\n {imagePrompts.map((prompt: string, index: number) => (\n \n ))}\n

\n \n

\n );\n }\n\n // Default JSON rendering\n return (\n \n );\n } catch (e) {\n return content;\n }\n }\n return content;\n };\n\n const sendMessage = async () => {\n if (!inputMessage.trim()) {\n log(\"WARN\", \"Attempted to send empty message\");\n return;\n }\n\n // Reset previous errors\n setError(null);\n\n // Add user message to chat\n const userMessage: ChatMessage = {\n id: uuidv4(),\n role: \"user\",\n content: inputMessage,\n timestamp: new Date().toISOString(),\n };\n\n setMessages(prev => [...prev, userMessage]);\n setInputMessage(\"\");\n setIsLoading(true);\n\n try {\n log(\"INFO\", \"Sending message to agent\", {\n messageLength: inputMessage.length,\n });\n\n // Send request to AgentA\n const response = await fetch(\"/process\", {\n method: \"POST\",\n headers: {\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify({\n messages: [...messages, userMessage].map(({ role, content }) => ({ role, content })),\n }),\n });\n\n if (!response.ok) {\n const errorText = await response.text();\n throw new Error(`HTTP error! status: ${response.status}, message: ${errorText}`);\n }\n\n const data: AgentResponse = await response.json();\n\n if (!data.response) {\n throw new Error(\"No response received from agent\");\n }\n\n log(\"INFO\", \"Received agent response\", {\n actionType: data.actionType,\n responseLength: data.response.length,\n });\n\n // Add agent response to chat\n const agentMessage: ChatMessage = {\n id: uuidv4(),\n role: \"agent\",\n content: data.response,\n timestamp: new Date().toISOString(),\n };\n setMessages(prev => [...prev, agentMessage]);\n\n // If the action type requires using a tool, make a tool request\n if (data.actionType === \"use_tool\" && data.tool) {\n try {\n const toolResponse = await fetch(\"/tool\", {\n method: \"POST\",\n headers: {\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify(data.tool),\n });\n\n if (!toolResponse.ok) {\n throw new Error(`Tool request failed with status: ${toolResponse.status}`);\n }\n\n const toolData: ToolResponse = await toolResponse.json();\n\n // Add tool response to chat\n const toolMessage: ChatMessage = {\n id: uuidv4(),\n role: \"tool\",\n content: toolData.success\n ? JSON.stringify(toolData.data)\n : `Tool error: ${toolData.error || \"Unknown error\"}`,\n timestamp: new Date().toISOString(),\n };\n setMessages(prev => [...prev, toolMessage]);\n\n // Update generated images if any\n if (toolData.images && toolData.images.length > 0) {\n setGeneratedImages(toolData.images);\n }\n } catch (toolError) {\n const errorMessage = toolError instanceof Error\n ? toolError.message\n : \"An unknown tool error occurred\";\n\n log(\"ERROR\", \"Tool request failed\", {\n errorMessage,\n tool: data.tool,\n });\n\n setMessages(prev => [...prev, {\n id: uuidv4(),\n role: \"system\",\n content: `Tool Error: ${errorMessage}. Please try again.`,\n timestamp: new Date().toISOString(),\n }]);\n }\n }\n } catch (error) {\n const errorMessage = error instanceof Error\n ? error.message\n : \"An unknown error occurred\";\n\n log(\"ERROR\", \"Message processing failed\", {\n errorMessage,\n inputMessage: userMessage.content,\n });\n\n setError(errorMessage);\n setMessages(prev => [...prev, {\n id: uuidv4(),\n role: \"system\",\n content: `Error: ${errorMessage}. Please try again.`,\n timestamp: new Date().toISOString(),\n }]);\n } finally {\n setIsLoading(false);\n }\n };\n\n // Get message style based on role\n const getMessageStyle = (role: string) => {\n switch (role) {\n case \"user\":\n return \"bg-blue-600 text-white ml-auto rounded-tl-lg rounded-bl-lg rounded-tr-lg\";\n case \"agent\":\n return \"bg-gray-100 border border-gray-200 mr-auto rounded-tl-lg rounded-br-lg rounded-tr-lg\";\n case \"tool\":\n return \"bg-green-50 border-l-4 border-green-500 mr-auto rounded-tr-lg rounded-br-lg\";\n default:\n return \"bg-yellow-50 border-l-4 border-yellow-400 mx-auto w-full text-center italic\";\n }\n };\n\n // Get avatar for each role\n const getAvatarForRole = (role: string) => {\n switch (role) {\n case \"user\":\n return (\n

\n U\n

\n );\n case \"agent\":\n return (\n

\n A\n

\n );\n case \"tool\":\n return (\n

\n T\n

\n );\n default:\n return (\n

\n S\n

\n );\n }\n };\n\n // Format timestamp\n const formatTime = (timestamp: string) => {\n try {\n const date = new Date(timestamp);\n return date.toLocaleTimeString([], { hour: \"2-digit\", minute: \"2-digit\" });\n } catch (e) {\n return \"\";\n }\n };\n\n return (\n

\n \n

Agent Information System

\n setIsExpanded(!isExpanded)}\n className=\"text-white hover:text-gray-200 transition-colors\"\n >\n \n \n \n \n

\n\n {isExpanded && (\n

About this System

\n This agent can create and use custom information tools to help answer your questions. It can analyze your\n request, define a schema for the information, and process your query.\n

\n Try asking questions about topics like weather forecasts, stock information, recipe recommendations, or\n any other data-driven query.\n

\n )}\n\n {error && (\n

\n \n

{error}

\n )}\n\n

\n {messages.map((message) => (\n

\n {message.role !== \"user\" && !isMobile && (\n

\n {getAvatarForRole(message.role)}\n

\n )}\n

\n \n {message.role === \"user\"\n ? \"You\"\n : message.role === \"agent\"\n ? \"Agent\"\n : message.role === \"tool\"\n ? \"Tool\"\n : \"System\"}\n \n {formatTime(message.timestamp)}\n

\n {formatContent(message.content, message.role)}\n

\n {message.role === \"user\" && !isMobile && (\n

\n {getAvatarForRole(message.role)}\n

\n )}\n

\n ))}\n {isLoading && (\n

\n {getAvatarForRole(\"agent\")}\n

\n )}\n

\n\n

\n setInputMessage(e.target.value)}\n onKeyPress={(e) => e.key === \"Enter\" && !e.shiftKey && (e.preventDefault(), sendMessage())}\n placeholder=\"Ask me anything...\"\n rows={1}\n disabled={isLoading}\n style={{ minHeight: \"44px\", maxHeight: \"120px\" }}\n />\n

\n \n \n \n

\n );\n}\n\nfunction client() {\n createRoot(document.getElementById(\"root\")).render();\n}\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 // 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 = 3; // Increment schema version for tool 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 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\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 You obey gricean maxims aiming for optimal information absorbtion and density. Utilize zettlekasten techniques to return a comprehensive response.\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 \"images\": [] // required array of image generation prompts\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 // Fallback response if OpenAI fails\n let agentAResponse: AgentResponse = {\n actionType: \"direct_response\",\n response: \"I'm having trouble processing your request right now. Please try again.\",\n };\n\n try {\n // AgentA decides what to do with the query\n const agentAPrompt = `\n You are AgentA, an advanced AI assistant that can define information tools to help users.\n When a user asks for information, you should:\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, a specialized tool agent designed to process specific information requests.\n \n If a schema already exists for their request type, use that. Otherwise, create a new one.\n \n Current conversation history:\n ${messages.map(m => `${m.role}: ${m.content}`).join(\"\\n\")}\n \n Now, decide:\n 1. Does this request require creating a new information tool?\n 2. Can we use an existing tool?\n \n Format your thinking as JSON with this structure:\n {\n \"actionType\": \"use_tool\",\n \"reasoning\": \"your reasoning here\",\n \"response\": \"your response to the user\",\n \"tool\": { // Only if actionType is create_tool or use_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\": {} parameters needed for the query, must include all.\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 agentAResponse = JSON.parse(completionContent);\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 } catch (aiProcessingError) {\n serverLog(\"ERROR\", \"AI processing failed\", {\n error: aiProcessingError instanceof Error ? aiProcessingError.message : String(aiProcessingError),\n });\n // Use fallback response if AI processing fails\n }\n\n // Always return a JSON response\n return new Response(JSON.stringify(agentAResponse), {\n headers: { \"Content-Type\": \"application/json\" },\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: \"Comprehensive processing error\",\n details: error instanceof Error ? error.message : String(error),\n suggestion: \"Please check your input and try again.\",\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 Agent Information System\n \n \n \n \n \n \n \n

\n \n \n \n \n `,\n {\n headers: {\n \"Content-Type\": \"text/html\",\n },\n },\n );\n }\n\n // If no route matches, return a 404\n return new Response(\"Not Found\", { status: 404 });\n}","deleted","label",[99],"permalink","https://www.val.town/x/DocJoc/DataAgent/code/main.tsx","extension","actionData","errors"]