mirror of
https://github.com/luckyyzh/pi-agent-integrated.git
synced 2026-10-03 02:59:35 +00:00
feat: add vision and extension management to WebUI
This commit is contained in:
luckyyzh
@@ -3,7 +3,8 @@
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"private": true,
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"pi": {
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"extensions": [
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"./searxng-search.ts"
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"./searxng-search.ts",
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"./vision.ts"
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]
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}
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}
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@@ -0,0 +1,539 @@
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/**
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* Vision extension: describe images for text-only main models (e.g. DeepSeek).
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*
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* Two interchangeable backends, selected by VISION_BACKEND (default "ollama"):
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* - "ollama": local Ollama vision model via the native /api/chat endpoint.
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* OLLAMA_HOST (default http://localhost:11434)
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* OLLAMA_VISION_MODEL (default qwen3-vl:8b)
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* - "openai": any OpenAI-compatible vision API (cloud or self-hosted).
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* VISION_OPENAI_BASE_URL e.g. https://api.openai.com/v1
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* VISION_OPENAI_API_KEY
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* VISION_OPENAI_MODEL e.g. gpt-4o-mini, glm-4.5v, qwen-vl-max
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*
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* Why not Ollama's OpenAI-compatible /v1 endpoint: it moves qwen3-family
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* reasoning output into the `reasoning` field with an empty `content`, which Pi
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* treats as an empty reply. The native /api/chat with `think: false` returns a
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* normal textual answer, so the Ollama backend deliberately bypasses /v1.
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*/
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import type { ExtensionAPI } from "@earendil-works/pi-coding-agent";
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import { createHash } from "node:crypto";
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import { readFile, stat } from "node:fs/promises";
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import { homedir } from "node:os";
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import { join } from "node:path";
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import { Type } from "typebox";
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const REQUEST_TIMEOUT_MS = 180_000;
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const MAX_IMAGE_BYTES = 7 * 1024 * 1024; // Ollama's built-in per-image limit
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const DEFAULT_MAX_TOKENS = 4096; // generous: thinking-based APIs spend budget on reasoning first
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const DEFAULT_PROMPT = [
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"请详细描述这张图片，输出要求：",
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"1. 完整 OCR：按阅读顺序逐字转录所有可见文字（含 UI 标签、按钮、错误信息、代码、数字、日期），保留版式线索。",
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"2. 版式与视觉结构：区域、颜色、形状；UI 截图、图表（坐标轴/数值/趋势）、表格用 markdown 重建、流程图（节点/连线/方向）。",
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"3. 语义总结：2-3 句概括图片内容与关键信息。",
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"4. 模糊/截断/有歧义处明确说明，不要猜测。",
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"5. 多张图片时，分别描述每张，用【图片1】【图片2】…标注。",
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"主模型看不到图，完全依赖你的转录，文字务必穷尽。",
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].join("\n");
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const visionParams = Type.Object({
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image_paths: Type.Array(
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Type.String({
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description: "图片文件路径（绝对或相对路径），至少一个",
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minLength: 1,
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}),
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{ minItems: 1, maxItems: 8 },
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),
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backend: Type.Optional(
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Type.Union([Type.Literal("ollama"), Type.Literal("openai")], {
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description:
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"视觉后端：ollama（本地，默认）或 openai（OpenAI 兼容 API）。默认取 VISION_BACKEND 环境变量",
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}),
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),
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model: Type.Optional(
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Type.String({
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description:
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"视觉模型 tag/ID，默认取后端对应环境变量（OLLAMA_VISION_MODEL 或 VISION_OPENAI_MODEL）",
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}),
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),
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prompt: Type.Optional(
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Type.String({
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description: `自定义识图指令，默认：${DEFAULT_PROMPT.split("\n")[0]}`,
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}),
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),
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});
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function envOr(name: string, fallback: string): string {
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const value = process.env[name]?.trim();
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return value && value.length > 0 ? value : fallback;
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}
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function mimeFromPath(filePath: string): string {
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const ext = filePath.split(".").pop()?.toLowerCase() ?? "";
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switch (ext) {
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case "png":
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return "image/png";
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case "jpg":
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case "jpeg":
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return "image/jpeg";
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case "webp":
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return "image/webp";
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case "gif":
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return "image/gif";
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case "bmp":
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return "image/bmp";
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default:
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return "application/octet-stream";
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}
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}
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interface LoadedImage {
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base64: string;
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mime: string;
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}
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async function loadImages(imagePaths: string[]): Promise<LoadedImage[]> {
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const images: LoadedImage[] = [];
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for (const filePath of imagePaths) {
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const fileStat = await stat(filePath).catch(() => null);
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if (!fileStat) throw new Error(`vision: file not found: ${filePath}`);
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if (!fileStat.isFile())
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throw new Error(`vision: not a regular file: ${filePath}`);
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if (fileStat.size === 0) throw new Error(`vision: empty file: ${filePath}`);
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if (fileStat.size > MAX_IMAGE_BYTES) {
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throw new Error(
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`vision: ${filePath} is ${fileStat.size} bytes, exceeding the ${MAX_IMAGE_BYTES} byte limit. Resize or compress the image first.`,
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);
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}
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const buffer = await readFile(filePath);
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images.push({
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base64: buffer.toString("base64"),
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mime: mimeFromPath(filePath),
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});
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}
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return images;
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}
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function requestSignal(signal: AbortSignal | undefined): AbortSignal {
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const timeoutSignal = AbortSignal.timeout(REQUEST_TIMEOUT_MS);
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return signal ? AbortSignal.any([signal, timeoutSignal]) : timeoutSignal;
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}
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async function describeWithOllama(
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baseUrl: string,
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model: string,
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prompt: string,
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images: LoadedImage[],
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signal: AbortSignal | undefined,
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): Promise<string> {
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// Ollama defaults to a small num_ctx (4096 on this setup); multi-image
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// requests blow past it. Raise explicitly — the model supports 262k.
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const numCtx = parseInt(envOr("OLLAMA_NUM_CTX", "16384"), 10) || 16384;
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const body = {
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model,
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messages: [
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{
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role: "user",
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content: prompt,
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images: images.map((img) => img.base64),
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},
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],
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stream: false,
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think: false,
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options: { temperature: 0, num_ctx: numCtx },
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};
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let response: Response;
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try {
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response = await fetch(`${baseUrl.replace(/\/+$/, "")}/api/chat`, {
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method: "POST",
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headers: { "Content-Type": "application/json" },
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body: JSON.stringify(body),
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signal: requestSignal(signal),
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});
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} catch (error) {
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const cause = error instanceof Error ? error.message : String(error);
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throw new Error(
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`vision: cannot reach Ollama at ${baseUrl} (${cause}). Is Ollama running?`,
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);
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}
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if (!response.ok) {
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const detail = (await response.text().catch(() => "")).slice(0, 500);
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throw new Error(
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`vision: Ollama returned HTTP ${response.status}${detail ? `: ${detail}` : ""}`,
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);
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}
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const data = (await response.json()) as { message?: { content?: string } };
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const content = data.message?.content?.trim();
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if (!content) {
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throw new Error(
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`vision: Ollama model ${model} returned an empty response. ` +
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"Check `ollama pull qwen3-vl:8b` and that the model supports vision.",
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);
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}
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return content;
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}
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async function describeWithOpenAI(
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baseUrl: string,
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apiKey: string,
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model: string,
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prompt: string,
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images: LoadedImage[],
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signal: AbortSignal | undefined,
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): Promise<string> {
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const body = {
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model,
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messages: [
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{
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role: "user",
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content: [
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{ type: "text", text: prompt },
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...images.map((img) => ({
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type: "image_url",
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image_url: { url: `data:${img.mime};base64,${img.base64}` },
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})),
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],
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},
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],
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max_tokens: DEFAULT_MAX_TOKENS,
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temperature: 0,
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};
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let response: Response;
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try {
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response = await fetch(`${baseUrl.replace(/\/+$/, "")}/chat/completions`, {
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method: "POST",
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headers: {
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"Content-Type": "application/json",
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Authorization: `Bearer ${apiKey}`,
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},
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body: JSON.stringify(body),
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signal: requestSignal(signal),
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});
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} catch (error) {
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const cause = error instanceof Error ? error.message : String(error);
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throw new Error(`vision: cannot reach ${baseUrl} (${cause}).`);
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}
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if (!response.ok) {
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const detail = (await response.text().catch(() => "")).slice(0, 500);
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throw new Error(
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`vision: API returned HTTP ${response.status}${detail ? `: ${detail}` : ""}. ` +
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"Check VISION_OPENAI_BASE_URL / VISION_OPENAI_API_KEY / VISION_OPENAI_MODEL.",
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);
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}
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const data = (await response.json()) as {
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choices?: Array<{
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message?: {
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content?: string;
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reasoning_content?: string;
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reasoning?: string;
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};
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finish_reason?: string;
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}>;
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};
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const choice = data.choices?.[0];
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const content = choice?.message?.content?.trim();
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if (content) return content;
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const reasoning =
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choice?.message?.reasoning_content || choice?.message?.reasoning;
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const finish = choice?.finish_reason ?? "unknown";
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if (reasoning) {
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throw new Error(
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`vision: model ${model} returned only reasoning (finish=${finish}). ` +
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"If it is a thinking model, pick a non-thinking vision model or raise max_tokens.",
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);
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}
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throw new Error(
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`vision: model ${model} returned an empty response (finish=${finish}).`,
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);
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}
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interface DescribeOptions {
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backend?: string;
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model?: string;
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prompt?: string;
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signal?: AbortSignal;
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}
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/**
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* Optional file-based configuration, edited from the Web UI vision panel
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* (writes `$PI_CODING_AGENT_DIR/vision.json`). Environment variables and
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* per-call parameters take precedence over this file; every read is fresh, so
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* saving the panel takes effect on the next image request without a restart.
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*/
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interface VisionFileConfig {
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backend?: "ollama" | "openai";
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ollama?: { host?: string; model?: string };
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openai?: { baseUrl?: string; apiKey?: string; model?: string };
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}
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const VISION_CONFIG_DEFAULTS: VisionFileConfig = {
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backend: "ollama",
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ollama: { host: "http://localhost:11434", model: "qwen3-vl:8b" },
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openai: { baseUrl: "", apiKey: "", model: "" },
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};
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function visionConfigPath(): string {
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const agentDir = process.env.PI_CODING_AGENT_DIR?.trim();
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return (
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(agentDir && agentDir.length > 0
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? agentDir
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: join(homedir(), ".pi", "agent")) + "/vision.json"
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);
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}
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async function loadVisionFileConfig(): Promise<VisionFileConfig> {
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try {
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const raw = await readFile(visionConfigPath(), "utf8");
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const parsed = JSON.parse(raw) as VisionFileConfig;
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return parsed && typeof parsed === "object" ? parsed : {};
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} catch {
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return {};
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}
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}
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async function describeImages(
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images: LoadedImage[],
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options: DescribeOptions,
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): Promise<string> {
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const fileConfig = await loadVisionFileConfig();
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const backend =
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options.backend ??
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envOr(
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"VISION_BACKEND",
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fileConfig.backend ?? VISION_CONFIG_DEFAULTS.backend ?? "ollama",
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);
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const prompt = options.prompt ?? DEFAULT_PROMPT;
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if (backend === "ollama") {
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const baseUrl = envOr(
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"OLLAMA_HOST",
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fileConfig.ollama?.host ??
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VISION_CONFIG_DEFAULTS.ollama?.host ??
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"http://localhost:11434",
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);
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const model =
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options.model ??
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envOr(
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"OLLAMA_VISION_MODEL",
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fileConfig.ollama?.model ??
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VISION_CONFIG_DEFAULTS.ollama?.model ??
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"qwen3-vl:8b",
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);
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return describeWithOllama(baseUrl, model, prompt, images, options.signal);
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}
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const baseUrl = envOr(
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"VISION_OPENAI_BASE_URL",
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fileConfig.openai?.baseUrl ?? "",
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);
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const apiKey = envOr(
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"VISION_OPENAI_API_KEY",
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fileConfig.openai?.apiKey ?? "",
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);
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const model =
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options.model ??
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envOr("VISION_OPENAI_MODEL", fileConfig.openai?.model ?? "");
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if (!baseUrl || !apiKey || !model) {
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throw new Error(
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"vision: the openai backend needs a base URL, API key and model. " +
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"Configure them in the Web UI vision panel (lower-left) or via " +
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"VISION_OPENAI_BASE_URL / VISION_OPENAI_API_KEY / VISION_OPENAI_MODEL. " +
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"Example: https://api.openai.com/v1 + gpt-4o-mini.",
|
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);
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}
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return describeWithOpenAI(
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baseUrl,
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apiKey,
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||||
model,
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prompt,
|
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images,
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options.signal,
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);
|
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}
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|
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/**
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* Session-scoped description cache: compaction, session restore, or repeated
|
||||
* turns replay the same image parts; avoid re-running the vision model each time.
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*/
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const IMAGE_DESCRIPTION_CACHE = new Map<string, string>();
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||||
function dataUrlToLoadedImage(url: string): LoadedImage | null {
|
||||
const match = /^data:(image\/[a-z0-9.+-]+);base64,(.+)$/i.exec(url);
|
||||
if (!match) return null;
|
||||
return { base64: match[2], mime: match[1] };
|
||||
}
|
||||
|
||||
function imageCacheKey(image: LoadedImage): string {
|
||||
// Whole-image hash: PNG/JPG headers repeat for same dimensions, so a short
|
||||
// prefix would collide across different images of the same size.
|
||||
return createHash("md5").update(image.base64).digest("hex");
|
||||
}
|
||||
|
||||
/**
|
||||
* Per-image description cache. Every request rebuilds the payload from the
|
||||
* session file, which keeps the original image parts, so without this cache
|
||||
* the whole conversation's images would be re-transcribed every turn and
|
||||
* eventually exceed Ollama's context window. Single-image granularity means
|
||||
* only genuinely new images hit the vision model.
|
||||
*/
|
||||
async function getImageDescription(
|
||||
image: LoadedImage,
|
||||
prompt: string,
|
||||
signal: AbortSignal | undefined,
|
||||
): Promise<string> {
|
||||
const key = imageCacheKey(image);
|
||||
const cached = IMAGE_DESCRIPTION_CACHE.get(key);
|
||||
if (cached) return cached;
|
||||
const description = await describeImages([image], { prompt, signal });
|
||||
IMAGE_DESCRIPTION_CACHE.set(key, description);
|
||||
if (IMAGE_DESCRIPTION_CACHE.size > 64) {
|
||||
const oldest = IMAGE_DESCRIPTION_CACHE.keys().next().value;
|
||||
if (oldest !== undefined) IMAGE_DESCRIPTION_CACHE.delete(oldest);
|
||||
}
|
||||
return description;
|
||||
}
|
||||
|
||||
function isTextOnlyModel(model: unknown): boolean {
|
||||
const input = (model as { input?: string[] } | undefined)?.input;
|
||||
return Array.isArray(input) && !input.includes("image");
|
||||
}
|
||||
|
||||
export default function visionExtension(pi: ExtensionAPI) {
|
||||
// Keep user-message images intact for text-only models so the hook below can
|
||||
// transcribe them (pi-ai would otherwise replace them with a text placeholder
|
||||
// before before_provider_request runs). The matching pi-ai patch is applied
|
||||
// and replayed by scripts/configure-pi-ai-vision.mjs.
|
||||
process.env.PI_VISION_PASSTHROUGH_IMAGES = "1";
|
||||
pi.registerTool({
|
||||
name: "vision",
|
||||
label: "Vision (image description)",
|
||||
description:
|
||||
"用视觉模型描述本地图片并返回详细文本（完整 OCR、版式结构、语义总结）。" +
|
||||
"适用于主模型不支持图片输入（如 DeepSeek）时查看截图/图表/文档/照片。" +
|
||||
"后端可配置：ollama（默认，本地 qwen3-vl:8b，免费私密）或 openai（任意 OpenAI 兼容视觉 API，" +
|
||||
"设 VISION_BACKEND=openai + VISION_OPENAI_BASE_URL/API_KEY/MODEL）。",
|
||||
promptSnippet:
|
||||
"Describe local images using a vision model (Ollama or OpenAI-compatible)",
|
||||
promptGuidelines: [
|
||||
"Use vision when the user asks you to look at an image (screenshot, diagram, chart, document, photo) and the current model cannot receive image attachments.",
|
||||
"Pass image file paths that exist on disk; the tool reads and encodes them itself.",
|
||||
"One call can describe up to 8 images; prefer batching related images into a single call.",
|
||||
"The returned text is the vision model's transcription — relay it faithfully, quoting OCR text verbatim.",
|
||||
"If the call fails because no vision backend is configured, report the missing environment variables.",
|
||||
],
|
||||
parameters: visionParams,
|
||||
executionMode: "sequential",
|
||||
async execute(_toolCallId, params, signal) {
|
||||
const prompt = params.prompt ?? DEFAULT_PROMPT;
|
||||
const images = await loadImages(params.image_paths);
|
||||
const content = await describeImages(images, {
|
||||
backend: params.backend,
|
||||
model: params.model,
|
||||
prompt,
|
||||
signal,
|
||||
});
|
||||
|
||||
return {
|
||||
content: [{ type: "text", text: content }],
|
||||
details: {
|
||||
backend: params.backend ?? envOr("VISION_BACKEND", "ollama"),
|
||||
model: params.model ?? undefined,
|
||||
imageCount: images.length,
|
||||
},
|
||||
};
|
||||
},
|
||||
});
|
||||
|
||||
// --- Automatic image transcription for text-only main models ---
|
||||
// Web UI image uploads arrive as base64 image_url parts in the user message.
|
||||
// A text-only model (e.g. DeepSeek) cannot receive them — DeepSeek rejects
|
||||
// the request with HTTP 400. This hook transcribes the images through the
|
||||
// configured vision backend and replaces them with text before the request
|
||||
// is sent, so the main model keeps working seamlessly.
|
||||
pi.on("before_provider_request", async (event, ctx) => {
|
||||
const payload = event.payload as
|
||||
| { messages?: Array<{ role?: string; content?: unknown }> }
|
||||
| undefined;
|
||||
const messages = payload?.messages;
|
||||
if (!Array.isArray(messages) || messages.length === 0) return;
|
||||
if (!isTextOnlyModel(ctx.model)) return; // vision-capable models pass through untouched
|
||||
|
||||
// Collect images and replace each with a numbered text placeholder.
|
||||
const images: LoadedImage[] = [];
|
||||
let dirty = false;
|
||||
for (const msg of messages) {
|
||||
if (msg?.role !== "user" || !Array.isArray(msg.content)) continue;
|
||||
const newContent: unknown[] = [];
|
||||
for (const part of msg.content) {
|
||||
const p = part as { type?: string; image_url?: { url?: string } };
|
||||
if (p?.type === "image_url" && typeof p.image_url?.url === "string") {
|
||||
const img = dataUrlToLoadedImage(p.image_url.url);
|
||||
if (img) {
|
||||
images.push(img);
|
||||
newContent.push({ type: "text", text: `[图片 ${images.length}]` });
|
||||
dirty = true;
|
||||
} else {
|
||||
newContent.push(part);
|
||||
}
|
||||
} else {
|
||||
newContent.push(part);
|
||||
}
|
||||
}
|
||||
msg.content = newContent;
|
||||
}
|
||||
if (!dirty || images.length === 0) return;
|
||||
|
||||
// Transcribe per image: only genuinely new images hit the vision model
|
||||
// (history resolves from cache), so one request never re-batches the
|
||||
// whole conversation's images and exceeds Ollama's context window.
|
||||
const transcribed: string[] = [];
|
||||
for (let i = 0; i < images.length; i++) {
|
||||
try {
|
||||
const desc = await getImageDescription(
|
||||
images[i],
|
||||
DEFAULT_PROMPT,
|
||||
ctx.signal,
|
||||
);
|
||||
transcribed.push(`【图片${i + 1}】\n${desc}`);
|
||||
} catch (error) {
|
||||
const cause = error instanceof Error ? error.message : String(error);
|
||||
transcribed.push(`【图片${i + 1}】\n[图片处理失败：${cause}]`);
|
||||
}
|
||||
}
|
||||
const description = transcribed.join("\n\n");
|
||||
|
||||
// Place the full transcription on the LAST image-carrying message (the one
|
||||
// the model is actively processing); earlier image messages reference it.
|
||||
// Putting it on the first message instead hid it in history.
|
||||
const targets: Array<{ part: { type?: string; text?: string } }> = [];
|
||||
for (const msg of messages) {
|
||||
if (msg?.role !== "user" || !Array.isArray(msg.content)) continue;
|
||||
for (const part of msg.content) {
|
||||
const p = part as { type?: string; text?: string };
|
||||
if (
|
||||
p?.type === "text" &&
|
||||
typeof p.text === "string" &&
|
||||
p.text.startsWith("[图片 ")
|
||||
) {
|
||||
targets.push({ part: p });
|
||||
}
|
||||
}
|
||||
}
|
||||
const lastTarget = targets[targets.length - 1];
|
||||
if (lastTarget) {
|
||||
lastTarget.part.text = `[用户上传了 ${images.length} 张图片，以下为视觉模型转录的文本描述]\n\n${description}`;
|
||||
}
|
||||
for (const { part } of targets) {
|
||||
if (part !== lastTarget?.part) {
|
||||
part.text = "（图片描述见最新消息中的综合转录）";
|
||||
}
|
||||
}
|
||||
return payload;
|
||||
});
|
||||
}
|
||||
Reference in New Issue
Block a user