DataNexus

\n Intelligent Data Discovery\n

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\n );\n}\n\nfunction client() {\n createRoot(document.getElementById(\"root\")).render();\n}\nif (typeof document !== \"undefined\") { client(); }\n\nexport default async function server(request: Request): Promise {\n const { OpenAI } = await import(\"https://esm.town/v/std/openai\");\n const { sqlite } = await import(\"https://esm.town/v/stevekrouse/sqlite\");\n\n // Cache configuration\n const CACHE_CONFIG: CacheConfig = {\n enabled: true,\n ttl: 24 * 60 * 60 * 1000, // 24 hours in milliseconds\n };\n\n // Enhanced server-side logging\n function serverLog(level: \"INFO\" | \"WARN\" | \"ERROR\", message: string, context?: any) {\n const timestamp = new Date().toISOString();\n const logEntry = {\n timestamp,\n level,\n message,\n context,\n };\n console.log(JSON.stringify(logEntry));\n return logEntry;\n }\n\n // Robust key extraction with fallback\n const extractKey = (url: URL): string => {\n try {\n const pathParts = url.pathname.split(\"/\").filter(Boolean);\n // Use the last part of the path, or fallback to a default key\n return pathParts[pathParts.length - 1] || \"default_agent_api\";\n } catch (error) {\n console.error(\"Failed to extract key:\", error);\n return \"fallback_agent_api\";\n }\n };\n\n const openai = new OpenAI();\n const KEY = extractKey(new URL(import.meta.url));\n const SCHEMA_VERSION = 4; // Increment schema version for cache support\n\n try {\n // Initialize database with enhanced error handling\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS ${KEY}_tools_${SCHEMA_VERSION} (\n id TEXT PRIMARY KEY,\n name TEXT,\n description TEXT,\n schema TEXT,\n created_at DATETIME DEFAULT CURRENT_TIMESTAMP,\n error_log TEXT\n )\n `);\n\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS ${KEY}_requests_${SCHEMA_VERSION} (\n id TEXT PRIMARY KEY,\n tool_id TEXT,\n query TEXT,\n parameters TEXT,\n response TEXT,\n error_log TEXT,\n created_at DATETIME DEFAULT CURRENT_TIMESTAMP\n )\n `);\n\n // New table for cache\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS ${KEY}_cache_${SCHEMA_VERSION} (\n path TEXT PRIMARY KEY,\n data TEXT,\n created_at DATETIME DEFAULT CURRENT_TIMESTAMP,\n expires_at DATETIME\n )\n `);\n\n serverLog(\"INFO\", \"Database initialized successfully\", {\n key: KEY,\n schemaVersion: SCHEMA_VERSION,\n });\n } catch (dbInitError) {\n serverLog(\"ERROR\", \"Database initialization failed\", {\n error: dbInitError instanceof Error ? dbInitError.message : String(dbInitError),\n key: KEY,\n });\n return new Response(\n JSON.stringify({\n error: \"Database initialization failed\",\n details: String(dbInitError),\n }),\n {\n status: 500,\n headers: { \"Content-Type\": \"application/json\" },\n },\n );\n }\n\n const url = new URL(request.url);\n const path = url.pathname.split(\"/\").filter(Boolean);\n const fullPath = url.pathname;\n\n // Check cache for existing data\n async function checkCache(path: string): Promise<{ hit: boolean; data?: any }> {\n if (!CACHE_CONFIG.enabled) {\n return { hit: false };\n }\n\n try {\n const result = await sqlite.query(\n `\n SELECT data FROM ${KEY}_cache_${SCHEMA_VERSION}\n WHERE path = ? AND expires_at > datetime('now')\n `,\n [path],\n );\n\n if (result.length > 0) {\n serverLog(\"INFO\", \"Cache hit\", { path });\n return {\n hit: true,\n data: JSON.parse(result[0].data),\n };\n }\n\n return { hit: false };\n } catch (error) {\n serverLog(\"WARN\", \"Cache check failed\", {\n path,\n error: error instanceof Error ? error.message : String(error),\n });\n return { hit: false };\n }\n }\n\n // Save data to cache\n async function saveToCache(path: string, data: any): Promise {\n if (!CACHE_CONFIG.enabled) {\n return;\n }\n\n try {\n const now = new Date();\n const expires = new Date(now.getTime() + CACHE_CONFIG.ttl);\n\n // Delete any existing entries for this path\n await sqlite.execute(\n `\n DELETE FROM ${KEY}_cache_${SCHEMA_VERSION} WHERE path = ?\n `,\n [path],\n );\n\n // Insert new cache entry\n await sqlite.execute(\n `\n INSERT INTO ${KEY}_cache_${SCHEMA_VERSION}\n (path, data, created_at, expires_at)\n VALUES (?, ?, datetime('now'), ?)\n `,\n [\n path,\n JSON.stringify(data),\n expires.toISOString(),\n ],\n );\n\n serverLog(\"INFO\", \"Cache updated\", { path });\n } catch (error) {\n serverLog(\"ERROR\", \"Cache update failed\", {\n path,\n error: error instanceof Error ? error.message : String(error),\n });\n }\n }\n\n // NEW: Handle dynamic data endpoints - trigger AgentA and AgentB for any path\n if (path.length > 0 && path[0] !== \"process\" && path[0] !== \"tool\") {\n try {\n serverLog(\"INFO\", \"Dynamic data endpoint requested\", {\n path: path.join(\"/\"),\n });\n\n // Check cache first\n const cacheResult = await checkCache(fullPath);\n if (cacheResult.hit) {\n return new Response(JSON.stringify(cacheResult.data), {\n headers: {\n \"Content-Type\": \"application/json\",\n \"X-Cache\": \"HIT\",\n },\n });\n }\n\n // Construct the topic from the path\n const topic = path.join(\" \");\n\n // Simulate a user request to AgentA\n const agentAPrompt = `\n You are AgentA, an advanced AI assistant that creates comprehensive information tools.\n \n A user has requested information about: \"${topic}\"\n \n Define a schema for this information topic and create a query for AgentB to fetch detailed data.\n Your goal is to return the most comprehensive, detailed, and accurate information about this topic.\n \n Format your thinking as JSON with this structure:\n {\n \"actionType\": \"use_tool\",\n \"reasoning\": \"your reasoning here\",\n \"response\": \"This is comprehensive information about ${topic}\",\n \"tool\": {\n \"id\": \"${crypto.randomUUID()}\",\n \"schema\": {\n \"name\": \"${topic.replace(/\\s+/g, \"_\")}_info\",\n \"description\": \"Comprehensive information about ${topic}\",\n \"fields\": [\n {\"name\": \"data\", \"type\": \"object\", \"description\": \"Complete structured data about ${topic}\"}\n ]\n },\n \"query\": \"Provide comprehensive, detailed information about ${topic}. Include all relevant subcategories, statistics, examples, and structured data.\",\n \"parameters\": {\"depth\": \"complete\", \"format\": \"structured\"}\n }\n }\n `;\n\n // Call AgentA to create the tool definition\n const agentACompletion = await openai.chat.completions.create({\n model: \"gpt-4o-mini\",\n response_format: { type: \"json_object\" },\n messages: [{ role: \"user\", content: agentAPrompt }],\n });\n\n // Parse AgentA response\n const completionContent = agentACompletion.choices[0].message.content || \"{}\";\n const agentAResponse: AgentResponse = JSON.parse(completionContent);\n\n // If AgentA didn't provide a tool, return an error\n if (!agentAResponse.tool) {\n throw new Error(\"Failed to create a tool for this topic\");\n }\n\n // Now use AgentB to process the tool request (similar to the /tool endpoint)\n const toolRequest = agentAResponse.tool;\n\n // Store the tool in the database\n const toolId = toolRequest.id || crypto.randomUUID();\n await sqlite.execute(\n `\n INSERT INTO ${KEY}_tools_${SCHEMA_VERSION} \n (id, name, description, schema) \n VALUES (?, ?, ?, ?)\n `,\n [\n toolId,\n toolRequest.schema.name,\n toolRequest.schema.description,\n JSON.stringify(toolRequest.schema),\n ],\n );\n\n // Call AgentB with the tool request\n const agentBPrompt = `\n You are AgentB, a specialized tool agent designed to provide comprehensive, accurate information.\n \n Tool Schema:\n ${JSON.stringify(toolRequest.schema, null, 2)}\n \n Query: ${toolRequest.query}\n \n Parameters: ${JSON.stringify(toolRequest.parameters || {})}\n \n Instructions:\n 1. Your task is to provide complete, accurate, and detailed information about \"${topic}\".\n 2. Return structured data that covers all relevant aspects of the topic.\n 3. Include subcategories, examples, definitions, comparisons, and any other relevant information.\n 4. Organize the information hierarchically where appropriate.\n 5. If the topic is about a list or collection, make sure to provide an extensive list.\n 6. For numeric data, provide precise figures where possible.\n 7. Include metadata about your response if relevant.\n \n Process this request and return a JSON response with:\n {\n \"success\": true,\n \"data\": {\n // Complete structured information about ${topic}\n }\n }\n `;\n\n const agentBCompletion = await openai.chat.completions.create({\n model: \"gpt-4o-mini\",\n response_format: { type: \"json_object\" },\n messages: [{ role: \"user\", content: agentBPrompt }],\n });\n\n // Parse AgentB response\n const agentBContent = agentBCompletion.choices[0].message.content || \"{}\";\n const toolResponse: ToolResponse = JSON.parse(agentBContent);\n\n // Store the tool request and response in the database\n const requestId = crypto.randomUUID();\n await sqlite.execute(\n `\n INSERT INTO ${KEY}_requests_${SCHEMA_VERSION} \n (id, tool_id, query, parameters, response, error_log) \n VALUES (?, ?, ?, ?, ?, ?)\n `,\n [\n requestId,\n toolId,\n toolRequest.query,\n JSON.stringify(toolRequest.parameters || {}),\n JSON.stringify(toolResponse.data),\n toolResponse.success ? null : toolResponse.error,\n ],\n );\n\n // Save to cache\n await saveToCache(fullPath, toolResponse.data);\n\n serverLog(\"INFO\", \"Dynamic endpoint processed successfully\", {\n path: path.join(\"/\"),\n success: toolResponse.success,\n });\n\n // Return the data as JSON\n return new Response(JSON.stringify(toolResponse.data), {\n headers: {\n \"Content-Type\": \"application/json\",\n \"X-Cache\": \"MISS\",\n },\n });\n } catch (error) {\n serverLog(\"ERROR\", \"Dynamic endpoint processing failed\", {\n path: path.join(\"/\"),\n error: error instanceof Error ? error.message : String(error),\n });\n\n return new Response(\n JSON.stringify({\n error: \"Failed to process this data endpoint\",\n details: error instanceof Error ? error.message : String(error),\n }),\n {\n status: 500,\n headers: { \"Content-Type\": \"application/json\" },\n },\n );\n }\n }\n\n // Route for tool processing (AgentB)\n if (path[0] === \"tool\") {\n try {\n const toolRequest: ToolRequest = await request.json();\n serverLog(\"INFO\", \"Processing tool request\", {\n toolName: toolRequest.schema.name,\n query: toolRequest.query,\n });\n\n // Use AgentB (a separate OpenAI instance) to process the tool request\n const agentBPrompt = `\n You are AgentB, a specialized tool agent designed to process specific information requests.\n \n Tool Schema:\n ${JSON.stringify(toolRequest.schema, null, 2)}\n \n Query: ${toolRequest.query}\n \n Parameters: ${JSON.stringify(toolRequest.parameters || {})}\n \n Process this request and return a JSON response with:\n {\n \"success\": true/false,\n \"data\": {}, // The result of processing the request\n \"error\": \"\" // Optional error message if something goes wrong\n }\n `;\n\n const agentBCompletion = await openai.chat.completions.create({\n model: \"gpt-4o-mini\",\n response_format: { type: \"json_object\" },\n messages: [{ role: \"user\", content: agentBPrompt }],\n });\n\n // Safely parse the response\n const completionContent = agentBCompletion.choices[0].message.content || \"{}\";\n const toolResponse: ToolResponse = JSON.parse(completionContent);\n\n // Store the tool request and response in the database\n const requestId = toolRequest.id || crypto.randomUUID();\n await sqlite.execute(\n `\n INSERT INTO ${KEY}_requests_${SCHEMA_VERSION} \n (id, tool_id, query, parameters, response, error_log) \n VALUES (?, ?, ?, ?, ?, ?)\n `,\n [\n requestId,\n toolRequest.schema.name,\n toolRequest.query,\n JSON.stringify(toolRequest.parameters || {}),\n JSON.stringify(toolResponse.data),\n toolResponse.success ? null : toolResponse.error,\n ],\n );\n\n serverLog(\"INFO\", \"Tool request processed\", {\n success: toolResponse.success,\n });\n\n return new Response(JSON.stringify(toolResponse), {\n headers: { \"Content-Type\": \"application/json\" },\n });\n } catch (error) {\n serverLog(\"ERROR\", \"Tool processing failed\", {\n error: error instanceof Error ? error.message : String(error),\n });\n\n return new Response(\n JSON.stringify({\n success: false,\n error: \"Tool processing error\",\n details: error instanceof Error ? error.message : String(error),\n }),\n {\n status: 500,\n headers: { \"Content-Type\": \"application/json\" },\n },\n );\n }\n }\n\n // Route for main agent processing (AgentA)\n if (path[0] === \"process\") {\n try {\n const { messages } = await request.json();\n serverLog(\"INFO\", \"Processing agent request\", {\n messageCount: messages.length,\n });\n\n // User's latest message\n const userMessage = messages.filter(m => m.role === \"user\").pop();\n\n if (!userMessage) {\n throw new Error(\"No user message found in the request\");\n }\n\n try {\n // Force AgentA to always create or use a tool for every query\n const agentAPrompt = `\n You are AgentA, an advanced AI assistant that MUST define or use information tools to help users.\n When a user asks for information, you MUST:\n \n 1. Analyze what kind of information they need\n 2. Define a schema for that information\n 3. Create a query to fetch the information from AgentB\n \n IMPORTANT: You must ALWAYS use a tool for EVERY user query, regardless of how simple it seems.\n Never use \"direct_response\" as an actionType. Always use either \"create_tool\" or \"use_tool\".\n \n Current conversation history:\n ${messages.map(m => `${m.role}: ${m.content}`).join(\"\\n\")}\n \n Format your response as JSON with this structure:\n {\n \"actionType\": \"create_tool\" | \"use_tool\", // NEVER use \"direct_response\"\n \"reasoning\": \"your reasoning here\",\n \"response\": \"Tell the user you're retrieving the information and explain the tool you're using\",\n \"tool\": {\n \"id\": \"optional unique identifier\",\n \"schema\": {\n \"name\": \"tool name\",\n \"description\": \"tool description\",\n \"fields\": [\n {\"name\": \"field1\", \"type\": \"string | number | boolean | array | object\", \"description\": \"field description\"}\n ]\n },\n \"query\": \"specific query for AgentB\",\n \"parameters\": {} // Any parameters needed for the query\n }\n }\n `;\n\n const agentACompletion = await openai.chat.completions.create({\n model: \"gpt-4o-mini\",\n response_format: { type: \"json_object\" },\n messages: [{ role: \"user\", content: agentAPrompt }],\n });\n\n // Safely parse the response\n const completionContent = agentACompletion.choices[0].message.content || \"{}\";\n let agentAResponse = JSON.parse(completionContent);\n\n // Ensure it's not a direct response (force tool usage)\n if (agentAResponse.actionType === \"direct_response\" || !agentAResponse.tool) {\n // Override with a default tool if somehow direct_response slipped through\n serverLog(\"WARN\", \"Forcing tool usage - direct response was returned\", {\n originalActionType: agentAResponse.actionType,\n });\n\n // Create a generic information tool based on the user query\n agentAResponse = {\n actionType: \"create_tool\",\n reasoning: \"Converting direct response to tool request\",\n response: `I'm retrieving information about \"${userMessage.content}\". One moment please...`,\n tool: {\n schema: {\n name: \"general_information\",\n description: `Information about: ${userMessage.content}`,\n fields: [\n { name: \"data\", type: \"object\", description: \"Complete information on the topic\" },\n ],\n },\n query: userMessage.content,\n parameters: { depth: \"complete\", format: \"structured\" },\n },\n };\n }\n\n // If it's a new tool, store it in the database\n if (agentAResponse.tool) {\n const toolId = crypto.randomUUID();\n await sqlite.execute(\n `\n INSERT INTO ${KEY}_tools_${SCHEMA_VERSION} \n (id, name, description, schema) \n VALUES (?, ?, ?, ?)\n `,\n [\n toolId,\n agentAResponse.tool.schema.name,\n agentAResponse.tool.schema.description,\n JSON.stringify(agentAResponse.tool.schema),\n ],\n );\n\n // Attach the generated ID to the tool\n agentAResponse.tool.id = toolId;\n }\n\n serverLog(\"INFO\", \"AgentA response received\", {\n actionType: agentAResponse.actionType,\n });\n\n // Always return the tool-based response\n return new Response(JSON.stringify(agentAResponse), {\n headers: { \"Content-Type\": \"application/json\" },\n });\n } catch (aiProcessingError) {\n serverLog(\"ERROR\", \"AI processing failed\", {\n error: aiProcessingError instanceof Error ? aiProcessingError.message : String(aiProcessingError),\n });\n\n // Even in failure case, create a generic tool\n const fallbackResponse = {\n actionType: \"create_tool\",\n response: \"I'm processing your request to find relevant information. Let me check my data sources...\",\n tool: {\n schema: {\n name: \"emergency_information\",\n description: \"Emergency fallback information retrieval\",\n fields: [\n { name: \"results\", type: \"object\", description: \"Available information on the topic\" },\n ],\n },\n query: userMessage.content,\n parameters: { format: \"simple\" },\n },\n };\n\n return new Response(JSON.stringify(fallbackResponse), {\n headers: { \"Content-Type\": \"application/json\" },\n });\n }\n } catch (error) {\n serverLog(\"ERROR\", \"Agent processing failed\", {\n error: error instanceof Error ? error.message : String(error),\n });\n\n return new Response(\n JSON.stringify({\n error: \"Processing error\",\n details: error instanceof Error ? error.message : String(error),\n }),\n {\n status: 500,\n headers: { \"Content-Type\": \"application/json\" },\n },\n );\n }\n }\n\n // Separate route for serving the HTML\n if (path.length === 0) {\n return new Response(\n `\n \n \n \n \n DataNexus - Intelligent Data Discovery\n \n \n \n \n \n \n \n \n \n \n \n \n

\n \n \n \n `,\n {\n headers: { \"Content-Type\": \"text/html\" },\n },\n );\n }\n\n // Default response for unhandled routes\n return new Response(\n JSON.stringify({\n error: \"Route not found\",\n availableRoutes: [\"/\", \"/process\", \"/tool\", \"/{dynamic data paths}\"],\n }),\n {\n status: 404,\n headers: { \"Content-Type\": \"application/json\" },\n },\n );\n}","deleted","label",[98],"permalink","https://www.val.town/x/data/tool/code/main.tsx","extension","actionData","errors"]