Mckay Wrigley
6500db9c1c
* move index to home folder, create state and context files and barrell folder * Sanity Check Commit: reducer added to home.tsx manual QA all working * WIP: promptBar * fix missing json parse on folders and prompts * split context and add promptbar context * add context to nested prompt componets and componetize Folder componet * remove log * Create buttons folder and componetize sidebar action button * tidy up prompt handlers * componetized sidebar * added back chatbar componet to left side sidebar * monster commit: Componetized the common code between chatbar and promptbar into new componet Sidebar and added context to both bars * add useFetch service * added prettier import sort to keep imports ordered and easier to indentify * added react query and useFetch to work with RQ * added apiService, errorService and reactQuery * add callback and tidy up error service * refactor chat and child componets to useContext * fix extra calls and bad calls to mel endpoint * minor import cleanup --------- Co-authored-by: jc.durbin <jc.durbin@ardanis.com>
64 lines
1.7 KiB
TypeScript
64 lines
1.7 KiB
TypeScript
import { DEFAULT_SYSTEM_PROMPT } from '@/utils/app/const';
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import { OpenAIError, OpenAIStream } from '@/utils/server';
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import { ChatBody, Message } from '@/types/chat';
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// @ts-expect-error
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import wasm from '../../node_modules/@dqbd/tiktoken/lite/tiktoken_bg.wasm?module';
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import tiktokenModel from '@dqbd/tiktoken/encoders/cl100k_base.json';
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import { Tiktoken, init } from '@dqbd/tiktoken/lite/init';
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export const config = {
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runtime: 'edge',
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};
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const handler = async (req: Request): Promise<Response> => {
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try {
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const { model, messages, key, prompt } = (await req.json()) as ChatBody;
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await init((imports) => WebAssembly.instantiate(wasm, imports));
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const encoding = new Tiktoken(
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tiktokenModel.bpe_ranks,
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tiktokenModel.special_tokens,
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tiktokenModel.pat_str,
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);
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let promptToSend = prompt;
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if (!promptToSend) {
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promptToSend = DEFAULT_SYSTEM_PROMPT;
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}
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const prompt_tokens = encoding.encode(promptToSend);
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let tokenCount = prompt_tokens.length;
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let messagesToSend: Message[] = [];
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for (let i = messages.length - 1; i >= 0; i--) {
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const message = messages[i];
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const tokens = encoding.encode(message.content);
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if (tokenCount + tokens.length + 1000 > model.tokenLimit) {
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break;
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}
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tokenCount += tokens.length;
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messagesToSend = [message, ...messagesToSend];
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}
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encoding.free();
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const stream = await OpenAIStream(model, promptToSend, key, messagesToSend);
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return new Response(stream);
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} catch (error) {
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console.error(error);
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if (error instanceof OpenAIError) {
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return new Response('Error', { status: 500, statusText: error.message });
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} else {
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return new Response('Error', { status: 500 });
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}
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}
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};
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export default handler;
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