refactor: flatten structure + remove dead code
- 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=
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Before Width: | Height: | Size: 332 KiB After Width: | Height: | Size: 332 KiB |
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Submodule
+1
Submodule LiteRT-LM added at 24a8bb189f.
File renamed without changes.
@@ -50,7 +50,7 @@
|
||||
4DD9808FE93325C3CE4CFA00 /* PhoneClawApp.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = PhoneClawApp.swift; sourceTree = "<group>"; };
|
||||
52F30C6348B834E5D157205F /* Info.plist */ = {isa = PBXFileReference; lastKnownFileType = text.plist; path = Info.plist; sourceTree = "<group>"; };
|
||||
571908036E6DCED1D58C1E9E /* Skills.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = Skills.swift; sourceTree = "<group>"; };
|
||||
772CB5029014926E172C0BF1 /* LocalLLMService.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = LocalLLMService.swift; sourceTree = "<group>"; };
|
||||
|
||||
AA2000000000000000000001 /* LLMEngine.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = LLMEngine.swift; sourceTree = "<group>"; };
|
||||
AA2000000000000000000002 /* MLXLocalLLMService.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = MLXLocalLLMService.swift; sourceTree = "<group>"; };
|
||||
AA2000000000000000000003 /* Gemma4Config.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = Gemma4Config.swift; sourceTree = "<group>"; };
|
||||
@@ -146,7 +146,6 @@
|
||||
AA2000000000000000000001 /* LLMEngine.swift */,
|
||||
AA3000000000000000000001 /* MLX */,
|
||||
AA2000000000000000000002 /* MLXLocalLLMService.swift */,
|
||||
772CB5029014926E172C0BF1 /* LocalLLMService.swift */,
|
||||
2E7C06DA449D1395217A42D5 /* PromptBuilder.swift */,
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||||
);
|
||||
path = LLM;
|
||||
@@ -180,7 +179,7 @@
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||||
98EFD7CCA7E78715F07BABE3 /* Skills */,
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||||
EF4D07A808616D6F13771DBE /* UI */,
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||||
);
|
||||
path = PhoneClaw;
|
||||
name = PhoneClaw;
|
||||
sourceTree = "<group>";
|
||||
};
|
||||
E0660A5AE3C683B7B20600EE /* Agent */ = {
|
||||
@@ -492,11 +491,11 @@
|
||||
baseConfigurationReference = 098B30935B91CD0BEF3A5056 /* Pods-PhoneClaw.release.xcconfig */;
|
||||
buildSettings = {
|
||||
ASSETCATALOG_COMPILER_APPICON_NAME = AppIcon;
|
||||
CODE_SIGN_ENTITLEMENTS = PhoneClaw/PhoneClaw.entitlements;
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||||
CODE_SIGN_ENTITLEMENTS = PhoneClaw.entitlements;
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||||
CODE_SIGN_IDENTITY = "iPhone Developer";
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||||
CURRENT_PROJECT_VERSION = 1;
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||||
DEVELOPMENT_TEAM = ZP4QNJ35TH;
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||||
INFOPLIST_FILE = PhoneClaw/Info.plist;
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INFOPLIST_FILE = Info.plist;
|
||||
LD_RUNPATH_SEARCH_PATHS = (
|
||||
"$(inherited)",
|
||||
"@executable_path/Frameworks",
|
||||
@@ -504,7 +503,6 @@
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||||
MARKETING_VERSION = 1.0.0;
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||||
PRODUCT_BUNDLE_IDENTIFIER = com.zxw.phoneclaw2026;
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||||
SDKROOT = iphoneos;
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||||
SWIFT_OBJC_BRIDGING_HEADER = "PhoneClaw/PhoneClaw-Bridging-Header.h";
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||||
TARGETED_DEVICE_FAMILY = "1,2";
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||||
};
|
||||
name = Release;
|
||||
@@ -514,11 +512,11 @@
|
||||
baseConfigurationReference = A1AB7F250BEEFF938318AA75 /* Pods-PhoneClaw.debug.xcconfig */;
|
||||
buildSettings = {
|
||||
ASSETCATALOG_COMPILER_APPICON_NAME = AppIcon;
|
||||
CODE_SIGN_ENTITLEMENTS = PhoneClaw/PhoneClaw.entitlements;
|
||||
CODE_SIGN_ENTITLEMENTS = PhoneClaw.entitlements;
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CODE_SIGN_IDENTITY = "iPhone Developer";
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CURRENT_PROJECT_VERSION = 1;
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DEVELOPMENT_TEAM = ZP4QNJ35TH;
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INFOPLIST_FILE = PhoneClaw/Info.plist;
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INFOPLIST_FILE = Info.plist;
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||||
LD_RUNPATH_SEARCH_PATHS = (
|
||||
"$(inherited)",
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||||
"@executable_path/Frameworks",
|
||||
@@ -526,7 +524,6 @@
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||||
MARKETING_VERSION = 1.0.0;
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||||
PRODUCT_BUNDLE_IDENTIFIER = com.zxw.phoneclaw2026;
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||||
SDKROOT = iphoneos;
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||||
SWIFT_OBJC_BRIDGING_HEADER = "PhoneClaw/PhoneClaw-Bridging-Header.h";
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||||
TARGETED_DEVICE_FAMILY = "1,2";
|
||||
};
|
||||
name = Debug;
|
||||
|
||||
@@ -1,187 +0,0 @@
|
||||
import Foundation
|
||||
import MediaPipeTasksGenAI
|
||||
|
||||
// MARK: - 使用 MediaPipe 高级 Swift API（和 Google Gallery 相同）
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|
||||
@Observable
|
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class LocalLLMService {
|
||||
|
||||
private var inference: LlmInference?
|
||||
private var currentSession: LlmInference.Session?
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||||
var isLoaded = false
|
||||
var isGenerating = false
|
||||
var statusMessage = "等待加载模型..."
|
||||
|
||||
// MARK: - 后端与采样参数（AgentEngine 可动态修改）
|
||||
var useGPU: Bool = false // 需要 extended-virtual-addressing entitlement 才能开启
|
||||
var samplingTopK: Int = 40
|
||||
var samplingTopP: Float = 0.95
|
||||
var samplingTemperature: Float = 0.8
|
||||
var maxOutputTokens: Int = 2048
|
||||
|
||||
static let modelFileName = "gemma-4-E4B-it"
|
||||
static let modelFileExtension = "litertlm"
|
||||
|
||||
deinit {
|
||||
currentSession = nil
|
||||
inference = nil
|
||||
}
|
||||
|
||||
// MARK: - 加载模型
|
||||
|
||||
func loadModel() {
|
||||
DispatchQueue.main.async { self.statusMessage = "正在加载 E4B 模型..." }
|
||||
|
||||
var modelPath = Bundle.main.path(forResource: Self.modelFileName, ofType: Self.modelFileExtension)
|
||||
if modelPath == nil {
|
||||
let docs = FileManager.default.urls(for: .documentDirectory, in: .userDomainMask).first!
|
||||
let docFile = docs.appendingPathComponent("\(Self.modelFileName).\(Self.modelFileExtension)")
|
||||
if FileManager.default.fileExists(atPath: docFile.path) {
|
||||
modelPath = docFile.path
|
||||
}
|
||||
}
|
||||
guard let path = modelPath else {
|
||||
DispatchQueue.main.async { self.statusMessage = "❌ 模型文件不存在" }
|
||||
print("[LLM] 找不到模型")
|
||||
return
|
||||
}
|
||||
|
||||
print("[LLM] 开始加载: \(path)")
|
||||
let startTime = CFAbsoluteTimeGetCurrent()
|
||||
|
||||
do {
|
||||
let options = LlmInference.Options(modelPath: path)
|
||||
// GPU 需要 extended-virtual-addressing entitlement，否则 E4B 模型会 OOM
|
||||
// 获得 entitlement 后改为 .gpu 即可启用 Metal 加速
|
||||
options.preferredBackend = useGPU ? .gpu : .cpu
|
||||
options.maxTokens = 512
|
||||
options.maxTopk = useGPU ? 40 : 1
|
||||
options.waitForWeightUploads = false
|
||||
options.sequenceBatchSize = 0
|
||||
|
||||
let label = useGPU ? "GPU" : "CPU"
|
||||
print("[LLM] 使用 \(label) 后端加载...")
|
||||
let llm = try LlmInference(options: options)
|
||||
self.inference = llm
|
||||
|
||||
let elapsed = CFAbsoluteTimeGetCurrent() - startTime
|
||||
DispatchQueue.main.async {
|
||||
self.isLoaded = true
|
||||
self.statusMessage = "\(label) 模型已就绪 ✅"
|
||||
}
|
||||
print("[LLM] Engine 创建成功 (\(label))，耗时: \(String(format: "%.1f", elapsed))秒")
|
||||
} catch {
|
||||
DispatchQueue.main.async { self.statusMessage = "❌ \(error.localizedDescription)" }
|
||||
print("[LLM] Engine 创建失败: \(error)")
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - Session 管理
|
||||
|
||||
private func createSession() -> Bool {
|
||||
currentSession = nil
|
||||
guard let llm = inference else { return false }
|
||||
|
||||
do {
|
||||
let sessionOptions = LlmInference.Session.Options()
|
||||
sessionOptions.topk = min(samplingTopK, useGPU ? 40 : 1) // 不能超过 maxTopk
|
||||
sessionOptions.topp = samplingTopP
|
||||
sessionOptions.temperature = samplingTemperature
|
||||
sessionOptions.randomSeed = 0
|
||||
|
||||
self.currentSession = try LlmInference.Session(llmInference: llm, options: sessionOptions)
|
||||
return true
|
||||
} catch {
|
||||
print("[LLM] Session 创建失败: \(error)")
|
||||
return false
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - 流式生成
|
||||
|
||||
func generateStream(prompt: String, onToken: @escaping (String) -> Void, onComplete: @escaping (Result<String, Error>) -> Void) {
|
||||
guard inference != nil else {
|
||||
onComplete(.failure(LLMError.notLoaded))
|
||||
return
|
||||
}
|
||||
|
||||
DispatchQueue.main.async { self.isGenerating = true }
|
||||
|
||||
DispatchQueue.global(qos: .userInitiated).async { [weak self] in
|
||||
guard let self = self else { return }
|
||||
|
||||
guard self.createSession(), let sess = self.currentSession else {
|
||||
DispatchQueue.main.async {
|
||||
self.isGenerating = false
|
||||
onComplete(.failure(LLMError.sessionFailed))
|
||||
}
|
||||
return
|
||||
}
|
||||
|
||||
do {
|
||||
try sess.addQueryChunk(inputText: prompt)
|
||||
} catch {
|
||||
print("[LLM] AddQueryChunk 失败: \(error)")
|
||||
DispatchQueue.main.async {
|
||||
self.isGenerating = false
|
||||
onComplete(.failure(LLMError.addChunkFailed))
|
||||
}
|
||||
return
|
||||
}
|
||||
|
||||
var fullResponse = ""
|
||||
|
||||
do {
|
||||
try sess.generateResponseAsync { [weak self] partialResponse, error in
|
||||
// progress callback — 每个 token
|
||||
if let err = error {
|
||||
print("[LLM] 流式错误: \(err)")
|
||||
return
|
||||
}
|
||||
if let token = partialResponse {
|
||||
fullResponse += token
|
||||
DispatchQueue.main.async {
|
||||
onToken(token)
|
||||
}
|
||||
}
|
||||
} completion: { [weak self] in
|
||||
// 完成回调
|
||||
print("[LLM] 流式完成，长度: \(fullResponse.count)")
|
||||
DispatchQueue.main.async {
|
||||
self?.isGenerating = false
|
||||
onComplete(.success(fullResponse))
|
||||
}
|
||||
}
|
||||
} catch {
|
||||
print("[LLM] GenerateResponseAsync 失败: \(error)")
|
||||
DispatchQueue.main.async {
|
||||
self.isGenerating = false
|
||||
onComplete(.failure(LLMError.predictFailed))
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - 同步生成（备用）
|
||||
|
||||
func generate(prompt: String) throws -> String {
|
||||
guard inference != nil else { throw LLMError.notLoaded }
|
||||
guard createSession(), let sess = currentSession else { throw LLMError.sessionFailed }
|
||||
|
||||
try sess.addQueryChunk(inputText: prompt)
|
||||
let response = try sess.generateResponse()
|
||||
return response
|
||||
}
|
||||
|
||||
enum LLMError: LocalizedError {
|
||||
case notLoaded, sessionFailed, addChunkFailed, predictFailed
|
||||
var errorDescription: String? {
|
||||
switch self {
|
||||
case .notLoaded: return "模型尚未加载"
|
||||
case .sessionFailed: return "Session 创建失败"
|
||||
case .addChunkFailed: return "Prompt 添加失败"
|
||||
case .predictFailed: return "推理失败"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,6 +0,0 @@
|
||||
//
|
||||
// PhoneClaw-Bridging-Header.h
|
||||
// 直接调用 LiteRT-LM C API
|
||||
//
|
||||
|
||||
#include "llm_inference_engine.h"
|
||||
@@ -1,345 +0,0 @@
|
||||
// Copyright 2024 The ODML Authors.
|
||||
//
|
||||
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||
// you may not use this file except in compliance with the License.
|
||||
// You may obtain a copy of the License at
|
||||
//
|
||||
// http://www.apache.org/licenses/LICENSE-2.0
|
||||
//
|
||||
// Unless required by applicable law or agreed to in writing, software
|
||||
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
||||
|
||||
#ifndef THIRD_PARTY_ODML_INFRA_GENAI_INFERENCE_C_LLM_INFERENCE_ENGINE_H_
|
||||
#define THIRD_PARTY_ODML_INFRA_GENAI_INFERENCE_C_LLM_INFERENCE_ENGINE_H_
|
||||
|
||||
#ifdef __cplusplus
|
||||
#include <cstddef>
|
||||
#else
|
||||
#include <stdbool.h>
|
||||
#include <stddef.h>
|
||||
#include <stdint.h>
|
||||
#endif
|
||||
|
||||
#ifdef __EMSCRIPTEN__
|
||||
#include <functional>
|
||||
#endif // __EMSCRIPTEN__
|
||||
|
||||
#ifndef ODML_EXPORT
|
||||
#define ODML_EXPORT __attribute__((visibility("default")))
|
||||
#endif // ODML_EXPORT
|
||||
|
||||
#ifdef __cplusplus
|
||||
extern "C" {
|
||||
#endif
|
||||
|
||||
typedef void LlmInferenceEngine_Engine;
|
||||
|
||||
typedef void LlmInferenceEngine_Session;
|
||||
|
||||
typedef void SentencePieceProcessor;
|
||||
|
||||
typedef void Constraint;
|
||||
|
||||
// LlmActivationDataType defines the activation data type for the model.
|
||||
typedef enum {
|
||||
// Use Default activation data type mentioned in the model metadata file.
|
||||
kLlmActivationDataTypeDefault = 0,
|
||||
|
||||
// Use Float32 activation data type.
|
||||
kLlmActivationDataTypeFloat32 = 1,
|
||||
|
||||
// Use Float16 activation data type.
|
||||
kLlmActivationDataTypeFloat16 = 2,
|
||||
|
||||
// Use Int16 activation data type.
|
||||
kLlmActivationDataTypeInt16 = 3,
|
||||
|
||||
// Use Int8 activation data type.
|
||||
kLlmActivationDataTypeInt8 = 4,
|
||||
} LlmActivationDataType;
|
||||
|
||||
// Specify the LiteRT backend to use for the LLM model. If not specified, the
|
||||
// default backend will be used.
|
||||
typedef enum {
|
||||
// Use default backend extracted from the model.
|
||||
kLlmPreferredBackendDefault = 0,
|
||||
|
||||
// Use GPU backend.
|
||||
kLlmPreferredBackendGpu = 1,
|
||||
|
||||
// Use CPU backend.
|
||||
kLlmPreferredBackendCpu = 2,
|
||||
} LlmPreferredBackend;
|
||||
|
||||
// LlmSessionConfig configures how to execute the model.
|
||||
// LINT.IfChange
|
||||
typedef struct {
|
||||
// Path to the model artifact.
|
||||
const char* model_path;
|
||||
|
||||
#ifdef __EMSCRIPTEN__
|
||||
// Web only supports the following model loading methods:
|
||||
// * File System loading for converted LLMs. model_path has to be set and its
|
||||
// name has to end with .task.
|
||||
// * Stream loading for handwritten LLMs. read_model_fn has to be set.
|
||||
|
||||
// Function to read model file.
|
||||
// The function returns a pointer to heap memory that contains the model file
|
||||
// contents started from `offset` with `size`.
|
||||
// Since the model file is hosted on JavaScript layer and this function copies
|
||||
// the data to the heap memory, the `mode` instructs how the source model file
|
||||
// data should be manipulated:
|
||||
// 0: Data will be kept in memory after read.
|
||||
// 1: Data will not be accessed again and can be discarded.
|
||||
// 2: All data has been used and can be discarded.
|
||||
using ReadDataFn =
|
||||
std::function<void*(uint64_t offset, uint64_t size, int mode)>;
|
||||
ReadDataFn* read_model_fn;
|
||||
#endif // __EMSCRIPTEN__
|
||||
|
||||
// Path to the vision encoder to use for vision modality. Optional.
|
||||
const char* vision_encoder_path;
|
||||
|
||||
// Path to the vision adapter to use for vision modality. Optional.
|
||||
const char* vision_adapter_path;
|
||||
|
||||
// Directory path for storing model related tokenizer and cache weights. the
|
||||
// user is responsible for providing the directory that can be writable by the
|
||||
// program.
|
||||
const char* cache_dir;
|
||||
|
||||
// Maximum number of tokens for input and output.
|
||||
size_t max_num_tokens;
|
||||
|
||||
// Maximum number of images to be used for vision modality.
|
||||
size_t max_num_images;
|
||||
|
||||
// Number of decode steps per sync. Used by GPU only. The default value is 3.
|
||||
size_t num_decode_steps_per_sync;
|
||||
|
||||
// Sequence batch size for encoding. Used by GPU only. Number of input tokens
|
||||
// to process at a time for batch processing. Setting this value to 1 means
|
||||
// both the encoding and decoding share the same graph of sequence length
|
||||
// of 1. Setting this value to 0 means the batch size will be optimized
|
||||
// programmatically.
|
||||
size_t sequence_batch_size;
|
||||
|
||||
// Number of supported lora ranks for the base model. Used by GPU only.
|
||||
size_t number_of_supported_lora_ranks;
|
||||
|
||||
// The supported lora ranks for the base model. Used by GPU only.
|
||||
size_t* supported_lora_ranks;
|
||||
|
||||
// Maximum top k, which is the max Top-K value supported for all
|
||||
// sessions created with the engine, used by GPU only. If a session with Top-K
|
||||
// value larger than this is being asked to be created, it will be
|
||||
// rejected(throw error). If not provided, the max top k will be 1, which
|
||||
// means only greedy decoding is supported for any sessions created with this
|
||||
// engine.
|
||||
size_t max_top_k;
|
||||
|
||||
// Optional setting for specific activation data type.
|
||||
LlmActivationDataType llm_activation_data_type;
|
||||
|
||||
// Optional setting for the number of draft tokens to generate when using
|
||||
// speculative decoding. Setting to 0 will disable speculative decoding.
|
||||
size_t num_draft_tokens;
|
||||
|
||||
// If true, waits for weights to finish uploading when initializing. Otherwise
|
||||
// initialization may finish before weights have finished uploading which
|
||||
// might push some of the weight upload time into input processing.
|
||||
bool wait_for_weight_uploads;
|
||||
|
||||
// Whether the submodel should be used if available.
|
||||
bool use_submodel;
|
||||
|
||||
// Optional setting to prefer specific backend instead.
|
||||
LlmPreferredBackend preferred_backend;
|
||||
|
||||
// Whether to pass the audio modality settings.
|
||||
bool enable_audio_modality;
|
||||
// Maximum audio sequence length.
|
||||
size_t max_audio_sequence_length;
|
||||
} LlmModelSettings;
|
||||
// LINT.ThenChange(//depot/google3/third_party/mediapipe/tasks/java/com/google/mediapipe/tasks/genai/llminference/jni/llm.cc)
|
||||
|
||||
// LlmPromptTemplates defines the prompt templates for the session.
|
||||
typedef struct {
|
||||
// The prompt prefix for the user role.
|
||||
const char* user_prefix;
|
||||
|
||||
// The prompt suffix for the user role.
|
||||
const char* user_suffix;
|
||||
|
||||
// The prompt prefix for the model role.
|
||||
const char* model_prefix;
|
||||
|
||||
// The prompt suffix for the model role.
|
||||
const char* model_suffix;
|
||||
|
||||
// The prompt prefix for the system role.
|
||||
const char* system_prefix;
|
||||
|
||||
// The prompt suffix for the system role.
|
||||
const char* system_suffix;
|
||||
} LlmPromptTemplates;
|
||||
|
||||
// LlmSessionConfig configures how to execute the model.
|
||||
typedef struct {
|
||||
// Top K number of tokens to be sampled from for each decoding step.
|
||||
size_t topk;
|
||||
|
||||
// Maximum cumulative probability over the tokens to sample from in each
|
||||
// decoding step for top-p / nucleus sampling.
|
||||
float topp;
|
||||
|
||||
// Randomness when decoding the next token, 0.0f means greedy decoding.
|
||||
float temperature;
|
||||
|
||||
// random seed, for reproducible sampling.
|
||||
size_t random_seed;
|
||||
|
||||
// Path to the LoRA tflite flatbuffer file. Optional.
|
||||
// This is only compatible with GPU handwritten models and converter based
|
||||
// models.
|
||||
const char* lora_path;
|
||||
|
||||
// Whether to configure the graph to include the token cost calculator,
|
||||
// which allows users to only compute the cost of a prompt.
|
||||
bool include_token_cost_calculator;
|
||||
|
||||
// Whether to configure the graph to include the vision modality.
|
||||
bool enable_vision_modality;
|
||||
|
||||
// Whether to configure the graph to include the audio modality.
|
||||
bool enable_audio_modality;
|
||||
|
||||
// Prompt templates to use for the session.
|
||||
// If not provided, the default prompt templates will be used.
|
||||
const LlmPromptTemplates* prompt_templates;
|
||||
} LlmSessionConfig;
|
||||
|
||||
// The config used to update the runtime behavior of the session.
|
||||
typedef struct {
|
||||
// Top K number of tokens to be sampled from for each decoding step.
|
||||
size_t* topk;
|
||||
|
||||
// Maximum cumulative probability over the tokens to sample from in each
|
||||
// decoding step for top-p / nucleus sampling.
|
||||
float* topp;
|
||||
|
||||
// Randomness when decoding the next token, 0.0f means greedy decoding.
|
||||
float* temperature;
|
||||
|
||||
// random seed, for reproducible sampling.
|
||||
size_t* random_seed;
|
||||
|
||||
// The constraint to use for the session.
|
||||
Constraint* constraint;
|
||||
} SessionRuntimeConfig;
|
||||
|
||||
// LlmResponseContext is the return type for
|
||||
// LlmInferenceEngine_Session_PredictSync.
|
||||
typedef struct {
|
||||
// An array of strings. The size of the array depends on the number of
|
||||
// responses.
|
||||
char** response_array;
|
||||
|
||||
// Number of responses.
|
||||
int response_count;
|
||||
|
||||
// Done all outputs for this session.
|
||||
bool done;
|
||||
} LlmResponseContext;
|
||||
|
||||
// Frees all context within the LlmResponseContext.
|
||||
ODML_EXPORT void LlmInferenceEngine_CloseResponseContext(
|
||||
LlmResponseContext* response_context);
|
||||
|
||||
// Create a LlmInferenceEngine session for executing a query.
|
||||
ODML_EXPORT int LlmInferenceEngine_CreateEngine(
|
||||
const LlmModelSettings* model_settings,
|
||||
LlmInferenceEngine_Engine** engine_out, char** error_msg);
|
||||
|
||||
// Returns the SentencePieceProcessor handle used by the engine.
|
||||
ODML_EXPORT int LlmInferenceEngine_GetSentencePieceProcessor(
|
||||
LlmInferenceEngine_Engine* engine,
|
||||
const SentencePieceProcessor** processor_out, char** error_msg);
|
||||
|
||||
// Free the engine, will release ownership of resource held by the engine.
|
||||
// Resource might be freed if no sessions are referencing to it.
|
||||
ODML_EXPORT void LlmInferenceEngine_Engine_Delete(
|
||||
LlmInferenceEngine_Engine* engine);
|
||||
|
||||
// Create a LlmInferenceEngine session for executing a query.
|
||||
ODML_EXPORT int LlmInferenceEngine_CreateSession(
|
||||
LlmInferenceEngine_Engine* engine, const LlmSessionConfig* session_config,
|
||||
LlmInferenceEngine_Session** session_out, char** error_msg);
|
||||
|
||||
// Update the runtime config for the session.
|
||||
ODML_EXPORT int LlmInferenceEngine_UpdateRuntimeConfig(
|
||||
LlmInferenceEngine_Session* session,
|
||||
const SessionRuntimeConfig* runtime_config, char** error_msg);
|
||||
|
||||
// Free the session, will wait until graph is done executing.
|
||||
ODML_EXPORT int LlmInferenceEngine_Session_Delete(
|
||||
LlmInferenceEngine_Session* session);
|
||||
|
||||
// Add query chunk to the session. This can be called multiple times to add
|
||||
// multiple query chunks before calling `PredictSync` or `PredictAsync`. The
|
||||
// query chunks will be processed in the order they are added, similar to a
|
||||
// concatenated prompt, but able to be processed in chunks.
|
||||
ODML_EXPORT int LlmInferenceEngine_Session_AddQueryChunk(
|
||||
LlmInferenceEngine_Session* session, const char* input, char** error_msg);
|
||||
|
||||
// Adds an SKBitmap to the session.
|
||||
ODML_EXPORT int LlmInferenceEngine_Session_AddImage(
|
||||
LlmInferenceEngine_Session* session, const void* sk_bitmap,
|
||||
char** error_msg);
|
||||
|
||||
// Return the generated output based on the previously added query chunks in
|
||||
// sync mode.
|
||||
ODML_EXPORT int LlmInferenceEngine_Session_PredictSync(
|
||||
LlmInferenceEngine_Session* session, LlmResponseContext* response_context,
|
||||
char** error_msg);
|
||||
|
||||
// Run callback function in async mode.
|
||||
// The callback will be invoked multiple times until `response_context.done`
|
||||
// is `true`. You need to invoke `LlmInferenceEngine_CloseResponseContext` after
|
||||
// each invocation to free memory.
|
||||
// The callback context can be a pointer to any user defined data structure as
|
||||
// it is passed to the callback unmodified.
|
||||
ODML_EXPORT int LlmInferenceEngine_Session_PredictAsync(
|
||||
LlmInferenceEngine_Session* session, void* callback_context,
|
||||
char** error_msg,
|
||||
void (*callback)(void* callback_context,
|
||||
LlmResponseContext* response_context));
|
||||
|
||||
// Request cancellation for pending processes.
|
||||
ODML_EXPORT int LlmInferenceEngine_Session_PendingProcessCancellation(
|
||||
LlmInferenceEngine_Session* session, char** error_msg);
|
||||
|
||||
// Clone the provided session.
|
||||
ODML_EXPORT int LlmInferenceEngine_Session_Clone(
|
||||
LlmInferenceEngine_Session* session,
|
||||
LlmInferenceEngine_Session** cloned_session, char** error_msg);
|
||||
|
||||
// Tokenizes an input prompt using a pre-existing processor and returns its
|
||||
// length in tokens. Returns -1 if tokenization fails.
|
||||
ODML_EXPORT int LlmInferenceEngine_Session_SizeInTokens(
|
||||
LlmInferenceEngine_Session* session, const char* input, char** error_msg);
|
||||
|
||||
// Adds an audio to the session.
|
||||
// The audio_bytes is expected to be the raw data of a mono .wav file.
|
||||
ODML_EXPORT int LlmInferenceEngine_Session_AddAudio(
|
||||
LlmInferenceEngine_Session* session, const char* audio_bytes,
|
||||
int audio_bytes_size, char** error_msg);
|
||||
|
||||
#ifdef __cplusplus
|
||||
} // extern C
|
||||
#endif
|
||||
|
||||
#endif // THIRD_PARTY_ODML_INFRA_GENAI_INFERENCE_C_LLM_INFERENCE_ENGINE_H_
|
||||
@@ -1,33 +0,0 @@
|
||||
// swift-tools-version: 5.9
|
||||
import PackageDescription
|
||||
|
||||
let package = Package(
|
||||
name: "PhoneClawMac",
|
||||
platforms: [.macOS(.v14)],
|
||||
products: [
|
||||
.executable(name: "PhoneClawMac", targets: ["PhoneClawMac"]),
|
||||
],
|
||||
dependencies: [
|
||||
.package(url: "https://github.com/jpsim/Yams.git", from: "5.0.0"),
|
||||
],
|
||||
targets: [
|
||||
.systemLibrary(
|
||||
name: "CLiteRtLm",
|
||||
path: "Sources/CLiteRtLm",
|
||||
pkgConfig: nil,
|
||||
providers: nil
|
||||
),
|
||||
.executableTarget(
|
||||
name: "PhoneClawMac",
|
||||
dependencies: ["CLiteRtLm", "Yams"],
|
||||
path: "Sources/PhoneClawMac",
|
||||
linkerSettings: [
|
||||
.unsafeFlags([
|
||||
"-L", ".",
|
||||
"-Xlinker", "-rpath", "-Xlinker", "@executable_path",
|
||||
"-Xlinker", "-rpath", "-Xlinker", "@executable_path/../lib",
|
||||
]),
|
||||
]
|
||||
),
|
||||
]
|
||||
)
|
||||
@@ -1,502 +0,0 @@
|
||||
// Copyright 2025 The ODML Authors.
|
||||
//
|
||||
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||
// you may not use this file except in compliance with the License.
|
||||
// You may obtain a copy of the License at
|
||||
//
|
||||
// http://www.apache.org/licenses/LICENSE-2.0
|
||||
//
|
||||
// Unless required by applicable law or agreed to in writing, software
|
||||
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
||||
|
||||
#ifndef THIRD_PARTY_ODML_LITERT_LM_C_ENGINE_H_
|
||||
#define THIRD_PARTY_ODML_LITERT_LM_C_ENGINE_H_
|
||||
|
||||
#include <stdbool.h>
|
||||
#include <stddef.h>
|
||||
#include <stdint.h>
|
||||
|
||||
#ifdef __cplusplus
|
||||
extern "C" {
|
||||
#endif
|
||||
|
||||
// For Windows, __declspec( dllexport ) is required to export function in .dll.
|
||||
// https://learn.microsoft.com/en-us/cpp/cpp/using-dllimport-and-dllexport-in-cpp-classes?view=msvc-170
|
||||
//
|
||||
// _WIN32 is defined as 1 when the compilation target is 32-bit ARM, 64-bit ARM,
|
||||
// x86, x64, or ARM64EC. Otherwise, undefined.
|
||||
// https://learn.microsoft.com/en-us/cpp/preprocessor/predefined-macros
|
||||
#if defined(_WIN32)
|
||||
#define LITERT_LM_C_API_EXPORT __declspec(dllexport)
|
||||
#else
|
||||
#define LITERT_LM_C_API_EXPORT
|
||||
#endif
|
||||
|
||||
// Opaque pointer for the LiteRT LM Engine.
|
||||
typedef struct LiteRtLmEngine LiteRtLmEngine;
|
||||
|
||||
// Opaque pointer for the LiteRT LM Session.
|
||||
typedef struct LiteRtLmSession LiteRtLmSession;
|
||||
|
||||
// Opaque pointer for the LiteRT LM Responses.
|
||||
typedef struct LiteRtLmResponses LiteRtLmResponses;
|
||||
|
||||
// Opaque pointer for the LiteRT LM Engine Settings.
|
||||
typedef struct LiteRtLmEngineSettings LiteRtLmEngineSettings;
|
||||
|
||||
// Opaque pointer for the LiteRT LM Benchmark Info.
|
||||
typedef struct LiteRtLmBenchmarkInfo LiteRtLmBenchmarkInfo;
|
||||
|
||||
// Opaque pointer for the LiteRT LM Conversation.
|
||||
typedef struct LiteRtLmConversation LiteRtLmConversation;
|
||||
|
||||
// Opaque pointer for a JSON response.
|
||||
typedef struct LiteRtLmJsonResponse LiteRtLmJsonResponse;
|
||||
|
||||
// Opaque pointer for LiteRT LM Session Config.
|
||||
typedef struct LiteRtLmSessionConfig LiteRtLmSessionConfig;
|
||||
|
||||
// Opaque pointer for LiteRT LM Conversation Config.
|
||||
typedef struct LiteRtLmConversationConfig LiteRtLmConversationConfig;
|
||||
|
||||
// Represents the type of sampler.
|
||||
typedef enum {
|
||||
kTypeUnspecified = 0,
|
||||
// Probabilistically pick among the top k tokens.
|
||||
kTopK = 1,
|
||||
// Probabilistically pick among the tokens such that the sum is greater
|
||||
// than or equal to p tokens after first performing top-k sampling.
|
||||
kTopP = 2,
|
||||
// Pick the token with maximum logit (i.e., argmax).
|
||||
kGreedy = 3,
|
||||
} Type;
|
||||
|
||||
// Parameters for the sampler.
|
||||
typedef struct {
|
||||
Type type;
|
||||
int32_t top_k;
|
||||
float top_p;
|
||||
float temperature;
|
||||
int32_t seed;
|
||||
} LiteRtLmSamplerParams;
|
||||
|
||||
// Creates a LiteRT LM Session Config.
|
||||
// The caller is responsible for destroying the config using
|
||||
// `litert_lm_session_config_delete`.
|
||||
// @return A pointer to the created config, or NULL on failure.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
LiteRtLmSessionConfig* litert_lm_session_config_create();
|
||||
|
||||
// Sets the maximum number of output tokens per decode step for this session.
|
||||
// @param config The config to modify.
|
||||
// @param max_output_tokens The maximum number of output tokens.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
void litert_lm_session_config_set_max_output_tokens(
|
||||
LiteRtLmSessionConfig* config, int max_output_tokens);
|
||||
|
||||
// Sets the sampler parameters for this session config.
|
||||
// @param config The config to modify.
|
||||
// @param sampler_params The sampler parameters to use.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
void litert_lm_session_config_set_sampler_params(
|
||||
LiteRtLmSessionConfig* config, const LiteRtLmSamplerParams* sampler_params);
|
||||
|
||||
// Destroys a LiteRT LM Session Config.
|
||||
// @param config The config to destroy.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
void litert_lm_session_config_delete(LiteRtLmSessionConfig* config);
|
||||
|
||||
// Creates a LiteRT LM Conversation Config.
|
||||
// The caller is responsible for destroying the config using
|
||||
// `litert_lm_conversation_config_delete`.
|
||||
// @param engine The engine to use.
|
||||
// @param session_config The session config to use. If NULL, default
|
||||
// session config will be used.
|
||||
// @param system_message_json The system message in JSON format.
|
||||
// @param tools_json The tools description in JSON array format.
|
||||
// @param enable_constrained_decoding Whether to enable constrained decoding.
|
||||
// @return A pointer to the created config, or NULL on failure.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
LiteRtLmConversationConfig* litert_lm_conversation_config_create(
|
||||
LiteRtLmEngine* engine, const LiteRtLmSessionConfig* session_config,
|
||||
const char* system_message_json, const char* tools_json,
|
||||
const char* messages_json, bool enable_constrained_decoding);
|
||||
|
||||
// Destroys a LiteRT LM Conversation Config.
|
||||
// @param config The config to destroy.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
void litert_lm_conversation_config_delete(LiteRtLmConversationConfig* config);
|
||||
|
||||
// Sets the minimum log level for the LiteRT LM library.
|
||||
// Log levels are: 0=INFO, 1=WARNING, 2=ERROR, 3=FATAL.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
void litert_lm_set_min_log_level(int level);
|
||||
|
||||
// Represents the type of input data.
|
||||
typedef enum {
|
||||
kInputText,
|
||||
kInputImage,
|
||||
kInputImageEnd,
|
||||
kInputAudio,
|
||||
kInputAudioEnd,
|
||||
} InputDataType;
|
||||
|
||||
// Represents a single piece of input data.
|
||||
typedef struct {
|
||||
InputDataType type;
|
||||
// The data pointer. The interpretation depends on the `type`.
|
||||
// For kInputText, it's a UTF-8 string.
|
||||
// For kInputImage and kInputAudio, it's a pointer to the raw bytes.
|
||||
const void* data;
|
||||
// The size of the data in bytes.
|
||||
size_t size;
|
||||
} InputData;
|
||||
|
||||
// Creates LiteRT LM Engine Settings. The caller is responsible for destroying
|
||||
// the settings using `litert_lm_engine_settings_delete`.
|
||||
//
|
||||
// @param model_path The path to the model file.
|
||||
// @param backend_str The backend to use (e.g., "cpu", "gpu").
|
||||
// @param vision_backend_str The vision backend to use, or NULL if not set.
|
||||
// @param audio_backend_str The audio backend to use, or NULL if not set.
|
||||
// @return A pointer to the created settings, or NULL on failure.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
LiteRtLmEngineSettings* litert_lm_engine_settings_create(
|
||||
const char* model_path, const char* backend_str,
|
||||
const char* vision_backend_str, const char* audio_backend_str);
|
||||
|
||||
// Destroys LiteRT LM Engine Settings.
|
||||
//
|
||||
// @param settings The settings to destroy.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
void litert_lm_engine_settings_delete(LiteRtLmEngineSettings* settings);
|
||||
|
||||
// Sets the maximum number of tokens for the engine.
|
||||
//
|
||||
// @param settings The engine settings.
|
||||
// @param max_num_tokens The maximum number of tokens.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
void litert_lm_engine_settings_set_max_num_tokens(
|
||||
LiteRtLmEngineSettings* settings, int max_num_tokens);
|
||||
|
||||
// Sets the cache directory for the engine.
|
||||
//
|
||||
// @param settings The engine settings.
|
||||
// @param cache_dir The cache directory.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
void litert_lm_engine_settings_set_cache_dir(LiteRtLmEngineSettings* settings,
|
||||
const char* cache_dir);
|
||||
|
||||
// Sets the activation data type.
|
||||
//
|
||||
// @param settings The engine settings.
|
||||
// @param activation_data_type_int The activation data type. See
|
||||
// `ActivationDataType` in executor_settings_base.h for the possible values
|
||||
// (e.g., 0 for F32, 1 for F16, 2 for I16, 3 for I8).
|
||||
LITERT_LM_C_API_EXPORT
|
||||
void litert_lm_engine_settings_set_activation_data_type(
|
||||
LiteRtLmEngineSettings* settings, int activation_data_type_int);
|
||||
|
||||
// Sets the prefill chunk size for the engine. Only applicable for CPU backend
|
||||
// with dynamic models.
|
||||
//
|
||||
// @param settings The engine settings.
|
||||
// @param prefill_chunk_size The prefill chunk size.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
void litert_lm_engine_settings_set_prefill_chunk_size(
|
||||
LiteRtLmEngineSettings* settings, int prefill_chunk_size);
|
||||
|
||||
// Enables benchmarking for the engine.
|
||||
//
|
||||
// @param settings The engine settings.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
void litert_lm_engine_settings_enable_benchmark(
|
||||
LiteRtLmEngineSettings* settings);
|
||||
|
||||
// Sets the number of prefill tokens for benchmarking.
|
||||
//
|
||||
// @param settings The engine settings.
|
||||
// @param num_prefill_tokens The number of prefill tokens.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
void litert_lm_engine_settings_set_num_prefill_tokens(
|
||||
LiteRtLmEngineSettings* settings, int num_prefill_tokens);
|
||||
|
||||
// Sets the number of decode tokens for benchmarking.
|
||||
//
|
||||
// @param settings The engine settings.
|
||||
// @param num_decode_tokens The number of decode tokens.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
void litert_lm_engine_settings_set_num_decode_tokens(
|
||||
LiteRtLmEngineSettings* settings, int num_decode_tokens);
|
||||
|
||||
// Creates a LiteRT LM Engine from the given settings. The caller is responsible
|
||||
// for destroying the engine using `litert_lm_engine_delete`.
|
||||
//
|
||||
// @param settings The engine settings.
|
||||
// @return A pointer to the created engine, or NULL on failure.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
LiteRtLmEngine* litert_lm_engine_create(const LiteRtLmEngineSettings* settings);
|
||||
|
||||
// Destroys a LiteRT LM Engine.
|
||||
//
|
||||
// @param engine The engine to destroy.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
void litert_lm_engine_delete(LiteRtLmEngine* engine);
|
||||
|
||||
// Creates a LiteRT LM Session. The caller is responsible for destroying the
|
||||
// session using `litert_lm_session_delete`.
|
||||
//
|
||||
// @param engine The engine to create the session from.
|
||||
// @param config The session config of the session. If NULL, use the default
|
||||
// session config.
|
||||
// @return A pointer to the created session, or NULL on failure.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
LiteRtLmSession* litert_lm_engine_create_session(LiteRtLmEngine* engine,
|
||||
LiteRtLmSessionConfig* config);
|
||||
|
||||
// Destroys a LiteRT LM Session.
|
||||
//
|
||||
// @param session The session to destroy.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
void litert_lm_session_delete(LiteRtLmSession* session);
|
||||
|
||||
// Generates content from the input prompt.
|
||||
//
|
||||
// @param session The session to use for generation.
|
||||
// @param inputs An array of InputData structs representing the multimodal
|
||||
// input.
|
||||
// @param num_inputs The number of InputData structs in the array.
|
||||
// @return A pointer to the responses, or NULL on failure. The caller is
|
||||
// responsible for deleting the responses using `litert_lm_responses_delete`.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
LiteRtLmResponses* litert_lm_session_generate_content(LiteRtLmSession* session,
|
||||
const InputData* inputs,
|
||||
size_t num_inputs);
|
||||
// Destroys a LiteRT LM Responses object.
|
||||
//
|
||||
// @param responses The responses to destroy.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
void litert_lm_responses_delete(LiteRtLmResponses* responses);
|
||||
|
||||
// Returns the number of response candidates.
|
||||
//
|
||||
// @param responses The responses object.
|
||||
// @return The number of candidates.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
int litert_lm_responses_get_num_candidates(const LiteRtLmResponses* responses);
|
||||
|
||||
// Returns the response text at a given index.
|
||||
//
|
||||
// @param responses The responses object.
|
||||
// @param index The index of the response.
|
||||
// @return The response text. The returned string is owned by the `responses`
|
||||
// object and is valid only for its lifetime. Returns NULL if index is out of
|
||||
// bounds.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
const char* litert_lm_responses_get_response_text_at(
|
||||
const LiteRtLmResponses* responses, int index);
|
||||
|
||||
// Retrieves the benchmark information from the session. The caller is
|
||||
// responsible for destroying the benchmark info using
|
||||
// `litert_lm_benchmark_info_delete`.
|
||||
//
|
||||
// @param session The session to get the benchmark info from.
|
||||
// @return A pointer to the benchmark info, or NULL on failure.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
LiteRtLmBenchmarkInfo* litert_lm_session_get_benchmark_info(
|
||||
LiteRtLmSession* session);
|
||||
|
||||
// Destroys a LiteRT LM Benchmark Info object.
|
||||
//
|
||||
// @param benchmark_info The benchmark info to destroy.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
void litert_lm_benchmark_info_delete(LiteRtLmBenchmarkInfo* benchmark_info);
|
||||
|
||||
// Returns the time to the first token in seconds.
|
||||
//
|
||||
// Note that the first time to token doesn't include the time for
|
||||
// initialization. It is the sum of the prefill time for the first turn and
|
||||
// the time spent for decoding the first token.
|
||||
//
|
||||
// @param benchmark_info The benchmark info object.
|
||||
// @return The time to the first token in seconds.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
double litert_lm_benchmark_info_get_time_to_first_token(
|
||||
const LiteRtLmBenchmarkInfo* benchmark_info);
|
||||
|
||||
// Returns the total initialization time in seconds.
|
||||
//
|
||||
// @param benchmark_info The benchmark info object.
|
||||
// @return The total initialization time in seconds.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
double litert_lm_benchmark_info_get_total_init_time_in_second(
|
||||
const LiteRtLmBenchmarkInfo* benchmark_info);
|
||||
|
||||
// Returns the number of prefill turns.
|
||||
//
|
||||
// @param benchmark_info The benchmark info object.
|
||||
// @return The number of prefill turns.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
int litert_lm_benchmark_info_get_num_prefill_turns(
|
||||
const LiteRtLmBenchmarkInfo* benchmark_info);
|
||||
|
||||
// Returns the number of decode turns.
|
||||
//
|
||||
// @param benchmark_info The benchmark info object.
|
||||
// @return The number of decode turns.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
int litert_lm_benchmark_info_get_num_decode_turns(
|
||||
const LiteRtLmBenchmarkInfo* benchmark_info);
|
||||
|
||||
// Returns the prefill token count at a given turn index.
|
||||
//
|
||||
// @param benchmark_info The benchmark info object.
|
||||
// @param index The index of the prefill turn.
|
||||
// @return The prefill token count.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
int litert_lm_benchmark_info_get_prefill_token_count_at(
|
||||
const LiteRtLmBenchmarkInfo* benchmark_info, int index);
|
||||
|
||||
// Returns the decode token count at a given turn index.
|
||||
//
|
||||
// @param benchmark_info The benchmark info object.
|
||||
// @param index The index of the decode turn.
|
||||
// @return The decode token count.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
int litert_lm_benchmark_info_get_decode_token_count_at(
|
||||
const LiteRtLmBenchmarkInfo* benchmark_info, int index);
|
||||
|
||||
// Returns the prefill tokens per second at a given turn index.
|
||||
//
|
||||
// @param benchmark_info The benchmark info object.
|
||||
// @param index The index of the prefill turn.
|
||||
// @return The prefill tokens per second.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
double litert_lm_benchmark_info_get_prefill_tokens_per_sec_at(
|
||||
const LiteRtLmBenchmarkInfo* benchmark_info, int index);
|
||||
|
||||
// Returns the decode tokens per second at a given turn index.
|
||||
//
|
||||
// @param benchmark_info The benchmark info object.
|
||||
// @param index The index of the decode turn.
|
||||
// @return The decode tokens per second.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
double litert_lm_benchmark_info_get_decode_tokens_per_sec_at(
|
||||
const LiteRtLmBenchmarkInfo* benchmark_info, int index);
|
||||
|
||||
// Callback for streaming responses.
|
||||
// `callback_data` is a pointer to user-defined data passed to the stream
|
||||
// function. `chunk` is the piece of text from the stream. It's only valid for
|
||||
// the duration of the call. `is_final` is true if this is the last chunk in the
|
||||
// stream. `error_msg` is a null-terminated string with an error message, or
|
||||
// NULL on success.
|
||||
typedef void (*LiteRtLmStreamCallback)(void* callback_data, const char* chunk,
|
||||
bool is_final, const char* error_msg);
|
||||
|
||||
// Generates content from the input prompt and streams the response via a
|
||||
// callback. This is a non-blocking call that will invoke the callback from a
|
||||
// background thread for each chunk.
|
||||
//
|
||||
// @param session The session to use for generation.
|
||||
// @param inputs An array of InputData structs representing the multimodal
|
||||
// input.
|
||||
// @param num_inputs The number of InputData structs in the array.
|
||||
// @param callback The callback function to receive response chunks.
|
||||
// @param callback_data A pointer to user data that will be passed to the
|
||||
// callback.
|
||||
// @return 0 on success, non-zero on failure to start the stream.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
int litert_lm_session_generate_content_stream(LiteRtLmSession* session,
|
||||
const InputData* inputs,
|
||||
size_t num_inputs,
|
||||
LiteRtLmStreamCallback callback,
|
||||
void* callback_data);
|
||||
|
||||
// Creates a LiteRT LM Conversation. The caller is responsible for destroying
|
||||
// the conversation using `litert_lm_conversation_delete`.
|
||||
//
|
||||
// @param engine The engine to create the conversation from.
|
||||
// @param config The conversation config to use. If NULL, the default config
|
||||
// will be used.
|
||||
// @return A pointer to the created conversation, or NULL on failure.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
LiteRtLmConversation* litert_lm_conversation_create(
|
||||
LiteRtLmEngine* engine, LiteRtLmConversationConfig* config);
|
||||
|
||||
// Destroys a LiteRT LM Conversation.
|
||||
//
|
||||
// @param conversation The conversation to destroy.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
void litert_lm_conversation_delete(LiteRtLmConversation* conversation);
|
||||
|
||||
// Sends a message to the conversation and returns the response.
|
||||
// This is a blocking call.
|
||||
//
|
||||
// @param conversation The conversation to use.
|
||||
// @param message_json A JSON string representing the message to send.
|
||||
// @param extra_context A JSON string representing the extra context to use.
|
||||
// @return A pointer to the JSON response, or NULL on failure. The caller is
|
||||
// responsible for deleting the response using
|
||||
// `litert_lm_json_response_delete`.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
LiteRtLmJsonResponse* litert_lm_conversation_send_message(
|
||||
LiteRtLmConversation* conversation, const char* message_json,
|
||||
const char* extra_context);
|
||||
|
||||
// Destroys a LiteRT LM Json Response object.
|
||||
//
|
||||
// @param response The response to destroy.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
void litert_lm_json_response_delete(LiteRtLmJsonResponse* response);
|
||||
|
||||
// Returns the JSON response string from a response object.
|
||||
//
|
||||
// @param response The response object.
|
||||
// @return The response JSON string. The returned string is owned by the
|
||||
// `response` object and is valid only for its lifetime. Returns NULL if
|
||||
// response is NULL.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
const char* litert_lm_json_response_get_string(
|
||||
const LiteRtLmJsonResponse* response);
|
||||
|
||||
// Sends a message to the conversation and streams the response via a
|
||||
// callback. This is a non-blocking call that will invoke the callback from a
|
||||
// background thread for each chunk.
|
||||
//
|
||||
// @param conversation The conversation to use.
|
||||
// @param message_json A JSON string representing the message to send.
|
||||
// @param extra_context A JSON string representing the extra context to use.
|
||||
// @param callback The callback function to receive response chunks.
|
||||
// @param callback_data A pointer to user data that will be passed to the
|
||||
// callback.
|
||||
// @return 0 on success, non-zero on failure to start the stream.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
int litert_lm_conversation_send_message_stream(
|
||||
LiteRtLmConversation* conversation, const char* message_json,
|
||||
const char* extra_context, LiteRtLmStreamCallback callback,
|
||||
void* callback_data);
|
||||
|
||||
// Cancels the ongoing inference process, for asynchronous inference.
|
||||
//
|
||||
// @param conversation The conversation to cancel the inference for.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
void litert_lm_conversation_cancel_process(LiteRtLmConversation* conversation);
|
||||
|
||||
// Retrieves the benchmark information from the conversation. The caller is
|
||||
// responsible for destroying the benchmark info using
|
||||
// `litert_lm_benchmark_info_delete`.
|
||||
//
|
||||
// @param conversation The conversation to get the benchmark info from.
|
||||
// @return A pointer to the benchmark info, or NULL on failure.
|
||||
LITERT_LM_C_API_EXPORT
|
||||
LiteRtLmBenchmarkInfo* litert_lm_conversation_get_benchmark_info(
|
||||
LiteRtLmConversation* conversation);
|
||||
|
||||
#ifdef __cplusplus
|
||||
} // extern "C"
|
||||
#endif
|
||||
|
||||
#endif // THIRD_PARTY_ODML_LITERT_LM_C_ENGINE_H_
|
||||
@@ -1,5 +0,0 @@
|
||||
module CLiteRtLm {
|
||||
header "include/engine.h"
|
||||
link "LiteRtLmEngine"
|
||||
export *
|
||||
}
|
||||
@@ -1,2 +0,0 @@
|
||||
// Placeholder for CLiteRtLm C library
|
||||
// The actual implementation is in libLiteRtLmEngine.dylib
|
||||
@@ -1,648 +0,0 @@
|
||||
import Foundation
|
||||
|
||||
// MARK: - 模型/推理配置
|
||||
|
||||
@Observable
|
||||
class ModelConfig {
|
||||
var maxTokens = 4000
|
||||
var topK = 64
|
||||
var topP = 0.95
|
||||
var temperature = 1.0
|
||||
var useGPU = true
|
||||
var systemPrompt = """
|
||||
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。
|
||||
|
||||
你拥有以下能力（Skill）：
|
||||
|
||||
___SKILLS___
|
||||
|
||||
当用户的请求需要使用某个能力时，先调用 load_skill 加载该能力的详细指令：
|
||||
<tool_call>
|
||||
{"name": "load_skill", "arguments": {"skill": "能力名"}}
|
||||
</tool_call>
|
||||
|
||||
不需要能力时直接回复。用中文回答，简洁实用。
|
||||
"""
|
||||
}
|
||||
|
||||
// MARK: - 聊天消息
|
||||
|
||||
struct ChatMessage: Identifiable {
|
||||
let id: UUID
|
||||
var role: Role
|
||||
var content: String
|
||||
let timestamp = Date()
|
||||
var skillName: String? = nil
|
||||
|
||||
init(role: Role, content: String, skillName: String? = nil) {
|
||||
self.id = UUID()
|
||||
self.role = role
|
||||
self.content = content
|
||||
self.skillName = skillName
|
||||
}
|
||||
|
||||
/// 保持 id 不变地更新内容（避免 SwiftUI 重建视图）
|
||||
mutating func update(content: String) {
|
||||
// 只在内容真正变化时更新，减少不必要的 SwiftUI 通知
|
||||
guard self.content != content else { return }
|
||||
self.content = content
|
||||
}
|
||||
|
||||
mutating func update(role: Role, content: String, skillName: String? = nil) {
|
||||
self.role = role
|
||||
self.content = content
|
||||
self.skillName = skillName
|
||||
}
|
||||
|
||||
enum Role {
|
||||
case user, assistant, system, skillResult
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - Agent Engine
|
||||
|
||||
@Observable
|
||||
class AgentEngine {
|
||||
|
||||
let llm = LocalLLMService()
|
||||
var messages: [ChatMessage] = []
|
||||
var isProcessing = false
|
||||
var config = ModelConfig()
|
||||
|
||||
// 文件驱动的 Skill 系统
|
||||
let skillLoader = SkillLoader()
|
||||
let toolRegistry = ToolRegistry.shared
|
||||
|
||||
// Skill 条目（给 UI 管理用，可开关）
|
||||
var skillEntries: [SkillEntry] = []
|
||||
|
||||
// 默认系统提示
|
||||
var defaultSystemPrompt: String {
|
||||
ModelConfig().systemPrompt
|
||||
}
|
||||
|
||||
/// 已启用的 Skill 概要（只含 Skill 级别信息，不含 Tool 细节）
|
||||
/// 用于 system prompt 和 UI chip
|
||||
var enabledSkillInfos: [SkillInfo] {
|
||||
skillEntries.filter(\.isEnabled).map {
|
||||
SkillInfo(name: $0.id, description: $0.description,
|
||||
displayName: $0.name, icon: $0.icon, samplePrompt: $0.samplePrompt)
|
||||
}
|
||||
}
|
||||
|
||||
init() {
|
||||
loadSkillEntries()
|
||||
}
|
||||
|
||||
/// 从 SKILL.md 文件加载所有 Skill
|
||||
private func loadSkillEntries() {
|
||||
let definitions = skillLoader.discoverSkills()
|
||||
self.skillEntries = definitions.map { SkillEntry(from: $0, registry: toolRegistry) }
|
||||
}
|
||||
|
||||
/// 热重载所有 Skill（编辑/新增 SKILL.md 后调用）
|
||||
func reloadSkills() {
|
||||
// 保存当前启用状态
|
||||
let enabledState = Dictionary(uniqueKeysWithValues: skillEntries.map { ($0.id, $0.isEnabled) })
|
||||
loadSkillEntries()
|
||||
// 恢复启用状态
|
||||
for i in skillEntries.indices {
|
||||
if let wasEnabled = enabledState[skillEntries[i].id] {
|
||||
skillEntries[i].isEnabled = wasEnabled
|
||||
skillLoader.setEnabled(skillEntries[i].id, enabled: wasEnabled)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - Skill 查找（文件驱动）
|
||||
|
||||
/// 根据 Skill ID 或 Tool 名找到 Skill ID
|
||||
private func findSkillId(for name: String) -> String? {
|
||||
// 先按 Skill ID 匹配
|
||||
if skillLoader.getDefinition(name) != nil { return name }
|
||||
// 再按 Tool 名反查
|
||||
return skillLoader.findSkillId(forTool: name)
|
||||
}
|
||||
|
||||
/// 根据 Skill ID 或 Tool 名找到 displayName
|
||||
private func findDisplayName(for name: String) -> String {
|
||||
if let skillId = findSkillId(for: name),
|
||||
let def = skillLoader.getDefinition(skillId) {
|
||||
return def.metadata.name
|
||||
}
|
||||
return name
|
||||
}
|
||||
|
||||
/// 处理 load_skill 调用 → 返回 SKILL.md 的 body 作为 instructions
|
||||
private func handleLoadSkill(skillName: String) -> String? {
|
||||
// 检查是否启用
|
||||
guard let entry = skillEntries.first(where: { $0.id == skillName }),
|
||||
entry.isEnabled else {
|
||||
return nil
|
||||
}
|
||||
// 加载 SKILL.md body（渐进式披露）
|
||||
return skillLoader.loadBody(skillId: skillName)
|
||||
}
|
||||
|
||||
/// 处理具体 Tool 调用 → 通过 ToolRegistry 执行
|
||||
private func handleToolExecution(toolName: String, args: [String: Any]) async throws -> String {
|
||||
return try await toolRegistry.execute(name: toolName, args: args)
|
||||
}
|
||||
|
||||
// MARK: - 初始化
|
||||
|
||||
func setup() {
|
||||
applySamplingConfig()
|
||||
let backend = config.useGPU ? "gpu" : "cpu"
|
||||
DispatchQueue.global(qos: .userInitiated).async { [weak self] in
|
||||
self?.llm.loadModel(backend: backend)
|
||||
}
|
||||
}
|
||||
|
||||
/// 将 config 中的采样参数同步到 LLM
|
||||
func applySamplingConfig() {
|
||||
llm.samplingTopK = Int32(config.topK)
|
||||
llm.samplingTopP = Float(config.topP)
|
||||
llm.samplingTemperature = Float(config.temperature)
|
||||
llm.maxOutputTokens = config.maxTokens
|
||||
}
|
||||
|
||||
/// 重新加载模型（backend 变化时调用）
|
||||
func reloadModel() {
|
||||
let backend = config.useGPU ? "gpu" : "cpu"
|
||||
DispatchQueue.global(qos: .userInitiated).async { [weak self] in
|
||||
self?.llm.loadModel(backend: backend)
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - 处理用户输入（LiteRT-LM C API 流式输出）
|
||||
|
||||
func processInput(_ text: String) async {
|
||||
let trimmed = text.trimmingCharacters(in: .whitespacesAndNewlines)
|
||||
guard !trimmed.isEmpty, !isProcessing else { return }
|
||||
guard llm.isLoaded else {
|
||||
messages.append(ChatMessage(role: .system, content: "⏳ 模型还在加载中..."))
|
||||
return
|
||||
}
|
||||
|
||||
messages.append(ChatMessage(role: .user, content: trimmed))
|
||||
isProcessing = true
|
||||
|
||||
// ★ 每次生成前同步采样参数
|
||||
applySamplingConfig()
|
||||
|
||||
let activeSkillInfos = enabledSkillInfos
|
||||
let prompt = PromptBuilder.build(
|
||||
userMessage: trimmed,
|
||||
tools: activeSkillInfos,
|
||||
history: messages,
|
||||
systemPrompt: config.systemPrompt
|
||||
)
|
||||
|
||||
// 占位消息（▍ 不可见，只显示 spinner）
|
||||
messages.append(ChatMessage(role: .assistant, content: "▍"))
|
||||
let msgIndex = messages.count - 1
|
||||
|
||||
var detectedToolCall = false
|
||||
var buffer = "" // 缓冲区：积累 token，确认不是 tool_call 后才显示
|
||||
var bufferFlushed = false // 一旦 flush 过，后续 token 直接显示
|
||||
|
||||
llm.generateStream(prompt: prompt) { [weak self] token in
|
||||
guard let self = self else { return }
|
||||
|
||||
if detectedToolCall {
|
||||
buffer += token
|
||||
return // 静默积累，不更新 UI — 由 executeToolChain 统一管理卡片
|
||||
}
|
||||
|
||||
buffer += token
|
||||
|
||||
if buffer.contains("<tool_call>") {
|
||||
detectedToolCall = true
|
||||
return // 不转换消息角色，保持 assistant "▍" → 显示思考点
|
||||
}
|
||||
|
||||
if !bufferFlushed {
|
||||
let trimmed = buffer.trimmingCharacters(in: .whitespacesAndNewlines)
|
||||
if trimmed.isEmpty { return }
|
||||
if "<tool_call>".hasPrefix(trimmed) { return }
|
||||
bufferFlushed = true
|
||||
self.messages[msgIndex].update(content: self.cleanOutputStreaming(buffer))
|
||||
return
|
||||
}
|
||||
|
||||
// ★ 原地更新 content，保持 id 不变 → 无重排
|
||||
// 用 buffer（完整原始文本）做清理，避免累积误差
|
||||
let cleaned = self.cleanOutputStreaming(buffer)
|
||||
if !cleaned.isEmpty {
|
||||
self.messages[msgIndex].update(content: cleaned)
|
||||
}
|
||||
} onComplete: { [weak self] result in
|
||||
guard let self = self else { return }
|
||||
switch result {
|
||||
case .success(let fullText):
|
||||
log("[Agent] 1st raw: \(fullText.prefix(300))")
|
||||
|
||||
if self.parseToolCall(fullText) != nil {
|
||||
// ★ 清除原始占位消息（executeToolChain 会创建正确的 skill 卡片）
|
||||
self.messages[msgIndex].update(content: "")
|
||||
// ★ 进入工具链循环（支持多轮）
|
||||
Task {
|
||||
await self.executeToolChain(
|
||||
prompt: prompt,
|
||||
fullText: fullText,
|
||||
userQuestion: trimmed
|
||||
)
|
||||
}
|
||||
return
|
||||
} else {
|
||||
let cleaned = self.cleanOutput(fullText)
|
||||
self.messages[msgIndex].update(
|
||||
content: cleaned.isEmpty ? "（无回复）" : cleaned
|
||||
)
|
||||
}
|
||||
case .failure(let error):
|
||||
self.messages[msgIndex].update(role: .system, content: "❌ \(error.localizedDescription)")
|
||||
}
|
||||
self.isProcessing = false
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - Skill 结果后的后续推理（支持多轮工具链）
|
||||
|
||||
/// 执行一次 LLM 推理并返回原始输出
|
||||
private func streamLLM(prompt: String, msgIndex: Int) async -> String? {
|
||||
var buffer = ""
|
||||
return await withCheckedContinuation { (continuation: CheckedContinuation<String?, Never>) in
|
||||
var toolCallDetected = false
|
||||
var bufferFlushed = false // 是否已确认不是 tool_call 前缀
|
||||
llm.generateStream(prompt: prompt) { [weak self] token in
|
||||
guard let self = self else { return }
|
||||
buffer += token
|
||||
|
||||
// 检测到 tool_call 后静默积累
|
||||
if toolCallDetected { return }
|
||||
if buffer.contains("<tool_call>") {
|
||||
toolCallDetected = true
|
||||
// 清除之前可能已显示的过程文字
|
||||
if bufferFlushed && self.messages[msgIndex].role == .assistant {
|
||||
self.messages[msgIndex].update(content: "")
|
||||
}
|
||||
return
|
||||
}
|
||||
|
||||
// 等待确认不是 tool_call 前缀再显示
|
||||
if !bufferFlushed {
|
||||
let trimmed = buffer.trimmingCharacters(in: .whitespacesAndNewlines)
|
||||
if trimmed.isEmpty { return }
|
||||
if "<tool_call>".hasPrefix(trimmed) { return }
|
||||
bufferFlushed = true
|
||||
}
|
||||
|
||||
// 普通文字流式显示
|
||||
let cleaned = self.cleanOutputStreaming(buffer)
|
||||
if !cleaned.isEmpty && self.messages[msgIndex].role == .assistant {
|
||||
self.messages[msgIndex].update(content: cleaned)
|
||||
}
|
||||
} onComplete: { [weak self] result in
|
||||
guard let self = self else {
|
||||
continuation.resume(returning: nil)
|
||||
return
|
||||
}
|
||||
switch result {
|
||||
case .success(let text):
|
||||
log("[Agent] LLM raw: \(text.prefix(300))")
|
||||
continuation.resume(returning: text)
|
||||
case .failure(let error):
|
||||
self.messages[msgIndex].update(role: .system, content: "❌ \(error.localizedDescription)")
|
||||
continuation.resume(returning: nil)
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// 执行 tool_call → skill/tool → follow-up 循环（最多 maxRounds 轮）
|
||||
///
|
||||
/// 支持两种调用：
|
||||
/// - load_skill: 逐级披露，返回 Skill 的完整 instructions + Tool 定义
|
||||
/// - 具体 tool: 执行原生 API
|
||||
private func executeToolChain(
|
||||
prompt: String,
|
||||
fullText: String,
|
||||
userQuestion: String,
|
||||
round: Int = 1,
|
||||
maxRounds: Int = 10
|
||||
) async {
|
||||
guard round <= maxRounds else {
|
||||
log("[Agent] 达到最大工具链轮数 \(maxRounds)")
|
||||
isProcessing = false
|
||||
return
|
||||
}
|
||||
|
||||
guard let call = parseToolCall(fullText) else {
|
||||
let cleaned = cleanOutput(fullText)
|
||||
if let lastAssistant = messages.lastIndex(where: { $0.role == .assistant }) {
|
||||
messages[lastAssistant].update(content: cleaned.isEmpty ? "（无回复）" : cleaned)
|
||||
}
|
||||
isProcessing = false
|
||||
return
|
||||
}
|
||||
|
||||
log("[Agent] Round \(round): tool_call name=\(call.name)")
|
||||
|
||||
// ── load_skill：逐级披露，返回 Skill 指令 ──
|
||||
if call.name == "load_skill" {
|
||||
// ★ 检查是否有多个 load_skill（模型可能一次请求多个能力）
|
||||
let allCalls = parseAllToolCalls(fullText)
|
||||
let loadSkillCalls = allCalls.filter { $0.name == "load_skill" }
|
||||
|
||||
var allInstructions = ""
|
||||
for lsCall in loadSkillCalls {
|
||||
let skillName = (lsCall.arguments["skill"] as? String)
|
||||
?? (lsCall.arguments["name"] as? String)
|
||||
?? ""
|
||||
log("[Agent] load_skill: \(skillName)")
|
||||
|
||||
let displayName = findDisplayName(for: skillName)
|
||||
messages.append(ChatMessage(role: .system, content: "identified", skillName: displayName))
|
||||
let cardIdx = messages.count - 1
|
||||
|
||||
guard let instructions = handleLoadSkill(skillName: skillName) else {
|
||||
messages[cardIdx].update(role: .system, content: "done", skillName: displayName)
|
||||
continue
|
||||
}
|
||||
|
||||
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
|
||||
}
|
||||
|
||||
// 构建 follow-up：把所有 instructions 注入 LLM 上下文
|
||||
let followUpPrompt = PromptBuilder.buildFollowUp(
|
||||
originalPrompt: prompt,
|
||||
modelResponse: fullText,
|
||||
skillName: "load_skill",
|
||||
skillResult: allInstructions,
|
||||
userQuestion: userQuestion,
|
||||
isLoadSkill: true
|
||||
)
|
||||
|
||||
messages.append(ChatMessage(role: .assistant, content: "▍"))
|
||||
let followUpIndex = messages.count - 1
|
||||
|
||||
guard let nextText = await streamLLM(prompt: followUpPrompt, msgIndex: followUpIndex) 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, round: round + 1, maxRounds: maxRounds
|
||||
)
|
||||
} else {
|
||||
let cleaned = cleanOutput(nextText)
|
||||
messages[followUpIndex].update(content: cleaned.isEmpty ? "（无回复）" : cleaned)
|
||||
isProcessing = false
|
||||
}
|
||||
return
|
||||
}
|
||||
|
||||
// ── 具体 Tool 调用：执行原生 API ──
|
||||
|
||||
// 定位所属 Skill（用于 UI 展示）
|
||||
let ownerSkillId = findSkillId(for: call.name)
|
||||
let displayName = findDisplayName(for: call.name)
|
||||
|
||||
// 找已有的 Skill 卡片（load_skill 阶段创建的，状态为 "loaded"）
|
||||
// 复用它来继续显示 Step 3: executing
|
||||
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 {
|
||||
// 没有已有卡片（直接 Tool 调用，跳过 load_skill），创建新卡片
|
||||
messages.append(ChatMessage(role: .system, content: "identified", skillName: displayName))
|
||||
cardIndex = messages.count - 1
|
||||
}
|
||||
|
||||
// ★ 未知工具：关闭卡片 + 报错
|
||||
guard ownerSkillId != nil else {
|
||||
log("[Agent] 未知工具: \(call.name)")
|
||||
messages[cardIndex].update(role: .system, content: "done", skillName: displayName)
|
||||
messages.append(ChatMessage(role: .assistant, content: "⚠️ 未知工具: \(call.name)"))
|
||||
isProcessing = false
|
||||
return
|
||||
}
|
||||
|
||||
// ★ Skill 未启用：关闭卡片 + 报错
|
||||
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
|
||||
}
|
||||
|
||||
// Step 3: executing（带具体 Tool 名）
|
||||
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
|
||||
)
|
||||
|
||||
messages.append(ChatMessage(role: .assistant, content: "▍"))
|
||||
let followUpIndex = messages.count - 1
|
||||
|
||||
guard let nextText = await streamLLM(prompt: followUpPrompt, msgIndex: followUpIndex) 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, round: round + 1, maxRounds: maxRounds
|
||||
)
|
||||
} else {
|
||||
let cleaned = cleanOutput(nextText)
|
||||
messages[followUpIndex].update(content: cleaned.isEmpty ? "（无回复）" : 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 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
|
||||
}
|
||||
|
||||
/// 解析所有 tool_call（支持模型在一次回复中输出多个）
|
||||
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
|
||||
}
|
||||
|
||||
/// 从部分流式内容中提取 skill 名（不需要完整 JSON）
|
||||
private func extractSkillName(from text: String) -> String? {
|
||||
// 匹配 "name": "xxx" 或 "name":"xxx"
|
||||
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])
|
||||
}
|
||||
|
||||
/// 流式输出时的轻量清理：只移除已完成的 token，不做尾部裁剪
|
||||
/// 保证文本长度单调递增，避免视觉跳动
|
||||
private func cleanOutputStreaming(_ text: String) -> String {
|
||||
var result = text
|
||||
|
||||
// ★ 截断：遇到 <tool_call> 只保留之前的内容
|
||||
if let tcRange = result.range(of: "<tool_call>") {
|
||||
result = String(result[result.startIndex..<tcRange.lowerBound])
|
||||
}
|
||||
|
||||
// 截断：第一个 end-of-turn token
|
||||
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
|
||||
}
|
||||
}
|
||||
|
||||
// 移除完整闭合的 <tool_call>...</tool_call> 块
|
||||
if let regex = try? NSRegularExpression(pattern: "<tool_call>.*?</tool_call>", options: .dotMatchesLineSeparators) {
|
||||
result = regex.stringByReplacingMatches(in: result, range: NSRange(result.startIndex..., in: result), withTemplate: "")
|
||||
}
|
||||
|
||||
// 只移除已完整闭合的 token（不动尾部未闭合的）
|
||||
result = result.replacingOccurrences(
|
||||
of: "<\\|?[/a-z_]+\\|?>",
|
||||
with: "",
|
||||
options: .regularExpression
|
||||
)
|
||||
|
||||
// 清理开头的 "model\n"
|
||||
if result.hasPrefix("model\n") {
|
||||
result = String(result.dropFirst(6))
|
||||
} else if result == "model" {
|
||||
return ""
|
||||
}
|
||||
|
||||
// ★ 流式时不做 trimmingCharacters，避免尾部空白被反复移除/恢复
|
||||
// 只移除开头空白
|
||||
return String(result.drop(while: { $0.isWhitespace || $0.isNewline }))
|
||||
}
|
||||
|
||||
/// 完成时的完整清理：包括尾部裁剪和空白处理
|
||||
private func cleanOutput(_ text: String) -> String {
|
||||
var result = text
|
||||
|
||||
// ★ 移除完整的 <tool_call>...</tool_call> 块
|
||||
if let regex = try? NSRegularExpression(pattern: "<tool_call>.*?</tool_call>", options: .dotMatchesLineSeparators) {
|
||||
result = regex.stringByReplacingMatches(in: result, range: NSRange(result.startIndex..., in: result), withTemplate: "")
|
||||
}
|
||||
|
||||
// ★ 截断：遇到未闭合的 <tool_call 也截断
|
||||
if let tcRange = result.range(of: "<tool_call>") {
|
||||
result = String(result[result.startIndex..<tcRange.lowerBound])
|
||||
}
|
||||
|
||||
// 截断：第一个 end-of-turn token
|
||||
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
|
||||
}
|
||||
}
|
||||
|
||||
// 清理所有 Gemma 4 特殊 token
|
||||
result = result.replacingOccurrences(
|
||||
of: "<\\|?[/a-z_]+\\|?>",
|
||||
with: "",
|
||||
options: .regularExpression
|
||||
)
|
||||
|
||||
// 清理未闭合的尾部 < 残留（仅完成时做）
|
||||
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])
|
||||
}
|
||||
}
|
||||
|
||||
// 清理开头的 "model\n"
|
||||
if result.hasPrefix("model\n") {
|
||||
result = String(result.dropFirst(6))
|
||||
} else if result == "model" {
|
||||
result = ""
|
||||
}
|
||||
|
||||
return result.trimmingCharacters(in: .whitespacesAndNewlines)
|
||||
}
|
||||
}
|
||||
@@ -1,36 +0,0 @@
|
||||
import SwiftUI
|
||||
import AppKit
|
||||
import Foundation
|
||||
|
||||
/// 输出到 stderr（GUI 进程的 stdout 不可见）
|
||||
func log(_ items: Any...) {
|
||||
let msg = items.map { String(describing: $0) }.joined(separator: " ")
|
||||
FileHandle.standardError.write(Data((msg + "\n").utf8))
|
||||
}
|
||||
|
||||
@main
|
||||
struct PhoneClawMacApp: App {
|
||||
@NSApplicationDelegateAdaptor(AppDelegate.self) var appDelegate
|
||||
|
||||
var body: some Scene {
|
||||
WindowGroup {
|
||||
ContentView()
|
||||
.frame(minWidth: 500, idealWidth: 600, minHeight: 700, idealHeight: 800)
|
||||
}
|
||||
.windowStyle(.hiddenTitleBar)
|
||||
.defaultSize(width: 600, height: 800)
|
||||
}
|
||||
}
|
||||
|
||||
class AppDelegate: NSObject, NSApplicationDelegate {
|
||||
func applicationDidFinishLaunching(_ notification: Notification) {
|
||||
// 命令行启动 SwiftUI 必须手动设置，否则窗口不显示
|
||||
NSApp.setActivationPolicy(.regular)
|
||||
NSApp.activate(ignoringOtherApps: true)
|
||||
|
||||
// 确保窗口在最前面
|
||||
DispatchQueue.main.asyncAfter(deadline: .now() + 0.5) {
|
||||
NSApp.windows.first?.makeKeyAndOrderFront(nil)
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,287 +0,0 @@
|
||||
import Foundation
|
||||
import CLiteRtLm
|
||||
|
||||
// MARK: - LiteRT-LM C API (engine.h) → Swift 桥接
|
||||
//
|
||||
// 使用 Session API（非 Conversation API），因为 PromptBuilder 已经
|
||||
// 构建了完整的 Gemma 4 格式 prompt。Session API 直接处理原始文本。
|
||||
|
||||
@Observable
|
||||
class LocalLLMService {
|
||||
|
||||
private var engine: OpaquePointer?
|
||||
/// 当前活跃的 session（引擎同时只支持一个）
|
||||
private var activeSession: OpaquePointer?
|
||||
private let sessionLock = NSLock()
|
||||
|
||||
var isLoaded = false
|
||||
var isGenerating = false
|
||||
var statusMessage = "等待加载模型..."
|
||||
|
||||
static let modelFileName = "gemma-4-E4B-it"
|
||||
static let modelFileExtension = "litertlm"
|
||||
|
||||
deinit {
|
||||
cleanupSession()
|
||||
if let eng = engine {
|
||||
litert_lm_engine_delete(eng)
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - Session 生命周期
|
||||
|
||||
/// 安全清理上一个 session（在回调外部调用）
|
||||
private func cleanupSession() {
|
||||
sessionLock.lock()
|
||||
if let sess = activeSession {
|
||||
litert_lm_session_delete(sess)
|
||||
activeSession = nil
|
||||
log("[LLM] Session 已清理")
|
||||
}
|
||||
sessionLock.unlock()
|
||||
}
|
||||
|
||||
// MARK: - 加载模型
|
||||
|
||||
/// 当前使用的 backend
|
||||
var currentBackend = "cpu"
|
||||
|
||||
func loadModel(backend: String = "cpu") {
|
||||
DispatchQueue.main.async {
|
||||
self.statusMessage = "正在加载 E4B 模型..."
|
||||
self.isLoaded = false
|
||||
}
|
||||
|
||||
let modelPath = findModelPath()
|
||||
guard let path = modelPath else {
|
||||
DispatchQueue.main.async { self.statusMessage = "❌ 模型文件不存在" }
|
||||
log("[LLM] 找不到模型文件")
|
||||
return
|
||||
}
|
||||
|
||||
// 如果已有引擎，先销毁
|
||||
cleanupSession()
|
||||
if let eng = engine {
|
||||
litert_lm_engine_delete(eng)
|
||||
engine = nil
|
||||
}
|
||||
|
||||
currentBackend = backend
|
||||
log("[LLM] 开始加载: \(path) (backend: \(backend))")
|
||||
let startTime = CFAbsoluteTimeGetCurrent()
|
||||
|
||||
let cPath = strdup(path)!
|
||||
let cBackend = strdup(backend)!
|
||||
let settings = litert_lm_engine_settings_create(cPath, cBackend, nil, nil)
|
||||
free(cPath)
|
||||
free(cBackend)
|
||||
|
||||
guard let settings = settings else {
|
||||
DispatchQueue.main.async { self.statusMessage = "❌ 创建引擎设置失败" }
|
||||
return
|
||||
}
|
||||
|
||||
litert_lm_engine_settings_set_max_num_tokens(settings, 4096)
|
||||
|
||||
let cacheDir = FileManager.default.urls(for: .cachesDirectory, in: .userDomainMask).first!.path
|
||||
let cCacheDir = strdup(cacheDir)!
|
||||
litert_lm_engine_settings_set_cache_dir(settings, cCacheDir)
|
||||
free(cCacheDir)
|
||||
|
||||
let eng = litert_lm_engine_create(settings)
|
||||
litert_lm_engine_settings_delete(settings)
|
||||
|
||||
if let eng = eng {
|
||||
self.engine = eng
|
||||
let elapsed = CFAbsoluteTimeGetCurrent() - startTime
|
||||
DispatchQueue.main.async {
|
||||
self.isLoaded = true
|
||||
self.statusMessage = "模型已就绪 ✅"
|
||||
}
|
||||
log("[LLM] Engine 创建成功，耗时: \(String(format: "%.1f", elapsed))秒")
|
||||
} else {
|
||||
DispatchQueue.main.async { self.statusMessage = "❌ Engine 创建失败" }
|
||||
log("[LLM] Engine 创建失败")
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - Session API 流式生成
|
||||
|
||||
/// 采样参数（每次 generate 前由 AgentEngine 设置）
|
||||
var samplingTopK: Int32 = 64
|
||||
var samplingTopP: Float = 0.95
|
||||
var samplingTemperature: Float = 1.0
|
||||
var maxOutputTokens: Int = 4000
|
||||
|
||||
func generateStream(
|
||||
prompt: String,
|
||||
onToken: @escaping (String) -> Void,
|
||||
onComplete: @escaping (Result<String, Error>) -> Void
|
||||
) {
|
||||
guard let engine = engine else {
|
||||
onComplete(.failure(LLMError.notLoaded))
|
||||
return
|
||||
}
|
||||
|
||||
DispatchQueue.main.async { self.isGenerating = true }
|
||||
|
||||
DispatchQueue.global(qos: .userInitiated).async { [weak self] in
|
||||
guard let self = self else { return }
|
||||
|
||||
// ★ 清理上一个 session
|
||||
self.cleanupSession()
|
||||
|
||||
// 创建 session config（应用采样参数）
|
||||
let sessionConfig = litert_lm_session_config_create()
|
||||
if let cfg = sessionConfig {
|
||||
litert_lm_session_config_set_max_output_tokens(cfg, Int32(self.maxOutputTokens))
|
||||
var params = LiteRtLmSamplerParams()
|
||||
params.top_k = self.samplingTopK
|
||||
params.top_p = self.samplingTopP
|
||||
params.temperature = self.samplingTemperature
|
||||
params.seed = 0
|
||||
litert_lm_session_config_set_sampler_params(cfg, ¶ms)
|
||||
log("[LLM] Session config: topK=\(params.top_k) topP=\(params.top_p) temp=\(params.temperature) maxTokens=\(self.maxOutputTokens)")
|
||||
}
|
||||
|
||||
// 创建新 session（传入 config）
|
||||
let session = litert_lm_engine_create_session(engine, sessionConfig)
|
||||
if let cfg = sessionConfig { litert_lm_session_config_delete(cfg) }
|
||||
guard let session = session else {
|
||||
DispatchQueue.main.async {
|
||||
self.isGenerating = false
|
||||
onComplete(.failure(LLMError.sessionFailed))
|
||||
}
|
||||
log("[LLM] Session 创建失败")
|
||||
return
|
||||
}
|
||||
|
||||
self.sessionLock.lock()
|
||||
self.activeSession = session
|
||||
self.sessionLock.unlock()
|
||||
log("[LLM] Session 创建成功")
|
||||
|
||||
// 构建 InputData（原始文本）
|
||||
let cPrompt = strdup(prompt)!
|
||||
var input = InputData()
|
||||
input.type = kInputText
|
||||
input.data = UnsafeRawPointer(cPrompt)
|
||||
input.size = strlen(cPrompt)
|
||||
|
||||
// 创建流式回调上下文
|
||||
let context = StreamContext(onToken: onToken, onComplete: { [weak self] result in
|
||||
DispatchQueue.main.async {
|
||||
self?.isGenerating = false
|
||||
onComplete(result)
|
||||
}
|
||||
// ★ 不在回调中删除 session，由下次 generateStream 清理
|
||||
})
|
||||
let contextPtr = Unmanaged.passRetained(context).toOpaque()
|
||||
|
||||
let result = litert_lm_session_generate_content_stream(
|
||||
session,
|
||||
&input,
|
||||
1,
|
||||
{ callbackData, chunk, isFinal, errorMsg in
|
||||
guard let ctx = callbackData else { return }
|
||||
let context = Unmanaged<StreamContext>.fromOpaque(ctx).takeUnretainedValue()
|
||||
|
||||
if let err = errorMsg {
|
||||
let errStr = String(cString: err)
|
||||
context.finish(with: .failure(LLMError.inferenceError(errStr)))
|
||||
Unmanaged<StreamContext>.fromOpaque(ctx).release()
|
||||
return
|
||||
}
|
||||
|
||||
if let chunk = chunk {
|
||||
let token = String(cString: chunk)
|
||||
// Session API 返回纯文本 token（不是 JSON）
|
||||
context.fullResponse += token
|
||||
DispatchQueue.main.async {
|
||||
context.onToken(token)
|
||||
}
|
||||
}
|
||||
|
||||
if isFinal {
|
||||
context.finish(with: .success(context.fullResponse))
|
||||
Unmanaged<StreamContext>.fromOpaque(ctx).release()
|
||||
}
|
||||
},
|
||||
contextPtr
|
||||
)
|
||||
|
||||
free(cPrompt)
|
||||
|
||||
if result != 0 {
|
||||
Unmanaged<StreamContext>.fromOpaque(contextPtr).release()
|
||||
DispatchQueue.main.async {
|
||||
self.isGenerating = false
|
||||
onComplete(.failure(LLMError.predictFailed))
|
||||
}
|
||||
log("[LLM] 流式生成启动失败, code=\(result)")
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - Helpers
|
||||
|
||||
private func findModelPath() -> String? {
|
||||
let candidates = [
|
||||
Bundle.main.path(forResource: Self.modelFileName, ofType: Self.modelFileExtension),
|
||||
"/Users/zxw/AITOOL/phoneclaw/Models/\(Self.modelFileName).\(Self.modelFileExtension)",
|
||||
]
|
||||
for path in candidates {
|
||||
if let p = path, FileManager.default.fileExists(atPath: p) {
|
||||
return p
|
||||
}
|
||||
}
|
||||
let docs = FileManager.default.urls(for: .documentDirectory, in: .userDomainMask).first!
|
||||
let docFile = docs.appendingPathComponent("\(Self.modelFileName).\(Self.modelFileExtension)")
|
||||
if FileManager.default.fileExists(atPath: docFile.path) {
|
||||
return docFile.path
|
||||
}
|
||||
return nil
|
||||
}
|
||||
|
||||
static func jsonEscape(_ str: String) -> String {
|
||||
let data = try! JSONSerialization.data(withJSONObject: [str])
|
||||
let arrayStr = String(data: data, encoding: .utf8)!
|
||||
let start = arrayStr.index(after: arrayStr.startIndex)
|
||||
let end = arrayStr.index(before: arrayStr.endIndex)
|
||||
return String(arrayStr[start..<end])
|
||||
}
|
||||
|
||||
enum LLMError: LocalizedError {
|
||||
case notLoaded, sessionFailed, addChunkFailed, predictFailed
|
||||
case inferenceError(String)
|
||||
var errorDescription: String? {
|
||||
switch self {
|
||||
case .notLoaded: return "模型尚未加载"
|
||||
case .sessionFailed: return "Session 创建失败"
|
||||
case .addChunkFailed: return "Prompt 添加失败"
|
||||
case .predictFailed: return "推理失败"
|
||||
case .inferenceError(let msg): return "推理错误: \(msg)"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - Stream Context
|
||||
|
||||
private class StreamContext {
|
||||
var fullResponse = ""
|
||||
var completed = false
|
||||
let onToken: (String) -> Void
|
||||
let onComplete: (Result<String, Error>) -> Void
|
||||
|
||||
init(onToken: @escaping (String) -> Void, onComplete: @escaping (Result<String, Error>) -> Void) {
|
||||
self.onToken = onToken
|
||||
self.onComplete = onComplete
|
||||
}
|
||||
|
||||
func finish(with result: Result<String, Error>) {
|
||||
guard !completed else { return }
|
||||
completed = true
|
||||
onComplete(result)
|
||||
}
|
||||
}
|
||||
@@ -1,117 +0,0 @@
|
||||
import Foundation
|
||||
|
||||
// MARK: - Prompt 构造器（Gemma 4 对话模板 + Function Calling）
|
||||
//
|
||||
// Gemma 4 E4B 使用新 token 格式：
|
||||
// <|turn>system\n ... <turn|>
|
||||
// <|turn>user\n ... <turn|>
|
||||
// <|turn>model\n ... <turn|>
|
||||
|
||||
struct PromptBuilder {
|
||||
|
||||
static let defaultSystemPrompt = "你是 PhoneClaw，一个运行在本地的私人 AI 助手。你完全运行在设备上，不联网。"
|
||||
|
||||
/// 构造完整 Prompt（包含工具定义 + 对话历史）
|
||||
static func build(
|
||||
userMessage: String,
|
||||
tools: [SkillInfo],
|
||||
history: [ChatMessage] = [],
|
||||
systemPrompt: String? = nil
|
||||
) -> String {
|
||||
var prompt = "<|turn>system\n"
|
||||
|
||||
// ★ 使用自定义 system prompt（如果有），否则用默认
|
||||
let basePrompt = systemPrompt ?? defaultSystemPrompt
|
||||
|
||||
// 构建 Skill 概要列表（只列名称 + 一句话描述，不暴露 Tool）
|
||||
var skillListText = ""
|
||||
for skill in tools {
|
||||
skillListText += "- **\(skill.name)**: \(skill.description)\n"
|
||||
}
|
||||
|
||||
// 处理 ___SKILLS___ 占位符
|
||||
if basePrompt.contains("___SKILLS___") {
|
||||
prompt += basePrompt.replacingOccurrences(of: "___SKILLS___", with: skillListText)
|
||||
} else {
|
||||
prompt += basePrompt
|
||||
if !tools.isEmpty {
|
||||
prompt += "\n\n你拥有以下能力（Skill）：\n\n" + skillListText
|
||||
prompt += "\n当用户的请求需要使用某个能力时，先调用 load_skill：\n<tool_call>\n{\"name\": \"load_skill\", \"arguments\": {\"skill\": \"能力名\"}}\n</tool_call>\n\n不需要能力时直接回复。用中文回答，简洁实用。"
|
||||
}
|
||||
}
|
||||
|
||||
prompt += "\n<turn|>\n"
|
||||
|
||||
// 对话历史（最近几轮，排除最后一条当前用户消息，避免重复）
|
||||
let recentHistory = history.suffix(12)
|
||||
for msg in recentHistory {
|
||||
// ★ 跳过最后一条 user 消息（等下面单独加）
|
||||
if msg.role == .user && msg.id == recentHistory.last?.id { continue }
|
||||
switch msg.role {
|
||||
case .user:
|
||||
prompt += "<|turn>user\n\(msg.content)<turn|>\n"
|
||||
case .assistant:
|
||||
prompt += "<|turn>model\n\(msg.content)<turn|>\n"
|
||||
case .system:
|
||||
if let skillName = msg.skillName {
|
||||
prompt += "<|turn>model\n<tool_call>\n{\"name\": \"\(skillName)\", \"arguments\": {}}\n</tool_call><turn|>\n"
|
||||
}
|
||||
case .skillResult:
|
||||
let skillLabel = msg.skillName ?? "tool"
|
||||
prompt += "<|turn>user\n工具 \(skillLabel) 的执行结果：\(msg.content)<turn|>\n"
|
||||
}
|
||||
}
|
||||
|
||||
// 当前用户消息
|
||||
prompt += "<|turn>user\n\(userMessage)<turn|>\n"
|
||||
prompt += "<|turn>model\n"
|
||||
|
||||
return prompt
|
||||
}
|
||||
|
||||
/// 构造工具/Skill 结果后的 follow-up prompt
|
||||
///
|
||||
/// - isLoadSkill=true: load_skill 返回了 instructions，LLM 应按指令继续执行
|
||||
/// - isLoadSkill=false: Tool 执行完毕，LLM 应根据结果回答用户
|
||||
static func buildFollowUp(
|
||||
originalPrompt: String,
|
||||
modelResponse: String,
|
||||
skillName: String,
|
||||
skillResult: String,
|
||||
userQuestion: String,
|
||||
isLoadSkill: Bool = false
|
||||
) -> String {
|
||||
var cleanedResponse = modelResponse
|
||||
for pat in ["<turn|>", "<end_of_turn>", "<eos>"] {
|
||||
cleanedResponse = cleanedResponse.replacingOccurrences(of: pat, with: "")
|
||||
}
|
||||
|
||||
if isLoadSkill {
|
||||
// load_skill 返回的是 Skill instructions → 让 LLM 按指令继续
|
||||
return originalPrompt + cleanedResponse + """
|
||||
<turn|>
|
||||
<|turn>user
|
||||
Skill 指令已加载：
|
||||
\(skillResult)
|
||||
|
||||
请根据以上指令，调用对应的工具来完成用户的请求："\(userQuestion)"
|
||||
<turn|>
|
||||
<|turn>model
|
||||
|
||||
"""
|
||||
} else {
|
||||
// Tool 执行结果 → 让 LLM 生成自然语言回复
|
||||
return originalPrompt + cleanedResponse + """
|
||||
<turn|>
|
||||
<|turn>user
|
||||
工具 \(skillName) 执行结果：
|
||||
\(skillResult)
|
||||
|
||||
请根据以上结果简洁回答我的问题："\(userQuestion)"
|
||||
<turn|>
|
||||
<|turn>model
|
||||
|
||||
"""
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,363 +0,0 @@
|
||||
import Foundation
|
||||
import Yams
|
||||
|
||||
// MARK: - SKILL.md 解析器 + 加载器
|
||||
//
|
||||
// 参考 Vera 项目的 skill_loader.py，实现 Swift 版。
|
||||
// 渐进式加载：
|
||||
// 1. 启动时：只加载 YAML frontmatter（元数据）
|
||||
// 2. load_skill 时：加载完整 body（指令体）
|
||||
|
||||
// MARK: - 数据模型
|
||||
|
||||
struct SkillExample {
|
||||
let query: String
|
||||
let scenario: String
|
||||
}
|
||||
|
||||
struct SkillMetadata {
|
||||
let id: String // 目录名 "clipboard"
|
||||
let name: String // "剪贴板"
|
||||
let description: String
|
||||
let version: String
|
||||
let icon: String
|
||||
let disabled: Bool
|
||||
let triggers: [String]
|
||||
let allowedTools: [String]
|
||||
let examples: [SkillExample]
|
||||
}
|
||||
|
||||
struct SkillDefinition: Identifiable {
|
||||
let id: String
|
||||
let filePath: URL
|
||||
let metadata: SkillMetadata
|
||||
var body: String? // Markdown body（懒加载）
|
||||
var isEnabled: Bool
|
||||
|
||||
/// 完整的 SKILL.md 原始内容
|
||||
var rawContent: String? {
|
||||
try? String(contentsOf: filePath, encoding: .utf8)
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - Skill Loader
|
||||
|
||||
class SkillLoader {
|
||||
|
||||
let skillsDirectory: URL
|
||||
private var cache: [String: SkillDefinition] = [:]
|
||||
|
||||
init() {
|
||||
let appSupport = FileManager.default.urls(for: .applicationSupportDirectory, in: .userDomainMask).first!
|
||||
self.skillsDirectory = appSupport.appendingPathComponent("PhoneClaw/skills", isDirectory: true)
|
||||
ensureDefaultSkills()
|
||||
}
|
||||
|
||||
// MARK: - 公开接口
|
||||
|
||||
/// 发现并加载所有 Skill 的元数据
|
||||
func discoverSkills() -> [SkillDefinition] {
|
||||
cache.removeAll()
|
||||
guard let items = try? FileManager.default.contentsOfDirectory(
|
||||
at: skillsDirectory,
|
||||
includingPropertiesForKeys: [.isDirectoryKey],
|
||||
options: .skipsHiddenFiles
|
||||
) else { return [] }
|
||||
|
||||
var results: [SkillDefinition] = []
|
||||
for item in items.sorted(by: { $0.lastPathComponent < $1.lastPathComponent }) {
|
||||
var isDir: ObjCBool = false
|
||||
FileManager.default.fileExists(atPath: item.path, isDirectory: &isDir)
|
||||
guard isDir.boolValue else { continue }
|
||||
|
||||
let skillFile = item.appendingPathComponent("SKILL.md")
|
||||
guard FileManager.default.fileExists(atPath: skillFile.path) else { continue }
|
||||
|
||||
let skillId = item.lastPathComponent
|
||||
if let def = loadDefinition(skillId: skillId, file: skillFile) {
|
||||
cache[skillId] = def
|
||||
results.append(def)
|
||||
}
|
||||
}
|
||||
return results
|
||||
}
|
||||
|
||||
/// 完整加载 Skill（包括 body）— load_skill 时调用
|
||||
func loadBody(skillId: String) -> String? {
|
||||
if let cached = cache[skillId], cached.body != nil {
|
||||
return cached.body
|
||||
}
|
||||
let skillFile = skillsDirectory
|
||||
.appendingPathComponent(skillId, isDirectory: true)
|
||||
.appendingPathComponent("SKILL.md")
|
||||
|
||||
guard let content = try? String(contentsOf: skillFile, encoding: .utf8) else { return nil }
|
||||
let body = parseBody(content)
|
||||
cache[skillId]?.body = body
|
||||
return body
|
||||
}
|
||||
|
||||
/// 保存 SKILL.md（编辑后写回）
|
||||
func saveSkill(skillId: String, content: String) throws {
|
||||
let skillFile = skillsDirectory
|
||||
.appendingPathComponent(skillId, isDirectory: true)
|
||||
.appendingPathComponent("SKILL.md")
|
||||
try content.write(to: skillFile, atomically: true, encoding: .utf8)
|
||||
// 清缓存，下次重新解析
|
||||
cache.removeValue(forKey: skillId)
|
||||
}
|
||||
|
||||
/// 重新加载所有（热更新入口）
|
||||
func reloadAll() -> [SkillDefinition] {
|
||||
return discoverSkills()
|
||||
}
|
||||
|
||||
/// 根据工具名反查 Skill ID
|
||||
func findSkillId(forTool toolName: String) -> String? {
|
||||
for (id, def) in cache {
|
||||
if def.metadata.allowedTools.contains(toolName) {
|
||||
return id
|
||||
}
|
||||
}
|
||||
return nil
|
||||
}
|
||||
|
||||
/// 获取缓存的 SkillDefinition
|
||||
func getDefinition(_ skillId: String) -> SkillDefinition? {
|
||||
cache[skillId]
|
||||
}
|
||||
|
||||
/// 更新启用状态
|
||||
func setEnabled(_ skillId: String, enabled: Bool) {
|
||||
cache[skillId]?.isEnabled = enabled
|
||||
}
|
||||
|
||||
// MARK: - 解析
|
||||
|
||||
private func loadDefinition(skillId: String, file: URL) -> SkillDefinition? {
|
||||
guard let content = try? String(contentsOf: file, encoding: .utf8) else { return nil }
|
||||
guard let frontmatter = parseFrontmatter(content) else { return nil }
|
||||
|
||||
let metadata = SkillMetadata(
|
||||
id: skillId,
|
||||
name: frontmatter["name"] as? String ?? skillId,
|
||||
description: frontmatter["description"] as? String ?? "",
|
||||
version: frontmatter["version"] as? String ?? "1.0.0",
|
||||
icon: frontmatter["icon"] as? String ?? "wrench",
|
||||
disabled: frontmatter["disabled"] as? Bool ?? false,
|
||||
triggers: frontmatter["triggers"] as? [String] ?? [],
|
||||
allowedTools: frontmatter["allowed-tools"] as? [String] ?? [],
|
||||
examples: parseExamples(frontmatter["examples"])
|
||||
)
|
||||
|
||||
return SkillDefinition(
|
||||
id: skillId,
|
||||
filePath: file,
|
||||
metadata: metadata,
|
||||
body: nil, // 懒加载
|
||||
isEnabled: !metadata.disabled
|
||||
)
|
||||
}
|
||||
|
||||
private func parseFrontmatter(_ content: String) -> [String: Any]? {
|
||||
// 匹配 --- ... --- 包裹的 YAML
|
||||
guard let regex = try? NSRegularExpression(
|
||||
pattern: "^---\\s*\\n(.*?)\\n---\\s*\\n",
|
||||
options: .dotMatchesLineSeparators
|
||||
) else { return nil }
|
||||
|
||||
let range = NSRange(content.startIndex..., in: content)
|
||||
guard let match = regex.firstMatch(in: content, range: range),
|
||||
let yamlRange = Range(match.range(at: 1), in: content) else { return nil }
|
||||
|
||||
let yamlString = String(content[yamlRange])
|
||||
// 用 Yams 解析
|
||||
guard let parsed = try? Yams.load(yaml: yamlString) as? [String: Any] else { return nil }
|
||||
return parsed
|
||||
}
|
||||
|
||||
private func parseBody(_ content: String) -> String {
|
||||
guard let regex = try? NSRegularExpression(
|
||||
pattern: "^---\\s*\\n.*?\\n---\\s*\\n(.*)$",
|
||||
options: .dotMatchesLineSeparators
|
||||
) else { return content }
|
||||
|
||||
let range = NSRange(content.startIndex..., in: content)
|
||||
if let match = regex.firstMatch(in: content, range: range),
|
||||
let bodyRange = Range(match.range(at: 1), in: content) {
|
||||
return String(content[bodyRange]).trimmingCharacters(in: .whitespacesAndNewlines)
|
||||
}
|
||||
return content.trimmingCharacters(in: .whitespacesAndNewlines)
|
||||
}
|
||||
|
||||
private func parseExamples(_ raw: Any?) -> [SkillExample] {
|
||||
guard let list = raw as? [[String: Any]] else { return [] }
|
||||
return list.compactMap { dict in
|
||||
guard let query = dict["query"] as? String,
|
||||
let scenario = dict["scenario"] as? String else { return nil }
|
||||
return SkillExample(query: query, scenario: scenario)
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - 首次启动：写入默认 Skill
|
||||
|
||||
private func ensureDefaultSkills() {
|
||||
let fm = FileManager.default
|
||||
if !fm.fileExists(atPath: skillsDirectory.path) {
|
||||
try? fm.createDirectory(at: skillsDirectory, withIntermediateDirectories: true)
|
||||
}
|
||||
|
||||
for (dirName, content) in Self.defaultSkills {
|
||||
let dir = skillsDirectory.appendingPathComponent(dirName, isDirectory: true)
|
||||
let file = dir.appendingPathComponent("SKILL.md")
|
||||
if !fm.fileExists(atPath: file.path) {
|
||||
try? fm.createDirectory(at: dir, withIntermediateDirectories: true)
|
||||
try? content.write(to: file, atomically: true, encoding: .utf8)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - 内置默认 SKILL.md
|
||||
|
||||
static let defaultSkills: [(String, String)] = [
|
||||
("clipboard", """
|
||||
---
|
||||
name: Clipboard
|
||||
description: '读写系统剪贴板内容。当用户需要读取、复制或操作剪贴板时使用。'
|
||||
version: "1.0.0"
|
||||
icon: doc.on.clipboard
|
||||
disabled: false
|
||||
|
||||
triggers:
|
||||
- 剪贴板
|
||||
- 粘贴
|
||||
- 复制
|
||||
- clipboard
|
||||
|
||||
allowed-tools:
|
||||
- clipboard-read
|
||||
- clipboard-write
|
||||
|
||||
examples:
|
||||
- query: "读取我的剪贴板内容"
|
||||
scenario: "读取剪贴板"
|
||||
- query: "把这段文字复制到剪贴板"
|
||||
scenario: "写入剪贴板"
|
||||
---
|
||||
|
||||
# 剪贴板操作
|
||||
|
||||
你负责帮助用户读写系统剪贴板。
|
||||
|
||||
## 可用工具
|
||||
|
||||
- **clipboard-read**: 读取剪贴板当前内容（无参数）
|
||||
- **clipboard-write**: 将文本写入剪贴板（参数: text — 要复制的文本）
|
||||
|
||||
## 执行流程
|
||||
|
||||
1. 用户要求读取 → 调用 `clipboard-read`
|
||||
2. 用户要求复制/写入 → 调用 `clipboard-write`，传入 text 参数
|
||||
3. 根据工具返回结果，简洁回答用户
|
||||
|
||||
## 调用格式
|
||||
|
||||
<tool_call>
|
||||
{"name": "工具名", "arguments": {}}
|
||||
</tool_call>
|
||||
"""),
|
||||
|
||||
("device", """
|
||||
---
|
||||
name: Device
|
||||
description: '获取设备硬件和系统信息。当用户询问电脑配置、系统版本时使用。'
|
||||
version: "1.0.0"
|
||||
icon: desktopcomputer
|
||||
disabled: false
|
||||
|
||||
triggers:
|
||||
- 设备
|
||||
- 电脑
|
||||
- 系统信息
|
||||
- 配置
|
||||
- 硬件
|
||||
|
||||
allowed-tools:
|
||||
- device-info
|
||||
|
||||
examples:
|
||||
- query: "我的电脑是什么配置"
|
||||
scenario: "查看设备信息"
|
||||
- query: "系统版本是多少"
|
||||
scenario: "查看系统版本"
|
||||
---
|
||||
|
||||
# 设备信息查询
|
||||
|
||||
你负责帮助用户查看设备的硬件和系统信息。
|
||||
|
||||
## 可用工具
|
||||
|
||||
- **device-info**: 获取设备型号、系统版本、内存、处理器信息（无参数）
|
||||
|
||||
## 执行流程
|
||||
|
||||
1. 调用 `device-info` 获取设备信息
|
||||
2. 将返回的 JSON 转换为用户友好的描述
|
||||
|
||||
## 调用格式
|
||||
|
||||
<tool_call>
|
||||
{"name": "device-info", "arguments": {}}
|
||||
</tool_call>
|
||||
"""),
|
||||
|
||||
("text", """
|
||||
---
|
||||
name: Text
|
||||
description: '文本处理工具：哈希计算、翻转等。当用户需要对文本进行处理或转换时使用。'
|
||||
version: "1.0.0"
|
||||
icon: textformat
|
||||
disabled: false
|
||||
|
||||
triggers:
|
||||
- 哈希
|
||||
- hash
|
||||
- 翻转
|
||||
- 反转
|
||||
- 文本处理
|
||||
|
||||
allowed-tools:
|
||||
- calculate-hash
|
||||
- text-reverse
|
||||
|
||||
examples:
|
||||
- query: "计算 Hello World 的哈希值"
|
||||
scenario: "哈希计算"
|
||||
- query: "把这段文字翻转过来"
|
||||
scenario: "文本翻转"
|
||||
---
|
||||
|
||||
# 文本处理
|
||||
|
||||
你负责帮助用户进行文本处理操作。
|
||||
|
||||
## 可用工具
|
||||
|
||||
- **calculate-hash**: 计算文本的哈希值（参数: text — 要计算哈希的文本）
|
||||
- **text-reverse**: 翻转文本（参数: text — 要翻转的文本）
|
||||
|
||||
## 执行流程
|
||||
|
||||
1. 判断用户需要哪种文本操作
|
||||
2. 调用对应工具，传入 text 参数
|
||||
3. 返回处理结果
|
||||
|
||||
## 调用格式
|
||||
|
||||
<tool_call>
|
||||
{"name": "工具名", "arguments": {"text": "要处理的文本"}}
|
||||
</tool_call>
|
||||
"""),
|
||||
]
|
||||
}
|
||||
@@ -1,54 +0,0 @@
|
||||
import Foundation
|
||||
|
||||
// MARK: - Skill 数据模型
|
||||
//
|
||||
// 文件驱动架构：
|
||||
// - SKILL.md 定义 Skill 元数据 + 指令体（热更新）
|
||||
// - ToolRegistry.swift 注册原生工具实现（编译时）
|
||||
// - SkillLoader.swift 解析和加载 SKILL.md
|
||||
//
|
||||
// 以下仅为给 UI 和 PromptBuilder 使用的精简数据结构。
|
||||
|
||||
// MARK: - Skill 条目（给 UI 管理用）
|
||||
|
||||
struct ToolInfo: Equatable {
|
||||
let name: String
|
||||
let description: String
|
||||
let parameters: String
|
||||
}
|
||||
|
||||
struct SkillEntry: Identifiable {
|
||||
let id: String // skill directory name, e.g. "clipboard"
|
||||
var name: String // display name, e.g. "Clipboard"
|
||||
var description: String
|
||||
var icon: String
|
||||
var samplePrompt: String
|
||||
var tools: [ToolInfo] = []
|
||||
var isEnabled: Bool = true
|
||||
var filePath: URL? // SKILL.md 路径（用于编辑）
|
||||
|
||||
/// 从 SkillDefinition 转换
|
||||
init(from def: SkillDefinition, registry: ToolRegistry) {
|
||||
self.id = def.id
|
||||
self.name = def.metadata.name
|
||||
self.description = def.metadata.description
|
||||
self.icon = def.metadata.icon
|
||||
self.samplePrompt = def.metadata.examples.first?.query ?? ""
|
||||
self.isEnabled = def.isEnabled
|
||||
self.filePath = def.filePath
|
||||
self.tools = def.metadata.allowedTools.compactMap { toolName in
|
||||
guard let tool = registry.find(name: toolName) else { return nil }
|
||||
return ToolInfo(name: tool.name, description: tool.description, parameters: tool.parameters)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - SkillInfo（给 PromptBuilder 用的精简描述）
|
||||
|
||||
struct SkillInfo {
|
||||
let name: String // skill id, e.g. "clipboard"
|
||||
let description: String
|
||||
var displayName: String = ""
|
||||
var icon: String = "wrench"
|
||||
var samplePrompt: String = ""
|
||||
}
|
||||
@@ -1,144 +0,0 @@
|
||||
import AppKit
|
||||
import Foundation
|
||||
|
||||
// MARK: - 原生工具注册表
|
||||
//
|
||||
// 所有原生 API 封装集中注册在这里。
|
||||
// SKILL.md 通过 allowed-tools 字段引用工具名。
|
||||
|
||||
struct RegisteredTool {
|
||||
let name: String
|
||||
let description: String
|
||||
let parameters: String
|
||||
let execute: ([String: Any]) async throws -> String
|
||||
}
|
||||
|
||||
class ToolRegistry {
|
||||
static let shared = ToolRegistry()
|
||||
|
||||
private var tools: [String: RegisteredTool] = [:]
|
||||
|
||||
init() {
|
||||
registerBuiltInTools()
|
||||
}
|
||||
|
||||
// MARK: - 公开接口
|
||||
|
||||
func register(_ tool: RegisteredTool) {
|
||||
tools[tool.name] = tool
|
||||
}
|
||||
|
||||
func find(name: String) -> RegisteredTool? {
|
||||
tools[name]
|
||||
}
|
||||
|
||||
func execute(name: String, args: [String: Any]) async throws -> String {
|
||||
guard let tool = tools[name] else {
|
||||
return "{\"success\": false, \"error\": \"未知工具: \(name)\"}"
|
||||
}
|
||||
return try await tool.execute(args)
|
||||
}
|
||||
|
||||
/// 根据名称列表获取工具（用于 SKILL.md 的 allowed-tools）
|
||||
func toolsFor(names: [String]) -> [RegisteredTool] {
|
||||
names.compactMap { tools[$0] }
|
||||
}
|
||||
|
||||
/// 根据工具名反查：它属于哪些 allowed-tools 列表
|
||||
/// 返回 true 如果该工具已注册
|
||||
func hasToolNamed(_ name: String) -> Bool {
|
||||
tools[name] != nil
|
||||
}
|
||||
|
||||
/// 所有已注册的工具名
|
||||
var allToolNames: [String] {
|
||||
Array(tools.keys).sorted()
|
||||
}
|
||||
|
||||
// MARK: - 内置工具注册
|
||||
|
||||
private func registerBuiltInTools() {
|
||||
// ── Clipboard ──
|
||||
register(RegisteredTool(
|
||||
name: "clipboard-read",
|
||||
description: "读取剪贴板当前内容",
|
||||
parameters: "无"
|
||||
) { _ in
|
||||
let content = NSPasteboard.general.string(forType: .string)
|
||||
if let raw = content, !raw.isEmpty {
|
||||
let text = raw.trimmingCharacters(in: .whitespacesAndNewlines)
|
||||
guard !text.isEmpty else { return "{\"success\": false, \"error\": \"剪贴板为空\"}" }
|
||||
return "{\"success\": true, \"type\": \"text\", \"content\": \"\(jsonEscape(String(text.prefix(500))))\", \"length\": \(text.count)}"
|
||||
}
|
||||
return "{\"success\": false, \"error\": \"剪贴板为空\"}"
|
||||
})
|
||||
|
||||
register(RegisteredTool(
|
||||
name: "clipboard-write",
|
||||
description: "将文本写入剪贴板",
|
||||
parameters: "text: 要复制的文本内容"
|
||||
) { args in
|
||||
guard let text = args["text"] as? String else {
|
||||
return "{\"success\": false, \"error\": \"缺少 text 参数\"}"
|
||||
}
|
||||
NSPasteboard.general.clearContents()
|
||||
NSPasteboard.general.setString(text, forType: .string)
|
||||
return "{\"success\": true, \"copied_length\": \(text.count)}"
|
||||
})
|
||||
|
||||
// ── Device ──
|
||||
register(RegisteredTool(
|
||||
name: "device-info",
|
||||
description: "获取设备型号、系统版本、内存、处理器信息",
|
||||
parameters: "无"
|
||||
) { _ in
|
||||
let info = ProcessInfo.processInfo
|
||||
let hostname = Host.current().localizedName ?? "Unknown"
|
||||
let osVersion = info.operatingSystemVersionString
|
||||
let physicalMemory = info.physicalMemory / (1024 * 1024 * 1024)
|
||||
let processorCount = info.processorCount
|
||||
|
||||
var size: size_t = 0
|
||||
sysctlbyname("hw.model", nil, &size, nil, 0)
|
||||
var model = [CChar](repeating: 0, count: size)
|
||||
sysctlbyname("hw.model", &model, &size, nil, 0)
|
||||
let modelStr = String(cString: model)
|
||||
|
||||
return "{\"success\": true, \"name\": \"\(hostname)\", \"model\": \"\(modelStr)\", \"system\": \"macOS \(osVersion)\", \"memory_gb\": \(physicalMemory), \"processors\": \(processorCount)}"
|
||||
})
|
||||
|
||||
// ── Text ──
|
||||
register(RegisteredTool(
|
||||
name: "calculate-hash",
|
||||
description: "计算文本的哈希值",
|
||||
parameters: "text: 要计算哈希的文本"
|
||||
) { args in
|
||||
guard let text = args["text"] as? String else {
|
||||
return "{\"success\": false, \"error\": \"缺少 text 参数\"}"
|
||||
}
|
||||
let hash = text.hashValue
|
||||
return "{\"success\": true, \"input\": \"\(jsonEscape(text))\", \"hash\": \(hash)}"
|
||||
})
|
||||
|
||||
register(RegisteredTool(
|
||||
name: "text-reverse",
|
||||
description: "翻转文本",
|
||||
parameters: "text: 要翻转的文本"
|
||||
) { args in
|
||||
guard let text = args["text"] as? String else {
|
||||
return "{\"success\": false, \"error\": \"缺少 text 参数\"}"
|
||||
}
|
||||
let reversed = String(text.reversed())
|
||||
return "{\"success\": true, \"original\": \"\(jsonEscape(text))\", \"reversed\": \"\(jsonEscape(reversed))\"}"
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - Helpers
|
||||
|
||||
func jsonEscape(_ str: String) -> String {
|
||||
str.replacingOccurrences(of: "\\", with: "\\\\")
|
||||
.replacingOccurrences(of: "\"", with: "\\\"")
|
||||
.replacingOccurrences(of: "\n", with: "\\n")
|
||||
.replacingOccurrences(of: "\t", with: "\\t")
|
||||
}
|
||||
@@ -1,101 +0,0 @@
|
||||
import SwiftUI
|
||||
|
||||
// MARK: - 显示模型（跨平台共享）
|
||||
|
||||
/// 单个 Skill 卡片数据
|
||||
struct SkillCard: Identifiable, Equatable {
|
||||
let id: UUID
|
||||
var skillName: String
|
||||
var skillStatus: String? // "identified", "loaded", "executing", "done"
|
||||
var toolName: String? // 正在执行的具体 Tool 名（如 "device-info"）
|
||||
}
|
||||
|
||||
/// AI 回复块：多张 skill 卡片 + 思考动画 + 回复文本
|
||||
struct ResponseBlock: Identifiable, Equatable {
|
||||
let id: UUID
|
||||
var skills: [SkillCard] = []
|
||||
var responseText: String?
|
||||
var isThinking: Bool
|
||||
}
|
||||
|
||||
/// 聊天列表的统一显示项
|
||||
enum DisplayItem: Identifiable {
|
||||
case user(ChatMessage)
|
||||
case response(ResponseBlock)
|
||||
|
||||
var id: UUID {
|
||||
switch self {
|
||||
case .user(let msg): return msg.id
|
||||
case .response(let block): return block.id
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - Messages → DisplayItems 转换（跨平台共享）
|
||||
|
||||
/// 用于纯思考占位的稳定 ID
|
||||
private let thinkingPlaceholderID = UUID()
|
||||
|
||||
func buildDisplayItems(from messages: [ChatMessage], isProcessing: Bool) -> [DisplayItem] {
|
||||
var items: [DisplayItem] = []
|
||||
var block: ResponseBlock? = nil
|
||||
|
||||
func flush() {
|
||||
if let b = block { items.append(.response(b)); block = nil }
|
||||
}
|
||||
|
||||
for msg in messages {
|
||||
switch msg.role {
|
||||
case .user:
|
||||
flush()
|
||||
items.append(.user(msg))
|
||||
case .system:
|
||||
if let name = msg.skillName {
|
||||
if block == nil { block = ResponseBlock(id: msg.id, isThinking: false) }
|
||||
|
||||
let content = msg.content
|
||||
// 查找已有的同名卡片 → 更新状态；否则新建
|
||||
if let idx = block?.skills.firstIndex(where: { $0.skillName == name }) {
|
||||
if content.hasPrefix("executing:") {
|
||||
block?.skills[idx].skillStatus = "executing"
|
||||
block?.skills[idx].toolName = String(content.dropFirst("executing:".count))
|
||||
} else {
|
||||
block?.skills[idx].skillStatus = content
|
||||
}
|
||||
} else {
|
||||
var card = SkillCard(id: msg.id, skillName: name)
|
||||
if content.hasPrefix("executing:") {
|
||||
card.skillStatus = "executing"
|
||||
card.toolName = String(content.dropFirst("executing:".count))
|
||||
} else {
|
||||
card.skillStatus = content
|
||||
}
|
||||
block?.skills.append(card)
|
||||
}
|
||||
}
|
||||
case .skillResult:
|
||||
break
|
||||
case .assistant:
|
||||
if block == nil { block = ResponseBlock(id: msg.id, isThinking: false) }
|
||||
if msg.content != "▍" && !msg.content.isEmpty {
|
||||
block?.responseText = msg.content
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if var b = block, !b.skills.isEmpty || b.responseText != nil {
|
||||
if isProcessing && !b.skills.isEmpty && b.responseText == nil {
|
||||
b.isThinking = true
|
||||
}
|
||||
items.append(.response(b))
|
||||
}
|
||||
|
||||
if isProcessing {
|
||||
let hasAI = block.map { !$0.skills.isEmpty || $0.responseText != nil } ?? false
|
||||
if !hasAI {
|
||||
items.append(.response(ResponseBlock(id: thinkingPlaceholderID, isThinking: true)))
|
||||
}
|
||||
}
|
||||
|
||||
return items
|
||||
}
|
||||
@@ -1,244 +0,0 @@
|
||||
import SwiftUI
|
||||
|
||||
// MARK: - Configurations 弹窗（macOS 版，适配 Theme 暖色系）
|
||||
|
||||
struct ConfigurationsView: View {
|
||||
@Bindable var engine: AgentEngine
|
||||
@Environment(\.dismiss) private var dismiss
|
||||
@State private var selectedTab = 0 // 0=Model Configs, 1=System Prompt
|
||||
|
||||
// 本地编辑状态（确认后才应用）
|
||||
@State private var maxTokens: Double = 4000
|
||||
@State private var topK: Double = 64
|
||||
@State private var topP: Double = 0.95
|
||||
@State private var temperature: Double = 1.0
|
||||
@State private var selectedBackend = 1 // 0=CPU, 1=GPU
|
||||
@State private var systemPrompt: String = ""
|
||||
|
||||
var body: some View {
|
||||
VStack(spacing: 0) {
|
||||
// 标题
|
||||
Text("Configurations")
|
||||
.font(.title2.weight(.bold))
|
||||
.foregroundStyle(Theme.textPrimary)
|
||||
.frame(maxWidth: .infinity, alignment: .leading)
|
||||
.padding()
|
||||
|
||||
// Tab 切换
|
||||
HStack(spacing: 0) {
|
||||
tabButton("Model Configs", tag: 0)
|
||||
tabButton("System Prompt", tag: 1)
|
||||
}
|
||||
.padding(.horizontal)
|
||||
|
||||
Rectangle().fill(Theme.border).frame(height: 1)
|
||||
|
||||
Group {
|
||||
if selectedTab == 0 {
|
||||
modelConfigsTab
|
||||
} else {
|
||||
systemPromptTab
|
||||
}
|
||||
}
|
||||
.frame(height: 360)
|
||||
|
||||
Rectangle().fill(Theme.border).frame(height: 1)
|
||||
|
||||
// 底部按钮
|
||||
HStack(spacing: 20) {
|
||||
Spacer()
|
||||
Button("Cancel") { dismiss() }
|
||||
.foregroundStyle(Theme.textSecondary)
|
||||
.buttonStyle(.plain)
|
||||
Button("OK") {
|
||||
applySettings()
|
||||
dismiss()
|
||||
}
|
||||
.font(.body.weight(.semibold))
|
||||
.foregroundStyle(Theme.accent)
|
||||
.padding(.horizontal, 24)
|
||||
.padding(.vertical, 8)
|
||||
.background(Theme.accent.opacity(0.15), in: Capsule())
|
||||
.buttonStyle(.plain)
|
||||
}
|
||||
.padding()
|
||||
}
|
||||
.frame(width: 420)
|
||||
.background(Theme.bgElevated)
|
||||
.clipShape(RoundedRectangle(cornerRadius: 16))
|
||||
.preferredColorScheme(.dark)
|
||||
.onAppear { loadCurrentSettings() }
|
||||
}
|
||||
|
||||
// MARK: - Tab 按钮
|
||||
|
||||
private func tabButton(_ title: String, tag: Int) -> some View {
|
||||
Button {
|
||||
withAnimation(.easeInOut(duration: 0.2)) { selectedTab = tag }
|
||||
} label: {
|
||||
VStack(spacing: 8) {
|
||||
Text(title)
|
||||
.font(.subheadline.weight(selectedTab == tag ? .semibold : .regular))
|
||||
.foregroundStyle(selectedTab == tag ? Theme.textPrimary : Theme.textTertiary)
|
||||
|
||||
Rectangle()
|
||||
.fill(selectedTab == tag ? Theme.accent : .clear)
|
||||
.frame(height: 2)
|
||||
}
|
||||
}
|
||||
.buttonStyle(.plain)
|
||||
.frame(maxWidth: .infinity)
|
||||
}
|
||||
|
||||
// MARK: - Model Configs
|
||||
|
||||
private var modelConfigsTab: some View {
|
||||
ScrollView {
|
||||
VStack(alignment: .leading, spacing: 20) {
|
||||
configSlider(
|
||||
title: "Max Tokens",
|
||||
value: $maxTokens,
|
||||
range: 128...8192,
|
||||
displayValue: "\(Int(maxTokens))"
|
||||
)
|
||||
configSlider(
|
||||
title: "TopK",
|
||||
value: $topK,
|
||||
range: 1...128,
|
||||
displayValue: "\(Int(topK))"
|
||||
)
|
||||
configSlider(
|
||||
title: "TopP",
|
||||
value: $topP,
|
||||
range: 0...1,
|
||||
displayValue: String(format: "%.2f", topP)
|
||||
)
|
||||
configSlider(
|
||||
title: "Temperature",
|
||||
value: $temperature,
|
||||
range: 0...2,
|
||||
displayValue: String(format: "%.2f", temperature)
|
||||
)
|
||||
|
||||
// Accelerator
|
||||
VStack(alignment: .leading, spacing: 8) {
|
||||
Text("Choose accelerator")
|
||||
.font(.subheadline)
|
||||
.foregroundStyle(Theme.textSecondary)
|
||||
|
||||
HStack(spacing: 0) {
|
||||
acceleratorButton("CPU", tag: 0)
|
||||
acceleratorButton("GPU", tag: 1)
|
||||
}
|
||||
.background(Theme.bg, in: RoundedRectangle(cornerRadius: 8))
|
||||
}
|
||||
}
|
||||
.padding()
|
||||
}
|
||||
.frame(maxHeight: 360)
|
||||
}
|
||||
|
||||
// MARK: - System Prompt
|
||||
|
||||
private var systemPromptTab: some View {
|
||||
VStack(alignment: .leading, spacing: 12) {
|
||||
TextEditor(text: $systemPrompt)
|
||||
.font(.system(size: 13))
|
||||
.foregroundStyle(Theme.textPrimary)
|
||||
.scrollContentBackground(.hidden)
|
||||
.padding(12)
|
||||
.background(Theme.bg, in: RoundedRectangle(cornerRadius: 8))
|
||||
.overlay(
|
||||
RoundedRectangle(cornerRadius: 8)
|
||||
.strokeBorder(Theme.border, lineWidth: 1)
|
||||
)
|
||||
.frame(maxHeight: 280)
|
||||
|
||||
Button("Restore default") {
|
||||
systemPrompt = engine.defaultSystemPrompt
|
||||
}
|
||||
.font(.subheadline)
|
||||
.foregroundStyle(Theme.accent)
|
||||
.buttonStyle(.plain)
|
||||
}
|
||||
.padding()
|
||||
}
|
||||
|
||||
|
||||
|
||||
// MARK: - 配置 Slider
|
||||
|
||||
private func configSlider(
|
||||
title: String,
|
||||
value: Binding<Double>,
|
||||
range: ClosedRange<Double>,
|
||||
displayValue: String
|
||||
) -> some View {
|
||||
VStack(alignment: .leading, spacing: 6) {
|
||||
Text(title)
|
||||
.font(.subheadline)
|
||||
.foregroundStyle(Theme.textSecondary)
|
||||
|
||||
HStack(spacing: 12) {
|
||||
Slider(value: value, in: range)
|
||||
.tint(Theme.accent)
|
||||
|
||||
Text(displayValue)
|
||||
.font(.body.monospaced())
|
||||
.foregroundStyle(Theme.textPrimary.opacity(0.8))
|
||||
.frame(width: 56)
|
||||
.padding(.horizontal, 8)
|
||||
.padding(.vertical, 5)
|
||||
.background(Theme.bg, in: RoundedRectangle(cornerRadius: 6))
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - Accelerator 按钮
|
||||
|
||||
private func acceleratorButton(_ title: String, tag: Int) -> some View {
|
||||
Button {
|
||||
selectedBackend = tag
|
||||
} label: {
|
||||
Text(title)
|
||||
.font(.subheadline.weight(.medium))
|
||||
.foregroundStyle(Theme.textPrimary)
|
||||
.frame(maxWidth: .infinity)
|
||||
.padding(.vertical, 8)
|
||||
.background(
|
||||
selectedBackend == tag ? Theme.bgHover : .clear,
|
||||
in: RoundedRectangle(cornerRadius: 6)
|
||||
)
|
||||
}
|
||||
.buttonStyle(.plain)
|
||||
}
|
||||
|
||||
// MARK: - 加载 / 应用
|
||||
|
||||
private func loadCurrentSettings() {
|
||||
maxTokens = Double(engine.config.maxTokens)
|
||||
topK = Double(engine.config.topK)
|
||||
topP = engine.config.topP
|
||||
temperature = engine.config.temperature
|
||||
selectedBackend = engine.config.useGPU ? 1 : 0
|
||||
systemPrompt = engine.config.systemPrompt
|
||||
}
|
||||
|
||||
private func applySettings() {
|
||||
let oldBackend = engine.config.useGPU
|
||||
engine.config.maxTokens = Int(maxTokens)
|
||||
engine.config.topK = Int(topK)
|
||||
engine.config.topP = topP
|
||||
engine.config.temperature = temperature
|
||||
engine.config.useGPU = selectedBackend == 1
|
||||
engine.config.systemPrompt = systemPrompt
|
||||
|
||||
// 同步采样参数到 LLM（下次生成立即生效）
|
||||
engine.applySamplingConfig()
|
||||
|
||||
// 如果 backend 变了，需要重载模型
|
||||
if oldBackend != engine.config.useGPU {
|
||||
engine.reloadModel()
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,487 +0,0 @@
|
||||
import SwiftUI
|
||||
|
||||
// MARK: - 主入口
|
||||
|
||||
struct ContentView: View {
|
||||
@State private var engine = AgentEngine()
|
||||
@State private var inputText = ""
|
||||
@State private var showConfigurations = false
|
||||
@State private var showSkillsManager = false
|
||||
/// 记录每个 skill 卡片的展开状态（key = ResponseBlock.id）
|
||||
@State private var expandedSkills: Set<UUID> = []
|
||||
|
||||
private var displayItems: [DisplayItem] {
|
||||
buildDisplayItems(from: engine.messages, isProcessing: engine.isProcessing)
|
||||
}
|
||||
|
||||
var body: some View {
|
||||
ZStack {
|
||||
Theme.bg.ignoresSafeArea()
|
||||
|
||||
VStack(spacing: 0) {
|
||||
topBar
|
||||
|
||||
if engine.messages.isEmpty {
|
||||
welcomeView
|
||||
} else {
|
||||
chatList
|
||||
}
|
||||
|
||||
if engine.messages.isEmpty {
|
||||
skillChips.padding(.bottom, 8)
|
||||
}
|
||||
|
||||
inputBar
|
||||
}
|
||||
}
|
||||
.preferredColorScheme(.dark)
|
||||
.task { engine.setup() }
|
||||
.sheet(isPresented: $showConfigurations) {
|
||||
ConfigurationsView(engine: engine)
|
||||
}
|
||||
.sheet(isPresented: $showSkillsManager) {
|
||||
SkillsManagerView(engine: engine)
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - 聊天列表
|
||||
|
||||
private var chatList: some View {
|
||||
ScrollViewReader { proxy in
|
||||
ScrollView {
|
||||
VStack(spacing: Theme.chatSpacing) {
|
||||
ForEach(displayItems) { item in
|
||||
switch item {
|
||||
case .user(let msg):
|
||||
UserBubble(text: msg.content)
|
||||
case .response(let block):
|
||||
AIResponseView(
|
||||
block: block,
|
||||
expandedSkills: expandedSkills,
|
||||
onToggle: { toggleExpand($0) }
|
||||
)
|
||||
}
|
||||
}
|
||||
}
|
||||
.padding(.horizontal, Theme.chatPadH)
|
||||
.padding(.vertical, 20)
|
||||
}
|
||||
.scrollIndicators(.hidden)
|
||||
.onChange(of: engine.messages.count) { scrollTo(proxy) }
|
||||
.onChange(of: engine.isProcessing) { scrollTo(proxy) }
|
||||
}
|
||||
}
|
||||
|
||||
private func scrollTo(_ proxy: ScrollViewProxy) {
|
||||
guard let last = displayItems.last else { return }
|
||||
withAnimation(.easeOut(duration: 0.3)) {
|
||||
proxy.scrollTo(last.id, anchor: .bottom)
|
||||
}
|
||||
}
|
||||
|
||||
private func toggleExpand(_ id: UUID) {
|
||||
withAnimation(.easeInOut(duration: 0.25)) {
|
||||
if expandedSkills.contains(id) {
|
||||
expandedSkills.remove(id)
|
||||
} else {
|
||||
expandedSkills.insert(id)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - 顶部栏
|
||||
|
||||
private var topBar: some View {
|
||||
HStack(spacing: 0) {
|
||||
// 左：新会话
|
||||
Button(action: { engine.clearMessages() }) {
|
||||
Image(systemName: "square.and.pencil")
|
||||
.font(.system(size: 15, weight: .medium))
|
||||
.foregroundStyle(Theme.textSecondary)
|
||||
.frame(width: 34, height: 34)
|
||||
.background(Theme.bgElevated, in: RoundedRectangle(cornerRadius: 9))
|
||||
}
|
||||
.buttonStyle(.plain)
|
||||
|
||||
Spacer()
|
||||
|
||||
// 中：模型状态
|
||||
HStack(spacing: 6) {
|
||||
Circle()
|
||||
.fill(engine.llm.isLoaded ? Theme.accentGreen : Theme.accent)
|
||||
.frame(width: 6, height: 6)
|
||||
Text(engine.llm.isLoaded ? "Gemma 4 E4B" : engine.llm.statusMessage)
|
||||
.font(.system(size: 12, weight: .medium, design: .rounded))
|
||||
.foregroundStyle(Theme.textSecondary)
|
||||
}
|
||||
|
||||
Spacer()
|
||||
|
||||
// 右：Skills + 设置
|
||||
HStack(spacing: 6) {
|
||||
Button(action: { showSkillsManager = true }) {
|
||||
Image(systemName: "puzzlepiece.extension")
|
||||
.font(.system(size: 14, weight: .medium))
|
||||
.foregroundStyle(Theme.textSecondary)
|
||||
.frame(width: 34, height: 34)
|
||||
.background(Theme.bgElevated, in: RoundedRectangle(cornerRadius: 9))
|
||||
}
|
||||
.buttonStyle(.plain)
|
||||
|
||||
Button(action: { showConfigurations = true }) {
|
||||
Image(systemName: "slider.horizontal.3")
|
||||
.font(.system(size: 15, weight: .medium))
|
||||
.foregroundStyle(Theme.textSecondary)
|
||||
.frame(width: 34, height: 34)
|
||||
.background(Theme.bgElevated, in: RoundedRectangle(cornerRadius: 9))
|
||||
}
|
||||
.buttonStyle(.plain)
|
||||
}
|
||||
}
|
||||
.padding(.horizontal, Theme.inputPadH)
|
||||
.padding(.vertical, 10)
|
||||
}
|
||||
|
||||
// MARK: - 欢迎页
|
||||
|
||||
private var welcomeView: some View {
|
||||
VStack(spacing: 0) {
|
||||
Spacer()
|
||||
ZStack {
|
||||
Circle().fill(Theme.accentSubtle).frame(width: 60, height: 60)
|
||||
Image(systemName: "sparkles")
|
||||
.font(.system(size: 24, weight: .medium))
|
||||
.foregroundStyle(Theme.accent)
|
||||
}
|
||||
Text("PhoneClaw")
|
||||
.font(.system(size: 26, weight: .semibold, design: .rounded))
|
||||
.foregroundStyle(Theme.textPrimary)
|
||||
.padding(.top, 16)
|
||||
Text("On-device AI Agent")
|
||||
.font(.system(size: 14))
|
||||
.foregroundStyle(Theme.textTertiary)
|
||||
.padding(.top, 4)
|
||||
Spacer()
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - Skill 快捷标签
|
||||
|
||||
private var skillChips: some View {
|
||||
ScrollView(.horizontal, showsIndicators: false) {
|
||||
HStack(spacing: 8) {
|
||||
ForEach(engine.enabledSkillInfos, id: \.name) { skill in
|
||||
Button {
|
||||
inputText = skill.samplePrompt
|
||||
Task { await send() }
|
||||
} label: {
|
||||
HStack(spacing: 5) {
|
||||
Image(systemName: skill.icon).font(.system(size: 11))
|
||||
Text(skill.displayName).font(.system(size: 12, weight: .medium))
|
||||
}
|
||||
.foregroundStyle(Theme.textSecondary)
|
||||
.padding(.horizontal, 12)
|
||||
.padding(.vertical, 8)
|
||||
.background(
|
||||
RoundedRectangle(cornerRadius: 20)
|
||||
.strokeBorder(Theme.border, lineWidth: 1)
|
||||
)
|
||||
}
|
||||
.buttonStyle(.plain)
|
||||
}
|
||||
}
|
||||
.padding(.horizontal, Theme.chatPadH)
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - 输入栏
|
||||
|
||||
private var inputBar: some View {
|
||||
HStack(spacing: 10) {
|
||||
#if os(macOS)
|
||||
TextField("Message…", text: $inputText)
|
||||
.textFieldStyle(.plain)
|
||||
.font(.system(size: 15))
|
||||
.foregroundStyle(Theme.textPrimary)
|
||||
.padding(.horizontal, 16)
|
||||
.padding(.vertical, 11)
|
||||
.background(Theme.bgElevated, in: RoundedRectangle(cornerRadius: 22))
|
||||
.overlay(RoundedRectangle(cornerRadius: 22).strokeBorder(Theme.border, lineWidth: 1))
|
||||
.onSubmit { Task { await send() } }
|
||||
#else
|
||||
TextField("Message…", text: $inputText, axis: .vertical)
|
||||
.lineLimit(1...5)
|
||||
.font(.system(size: 15))
|
||||
.foregroundStyle(Theme.textPrimary)
|
||||
.padding(.horizontal, 16)
|
||||
.padding(.vertical, 11)
|
||||
.background(Theme.bgElevated, in: RoundedRectangle(cornerRadius: 22))
|
||||
.overlay(RoundedRectangle(cornerRadius: 22).strokeBorder(Theme.border, lineWidth: 1))
|
||||
.onSubmit { Task { await send() } }
|
||||
#endif
|
||||
|
||||
Button { Task { await send() } } label: {
|
||||
Image(systemName: "arrow.up")
|
||||
.font(.system(size: 14, weight: .bold))
|
||||
.foregroundStyle(canSend ? Theme.bg : Theme.textTertiary)
|
||||
.frame(width: 34, height: 34)
|
||||
.background(canSend ? Theme.accent : Theme.bgElevated, in: Circle())
|
||||
.overlay(Circle().strokeBorder(canSend ? .clear : Theme.border, lineWidth: 1))
|
||||
}
|
||||
.buttonStyle(.plain)
|
||||
.disabled(!canSend)
|
||||
.animation(.easeInOut(duration: 0.15), value: canSend)
|
||||
}
|
||||
.padding(.horizontal, Theme.inputPadH)
|
||||
.padding(.vertical, 14)
|
||||
.background(Theme.bg)
|
||||
}
|
||||
|
||||
private var canSend: Bool {
|
||||
!inputText.trimmingCharacters(in: .whitespaces).isEmpty
|
||||
&& !engine.isProcessing && engine.llm.isLoaded
|
||||
}
|
||||
|
||||
private func send() async {
|
||||
let text = inputText
|
||||
inputText = ""
|
||||
await engine.processInput(text)
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - 用户气泡
|
||||
|
||||
struct UserBubble: View {
|
||||
let text: String
|
||||
var body: some View {
|
||||
HStack {
|
||||
Spacer(minLength: Theme.bubbleMinSpacer)
|
||||
Text(text)
|
||||
.font(.system(size: 15))
|
||||
.foregroundStyle(Theme.userText)
|
||||
.padding(.horizontal, 16)
|
||||
.padding(.vertical, 10)
|
||||
.background(Theme.userBubble, in: UserBubbleShape())
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
struct UserBubbleShape: Shape {
|
||||
func path(in rect: CGRect) -> Path {
|
||||
let r: CGFloat = 18, sr: CGFloat = 4
|
||||
return Path { p in
|
||||
p.move(to: CGPoint(x: rect.minX + r, y: rect.minY))
|
||||
p.addLine(to: CGPoint(x: rect.maxX - r, y: rect.minY))
|
||||
p.addArc(tangent1End: CGPoint(x: rect.maxX, y: rect.minY),
|
||||
tangent2End: CGPoint(x: rect.maxX, y: rect.minY + r), radius: r)
|
||||
p.addLine(to: CGPoint(x: rect.maxX, y: rect.maxY - sr))
|
||||
p.addArc(tangent1End: CGPoint(x: rect.maxX, y: rect.maxY),
|
||||
tangent2End: CGPoint(x: rect.maxX - sr, y: rect.maxY), radius: sr)
|
||||
p.addLine(to: CGPoint(x: rect.minX + r, y: rect.maxY))
|
||||
p.addArc(tangent1End: CGPoint(x: rect.minX, y: rect.maxY),
|
||||
tangent2End: CGPoint(x: rect.minX, y: rect.maxY - r), radius: r)
|
||||
p.addLine(to: CGPoint(x: rect.minX, y: rect.minY + r))
|
||||
p.addArc(tangent1End: CGPoint(x: rect.minX, y: rect.minY),
|
||||
tangent2End: CGPoint(x: rect.minX + r, y: rect.minY), radius: r)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - AI 回复
|
||||
|
||||
struct AIResponseView: View {
|
||||
let block: ResponseBlock
|
||||
let expandedSkills: Set<UUID>
|
||||
let onToggle: (UUID) -> Void
|
||||
|
||||
private var hasSkill: Bool { !block.skills.isEmpty }
|
||||
private var isPureThinking: Bool { !hasSkill && block.responseText == nil && block.isThinking }
|
||||
|
||||
var body: some View {
|
||||
HStack {
|
||||
VStack(alignment: .leading, spacing: 12) {
|
||||
// ★ 纯思考状态（无 skill、无文字）→ 第一张卡片出现后淡出
|
||||
if isPureThinking {
|
||||
ThinkingIndicator()
|
||||
.padding(.leading, 12)
|
||||
.padding(.vertical, 10)
|
||||
}
|
||||
|
||||
// ★ 所有 Skill 卡片（支持多张）
|
||||
ForEach(block.skills) { card in
|
||||
SkillCardView(
|
||||
card: card,
|
||||
isExpanded: expandedSkills.contains(card.id),
|
||||
onToggle: { onToggle(card.id) }
|
||||
)
|
||||
.transition(.opacity.combined(with: .scale(scale: 0.95)))
|
||||
}
|
||||
|
||||
// Skill 完成后等待 follow-up 文字
|
||||
if hasSkill && block.isThinking && block.responseText == nil {
|
||||
ThinkingIndicator()
|
||||
.padding(.leading, 12)
|
||||
}
|
||||
|
||||
// 回复文本
|
||||
if let text = block.responseText {
|
||||
Text(text)
|
||||
.font(.system(size: 15))
|
||||
.foregroundStyle(Theme.textPrimary)
|
||||
.textSelection(.enabled)
|
||||
.lineSpacing(5)
|
||||
.frame(maxWidth: .infinity, alignment: .leading)
|
||||
.fixedSize(horizontal: false, vertical: true)
|
||||
.padding(.leading, 4)
|
||||
.animation(nil, value: text)
|
||||
}
|
||||
}
|
||||
.animation(.easeInOut(duration: 0.3), value: block.skills.count)
|
||||
|
||||
Spacer(minLength: Theme.aiMinSpacer)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - 单个 Skill 卡片（4 步进度）
|
||||
|
||||
struct SkillCardView: View {
|
||||
let card: SkillCard
|
||||
let isExpanded: Bool
|
||||
let onToggle: () -> Void
|
||||
|
||||
private var isSkillDone: Bool { card.skillStatus == "done" }
|
||||
|
||||
private var currentStep: Int {
|
||||
switch card.skillStatus {
|
||||
case "identified": return 0
|
||||
case "loaded": return 1
|
||||
case let s where s?.hasPrefix("executing") == true: return 2
|
||||
case "done": return 3
|
||||
default: return 0
|
||||
}
|
||||
}
|
||||
|
||||
var body: some View {
|
||||
VStack(alignment: .leading, spacing: 0) {
|
||||
// 卡片头部
|
||||
HStack(spacing: 10) {
|
||||
// 图标：spinner → 扎手
|
||||
ZStack {
|
||||
Image(systemName: "wrench.and.screwdriver.fill")
|
||||
.font(.system(size: 13))
|
||||
.foregroundStyle(Theme.accent)
|
||||
.frame(width: 26, height: 26)
|
||||
.background(Theme.accentSubtle, in: RoundedRectangle(cornerRadius: 7))
|
||||
.opacity(isSkillDone ? 1 : 0)
|
||||
|
||||
SpinnerIcon()
|
||||
.frame(width: 26, height: 26)
|
||||
.opacity(isSkillDone ? 0 : 1)
|
||||
}
|
||||
.animation(.easeInOut(duration: 0.3), value: isSkillDone)
|
||||
|
||||
Text(isSkillDone ? "Used \"\(card.skillName)\"" : "Running \"\(card.skillName)\"…")
|
||||
.font(.system(size: 13, weight: .medium))
|
||||
.foregroundStyle(Theme.textPrimary)
|
||||
.lineLimit(1)
|
||||
.contentTransition(.opacity)
|
||||
.animation(.easeInOut(duration: 0.2), value: isSkillDone)
|
||||
|
||||
Spacer()
|
||||
|
||||
Image(systemName: "chevron.down")
|
||||
.font(.system(size: 10, weight: .semibold))
|
||||
.foregroundStyle(Theme.textTertiary)
|
||||
.rotationEffect(.degrees(isExpanded ? -180 : 0))
|
||||
.animation(.easeInOut(duration: 0.25), value: isExpanded)
|
||||
}
|
||||
.padding(.horizontal, 12)
|
||||
.padding(.vertical, 10)
|
||||
.contentShape(Rectangle())
|
||||
.onTapGesture { onToggle() }
|
||||
|
||||
// 展开：4 步进度
|
||||
if isExpanded {
|
||||
Rectangle().fill(Theme.borderSubtle).frame(height: 1)
|
||||
|
||||
VStack(alignment: .leading, spacing: 6) {
|
||||
stepRow(label: "识别能力: \(card.skillName)",
|
||||
done: currentStep > 0,
|
||||
active: currentStep == 0)
|
||||
stepRow(label: "加载 Skill 指令",
|
||||
done: currentStep > 1,
|
||||
active: currentStep == 1)
|
||||
stepRow(label: card.toolName != nil ? "执行 \(card.toolName!)" : "执行工具",
|
||||
done: currentStep > 2,
|
||||
active: currentStep == 2)
|
||||
stepRow(label: "生成回复",
|
||||
done: isSkillDone,
|
||||
active: false)
|
||||
}
|
||||
.padding(.horizontal, 12)
|
||||
.padding(.vertical, 10)
|
||||
.animation(.easeInOut(duration: 0.2), value: currentStep)
|
||||
}
|
||||
}
|
||||
.background(Theme.bgElevated, in: RoundedRectangle(cornerRadius: 12))
|
||||
.overlay(RoundedRectangle(cornerRadius: 12).strokeBorder(Theme.border, lineWidth: 1))
|
||||
}
|
||||
|
||||
private func stepRow(label: String, done: Bool, active: Bool = false) -> some View {
|
||||
HStack(spacing: 8) {
|
||||
Group {
|
||||
if done {
|
||||
Image(systemName: "checkmark.circle.fill")
|
||||
.font(.system(size: 11))
|
||||
.foregroundStyle(Theme.accentGreen)
|
||||
} else if active {
|
||||
ProgressView().controlSize(.mini).tint(Theme.textTertiary)
|
||||
} else {
|
||||
Circle().fill(Theme.textTertiary.opacity(0.3)).frame(width: 6, height: 6)
|
||||
}
|
||||
}
|
||||
.frame(width: 14, height: 14)
|
||||
|
||||
Text(label)
|
||||
.font(.system(size: 12))
|
||||
.foregroundStyle(done ? Theme.textSecondary : Theme.textTertiary)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - 旋转 Spinner
|
||||
|
||||
struct SpinnerIcon: View {
|
||||
@State private var rotating = false
|
||||
var body: some View {
|
||||
Image(systemName: "asterisk")
|
||||
.font(.system(size: 12, weight: .medium))
|
||||
.foregroundStyle(Theme.textTertiary)
|
||||
.rotationEffect(.degrees(rotating ? 360 : 0))
|
||||
.animation(.linear(duration: 1.2).repeatForever(autoreverses: false), value: rotating)
|
||||
.onAppear { rotating = true }
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - 思考动画
|
||||
|
||||
struct ThinkingIndicator: View {
|
||||
@State private var active = 0
|
||||
let timer = Timer.publish(every: 0.45, on: .main, in: .common).autoconnect()
|
||||
|
||||
var body: some View {
|
||||
HStack(spacing: 5) {
|
||||
ForEach(0..<3, id: \.self) { i in
|
||||
Circle()
|
||||
.fill(Theme.textTertiary)
|
||||
.frame(width: 6, height: 6)
|
||||
.opacity(active == i ? 1.0 : 0.3)
|
||||
.scaleEffect(active == i ? 1.0 : 0.75)
|
||||
.animation(.easeInOut(duration: 0.35), value: active)
|
||||
}
|
||||
}
|
||||
.frame(height: 20)
|
||||
.onReceive(timer) { _ in active = (active + 1) % 3 }
|
||||
}
|
||||
}
|
||||
@@ -1,408 +0,0 @@
|
||||
import SwiftUI
|
||||
|
||||
// MARK: - Skills 管理面板（独立面板）
|
||||
|
||||
struct SkillsManagerView: View {
|
||||
@Bindable var engine: AgentEngine
|
||||
@Environment(\.dismiss) private var dismiss
|
||||
@State private var expandedSkills: Set<String> = []
|
||||
|
||||
private var enabledCount: Int { engine.skillEntries.filter(\.isEnabled).count }
|
||||
|
||||
var body: some View {
|
||||
VStack(spacing: 0) {
|
||||
// 标题栏
|
||||
HStack {
|
||||
Text("Skills")
|
||||
.font(.title2.weight(.bold))
|
||||
.foregroundStyle(Theme.textPrimary)
|
||||
|
||||
Spacer()
|
||||
|
||||
HStack(spacing: 12) {
|
||||
Button { engine.reloadSkills() } label: {
|
||||
Image(systemName: "arrow.clockwise")
|
||||
.font(.system(size: 12, weight: .medium))
|
||||
.foregroundStyle(Theme.accent)
|
||||
}
|
||||
.buttonStyle(.plain)
|
||||
.help("Reload all skills from disk")
|
||||
|
||||
Button("Enable all") { engine.setAllSkills(enabled: true) }
|
||||
.font(.system(size: 12, weight: .medium))
|
||||
.foregroundStyle(Theme.accent)
|
||||
.buttonStyle(.plain)
|
||||
Button("Disable all") { engine.setAllSkills(enabled: false) }
|
||||
.font(.system(size: 12, weight: .medium))
|
||||
.foregroundStyle(Theme.textTertiary)
|
||||
.buttonStyle(.plain)
|
||||
}
|
||||
}
|
||||
.padding(.horizontal, 20)
|
||||
.padding(.top, 18)
|
||||
.padding(.bottom, 12)
|
||||
|
||||
Rectangle().fill(Theme.border).frame(height: 1)
|
||||
|
||||
// Skill 列表
|
||||
ScrollView {
|
||||
VStack(spacing: 10) {
|
||||
ForEach(engine.skillEntries.indices, id: \.self) { i in
|
||||
SkillDetailCard(
|
||||
entry: $engine.skillEntries[i],
|
||||
isExpanded: expandedSkills.contains(engine.skillEntries[i].id),
|
||||
onToggleExpand: { toggleExpand(engine.skillEntries[i].id) },
|
||||
onSave: { content in
|
||||
try? engine.skillLoader.saveSkill(skillId: engine.skillEntries[i].id, content: content)
|
||||
engine.reloadSkills()
|
||||
}
|
||||
)
|
||||
}
|
||||
}
|
||||
.padding(16)
|
||||
}
|
||||
|
||||
Rectangle().fill(Theme.border).frame(height: 1)
|
||||
|
||||
// 底部
|
||||
HStack {
|
||||
Text("\(enabledCount)/\(engine.skillEntries.count) enabled")
|
||||
.font(.system(size: 12))
|
||||
.foregroundStyle(Theme.textTertiary)
|
||||
|
||||
Spacer()
|
||||
|
||||
Button("Done") { dismiss() }
|
||||
.font(.body.weight(.semibold))
|
||||
.foregroundStyle(Theme.accent)
|
||||
.padding(.horizontal, 24)
|
||||
.padding(.vertical, 7)
|
||||
.background(Theme.accent.opacity(0.15), in: Capsule())
|
||||
.buttonStyle(.plain)
|
||||
}
|
||||
.padding(.horizontal, 20)
|
||||
.padding(.vertical, 12)
|
||||
}
|
||||
.frame(width: 520, height: 640)
|
||||
.background(Theme.bg)
|
||||
.clipShape(RoundedRectangle(cornerRadius: 16))
|
||||
.preferredColorScheme(.dark)
|
||||
}
|
||||
|
||||
private func toggleExpand(_ id: String) {
|
||||
withAnimation(.easeInOut(duration: 0.25)) {
|
||||
if expandedSkills.contains(id) {
|
||||
expandedSkills.remove(id)
|
||||
} else {
|
||||
expandedSkills.insert(id)
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - 单个 Skill 详情卡片（三层架构展示 + 编辑）
|
||||
|
||||
struct SkillDetailCard: View {
|
||||
@Binding var entry: SkillEntry
|
||||
let isExpanded: Bool
|
||||
let onToggleExpand: () -> Void
|
||||
let onSave: (String) -> Void
|
||||
|
||||
@State private var isEditing = false
|
||||
@State private var editText = ""
|
||||
@State private var showSource = false
|
||||
@State private var saveFlash = false
|
||||
|
||||
/// L2: SKILL.md 的 Markdown body（指令体，注入 LLM）
|
||||
private var skillBody: String? {
|
||||
guard let url = entry.filePath,
|
||||
let content = try? String(contentsOf: url, encoding: .utf8) else { return nil }
|
||||
guard let regex = try? NSRegularExpression(
|
||||
pattern: "^---\\s*\\n.*?\\n---\\s*\\n(.*)$",
|
||||
options: .dotMatchesLineSeparators
|
||||
) else { return nil }
|
||||
let range = NSRange(content.startIndex..., in: content)
|
||||
if let match = regex.firstMatch(in: content, range: range),
|
||||
let bodyRange = Range(match.range(at: 1), in: content) {
|
||||
return String(content[bodyRange]).trimmingCharacters(in: .whitespacesAndNewlines)
|
||||
}
|
||||
return nil
|
||||
}
|
||||
|
||||
/// 完整 SKILL.md 原始内容
|
||||
private var rawContent: String {
|
||||
guard let url = entry.filePath,
|
||||
let content = try? String(contentsOf: url, encoding: .utf8) else { return "" }
|
||||
return content
|
||||
}
|
||||
|
||||
var body: some View {
|
||||
VStack(alignment: .leading, spacing: 0) {
|
||||
// ── 头部 ──
|
||||
HStack(spacing: 10) {
|
||||
Image(systemName: entry.icon)
|
||||
.font(.system(size: 15))
|
||||
.foregroundStyle(entry.isEnabled ? Theme.accent : Theme.textTertiary)
|
||||
.frame(width: 32, height: 32)
|
||||
.background(
|
||||
entry.isEnabled ? Theme.accentSubtle : Theme.textTertiary.opacity(0.08),
|
||||
in: RoundedRectangle(cornerRadius: 8)
|
||||
)
|
||||
|
||||
VStack(alignment: .leading, spacing: 2) {
|
||||
Text(entry.name)
|
||||
.font(.system(size: 14, weight: .semibold))
|
||||
.foregroundStyle(Theme.textPrimary)
|
||||
Text(entry.description)
|
||||
.font(.system(size: 11))
|
||||
.foregroundStyle(Theme.textTertiary)
|
||||
.lineLimit(1)
|
||||
}
|
||||
|
||||
Spacer()
|
||||
|
||||
Toggle("", isOn: $entry.isEnabled)
|
||||
.toggleStyle(.switch)
|
||||
.controlSize(.small)
|
||||
.labelsHidden()
|
||||
|
||||
Image(systemName: "chevron.down")
|
||||
.font(.system(size: 10, weight: .semibold))
|
||||
.foregroundStyle(Theme.textTertiary)
|
||||
.rotationEffect(.degrees(isExpanded ? -180 : 0))
|
||||
.animation(.easeInOut(duration: 0.25), value: isExpanded)
|
||||
}
|
||||
.padding(.horizontal, 12)
|
||||
.padding(.vertical, 10)
|
||||
.contentShape(Rectangle())
|
||||
.onTapGesture { onToggleExpand() }
|
||||
|
||||
// ── 展开详情：三层架构 ──
|
||||
if isExpanded {
|
||||
Rectangle().fill(Theme.border).frame(height: 1)
|
||||
|
||||
VStack(alignment: .leading, spacing: 14) {
|
||||
|
||||
// ━━ L1: TOOLS（原生工具 · ToolRegistry） ━━
|
||||
if !entry.tools.isEmpty {
|
||||
VStack(alignment: .leading, spacing: 8) {
|
||||
HStack(spacing: 6) {
|
||||
Text("TOOLS")
|
||||
.font(.system(size: 10, weight: .bold))
|
||||
.foregroundStyle(Theme.textTertiary)
|
||||
.kerning(1)
|
||||
Text("· ToolRegistry")
|
||||
.font(.system(size: 9, design: .monospaced))
|
||||
.foregroundStyle(Theme.textTertiary.opacity(0.6))
|
||||
}
|
||||
|
||||
ForEach(entry.tools, id: \.name) { tool in
|
||||
HStack(alignment: .top, spacing: 8) {
|
||||
// ★ 注册状态标注
|
||||
Image(systemName: ToolRegistry.shared.hasToolNamed(tool.name) ? "checkmark.circle.fill" : "exclamationmark.triangle.fill")
|
||||
.font(.system(size: 10))
|
||||
.foregroundStyle(ToolRegistry.shared.hasToolNamed(tool.name) ? Theme.accentGreen : .orange)
|
||||
.frame(width: 14)
|
||||
.padding(.top, 2)
|
||||
|
||||
VStack(alignment: .leading, spacing: 3) {
|
||||
HStack(spacing: 6) {
|
||||
Text(tool.name)
|
||||
.font(.system(size: 12, weight: .medium, design: .monospaced))
|
||||
.foregroundStyle(Theme.textPrimary)
|
||||
// ★ 注册/未注册 badge
|
||||
Text(ToolRegistry.shared.hasToolNamed(tool.name) ? "registered" : "missing")
|
||||
.font(.system(size: 8, weight: .semibold, design: .monospaced))
|
||||
.foregroundStyle(ToolRegistry.shared.hasToolNamed(tool.name) ? Theme.accentGreen : .orange)
|
||||
.padding(.horizontal, 5)
|
||||
.padding(.vertical, 1)
|
||||
.background(
|
||||
(ToolRegistry.shared.hasToolNamed(tool.name) ? Theme.accentGreen : Color.orange).opacity(0.12),
|
||||
in: Capsule()
|
||||
)
|
||||
}
|
||||
Text(tool.description)
|
||||
.font(.system(size: 11))
|
||||
.foregroundStyle(Theme.textSecondary)
|
||||
if tool.parameters != "无" {
|
||||
HStack(spacing: 4) {
|
||||
Text("参数:")
|
||||
.foregroundStyle(Theme.textTertiary)
|
||||
Text(tool.parameters)
|
||||
.foregroundStyle(Theme.textSecondary)
|
||||
}
|
||||
.font(.system(size: 10))
|
||||
}
|
||||
}
|
||||
}
|
||||
.padding(8)
|
||||
.frame(maxWidth: .infinity, alignment: .leading)
|
||||
.background(Theme.bg, in: RoundedRectangle(cornerRadius: 6))
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// ━━ EXAMPLE ━━
|
||||
VStack(alignment: .leading, spacing: 4) {
|
||||
Text("EXAMPLE")
|
||||
.font(.system(size: 10, weight: .bold))
|
||||
.foregroundStyle(Theme.textTertiary)
|
||||
.kerning(1)
|
||||
|
||||
Text("\"\(entry.samplePrompt)\"")
|
||||
.font(.system(size: 12, design: .rounded))
|
||||
.foregroundStyle(Theme.textSecondary)
|
||||
.italic()
|
||||
}
|
||||
|
||||
// ━━ L2: INSTRUCTIONS（Markdown body · 注入 LLM 上下文） ━━
|
||||
if let body = skillBody {
|
||||
VStack(alignment: .leading, spacing: 6) {
|
||||
HStack(spacing: 6) {
|
||||
Text("INSTRUCTIONS")
|
||||
.font(.system(size: 10, weight: .bold))
|
||||
.foregroundStyle(Theme.textTertiary)
|
||||
.kerning(1)
|
||||
Text("· LLM Context")
|
||||
.font(.system(size: 9, design: .monospaced))
|
||||
.foregroundStyle(Theme.textTertiary.opacity(0.6))
|
||||
}
|
||||
|
||||
Text(body)
|
||||
.font(.system(size: 11))
|
||||
.foregroundStyle(Theme.textSecondary)
|
||||
.lineSpacing(3)
|
||||
.frame(maxWidth: .infinity, alignment: .leading)
|
||||
.padding(8)
|
||||
.background(Theme.bg, in: RoundedRectangle(cornerRadius: 6))
|
||||
}
|
||||
}
|
||||
|
||||
// ━━ SKILL.MD 源文件（查看 / 编辑 / 保存 / 热重载） ━━
|
||||
VStack(alignment: .leading, spacing: 6) {
|
||||
HStack(spacing: 6) {
|
||||
Text("SKILL.MD")
|
||||
.font(.system(size: 10, weight: .bold))
|
||||
.foregroundStyle(Theme.textTertiary)
|
||||
.kerning(1)
|
||||
|
||||
if let path = entry.filePath?.path {
|
||||
Text(path.replacingOccurrences(of: NSHomeDirectory(), with: "~"))
|
||||
.font(.system(size: 8, design: .monospaced))
|
||||
.foregroundStyle(Theme.textTertiary.opacity(0.5))
|
||||
.lineLimit(1)
|
||||
.truncationMode(.middle)
|
||||
}
|
||||
|
||||
Spacer()
|
||||
|
||||
if isEditing {
|
||||
Button("Cancel") {
|
||||
isEditing = false
|
||||
}
|
||||
.font(.system(size: 11, weight: .medium))
|
||||
.foregroundStyle(Theme.textTertiary)
|
||||
.buttonStyle(.plain)
|
||||
|
||||
Button {
|
||||
onSave(editText)
|
||||
isEditing = false
|
||||
showSource = true
|
||||
saveFlash = true
|
||||
DispatchQueue.main.asyncAfter(deadline: .now() + 1.5) {
|
||||
saveFlash = false
|
||||
}
|
||||
} label: {
|
||||
HStack(spacing: 3) {
|
||||
Image(systemName: "arrow.down.doc")
|
||||
.font(.system(size: 9))
|
||||
Text("Save & Reload")
|
||||
.font(.system(size: 11, weight: .semibold))
|
||||
}
|
||||
.foregroundStyle(Theme.bg)
|
||||
.padding(.horizontal, 10)
|
||||
.padding(.vertical, 4)
|
||||
.background(Theme.accent, in: Capsule())
|
||||
}
|
||||
.buttonStyle(.plain)
|
||||
} else {
|
||||
Button {
|
||||
editText = rawContent
|
||||
isEditing = true
|
||||
showSource = true
|
||||
} label: {
|
||||
HStack(spacing: 3) {
|
||||
Image(systemName: "pencil")
|
||||
.font(.system(size: 9))
|
||||
Text("Edit")
|
||||
.font(.system(size: 11, weight: .medium))
|
||||
}
|
||||
.foregroundStyle(Theme.accent)
|
||||
}
|
||||
.buttonStyle(.plain)
|
||||
|
||||
Button {
|
||||
if !showSource { editText = rawContent }
|
||||
showSource.toggle()
|
||||
} label: {
|
||||
HStack(spacing: 3) {
|
||||
Image(systemName: showSource ? "eye.slash" : "eye")
|
||||
.font(.system(size: 9))
|
||||
Text(showSource ? "Hide" : "View")
|
||||
.font(.system(size: 11, weight: .medium))
|
||||
}
|
||||
.foregroundStyle(Theme.textSecondary)
|
||||
}
|
||||
.buttonStyle(.plain)
|
||||
}
|
||||
}
|
||||
|
||||
if saveFlash {
|
||||
HStack(spacing: 4) {
|
||||
Image(systemName: "checkmark.circle.fill")
|
||||
.font(.system(size: 10))
|
||||
Text("Saved & reloaded")
|
||||
.font(.system(size: 11, weight: .medium))
|
||||
}
|
||||
.foregroundStyle(Theme.accentGreen)
|
||||
.transition(.opacity)
|
||||
}
|
||||
|
||||
if showSource || isEditing {
|
||||
if isEditing {
|
||||
TextEditor(text: $editText)
|
||||
.font(.system(size: 11, design: .monospaced))
|
||||
.foregroundStyle(Theme.textPrimary)
|
||||
.scrollContentBackground(.hidden)
|
||||
.frame(minHeight: 180, maxHeight: 300)
|
||||
.padding(6)
|
||||
.background(Theme.bg, in: RoundedRectangle(cornerRadius: 6))
|
||||
.overlay(
|
||||
RoundedRectangle(cornerRadius: 6)
|
||||
.strokeBorder(Theme.accent.opacity(0.4), lineWidth: 1)
|
||||
)
|
||||
} else {
|
||||
ScrollView {
|
||||
Text(editText)
|
||||
.font(.system(size: 11, design: .monospaced))
|
||||
.foregroundStyle(Theme.textSecondary)
|
||||
.frame(maxWidth: .infinity, alignment: .leading)
|
||||
.textSelection(.enabled)
|
||||
}
|
||||
.frame(maxHeight: 200)
|
||||
.padding(6)
|
||||
.background(Theme.bg, in: RoundedRectangle(cornerRadius: 6))
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
.padding(12)
|
||||
}
|
||||
}
|
||||
.background(Theme.bgElevated, in: RoundedRectangle(cornerRadius: 12))
|
||||
.overlay(RoundedRectangle(cornerRadius: 12).strokeBorder(Theme.border, lineWidth: 1))
|
||||
.opacity(entry.isEnabled ? 1.0 : 0.6)
|
||||
.animation(.easeInOut(duration: 0.2), value: entry.isEnabled)
|
||||
.animation(.easeInOut(duration: 0.3), value: saveFlash)
|
||||
}
|
||||
}
|
||||
@@ -1,61 +0,0 @@
|
||||
import SwiftUI
|
||||
|
||||
// MARK: - PhoneClaw 设计系统（Claude 风格）
|
||||
// 跨平台共享：macOS + iOS
|
||||
|
||||
struct Theme {
|
||||
// MARK: 背景
|
||||
static let bg = Color(hex: "1A1915") // 深暖棕
|
||||
static let bgElevated = Color(hex: "24221D") // 卡片/输入框
|
||||
static let bgHover = Color(hex: "2E2B25") // hover / pressed
|
||||
|
||||
// MARK: 文字
|
||||
static let textPrimary = Color(hex: "ECECEA") // 主文字 暖白
|
||||
static let textSecondary = Color(hex: "A8A8A0") // 次要
|
||||
static let textTertiary = Color(hex: "6B6B63") // 辅助/placeholder
|
||||
|
||||
// MARK: 强调色
|
||||
static let accent = Color(hex: "D4A574") // 沙金/赭石
|
||||
static let accentSubtle = Color(hex: "D4A574").opacity(0.12)
|
||||
static let accentGreen = Color(hex: "7CB87C") // 成功/在线
|
||||
|
||||
// MARK: 用户气泡
|
||||
static let userBubble = Color(hex: "D4A574")
|
||||
static let userText = Color(hex: "1A1915")
|
||||
|
||||
// MARK: 边框
|
||||
static let border = Color(hex: "3A3732")
|
||||
static let borderSubtle = Color(hex: "2E2B25")
|
||||
|
||||
// MARK: 响应式间距
|
||||
#if os(macOS)
|
||||
static let chatPadH: CGFloat = 24
|
||||
static let chatSpacing: CGFloat = 24
|
||||
static let inputPadH: CGFloat = 20
|
||||
static let bubbleMinSpacer: CGFloat = 80
|
||||
static let aiMinSpacer: CGFloat = 40
|
||||
#else
|
||||
static let chatPadH: CGFloat = 16
|
||||
static let chatSpacing: CGFloat = 20
|
||||
static let inputPadH: CGFloat = 16
|
||||
static let bubbleMinSpacer: CGFloat = 60
|
||||
static let aiMinSpacer: CGFloat = 24
|
||||
#endif
|
||||
}
|
||||
|
||||
// MARK: - Hex Color（跨平台）
|
||||
|
||||
extension Color {
|
||||
init(hex: String) {
|
||||
let hex = hex.trimmingCharacters(in: CharacterSet.alphanumerics.inverted)
|
||||
var int: UInt64 = 0
|
||||
Scanner(string: hex).scanHexInt64(&int)
|
||||
let a, r, g, b: UInt64
|
||||
switch hex.count {
|
||||
case 6: (a, r, g, b) = (255, int >> 16, int >> 8 & 0xFF, int & 0xFF)
|
||||
case 8: (a, r, g, b) = (int >> 24, int >> 16 & 0xFF, int >> 8 & 0xFF, int & 0xFF)
|
||||
default: (a, r, g, b) = (255, 0, 0, 0)
|
||||
}
|
||||
self.init(.sRGB, red: Double(r)/255, green: Double(g)/255, blue: Double(b)/255, opacity: Double(a)/255)
|
||||
}
|
||||
}
|
||||
File renamed without changes.
File renamed without changes.
File renamed without changes.
@@ -1,117 +0,0 @@
|
||||
# PhoneClaw 技术路径分析
|
||||
|
||||
> 基于对 Google AI Edge Gallery、LiteRT-LM、Gemma 4 E4B 的实际代码和文档研究
|
||||
|
||||
## 关键发现
|
||||
|
||||
### 1. 你手机上跑的 App = Google AI Edge Gallery
|
||||
- App Store: https://apps.apple.com/us/app/google-ai-edge-gallery/id6749645337
|
||||
- GitHub: https://github.com/google-ai-edge/gallery
|
||||
- **但 iOS 源码未开源**（GitHub 上只有 Android Kotlin 代码，占 91%）
|
||||
- iOS 版由 Google 内部开发，未公开 Swift 源码
|
||||
|
||||
### 2. Gemma 4 E4B 的推理引擎 = LiteRT-LM
|
||||
- GitHub: https://github.com/google-ai-edge/LiteRT-LM
|
||||
- 语言: C++ (76.5%), 有 C API 绑定
|
||||
- **iOS 支持**: 有（Metal GPU 加速），但 **Swift API 标注 "Coming Soon"**
|
||||
- 当前可用: C/C++ API → 可通过 Swift Bridging Header 调用
|
||||
- 内置 Function Calling / Tool Use 支持（核心！）
|
||||
|
||||
### 3. Gallery 的 Agent Skills 系统
|
||||
- skills/ 目录包含模块化技能定义（平台无关的 Markdown 文件）
|
||||
- 技能格式: SKILL.md (YAML frontmatter + 指令)
|
||||
- Gallery 支持**通过 URL 加载自定义 Skill**
|
||||
- Function_Calling_Guide.md 详细说明了工具定义方式
|
||||
|
||||
---
|
||||
|
||||
## 三条可行路径
|
||||
|
||||
### 路径 A：在 Gallery App 上加载自定义 Skill ⭐ 最快
|
||||
```
|
||||
成本: 0 代码 时间: 30 分钟
|
||||
原理: Gallery App 原生支持从 URL 加载 Skill
|
||||
你只需要写 SKILL.md 文件，托管在 GitHub 上
|
||||
```
|
||||
|
||||
**优点**: 零开发成本，直接用 E4B，Gallery 已处理好所有推理/UI/权限
|
||||
**缺点**: Gallery 是沙箱 App，不能调用你自定义的 iOS API（相机/日历等）
|
||||
**适合**: 验证 Skill 提示词和交互设计
|
||||
|
||||
---
|
||||
|
||||
### 路径 B：Fork Gallery + LiteRT-LM C API → Swift 自定义 App ⭐⭐ 推荐
|
||||
```
|
||||
成本: 中等 时间: 1-2 周
|
||||
原理: 用 LiteRT-LM 的 C API 从 Swift 调用 E4B 推理
|
||||
自己实现 Skill Router + iOS API 调用
|
||||
```
|
||||
|
||||
**技术栈**:
|
||||
```
|
||||
Swift App
|
||||
├── LiteRT-LM C API (via Bridging Header)
|
||||
│ └── 加载 .litertlm 模型文件
|
||||
│ └── Conversation API (含 tool use)
|
||||
├── Skill Router (Swift)
|
||||
│ └── 解析 function call → 执行 iOS API
|
||||
└── iOS Skills (Swift)
|
||||
└── 相机/剪贴板/日历/健康...
|
||||
```
|
||||
|
||||
**LiteRT-LM C API 核心接口**:
|
||||
```c
|
||||
// 创建引擎
|
||||
LiteRtLmEngine* engine = LiteRtLmEngineCreate(settings);
|
||||
|
||||
// 创建对话
|
||||
LiteRtLmConversation* conv = LiteRtLmConversationCreate(engine);
|
||||
|
||||
// 发送消息（含工具定义）
|
||||
LiteRtLmConversationSendMessage(conv, message, callback);
|
||||
|
||||
// 返回工具结果
|
||||
LiteRtLmConversationSendToolResult(conv, tool_call_id, result);
|
||||
```
|
||||
|
||||
**优点**: 完全控制，能调用所有 iOS API，真正的端侧 Agent
|
||||
**缺点**: 需要编译 LiteRT-LM 的 iOS 库（CMake/Bazel）
|
||||
|
||||
---
|
||||
|
||||
### 路径 C：MediaPipe LLM Inference API (CocoaPods)
|
||||
```
|
||||
成本: 中等 时间: 3-5 天
|
||||
原理: Google 的旧版 API，有 iOS CocoaPods 支持
|
||||
```
|
||||
|
||||
**Podfile**:
|
||||
```ruby
|
||||
pod 'MediaPipeTasksGenAI'
|
||||
pod 'MediaPipeTasksGenAIC'
|
||||
```
|
||||
|
||||
**优点**: 集成简单，CocoaPods 一键安装
|
||||
**缺点**: 较旧的 API，不确定是否支持 Gemma 4 E4B 格式(.litertlm)
|
||||
Google 建议迁移到 LiteRT-LM
|
||||
|
||||
---
|
||||
|
||||
## 推荐策略：A → B 渐进
|
||||
|
||||
### 阶段 1（今天）：路径 A — Skill 验证
|
||||
- 写一个自定义 SKILL.md
|
||||
- 在 Gallery App 中加载运行
|
||||
- 验证 E4B 的 function calling 能力和提示词
|
||||
|
||||
### 阶段 2（本周）：路径 B — 搭建独立 App
|
||||
- 编译 LiteRT-LM iOS 库
|
||||
- Swift Bridging Header 接入 C API
|
||||
- 实现第一个真正有 iOS 权限调用的 Skill（clipboard_read）
|
||||
|
||||
---
|
||||
|
||||
## 接下来你想先走哪一步？
|
||||
|
||||
- **A**: 我现在就帮你写一个 SKILL.md，放到 Gallery 里测试
|
||||
- **B**: 直接开始搭建独立 App（编译 LiteRT-LM）
|
||||
File renamed without changes.
File renamed without changes.
File renamed without changes.
File renamed without changes.
File renamed without changes.
@@ -1,16 +0,0 @@
|
||||
{
|
||||
"folders": [
|
||||
{
|
||||
"name": "PhoneClaw",
|
||||
"path": "."
|
||||
}
|
||||
],
|
||||
"settings": {
|
||||
"git.ignoreLimitWarning": true,
|
||||
"git.ignoredRepositories": [
|
||||
"LiteRT-LM",
|
||||
"_mlx-swift-lm",
|
||||
"_mediapipe-samples"
|
||||
]
|
||||
}
|
||||
}
|
||||
-30
@@ -1,30 +0,0 @@
|
||||
name: PhoneClaw
|
||||
options:
|
||||
bundleIdPrefix: com.phoneclaw
|
||||
deploymentTarget:
|
||||
iOS: "17.0"
|
||||
xcodeVersion: "16.0"
|
||||
createIntermediateGroups: true
|
||||
|
||||
settings:
|
||||
base:
|
||||
SWIFT_VERSION: "5.9"
|
||||
TARGETED_DEVICE_FAMILY: "1" # iPhone only
|
||||
|
||||
targets:
|
||||
PhoneClaw:
|
||||
type: application
|
||||
platform: iOS
|
||||
sources:
|
||||
- path: PhoneClaw
|
||||
excludes:
|
||||
- "*.h"
|
||||
settings:
|
||||
base:
|
||||
INFOPLIST_FILE: PhoneClaw/Info.plist
|
||||
CODE_SIGN_ENTITLEMENTS: PhoneClaw/PhoneClaw.entitlements
|
||||
PRODUCT_BUNDLE_IDENTIFIER: com.zxw.phoneclaw2026
|
||||
MARKETING_VERSION: "1.0.0"
|
||||
CURRENT_PROJECT_VERSION: "1"
|
||||
ASSETCATALOG_COMPILER_APPICON_NAME: AppIcon
|
||||
SWIFT_OBJC_BRIDGING_HEADER: PhoneClaw/PhoneClaw-Bridging-Header.h
|
||||
Reference in new issue
Block a user