|
||||
|
||||
import { Alert, Typography } from '@mui/joy';
|
||||
|
||||
import { AlreadySet } from '~/common/components/AlreadySet';
|
||||
import { FormInputKey } from '~/common/components/forms/FormInputKey';
|
||||
import { FormSwitchControl } from '~/common/components/forms/FormSwitchControl';
|
||||
import { InlineError } from '~/common/components/InlineError';
|
||||
@@ -49,7 +48,7 @@ export function TogetherAISourceSetup(props: { sourceId: DModelSourceId }) {
|
||||
autoCompleteId='togetherai-key' label='Together AI Key'
|
||||
rightLabel={<>{needsUserKey
|
||||
? !togetherKey && <Link level='body-sm' href={TOGETHERAI_REG_LINK} target='_blank'>request Key</Link>
|
||||
: <AlreadySet />}
|
||||
: '✔️ already set in server'}
|
||||
</>}
|
||||
value={togetherKey} onChange={value => updateSetup({ togetherKey: value })}
|
||||
required={needsUserKey} isError={showKeyError}
|
||||
|
||||
+1
-7
@@ -3,7 +3,7 @@ import { frontendSideFetch } from '~/common/util/clientFetchers';
|
||||
|
||||
import type { ChatStreamingInputSchema, ChatStreamingPreambleModelSchema, ChatStreamingPreambleStartSchema } from '../server/llm.server.streaming';
|
||||
import type { DLLMId } from '../store-llms';
|
||||
import type { VChatContextRef, VChatFunctionIn, VChatMessageIn, VChatStreamContextName } from '../llm.client';
|
||||
import type { VChatFunctionIn, VChatMessageIn } from '../llm.client';
|
||||
|
||||
import type { OpenAIAccessSchema } from '../server/openai/openai.router';
|
||||
import type { OpenAIWire } from '../server/openai/openai.wiretypes';
|
||||
@@ -29,7 +29,6 @@ export async function unifiedStreamingClient<TSourceSetup = unknown, TLLMOptions
|
||||
llmId: DLLMId,
|
||||
llmOptions: TLLMOptions,
|
||||
messages: VChatMessageIn[],
|
||||
contextName: VChatStreamContextName, contextRef: VChatContextRef,
|
||||
functions: VChatFunctionIn[] | null, forceFunctionName: string | null,
|
||||
abortSignal: AbortSignal,
|
||||
onUpdate: (update: StreamingClientUpdate, done: boolean) => void,
|
||||
@@ -56,11 +55,6 @@ export async function unifiedStreamingClient<TSourceSetup = unknown, TLLMOptions
|
||||
...(llmResponseTokens ? { maxTokens: llmResponseTokens } : {}),
|
||||
},
|
||||
history: messages,
|
||||
context: {
|
||||
method: 'chat-stream',
|
||||
name: contextName, // this errors if the client VChatContextName mismatches the server z.enum
|
||||
ref: contextRef,
|
||||
},
|
||||
};
|
||||
|
||||
// connect to the server-side streaming endpoint
|
||||
|
||||
@@ -10,7 +10,6 @@ import StayPrimaryPortraitIcon from '@mui/icons-material/StayPrimaryPortrait';
|
||||
|
||||
import { getBackendCapabilities } from '~/modules/backend/store-backend-capabilities';
|
||||
|
||||
import { AlreadySet } from '~/common/components/AlreadySet';
|
||||
import { FormInputKey } from '~/common/components/forms/FormInputKey';
|
||||
import { FormLabelStart } from '~/common/components/forms/FormLabelStart';
|
||||
import { FormRadioControl } from '~/common/components/forms/FormRadioControl';
|
||||
@@ -81,7 +80,7 @@ export function ProdiaSettings(props: { noSkipKey?: boolean }) {
|
||||
|
||||
{!backendHasProdia && !!props.noSkipKey && <FormInputKey
|
||||
autoCompleteId='prodia-key' label='Prodia API Key'
|
||||
rightLabel={<AlreadySet required={!backendHasProdia} />}
|
||||
rightLabel={backendHasProdia ? '✔️ already set in server' : 'required'}
|
||||
value={apiKey} onChange={setApiKey}
|
||||
required={!backendHasProdia} isError={!isValidKey}
|
||||
/>}
|
||||
|
||||
Reference in New Issue
Block a user