...

lib/ai/client/aiclient_validate.v

@@ -10,11 +10,10 @@ pub fn validate_vlang_content(path pathlib.Path) !string {
 	// Use `v fmt -check` to validate V language syntax
 	// If there are any formatting issues, `v fmt -check` will return a non-zero exit code
 	// and print the issues to stderr.
-	res := os.system('v fmt -check ${path.str()}')
+	res := os.system('v fmt -check "${path.str()}" 2>/dev/null')
 	if res != 0 {
-		return 'V language syntax validation failed. Please check the file for errors.'
+		return 'V language syntax validation failed. File has formatting or syntax errors.'
 	}
-	// TODO: do 'v filepath' d and check if errors return, if no, then remove the compiled binary if its there, if it goes wrong do same
 	return '' // empty means no error
 }
 

lib/ai/client/aiclient_write.v

@@ -5,72 +5,173 @@ import incubaid.herolib.ui.console
 import incubaid.herolib.clients.openai
 import os
 
-// TODO: do as params for the function
+// WritePromptArgs holds the parameters for write_from_prompt function
+@[params]
+pub struct WritePromptArgs {
+pub mut:
+	path          pathlib.Path
+	prompt        string
+	models        []LLMEnum = [.best]
+	temperature   f64       = 0.5
+	max_tokens    int       = 16000
+	system_prompt string    = 'You are a helpful assistant that modifies files based on user instructions.'
+}
 
-pub fn (mut ac AIClient) write_from_prompt(path_ pathlib.Path, prompt string, models []LLMEnum) ! {
-	mut mypath := path_
+// write_from_prompt modifies a file based on AI-generated modification instructions
+//
+// The process:
+// 1. Uses the first model to generate modification instructions from the prompt
+// 2. Uses the morph model to apply those instructions to the original content
+// 3. Validates the result based on file type (.v, .md, .yaml, .json)
+// 4. On validation failure, retries with the next model in the list
+// 5. Restores from backup if all models fail
+pub fn (mut ac AIClient) write_from_prompt(args WritePromptArgs) ! {
+	mut mypath := args.path
 	original_content := mypath.read()!
 	mut backup_path := pathlib.get_file(path: '${mypath.path}.backup', create: true)!
 	backup_path.write(original_content)!
 
-	mut selected_models := models.clone()
+	mut selected_models := args.models.clone()
 	if selected_models.len == 0 {
-		selected_models = [.best] // Default to best model if none provided
+		selected_models = [.best]
 	}
 
 	for model_enum in selected_models {
-		model_name, base_url := llm_to_model_url(model_enum)!
-		mut llm_client := openai.get(name: model_enum.str())! // Assuming model_enum.str() matches the name used in llms_init
+		model_name, _ := llm_to_model_url(model_enum)!
 
-		// 3. Use first model (or default best) to process prompt
-		// This part needs to be implemented based on how the OpenAI client's chat completion works
-		// For now, let's assume a simple completion call
-		// This is a placeholder and needs actual implementation based on the OpenAI client's chat completion method
-		// For example:
-		// completion := llm_client.chat_completion(prompt)!
-		// instructions := completion.choices[0].message.content
+		// Step 1: Get modification instructions from the selected model
+		// Get the appropriate LLM client for instruction generation
+		mut llm_client := get_llm_client(mut ac, model_enum)
 
-		// For now, let's just use the prompt as the "instructions" for modification
-		instructions := prompt
+		instruction_prompt := generate_instruction_prompt(original_content, mypath.ext()!,
+			args.prompt)
 
-		// 5. Use morph model to merge original + instructions
-		// This is a placeholder for the merging logic
-		// For now, let's just replace the content with instructions
-		new_content := instructions // This needs to be replaced with actual merging logic
+		instructions_response := llm_client.chat_completion(
+			message:               instruction_prompt
+			temperature:           args.temperature
+			max_completion_tokens: args.max_tokens
+		)!
 
-		// 6. Validate content based on file extension
+		instructions := instructions_response.result.trim_space()
+
+		// Step 2: Use morph model to apply instructions to original content
+		morph_prompt := generate_morph_prompt(original_content, instructions)
+
+		morph_response := ac.llms.llm_morph.chat_completion(
+			message:               morph_prompt
+			temperature:           args.temperature
+			max_completion_tokens: args.max_tokens
+		)!
+
+		new_content := morph_response.result.trim_space()
+
+		// Step 3: Validate content based on file extension
 		mut validation_error := ''
-		match mypath.ext()! {
+
+		// Create a temporary file for validation
+		file_ext := mypath.ext()!
+		mut temp_path := pathlib.get_file(
+			path:   '${mypath.path}.validate_temp${file_ext}'
+			create: true
+		)!
+		temp_path.write(new_content)!
+
+		match file_ext {
 			'.v' {
-				validation_error = validate_vlang_content(mypath)!
+				validation_error = validate_vlang_content(temp_path)!
 			}
 			'.md' {
-				validation_error = validate_markdown_content(mypath)!
+				validation_error = validate_markdown_content(temp_path)!
 			}
 			'.yaml', '.yml' {
-				validation_error = validate_yaml_content(mypath)!
+				validation_error = validate_yaml_content(temp_path)!
 			}
 			'.json' {
-				validation_error = validate_json_content(mypath)!
+				validation_error = validate_json_content(temp_path)!
 			}
 			else {
 				// No specific validation for other file types
 			}
 		}
 
+		// Clean up temporary validation file
+		if temp_path.exists() {
+			temp_path.delete()!
+		}
+
 		if validation_error == '' {
 			// Validation passed - write new content
 			mypath.write(new_content)!
 			backup_path.delete()! // Remove backup on success
+			console.print_stdout('✓ Successfully modified ${mypath.str()} using model ${model_name}')
 			return
 		} else {
-			console.print_stderr('Validation failed for model ${model_name}. Error: ${validation_error}. Trying next model...')
+			console.print_stderr('✗ Validation failed for model ${model_name}. Error: ${validation_error}. Trying next model...')
 		}
 	}
 
-	// 8. If all fail, restore .backup and error
+	// Step 4: If all models fail, restore backup and error
 	original_backup := backup_path.read()!
 	mypath.write(original_backup)!
 	backup_path.delete()!
-	return error('All models failed to generate valid content. Original file restored.')
+	return error('All models failed to generate valid content. Original file restored from backup.')
+}
+
+// get_llm_client returns the appropriate LLM client for the given model enum
+fn get_llm_client(mut ac AIClient, model LLMEnum) &openai.OpenAI {
+	return match model {
+		.maverick { ac.llms.llm_maverick }
+		.qwen { ac.llms.llm_qwen }
+		.embed { ac.llms.llm_embed }
+		.llm_120b { ac.llms.llm_120b }
+		.best { ac.llms.llm_best }
+		.flash { ac.llms.llm_flash }
+		.pro { ac.llms.llm_pro }
+		.morph { ac.llms.llm_morph }
+		.local { ac.llms.llm_local }
+	}
+}
+
+// generate_instruction_prompt creates the prompt for generating modification instructions
+fn generate_instruction_prompt(content string, file_ext string, user_prompt string) string {
+	return 'You are a file modification assistant specializing in ${file_ext} files.
+
+The user will provide a file and a modification request. Your task is to analyze the request and respond with ONLY clear, concise modification instructions.
+
+Do NOT apply the modifications yourself. Just provide step-by-step instructions that could be applied to transform the file.
+
+Original file content:
+\`\`\`${file_ext}
+${content}
+\`\`\`
+
+File type: ${file_ext}
+
+User modification request:
+${user_prompt}
+
+Provide only the modification instructions. Be specific and clear. Format your response as a numbered list of changes to make.'
+}
+
+// generate_morph_prompt creates the prompt for the morph model to apply instructions
+fn generate_morph_prompt(original_content string, instructions string) string {
+	return 'You are an expert code and file modifier. Your task is to apply modification instructions to existing file content.
+
+Take the original file content and the modification instructions, then generate the modified version.
+
+IMPORTANT: Return ONLY the modified file content. Do NOT include:
+- Markdown formatting or code blocks
+- Explanations or commentary
+- "Here is the modified file:" prefixes
+- Any text other than the actual modified content
+
+Original file content:
+\`\`\`
+${original_content}
+\`\`\`
+
+Modification instructions to apply:
+${instructions}
+
+Return the complete modified file content:'
 }