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import { HF_ACCESS_TOKEN, MODELS, OLD_MODELS } from "$env/static/private";
import type {
ChatTemplateInput,
WebSearchQueryTemplateInput,
WebSearchSummaryTemplateInput,
} from "$lib/types/Template";
import { compileTemplate } from "$lib/utils/template";
import { z } from "zod";
type Optional<T, K extends keyof T> = Pick<Partial<T>, K> & Omit<T, K>;
const sagemakerEndpoint = z.object({
host: z.literal("sagemaker"),
url: z.string().url(),
accessKey: z.string().min(1),
secretKey: z.string().min(1),
sessionToken: z.string().optional(),
});
const tgiEndpoint = z.object({
host: z.union([z.literal("tgi"), z.undefined()]),
url: z.string().url(),
authorization: z.string().min(1).default(`Bearer ${HF_ACCESS_TOKEN}`),
});
const localEndpoint = z.object({
host: z.union([z.literal("local"), z.undefined()]),
model: z.string(),
url: z.string().url(),
authorization: z.string().min(1).default(`Bearer ${HF_ACCESS_TOKEN}`),
});
const commonEndpoint = z.object({
weight: z.number().int().positive().default(1),
});
const endpoint = z.lazy(() =>
z.union([
sagemakerEndpoint.merge(commonEndpoint),
tgiEndpoint.merge(commonEndpoint),
localEndpoint.merge(commonEndpoint),
])
);
const combinedEndpoint = endpoint.transform((data) => {
if (data.host === "tgi" || data.host === undefined) {
return tgiEndpoint.merge(commonEndpoint).parse(data);
} else if (data.host === "sagemaker") {
return sagemakerEndpoint.merge(commonEndpoint).parse(data);
} else if (data.host === "local") {
return localEndpoint.merge(commonEndpoint).parse(data);
} else {
throw new Error(`Invalid host: ${data.host}`);
}
});
const modelsRaw = z
.array(
z.object({
/** Used as an identifier in DB */
id: z.string().optional(),
/** Used to link to the model page, and for inference */
name: z.string().min(1),
displayName: z.string().min(1).optional(),
description: z.string().min(1).optional(),
is_local: z.boolean().optional(),
websiteUrl: z.string().url().optional(),
modelUrl: z.string().url().optional(),
datasetName: z.string().min(1).optional(),
datasetUrl: z.string().url().optional(),
userMessageToken: z.string().default(""),
userMessageEndToken: z.string().default(""),
assistantMessageToken: z.string().default(""),
assistantMessageEndToken: z.string().default(""),
messageEndToken: z.string().default(""),
preprompt: z.string().default(""),
prepromptUrl: z.string().url().optional(),
chatPromptTemplate: z
.string()
.default(
"{{preprompt}}" +
"{{#each messages}}" +
"{{#ifUser}}{{@root.userMessageToken}}{{content}}{{@root.userMessageEndToken}}{{/ifUser}}" +
"{{#ifAssistant}}{{@root.assistantMessageToken}}{{content}}{{@root.assistantMessageEndToken}}{{/ifAssistant}}" +
"{{/each}}" +
"{{assistantMessageToken}}"
),
webSearchSummaryPromptTemplate: z
.string()
.default(
"{{userMessageToken}}{{answer}}{{userMessageEndToken}}" +
"{{userMessageToken}}" +
"The text above should be summarized to best answer the query: {{query}}." +
"{{userMessageEndToken}}" +
"{{assistantMessageToken}}Summary: "
),
webSearchQueryPromptTemplate: z
.string()
.default(
"{{userMessageToken}}" +
"The following messages were written by a user, trying to answer a question." +
"{{userMessageEndToken}}" +
"{{#each messages}}" +
"{{#ifUser}}{{@root.userMessageToken}}{{content}}{{@root.userMessageEndToken}}{{/ifUser}}" +
"{{/each}}" +
"{{userMessageToken}}" +
"What plain-text english sentence would you input into Google to answer the last question? Answer with a short (10 words max) simple sentence." +
"{{userMessageEndToken}}" +
"{{assistantMessageToken}}Query: "
),
promptExamples: z
.array(
z.object({
title: z.string().min(1),
prompt: z.string().min(1),
})
)
.optional(),
endpoints: z.array(combinedEndpoint).optional(),
parameters: z
.object({
temperature: z.number().min(0).max(1),
truncate: z.number().int().positive(),
max_new_tokens: z.number().int().positive(),
stop: z.array(z.string()).optional(),
})
.passthrough()
.optional(),
})
)
.parse(JSON.parse(MODELS));
export const models = await Promise.all(
modelsRaw.map(async (m) => ({
...m,
userMessageEndToken: m?.userMessageEndToken || m?.messageEndToken,
assistantMessageEndToken: m?.assistantMessageEndToken || m?.messageEndToken,
chatPromptRender: compileTemplate<ChatTemplateInput>(m.chatPromptTemplate, m),
webSearchSummaryPromptRender: compileTemplate<WebSearchSummaryTemplateInput>(
m.webSearchSummaryPromptTemplate,
m
),
webSearchQueryPromptRender: compileTemplate<WebSearchQueryTemplateInput>(
m.webSearchQueryPromptTemplate,
m
),
id: m.id || m.name,
displayName: m.displayName || m.name,
preprompt: m.prepromptUrl ? await fetch(m.prepromptUrl).then((r) => r.text()) : m.preprompt,
}))
);
// Models that have been deprecated
export const oldModels = OLD_MODELS
? z
.array(
z.object({
id: z.string().optional(),
name: z.string().min(1),
displayName: z.string().min(1).optional(),
})
)
.parse(JSON.parse(OLD_MODELS))
.map((m) => ({ ...m, id: m.id || m.name, displayName: m.displayName || m.name }))
: [];
export type BackendModel = Optional<(typeof models)[0], "preprompt">;
export type Endpoint = z.infer<typeof endpoint>;
export const defaultModel = models[0];
export const validateModel = (_models: BackendModel[]) => {
// Zod enum function requires 2 parameters
return z.enum([_models[0].id, ..._models.slice(1).map((m) => m.id)]);
};
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