Val Town Code SearchReturn to Val Town

1import { fetch } from "https://esm.town/v/std/fetch";
2import { OpenAI } from "https://esm.town/v/std/openai";
3import { PDFExtract, PDFExtractOptions } from "npm:pdf.js-extract";
4
173}
174
175async function callOpenAI(
176  openaiInstance: OpenAI,
177  systemPrompt: string,
178  userMessage: string,
182  agentName: string,
183): Promise<object | string> {
184  log.push({ agent: agentName, type: "step", message: `Calling OpenAI model ${model}...` });
185  try {
186    const response = await openaiInstance.chat.completions.create({
187      model: model,
188      messages: [
222    }
223  } catch (error) {
224    console.error(agentName, "OpenAI API call error:", error);
225    let errMsg = "AI communication error.";
226    if (error.message) errMsg += ` Message: ${error.message}`;
1072                    <li>**No client-side persistence of documents and analyses**</li>
1073                </ul>
1074                <p>This application uses OpenAI's GPT models for its AI capabilities. Data submitted will be processed by OpenAI.</p>
1075            </div>
1076        \`;
1085
1086export default async function(req: Request) {
1087  const openai = new OpenAI();
1088  const url = new URL(req.url);
1089  const format = url.searchParams.get("format");
1154    const suggPrompt = legalTaskSuggestionSystemPromptTemplate.replace("%%DOCUMENT_TEXT%%", suggText);
1155    const suggAgent = "Task Suggestion AI (LLM1)";
1156    const suggRes = await callOpenAI(
1157      openai,
1158      suggPrompt,
1159      "Generate task suggestions based on the provided document text.",
1303
1304      const analysisAgent = "Legal Analysis AI (LLM2)";
1305      const aiRes = await callOpenAI(openai, finalPrompt, docToAnalyze, "gpt-4o", true, log, analysisAgent);
1306
1307      if (typeof aiRes === "object" && (aiRes as any).error) {

4
5// Environment Variables to set in Val.Town:
6// - OPENAI_KEY: Your OpenAI API Key
7// - NOTION_KEY: Your Notion API Key
8// - NOTION_DATABASE_ID: The Database ID for your "Reflections" database (submissions)
114
115    console.log("Main Submission Handler: Starting AI Analysis for:", userNumberFromForm);
116    const OPENAI_KEY = getEnv("OPENAI_KEY") as string;
117    let aiAnalysisResult = {
118      summary: "AI summary not generated.",
173    const userMessage =
174      `Please analyze the following user reflection:\n\nUser Reflection Text:\n\"\"\"\n${combinedResponsesText}\n\"\"\"\n\nProvide your analysis as a single JSON object.`;
175    const openAIPayload = {
176      model: "gpt-3.5-turbo",
177      messages: [{ role: "system", content: systemMessage }, { role: "user", content: userMessage }],
180    };
181    try {
182      const openAIResponse = await fetch("https://api.openai.com/v1/chat/completions", {
183        method: "POST",
184        headers: { "Content-Type": "application/json", "Authorization": `Bearer ${OPENAI_KEY}` },
185        body: JSON.stringify(openAIPayload),
186      });
187      if (!openAIResponse.ok) {
188        const errorBody = await openAIResponse.text();
189        console.error("Main Submission Handler: OpenAI API Error:", openAIResponse.status, errorBody);
190      }
191      else {
192        const openAIData = await openAIResponse.json();
193        const aiContent = openAIData.choices[0]?.message?.content;
194        if (aiContent) {
195          try {
210          } catch (parseError) {
211            console.error(
212              "Main Submission Handler: Failed to parse OpenAI JSON response:",
213              parseError,
214              "Raw AI content:",
217          }
218        } else {
219          console.warn("Main Submission Handler: OpenAI response content was empty.");
220        }
221      }
222    } catch (aiError) {
223      console.error("Main Submission Handler: Error calling OpenAI API:", aiError);
224    }
225

76  const NOTION_USERS_DB_ID = getEnv("NOTION_USERS_DATABASE_ID") as string;
77  const NOTION_REFLECTIONS_DB_ID = getEnv("NOTION_DATABASE_ID") as string;
78  const OPENAI_KEY = getEnv("OPENAI_KEY") as string;
79
80  let isFirstTimeUser = true;
187Output ONLY the welcome message text.`;
188
189              const openAIPayload = {
190                model: "gpt-3.5-turbo",
191                messages: [{ role: "system", content: systemPrompt }],
194              };
195
196              console.log("getUserReflectionContext: Calling OpenAI for personalized welcome...");
197              try {
198                const openAIResponse = await fetch("https://api.openai.com/v1/chat/completions", {
199                  method: "POST",
200                  headers: { "Content-Type": "application/json", "Authorization": `Bearer ${OPENAI_KEY}` },
201                  body: JSON.stringify(openAIPayload),
202                });
203
204                if (!openAIResponse.ok) {
205                  const errorBody = await openAIResponse.text();
206                  console.error("getUserReflectionContext: OpenAI API Error:", openAIResponse.status, errorBody);
207                  welcomeMessage = DEFAULT_RETURNING_USER_MESSAGE;
208                } else {
209                  const openAIData = await openAIResponse.json();
210                  if (openAIData.choices && openAIData.choices[0] && openAIData.choices[0].message) {
211                    welcomeMessage = openAIData.choices[0].message.content.trim();
212                    console.log("getUserReflectionContext: OpenAI generated welcome:", welcomeMessage);
213                  } else {
214                    welcomeMessage = DEFAULT_RETURNING_USER_MESSAGE;
215                    console.warn(
216                      "getUserReflectionContext: OpenAI response structure unexpected, using default welcome.",
217                    );
218                  }
219                }
220              } catch (aiError) {
221                console.error("getUserReflectionContext: Error calling OpenAI:", aiError);
222                welcomeMessage = DEFAULT_RETURNING_USER_MESSAGE;
223              }

11const aiTools: AITool[] = [
12  // AI Assistants & Chatbots
13  { name: "ChatGPT", url: "https://chatgpt.com/", category: "Assistant", description: "OpenAI's conversational AI assistant" },
14  { name: "Claude", url: "https://claude.ai/", category: "Assistant", description: "Anthropic's AI assistant for various tasks and conversations" },
15  { name: "Gemini", url: "https://gemini.google.com/", category: "Assistant", description: "Google's advanced AI assistant" },
27  // Image Generation & Editing
28  { name: "Midjourney", url: "https://www.midjourney.com/", category: "Images", description: "AI art and image generation platform" },
29  { name: "DALL·E 3", url: "https://openai.com/dall-e-3", category: "Images", description: "OpenAI's advanced image generation model" },
30  { name: "Stable Diffusion", url: "https://stability.ai/", category: "Images", description: "Open-source AI image generation" },
31  { name: "Lexica", url: "https://lexica.art/", category: "Images", description: "AI art search engine and generator" },
73  { name: "Runway", url: "https://runwayml.com/", category: "Video", description: "AI video editing and generation tools" },
74  { name: "Synthesia", url: "https://www.synthesia.io/", category: "Video", description: "AI video generation with virtual avatars" },
75  { name: "Sora", url: "https://openai.com/sora", category: "Video", description: "OpenAI's text-to-video generation model" },
76  { name: "Kling", url: "https://klingai.com/", category: "Video", description: "AI video generation platform" },
77  { name: "Hailuo", url: "https://hailuo.ai/", category: "Video", description: "AI video creation tool" },
318
319  // AI Research & Platforms
320  { name: "OpenAI", url: "https://openai.com/", category: "Research", description: "AI research and deployment company" },
321  { name: "Anthropic", url: "https://www.anthropic.com/", category: "Research", description: "AI safety research company" },
322  { name: "xAI", url: "https://x.ai/", category: "Research", description: "Elon Musk's AI company" },

88Note: When changing a SQLite table's schema, change the table's name (e.g., add _2 or _3) to create a fresh table.
89
90### OpenAI
91
92```ts
93import { OpenAI } from "https://esm.town/v/std/openai";
94const openai = new OpenAI();
95const completion = await openai.chat.completions.create({
96  messages: [
97    { role: "user", content: "Say hello in a creative way" },

2import process from "node:process";
3import { marked } from "npm:marked";
4import { OpenAI } from "npm:openai";
5
6function pm(...lines: string[]): string {
16  );
17
18  const client = new OpenAI({ apiKey: process.env.PERPLEXITY_API_KEY, baseURL: "https://api.perplexity.ai" });
19  const response = await client.chat.completions.create({
20    model: "sonar",

1import { fetch } from "https://esm.town/v/std/fetch";
2import { OpenAI } from "https://esm.town/v/std/openai";
3import { z } from "npm:zod";
4
449}
450
451async function callOpenAI(
452  sysPrompt: string,
453  userPrompt: string,
458  lg(
459    "DEBUG",
460    "callOpenAI",
461    `Initiating OpenAI call tid=${tid}`,
462    { spLen: sysPrompt.length, upLen: userPrompt.length },
463    mid,
465  );
466  try {
467    const openai = new OpenAI();
468    const completion = await openai.chat.completions.create({
469      model: "gpt-4o-mini",
470      messages: [{ role: "system", content: sysPrompt }, { role: "user", content: userPrompt }],
477      lg(
478        "WARN",
479        "callOpenAI",
480        `OpenAI call returned no text tid=${tid}.`,
481        { usage: usage, finishReason: completion.choices[0]?.finish_reason },
482        mid,
487    lg(
488      "INFO",
489      "callOpenAI",
490      `OpenAI call OK tid=${tid}`,
491      { resLen: resText.length, usage: usage, finishReason: completion.choices[0]?.finish_reason },
492      mid,
496  } catch (err: any) {
497    const errDtls = { msg: err.message, name: err.name, status: err.status, type: err.type, code: err.code };
498    lg("ERROR", "callOpenAI", `OpenAI API call failed tid=${tid}`, { error: errDtls }, mid, tid);
499    throw new Error(`OpenAI API failed: ${err.message}` + (err.code ? ` (Code: ${err.code})` : ""));
500  }
501}
910    `Generate the ${p.cType} for the specified platform based on the context provided in the system prompt.`;
911  try {
912    const genContent = await callOpenAI(sysP, userP, mid, tid, lg);
913    if (!genContent) {
914      lg("WARN", "CntAgent", `LLM returned no content tid=${tid}.`, undefined, mid, tid);
956  const userP = `Develop the marketing strategy based on the system prompt context & framework.`;
957  try {
958    const stratContent = await callOpenAI(sysP, userP, mid, tid, lg);
959    if (!stratContent) {
960      lg("WARN", "StratAgent", `LLM no content for strat tid=${tid}.`, undefined, mid, tid);
1007    `Generate 2-3 distinct logo concepts for "${p.bName}" based on the system prompt. Provide descriptions and AI prompts.`;
1008  try {
1009    const conceptContent = await callOpenAI(sysP, userP, mid, tid, lg);
1010    if (!conceptContent) {
1011      lg("WARN", "LogoAgent", `LLM no content for logo tid=${tid}.`, undefined, mid, tid);
1084    `Develop foundational brand ID guide for "${p.bName}" based on system prompt context/instructions. Ensure cohesive & practical.`;
1085  try {
1086    const idContent = await callOpenAI(sysP, userP, mid, tid, lg);
1087    if (!idContent) {
1088      lg("WARN", "BrandAgent", `LLM no content for brand ID tid=${tid}.`, undefined, mid, tid);
1148  const userP = `Run simulation type '${p.simType}' based on system prompt context/params. Follow format.`;
1149  try {
1150    const simContent = await callOpenAI(sysP, userP, mid, tid, lg);
1151    if (!simContent) {
1152      lg("WARN", "SimAgent", `LLM no content for sim tid=${tid}.`, undefined, mid, tid);
1196    `Based on my request in system context, generate/refine the system prompt per guidelines. Output only resulting prompt text.`;
1197  try {
1198    const genSysP = await callOpenAI(sysP, userP, mid, tid, lg);
1199    if (!genSysP) {
1200      lg("WARN", "MetaAgent", `LLM no content for meta-prompt tid=${tid}.`, undefined, mid, tid);

14
15### Core Cognitive Tools
16- **AI-Enhanced Thought Forking**: Automatically generate parallel explorations using OpenAI
17- **Goal Tracking**: Create, update, and monitor goals with hierarchical structure
18- **Task Management**: Break down goals into actionable tasks with state tracking

4  "projects": [
5    {
6      "raw": "# bolt.diy\r\n\r\n### Repository URL\r\n\r\nhttps://github.com/stackblitz-labs/bolt.diy\r\n\r\n### Project Description\r\n\r\nbolt.diy is an open-source platform that enables users to prompt, run, edit, and deploy full-stack web applications directly in the browser using various Large Language Models (LLMs). It supports integration with multiple LLM providers, including OpenAI, Anthropic, HuggingFace, and more. The platform offers features like an integrated terminal, code versioning, and the ability to attach images to prompts, facilitating a seamless AI-powered development experience. \r\n\r\n\r\n### Potential Contribution Areas\r\n\r\nAgent Collaboration: Enhance bolt.diy to support MCP tool access and A2A-based agent collaboration, fostering better interoperability between SAP agents and bolt.diy projects.\r\nSAP Integration: Develop adapters or plugins to integrate bolt.diy with SAP's App Router or Cloud Foundry, enabling seamless deployment of applications within the SAP ecosystem.\r\n\r\n### Estimated Time Commitment\r\n\r\nMedium (4-8 hours/week)\r\n\r\n### Required Skills\r\n\r\nTypeScript\r\n\r\n### License\r\n\r\nMIT\r\n\r\n### Additional Information\r\n\r\n_No response_",
7      "id": 22,
8      "title": "bolt.diy",
39      "projectName": "",
40      "repositoryUrl": "https://github.com/stackblitz-labs/bolt.diy",
41      "description": "bolt.diy is an open-source platform that enables users to prompt, run, edit, and deploy full-stack web applications directly in the browser using various Large Language Models (LLMs). It supports integration with multiple LLM providers, including OpenAI, Anthropic, HuggingFace, and more. The platform offers features like an integrated terminal, code versioning, and the ability to attach images to prompts, facilitating a seamless AI-powered development experience.",
42      "contribution": "Agent Collaboration: Enhance bolt.diy to support MCP tool access and A2A-based agent collaboration, fostering better interoperability between SAP agents and bolt.diy projects.\r\nSAP Integration: Develop adapters or plugins to integrate bolt.diy with SAP's App Router or Cloud Foundry, enabling seamless deployment of applications within the SAP ecosystem.",
43      "timeCommitment": "Medium (4-8 hours/week)",
222    },
223    {
224      "raw": "### Project Name\n\nLLM Proxy Server for SAP AI Core\n\n### Repository URL\n\nhttps://github.com/sap-samples/llm-proxy-sap-ai-core\n\n### Project Description\n\nThe LLM Proxy Server for SAP AI Core is an open-source project designed to serve as a lightweight, performant, and extensible intermediary between SAP AI Core and a variety of large language model (LLM) backends. Inspired by solutions like LightLLM, this proxy server standardizes interactions with different model providers (OpenAI, Anthropic, HuggingFace, etc.), handles rate limiting and caching, and simplifies integration for enterprise use cases. Its purpose is to provide a scalable, multi-tenant LLM gateway that plugs seamlessly into the SAP AI Core inference pipeline.\n\n### Potential Contribution Areas\n\n- Model Adapter Layer: Implement new adapters to support additional LLM providers or fine-tuned models hosted on SAP AI Core.\r\n- Request Routing & Optimization: Improve routing logic, caching strategies, and load balancing between LLM providers.\r\n- Security & Multi-Tenancy: Enhance authentication, logging, and quota management to support secure, tenant-aware deployments.\r\n- SAP Integration: Develop SDK components or API contracts to enable easy consumption of the proxy within SAP BTP-based applications.\n\n### Estimated Time Commitment\n\nMedium (4-8 hours/week)\n\n### Required Skills\n\n_No response_\n\n### License\n\n_No response_\n\n### Additional Information\n\n_No response_",
225      "id": 23,
226      "title": "LLM Proxy Server for SAP AI Core",
256      "projectName": "LLM Proxy Server for SAP AI Core",
257      "repositoryUrl": "https://github.com/sap-samples/llm-proxy-sap-ai-core",
258      "description": "The LLM Proxy Server for SAP AI Core is an open-source project designed to serve as a lightweight, performant, and extensible intermediary between SAP AI Core and a variety of large language model (LLM) backends. Inspired by solutions like LightLLM, this proxy server standardizes interactions with different model providers (OpenAI, Anthropic, HuggingFace, etc.), handles rate limiting and caching, and simplifies integration for enterprise use cases. Its purpose is to provide a scalable, multi-tenant LLM gateway that plugs seamlessly into the SAP AI Core inference pipeline.",
259      "contribution": "- Model Adapter Layer: Implement new adapters to support additional LLM providers or fine-tuned models hosted on SAP AI Core.\r\n- Request Routing & Optimization: Improve routing logic, caching strategies, and load balancing between LLM providers.\r\n- Security & Multi-Tenancy: Enhance authentication, logging, and quota management to support secure, tenant-aware deployments.\r\n- SAP Integration: Develop SDK components or API contracts to enable easy consumption of the proxy within SAP BTP-based applications.",
260      "timeCommitment": "Medium (4-8 hours/week)",
746    },
747    {
748      "raw": "# Open WebUI    \r\n  ---\r\n  \r\n  ## 🔗 Repository URL  \r\n  https://github.com/open-webui/open-webui\r\n  \r\n  ## 🧠 Project Description  \r\n  Local web UI for interacting with LLMs like OpenAI, Ollama, LM Studio, and others.\r\n  \r\n  ## 🧩 Interoperability: Agent & Tool Protocol Fit  \r\n  Could integrate MCP to allow access to external tool APIs and A2A for backend AI agent interaction.\r\n  \r\n  ## 🛠️ How AI Guild Can Contribute  \r\n  - Add SAP API Hub wrapper as MCP server\r\n  - Build agent-to-agent pipeline that connects support tooling to dev tools\r\n  \r\n  ## 🕒 Estimated Time Commitment  \r\n  Medium (4-8 hours/week)\r\n\r\n  \r\n  ## 🧪 Required Skills  \r\n  TypeScript, Python, LLM APIs, Web UI Development\r\n   \r\n  ## ⚖️ License  \r\n  \r\n  \r\n  ---\r\n  \r\n  ## 💬 Additional Information  \r\n  \r\n  \r\n  📎 Linked source: `Open WebUI.json`\r\n  ",
749      "id": 9,
750      "title": "Open WebUI",

5import { discordWebhook } from "https://esm.town/v/stevekrouse/discordWebhook";
6import { Octokit } from "npm:octokit";
7import { OpenAI } from "https://esm.town/v/std/openai";
8
9// Environment variables, set them in the left sidebar
25 */
26async function generateUserFocusedSummary(fullCommitMessage: string, commitType: string): Promise<string> {
27  const openai = new OpenAI();
28  
29  const prompt = `You are writing release notes for a developer tool. Based on this commit message, write a concise one-liner.
51
52  try {
53    const completion = await openai.chat.completions.create({
54      messages: [{ role: "user", content: prompt }],
55      model: "gpt-4o-mini",