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v2.2.1: fix compaction orphaning tool outputs causing Crof incomplete

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- Fix compaction cutting between function_call and function_call_output pairs
- Orphaned tool results confused Crof models causing finish_reason=length
- reasoning_effort=none now always sends enable_thinking=false too
- Added Crof upstream debug logging
This commit is contained in:
Roman
2026-05-20 13:14:40 +04:00
Unverified
parent 9bcb6998e0
commit 6c67642522
5 changed files with 40 additions and 5 deletions
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28
src/translate-proxy.py
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@@ -238,8 +238,16 @@ def _compact_input(input_data):
break
head = input_data[:head_end]
tail = input_data[-_COMPACT_KEEP_RECENT:]
body = input_data[head_end:-_COMPACT_KEEP_RECENT]
tail_start = len(input_data) - _COMPACT_KEEP_RECENT
while tail_start > head_end:
if input_data[tail_start].get("type") == "function_call_output":
tail_start -= 1
elif input_data[tail_start].get("type") == "message" and input_data[tail_start].get("role") == "assistant":
tail_start -= 1
else:
break
tail = input_data[tail_start:]
body = input_data[head_end:tail_start]
if not body:
return head + tail
@@ -891,9 +899,11 @@ class Handler(http.server.BaseHTTPRequestHandler):
if body.get("tool_choice"):
chat_body["tool_choice"] = body["tool_choice"]
chat_body["stream"] = stream
if not REASONING_ENABLED:
if not REASONING_ENABLED or REASONING_EFFORT == "none":
chat_body["enable_thinking"] = False
chat_body["reasoning_effort"] = REASONING_EFFORT if REASONING_ENABLED else "none"
chat_body["reasoning_effort"] = "none"
else:
chat_body["reasoning_effort"] = REASONING_EFFORT
target = upstream_target(TARGET_URL, "/chat/completions")
fwd = forwarded_headers(self.headers, {
@@ -901,6 +911,16 @@ class Handler(http.server.BaseHTTPRequestHandler):
"Authorization": f"Bearer {API_KEY}",
}, browser_ua=True)
print(f"[translate-proxy] POST {target} model={model} stream={stream} ua={fwd.get('User-Agent','')[:50]}", file=sys.stderr)
_crof_debug_path = os.path.join(_LOG_DIR, "crof-upstream.jsonl")
with open(_crof_debug_path, "a") as _cdf:
_cdf.write(json.dumps({
"ts": _ts, "model": model, "max_tokens": chat_body.get("max_tokens"),
"reasoning_enabled": REASONING_ENABLED, "reasoning_effort": chat_body.get("reasoning_effort"),
"enable_thinking": chat_body.get("enable_thinking", "NOT_SENT"),
"n_messages": len(chat_body.get("messages", [])),
"has_tools": bool(chat_body.get("tools")),
"messages_summary": [{"role": m.get("role"), "tc": len(m.get("tool_calls", [])), "content_len": len(str(m.get("content", "")))[:6], "tool_call_id": m.get("tool_call_id")} for m in chat_body.get("messages", [])],
}) + "\n")
req = urllib.request.Request(
target,
data=json.dumps(chat_body).encode(),