14125ed57c
- tools state now held by Session (NativeClaudeSession.tools) - MixinSession.__setattr__ broadcasts tools/system to all sub-sessions - NativeToolClient no longer duplicates tools storage - fix: use type(s) is instead of isinstance to avoid catching NativeOAISession subclass
843 lines
46 KiB
Python
843 lines
46 KiB
Python
import os, json, re, time, requests, sys, threading, urllib3, base64, mimetypes, uuid
|
||
from datetime import datetime
|
||
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
|
||
|
||
def _load_mykeys():
|
||
try:
|
||
import mykey; return {k: v for k, v in vars(mykey).items() if not k.startswith('_')}
|
||
except ImportError: pass
|
||
p = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'mykey.json')
|
||
if not os.path.exists(p): raise Exception('[ERROR] mykey.py or mykey.json not found, please create one from mykey_template.')
|
||
with open(p, encoding='utf-8') as f: return json.load(f)
|
||
|
||
mykeys = _load_mykeys()
|
||
proxy = mykeys.get("proxy", 'http://127.0.0.1:2082')
|
||
proxies = {"http": proxy, "https": proxy} if proxy else None
|
||
|
||
def compress_history_tags(messages, keep_recent=10, max_len=800):
|
||
"""Compress <thinking>/<tool_use>/<tool_result> tags in older messages to save tokens."""
|
||
compress_history_tags._cd = getattr(compress_history_tags, '_cd', 0) + 1
|
||
if compress_history_tags._cd % 5 != 0: return messages
|
||
_before = sum(len(json.dumps(m, ensure_ascii=False)) for m in messages)
|
||
_pats = {tag: re.compile(rf'(<{tag}>)([\s\S]*?)(</{tag}>)') for tag in ('thinking', 'think', 'tool_use', 'tool_result')}
|
||
_hist_pat = re.compile(r'<(history|key_info)>[\s\S]*?</\1>')
|
||
def _trunc(text):
|
||
text = _hist_pat.sub(lambda m: f'<{m.group(1)}>[...]</{m.group(1)}>', text)
|
||
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)
|
||
return text
|
||
for i, msg in enumerate(messages):
|
||
if i >= len(messages) - keep_recent: break
|
||
c = msg['content']
|
||
if isinstance(c, str): msg['content'] = _trunc(c)
|
||
elif isinstance(c, list):
|
||
for block in c:
|
||
if isinstance(block, dict) and block.get('type') == 'text' and isinstance(block.get('text'), str):
|
||
block['text'] = _trunc(block['text'])
|
||
print(f"[Cut] {_before} -> {sum(len(json.dumps(m, ensure_ascii=False)) for m in messages)}")
|
||
return messages
|
||
|
||
def _sanitize_leading_user_msg(msg):
|
||
"""把 user 消息里的 tool_result 块改写成纯文本，避免孤立引用。
|
||
history 统一使用 Claude content-block 格式：content 是 list of blocks。"""
|
||
msg = dict(msg) # 浅拷贝外层 dict
|
||
content = msg.get('content')
|
||
if not isinstance(content, list): return msg
|
||
texts = []
|
||
for block in content:
|
||
if not isinstance(block, dict): continue
|
||
if block.get('type') == 'tool_result':
|
||
c = block.get('content', '')
|
||
if isinstance(c, list): # content 本身也可能是 list[{type:text,text:...}]
|
||
texts.extend(b.get('text', '') for b in c if isinstance(b, dict))
|
||
else: texts.append(str(c))
|
||
elif block.get('type') == 'text':
|
||
texts.append(block.get('text', ''))
|
||
msg['content'] = [{"type": "text", "text": '\n'.join(t for t in texts if t)}]
|
||
return msg
|
||
|
||
def trim_messages_history(history, context_win):
|
||
compress_history_tags(history)
|
||
cost = sum(len(json.dumps(m, ensure_ascii=False)) for m in history)
|
||
print(f'[Debug] Current context: {cost} chars, {len(history)} messages.')
|
||
if cost > context_win * 3:
|
||
target = context_win * 3 * 0.6
|
||
while len(history) > 5 and cost > target:
|
||
history.pop(0)
|
||
while history and history[0].get('role') != 'user': history.pop(0)
|
||
if history and history[0].get('role') == 'user': history[0] = _sanitize_leading_user_msg(history[0])
|
||
cost = sum(len(json.dumps(m, ensure_ascii=False)) for m in history)
|
||
print(f'[Debug] Trimmed context, current: {cost} chars, {len(history)} messages.')
|
||
|
||
def auto_make_url(base, path):
|
||
b, p = base.rstrip('/'), path.strip('/')
|
||
if b.endswith('$'): return b[:-1].rstrip('/')
|
||
return b if b.endswith(p) else f"{b}/{p}" if re.search(r'/v\d+$', b) else f"{b}/v1/{p}"
|
||
|
||
def build_multimodal_content(prompt_text, image_paths):
|
||
parts = []
|
||
text = prompt_text if isinstance(prompt_text, str) else str(prompt_text or "")
|
||
if text.strip():
|
||
parts.append({"type": "text", "text": text})
|
||
else:
|
||
parts.append({"type": "text", "text": "请查看图片并理解用户意图。"})
|
||
for path in image_paths or []:
|
||
if not path or not os.path.isfile(path): continue
|
||
try:
|
||
mime = mimetypes.guess_type(path)[0] or "image/png"
|
||
if not mime.startswith("image/"): mime = "image/png"
|
||
with open(path, "rb") as f:
|
||
data_url = f"data:{mime};base64,{base64.b64encode(f.read()).decode('ascii')}"
|
||
parts.append({"type": "image_url", "image_url": {"url": data_url}})
|
||
except Exception as e:
|
||
print(f"[WARN] encode image failed {path}: {e}")
|
||
return parts
|
||
|
||
class SiderLLMSession:
|
||
def __init__(self, cfg):
|
||
from sider_ai_api import Session # 不使用sider的话没必要安装这个包
|
||
self._core = Session(cookie=cfg['apikey'], proxies=proxies)
|
||
self.default_model = cfg.get('model', 'gemini-3.0-flash')
|
||
def ask(self, prompt, model=None, stream=False):
|
||
if model is None: model = self.default_model
|
||
if len(prompt) > 28000:
|
||
print(f"[Warn] Prompt too long ({len(prompt)} chars), truncating.")
|
||
prompt = prompt[-28000:]
|
||
full_text = self._core.chat(prompt, model, stream=False)
|
||
if stream: return iter([full_text]) # gen有奇怪的空回复或死循环行为，sider足够快
|
||
return full_text
|
||
|
||
def _parse_claude_sse(resp_lines):
|
||
"""Parse Anthropic SSE stream. Yields text chunks, returns list[content_block]."""
|
||
content_blocks = []; current_block = None; tool_json_buf = ""
|
||
stop_reason = None; got_message_stop = False
|
||
for line in resp_lines:
|
||
if not line: continue
|
||
line = line.decode('utf-8') if isinstance(line, bytes) else line
|
||
if not line.startswith("data:"): continue
|
||
data_str = line[5:].lstrip()
|
||
if data_str == "[DONE]": break
|
||
try: evt = json.loads(data_str)
|
||
except Exception as e:
|
||
print(f"[SSE] JSON parse error: {e}, line: {data_str[:200]}")
|
||
continue
|
||
evt_type = evt.get("type", "")
|
||
if evt_type == "message_start":
|
||
usage = evt.get("message", {}).get("usage", {})
|
||
ci, cr, inp = usage.get("cache_creation_input_tokens", 0), usage.get("cache_read_input_tokens", 0), usage.get("input_tokens", 0)
|
||
print(f"[Cache] input={inp} creation={ci} read={cr}")
|
||
elif 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 and current_block.get("type") == "text": current_block["text"] += text
|
||
if 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
|
||
elif evt_type == "message_delta":
|
||
delta = evt.get("delta", {})
|
||
stop_reason = delta.get("stop_reason", stop_reason)
|
||
out_usage = evt.get("usage", {})
|
||
out_tokens = out_usage.get("output_tokens", 0)
|
||
if out_tokens: print(f"[Output] tokens={out_tokens} stop_reason={stop_reason}")
|
||
elif evt_type == "message_stop": got_message_stop = True
|
||
elif evt_type == "error":
|
||
err = evt.get("error", {})
|
||
emsg = err.get("message", str(err)) if isinstance(err, dict) else str(err)
|
||
print(f"[SSE ERROR] {emsg}")
|
||
yield f"\n\n[SSE Error: {emsg}]"
|
||
break
|
||
if not got_message_stop and not stop_reason:
|
||
print("[WARN] SSE stream ended without message_stop - possible network interruption")
|
||
yield "\n\n[!!! 流异常中断，未收到完整响应 !!!]"
|
||
elif stop_reason == "max_tokens":
|
||
print(f"[WARN] Response truncated: max_tokens")
|
||
yield "\n\n[!!! Response truncated: max_tokens !!!]"
|
||
return content_blocks
|
||
|
||
def _parse_openai_sse(resp_lines, api_mode="chat_completions"):
|
||
"""Parse OpenAI SSE stream (chat_completions or responses API).
|
||
Yields text chunks, returns list[content_block].
|
||
content_block: {type:'text', text:str} | {type:'tool_use', id:str, name:str, input:dict}
|
||
"""
|
||
content_text = ""
|
||
if api_mode == "responses":
|
||
seen_delta = False; fc_buf = {}; current_fc_idx = None
|
||
for line in resp_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[5:].lstrip()
|
||
if data_str == "[DONE]": break
|
||
try: evt = json.loads(data_str)
|
||
except: continue
|
||
etype = evt.get("type", "")
|
||
if etype == "response.output_text.delta":
|
||
delta = evt.get("delta", "")
|
||
if delta: seen_delta = True; content_text += delta; yield delta
|
||
elif etype == "response.output_text.done" and not seen_delta:
|
||
text = evt.get("text", "")
|
||
if text: content_text += text; yield text
|
||
elif etype == "response.output_item.added":
|
||
item = evt.get("item", {})
|
||
if item.get("type") == "function_call":
|
||
idx = evt.get("output_index", 0)
|
||
fc_buf[idx] = {"id": item.get("call_id", item.get("id", "")), "name": item.get("name", ""), "args": ""}
|
||
current_fc_idx = idx
|
||
elif etype == "response.function_call_arguments.delta":
|
||
idx = evt.get("output_index", current_fc_idx or 0)
|
||
if idx in fc_buf: fc_buf[idx]["args"] += evt.get("delta", "")
|
||
elif etype == "response.function_call_arguments.done":
|
||
idx = evt.get("output_index", current_fc_idx or 0)
|
||
if idx in fc_buf: fc_buf[idx]["args"] = evt.get("arguments", fc_buf[idx]["args"])
|
||
elif etype == "error":
|
||
err = evt.get("error", {})
|
||
emsg = err.get("message", str(err)) if isinstance(err, dict) else str(err)
|
||
if emsg: content_text += f"Error: {emsg}"; yield f"Error: {emsg}"
|
||
break
|
||
elif etype == "response.completed":
|
||
usage = evt.get("response", {}).get("usage", {})
|
||
cached = (usage.get("input_tokens_details") or {}).get("cached_tokens", 0)
|
||
inp = usage.get("input_tokens", 0)
|
||
if inp: print(f"[Cache] input={inp} cached={cached}")
|
||
break
|
||
blocks = []
|
||
if content_text: blocks.append({"type": "text", "text": content_text})
|
||
for idx in sorted(fc_buf):
|
||
fc = fc_buf[idx]
|
||
try: inp = json.loads(fc["args"]) if fc["args"] else {}
|
||
except: inp = {"_raw": fc["args"]}
|
||
blocks.append({"type": "tool_use", "id": fc["id"], "name": fc["name"], "input": inp})
|
||
return blocks
|
||
else:
|
||
tc_buf = {} # index -> {id, name, args}
|
||
for line in resp_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[5:].lstrip()
|
||
if data_str == "[DONE]": break
|
||
try: evt = json.loads(data_str)
|
||
except: continue
|
||
ch = (evt.get("choices") or [{}])[0]
|
||
delta = ch.get("delta") or {}
|
||
if delta.get("content"):
|
||
text = delta["content"]; content_text += text; yield text
|
||
for tc in (delta.get("tool_calls") or []):
|
||
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"]
|
||
usage = evt.get("usage")
|
||
if usage:
|
||
cached = (usage.get("prompt_tokens_details") or {}).get("cached_tokens", 0)
|
||
print(f"[Cache] input={usage.get('prompt_tokens',0)} cached={cached}")
|
||
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 _openai_stream(api_base, api_key, messages, model, api_mode='chat_completions', *,
|
||
temperature=0.5, max_tokens=None, tools=None, reasoning_effort=None,
|
||
max_retries=0, connect_timeout=10, read_timeout=300, proxies=None):
|
||
"""Shared OpenAI-compatible streaming request with retry. Yields text chunks, returns list[content_block]."""
|
||
ml = model.lower()
|
||
if 'kimi' in ml or 'moonshot' in ml: temperature = 1.0
|
||
elif 'minimax' in ml: temperature = max(0.01, min(temperature, 1.0)) # MiniMax requires temp in (0, 1]
|
||
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json", "Accept": "text/event-stream"}
|
||
if api_mode == "responses":
|
||
url = auto_make_url(api_base, "responses")
|
||
payload = {"model": model, "input": _to_responses_input(messages), "stream": True}
|
||
if reasoning_effort: payload["reasoning"] = {"effort": reasoning_effort}
|
||
else:
|
||
url = auto_make_url(api_base, "chat/completions")
|
||
payload = {"model": model, "messages": messages, "temperature": temperature, "stream": True, "stream_options": {"include_usage": True}}
|
||
if max_tokens: payload["max_tokens"] = max_tokens
|
||
if reasoning_effort: payload["reasoning_effort"] = reasoning_effort
|
||
if tools:
|
||
if api_mode == "responses":
|
||
# Responses API: flatten {type, function: {name, ...}} -> {type, name, ...}
|
||
resp_tools = []
|
||
for t in tools:
|
||
if t.get("type") == "function" and "function" in t:
|
||
rt = {"type": "function"}
|
||
rt.update(t["function"])
|
||
resp_tools.append(rt)
|
||
else: resp_tools.append(t)
|
||
payload["tools"] = resp_tools
|
||
else: payload["tools"] = tools
|
||
RETRYABLE = {408, 409, 425, 429, 500, 502, 503, 504}
|
||
def _delay(resp, attempt):
|
||
try: ra = float((resp.headers or {}).get("retry-after"))
|
||
except: ra = None
|
||
return max(0.5, ra if ra is not None else min(30.0, 1.5 * (2 ** attempt)))
|
||
for attempt in range(max_retries + 1):
|
||
streamed = False
|
||
try:
|
||
with requests.post(url, headers=headers, json=payload, stream=True,
|
||
timeout=(connect_timeout, read_timeout), proxies=proxies) as r:
|
||
if r.status_code >= 400:
|
||
if r.status_code in RETRYABLE and attempt < max_retries:
|
||
d = _delay(r, attempt)
|
||
print(f"[LLM Retry] HTTP {r.status_code}, retry in {d:.1f}s ({attempt+1}/{max_retries+1})")
|
||
time.sleep(d); continue
|
||
# Read error body before raise (stream mode closes connection after raise)
|
||
err_body = ""
|
||
try: err_body = r.text.strip()[:1200]
|
||
except: pass
|
||
try: r.raise_for_status()
|
||
except requests.HTTPError as e:
|
||
e._err_body = err_body; raise
|
||
gen = _parse_openai_sse(r.iter_lines(), api_mode)
|
||
try:
|
||
while True: streamed = True; yield next(gen)
|
||
except StopIteration as e:
|
||
return e.value or []
|
||
except requests.HTTPError as e:
|
||
resp = getattr(e, "response", None); status = getattr(resp, "status_code", None)
|
||
if status in RETRYABLE and attempt < max_retries and not streamed:
|
||
d = _delay(resp, attempt)
|
||
print(f"[LLM Retry] HTTP {status}, retry in {d:.1f}s ({attempt+1}/{max_retries+1})")
|
||
time.sleep(d); continue
|
||
body = ""; rid = ""; ra = ""; ct = ""
|
||
try: body = getattr(e, '_err_body', '') or (resp.text or "").strip()[:1200]
|
||
except: pass
|
||
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","")
|
||
except: pass
|
||
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>'}"
|
||
yield err; return [{"type": "text", "text": err}]
|
||
except (requests.Timeout, requests.ConnectionError) as e:
|
||
if attempt < max_retries and not streamed:
|
||
d = _delay(None, attempt)
|
||
print(f"[LLM Retry] {type(e).__name__}, retry in {d:.1f}s ({attempt+1}/{max_retries+1})")
|
||
time.sleep(d); continue
|
||
err = f"Error: {type(e).__name__}: {e}"
|
||
yield err; return [{"type": "text", "text": err}]
|
||
except Exception as e:
|
||
err = f"Error: {e}"
|
||
yield err; return [{"type": "text", "text": err}]
|
||
|
||
def _to_responses_input(messages):
|
||
result = []
|
||
for msg in messages:
|
||
role = str(msg.get("role", "user")).lower()
|
||
if role not in ["user", "assistant", "system", "developer"]: role = "user"
|
||
if role == "system": role = "developer" # Responses API uses 'developer' instead of 'system'
|
||
content = msg.get("content", "")
|
||
text_type = "output_text" if role == "assistant" else "input_text"
|
||
parts = []
|
||
if isinstance(content, str):
|
||
if content: parts.append({"type": text_type, "text": content})
|
||
elif isinstance(content, list):
|
||
for part in content:
|
||
if not isinstance(part, dict): continue
|
||
ptype = part.get("type")
|
||
if ptype == "text":
|
||
text = part.get("text", "")
|
||
if text: parts.append({"type": text_type, "text": text})
|
||
elif ptype == "image_url":
|
||
url = (part.get("image_url") or {}).get("url", "")
|
||
if url and role != "assistant": parts.append({"type": "input_image", "image_url": url})
|
||
if len(parts) == 0: parts = [{"type": text_type, "text": str(content)}]
|
||
result.append({"role": role, "content": parts})
|
||
return result
|
||
|
||
|
||
def _msgs_claude2oai(messages):
|
||
result = []
|
||
for msg in messages:
|
||
role = msg.get("role", "user")
|
||
content = msg.get("content", "")
|
||
blocks = content if isinstance(content, list) else [{"type": "text", "text": str(content)}]
|
||
if role == "assistant":
|
||
text_parts, tool_calls = [], []
|
||
for b in blocks:
|
||
if not isinstance(b, dict): continue
|
||
if b.get("type") == "text": text_parts.append({"type": "text", "text": b.get("text", "")})
|
||
elif b.get("type") == "tool_use":
|
||
tool_calls.append({
|
||
"id": b.get("id", ""), "type": "function",
|
||
"function": {"name": b.get("name", ""), "arguments": json.dumps(b.get("input", {}), ensure_ascii=False)}
|
||
})
|
||
m = {"role": "assistant"}
|
||
if text_parts: m["content"] = text_parts
|
||
else: m["content"] = ""
|
||
if tool_calls: m["tool_calls"] = tool_calls
|
||
result.append(m)
|
||
elif role == "user":
|
||
text_parts = []
|
||
for b in blocks:
|
||
if not isinstance(b, dict): continue
|
||
if b.get("type") == "tool_result":
|
||
if text_parts:
|
||
result.append({"role": "user", "content": text_parts})
|
||
text_parts = []
|
||
tr = b.get("content", "")
|
||
if isinstance(tr, list):
|
||
tr = "\n".join(x.get("text", "") for x in tr if isinstance(x, dict) and x.get("type") == "text")
|
||
result.append({"role": "tool", "tool_call_id": b.get("tool_use_id", ""), "content": tr if isinstance(tr, str) else str(tr)})
|
||
elif b.get("type") == "image":
|
||
src = b.get("source") or {}
|
||
if src.get("type") == "base64" and src.get("data"):
|
||
text_parts.append({"type": "image_url", "image_url": {"url": f"data:{src.get('media_type', 'image/png')};base64,{src.get('data', '')}"}})
|
||
elif b.get("type") == "image_url":
|
||
text_parts.append(b)
|
||
elif b.get("type") == "text":
|
||
text_parts.append({"type": "text", "text": b.get("text", "")})
|
||
if text_parts: result.append({"role": "user", "content": text_parts})
|
||
else:
|
||
result.append(msg)
|
||
return result
|
||
|
||
|
||
class BaseSession:
|
||
def __init__(self, cfg):
|
||
self.api_key = cfg['apikey']
|
||
self.api_base = cfg['apibase'].rstrip('/')
|
||
self.default_model = cfg.get('model', '')
|
||
self.context_win = cfg.get('context_win', 24000)
|
||
self.history = []
|
||
self.lock = threading.Lock()
|
||
self.system = ""
|
||
self.name = cfg.get('name', self.default_model)
|
||
proxy = cfg.get('proxy')
|
||
self.proxies = {"http": proxy, "https": proxy} if proxy else None
|
||
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 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 ask(self, prompt, model=None, stream=False):
|
||
def _ask_gen():
|
||
content = ''
|
||
with self.lock:
|
||
self.history.append({"role": "user", "content": [{"type": "text", "text": prompt}]})
|
||
trim_messages_history(self.history, self.context_win)
|
||
messages = self.make_messages(self.history)
|
||
content_blocks = None
|
||
gen = self.raw_ask(messages, model)
|
||
try:
|
||
while True: chunk = next(gen); content += chunk; yield chunk
|
||
except StopIteration as e: content_blocks = e.value or []
|
||
if len(content_blocks) > 1: print(f"[DEBUG BaseSession.ask] content_blocks: {content_blocks}")
|
||
for block in (content_blocks or []):
|
||
if block.get('type', '') == 'tool_use':
|
||
tu = {'name': block.get('name', ''), 'arguments': block.get('input', {})}
|
||
yield f'<tool_use>{json.dumps(tu, ensure_ascii=False)}</tool_use>'
|
||
if not content.startswith("Error:"): self.history.append({"role": "assistant", "content": [{"type": "text", "text": content}]})
|
||
return _ask_gen() if stream else ''.join(list(_ask_gen()))
|
||
|
||
class ClaudeSession(BaseSession):
|
||
def raw_ask(self, messages, model=None, temperature=0.5, max_tokens=6144):
|
||
model = model or self.default_model
|
||
ml = model.lower()
|
||
if 'kimi' in ml or 'moonshot' in ml: temperature = 1.0 # kimi/moonshot only accepts temp 1.0
|
||
elif 'minimax' in ml: temperature = max(0.01, min(temperature, 1.0)) # MiniMax requires temp in (0, 1]
|
||
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 self.system: payload["system"] = [{"type": "text", "text": self.system, "cache_control": {"type": "persistent"}}]
|
||
content_blocks = []
|
||
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()
|
||
content_blocks = yield from _parse_claude_sse(r.iter_lines())
|
||
except Exception as e: yield f"Error: {str(e)}"
|
||
return content_blocks or []
|
||
def make_messages(self, raw_list):
|
||
msgs = [{"role": m['role'], "content": list(m['content'])} for m in raw_list]
|
||
c = msgs[-1]["content"]
|
||
c[-1] = dict(c[-1], cache_control={"type": "ephemeral"})
|
||
return msgs
|
||
|
||
class LLMSession(BaseSession):
|
||
def raw_ask(self, messages, model=None, temperature=0.5):
|
||
if model is None: model = self.default_model
|
||
return (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): return _msgs_claude2oai(raw_list)
|
||
|
||
class NativeClaudeSession(BaseSession):
|
||
def __init__(self, cfg):
|
||
super().__init__(cfg)
|
||
self.context_win = cfg.get("context_win", 28000)
|
||
self.fake_cc_system_prompt = cfg.get("fake_cc_system_prompt", False)
|
||
self._session_id = str(uuid.uuid4())
|
||
self._account_uuid = str(uuid.uuid4())
|
||
self._device_id = uuid.uuid4().hex + uuid.uuid4().hex[:32]
|
||
self.tools = None
|
||
def raw_ask(self, messages, tools=None, system=None, model=None, temperature=0.5, max_tokens=6144):
|
||
model = model or self.default_model
|
||
headers = {"Content-Type": "application/json", "anthropic-version": "2023-06-01",
|
||
"anthropic-beta": "prompt-caching-2024-07-31", "x-app": "cli", "user-agent": "claude-cli/2.1.80 (external, cli)"}
|
||
if self.api_key.startswith("cr_"): headers["authorization"] = f"Bearer {self.api_key}"
|
||
else: headers["x-api-key"] = self.api_key
|
||
payload = {"model": model, "messages": messages, "temperature": temperature, "max_tokens": max_tokens, "stream": True}
|
||
payload["metadata"] = {"user_id": json.dumps({"device_id": self._device_id, "account_uuid": self._account_uuid, "session_id": self._session_id}, separators=(',', ':'))}
|
||
if tools:
|
||
tools = [dict(t) for t in tools]; tools[-1]["cache_control"] = {"type": "ephemeral"}
|
||
payload["tools"] = tools
|
||
payload['system'] = [{"type": "text", "text": "You are Claude Code, Anthropic's official CLI for Claude.", "cache_control": {"type": "ephemeral"}}]
|
||
if system:
|
||
if self.fake_cc_system_prompt: messages[0]["content"].insert(0, {"type": "text", "text": system})
|
||
else: payload["system"] = [{"type": "text", "text": system}]
|
||
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, model=None):
|
||
assert type(msg) is dict
|
||
with self.lock:
|
||
self.history.append(msg)
|
||
trim_messages_history(self.history, self.context_win)
|
||
messages = [{"role": m["role"], "content": list(m["content"])} for m in self.history]
|
||
|
||
content_blocks = None
|
||
gen = self.raw_ask(messages, self.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('}]'):
|
||
_pat = next((p for p in ['[{"type":"tool_use"', '[{"type": "tool_use"'] if p in content), None)
|
||
if _pat:
|
||
try:
|
||
idx = content.index(_pat); 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 NativeOAISession(NativeClaudeSession):
|
||
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 []) + _msgs_claude2oai(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 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.name = self.backend.name
|
||
self.total_cd_tokens = 0
|
||
|
||
def chat(self, messages, tools=None):
|
||
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：
|
||
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。
|
||
格式: ```<tool_use>{{"name": "工具名", "arguments": {{参数}}}}</tool_use>```
|
||
|
||
### 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_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 = ""; user = ""
|
||
if system_content: system += f"{system_content}\n"
|
||
system += f"{tool_instruction}"
|
||
for m in history_msgs:
|
||
role = "USER" if m['role'] == 'user' else "ASSISTANT"
|
||
user += f"=== {role} ===\n"
|
||
for tr in m.get('tool_results', []): user += f'<tool_result>{tr["content"]}</tool_result>\n'
|
||
user += str(m['content']) + "\n"
|
||
self.total_cd_tokens += self._estimate_content_len(user)
|
||
if self.total_cd_tokens > 9000: 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_call)>((?:(?!<(?:tool_use|tool_call)>).){15,}?)</(?:tool_use|tool_call)>"
|
||
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().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)
|
||
if json_match:
|
||
json_str = json_match.group(0).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 if isinstance(i, int) else
|
||
next(s.backend for s in all_sessions if type(s) is not dict and s.backend.name == i) for i in cfg.get('llm_nos', [])]
|
||
is_native = lambda s: 'Native' in s.__class__.__name__
|
||
groups = {is_native(s) for s in self._sessions}
|
||
assert len(groups) == 1, f"MixinSession: sessions must be in same group (Native or non-Native), got {[type(s).__name__ for s in self._sessions]}"
|
||
self.name = '|'.join(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)
|
||
def __setattr__(self, name, value):
|
||
if name in ('system', 'tools'):
|
||
for s in self._sessions:
|
||
v = openai_tools_to_claude(value) if name == 'tools' and type(s) is NativeClaudeSession else value
|
||
setattr(s, name, v)
|
||
else: object.__setattr__(self, name, value)
|
||
@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.name = self.backend.name
|
||
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.backend.tools = tools
|
||
combined_content = []; resp = None; tool_results = []
|
||
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 msg['role'] == 'user' and msg.get('tool_results'): tool_results.extend(msg['tool_results'])
|
||
tr_id_set = set(); tool_result_blocks = []
|
||
for tr in tool_results:
|
||
tool_use_id, content = tr.get("tool_use_id", ""), tr.get("content", "")
|
||
tr_id_set.add(tool_use_id)
|
||
if tool_use_id: tool_result_blocks.append({"type": "tool_result", "tool_use_id": tool_use_id, "content": tr.get("content", "")})
|
||
else: combined_content = [{"type": "text", "text": f'<tool_result>{content}</tool_result>'}] + combined_content
|
||
for tid in self._pending_tool_ids:
|
||
if tid not in tr_id_set: tool_result_blocks.append({"type": "tool_result", "tool_use_id": tid, "content": ""})
|
||
self._pending_tool_ids = []
|
||
merged = {"role": "user", "content": tool_result_blocks + combined_content}
|
||
_write_llm_log('Prompt', json.dumps(merged, ensure_ascii=False, indent=2))
|
||
gen = self.backend.ask(merged)
|
||
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: self._pending_tool_ids = [tc.id for tc in resp.tool_calls]
|
||
return resp |