Add the harness, envs, per-bot models and chat attachments
- Harness: providers, model catalog synced hourly, automatic free picks, cooldowns, tool loop with context trimming, MCP computer tools - Bots queue runs replies and tasks through the harness; jobs wait (attempt refunded) while every model is resting - Envs: Settings > Environment, scoped general/workspace/computer, written to computers and redacted from tool output - Models: Settings > Models, per-harness provider select defaulting to OpenRouter, and a models modal on each bot's chat (the coordinator's edits the defaults) - Chat: thinking row while a reply runs, file attachments (upload, paste, drop) that replies read as images, audio and document text - Fix media uploads failing after a user's first file, and private files being served from the public media path Co-Authored-By: Claude Opus 5.5 <[email protected]>
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import { ReadImagePrompt } from "@/src/data/harness-prompts";
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import HarnessError from "../harness-error";
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import grabHarnessAdapter from "../providers/grab-harness-adapter";
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import resolveHarnessCandidates from "../resolve/resolve-harness-candidates";
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import withCandidates from "../resolve/with-candidates";
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type Params = {
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/**
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* An https or `data:` URL.
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*/
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url: string;
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/**
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* What to look for, added to the standard reading prompt.
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*/
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focus?: string;
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signal?: AbortSignal;
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/**
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* Uses this bot's media reading model when it has its own.
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*/
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bot_id?: number | string;
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};
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/**
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* Reads an image with the media reading (vision) harness and returns what
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* it sees, as text for a model that can't see.
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*/
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export default async function readImage({ url, focus, signal, bot_id }: Params) {
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const candidates = await resolveHarnessCandidates({
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harness: "vision",
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bot_id,
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});
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const { result } = await withCandidates({
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harness: "vision",
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candidates,
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run: async (candidate) => {
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const answer = await grabHarnessAdapter({
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kind: candidate.provider.kind,
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}).chat({
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candidate,
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messages: [
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{
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role: "user",
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content: [
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{
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type: "text",
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text: focus
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? `${ReadImagePrompt}\n\nPay special attention to: ${focus}`
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: ReadImagePrompt,
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},
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{ type: "image", url },
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],
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},
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],
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temperature: 0,
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signal: signal || new AbortController().signal,
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});
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if (!answer.text?.trim()) {
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throw new HarnessError({
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message: `The model couldn't read the image.`,
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retryable: true,
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candidate_key: candidate.key,
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});
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}
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return answer.text.trim();
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},
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});
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return result;
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}
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@@ -0,0 +1,97 @@
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import fileToText from "@/src/functions/backend/db/users/media/file-to-text";
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import AppError from "@/src/utils/app-error";
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import transcribeAudio from "../audio/transcribe-audio";
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import readImage from "./read-image";
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type Params = {
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data: Uint8Array;
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/**
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* e.g. `image/png`, `audio/webm`, `application/pdf`.
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*/
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media_type: string;
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/**
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* File name, used for its extension when the type is vague.
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*/
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name?: string;
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signal?: AbortSignal;
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/**
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* Reads with this bot's models when it has its own.
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*/
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bot_id?: number | string;
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};
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const DocumentExtensions = ["pdf", "docx", "txt", "md", "csv", "json", "html", "xml", "yaml", "yml"];
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const TypeExtensions: Record<string, string> = {
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"application/pdf": "pdf",
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"application/vnd.openxmlformats-officedocument.wordprocessingml.document":
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"docx",
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"text/plain": "txt",
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"text/markdown": "md",
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"text/csv": "csv",
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"application/json": "json",
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};
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/**
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* Reads any attachment into text for the text harness: documents are
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* extracted on this machine, images go to the media reading harness and
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* audio to the transcription harness.
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*/
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export default async function readMedia({
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data,
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media_type,
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name,
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signal,
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bot_id,
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}: Params): Promise<string> {
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const type = media_type.toLowerCase().split(";")[0]!.trim();
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const ext = (
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name?.includes(".")
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? name.split(".").pop()
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: TypeExtensions[type] || type.split("/").pop()
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)?.toLowerCase() || "";
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if (type.startsWith("image/")) {
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return await readImage({
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url: `data:${type};base64,${Buffer.from(data).toString("base64")}`,
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signal,
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bot_id,
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});
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}
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if (type.startsWith("audio/")) {
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return await transcribeAudio({
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audio: { data, format: audioFormat({ type, ext }) },
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signal,
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bot_id,
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});
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}
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if (type.startsWith("text/") || DocumentExtensions.includes(ext)) {
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const text = await fileToText({
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file_stream: Buffer.from(data),
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file_type: ext,
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});
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if (!text?.trim()) {
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throw new AppError(
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ext == "pdf"
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? `This PDF has no text in it (it may be a scan). Send its pages as images instead.`
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: `This file has no text in it.`,
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);
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}
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return text;
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}
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throw new AppError(`Bots can't read ${ext ? `.${ext}` : "this kind of"} files yet.`);
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}
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function audioFormat({ type, ext }: { type: string; ext: string }) {
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if (type.includes("mpeg") || ext == "mp3") return "mp3";
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if (type.includes("mp4") || ext == "m4a") return "m4a";
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if (type.includes("wav")) return "wav";
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if (type.includes("ogg")) return "ogg";
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if (type.includes("flac")) return "flac";
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return ext || "webm";
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}
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