900 lines
49 KiB
Python
900 lines
49 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=10, max_len=800):
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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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_before = sum(len(json.dumps(m, ensure_ascii=False)) for m in messages)
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_pats = {tag: re.compile(rf'(<{tag}>)([\s\S]*?)(</{tag}>)') for tag in ('thinking', 'think', '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: 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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print(f"[Cut] {_before} -> {sum(len(json.dumps(m, ensure_ascii=False)) for m in messages)}")
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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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def _parse_claude_sse(resp_lines):
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"""Parse Anthropic SSE stream. Yields text chunks, returns list[content_block]."""
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content_blocks = []; current_block = None; tool_json_buf = ""
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stop_reason = None; got_message_stop = False
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for line in resp_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_str = line[5:].lstrip()
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if data_str == "[DONE]": break
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try: evt = json.loads(data_str)
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except Exception as e:
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print(f"[SSE] JSON parse error: {e}, line: {data_str[:200]}")
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continue
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evt_type = evt.get("type", "")
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if evt_type == "message_start":
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usage = evt.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 evt_type == "content_block_start":
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block = evt.get("content_block", {})
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if block.get("type") == "text": current_block = {"type": "text", "text": ""}
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elif block.get("type") == "tool_use":
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current_block = {"type": "tool_use", "id": block.get("id", ""), "name": block.get("name", ""), "input": {}}
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tool_json_buf = ""
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elif evt_type == "content_block_delta":
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delta = evt.get("delta", {})
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if delta.get("type") == "text_delta":
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text = delta.get("text", "")
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if current_block and current_block.get("type") == "text": current_block["text"] += text
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if text: yield text
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elif delta.get("type") == "input_json_delta": tool_json_buf += delta.get("partial_json", "")
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elif evt_type == "content_block_stop":
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if current_block:
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if current_block["type"] == "tool_use":
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try: current_block["input"] = json.loads(tool_json_buf) if tool_json_buf else {}
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except: current_block["input"] = {"_raw": tool_json_buf}
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content_blocks.append(current_block)
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current_block = None
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elif evt_type == "message_delta":
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delta = evt.get("delta", {})
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stop_reason = delta.get("stop_reason", stop_reason)
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out_usage = evt.get("usage", {})
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out_tokens = out_usage.get("output_tokens", 0)
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if out_tokens: print(f"[Output] tokens={out_tokens} stop_reason={stop_reason}")
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elif evt_type == "message_stop": got_message_stop = True
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elif evt_type == "error":
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err = evt.get("error", {})
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emsg = err.get("message", str(err)) if isinstance(err, dict) else str(err)
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print(f"[SSE ERROR] {emsg}")
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yield f"\n\n[SSE Error: {emsg}]"
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break
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if not got_message_stop and not stop_reason:
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print("[WARN] SSE stream ended without message_stop - possible network interruption")
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yield "\n\n[!!! 流异常中断，未收到完整响应 !!!]"
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elif stop_reason == "max_tokens":
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print(f"[WARN] Response truncated: max_tokens")
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yield "\n\n[!!! Response truncated: max_tokens !!!]"
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return content_blocks
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def _parse_openai_sse(resp_lines, api_mode="chat_completions"):
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"""Parse OpenAI SSE stream (chat_completions or responses API).
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Yields text chunks, returns list[content_block].
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content_block: {type:'text', text:str} | {type:'tool_use', id:str, name:str, input:dict}
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"""
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content_text = ""
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if api_mode == "responses":
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seen_delta = False; fc_buf = {}; current_fc_idx = None
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for line in resp_lines:
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if not line: continue
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line = line.decode('utf-8', errors='replace') if isinstance(line, bytes) else line
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if not line.startswith("data:"): continue
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data_str = line[5:].lstrip()
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if data_str == "[DONE]": break
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try: evt = json.loads(data_str)
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except: continue
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etype = evt.get("type", "")
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if etype == "response.output_text.delta":
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delta = evt.get("delta", "")
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if delta: seen_delta = True; content_text += delta; yield delta
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elif etype == "response.output_text.done" and not seen_delta:
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text = evt.get("text", "")
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if text: content_text += text; yield text
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elif etype == "response.output_item.added":
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item = evt.get("item", {})
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if item.get("type") == "function_call":
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idx = evt.get("output_index", 0)
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fc_buf[idx] = {"id": item.get("call_id", item.get("id", "")), "name": item.get("name", ""), "args": ""}
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current_fc_idx = idx
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elif etype == "response.function_call_arguments.delta":
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idx = evt.get("output_index", current_fc_idx or 0)
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if idx in fc_buf: fc_buf[idx]["args"] += evt.get("delta", "")
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elif etype == "response.function_call_arguments.done":
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idx = evt.get("output_index", current_fc_idx or 0)
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if idx in fc_buf: fc_buf[idx]["args"] = evt.get("arguments", fc_buf[idx]["args"])
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elif etype == "error":
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err = evt.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: content_text += f"Error: {emsg}"; yield f"Error: {emsg}"
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break
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elif etype == "response.completed":
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usage = evt.get("response", {}).get("usage", {})
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cached = (usage.get("input_tokens_details") or {}).get("cached_tokens", 0)
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inp = usage.get("input_tokens", 0)
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if inp: print(f"[Cache] input={inp} cached={cached}")
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break
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blocks = []
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if content_text: blocks.append({"type": "text", "text": content_text})
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for idx in sorted(fc_buf):
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fc = fc_buf[idx]
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try: inp = json.loads(fc["args"]) if fc["args"] else {}
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except: inp = {"_raw": fc["args"]}
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blocks.append({"type": "tool_use", "id": fc["id"], "name": fc["name"], "input": inp})
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return blocks
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else:
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tc_buf = {} # index -> {id, name, args}
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for line in resp_lines:
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if not line: continue
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line = line.decode('utf-8', errors='replace') if isinstance(line, bytes) else line
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if not line.startswith("data:"): continue
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data_str = line[5:].lstrip()
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if data_str == "[DONE]": break
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try: evt = json.loads(data_str)
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except: continue
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ch = (evt.get("choices") or [{}])[0]
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delta = ch.get("delta", {})
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if delta.get("content"):
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text = delta["content"]; content_text += text; yield text
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for tc in delta.get("tool_calls", []):
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idx = tc.get("index", 0)
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if idx not in tc_buf: tc_buf[idx] = {"id": tc.get("id", ""), "name": "", "args": ""}
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if tc.get("function", {}).get("name"): tc_buf[idx]["name"] = tc["function"]["name"]
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if tc.get("function", {}).get("arguments"): tc_buf[idx]["args"] += tc["function"]["arguments"]
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usage = evt.get("usage")
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if usage:
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cached = (usage.get("prompt_tokens_details") or {}).get("cached_tokens", 0)
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print(f"[Cache] input={usage.get('prompt_tokens',0)} cached={cached}")
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blocks = []
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if content_text: blocks.append({"type": "text", "text": content_text})
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for idx in sorted(tc_buf):
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tc = tc_buf[idx]
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try: inp = json.loads(tc["args"]) if tc["args"] else {}
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except: inp = {"_raw": tc["args"]}
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blocks.append({"type": "tool_use", "id": tc["id"], "name": tc["name"], "input": inp})
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return blocks
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def _openai_stream(api_base, api_key, messages, model, api_mode='chat_completions', *,
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temperature=0.5, max_tokens=None, tools=None, reasoning_effort=None,
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max_retries=0, connect_timeout=10, read_timeout=300, proxies=None):
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"""Shared OpenAI-compatible streaming request with retry. Yields text chunks, returns list[content_block]."""
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ml = model.lower()
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if 'kimi' in ml or 'moonshot' in ml: temperature = 1.0
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elif 'minimax' in ml: temperature = max(0.01, min(temperature, 1.0)) # MiniMax requires temp in (0, 1]
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headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json", "Accept": "text/event-stream"}
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if api_mode == "responses":
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url = auto_make_url(api_base, "responses")
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payload = {"model": model, "input": _to_responses_input(messages), "stream": True}
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if reasoning_effort: payload["reasoning"] = {"effort": reasoning_effort}
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else:
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url = auto_make_url(api_base, "chat/completions")
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payload = {"model": model, "messages": messages, "temperature": temperature, "stream": True, "stream_options": {"include_usage": True}}
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if max_tokens: payload["max_tokens"] = max_tokens
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if reasoning_effort: payload["reasoning_effort"] = reasoning_effort
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if tools:
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if api_mode == "responses":
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# Responses API: flatten {type, function: {name, ...}} -> {type, name, ...}
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resp_tools = []
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for t in tools:
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if t.get("type") == "function" and "function" in t:
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rt = {"type": "function"}
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rt.update(t["function"])
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resp_tools.append(rt)
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else: resp_tools.append(t)
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payload["tools"] = resp_tools
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else: payload["tools"] = tools
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RETRYABLE = {408, 409, 425, 429, 500, 502, 503, 504}
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def _delay(resp, attempt):
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try: ra = float((resp.headers or {}).get("retry-after"))
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except: ra = None
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return max(0.5, ra if ra is not None else min(30.0, 1.5 * (2 ** attempt)))
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for attempt in range(max_retries + 1):
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streamed = 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=(connect_timeout, read_timeout), proxies=proxies) as r:
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if r.status_code >= 400:
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if r.status_code in RETRYABLE and attempt < max_retries:
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d = _delay(r, attempt)
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print(f"[LLM Retry] HTTP {r.status_code}, retry in {d:.1f}s ({attempt+1}/{max_retries+1})")
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time.sleep(d); continue
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# Read error body before raise (stream mode closes connection after raise)
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err_body = ""
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try: err_body = r.text.strip()[:1200]
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except: pass
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try: r.raise_for_status()
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except requests.HTTPError as e:
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e._err_body = err_body; raise
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gen = _parse_openai_sse(r.iter_lines(), api_mode)
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try:
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while True: streamed = True; yield next(gen)
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except StopIteration as e:
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return e.value or []
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except requests.HTTPError as e:
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resp = getattr(e, "response", None); status = getattr(resp, "status_code", None)
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if status in RETRYABLE and attempt < max_retries and not streamed:
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d = _delay(resp, attempt)
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print(f"[LLM Retry] HTTP {status}, retry in {d:.1f}s ({attempt+1}/{max_retries+1})")
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time.sleep(d); continue
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body = ""; rid = ""; ra = ""; ct = ""
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try: body = getattr(e, '_err_body', '') or (resp.text or "").strip()[:1200]
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except: pass
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try: h = resp.headers or {}; rid = h.get("x-request-id","") or h.get("request-id",""); ra = h.get("retry-after",""); ct = h.get("content-type","")
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except: pass
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err = f"Error: HTTP {status} {e}; content_type: {ct or '<empty>'}; retry_after: {ra or '<empty>'}; request_id: {rid or '<empty>'}; body: {body or '<empty>'}"
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yield err; return [{"type": "text", "text": err}]
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except (requests.Timeout, requests.ConnectionError) as e:
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if attempt < max_retries and not streamed:
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d = _delay(None, attempt)
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print(f"[LLM Retry] {type(e).__name__}, retry in {d:.1f}s ({attempt+1}/{max_retries+1})")
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time.sleep(d); continue
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err = f"Error: {type(e).__name__}: {e}"
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yield err; return [{"type": "text", "text": err}]
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except Exception as e:
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err = f"Error: {e}"
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yield err; return [{"type": "text", "text": err}]
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def _to_responses_input(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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if role == "system": role = "developer" # Responses API uses 'developer' instead of 'system'
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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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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', 20000)
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self.raw_msgs, self.lock = [], threading.Lock()
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self.system = ""
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def _trim_messages(self, raw_msgs):
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compress_history_tags(raw_msgs)
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total = sum(len(m['prompt']) for m in raw_msgs)
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print(f'[Debug] Current context: {total} chars, {len(raw_msgs)} messages.')
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if total <= self.context_win * 3: return raw_msgs
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target, current, result = self.context_win * 3 * 0.6, 0, []
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for msg in reversed(raw_msgs):
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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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print(f'[Debug] Trimmed context, current: {current} chars, {len(result)} messages.')
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return result[::-1] or raw_msgs[-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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ml = model.lower()
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if 'kimi' in ml or 'moonshot' in ml: temperature = 1.0 # kimi/moonshot only accepts temp 1.0
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elif 'minimax' in ml: temperature = max(0.01, min(temperature, 1.0)) # MiniMax requires temp in (0, 1]
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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}
|
|
if self.system: payload["system"] = [{"type": "text", "text": self.system, "cache_control": {"type": "persistent"}}]
|
|
try:
|
|
with requests.post(auto_make_url(self.api_base, "messages"), headers=headers, json=payload, stream=True, timeout=(5,30)) as r:
|
|
r.raise_for_status()
|
|
yield from _parse_claude_sse(r.iter_lines())
|
|
except Exception as e: yield f"Error: {str(e)}"
|
|
def make_messages(self, raw_list):
|
|
msgs = [{"role": m['role'], "content": [{"type": "text", "text": m['prompt']}]} for m in raw_list]
|
|
c = msgs[-1]["content"]
|
|
c[-1] = dict(c[-1], cache_control={"type": "ephemeral"})
|
|
return msgs
|
|
def ask(self, prompt, model=None, stream=False):
|
|
def _ask_gen():
|
|
content = ''
|
|
with self.lock:
|
|
self.raw_msgs.append({"role": "user", "prompt": prompt})
|
|
self.raw_msgs = self._trim_messages(self.raw_msgs)
|
|
messages = self.make_messages(self.raw_msgs)
|
|
for chunk in self.raw_ask(messages, model):
|
|
content += chunk; yield chunk
|
|
if not content.startswith("Error:"): self.raw_msgs.append({"role": "assistant", "prompt": content})
|
|
return _ask_gen() if stream else ''.join(list(_ask_gen()))
|
|
|
|
class LLMSession:
|
|
def __init__(self, cfg):
|
|
self.api_key = cfg['apikey']; self.api_base = cfg['apibase'].rstrip('/')
|
|
self.default_model = cfg['model']
|
|
self.context_win = cfg.get('context_win', 20000)
|
|
self.raw_msgs, self.messages = [], []
|
|
proxy = cfg.get('proxy')
|
|
self.proxies = {"http": proxy, "https": proxy} if proxy else None
|
|
self.lock = threading.Lock()
|
|
self.max_retries = max(0, int(cfg.get('max_retries', 2)))
|
|
self.connect_timeout = max(1, int(cfg.get('connect_timeout', 10)))
|
|
self.read_timeout = max(5, int(cfg.get('read_timeout', 120)))
|
|
effort = cfg.get('reasoning_effort')
|
|
effort = None if effort is None else str(effort).strip().lower()
|
|
self.reasoning_effort = effort if effort in ['none', 'minimal','low', 'medium', 'high', 'xhigh'] else None
|
|
if effort and self.reasoning_effort is None: print(f"[WARN] Invalid reasoning_effort {effort!r}, ignored.")
|
|
mode = str(cfg.get('api_mode', 'chat_completions')).strip().lower().replace('-', '_')
|
|
if mode in ["responses", "response"]: self.api_mode = "responses"
|
|
else: self.api_mode = "chat_completions"
|
|
|
|
def raw_ask(self, messages, model=None, temperature=0.5):
|
|
if model is None: model = self.default_model
|
|
yield from _openai_stream(self.api_base, self.api_key, messages, model, self.api_mode,
|
|
temperature=temperature, reasoning_effort=self.reasoning_effort,
|
|
max_retries=self.max_retries, connect_timeout=self.connect_timeout,
|
|
read_timeout=self.read_timeout, proxies=self.proxies)
|
|
|
|
def make_messages(self, raw_list, omit_images=True):
|
|
compress_history_tags(raw_list)
|
|
messages = []
|
|
for i, msg in enumerate(raw_list):
|
|
prompt = msg['prompt']
|
|
image = msg.get('image')
|
|
if omit_images and image: messages.append({"role": msg['role'], "content": "[Image omitted, if you needed it, ask me]\n" + prompt})
|
|
elif not omit_images and image:
|
|
messages.append({"role": msg['role'], "content": [
|
|
{"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image}"}},
|
|
{"type": "text", "text": prompt} ]})
|
|
else:
|
|
messages.append({"role": msg['role'], "content": prompt})
|
|
return messages
|
|
|
|
def summary_history(self, model=None):
|
|
if model is None: model = self.default_model
|
|
with self.lock:
|
|
keep = 0; tok = 0
|
|
for m in reversed(self.raw_msgs):
|
|
l = len(str(m))//3
|
|
if tok + l > self.context_win*0.2: break
|
|
tok += l; keep += 1
|
|
keep = max(2, keep)
|
|
old, self.raw_msgs = self.raw_msgs[:-keep], self.raw_msgs[-keep:]
|
|
if len(old) == 0: old = self.raw_msgs; self.raw_msgs = []
|
|
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"
|
|
messages = self.make_messages(old, omit_images=True)
|
|
messages += [{"role":"user", "content":p}]
|
|
msg_lens = [1000 if isinstance(m["content"], list) else len(str(m["content"]))//3 for m in messages]
|
|
summary = ''.join(list(self.raw_ask(messages, model, temperature=0.1)))
|
|
print('[Debug] Summary length:', len(summary)//3, '; Orig context lengths:', str(msg_lens))
|
|
if not summary.startswith("Error:"):
|
|
self.raw_msgs.insert(0, {"role":"assistant", "prompt":"Prev summary:\n"+summary, "image":None})
|
|
else: self.raw_msgs = old + self.raw_msgs # 不做了，下次再做
|
|
|
|
def ask(self, prompt, model=None, image_base64=None, stream=False):
|
|
if model is None: model = self.default_model
|
|
def _ask_gen():
|
|
content = ''
|
|
with self.lock:
|
|
self.raw_msgs.append({"role": "user", "prompt": prompt, "image": image_base64})
|
|
messages = self.make_messages(self.raw_msgs[:-1], omit_images=True)
|
|
messages += self.make_messages([self.raw_msgs[-1]], omit_images=False)
|
|
msg_lens = [1000 if isinstance(m["content"], list) else len(str(m["content"]))//3 for m in messages]
|
|
total_len = sum(msg_lens) # estimate token count
|
|
gen = self.raw_ask(messages, model)
|
|
for chunk in gen:
|
|
content += chunk; yield chunk
|
|
if not content.startswith("Error:"):
|
|
self.raw_msgs.append({"role": "assistant", "prompt": content, "image": None})
|
|
if total_len > self.context_win // 2: print(f"[Debug] Whole context length {total_len} {str(msg_lens)}.")
|
|
if total_len > self.context_win:
|
|
yield '[NextWillSummary]'
|
|
threading.Thread(target=self.summary_history, daemon=True).start()
|
|
if stream: return _ask_gen()
|
|
return ''.join(list(_ask_gen()))
|
|
|
|
|
|
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 = ''; self.lock = threading.Lock()
|
|
self.max_retries = max(0, int(cfg.get('max_retries', 2)))
|
|
self.connect_timeout = max(1, int(cfg.get('connect_timeout', 10)))
|
|
self.read_timeout = max(5, int(cfg.get('read_timeout', 120)))
|
|
effort = cfg.get('reasoning_effort')
|
|
effort = None if effort is None else str(effort).strip().lower()
|
|
self.reasoning_effort = effort if effort in ('low', 'medium', 'high') else None
|
|
if effort and not self.reasoning_effort: print(f"[WARN] Invalid reasoning_effort {effort!r}, ignored.")
|
|
mode = str(cfg.get('api_mode', 'chat_completions')).strip().lower().replace('-', '_')
|
|
self.api_mode = 'responses' if mode in ('responses', 'response') else 'chat_completions'
|
|
|
|
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
|
|
msgs = ([{"role": "system", "content": system}] if system else []) + messages
|
|
return (yield from _openai_stream(self.api_base, self.api_key, msgs, model, self.api_mode,
|
|
temperature=temperature, max_tokens=max_tokens, tools=tools,
|
|
reasoning_effort=self.reasoning_effort,
|
|
max_retries=self.max_retries, connect_timeout=self.connect_timeout,
|
|
read_timeout=self.read_timeout, proxies=self.proxies))
|
|
|
|
def ask(self, msg, tools=None, model=None, **kw):
|
|
assert type(msg) is dict
|
|
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)
|
|
print(f'[Debug] Current context: {cost} chars, {len(self.history)} messages.')
|
|
if cost > self.context_win * 3:
|
|
target = self.context_win * 3 * 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)
|
|
print(f'[Debug] Trimmed context, current: {cost} chars, {len(self.history)} messages.')
|
|
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"]
|
|
if len(tool_calls) == 0 and content.endswith('}]') and '[{"type":"tool_use"' in content:
|
|
try:
|
|
idx = content.index('[{"type":"tool_use"')
|
|
raw = json.loads(content[idx:])
|
|
tool_calls = [MockToolCall(b["name"], b.get("input", {}), id=b.get("id", "")) for b in raw if b.get("type") == "tool_use"]
|
|
content = content[:idx].strip()
|
|
except: pass
|
|
think_pattern = r"<think(?:ing)?>(.*?)</think(?:ing)?>"; thinking = ''
|
|
think_match = re.search(think_pattern, content, re.DOTALL)
|
|
if think_match:
|
|
thinking = think_match.group(1).strip()
|
|
content = re.sub(think_pattern, "", content, flags=re.DOTALL)
|
|
return MockResponse(thinking, 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', 24000)
|
|
self.history = []; self.system = ''; self.lock = threading.Lock()
|
|
|
|
def raw_ask(self, messages, tools=None, system=None, model=None, temperature=0.5, max_tokens=6144):
|
|
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"}}]
|
|
messages[-1] = {**messages[-1], "content": list(messages[-1]["content"])}
|
|
messages[-1]["content"][-1] = dict(messages[-1]["content"][-1], cache_control={"type": "ephemeral"})
|
|
try:
|
|
resp = requests.post(auto_make_url(self.api_base, "messages"), headers=headers, json=payload, stream=True, timeout=60)
|
|
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 = yield from _parse_claude_sse(resp.iter_lines())
|
|
return content_blocks or []
|
|
|
|
def ask(self, msg, tools=None, model=None):
|
|
assert type(msg) is dict
|
|
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)
|
|
print(f'[Debug] Current context: {cost} chars, {len(self.history)} messages.')
|
|
if cost > self.context_win * 3:
|
|
target = self.context_win * 3 * 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)
|
|
print(f'[Debug] Trimmed context, current: {cost} chars, {len(self.history)} messages.')
|
|
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, backend, auto_save_tokens=True):
|
|
self.backend = backend
|
|
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
|
|
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)
|
|
system = ""
|
|
if system_content: system += f"{system_content}\n"
|
|
system += f"{tool_instruction}"
|
|
user = ""
|
|
for m in history_msgs:
|
|
role = "USER" if m['role'] == 'user' else "ASSISTANT"
|
|
user += 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 = ''
|
|
user += "=== ASSISTANT ===\n"
|
|
return system + user
|
|
|
|
def _parse_mixed_response(self, text):
|
|
remaining_text = text; thinking = ''
|
|
think_pattern = r"<think(?:ing)?>(.*?)</think(?:ing)?>"
|
|
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_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'temp/model_responses')
|
|
os.makedirs(log_dir, exist_ok=True)
|
|
log_path = os.path.join(log_dir, f'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 MixinSession:
|
|
"""Multi-session fallback with spring-back to primary."""
|
|
def __init__(self, all_sessions, cfg):
|
|
self._retries, self._base_delay = cfg.get('max_retries', 3), cfg.get('base_delay', 1.5)
|
|
self._spring_sec = cfg.get('spring_back', 300)
|
|
self._sessions = [all_sessions[i].backend for i in cfg.get('llm_nos', [])]
|
|
assert 'Native' not in self._sessions[0].__class__.__name__
|
|
assert len(set(type(s) for s in self._sessions)) == 1, f'MixinSession: all sessions must be same type, got {[type(s).__name__ for s in self._sessions]}'
|
|
self._orig_raw_asks = [s.raw_ask for s in self._sessions]
|
|
self._sessions[0].raw_ask = self._raw_ask
|
|
self.default_model = getattr(self._sessions[0], 'default_model', None)
|
|
self._cur_idx, self._switched_at = 0, 0.0
|
|
def __getattr__(self, name): return getattr(self._sessions[0], name)
|
|
@property
|
|
def primary(self): return self._sessions[0]
|
|
def _pick(self):
|
|
if self._cur_idx and time.time() - self._switched_at > self._spring_sec: self._cur_idx = 0
|
|
return self._cur_idx
|
|
def _raw_ask(self, *args, **kwargs):
|
|
base, n = self._pick(), len(self._sessions)
|
|
test_error = lambda x: isinstance(x, str) and (x.startswith('Error:') or x.startswith('[Error:'))
|
|
for attempt in range(self._retries + 1):
|
|
idx = (base + attempt) % n
|
|
gen = self._orig_raw_asks[idx](*args, **kwargs)
|
|
last_chunk, return_val, yielded = None, [], False
|
|
try:
|
|
while True:
|
|
chunk = next(gen); last_chunk = chunk
|
|
if not yielded and test_error(chunk): continue
|
|
yield chunk; yielded = True
|
|
except StopIteration as e: return_val = e.value or []
|
|
is_err = test_error(last_chunk)
|
|
if not is_err:
|
|
if attempt > 0: self._cur_idx = idx; self._switched_at = time.time()
|
|
return return_val
|
|
if attempt >= self._retries:
|
|
yield last_chunk; return return_val
|
|
nxt = (base + attempt + 1) % n
|
|
if nxt == base: # full round failed, delay before next
|
|
rnd = (attempt + 1) // n
|
|
delay = min(30, self._base_delay * (2 ** rnd))
|
|
print(f'[MixinSession] {last_chunk[:80]}, round {rnd} exhausted, retry in {delay:.1f}s')
|
|
time.sleep(delay)
|
|
else: print(f'[MixinSession] {last_chunk[:80]}, retry {attempt+1}/{self._retries} (s{idx}→s{nxt})')
|
|
|
|
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. 然后才能输出工具调用
|
|
""".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
|
|
if combined != self.backend.system: print(f"[Debug] Updated system prompt, length {len(combined)} chars.")
|
|
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 msg['role'] == 'system':
|
|
self.set_system(c); continue
|
|
if isinstance(c, str): combined_content.append({"type": "text", "text": c})
|
|
elif isinstance(c, list): 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
|
|
if resp:
|
|
_write_llm_log('Response', resp.raw)
|
|
text = resp.content
|
|
think_match = re.search(r'<think(?:ing)?>(.*?)</think(?:ing)?>', text, re.DOTALL)
|
|
if think_match:
|
|
resp.thinking = think_match.group(1).strip()
|
|
text = re.sub(r'<think(?:ing)?>.*?</think(?:ing)?>', '', 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 |