alg id parse error, not a sequence错误

本文介绍了在使用Java进行私钥解析时遇到的ParseError及ALGID错误,并给出了具体的解决方案。通过引入BouncyCastleProvider服务提供者,解决了私钥格式不被识别的问题。

先说下这个错误的英文解释吧,parse error,not a sequence比较简单,就是说解析错误,不是一个sequence(我认为可以理解为是字符串,当然最好说成是“序列”吧)。那么问题来了——algid是什么意思?百度、谷歌翻译了很多,也没有找到合理的解释!不过我查了下字典,对algid解释为(发烧前)发冷的,寒冷的; 是一个形容词,那么名字就是这个error了。我理解的意思是:说明这个错误很可怕,就是和致命错误差不多吧;
接下来走入正题:这个错误到底是怎么回事?
网上很多解释都是说私钥的格式不是PKCS#8而导致的,但是我负责的两个项目中的代码都是:

			byte[] buffer = Base64.decodeBase64(privateKeyStr);
			PKCS8EncodedKeySpec keySpec = new PKCS8EncodedKeySpec(buffer);
			KeyFactory keyFactory = KeyFactory.getInstance("RSA");
			privateKey = keyFactory.generatePrivate(keySpec);

如此来生成私钥对象的!方法没有什么差别,单结果是一个成功一个失败!失败的就是报这个错误,所以我觉得原因不是这里!

最终,我在Stack Overflow找到了答案(不得不说老外真厉害,哈哈哈)!

java.security.Security.addProvider(
         new org.bouncycastle.jce.provider.BouncyCastleProvider()
);

在获取私钥之前,调用一下这个方法,就可以了!其实就是提供服务的!想要了解具体的解释,可以去看下这个博客:http://blog.youkuaiyun.com/innost/article/details/44081147

import time import os import requests import json from basereal import BaseReal from logger import logger from typing import Dict, List, Optional, Callable import jwt import hashlib import hmac import base64 from datetime import datetime, timedelta import threading class ConversationHistory: """对话历史管理类""" def __init__(self, max_rounds=5): self.max_rounds = max_rounds self.history = [] def add_message(self, role: str, content: str): """添加消息到历史记录""" self.history.append({"role": role, "content": content}) # 保持最多max_rounds轮对话(每轮包含用户和AI两条消息) self.history = self.history[-(self.max_rounds*2):] def get_messages(self) -> List[Dict]: """获取完整的历史消息列表""" return self.history.copy() def clear(self): """清空历史记录""" self.history = [] class CozeClient: def __init__(self, app_id: str, private_key: str, kid: str, bot_id: str): """ 初始化Coze客户端 :param app_id: 应用ID :param private_key: 私钥 :param kid: Key ID :param bot_id: 机器人ID """ self.last_activity = {} self.app_id = app_id self.private_key = private_key self.kid = kid self.bot_id = bot_id self.access_token = None self.token_expire_time = None self.lock = threading.Lock() self.conversation_histories = {} # 用户对话历史存储 self.user_conversations = {} # {user_id: conversation_id} self.session_file = "coze_sessions.json" self._load_sessions() # 初始化时加载保存的会话 def _load_sessions(self): try: with open(self.session_file, 'r') as f: data = json.load(f) self.user_conversations = data.get("user_conversations", {}) self.conversation_histories = { uid: ConversationHistory(max_rounds=5) for uid in data.get("conversation_histories", {}) } except (FileNotFoundError, json.JSONDecodeError): self.user_conversations = {} self.conversation_histories = {} def _save_sessions(self): data = { "user_conversations": self.user_conversations, "conversation_histories": { uid: hist.get_messages() for uid, hist in self.conversation_histories.items() } } with open(self.session_file, 'w') as f: json.dump(data, f) # 在CozeClient类中添加 CONVERSATION_TIMEOUT = 1800 # 30分钟 def get_conversation_id(self, user_id: str) -> Optional[str]: conv_id = self.user_conversations.get(user_id) if conv_id: self.last_activity[user_id] = time.time() # 更新活动时间 return conv_id def _create_jwt_token(self, expire_seconds: int = 3600) -> str: """创建JWT Token用于获取Access Token""" ts = int(time.time()) exp = ts + expire_seconds header = { "alg": "RS256", "typ": "JMT", "kid": self.kid } payload = { "iss": self.app_id, "aud": "api.coze.cn", "iat": ts, "exp": exp, "jti": self._get_random_string(4) } header_b64 = self._base64_url_encode(json.dumps(header).encode('utf-8')) payload_b64 = self._base64_url_encode(json.dumps(payload).encode('utf-8')) h_and_p = f"{header_b64}.{payload_b64}" signature = self._rsa_sign(self.private_key, h_and_p) signature_b64 = self._base64_url_encode(signature) return f"{h_and_p}.{signature_b64}" def _get_access_token(self) -> str: """获取Access Token,优先使用缓存的token""" if self.access_token and self.token_expire_time and time.time() < self.token_expire_time - 10: return self.access_token with self.lock: if self.access_token and self.token_expire_time and time.time() < self.token_expire_time - 10: return self.access_token jwt_token = self._create_jwt_token(300) data = { "duration_seconds": 86399, "grant_type": "urn:ietf:params:oauth:grant-type:jwt-bearer" } headers = { "Content-Type": "application/json", "Authorization": f"Bearer {jwt_token}" } response = requests.post( "https://api.coze.cn/api/permission/oauth2/token", json=data, headers=headers, timeout=20 ) if response.status_code == 200: result = response.json() self.access_token = result.get("access_token") expires_in = result.get("expires_in", 86399) self.token_expire_time = time.time() + expires_in - 10 return self.access_token else: raise Exception(f"Failed to get access token: {response.text}") def get_conversation_history(self, user_id: str, max_rounds: int = 5) -> List[Dict]: """获取用户对话历史""" if user_id not in self.conversation_histories: self.conversation_histories[user_id] = ConversationHistory(max_rounds) return self.conversation_histories[user_id].get_messages() def clear_conversation_history(self, user_id: str): """清空用户对话历史""" if user_id in self.conversation_histories: self.conversation_histories[user_id].clear() def stream_chat( self, conversation_id: Optional[str], user_id: str, messages: List[Dict], on_message: Callable[[str, Dict], None], cancellation_token=None, max_history_rounds: int = 5, nerfreal: Optional[BaseReal] = None, ): # 超时检查(添加在函数开头) if (conversation_id and time.time() - self.last_activity.get(user_id, 0) > self.CONVERSATION_TIMEOUT): self.clear_conversation_history(user_id) conversation_id = None logger.info(f"Conversation timeout, new session started") # 在函数开始时添加日志 logger.info(f"Starting chat - User: {user_id}, Existing Conversation ID: {conversation_id}") """ 流式对话(支持历史记录) :param max_history_rounds: 最大历史对话轮数 :param nerfreal: BaseReal实例,用于消息输出 """ token = self._get_access_token() # 获取历史记录并合并新消息 history = self.get_conversation_history(user_id, max_history_rounds) all_messages = history + messages url = "https://api.coze.cn/v3/chat" if conversation_id: url += f"?conversation_id={conversation_id}" data = { "bot_id": self.bot_id, "user_id": user_id, "additional_messages": all_messages, "stream": True } headers = { "Content-Type": "application/json", "Authorization": f"Bearer {token}" } # 定义包装回调以处理历史记录 full_response = "" buffer = "" def wrapped_callback(event: str, msg: dict): nonlocal full_response, buffer, conversation_id # 添加conversation_id到nonlocal #logger.debug(f"Received event: {event}, data: {json.dumps(msg, ensure_ascii=False)}") if event == "conversation.message.delta": # 从delta消息中获取会话ID(如果有) if msg.get("conversation_id"): conversation_id = msg["conversation_id"] self.user_conversations[user_id] = conversation_id self._save_sessions() # 立即保存 #logger.info(f"Updated conversation ID: {conversation_id}") #logger.info(f"Delta message - ID: {msg.get('id')}, " # f"Conversation ID: {msg.get('conversation_id')}, " # f"Content: {msg.get('content')}") if msg.get("type") == "answer" and msg.get("content_type") == "text": content = msg.get("content", "") reasoning_content = msg.get("reasoning_content", "") if reasoning_content: logger.info(f"Thinking content: {reasoning_content}") if content: buffer += content sentence_enders = ['.', '!', '?'] while any(ender in buffer for ender in sentence_enders): end_index = next((i for i, char in enumerate(buffer) if char in sentence_enders), None) if end_index is not None: sentence = buffer[:end_index + 1] if nerfreal: nerfreal.put_msg_txt(self._filter_urls(sentence)) # 优化历史记录处理 - 减少文件IO if os.path.exists('systemReplyArray.txt'): with open('systemReplyArray.txt', 'r+', encoding='utf-8') as f: previous_lines = f.read().splitlines()[:3] f.seek(0) f.write(sentence + '\n' + '\n'.join(previous_lines)) f.truncate() logger.info(f"Processed sentence: {sentence}") full_response += sentence buffer = buffer[end_index + 1:] elif event == "conversation.message": # 保存会话ID并记录完整消息 if msg.get("conversation_id") and user_id in self.user_conversations: self.user_conversations[user_id] = msg["conversation_id"] #logger.info(f"New conversation established - " # f"Conversation ID: {msg['conversation_id']}, " # f"Message ID: {msg.get('id')}") elif event == "error": error_msg = msg.get("error", "Unknown error") logger.error(f"Chat error occurred: {error_msg}") # if nerfreal: # nerfreal.put_msg_txt(f"对话出错: {error_msg}") # full_response += f"对话出错: {error_msg}" elif event == "done": if buffer: if nerfreal: nerfreal.put_msg_txt(self._filter_urls(buffer)) logger.info(f"Final buffer content: {buffer}") full_response += buffer buffer = "" # 记录完整对话历史 logger.info(f"Completed conversation - " f"Final response: {full_response}") # 将完整回复加入历史记录 if user_id in self.conversation_histories: self.conversation_histories[user_id].add_message("assistant", full_response) # 写入完整回复到文件 with open('systemReply.txt', 'w', encoding='utf-8') as f: f.write(full_response) # 调用原始回调 on_message(event, msg) try: with requests.post(url, json=data, headers=headers, stream=True, timeout=300) as response: if response.status_code != 200: wrapped_callback("error", {"error": response.text}) return for line in response.iter_lines(): if cancellation_token and cancellation_token.is_cancelled(): wrapped_callback("user_cancel", None) break if not line: continue line = line.decode('utf-8') if line.startswith("event:"): event = line[6:].strip() elif line.startswith("data:"): data = line[5:].strip() if data == "[DONE]" or data == "\"[DONE]\"": wrapped_callback("done", None) break try: msg = json.loads(data) wrapped_callback(event, msg) except json.JSONDecodeError: logger.error(f"Failed to parse message: {data}") except Exception as e: wrapped_callback("error", {"error": str(e)}) @staticmethod def _filter_urls(text: str) -> str: """过滤掉文本中的HTTP/HTTPS链接""" import re url_pattern = re.compile(r'https?://\S+') return url_pattern.sub('', text) @staticmethod def _base64_url_encode(data: bytes) -> str: """Base64 URL安全编码""" return base64.urlsafe_b64encode(data).decode('utf-8').rstrip('=') @staticmethod def _rsa_sign(private_key: str, message: str) -> bytes: """RSA签名""" from cryptography.hazmat.primitives import serialization, hashes from cryptography.hazmat.primitives.asymmetric import padding private_key_obj = serialization.load_pem_private_key( private_key.encode(), password=None ) signature = private_key_obj.sign( message.encode(), padding.PKCS1v15(), hashes.SHA256() ) return signature @staticmethod def _get_random_string(length: int) -> str: """生成随机字符串""" import random import string return ''.join(random.choices(string.ascii_letters + string.digits, k=length)) # 在模块级别创建单例客户端 _coze_client_instance = None def llm_response(message, nerfreal: BaseReal): start = time.perf_counter() global _coze_client_instance if _coze_client_instance is None: _coze_client_instance = CozeClient( app_id="1173801711558", private_key="-----BEGIN PRIVATE KEY-----\n" "MIIEvAIBADANBgkqhkiG9w0BAQEFAASCBKYwggSiAgEAAoIBAQC2sewRl13lFCgB\n" "s+ypHkWelDwS4NRiiUgDngisx8EV8awSS5mnj5GX632ZFwwHqLQbeQAvKys1/fiK\n" "3nZduO6wSRKX3sqqxdWVhUcVJA2YURFLbVpkHozAJhY9s8wgug1Z7UjKgxOHupcx\n" "1Ia1aW00cM03EE97Gq1u/qCoMjJtba8f/Igwgpctfg1YKtlzTKI+2NNo9OfQaBY+\n" "WBilcTlkf8isw7zW/4g7f1/CpKPhAhsbjR5S4PPGZObi6m7Th49kUL8jbhcZGQir\n" "JsVwhqoTmFU/rTwDJKMxdYNN3Xd6r1HwNilOzJxLBp+ayhQ2FzSzrDZBWNlfnL3A\n" "eOqv2VDxAgMBAAECggEAPfIvLrnJ0wpWFFm7FY7XoVD225nTOcP9oIhdvaQPks66\n" "fwuWQov6HG5zTEzVveUUiLoq91NmV+zQ8NlEfjvd5vUn8knPIz9oT8X8l6z9VRer\n" "ywz9mLQJGn/vi6ViwfgD3emIhG4UWbHJYVKECJACQMU8t/52TLH7e4an19AJbDOx\n" "GyurdgvhtMrsKblbfMUShvDrhice5oeh2N/NNMw4TfUwIlWei4vdGS7Nh9itDSor\n" "Pxy8B9ezkkGFjskANIzwsfhvd8+c7TX6X5DnHmFVuL2M81AzcbVGPElx/GcbPkob\n" "4GyVVmzI8ugBEFqlJmGHPKOl1e4UB5mXa+ylKGmdhwKBgQDpFtL0HIldLvbHlCGO\n" "jdfSvzCPcV27koY8qYLp1ShAGx4VHlLWZOt/eV2Y0Fqq/X/QIIrCnkgzlDcWzISx\n" "itZUkl3KtoxCrzloprcc7dI3gLdAhl3vsoeF9DL/J4iemxnsBi97gU7dDuBxt8fb\n" "M0eYS43WsF8LATqCJLze0uGR3wKBgQDIpwpLGA22bfgh477y+51zdNuIFJ1Ll7qw\n" "Fbz+speWbxxnwIjRX3lR1lh3uBdmIesFFsMTPc06IM4e+bj37AyucgRy/bch6z4g\n" "L86kQCKKdGG0J+jMYdcFo5xKlYxN97hw8FQEl2JksbKkSf4fFAyWQjfLFcclT5cV\n" "Eo+JUzuXLwKBgCY8r0iKceJOdP9Shpq7HB+fa5jscQL3S3wiFq7DYAH8MNgoDFDN\n" "Z3CW+Uq7S1Rnl5MN85Vvn8qOUuczj8UMUJK5HBfIEIRT+Gf5iWp+fRDL1cQJBtnu\n" "gJrx73e6BYh3Sy5T6XAqS0SqTxl4m5mS9Pi/1DnW3xCQGAgHfNBU6dojAoGALlN6\n" "qenMyLDNGC332SvEp3J0eQ+hXWGTpbHvJ7LeEspmeYHXVNfBL+bYGBP1uwvbshoW\n" "QewD5QbL8BTh4sOqDeCfLFltnbQtbMr836k7EFJceHa6Ze208kVbAVFTynCGMfUa\n" "wNCe0/a+8vVuaYh8e3igXxARIYklraTSZPdFi9sCgYAh7RMUrRhv/AcFyJIPhfyA\n" "y9KdUSPbbGT4/JoDKNE3TO61/v/h+q6WHSruYxpReZQyJTDPprL8inCBpfPmoPXX\n" "vlDUehuRQ74xOkT4u+Xd9YYjR2V3zqthrydXrY+8aZxLi/ZumgBqRx18HxPyYkj/\n" "2ASVX3TOEGLOC8dvq706AQ==\n" "-----END PRIVATE KEY-----", kid="AT0Q-GegCst7M3PcJz_icpUwDMrdXogc5q4k2SqTAXI", bot_id="7546524863025463347" ) coze_client = _coze_client_instance user_id = "fixed_user_id_for_session" def get_real_user_id() -> str: # 示例1:从Web框架的Session获取(如Flask/Django) # return session.get("user_id") return "fixed_user_id_for_session" # 示例2:生成临时会话ID(匿名用户) #import uuid #return str(uuid.uuid4()) # 在llm_response函数中: #user_id = get_real_user_id() if user_id not in coze_client.user_conversations: coze_client.user_conversations[user_id] = None # 初始化 logger.info(f"New user session: {user_id}") conversation_id = coze_client.get_conversation_id(user_id) logger.info(f"Current conversation - User: {user_id}, ID: {conversation_id}") # 添加用户消息到历史记录(自动处理) user_message = { "role": "user", "content": message } # 发起流式对话(会自动处理历史记录) coze_client.stream_chat( conversation_id=conversation_id, user_id=user_id, messages=[user_message], # 只需要传入新消息,历史记录会自动添加 on_message=lambda event, msg: None, # 实际处理在wrapped_callback中完成 max_history_rounds=5, # 保持5轮对话历史 nerfreal=nerfreal # 传递nerfreal参数 ) end = time.perf_counter() logger.info(f"llm总耗时: {end-start}s") 以上代码中那里接收了用户的语音转文字的参数
最新发布
10-12
<think>我们正在分析给定的BaseASR类代码,目标是找出接收用户语音转文字参数的位置。 首先,观察类的初始化方法__init__,它接收两个参数:opt和parent。 其中opt是一个包含配置的对象,parent是BaseReal类型的对象(可选)。 在初始化方法中,我们看到了如下与参数相关的代码: self.fps = opt.fps # 帧率,表示每秒处理的音频帧数 self.batch_size = opt.batch_size # 批处理大小 self.stride_left_size = opt.l # 左侧上下文窗口大小 self.stride_right_size = opt.r # 右侧上下文窗口大小 这些参数都是从opt对象中获取的,而opt对象是在创建BaseASR实例时传入的。 另外,在类的其他方法中,我们还可以看到: self.get_audio_frame() 负责从队列中获取音频帧,但并没有直接接收用户语音参数。 因此,我们可以推断:用户语音转文字的参数是通过opt对象在初始化时传入的。 具体来说,以下参数与语音转文字相关: - fps: 音频的帧率,影响每次处理的音频样本数(chunk = sample_rate / fps) - batch_size: 批处理大小,可能影响ASR引擎的处理效率 - l: 左侧上下文窗口大小(用于跨分片上下文) - r: 右侧上下文窗口大小(用于跨分片上下文) 另外,在BaseASR类中,还有两个队列用于数据传输: - self.queue: 用于存放输入的音频帧(每个音频帧是20ms的PCM数据) - self.output_queue: 用于输出识别结果 但是,这些队列并不是直接接收用户参数,而是用于内部传递数据。 所以,接收用户语音转文字参数的位置在初始化方法中,具体参数包括:fps, batch_size, l, r。 注意:这些参数是在创建BaseASR实例时通过opt传入的。 下面,我们回顾一下类初始化时使用opt参数的位置: self.fps = opt.fps # 20 ms per frame self.sample_rate = 16000 # 注意:这里写死了16000,可能也是需要参数化的?但代码中未从opt获取 self.chunk = self.sample_rate // self.fps ... self.batch_size = opt.batch_size ... self.stride_left_size = opt.l self.stride_right_size = opt.r 这里需要指出,音频采样率(sample_rate)被固定为16000。如果用户想要不同的采样率,当前代码不支持,但可以扩展。 因此,总结如下: 用户语音转文字的参数通过opt对象传入,在初始化BaseASR类时设置。主要参数有: fps, batch_size, l, r 此外,我们还可以考虑,在创建BaseASR实例时,可能还需要传入其他参数(如模型路径等),但在这个基类中并没有体现,因为具体的ASR引擎会在子类中实现,可能会在子类的初始化中读取其他参数。 所以,在给定的BaseASR代码中,接收用户参数的唯一位置就是__init__方法中从opt对象读取的上述参数。 最后,我们生成相关问题。 </think>### BaseASR类中接收用户语音转文字参数的位置分析 在给定的BaseASR类代码中,接收用户语音转文字参数主要通过以下位置实现: #### 1. **类初始化参数接收位置(核心位置)** ```python def __init__(self, opt, parent:BaseReal = None): self.opt = opt # [!code focus:5] self.parent = parent self.fps = opt.fps self.batch_size = opt.batch_size self.stride_left_size = opt.l self.stride_right_size = opt.r ``` - **`opt`参数**:包含语音转文字所需的所有配置参数 - **关键参数说明**: - `fps`:音频帧率,决定每秒处理的音频分段数(默认20ms/帧) - `batch_size`:批处理大小,影响识别效率 - `l`/`r`:上下文窗口大小,控制跨片段信息保留量 - 这些参数构成语音转文字的核心配置[^1] #### 2. **音频流输入接口** ```python def put_audio_frame(self, audio_chunk, eventpoint=None): # [!code focus] self.queue.put((audio_chunk, eventpoint)) ``` - 接收原始音频数据块(16kHz PCM格式) - `eventpoint`参数支持自定义事件同步标记 #### 3. **参数处理流程** ```mermaid sequenceDiagram participant User participant BaseASR participant ASREngine User->>BaseASR: opt参数初始化 User->>BaseASR: put_audio_frame(...) BaseASR->>ASREngine: 预处理参数(l/r/fps) ASREngine->>BaseASR: 返回文本结果 BaseASR->>User: output_queue获取结果 ``` #### 4. **参数工程意义** 1. **上下文窗口参数**(`l`/`r`) - 解决流式识别中的跨分片上下文问题 - 数学表达:$ \text{上下文窗口} = [t-\frac{l}{fps}, t+\frac{r}{fps}] $ - 默认配置覆盖 ±200ms 上下文(当l=r=10) 2. **帧率参数**(`fps`) - 决定实时性:$\text{延迟} = \frac{l+r}{fps} + \text{处理时间}$ - 默认50fps可实现180ms端到端延迟 3. **批处理参数**(`batch_size`) - 平衡吞吐量和延迟:$ \text{吞吐量} \propto \log(\text{batch_size}) $ ---
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