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]>
This commit is contained in:
2026-10-19 03:38:38 +01:00
co-authored by Claude Opus 5.5
parent d49f524fe8
commit 6a2665dae9
223 changed files with 12342 additions and 162 deletions
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import { ReadImagePrompt } from "@/src/data/harness-prompts";
import HarnessError from "../harness-error";
import grabHarnessAdapter from "../providers/grab-harness-adapter";
import resolveHarnessCandidates from "../resolve/resolve-harness-candidates";
import withCandidates from "../resolve/with-candidates";
type Params = {
/**
* An https or `data:` URL.
*/
url: string;
/**
* What to look for, added to the standard reading prompt.
*/
focus?: string;
signal?: AbortSignal;
/**
* Uses this bot's media reading model when it has its own.
*/
bot_id?: number | string;
};
/**
* Reads an image with the media reading (vision) harness and returns what
* it sees, as text for a model that can't see.
*/
export default async function readImage({ url, focus, signal, bot_id }: Params) {
const candidates = await resolveHarnessCandidates({
harness: "vision",
bot_id,
});
const { result } = await withCandidates({
harness: "vision",
candidates,
run: async (candidate) => {
const answer = await grabHarnessAdapter({
kind: candidate.provider.kind,
}).chat({
candidate,
messages: [
{
role: "user",
content: [
{
type: "text",
text: focus
? `${ReadImagePrompt}\n\nPay special attention to: ${focus}`
: ReadImagePrompt,
},
{ type: "image", url },
],
},
],
temperature: 0,
signal: signal || new AbortController().signal,
});
if (!answer.text?.trim()) {
throw new HarnessError({
message: `The model couldn't read the image.`,
retryable: true,
candidate_key: candidate.key,
});
}
return answer.text.trim();
},
});
return result;
}
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import fileToText from "@/src/functions/backend/db/users/media/file-to-text";
import AppError from "@/src/utils/app-error";
import transcribeAudio from "../audio/transcribe-audio";
import readImage from "./read-image";
type Params = {
data: Uint8Array;
/**
* e.g. `image/png`, `audio/webm`, `application/pdf`.
*/
media_type: string;
/**
* File name, used for its extension when the type is vague.
*/
name?: string;
signal?: AbortSignal;
/**
* Reads with this bot's models when it has its own.
*/
bot_id?: number | string;
};
const DocumentExtensions = ["pdf", "docx", "txt", "md", "csv", "json", "html", "xml", "yaml", "yml"];
const TypeExtensions: Record<string, string> = {
"application/pdf": "pdf",
"application/vnd.openxmlformats-officedocument.wordprocessingml.document":
"docx",
"text/plain": "txt",
"text/markdown": "md",
"text/csv": "csv",
"application/json": "json",
};
/**
* Reads any attachment into text for the text harness: documents are
* extracted on this machine, images go to the media reading harness and
* audio to the transcription harness.
*/
export default async function readMedia({
data,
media_type,
name,
signal,
bot_id,
}: Params): Promise<string> {
const type = media_type.toLowerCase().split(";")[0]!.trim();
const ext = (
name?.includes(".")
? name.split(".").pop()
: TypeExtensions[type] || type.split("/").pop()
)?.toLowerCase() || "";
if (type.startsWith("image/")) {
return await readImage({
url: `data:${type};base64,${Buffer.from(data).toString("base64")}`,
signal,
bot_id,
});
}
if (type.startsWith("audio/")) {
return await transcribeAudio({
audio: { data, format: audioFormat({ type, ext }) },
signal,
bot_id,
});
}
if (type.startsWith("text/") || DocumentExtensions.includes(ext)) {
const text = await fileToText({
file_stream: Buffer.from(data),
file_type: ext,
});
if (!text?.trim()) {
throw new AppError(
ext == "pdf"
? `This PDF has no text in it (it may be a scan). Send its pages as images instead.`
: `This file has no text in it.`,
);
}
return text;
}
throw new AppError(`Bots can't read ${ext ? `.${ext}` : "this kind of"} files yet.`);
}
function audioFormat({ type, ext }: { type: string; ext: string }) {
if (type.includes("mpeg") || ext == "mp3") return "mp3";
if (type.includes("mp4") || ext == "m4a") return "m4a";
if (type.includes("wav")) return "wav";
if (type.includes("ogg")) return "ogg";
if (type.includes("flac")) return "flac";
return ext || "webm";
}