Kellyvv
0f146d4e8f
- Move PhoneClaw/ contents to project root (no more nesting) - Delete PhoneClawMac/ (nested SPM package, duplicated iOS code) - Delete LLM/LocalLLMService.swift (dead MediaPipe implementation) - Delete llm_inference_engine.h + PhoneClaw-Bridging-Header.h (unused) - Delete Sources/ (empty dir), phoneclaw.code-workspace, TECH_PATH_ANALYSIS.md, project.yml - Fix project.pbxproj: update INFOPLIST_FILE, CODE_SIGN_ENTITLEMENTS paths - Fix project.pbxproj: remove SWIFT_OBJC_BRIDGING_HEADER setting - Fix project.pbxproj: remove LocalLLMService.swift file reference - Fix project.pbxproj: PhoneClaw group now uses name= instead of path=
808 lines
30 KiB
Swift
808 lines
30 KiB
Swift
import CoreImage
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import Foundation
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import MLXLMCommon
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import UIKit
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func log(_ message: String) {
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print(message)
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}
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// MARK: - 模型/推理配置
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@Observable
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class ModelConfig {
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var maxTokens = 4000
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var topK = 64
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var topP = 0.95
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var temperature = 1.0
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var useGPU = true
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/// System prompt — 由 AgentEngine.loadSystemPrompt() 从 SYSPROMPT.md 注入，不在代码里硬编码。
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var systemPrompt = ""
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}
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// MARK: - SYSPROMPT 默认内容（仅在文件不存在时写入磁盘）
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private let kDefaultSystemPrompt = """
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你是 PhoneClaw，一个运行在本地设备上的私人 AI 助手。你完全离线运行，不联网，保护用户隐私。
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你拥有以下能力（Skill）：
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___SKILLS___
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只有当用户明确要求执行某项设备内操作时，才调用 load_skill 加载该能力的详细指令。
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像"配置""信息""看看""帮我查一下"这类含糊词，不足以单独触发工具调用。
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如果用户只是普通聊天、追问上文、让你解释结果，直接回答，不要调用工具。
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如果确实需要某个能力，你必须自己调用 load_skill，不要让用户去"使用某个能力"或"打开某个 skill"。
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当且仅当确实需要某个能力时，先调用 load_skill：
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<tool_call>
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{"name": "load_skill", "arguments": {"skill": "能力名"}}
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</tool_call>
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在已经拿到工具结果后，优先直接给出最终答案，不要无谓追问。
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用中文回答，简洁实用。
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"""
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// MARK: - 聊天消息
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struct ChatImageAttachment: Identifiable {
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let id = UUID()
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let data: Data
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init?(image: UIImage) {
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if let jpeg = image.jpegData(compressionQuality: 0.92) {
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self.data = jpeg
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} else if let png = image.pngData() {
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self.data = png
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} else {
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return nil
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}
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}
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var uiImage: UIImage? {
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UIImage(data: data)
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}
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var ciImage: CIImage? {
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if let image = CIImage(
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data: data,
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options: [.applyOrientationProperty: true]
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) {
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return image
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}
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guard let uiImage else { return nil }
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if let ciImage = uiImage.ciImage {
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return ciImage
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}
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if let cgImage = uiImage.cgImage {
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return CIImage(cgImage: cgImage)
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}
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return CIImage(image: uiImage)
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}
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}
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struct ChatMessage: Identifiable {
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let id: UUID
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var role: Role
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var content: String
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var images: [ChatImageAttachment]
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let timestamp = Date()
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var skillName: String? = nil
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init(
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role: Role,
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content: String,
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images: [ChatImageAttachment] = [],
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skillName: String? = nil
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) {
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self.id = UUID()
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self.role = role
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self.content = content
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self.images = images
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self.skillName = skillName
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}
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mutating func update(content: String) {
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guard self.content != content else { return }
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self.content = content
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}
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mutating func update(role: Role, content: String, skillName: String? = nil) {
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self.role = role
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self.content = content
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self.skillName = skillName
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}
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enum Role {
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case user, assistant, system, skillResult
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}
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}
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// MARK: - Agent Engine
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@Observable
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class AgentEngine {
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let llm = MLXLocalLLMService()
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var messages: [ChatMessage] = []
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var isProcessing = false
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var config = ModelConfig()
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// 文件驱动的 Skill 系统
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let skillLoader = SkillLoader()
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let toolRegistry = ToolRegistry.shared
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// Skill 条目（给 UI 管理用，可开关）
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var skillEntries: [SkillEntry] = []
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var enabledSkillInfos: [SkillInfo] {
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skillEntries.filter(\.isEnabled).map {
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SkillInfo(name: $0.id, description: $0.description,
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displayName: $0.name, icon: $0.icon, samplePrompt: $0.samplePrompt)
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}
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}
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init() {
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loadSkillEntries()
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}
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private func loadSkillEntries() {
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let definitions = skillLoader.discoverSkills()
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self.skillEntries = definitions.map { SkillEntry(from: $0, registry: toolRegistry) }
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}
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func reloadSkills() {
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let enabledState = Dictionary(uniqueKeysWithValues: skillEntries.map { ($0.id, $0.isEnabled) })
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loadSkillEntries()
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for i in skillEntries.indices {
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if let wasEnabled = enabledState[skillEntries[i].id] {
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skillEntries[i].isEnabled = wasEnabled
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skillLoader.setEnabled(skillEntries[i].id, enabled: wasEnabled)
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}
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}
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}
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// MARK: - Skill 查找（文件驱动）
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private func findSkillId(for name: String) -> String? {
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if skillLoader.getDefinition(name) != nil { return name }
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return skillLoader.findSkillId(forTool: name)
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}
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private func findDisplayName(for name: String) -> String {
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if let skillId = findSkillId(for: name),
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let def = skillLoader.getDefinition(skillId) {
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return def.metadata.name
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}
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return name
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}
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private func handleLoadSkill(skillName: String) -> String? {
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guard let entry = skillEntries.first(where: { $0.id == skillName }),
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entry.isEnabled else {
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return nil
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}
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return skillLoader.loadBody(skillId: skillName)
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}
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private func handleToolExecution(toolName: String, args: [String: Any]) async throws -> String {
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return try await toolRegistry.execute(name: toolName, args: args)
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}
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private func fallbackReplyForEmptyToolFollowUp(toolName: String, toolResult: String) -> String {
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let trimmed = toolResult.trimmingCharacters(in: .whitespacesAndNewlines)
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if let data = trimmed.data(using: .utf8),
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let payload = try? JSONSerialization.jsonObject(with: data) as? [String: Any],
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let success = payload["success"] as? Bool,
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!success,
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let error = payload["error"] as? String,
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!error.isEmpty {
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return "工具 \(toolName) 执行失败：\(error)"
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}
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if trimmed.isEmpty {
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return "工具 \(toolName) 已执行，但没有返回内容。"
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}
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return """
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工具 \(toolName) 已执行完成，但模型没有生成最终回答。
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工具返回结果：
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\(trimmed)
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"""
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}
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private func fallbackReplyForEmptySkillFollowUp(skillName: String) -> String {
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"Skill \(skillName) 已加载，但模型没有继续生成工具调用或最终回答。请重试，或把问题说得更具体一些。"
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}
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// MARK: - 初始化
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/// ConfigurationsView 的"Restore default"按钮使用。
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var defaultSystemPrompt: String { kDefaultSystemPrompt }
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func setup() {
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loadSystemPrompt() // 从 SYSPROMPT.md 注入 system prompt
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applySamplingConfig()
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DispatchQueue.global(qos: .userInitiated).async { [weak self] in
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self?.llm.loadModel()
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}
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}
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// MARK: - SYSPROMPT 注入
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/// 从 ApplicationSupport/PhoneClaw/SYSPROMPT.md 读取 system prompt。
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/// 文件不存在时自动写入 kDefaultSystemPrompt（供用户后续编辑）。
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func loadSystemPrompt() {
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let fm = FileManager.default
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guard let supportDir = fm.urls(for: .applicationSupportDirectory,
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in: .userDomainMask).first else { return }
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let dir = supportDir.appendingPathComponent("PhoneClaw", isDirectory: true)
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let file = dir.appendingPathComponent("SYSPROMPT.md")
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if !fm.fileExists(atPath: dir.path) {
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try? fm.createDirectory(at: dir, withIntermediateDirectories: true)
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}
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if fm.fileExists(atPath: file.path),
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let content = try? String(contentsOf: file, encoding: .utf8),
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!content.trimmingCharacters(in: .whitespacesAndNewlines).isEmpty {
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config.systemPrompt = content
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print("[Agent] SYSPROMPT loaded (\(content.count) chars)")
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} else {
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try? kDefaultSystemPrompt.write(to: file, atomically: true, encoding: .utf8)
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config.systemPrompt = kDefaultSystemPrompt
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print("[Agent] SYSPROMPT not found — default written to \(file.path)")
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}
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}
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func applySamplingConfig() {
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llm.samplingTopK = config.topK
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llm.samplingTopP = Float(config.topP)
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llm.samplingTemperature = Float(config.temperature)
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llm.maxOutputTokens = config.maxTokens
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}
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func reloadModel() {
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DispatchQueue.global(qos: .userInitiated).async { [weak self] in
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self?.llm.loadModel()
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}
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}
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// MARK: - 处理用户输入（LiteRT-LM C API 流式输出）
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func processInput(_ text: String, images: [UIImage] = []) async {
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let trimmed = text.trimmingCharacters(in: .whitespacesAndNewlines)
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let normalizedText = trimmed.isEmpty && !images.isEmpty ? "请描述这张图片。" : trimmed
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guard !normalizedText.isEmpty, !isProcessing else { return }
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guard llm.isLoaded else {
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messages.append(ChatMessage(role: .system, content: "⏳ 模型还在加载中..."))
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return
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}
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let attachments = images.compactMap(ChatImageAttachment.init(image:))
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messages.append(ChatMessage(role: .user, content: normalizedText, images: attachments))
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isProcessing = true
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applySamplingConfig()
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let activeSkillInfos = attachments.isEmpty ? enabledSkillInfos : []
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let historyDepth = attachments.isEmpty ? llm.safeHistoryDepth : 0
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print("[MEM] safeHistoryDepth=\(historyDepth), headroom=\(llm.availableHeadroomMB) MB")
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let promptImages = promptImages(historyDepth: historyDepth, currentImages: attachments)
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print("[VLM] userAttachments=\(attachments.count), promptImages=\(promptImages.count)")
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messages.append(ChatMessage(role: .assistant, content: "▍"))
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let msgIndex = messages.count - 1
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if !attachments.isEmpty {
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let multimodalChat: [Chat.Message] = [
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.system(PromptBuilder.multimodalSystemPrompt),
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.user(
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normalizedText,
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images: promptImages.map { .ciImage($0) }
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),
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]
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llm.generateStream(chat: multimodalChat) { [weak self] token in
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guard let self = self else { return }
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let updated = self.messages[msgIndex].content == "▍"
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? token
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: self.messages[msgIndex].content.replacingOccurrences(of: "▍", with: "") + token
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self.messages[msgIndex].update(content: updated + "▍")
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} onComplete: { [weak self] result in
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guard let self = self else { return }
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switch result {
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case .success(let fullText):
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log("[Agent] 1st raw: \(fullText.prefix(300))")
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let cleaned = self.cleanOutput(fullText)
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self.messages[msgIndex].update(
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content: cleaned.isEmpty ? "（无回复）" : cleaned
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)
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case .failure(let error):
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self.messages[msgIndex].update(role: .system, content: "❌ \(error.localizedDescription)")
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}
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self.isProcessing = false
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}
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return
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}
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let prompt = PromptBuilder.build(
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userMessage: normalizedText,
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currentImageCount: attachments.count,
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tools: activeSkillInfos,
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history: messages,
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systemPrompt: config.systemPrompt,
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historyDepth: historyDepth
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)
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var detectedToolCall = false
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var buffer = ""
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var bufferFlushed = false
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llm.generateStream(prompt: prompt, images: promptImages) { [weak self] token in
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guard let self = self else { return }
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if detectedToolCall {
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buffer += token
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return
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}
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buffer += token
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if buffer.contains("<tool_call>") {
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detectedToolCall = true
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return
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}
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if !bufferFlushed {
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let trimmed = buffer.trimmingCharacters(in: .whitespacesAndNewlines)
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if trimmed.isEmpty { return }
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if "<tool_call>".hasPrefix(trimmed) { return }
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bufferFlushed = true
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self.messages[msgIndex].update(content: self.cleanOutputStreaming(buffer))
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return
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}
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let cleaned = self.cleanOutputStreaming(buffer)
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if !cleaned.isEmpty {
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self.messages[msgIndex].update(content: cleaned)
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}
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} onComplete: { [weak self] result in
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guard let self = self else { return }
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switch result {
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case .success(let fullText):
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log("[Agent] 1st raw: \(fullText.prefix(300))")
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if self.parseToolCall(fullText) != nil {
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self.messages[msgIndex].update(content: "")
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Task {
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await self.executeToolChain(
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prompt: prompt,
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fullText: fullText,
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userQuestion: normalizedText,
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images: promptImages
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)
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}
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return
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} else {
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let cleaned = self.cleanOutput(fullText)
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self.messages[msgIndex].update(
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content: cleaned.isEmpty ? "（无回复）" : cleaned
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)
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}
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case .failure(let error):
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self.messages[msgIndex].update(role: .system, content: "❌ \(error.localizedDescription)")
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}
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self.isProcessing = false
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}
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}
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// MARK: - Skill 结果后的后续推理（支持多轮工具链）
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private func streamLLM(prompt: String, msgIndex: Int, images: [CIImage]) async -> String? {
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var buffer = ""
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return await withCheckedContinuation { (continuation: CheckedContinuation<String?, Never>) in
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var toolCallDetected = false
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var bufferFlushed = false
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llm.generateStream(prompt: prompt, images: images) { [weak self] token in
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guard let self = self else { return }
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buffer += token
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if toolCallDetected { return }
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if buffer.contains("<tool_call>") {
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toolCallDetected = true
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if bufferFlushed && self.messages[msgIndex].role == .assistant {
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self.messages[msgIndex].update(content: "")
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}
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return
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}
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if !bufferFlushed {
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let trimmed = buffer.trimmingCharacters(in: .whitespacesAndNewlines)
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if trimmed.isEmpty { return }
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if "<tool_call>".hasPrefix(trimmed) { return }
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bufferFlushed = true
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}
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let cleaned = self.cleanOutputStreaming(buffer)
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if !cleaned.isEmpty && self.messages[msgIndex].role == .assistant {
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self.messages[msgIndex].update(content: cleaned)
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}
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} onComplete: { [weak self] result in
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guard let self = self else {
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continuation.resume(returning: nil)
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return
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}
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switch result {
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case .success(let text):
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log("[Agent] LLM raw: \(text.prefix(300))")
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continuation.resume(returning: text)
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case .failure(let error):
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self.messages[msgIndex].update(role: .system, content: "❌ \(error.localizedDescription)")
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continuation.resume(returning: nil)
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}
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}
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}
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}
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private func executeToolChain(
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prompt: String,
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fullText: String,
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userQuestion: String,
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images: [CIImage],
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round: Int = 1,
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maxRounds: Int = 10
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) async {
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guard round <= maxRounds else {
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log("[Agent] 达到最大工具链轮数 \(maxRounds)")
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isProcessing = false
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return
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}
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guard let call = parseToolCall(fullText) else {
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let cleaned = cleanOutput(fullText)
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if let lastAssistant = messages.lastIndex(where: { $0.role == .assistant }) {
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messages[lastAssistant].update(content: cleaned.isEmpty ? "（无回复）" : cleaned)
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}
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isProcessing = false
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return
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}
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log("[Agent] Round \(round): tool_call name=\(call.name)")
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// ── load_skill ──
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if call.name == "load_skill" {
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let allCalls = parseAllToolCalls(fullText)
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let loadSkillCalls = allCalls.filter { $0.name == "load_skill" }
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var allInstructions = ""
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var loadedDisplayNames: [String] = []
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for lsCall in loadSkillCalls {
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let skillName = (lsCall.arguments["skill"] as? String)
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?? (lsCall.arguments["name"] as? String)
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?? ""
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log("[Agent] load_skill: \(skillName)")
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let displayName = findDisplayName(for: skillName)
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loadedDisplayNames.append(displayName)
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messages.append(ChatMessage(role: .system, content: "identified", skillName: displayName))
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let cardIdx = messages.count - 1
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guard let instructions = handleLoadSkill(skillName: skillName) else {
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messages[cardIdx].update(role: .system, content: "done", skillName: displayName)
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continue
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}
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try? await Task.sleep(for: .milliseconds(300))
|
||
messages[cardIdx].update(role: .system, content: "loaded", skillName: displayName)
|
||
messages.append(ChatMessage(role: .skillResult, content: instructions, skillName: skillName))
|
||
allInstructions += instructions + "\n\n"
|
||
}
|
||
|
||
guard !allInstructions.isEmpty else {
|
||
isProcessing = false
|
||
return
|
||
}
|
||
|
||
let followUpPrompt = PromptBuilder.buildFollowUp(
|
||
originalPrompt: prompt,
|
||
modelResponse: fullText,
|
||
skillName: "load_skill",
|
||
skillResult: allInstructions,
|
||
userQuestion: userQuestion,
|
||
currentImageCount: images.count,
|
||
isLoadSkill: true
|
||
)
|
||
|
||
messages.append(ChatMessage(role: .assistant, content: "▍"))
|
||
let followUpIndex = messages.count - 1
|
||
|
||
guard let nextText = await streamLLM(prompt: followUpPrompt, msgIndex: followUpIndex, images: images) else {
|
||
isProcessing = false
|
||
return
|
||
}
|
||
|
||
if parseToolCall(nextText) != nil {
|
||
log("[Agent] load_skill 后检测到 tool 调用 (round \(round + 1))")
|
||
messages[followUpIndex].update(content: "")
|
||
await executeToolChain(
|
||
prompt: followUpPrompt, fullText: nextText,
|
||
userQuestion: userQuestion, images: images, round: round + 1, maxRounds: maxRounds
|
||
)
|
||
} else {
|
||
let cleaned = cleanOutput(nextText)
|
||
if cleaned.isEmpty {
|
||
let retryPrompt = PromptBuilder.buildForcedSkillContinuation(
|
||
priorPrompt: followUpPrompt,
|
||
userQuestion: userQuestion
|
||
)
|
||
|
||
guard let retryText = await streamLLM(prompt: retryPrompt, msgIndex: followUpIndex, images: images) else {
|
||
isProcessing = false
|
||
return
|
||
}
|
||
|
||
if parseToolCall(retryText) != nil {
|
||
log("[Agent] load_skill 重试后检测到 tool 调用 (round \(round + 1))")
|
||
messages[followUpIndex].update(content: "")
|
||
await executeToolChain(
|
||
prompt: retryPrompt,
|
||
fullText: retryText,
|
||
userQuestion: userQuestion,
|
||
images: images,
|
||
round: round + 1,
|
||
maxRounds: maxRounds
|
||
)
|
||
} else {
|
||
let retryCleaned = cleanOutput(retryText)
|
||
let loadedSkillName = loadedDisplayNames.joined(separator: ", ").isEmpty
|
||
? "已加载的能力"
|
||
: loadedDisplayNames.joined(separator: ", ")
|
||
messages[followUpIndex].update(content: retryCleaned.isEmpty
|
||
? fallbackReplyForEmptySkillFollowUp(skillName: loadedSkillName)
|
||
: retryCleaned)
|
||
isProcessing = false
|
||
}
|
||
} else {
|
||
messages[followUpIndex].update(content: cleaned)
|
||
isProcessing = false
|
||
}
|
||
}
|
||
return
|
||
}
|
||
|
||
// ── 具体 Tool 调用 ──
|
||
|
||
let ownerSkillId = findSkillId(for: call.name)
|
||
let displayName = findDisplayName(for: call.name)
|
||
|
||
let cardIndex: Int
|
||
if let idx = messages.lastIndex(where: {
|
||
$0.role == .system && ($0.skillName == displayName || $0.skillName == call.name)
|
||
&& ($0.content == "identified" || $0.content == "loaded")
|
||
}) {
|
||
cardIndex = idx
|
||
} else {
|
||
messages.append(ChatMessage(role: .system, content: "identified", skillName: displayName))
|
||
cardIndex = messages.count - 1
|
||
}
|
||
|
||
guard ownerSkillId != nil else {
|
||
messages[cardIndex].update(role: .system, content: "done", skillName: displayName)
|
||
messages.append(ChatMessage(role: .assistant, content: "⚠️ 未知工具: \(call.name)"))
|
||
isProcessing = false
|
||
return
|
||
}
|
||
|
||
let enabledIds = Set(skillEntries.filter(\.isEnabled).map(\.id))
|
||
guard enabledIds.contains(ownerSkillId!) else {
|
||
messages[cardIndex].update(role: .system, content: "done", skillName: displayName)
|
||
messages.append(ChatMessage(role: .assistant, content: "⚠️ Skill \(displayName) 未启用"))
|
||
isProcessing = false
|
||
return
|
||
}
|
||
|
||
messages[cardIndex].update(role: .system, content: "executing:\(call.name)", skillName: displayName)
|
||
|
||
do {
|
||
let toolResult = try await handleToolExecution(toolName: call.name, args: call.arguments)
|
||
messages[cardIndex].update(role: .system, content: "done", skillName: displayName)
|
||
messages.append(ChatMessage(role: .skillResult, content: toolResult, skillName: call.name))
|
||
log("[Agent] Tool \(call.name) round \(round) done")
|
||
|
||
let followUpPrompt = PromptBuilder.buildFollowUp(
|
||
originalPrompt: prompt,
|
||
modelResponse: fullText,
|
||
skillName: call.name,
|
||
skillResult: toolResult,
|
||
userQuestion: userQuestion,
|
||
currentImageCount: images.count
|
||
)
|
||
|
||
messages.append(ChatMessage(role: .assistant, content: "▍"))
|
||
let followUpIndex = messages.count - 1
|
||
|
||
guard let nextText = await streamLLM(prompt: followUpPrompt, msgIndex: followUpIndex, images: images) else {
|
||
isProcessing = false
|
||
return
|
||
}
|
||
|
||
if !parseAllToolCalls(nextText).isEmpty {
|
||
log("[Agent] 检测到第 \(round + 1) 轮工具调用")
|
||
messages[followUpIndex].update(content: "")
|
||
await executeToolChain(
|
||
prompt: followUpPrompt, fullText: nextText,
|
||
userQuestion: userQuestion, images: images, round: round + 1, maxRounds: maxRounds
|
||
)
|
||
} else {
|
||
let cleaned = cleanOutput(nextText)
|
||
if cleaned.isEmpty {
|
||
messages[followUpIndex].update(content: fallbackReplyForEmptyToolFollowUp(
|
||
toolName: call.name,
|
||
toolResult: toolResult
|
||
))
|
||
} else {
|
||
messages[followUpIndex].update(content: cleaned)
|
||
}
|
||
isProcessing = false
|
||
}
|
||
} catch {
|
||
messages[cardIndex].update(role: .system, content: "done", skillName: displayName)
|
||
messages.append(ChatMessage(role: .system, content: "❌ Tool 执行失败: \(error)"))
|
||
isProcessing = false
|
||
}
|
||
}
|
||
|
||
// MARK: - 工具
|
||
|
||
func clearMessages() {
|
||
messages.removeAll()
|
||
}
|
||
|
||
func cancelActiveGeneration() {
|
||
guard isProcessing || llm.isGenerating else { return }
|
||
llm.cancel()
|
||
isProcessing = false
|
||
|
||
if let lastAssistant = messages.lastIndex(where: { $0.role == .assistant }) {
|
||
let content = messages[lastAssistant].content.replacingOccurrences(of: "▍", with: "")
|
||
messages[lastAssistant].update(content: content.isEmpty ? "（已中断）" : content)
|
||
}
|
||
|
||
log("[Agent] Generation cancelled because the app left foreground")
|
||
}
|
||
|
||
private func promptImages(
|
||
historyDepth: Int,
|
||
currentImages: [ChatImageAttachment]
|
||
) -> [CIImage] {
|
||
_ = historyDepth
|
||
return Array(currentImages.prefix(1).compactMap(\.ciImage))
|
||
}
|
||
|
||
func setAllSkills(enabled: Bool) {
|
||
for i in skillEntries.indices {
|
||
skillEntries[i].isEnabled = enabled
|
||
}
|
||
}
|
||
|
||
// MARK: - 解析
|
||
|
||
private func parseToolCall(_ text: String) -> (name: String, arguments: [String: Any])? {
|
||
return parseAllToolCalls(text).first
|
||
}
|
||
|
||
private func parseAllToolCalls(_ text: String) -> [(name: String, arguments: [String: Any])] {
|
||
var results: [(name: String, arguments: [String: Any])] = []
|
||
let patterns = [
|
||
"<tool_call>\\s*(\\{.*?\\})\\s*</tool_call>",
|
||
"```json\\s*(\\{.*?\\})\\s*```",
|
||
"<function_call>\\s*(\\{.*?\\})\\s*</function_call>"
|
||
]
|
||
for pattern in patterns {
|
||
guard let regex = try? NSRegularExpression(pattern: pattern, options: .dotMatchesLineSeparators) else { continue }
|
||
let range = NSRange(text.startIndex..., in: text)
|
||
let matches = regex.matches(in: text, range: range)
|
||
for match in matches {
|
||
if let jsonRange = Range(match.range(at: 1), in: text) {
|
||
let json = String(text[jsonRange])
|
||
if let data = json.data(using: .utf8),
|
||
let dict = try? JSONSerialization.jsonObject(with: data) as? [String: Any],
|
||
let name = dict["name"] as? String {
|
||
results.append((name, dict["arguments"] as? [String: Any] ?? [:]))
|
||
}
|
||
}
|
||
}
|
||
if !results.isEmpty { break }
|
||
}
|
||
return results
|
||
}
|
||
|
||
private func extractSkillName(from text: String) -> String? {
|
||
guard let regex = try? NSRegularExpression(pattern: "\"name\"\\s*:\\s*\"([^\"]+)\""),
|
||
let match = regex.firstMatch(in: text, range: NSRange(text.startIndex..., in: text)),
|
||
let nameRange = Range(match.range(at: 1), in: text) else {
|
||
return nil
|
||
}
|
||
return String(text[nameRange])
|
||
}
|
||
|
||
private func cleanOutputStreaming(_ text: String) -> String {
|
||
var result = text
|
||
|
||
if let tcRange = result.range(of: "<tool_call>") {
|
||
result = String(result[result.startIndex..<tcRange.lowerBound])
|
||
}
|
||
|
||
let endPatterns = ["<turn|>", "<end_of_turn>", "<eos>"]
|
||
for pat in endPatterns {
|
||
if let range = result.range(of: pat) {
|
||
result = String(result[result.startIndex..<range.lowerBound])
|
||
break
|
||
}
|
||
}
|
||
|
||
if let regex = try? NSRegularExpression(pattern: "<tool_call>.*?</tool_call>", options: .dotMatchesLineSeparators) {
|
||
result = regex.stringByReplacingMatches(in: result, range: NSRange(result.startIndex..., in: result), withTemplate: "")
|
||
}
|
||
|
||
result = result.replacingOccurrences(
|
||
of: "<\\|?[/a-z_]+\\|?>",
|
||
with: "",
|
||
options: .regularExpression
|
||
)
|
||
|
||
if result.hasPrefix("model\n") {
|
||
result = String(result.dropFirst(6))
|
||
} else if result == "model" {
|
||
return ""
|
||
}
|
||
|
||
return String(result.drop(while: { $0.isWhitespace || $0.isNewline }))
|
||
}
|
||
|
||
private func cleanOutput(_ text: String) -> String {
|
||
var result = text
|
||
|
||
if let regex = try? NSRegularExpression(pattern: "<tool_call>.*?</tool_call>", options: .dotMatchesLineSeparators) {
|
||
result = regex.stringByReplacingMatches(in: result, range: NSRange(result.startIndex..., in: result), withTemplate: "")
|
||
}
|
||
|
||
if let tcRange = result.range(of: "<tool_call>") {
|
||
result = String(result[result.startIndex..<tcRange.lowerBound])
|
||
}
|
||
|
||
let endPatterns = ["<turn|>", "<end_of_turn>", "<eos>"]
|
||
for pat in endPatterns {
|
||
if let range = result.range(of: pat) {
|
||
result = String(result[result.startIndex..<range.lowerBound])
|
||
break
|
||
}
|
||
}
|
||
|
||
result = result.replacingOccurrences(
|
||
of: "<\\|?[/a-z_]+\\|?>",
|
||
with: "",
|
||
options: .regularExpression
|
||
)
|
||
|
||
if let lastOpen = result.lastIndex(of: "<") {
|
||
let tail = String(result[lastOpen...])
|
||
let tailBody = tail.dropFirst()
|
||
if !tailBody.isEmpty && tailBody.allSatisfy({ $0.isLetter || $0 == "_" || $0 == "/" || $0 == "|" }) {
|
||
result = String(result[result.startIndex..<lastOpen])
|
||
}
|
||
}
|
||
|
||
if result.hasPrefix("model\n") {
|
||
result = String(result.dropFirst(6))
|
||
} else if result == "model" {
|
||
result = ""
|
||
}
|
||
|
||
return result.trimmingCharacters(in: .whitespacesAndNewlines)
|
||
}
|
||
}
|