854 lines
46 KiB
Python
854 lines
46 KiB
Python
import os, json, re, time, requests, sys, threading, urllib3, base64, mimetypes
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from datetime import datetime
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urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
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def _load_mykeys():
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try:
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import mykey; return {k: v for k, v in vars(mykey).items() if not k.startswith('_')}
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except ImportError: pass
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p = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'mykey.json')
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if not os.path.exists(p): raise Exception('[ERROR] mykey.py or mykey.json not found, please create one from mykey_template.')
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with open(p, encoding='utf-8') as f: return json.load(f)
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mykeys = _load_mykeys()
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proxy = mykeys.get("proxy", 'http://127.0.0.1:2082')
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proxies = {"http": proxy, "https": proxy} if proxy else None
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def compress_history_tags(messages, keep_recent=12, max_len=1000):
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"""Compress <thinking>/<tool_use>/<tool_result> tags in older messages to save tokens.
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Supports both prompt-style (ClaudeSession/LLMSession) and content-style (NativeClaudeSession) messages."""
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compress_history_tags._cd = getattr(compress_history_tags, '_cd', 0) + 1
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if compress_history_tags._cd % 5 != 0: return messages
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_pats = {tag: re.compile(rf'(<{tag}>)([\s\S]*?)(</{tag}>)') for tag in ('thinking', 'tool_use', 'tool_result')}
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def _trunc(text):
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for pat in _pats.values(): text = pat.sub(lambda m: m.group(1) + m.group(2)[:max_len] + '...' + m.group(3) if len(m.group(2)) > max_len else m.group(0), text)
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return text
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for i, msg in enumerate(messages):
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if i >= len(messages) - keep_recent: break
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if 'prompt' in msg and 'orig' not in msg:
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msg['orig'] = msg['prompt']
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msg['prompt'] = _trunc(msg['prompt'])
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elif 'content' in msg and 'prompt' not in msg:
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c = msg['content']
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if isinstance(c, str): msg['content'] = _trunc(c)
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elif isinstance(c, list):
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for block in c:
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if isinstance(block, dict) and block.get('type') == 'text' and isinstance(block.get('text'), str):
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block['text'] = _trunc(block['text'])
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return messages
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def auto_make_url(base, path):
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b, p = base.rstrip('/'), path.strip('/')
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if b.endswith('$'): return b[:-1].rstrip('/')
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return b if b.endswith(p) else f"{b}/{p}" if re.search(r'/v\d+$', b) else f"{b}/v1/{p}"
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def build_multimodal_content(prompt_text, image_paths):
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parts = []
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text = prompt_text if isinstance(prompt_text, str) else str(prompt_text or "")
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if text.strip():
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parts.append({"type": "text", "text": text})
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else:
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parts.append({"type": "text", "text": "请查看图片并理解用户意图。"})
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for path in image_paths or []:
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if not path or not os.path.isfile(path): continue
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try:
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mime = mimetypes.guess_type(path)[0] or "image/png"
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if not mime.startswith("image/"): mime = "image/png"
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with open(path, "rb") as f:
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data_url = f"data:{mime};base64,{base64.b64encode(f.read()).decode('ascii')}"
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parts.append({"type": "image_url", "image_url": {"url": data_url}})
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except Exception as e:
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print(f"[WARN] encode image failed {path}: {e}")
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return parts
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class SiderLLMSession:
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def __init__(self, cfg):
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from sider_ai_api import Session # 不使用sider的话没必要安装这个包
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self._core = Session(cookie=cfg['apikey'], proxies=proxies)
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self.default_model = cfg.get('model', 'gemini-3.0-flash')
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def ask(self, prompt, model=None, stream=False):
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if model is None: model = self.default_model
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if len(prompt) > 28000:
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print(f"[Warn] Prompt too long ({len(prompt)} chars), truncating.")
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prompt = prompt[-28000:]
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full_text = self._core.chat(prompt, model, stream=False)
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if stream: return iter([full_text]) # gen有奇怪的空回复或死循环行为，sider足够快
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return full_text
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class ClaudeSession:
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def __init__(self, cfg):
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self.api_key = cfg['apikey']; self.api_base = cfg['apibase'].rstrip('/')
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self.default_model = cfg.get('model', 'claude-opus')
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self.context_win = cfg.get('context_win', 18000)
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self.raw_msgs, self.lock = [], threading.Lock()
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self.prompt_cache = cfg.get('prompt_cache', False)
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def _trim_messages(self, messages):
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if not self.prompt_cache: compress_history_tags(messages)
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total = sum(len(m['prompt']) for m in messages)
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if total <= self.context_win * 3: return messages
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target, current, result = self.context_win * 3 * 0.6, 0, []
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for msg in reversed(messages):
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if (msg_len := len(msg['prompt'])) + current <= target:
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result.append(msg); current += msg_len
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else: break
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if current > self.context_win * 2.7: print(f'[DEBUG] {len(result)} contexts, whole length {current//3} tokens.')
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return result[::-1] or messages[-2:]
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def raw_ask(self, messages, model=None, temperature=0.5, max_tokens=6144):
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model = model or self.default_model
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if 'kimi' in model.lower() or 'moonshot' in model.lower(): temperature = 1.0 # kimi/moonshot only accepts temp 1.0
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headers = {"x-api-key": self.api_key, "Content-Type": "application/json", "anthropic-version": "2023-06-01", "anthropic-beta": "prompt-caching-2024-07-31"}
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payload = {"model": model, "messages": messages, "temperature": temperature, "max_tokens": max_tokens, "stream": True}
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try:
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with requests.post(auto_make_url(self.api_base, "messages"), headers=headers, json=payload, stream=True, timeout=(5,30)) as r:
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r.raise_for_status()
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for line in r.iter_lines():
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if not line: continue
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line = line.decode("utf-8") if isinstance(line, bytes) else line
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if not line.startswith("data:"): continue
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data = line[5:].lstrip()
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if data == "[DONE]": break
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try:
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obj = json.loads(data)
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if obj.get("type") == "message_start":
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usage = obj.get("message", {}).get("usage", {})
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ci, cr, inp = usage.get("cache_creation_input_tokens", 0), usage.get("cache_read_input_tokens", 0), usage.get("input_tokens", 0)
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print(f"[Cache] input={inp} creation={ci} read={cr}")
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elif obj.get("type") == "content_block_delta" and obj.get("delta", {}).get("type") == "text_delta":
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text = obj["delta"].get("text", "")
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if text: yield text
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except: pass
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except Exception as e: yield f"Error: {str(e)}"
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def make_messages(self, raw_list):
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trimmed = self._trim_messages(raw_list)
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msgs = [{"role": m['role'], "content": m['prompt']} for m in trimmed]
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for i in range(len(msgs)-1, -1, -1):
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if msgs[i]["role"] == "assistant":
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msgs[i]["content"] = [{"type": "text", "text": msgs[i]["content"], "cache_control": {"type": "ephemeral"}}]
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break
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return msgs
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def ask(self, prompt, model=None, stream=False):
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def _ask_gen():
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content = ''
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with self.lock:
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self.raw_msgs.append({"role": "user", "prompt": prompt})
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messages = self.make_messages(self.raw_msgs)
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for chunk in self.raw_ask(messages, model):
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content += chunk; yield chunk
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if not content.startswith("Error:"): self.raw_msgs.append({"role": "assistant", "prompt": content})
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return _ask_gen() if stream else ''.join(list(_ask_gen()))
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class LLMSession:
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def __init__(self, cfg):
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self.api_key = cfg['apikey']; self.api_base = cfg['apibase'].rstrip('/')
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self.default_model = cfg['model']
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self.context_win = cfg.get('context_win', 18000)
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self.raw_msgs, self.messages = [], []
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proxy = cfg.get('proxy')
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self.proxies = {"http": proxy, "https": proxy} if proxy else None
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self.prompt_cache = cfg.get('prompt_cache', False)
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self.lock = threading.Lock()
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self.max_retries = max(0, int(cfg.get('max_retries', 2)))
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self.connect_timeout = max(1, int(cfg.get('connect_timeout', 10)))
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self.read_timeout = max(5, int(cfg.get('read_timeout', 120)))
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effort = cfg.get('reasoning_effort')
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effort = None if effort is None else str(effort).strip().lower()
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self.reasoning_effort = effort if effort in ['none', 'minimal','low', 'medium', 'high', 'xhigh'] else None
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if effort and self.reasoning_effort is None: print(f"[WARN] Invalid reasoning_effort {effort!r}, ignored.")
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mode = str(cfg.get('api_mode', 'chat_completions')).strip().lower().replace('-', '_')
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if mode in ["responses", "response"]: self.api_mode = "responses"
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else: self.api_mode = "chat_completions"
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def _retry_delay(self, resp, attempt):
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retry_after = None
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try:
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if resp is not None: retry_after = (resp.headers or {}).get("retry-after")
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if retry_after is not None: retry_after = float(retry_after)
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except: retry_after = None
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if retry_after is None: retry_after = min(30.0, 1.5 * (2 ** attempt))
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return max(0.5, float(retry_after))
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def _to_responses_input(self, messages):
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result = []
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for msg in messages:
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role = str(msg.get("role", "user")).lower()
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if role not in ["user", "assistant", "system", "developer"]: role = "user"
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content = msg.get("content", "")
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text_type = "output_text" if role == "assistant" else "input_text"
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parts = []
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if isinstance(content, str):
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if content: parts.append({"type": text_type, "text": content})
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elif isinstance(content, list):
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for part in content:
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if not isinstance(part, dict): continue
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ptype = part.get("type")
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if ptype == "text":
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text = part.get("text", "")
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if text: parts.append({"type": text_type, "text": text})
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elif ptype == "image_url":
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url = (part.get("image_url") or {}).get("url", "")
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if url and role != "assistant": parts.append({"type": "input_image", "image_url": url})
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if len(parts) == 0: parts = [{"type": text_type, "text": str(content)}]
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result.append({"role": role, "content": parts})
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return result
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def raw_ask(self, messages, model=None, temperature=0.5):
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if model is None: model = self.default_model
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if 'kimi' in model.lower() or 'moonshot' in model.lower(): temperature = 1.0 # kimi/moonshot only accepts temp 1.0
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headers = {"Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json", "Accept": "text/event-stream"}
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if self.api_mode == "responses":
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url = auto_make_url(self.api_base, "responses")
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payload = {"model": model, "input": self._to_responses_input(messages), "temperature": temperature, "stream": True}
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if self.reasoning_effort: payload["reasoning"] = {"effort": self.reasoning_effort}
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else:
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url = auto_make_url(self.api_base, "chat/completions")
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payload = {"model": model, "messages": messages, "temperature": temperature, "stream": True}
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if self.reasoning_effort: payload["reasoning_effort"] = self.reasoning_effort
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for attempt in range(self.max_retries + 1):
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streamed_any = False
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try:
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with requests.post(url, headers=headers, json=payload, stream=True,
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timeout=(self.connect_timeout, self.read_timeout), proxies=self.proxies) as r:
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if r.status_code >= 400:
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retryable = r.status_code in [408, 409, 425, 429, 500, 502, 503, 504]
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if retryable and attempt < self.max_retries:
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delay = self._retry_delay(r, attempt)
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print(f"[LLM Retry] HTTP {r.status_code}, retry in {delay:.1f}s ({attempt+1}/{self.max_retries+1})")
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time.sleep(delay)
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continue
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r.raise_for_status()
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buffer = ''; seen_delta = False
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for line in r.iter_lines():
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line = line.decode("utf-8") if isinstance(line, bytes) else line
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if not line or not line.startswith("data:"): continue
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data = line[5:].lstrip()
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if data == "[DONE]": break
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try: obj = json.loads(data)
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except: continue
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if self.api_mode == "responses":
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etype = obj.get("type", "")
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delta = obj.get("delta", "") if etype == "response.output_text.delta" else ""
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if delta:
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streamed_any = True; seen_delta = True
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yield delta; buffer += delta
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elif etype == "response.output_text.done" and not seen_delta:
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text = obj.get("text", "")
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if text:
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streamed_any = True
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yield text; buffer += text
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elif etype == "error":
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err = obj.get("error", {})
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emsg = err.get("message", str(err)) if isinstance(err, dict) else str(err)
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if emsg:
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yield f"Error: {emsg}"
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return
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elif etype == "response.completed":
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break
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else:
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ch = (obj.get("choices") or [{}])[0]
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finish_reason = ch.get("finish_reason")
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delta = (ch.get("delta") or {}).get("content")
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if delta:
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streamed_any = True
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yield delta; buffer += delta
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if finish_reason: break
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#if '</tool_use>' in buffer[-30:]: break
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return
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except requests.HTTPError as e:
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resp = getattr(e, "response", None)
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status = getattr(resp, "status_code", "unknown")
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retryable = isinstance(status, int) and status in [408, 409, 425, 429, 500, 502, 503, 504]
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if retryable and attempt < self.max_retries and not streamed_any:
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delay = self._retry_delay(resp, attempt)
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print(f"[LLM Retry] HTTP {status}, retry in {delay:.1f}s ({attempt+1}/{self.max_retries+1})")
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time.sleep(delay)
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continue
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body = ""
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try: body = (resp.text or "").strip()
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except: body = ""
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body = body[:1200] if body else "<empty>"
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rid = ""; retry_after = ""; ct = ""
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try:
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h = resp.headers or {}
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rid = h.get("x-request-id") or h.get("request-id") or ""
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retry_after = h.get("retry-after") or ""
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ct = h.get("content-type") or ""
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except: pass
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yield f"Error: HTTP {status} {str(e)}; content_type: {ct or '<empty>'}; retry_after: {retry_after or '<empty>'}; request_id: {rid or '<empty>'}; body: {body}"
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return
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except (requests.Timeout, requests.ConnectionError) as e:
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if attempt < self.max_retries and not streamed_any:
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delay = self._retry_delay(None, attempt)
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print(f"[LLM Retry] {type(e).__name__}, retry in {delay:.1f}s ({attempt+1}/{self.max_retries+1})")
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time.sleep(delay)
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continue
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yield f"Error: {type(e).__name__}: {str(e)}"
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return
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except Exception as e:
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yield f"Error: {str(e)}"
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return
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def make_messages(self, raw_list, omit_images=True):
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if not self.prompt_cache: compress_history_tags(raw_list)
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messages = []
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for i, msg in enumerate(raw_list):
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prompt = msg['prompt']
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image = msg.get('image')
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if omit_images and image: messages.append({"role": msg['role'], "content": "[Image omitted, if you needed it, ask me]\n" + prompt})
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elif not omit_images and image:
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messages.append({"role": msg['role'], "content": [
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{"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image}"}},
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{"type": "text", "text": prompt} ]})
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else:
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messages.append({"role": msg['role'], "content": prompt})
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return messages
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def summary_history(self, model=None):
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if model is None: model = self.default_model
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with self.lock:
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keep = 0; tok = 0
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for m in reversed(self.raw_msgs):
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l = len(str(m))//3
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if tok + l > self.context_win*0.2: break
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tok += l; keep += 1
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keep = max(2, keep)
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old, self.raw_msgs = self.raw_msgs[:-keep], self.raw_msgs[-keep:]
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if len(old) == 0: old = self.raw_msgs; self.raw_msgs = []
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p = "Summarize prev summary and prev conversations into compact memory (facts/decisions/constraints/open questions). Do NOT restate long schemas. The new summary should less than 1000 tokens. Permit dropping non-important things.\n"
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messages = self.make_messages(old, omit_images=True)
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messages += [{"role":"user", "content":p}]
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msg_lens = [1000 if isinstance(m["content"], list) else len(str(m["content"]))//3 for m in messages]
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summary = ''.join(list(self.raw_ask(messages, model, temperature=0.1)))
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print('[Debug] Summary length:', len(summary)//3, '; Orig context lengths:', str(msg_lens))
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if not summary.startswith("Error:"):
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self.raw_msgs.insert(0, {"role":"assistant", "prompt":"Prev summary:\n"+summary, "image":None})
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else: self.raw_msgs = old + self.raw_msgs # 不做了，下次再做
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def ask(self, prompt, model=None, image_base64=None, stream=False):
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if model is None: model = self.default_model
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def _ask_gen():
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content = ''
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with self.lock:
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self.raw_msgs.append({"role": "user", "prompt": prompt, "image": image_base64})
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messages = self.make_messages(self.raw_msgs[:-1], omit_images=True)
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messages += self.make_messages([self.raw_msgs[-1]], omit_images=False)
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msg_lens = [1000 if isinstance(m["content"], list) else len(str(m["content"]))//3 for m in messages]
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total_len = sum(msg_lens) # estimate token count
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gen = self.raw_ask(messages, model)
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for chunk in gen:
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content += chunk; yield chunk
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if not content.startswith("Error:"):
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self.raw_msgs.append({"role": "assistant", "prompt": content, "image": None})
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if total_len > self.context_win // 2: print(f"[Debug] Whole context length {total_len} {str(msg_lens)}.")
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if total_len > self.context_win:
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yield '[NextWillSummary]'
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threading.Thread(target=self.summary_history, daemon=True).start()
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if stream: return _ask_gen()
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return ''.join(list(_ask_gen()))
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class GeminiSession:
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def __init__(self, cfg):
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self.api_key = cfg.get('apikey')
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if not self.api_key: raise ValueError("google_api_key 未配置或为空，请在 mykey.py 中设置")
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self.default_model = cfg.get('model', 'gemini-2.0-flash-001')
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p = cfg.get('proxy', proxy)
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self.proxies = {"http":p, "https":p} if p else None
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def ask(self, prompt, model=None, stream=False):
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if model is None: model = self.default_model
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url = f"https://generativelanguage.googleapis.com/v1/models/{model}:generateContent?key={self.api_key}"
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headers = {"Content-Type":"application/json"}
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data = {"contents":[{"role":"user","parts":[{"text":prompt}]}]}
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try:
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kw = {"headers":headers, "json":data, "timeout":60, 'proxies': self.proxies}
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r = requests.post(url, **kw)
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except Exception as e:
|
||
return f"[GeminiError] request failed: {e}"
|
||
if r.status_code != 200:
|
||
body = r.text[:500].replace("\n"," ")
|
||
return f"[GeminiError] HTTP {r.status_code}: {body}"
|
||
try:
|
||
obj = r.json(); cands = obj.get("candidates") or []
|
||
if not cands: return "[GeminiError] empty candidates"
|
||
parts = (cands[0].get("content") or {}).get("parts") or []
|
||
full_text = "".join(p.get("text","") for p in parts)
|
||
except Exception as e:
|
||
return f"[GeminiError] invalid response format: {e}"
|
||
return iter([full_text]) if stream else full_text
|
||
|
||
class XaiSession:
|
||
def __init__(self, cfg):
|
||
import xai_sdk
|
||
from xai_sdk.chat import user, system
|
||
self._user, self._system = user, system
|
||
self.default_model = cfg.get('model', 'grok-4-1-fast-non-reasoning')
|
||
self._last_response_id = None # 多轮对话链
|
||
os.environ["XAI_API_KEY"] = cfg['apikey']
|
||
proxy = cfg.get('proxy', 'http://127.0.0.1:2082')
|
||
if not proxy.startswith("http"): proxy = f"http://{proxy}"
|
||
os.environ.setdefault("grpc_proxy", proxy)
|
||
self._client = xai_sdk.Client()
|
||
def ask(self, prompt, model=None, system_prompt=None, stream=False):
|
||
"""发送消息，自动串联多轮对话；stream=True返回生成器"""
|
||
mdl = model or self.default_model
|
||
try:
|
||
kw = dict(model=mdl, store_messages=True)
|
||
if self._last_response_id: kw["previous_response_id"] = self._last_response_id
|
||
chat = self._client.chat.create(**kw)
|
||
if system_prompt: chat.append(self._system(system_prompt))
|
||
chat.append(self._user(prompt))
|
||
if stream: return self._stream(chat)
|
||
resp = chat.sample()
|
||
self._last_response_id = resp.id
|
||
return resp.content
|
||
except Exception as e:
|
||
err = f"[XaiError] {e}"
|
||
return iter([err]) if stream else err
|
||
def _stream(self, chat):
|
||
try:
|
||
last_resp = None
|
||
for resp, chunk in chat.stream():
|
||
last_resp = resp
|
||
if chunk and chunk.content: yield chunk.content
|
||
if last_resp and hasattr(last_resp, 'id'): self._last_response_id = last_resp.id
|
||
except Exception as e:
|
||
yield f"[XaiError] {e}"
|
||
def reset(self): self._last_response_id = None
|
||
|
||
|
||
class NativeOAISession:
|
||
def __init__(self, cfg):
|
||
self.api_key = cfg['apikey']; self.api_base = cfg['apibase'].rstrip('/')
|
||
self.default_model = cfg.get('model', 'gpt-4o')
|
||
self.context_win = cfg.get('context_win', 24000)
|
||
proxy = cfg.get('proxy')
|
||
self.proxies = {"http": proxy, "https": proxy} if proxy else None
|
||
self.history = []; self.system = None; self.lock = threading.Lock()
|
||
def set_system(self, system_text): self.system = system_text
|
||
|
||
def raw_ask(self, messages, tools=None, system=None, model=None, temperature=0.5, max_tokens=6144, **kw):
|
||
"""OpenAI streaming. yields text chunks, generator return = list[content_block]"""
|
||
model = model or self.default_model
|
||
headers = {"Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json"}
|
||
msgs = ([{"role": "system", "content": system}] if system else []) + messages
|
||
payload = {"model": model, "messages": msgs, "temperature": temperature, "max_tokens": max_tokens, "stream": True}
|
||
if tools: payload["tools"] = tools
|
||
try:
|
||
resp = requests.post(auto_make_url(self.api_base, "chat/completions"), headers=headers, json=payload, stream=True, timeout=120, proxies=self.proxies)
|
||
if resp.status_code != 200:
|
||
err = f"Error: HTTP {resp.status_code} {resp.text[:500]}"; yield err; return [{"type": "text", "text": err}]
|
||
except Exception as e:
|
||
err = f"Error: {e}"; yield err; return [{"type": "text", "text": err}]
|
||
content_text = ""; tc_buf = {} # index -> {id, name, args_str}
|
||
for line in resp.iter_lines():
|
||
if not line: continue
|
||
line = line.decode('utf-8', errors='replace') if isinstance(line, bytes) else line
|
||
if not line.startswith("data: "): continue
|
||
data_str = line[6:]
|
||
if data_str.strip() == "[DONE]": break
|
||
try: evt = json.loads(data_str)
|
||
except: continue
|
||
delta = evt.get("choices", [{}])[0].get("delta", {})
|
||
if delta.get("content"):
|
||
text = delta["content"]; content_text += text; yield text
|
||
for tc in delta.get("tool_calls", []):
|
||
idx = tc.get("index", 0)
|
||
if idx not in tc_buf: tc_buf[idx] = {"id": tc.get("id", ""), "name": "", "args": ""}
|
||
if tc.get("function", {}).get("name"): tc_buf[idx]["name"] = tc["function"]["name"]
|
||
if tc.get("function", {}).get("arguments"): tc_buf[idx]["args"] += tc["function"]["arguments"]
|
||
blocks = []
|
||
if content_text: blocks.append({"type": "text", "text": content_text})
|
||
for idx in sorted(tc_buf):
|
||
tc = tc_buf[idx]
|
||
try: inp = json.loads(tc["args"]) if tc["args"] else {}
|
||
except: inp = {"_raw": tc["args"]}
|
||
blocks.append({"type": "tool_use", "id": tc["id"], "name": tc["name"], "input": inp})
|
||
return blocks
|
||
|
||
def ask(self, msg, tools=None, model=None, **kw):
|
||
"""Managed ask with history. yields text chunks, return MockResponse"""
|
||
if isinstance(msg, str): msg = {"role": "user", "content": msg}
|
||
elif isinstance(msg, list): msg = {"role": "user", "content": msg}
|
||
with self.lock:
|
||
self.history.append(msg)
|
||
while len(self.history) > 2:
|
||
cost = sum(len(json.dumps(m, ensure_ascii=False)) for m in self.history) + len(self.system or '')
|
||
if cost <= self.context_win * 4: break
|
||
self.history.pop(0); self.history.pop(0)
|
||
messages = list(self.history)
|
||
content_blocks = None
|
||
gen = self.raw_ask(messages, tools, self.system, model)
|
||
try:
|
||
while True: yield next(gen)
|
||
except StopIteration as e: content_blocks = e.value or []
|
||
if content_blocks and not (len(content_blocks) == 1 and content_blocks[0].get("text", "").startswith("Error:")):
|
||
self.history.append({"role": "assistant", "content": content_blocks})
|
||
text_parts = [b["text"] for b in content_blocks if b.get("type") == "text"]
|
||
content = "\n".join(text_parts).strip()
|
||
tool_calls = [MockToolCall(b["name"], b.get("input", {}), id=b.get("id", "")) for b in content_blocks if b.get("type") == "tool_use"]
|
||
return MockResponse("", content, tool_calls, str(content_blocks))
|
||
|
||
|
||
class NativeClaudeSession:
|
||
def __init__(self, cfg):
|
||
self.api_key = cfg['apikey']; self.api_base = cfg['apibase'].rstrip('/')
|
||
self.default_model = cfg.get('model', 'claude-opus')
|
||
self.context_win = cfg.get('context_win', 32000)
|
||
self.history = []
|
||
self.system = None
|
||
self.lock = threading.Lock()
|
||
def set_system(self, system_text): self.system = system_text
|
||
|
||
def raw_ask(self, messages, tools=None, system=None, model=None, temperature=0.5, max_tokens=6144):
|
||
"""底层API调用。yields text chunks，generator return = list[content_block]"""
|
||
model = model or self.default_model
|
||
headers = {"x-api-key": self.api_key, "Content-Type": "application/json", "anthropic-version": "2023-06-01", "anthropic-beta": "prompt-caching-2024-07-31"}
|
||
payload = {"model": model, "messages": messages, "temperature": temperature, "max_tokens": max_tokens, "stream": True}
|
||
if tools:
|
||
tools = [dict(t) for t in tools]; tools[-1]["cache_control"] = {"type": "ephemeral"}
|
||
payload["tools"] = tools
|
||
if system: payload["system"] = [{"type": "text", "text": system, "cache_control": {"type": "ephemeral"}}]
|
||
# 历史消息缓存：最后一个assistant消息加cache_control
|
||
for i in range(len(messages) - 1, -1, -1):
|
||
if messages[i]["role"] == "assistant":
|
||
c = messages[i].get("content", [])
|
||
if isinstance(c, list) and c: messages[i] = {**messages[i], "content": [*c[:-1], {**c[-1], "cache_control": {"type": "ephemeral"}}]}
|
||
break
|
||
try:
|
||
resp = requests.post(auto_make_url(self.api_base, "messages"), headers=headers, json=payload, stream=True, timeout=120)
|
||
if resp.status_code != 200:
|
||
error_msg = f"Error: HTTP {resp.status_code} {resp.text[:500]}"
|
||
yield error_msg
|
||
return [{"type": "text", "text": error_msg}]
|
||
except Exception as e:
|
||
error_msg = f"Error: {e}"
|
||
yield error_msg
|
||
return [{"type": "text", "text": error_msg}]
|
||
|
||
content_blocks = []; current_block = None; tool_json_buf = ""
|
||
for line in resp.iter_lines():
|
||
if not line: continue
|
||
line = line.decode('utf-8') if isinstance(line, bytes) else line
|
||
data_str = line[6:]
|
||
if data_str.strip() == "[DONE]": break
|
||
try: evt = json.loads(data_str)
|
||
except: continue
|
||
evt_type = evt.get("type", "")
|
||
if evt_type == "content_block_start":
|
||
block = evt.get("content_block", {})
|
||
if block.get("type") == "text": current_block = {"type": "text", "text": ""}
|
||
elif block.get("type") == "tool_use":
|
||
current_block = {"type": "tool_use", "id": block.get("id", ""), "name": block.get("name", ""), "input": {}}
|
||
tool_json_buf = ""
|
||
elif evt_type == "content_block_delta":
|
||
delta = evt.get("delta", {})
|
||
if delta.get("type") == "text_delta":
|
||
text = delta.get("text", "")
|
||
if current_block: current_block["text"] += text
|
||
yield text
|
||
elif delta.get("type") == "input_json_delta": tool_json_buf += delta.get("partial_json", "")
|
||
elif evt_type == "content_block_stop":
|
||
if current_block:
|
||
if current_block["type"] == "tool_use":
|
||
try: current_block["input"] = json.loads(tool_json_buf) if tool_json_buf else {}
|
||
except: current_block["input"] = {"_raw": tool_json_buf}
|
||
content_blocks.append(current_block)
|
||
current_block = None
|
||
return content_blocks
|
||
|
||
def ask(self, msg, tools=None, model=None):
|
||
"""增量ask。msg: str|list[content_block]|dict。yields text chunks, return MockResponse"""
|
||
if isinstance(msg, str): msg = {"role": "user", "content": msg}
|
||
elif isinstance(msg, list): msg = {"role": "user", "content": msg}
|
||
with self.lock:
|
||
self.history.append(msg)
|
||
compress_history_tags(self.history)
|
||
cost = sum(len(json.dumps(m, ensure_ascii=False)) for m in self.history)
|
||
if cost > self.context_win * 4:
|
||
target = self.context_win * 4 * 0.6
|
||
while len(self.history) > 2 and cost > target:
|
||
self.history.pop(0); self.history.pop(0)
|
||
cost = sum(len(json.dumps(m, ensure_ascii=False)) for m in self.history)
|
||
messages = list(self.history)
|
||
|
||
content_blocks = None
|
||
gen = self.raw_ask(messages, tools, self.system, model)
|
||
try:
|
||
while True: yield next(gen)
|
||
except StopIteration as e: content_blocks = e.value or []
|
||
if content_blocks and not (len(content_blocks) == 1 and content_blocks[0].get("text", "").startswith("Error:")):
|
||
self.history.append({"role": "assistant", "content": content_blocks})
|
||
thinking = ''
|
||
text_parts = [b["text"] for b in content_blocks if b.get("type") == "text"]
|
||
content = "\n".join(text_parts).strip()
|
||
tool_calls = []
|
||
for b in content_blocks:
|
||
if b.get("type") == "tool_use":
|
||
tool_calls.append(MockToolCall(b["name"], b.get("input", {}), id=b.get("id", "")))
|
||
return MockResponse(thinking, content, tool_calls, str(content_blocks))
|
||
|
||
def openai_tools_to_claude(tools):
|
||
"""[{type:'function', function:{name,description,parameters}}] → [{name,description,input_schema}]."""
|
||
result = []
|
||
for t in tools:
|
||
if 'input_schema' in t: result.append(t); continue # 已是claude格式
|
||
fn = t.get('function', t)
|
||
result.append({
|
||
'name': fn['name'], 'description': fn.get('description', ''),
|
||
'input_schema': fn.get('parameters', {'type': 'object', 'properties': {}})
|
||
})
|
||
return result
|
||
|
||
|
||
class MockFunction:
|
||
def __init__(self, name, arguments): self.name, self.arguments = name, arguments
|
||
|
||
class MockToolCall:
|
||
def __init__(self, name, args, id=''):
|
||
arg_str = json.dumps(args, ensure_ascii=False) if isinstance(args, dict) else args
|
||
self.function = MockFunction(name, arg_str); self.id = id
|
||
|
||
class MockResponse:
|
||
def __init__(self, thinking, content, tool_calls, raw, stop_reason='end_turn'):
|
||
self.thinking = thinking; self.content = content
|
||
self.tool_calls = tool_calls; self.raw = raw
|
||
self.stop_reason = 'tool_use' if tool_calls else stop_reason
|
||
def __repr__(self):
|
||
return f"<MockResponse thinking={bool(self.thinking)}, content='{self.content}', tools={bool(self.tool_calls)}>"
|
||
|
||
class ToolClient:
|
||
def __init__(self, backends, auto_save_tokens=True):
|
||
if isinstance(backends, list): self.backends = backends
|
||
else: self.backends = [backends]
|
||
self.backend = self.backends[0]
|
||
self.auto_save_tokens = auto_save_tokens
|
||
self.last_tools = ''
|
||
self.total_cd_tokens = 0
|
||
|
||
def chat(self, messages, tools=None):
|
||
if self._should_use_structured_messages(messages):
|
||
backend_messages = self._build_backend_messages(messages, tools)
|
||
print("Structured prompt length:", sum(self._estimate_content_len(m.get("content")) for m in backend_messages), 'chars')
|
||
prompt_log = self._serialize_messages_for_log(backend_messages)
|
||
gen = self.backend.raw_ask(backend_messages)
|
||
else:
|
||
full_prompt = self._build_protocol_prompt(messages, tools)
|
||
print("Full prompt length:", len(full_prompt), 'chars')
|
||
prompt_log = full_prompt
|
||
gen = self.backend.ask(full_prompt, stream=True)
|
||
_write_llm_log('Prompt', prompt_log)
|
||
raw_text = ''; summarytag = '[NextWillSummary]'
|
||
for chunk in gen:
|
||
raw_text += chunk
|
||
if chunk != summarytag: yield chunk
|
||
print('Complete response received.')
|
||
if raw_text.endswith(summarytag):
|
||
self.last_tools = ''; raw_text = raw_text[:-len(summarytag)]
|
||
_write_llm_log('Response', raw_text)
|
||
return self._parse_mixed_response(raw_text)
|
||
|
||
def _should_use_structured_messages(self, messages):
|
||
return isinstance(self.backend, LLMSession) and any(isinstance(m.get("content"), list) for m in messages)
|
||
|
||
def _estimate_content_len(self, content):
|
||
if isinstance(content, str): return len(content)
|
||
if isinstance(content, list):
|
||
total = 0
|
||
for part in content:
|
||
if not isinstance(part, dict): continue
|
||
if part.get("type") == "text":
|
||
total += len(part.get("text", ""))
|
||
elif part.get("type") == "image_url":
|
||
total += 1000
|
||
return total
|
||
return len(str(content))
|
||
|
||
def _prepare_tool_instruction(self, tools):
|
||
tool_instruction = ""
|
||
if not tools: return tool_instruction
|
||
tools_json = json.dumps(tools, ensure_ascii=False, separators=(',', ':'))
|
||
tool_instruction = f"""
|
||
### 交互协议 (必须严格遵守，持续有效)
|
||
请按照以下步骤思考并行动，标签之间需要回车换行：
|
||
1. **思考**: 在 `<thinking>` 标签中先进行思考，分析现状和策略。
|
||
2. **总结**: 在 `<summary>` 中输出*极为简短*的高度概括的单行（<30字）物理快照，包括上次工具调用结果产生的新信息+本次工具调用意图。此内容将进入长期工作记忆，记录关键信息，严禁输出无实际信息增量的描述。
|
||
3. **行动**: 如需调用工具，请在回复正文之后输出一个（或多个）**<tool_use>块**，然后结束，我会稍后给你返回<tool_result>块。
|
||
格式: ```<tool_use>\n{{"name": "工具名", "arguments": {{参数}}}}\n</tool_use>\n```
|
||
|
||
### 可用工具库（已挂载，持续有效）
|
||
{tools_json}
|
||
"""
|
||
if self.auto_save_tokens and self.last_tools == tools_json:
|
||
tool_instruction = "\n### 工具库状态：持续有效（code_run/file_read等），**可正常调用**。调用协议沿用。\n"
|
||
else:
|
||
self.total_cd_tokens = 0
|
||
self.last_tools = tools_json
|
||
return tool_instruction
|
||
|
||
def _build_backend_messages(self, messages, tools):
|
||
system_content = next((m['content'] for m in messages if m['role'].lower() == 'system'), "")
|
||
history_msgs = [m for m in messages if m['role'].lower() != 'system']
|
||
tool_instruction = self._prepare_tool_instruction(tools)
|
||
backend_messages = []
|
||
merged_system = f"{system_content}\n{tool_instruction}".strip() if tool_instruction else system_content
|
||
if merged_system:
|
||
backend_messages.append({"role": "system", "content": merged_system})
|
||
for m in history_msgs:
|
||
backend_messages.append({"role": m['role'], "content": m['content']})
|
||
self.total_cd_tokens += self._estimate_content_len(m['content'])
|
||
if self.total_cd_tokens > 6000: self.last_tools = ''
|
||
return backend_messages
|
||
|
||
def _serialize_messages_for_log(self, messages):
|
||
logged = []
|
||
for msg in messages:
|
||
content = msg.get("content")
|
||
if isinstance(content, list):
|
||
parts = []
|
||
for part in content:
|
||
if not isinstance(part, dict): continue
|
||
if part.get("type") == "text":
|
||
parts.append({"type": "text", "text": part.get("text", "")})
|
||
elif part.get("type") == "image_url":
|
||
url = (part.get("image_url") or {}).get("url", "")
|
||
prefix = url.split(",", 1)[0] if url else "data:image/unknown;base64"
|
||
parts.append({"type": "image_url", "image_url": {"url": prefix + ",<omitted>"}})
|
||
else:
|
||
parts.append(part)
|
||
logged.append({"role": msg.get("role"), "content": parts})
|
||
else:
|
||
logged.append(msg)
|
||
return json.dumps(logged, ensure_ascii=False, indent=2)
|
||
|
||
def _build_protocol_prompt(self, messages, tools):
|
||
system_content = next((m['content'] for m in messages if m['role'].lower() == 'system'), "")
|
||
history_msgs = [m for m in messages if m['role'].lower() != 'system']
|
||
tool_instruction = self._prepare_tool_instruction(tools)
|
||
|
||
prompt = ""
|
||
if system_content: prompt += f"=== SYSTEM ===\n{system_content}\n"
|
||
prompt += f"{tool_instruction}\n\n"
|
||
for m in history_msgs:
|
||
role = "USER" if m['role'] == 'user' else "ASSISTANT"
|
||
prompt += f"=== {role} ===\n{m['content']}\n\n"
|
||
self.total_cd_tokens += self._estimate_content_len(m['content'])
|
||
|
||
if self.total_cd_tokens > 6000: self.last_tools = ''
|
||
|
||
prompt += "=== ASSISTANT ===\n"
|
||
return prompt
|
||
|
||
def _parse_mixed_response(self, text):
|
||
remaining_text = text; thinking = ''
|
||
think_pattern = r"<thinking>(.*?)</thinking>"
|
||
think_match = re.search(think_pattern, text, re.DOTALL)
|
||
|
||
if think_match:
|
||
thinking = think_match.group(1).strip()
|
||
remaining_text = re.sub(think_pattern, "", remaining_text, flags=re.DOTALL)
|
||
|
||
tool_calls = []; json_strs = []; errors = []
|
||
tool_pattern = r"<tool_use>((?:(?!<tool_use>).){15,}?)</tool_use>"
|
||
tool_all = re.findall(tool_pattern, remaining_text, re.DOTALL)
|
||
|
||
if tool_all:
|
||
tool_all = [s.strip() for s in tool_all]
|
||
json_strs.extend([s for s in tool_all if s.startswith('{') and s.endswith('}')])
|
||
remaining_text = re.sub(tool_pattern, "", remaining_text, flags=re.DOTALL)
|
||
elif '<tool_use>' in remaining_text:
|
||
weaktoolstr = remaining_text.split('<tool_use>')[-1].strip()
|
||
json_str = weaktoolstr if weaktoolstr.endswith('}') else ''
|
||
if json_str == '' and '```' in weaktoolstr and weaktoolstr.split('```')[0].strip().endswith('}'):
|
||
json_str = weaktoolstr.split('```')[0].strip()
|
||
if json_str:
|
||
json_strs.append(json_str)
|
||
remaining_text = remaining_text.replace('<tool_use>'+weaktoolstr, "")
|
||
elif '"name":' in remaining_text and '"arguments":' in remaining_text:
|
||
json_match = re.search(r"(\{.*\"name\":.*?\})", remaining_text, re.DOTALL | re.MULTILINE)
|
||
if json_match:
|
||
json_str = json_match.group(1).strip()
|
||
json_strs.append(json_str)
|
||
remaining_text = remaining_text.replace(json_str, "").strip()
|
||
|
||
for json_str in json_strs:
|
||
try:
|
||
data = tryparse(json_str)
|
||
func_name = data.get('name') or data.get('function') or data.get('tool')
|
||
args = data.get('arguments') or data.get('args') or data.get('params') or data.get('parameters')
|
||
if args is None: args = data
|
||
if func_name: tool_calls.append(MockToolCall(func_name, args))
|
||
except json.JSONDecodeError as e:
|
||
errors.append({'err': f"[Warn] Failed to parse tool_use JSON: {json_str}", 'bad_json': f'Failed to parse tool_use JSON: {json_str[:200]}'})
|
||
self.last_tools = '' # llm肯定忘了tool schema了，再提供下
|
||
except Exception as e:
|
||
errors.append({'err': f'[Warn] Exception during tool_use parsing: {str(e)} {str(data)}'})
|
||
if len(tool_calls) == 0:
|
||
for e in errors:
|
||
print(e['err'])
|
||
if 'bad_json' in e: tool_calls.append(MockToolCall('bad_json', {'msg': e['bad_json']}))
|
||
content = remaining_text.strip()
|
||
return MockResponse(thinking, content, tool_calls, text)
|
||
|
||
def _write_llm_log(label, content):
|
||
log_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), f'temp/model_responses_{os.getpid()}.txt')
|
||
ts = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
|
||
with open(log_path, 'a', encoding='utf-8', errors='replace') as f:
|
||
f.write(f"=== {label} === {ts}\n{content}\n\n")
|
||
|
||
def tryparse(json_str):
|
||
try: return json.loads(json_str)
|
||
except: pass
|
||
json_str = json_str.strip().strip('`').replace('json\n', '', 1).strip()
|
||
try: return json.loads(json_str)
|
||
except: pass
|
||
try: return json.loads(json_str[:-1])
|
||
except: pass
|
||
if '}' in json_str: json_str = json_str[:json_str.rfind('}') + 1]
|
||
return json.loads(json_str)
|
||
|
||
|
||
class NativeToolClient:
|
||
THINKING_PROMPT = """
|
||
### 行动规范（持续有效）
|
||
每次回复请遵循：
|
||
1. 在 <thinking></thinking> 标签中先分析现状和策略
|
||
2. 在 <summary></summary> 中输出极简单行（<30字）物理快照：上次结果新信息+本次意图。此内容进入长期工作记忆。
|
||
3. 如需调用工具，直接使用工具调用能力，然后结束回复。
|
||
""".strip()
|
||
def __init__(self, backend):
|
||
self.backend = backend
|
||
self.backend.system = self.THINKING_PROMPT
|
||
self.tools = {}
|
||
self._pending_tool_ids = []
|
||
def set_system(self, extra_system):
|
||
combined = f"{extra_system}\n\n{self.THINKING_PROMPT}" if extra_system else self.THINKING_PROMPT
|
||
self.backend.system = combined
|
||
def chat(self, messages, tools=None):
|
||
if tools: self.tools = openai_tools_to_claude(tools) if isinstance(self.backend, NativeClaudeSession) else tools
|
||
combined_content = []; resp = None
|
||
for msg in messages:
|
||
c = msg.get('content', '')
|
||
if isinstance(c, str): combined_content.append({"type": "text", "text": c})
|
||
elif isinstance(c, list) or isinstance(c, dict): combined_content.extend(c)
|
||
if self._pending_tool_ids and isinstance(self.backend, NativeClaudeSession):
|
||
tool_result_blocks = [{"type": "tool_result", "tool_use_id": tid, "content": ""} for tid in self._pending_tool_ids]
|
||
combined_content = tool_result_blocks + combined_content
|
||
self._pending_tool_ids = []
|
||
merged = {"role": "user", "content": combined_content}
|
||
_write_llm_log('Prompt', json.dumps(merged, ensure_ascii=False, indent=2))
|
||
gen = self.backend.ask(merged, self.tools);
|
||
try:
|
||
while True:
|
||
chunk = next(gen); yield chunk
|
||
except StopIteration as e: resp = e.value
|
||
print('Complete response received.')
|
||
if resp:
|
||
_write_llm_log('Response', resp.raw)
|
||
text = resp.content
|
||
think_match = re.search(r'<thinking>(.*?)</thinking>', text, re.DOTALL)
|
||
if think_match:
|
||
resp.thinking = think_match.group(1).strip()
|
||
text = re.sub(r'<thinking>.*?</thinking>', '', text, flags=re.DOTALL)
|
||
resp.content = text.strip()
|
||
if resp and hasattr(resp, 'tool_calls') and resp.tool_calls and isinstance(self.backend, NativeClaudeSession):
|
||
self._pending_tool_ids = [tc.id for tc in resp.tool_calls]
|
||
return resp |