"""OpenAI-compatible provider (internal gateways, Azure OpenAI, LiteLLM, vLLM...).
Targets the ``POST {base_url}/chat/completions`` streaming endpoint with the
standard function-calling schema. Works with any server that speaks the OpenAI
Chat Completions API.
"""
from __future__ import annotations
import json
import threading
from typing import Any, Dict, List, Optional
import requests
from .base import (
CancelFn, CancelWatchdog, MODEL_NOT_FOUND_HINT, Provider, ProviderError,
TextCallback, ThinkStreamSplitter, ToolSpec,
)
_TIMEOUT = (5, 30) # (connect, read) seconds — lower for faster Stop response
_MAX_RETRIES = 6 # auto-retry on rate-limit (429) up to this many times
class OpenAICompatProvider(Provider):
name = "openai_compat"
supports_vision = True
def _url(self) -> str:
base = str(self.conf.get("base_url", "")).rstrip("/")
if not base:
raise ProviderError("base_url is not configured for the OpenAI-compatible provider.")
return f"{base}/chat/completions"
def _headers(self) -> Dict[str, str]:
headers = {"Content-Type": "application/json"}
key = self.conf.get("api_key")
if key:
headers["Authorization"] = f"Bearer {key}"
return headers
@staticmethod
def _to_api_messages(messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
out: List[Dict[str, Any]] = []
for m in messages:
role = m["role"]
if role == "assistant" and m.get("tool_calls"):
out.append({
"role": "assistant",
"content": m.get("content") or "",
"tool_calls": [{
"id": tc["id"],
"type": "function",
"function": {
"name": tc["name"],
"arguments": json.dumps(tc.get("arguments", {}), ensure_ascii=False),
},
} for tc in m["tool_calls"]],
})
elif role == "tool":
out.append({
"role": "tool",
"tool_call_id": m.get("tool_call_id", ""),
"content": m.get("content", ""),
})
else:
content = m.get("content", "")
if isinstance(content, list):
# Preview tab's region-selection → AI fix flow: a list of
# canonical content blocks (see providers/base.py docstring).
blocks = []
for block in content:
if block.get("type") == "image":
mime = block.get("mime", "image/png")
blocks.append({"type": "image_url", "image_url": {
"url": f"data:{mime};base64,{block.get('data', '')}",
}})
else:
blocks.append({"type": "text", "text": block.get("text", "")})
out.append({"role": role, "content": blocks})
else:
out.append({"role": role, "content": content})
return out
def chat(
self,
messages: List[Dict[str, Any]],
tools: Optional[List[ToolSpec]] = None,
on_text: Optional[TextCallback] = None,
cancel: Optional[CancelFn] = None,
on_reasoning: Optional[TextCallback] = None,
) -> Dict[str, Any]:
work = list(messages) # local copy we can trim on context overflow
payload: Dict[str, Any] = {"model": self.model, "stream": True}
if tools:
payload["tools"] = [t.to_openai() for t in tools]
payload["tool_choice"] = "auto"
text_parts: List[str] = []
# Accumulate tool-call fragments keyed by streamed index.
tool_acc: Dict[int, Dict[str, Any]] = {}
usage_seen: Dict[str, Any] = {} # final "usage" block, if the server sends one
# Some gateways inline reasoning as … in the content stream
# (rather than a separate reasoning_content field). Route that to
# on_reasoning (→ "Thinking" indicator) and keep the answer bubble clean.
def _emit_answer(t: str) -> None:
text_parts.append(t)
if on_text:
on_text(t)
splitter = ThinkStreamSplitter(on_text=_emit_answer, on_reasoning=on_reasoning)
for attempt in range(1, _MAX_RETRIES + 2):
payload["messages"] = self._to_api_messages(work)
try:
resp = self._request(
"POST", self._url(), headers=self._headers(), json=payload,
stream=True, timeout=_TIMEOUT,
)
except requests.RequestException as exc:
raise ProviderError(f"Could not reach the gateway: {exc}") from exc
# requests/urllib3 falls back to Latin-1 for text/* responses whose
# Content-Type omits an explicit charset (common for SSE streams) —
# every non-ASCII UTF-8 byte pair then gets misread as two Latin-1
# characters ("ô" → "ô"), corrupting every non-English reply. The
# body is always UTF-8 JSON/SSE in practice, so force it explicitly
# rather than trust the guess.
resp.encoding = "utf-8"
if resp.status_code >= 400:
code = resp.status_code
wait = self._retry_after(resp)
err = self._error_text(resp)
resp.close()
# Rate limited (TPM/RPM) — wait the suggested time and retry.
if code == 429 and attempt <= _MAX_RETRIES:
if self._wait_or_cancel(wait, cancel, on_text, attempt):
return _assemble_assistant(text_parts, tool_acc) # cancelled
continue
# Prompt too long — auto-compress and retry. First try dropping the
# oldest turn; if there's nothing left to drop (e.g. the very first
# message of a new conversation is itself oversized, typically from
# a large attachment), shrink that message's own content instead of
# giving up immediately.
if code == 400 and attempt <= _MAX_RETRIES and self._is_context_overflow(err):
work, changed = self._drop_oldest_turn(work)
note = "\n✂ Lịch sử quá dài — tự nén bớt rồi thử lại…\n"
if not changed:
work, changed = self._shrink_last_message(work)
note = "\n✂ Tin nhắn/đính kèm quá dài cho model này — tự cắt bớt nội dung rồi thử lại…\n"
if changed:
if on_text:
on_text(note)
continue
if self._is_context_overflow(err):
raise ProviderError(self._friendly_context_error(err))
raise ProviderError(err)
break # 200 OK → stream the response below
# Stream the body. A gateway/proxy can drop the connection mid-stream
# ("Response ended prematurely" / connection reset): if nothing was
# received yet, silently re-send the request a couple of times; if a
# partial answer already streamed, keep it and just note the cut —
# never surface the raw transport error over usable content.
stream_retries = 0
# If cancel is a threading.Event (new worker._stop_event), we can wait
# on it with a timeout in parallel with the streaming read — this makes
# Stop interrupt immediately even during LLM "thinking" silence.
cancel_event: Optional[threading.Event] = None
if isinstance(cancel, threading.Event):
cancel_event = cancel
elif hasattr(cancel, "is_set") and callable(getattr(cancel, "wait")):
# Duck-type: anything with is_set() and wait() counts as Event-like
cancel_event = cancel
def _wait_cancel(ev: threading.Event, resp: requests.Response) -> None:
"""Block until cancel is set, then close the response to unblock iter_lines."""
ev.wait()
try:
resp.close()
except Exception:
pass
cancel_thread: Optional[threading.Thread] = None
if cancel_event is not None:
cancel_thread = threading.Thread(
target=_wait_cancel, args=(cancel_event, resp), daemon=True)
cancel_thread.start()
while True:
try:
with CancelWatchdog(resp, cancel):
for raw in resp.iter_lines(decode_unicode=True):
if self._is_cancelled(cancel):
break
if not raw or not raw.startswith("data:"):
continue
data = raw[len("data:"):].strip()
if data == "[DONE]":
break
try:
chunk = json.loads(data)
except json.JSONDecodeError:
continue
choices = chunk.get("choices") or []
if chunk.get("usage"):
usage_seen = chunk["usage"]
if not choices:
continue
delta = choices[0].get("delta", {})
# Reasoning models (Qwen3, DeepSeek-R1, …) stream their private
# thinking in a separate field — surface it as "thinking" activity
# only, never as part of the answer.
rc = delta.get("reasoning_content") or delta.get("reasoning")
if rc and on_reasoning:
on_reasoning(rc)
piece = delta.get("content")
if piece:
splitter.feed(piece) # splits inline … out of the answer
for tc in delta.get("tool_calls", []) or []:
idx = tc.get("index", 0)
slot = tool_acc.setdefault(idx, {"id": "", "name": "", "args": ""})
if tc.get("id"):
slot["id"] = tc["id"]
fn = tc.get("function", {})
if fn.get("name"):
slot["name"] = fn["name"]
if fn.get("arguments"):
slot["args"] += fn["arguments"]
resp.close()
break # stream finished normally (or cancelled)
except requests.RequestException as exc:
resp.close()
# If cancel was requested, close cleanly without retry
if cancel_event is not None and cancel_event.is_set():
break
if self._is_cancelled(cancel):
break
if text_parts or tool_acc:
# Partial answer already on screen — keep it, note the cut.
if on_text:
on_text("\n⚠ Kết nối bị ngắt giữa chừng — hiển thị phần đã nhận được.\n")
break
stream_retries += 1
if stream_retries > 2:
raise ProviderError(
f"Kết nối tới gateway bị ngắt giữa chừng (đã thử lại {stream_retries - 1} lần): {exc}"
) from exc
if on_text:
on_text("\n⚠ Kết nối bị ngắt — đang thử lại…\n")
try:
resp = self._request(
"POST", self._url(), headers=self._headers(), json=payload,
stream=True, timeout=_TIMEOUT,
)
except requests.RequestException as exc2:
raise ProviderError(f"Could not reach the gateway: {exc2}") from exc2
resp.encoding = "utf-8" # same Latin-1-fallback fix as the initial request
if resp.status_code >= 400:
err = self._error_text(resp)
resp.close()
raise ProviderError(err)
splitter.flush() # emit any held-back tail (partial tag / trailing text)
self._record_usage(work, text_parts, tool_acc, usage_seen)
return _assemble_assistant(text_parts, tool_acc)
def _record_usage(self, messages, text_parts, tool_acc, usage_seen) -> None:
"""One Dashboard usage event per turn: real counts when the server's
final chunk carried a "usage" block, a ~4 chars/token estimate
otherwise. Never breaks the turn."""
try:
from ..core import usage_tracker as ut
if usage_seen:
ut.record(self.name, self.model,
usage_seen.get("prompt_tokens", 0),
usage_seen.get("completion_tokens", 0),
(usage_seen.get("prompt_tokens_details") or {}).get("cached_tokens", 0))
else:
sent = json.dumps(self._to_api_messages(messages), ensure_ascii=False)
got = "".join(text_parts) + "".join(s["args"] for s in tool_acc.values())
ut.record(self.name, self.model, ut.estimate_tokens(sent),
ut.estimate_tokens(got), 0, estimated=True)
except Exception: # noqa: BLE001
pass
def list_models(self):
self.last_error = ""
base = str(self.conf.get("base_url", "")).rstrip("/")
if not base:
self.last_error = "Base URL is not configured (Settings → OpenAI-compatible)."
return []
try:
resp = self._request("GET", f"{base}/models", headers=self._headers(),
timeout=(10, 30))
if resp.status_code >= 400:
self.last_error = self._error_text(resp)
return []
data = resp.json().get("data", [])
ids = [m.get("id") for m in data if isinstance(m, dict) and m.get("id")]
if not ids:
self.last_error = "Gateway responded but returned no models."
return ids
except requests.RequestException as exc:
self.last_error = f"Could not reach the gateway: {exc}"
return []
except ValueError as exc:
self.last_error = f"Gateway returned an invalid (non-JSON) response: {exc}"
return []
@staticmethod
def _error_text(resp: requests.Response) -> str:
try:
body = resp.json()
# Prefer the OpenAI-style {"error": {"message": ...}} shape; some
# gateways instead return a FLAT body like {"message": "Not found",
# "description": "...", "code": 404} — "description" is usually the
# human-readable one there, so try it before falling back to the
# generic top-level "message" (often just "Not found") or a raw dump.
err_obj = body.get("error")
msg = (
(err_obj.get("message") if isinstance(err_obj, dict) else None)
or body.get("description")
or body.get("message")
or json.dumps(body)
)
except ValueError:
msg = resp.text[:300]
text = f"Gateway error {resp.status_code}: {msg}"
if resp.status_code == 404 and "model" in msg.lower():
# A model-not-found/unavailable response — this is recoverable by
# just picking a different model, not a real outage. Say so
# explicitly so the user doesn't read it as the app being broken.
text += MODEL_NOT_FOUND_HINT
return text
def _assemble_assistant(text_parts: List[str], tool_acc: Dict[int, Dict[str, Any]]) -> Dict[str, Any]:
tool_calls: List[Dict[str, Any]] = []
for idx in sorted(tool_acc):
slot = tool_acc[idx]
if not slot["name"]:
continue
try:
args = json.loads(slot["args"]) if slot["args"].strip() else {}
except json.JSONDecodeError:
args = {"_raw": slot["args"]}
tool_calls.append({
"id": slot["id"] or f"call_{idx}",
"name": slot["name"],
"arguments": args,
})
return {
"role": "assistant",
"content": Provider.strip_think("".join(text_parts)),
"tool_calls": tool_calls,
}