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import {
VertexAI,
HarmCategory,
HarmBlockThreshold,
type Content,
type TextPart,
} from "@google-cloud/vertexai";
import type { Endpoint, TextGenerationStreamOutputWithToolsAndWebSources } from "../endpoints";
import { z } from "zod";
import type { Message } from "$lib/types/Message";
import { createImageProcessorOptionsValidator, makeImageProcessor } from "../images";
import { createDocumentProcessorOptionsValidator, makeDocumentProcessor } from "../document";
export const endpointVertexParametersSchema = z.object({
weight: z.number().int().positive().default(1),
model: z.any(), // allow optional and validate against emptiness
type: z.literal("vertex"),
location: z.string().default("europe-west1"),
extraBody: z.object({ model_version: z.string() }).optional(),
project: z.string(),
apiEndpoint: z.string().optional(),
safetyThreshold: z
.enum([
HarmBlockThreshold.HARM_BLOCK_THRESHOLD_UNSPECIFIED,
HarmBlockThreshold.BLOCK_LOW_AND_ABOVE,
HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
HarmBlockThreshold.BLOCK_NONE,
HarmBlockThreshold.BLOCK_ONLY_HIGH,
])
.optional(),
tools: z.array(z.any()).optional(),
multimodal: z
.object({
image: createImageProcessorOptionsValidator({
supportedMimeTypes: [
"image/png",
"image/jpeg",
"image/webp",
"image/avif",
"image/tiff",
"image/gif",
],
preferredMimeType: "image/webp",
maxSizeInMB: 20,
maxWidth: 4096,
maxHeight: 4096,
}),
document: createDocumentProcessorOptionsValidator({
supportedMimeTypes: ["application/pdf", "text/plain"],
maxSizeInMB: 20,
}),
})
.default({}),
});
export function endpointVertex(input: z.input<typeof endpointVertexParametersSchema>): Endpoint {
const { project, location, model, apiEndpoint, safetyThreshold, tools, multimodal, extraBody } =
endpointVertexParametersSchema.parse(input);
const vertex_ai = new VertexAI({
project,
location,
apiEndpoint,
});
return async ({ messages, preprompt, generateSettings }) => {
const parameters = { ...model.parameters, ...generateSettings };
const hasFiles = messages.some((message) => message.files && message.files.length > 0);
const generativeModel = vertex_ai.getGenerativeModel({
model: extraBody?.model_version ?? model.id ?? model.name,
safetySettings: safetyThreshold
? [
{
category: HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT,
threshold: safetyThreshold,
},
{
category: HarmCategory.HARM_CATEGORY_HARASSMENT,
threshold: safetyThreshold,
},
{
category: HarmCategory.HARM_CATEGORY_HATE_SPEECH,
threshold: safetyThreshold,
},
{
category: HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT,
threshold: safetyThreshold,
},
{
category: HarmCategory.HARM_CATEGORY_UNSPECIFIED,
threshold: safetyThreshold,
},
]
: undefined,
generationConfig: {
maxOutputTokens: parameters?.max_new_tokens ?? 4096,
stopSequences: parameters?.stop,
temperature: parameters?.temperature ?? 1,
},
// tools and multimodal are mutually exclusive
tools: !hasFiles ? tools : undefined,
});
// Preprompt is the same as the first system message.
let systemMessage = preprompt;
if (messages[0].from === "system") {
systemMessage = messages[0].content;
messages.shift();
}
const vertexMessages = await Promise.all(
messages.map(async ({ from, content, files }: Omit<Message, "id">): Promise<Content> => {
const imageProcessor = makeImageProcessor(multimodal.image);
const documentProcessor = makeDocumentProcessor(multimodal.document);
const processedFilesWithNull =
files && files.length > 0
? await Promise.all(
files.map(async (file) => {
if (file.mime.includes("image")) {
const { image, mime } = await imageProcessor(file);
return { file: image, mime };
} else if (file.mime === "application/pdf" || file.mime === "text/plain") {
return documentProcessor(file);
}
return null;
})
)
: [];
const processedFiles = processedFilesWithNull.filter((file) => file !== null);
return {
role: from === "user" ? "user" : "model",
parts: [
...processedFiles.map((processedFile) => ({
inlineData: {
data: processedFile.file.toString("base64"),
mimeType: processedFile.mime,
},
})),
{
text: content,
},
],
};
})
);
const result = await generativeModel.generateContentStream({
contents: vertexMessages,
systemInstruction: systemMessage
? {
role: "system",
parts: [
{
text: systemMessage,
},
],
}
: undefined,
});
let tokenId = 0;
return (async function* () {
let generatedText = "";
const webSources = [];
for await (const data of result.stream) {
if (!data?.candidates?.length) break; // Handle case where no candidates are present
const candidate = data.candidates[0];
if (!candidate.content?.parts?.length) continue; // Skip if no parts are present
const firstPart = candidate.content.parts.find((part) => "text" in part) as
| TextPart
| undefined;
if (!firstPart) continue; // Skip if no text part is found
const isLastChunk = !!candidate.finishReason;
const candidateWebSources = candidate.groundingMetadata?.groundingChunks
?.map((chunk) => {
const uri = chunk.web?.uri ?? chunk.retrievedContext?.uri;
const title = chunk.web?.title ?? chunk.retrievedContext?.title;
if (!uri || !title) {
return null;
}
return {
uri,
title,
};
})
.filter((source) => source !== null);
if (candidateWebSources) {
webSources.push(...candidateWebSources);
}
const content = firstPart.text;
generatedText += content;
const output: TextGenerationStreamOutputWithToolsAndWebSources = {
token: {
id: tokenId++,
text: content,
logprob: 0,
special: isLastChunk,
},
generated_text: isLastChunk ? generatedText : null,
details: null,
webSources,
};
yield output;
if (isLastChunk) break;
}
})();
};
}
export default endpointVertex;
|