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import { z } from "zod";
import { openAICompletionToTextGenerationStream } from "./openAICompletionToTextGenerationStream";
import { openAIChatToTextGenerationStream } from "./openAIChatToTextGenerationStream";
import type { CompletionCreateParamsStreaming } from "openai/resources/completions";
import type {
ChatCompletionCreateParamsStreaming,
ChatCompletionTool,
} from "openai/resources/chat/completions";
import type { FunctionDefinition, FunctionParameters } from "openai/resources/shared";
import { buildPrompt } from "$lib/buildPrompt";
import { env } from "$env/dynamic/private";
import type { Endpoint } from "../endpoints";
import type OpenAI from "openai";
import { createImageProcessorOptionsValidator, makeImageProcessor } from "../images";
import type { MessageFile } from "$lib/types/Message";
import { type Tool } from "$lib/types/Tool";
import type { EndpointMessage } from "../endpoints";
import { v4 as uuidv4 } from "uuid";
function createChatCompletionToolsArray(tools: Tool[] | undefined): ChatCompletionTool[] {
const toolChoices = [] as ChatCompletionTool[];
if (tools === undefined) {
return toolChoices;
}
for (const t of tools) {
const requiredProperties = [] as string[];
const properties = {} as Record<string, unknown>;
for (const idx in t.inputs) {
const parameterDefinition = t.inputs[idx];
const parameter = {} as Record<string, unknown>;
switch (parameterDefinition.type) {
case "str":
parameter.type = "string";
break;
case "float":
case "int":
parameter.type = "number";
break;
case "bool":
parameter.type = "boolean";
break;
case "file":
throw new Error("File type's currently not supported");
default:
throw new Error(`Unknown tool IO type: ${t}`);
}
if ("description" in parameterDefinition) {
parameter.description = parameterDefinition.description;
}
if (parameterDefinition.paramType == "required") {
requiredProperties.push(t.inputs[idx].name);
}
properties[t.inputs[idx].name] = parameter;
}
const functionParameters: FunctionParameters = {
type: "object",
...(requiredProperties.length > 0 ? { required: requiredProperties } : {}),
properties,
};
const functionDefinition: FunctionDefinition = {
name: t.name,
description: t.description,
parameters: functionParameters,
};
const toolDefinition: ChatCompletionTool = {
type: "function",
function: functionDefinition,
};
toolChoices.push(toolDefinition);
}
return toolChoices;
}
export const endpointOAIParametersSchema = z.object({
weight: z.number().int().positive().default(1),
model: z.any(),
type: z.literal("openai"),
baseURL: z.string().url().default("https://api.openai.com/v1"),
apiKey: z.string().default(env.OPENAI_API_KEY || env.HF_TOKEN || "sk-"),
completion: z
.union([z.literal("completions"), z.literal("chat_completions")])
.default("chat_completions"),
defaultHeaders: z.record(z.string()).optional(),
defaultQuery: z.record(z.string()).optional(),
extraBody: z.record(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: Infinity,
maxWidth: 4096,
maxHeight: 4096,
}),
})
.default({}),
/* enable use of max_completion_tokens in place of max_tokens */
useCompletionTokens: z.boolean().default(false),
});
export async function endpointOai(
input: z.input<typeof endpointOAIParametersSchema>
): Promise<Endpoint> {
const {
baseURL,
apiKey,
completion,
model,
defaultHeaders,
defaultQuery,
multimodal,
extraBody,
useCompletionTokens,
} = endpointOAIParametersSchema.parse(input);
let OpenAI;
try {
OpenAI = (await import("openai")).OpenAI;
} catch (e) {
throw new Error("Failed to import OpenAI", { cause: e });
}
const openai = new OpenAI({
apiKey: apiKey || "sk-",
baseURL,
defaultHeaders,
defaultQuery,
});
const imageProcessor = makeImageProcessor(multimodal.image);
if (completion === "completions") {
if (model.tools) {
throw new Error(
"Tools are not supported for 'completions' mode, switch to 'chat_completions' instead"
);
}
return async ({ messages, preprompt, continueMessage, generateSettings, conversationId }) => {
const prompt = await buildPrompt({
messages,
continueMessage,
preprompt,
model,
});
const parameters = { ...model.parameters, ...generateSettings };
const body: CompletionCreateParamsStreaming = {
model: model.id ?? model.name,
prompt,
stream: true,
max_tokens: parameters?.max_new_tokens,
stop: parameters?.stop,
temperature: parameters?.temperature,
top_p: parameters?.top_p,
frequency_penalty: parameters?.repetition_penalty,
presence_penalty: parameters?.presence_penalty,
};
const openAICompletion = await openai.completions.create(body, {
body: { ...body, ...extraBody },
headers: {
"ChatUI-Conversation-ID": conversationId?.toString() ?? "",
"X-use-cache": "false",
},
});
return openAICompletionToTextGenerationStream(openAICompletion);
};
} else if (completion === "chat_completions") {
return async ({
messages,
preprompt,
generateSettings,
tools,
toolResults,
conversationId,
}) => {
let messagesOpenAI: OpenAI.Chat.Completions.ChatCompletionMessageParam[] =
await prepareMessages(messages, imageProcessor, !model.tools && model.multimodal);
if (messagesOpenAI?.[0]?.role !== "system") {
messagesOpenAI = [{ role: "system", content: "" }, ...messagesOpenAI];
}
if (messagesOpenAI?.[0]) {
messagesOpenAI[0].content = preprompt ?? "";
}
// if system role is not supported, convert first message to a user message.
if (!model.systemRoleSupported && messagesOpenAI?.[0]?.role === "system") {
messagesOpenAI[0] = {
...messagesOpenAI[0],
role: "user",
};
}
if (toolResults && toolResults.length > 0) {
const toolCallRequests: OpenAI.Chat.Completions.ChatCompletionAssistantMessageParam = {
role: "assistant",
content: null,
tool_calls: [],
};
const responses: Array<OpenAI.Chat.Completions.ChatCompletionToolMessageParam> = [];
for (const result of toolResults) {
const id = uuidv4();
const toolCallResult: OpenAI.Chat.Completions.ChatCompletionMessageToolCall = {
type: "function",
function: {
name: result.call.name,
arguments: JSON.stringify(result.call.parameters),
},
id,
};
toolCallRequests.tool_calls?.push(toolCallResult);
const toolCallResponse: OpenAI.Chat.Completions.ChatCompletionToolMessageParam = {
role: "tool",
content: "",
tool_call_id: id,
};
if ("outputs" in result) {
toolCallResponse.content = JSON.stringify(result.outputs);
}
responses.push(toolCallResponse);
}
messagesOpenAI.push(toolCallRequests);
messagesOpenAI.push(...responses);
}
const parameters = { ...model.parameters, ...generateSettings };
const toolCallChoices = createChatCompletionToolsArray(tools);
const body: ChatCompletionCreateParamsStreaming = {
model: model.id ?? model.name,
messages: messagesOpenAI,
stream: true,
...(useCompletionTokens
? { max_completion_tokens: parameters?.max_new_tokens }
: { max_tokens: parameters?.max_new_tokens }),
stop: parameters?.stop,
temperature: parameters?.temperature,
top_p: parameters?.top_p,
frequency_penalty: parameters?.repetition_penalty,
presence_penalty: parameters?.presence_penalty,
...(toolCallChoices.length > 0 ? { tools: toolCallChoices, tool_choice: "auto" } : {}),
};
const openChatAICompletion = await openai.chat.completions.create(body, {
body: { ...body, ...extraBody },
headers: {
"ChatUI-Conversation-ID": conversationId?.toString() ?? "",
"X-use-cache": "false",
},
});
return openAIChatToTextGenerationStream(openChatAICompletion);
};
} else {
throw new Error("Invalid completion type");
}
}
async function prepareMessages(
messages: EndpointMessage[],
imageProcessor: ReturnType<typeof makeImageProcessor>,
isMultimodal: boolean
): Promise<OpenAI.Chat.Completions.ChatCompletionMessageParam[]> {
return Promise.all(
messages.map(async (message) => {
if (message.from === "user" && isMultimodal) {
return {
role: message.from,
content: [
...(await prepareFiles(imageProcessor, message.files ?? [])),
{ type: "text", text: message.content },
],
};
}
return {
role: message.from,
content: message.content,
};
})
);
}
async function prepareFiles(
imageProcessor: ReturnType<typeof makeImageProcessor>,
files: MessageFile[]
): Promise<OpenAI.Chat.Completions.ChatCompletionContentPartImage[]> {
const processedFiles = await Promise.all(
files.filter((file) => file.mime.startsWith("image/")).map(imageProcessor)
);
return processedFiles.map((file) => ({
type: "image_url" as const,
image_url: {
url: `data:${file.mime};base64,${file.image.toString("base64")}`,
},
}));
}