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fix(vision): Z.AI vision model compatibility — endpoint routing and max_tokens handling
Z.AI (智谱 GLM) vision models (glm-4v-flash, glm-4v-plus, etc.) have two
compatibility issues when used through the Anthropic-compatible endpoint:
1. **Error 1210 — max_tokens rejected on multimodal calls**: Z.AI rejects
the max_tokens parameter for vision model requests with error code 1210
("API 调用参数有误"). The error string does not contain "max_tokens",
so the existing unsupported-parameter retry logic never fires.
2. **Wrong endpoint inheritance**: When the main runtime provider uses Z.AI's
Anthropic-compatible endpoint (open.bigmodel.cn/api/anthropic), the vision
client inherits this endpoint. But Z.AI's Anthropic wire cannot properly
handle image content — models silently fail ("I can't see the image") or
reject max_tokens.
Changes:
- resolve_vision_provider_client(): force Z.AI vision to use OpenAI-compatible
endpoint (open.bigmodel.cn/api/paas/v4) instead of inheriting Anthropic wire
- _build_call_kwargs(): skip max_tokens for Z.AI vision models (4v/5v/-v suffix)
- _AnthropicCompletionsAdapter: support _skip_zai_max_tokens flag
- _to_openai_base_url(): rewrite Z.AI Anthropic URLs to OpenAI-compatible path
- call_llm() retry: detect Z.AI error 1210 and strip max_tokens before retry
This commit is contained in:
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1 changed files with 73 additions and 4 deletions
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@ -455,6 +455,12 @@ def _to_openai_base_url(base_url: str) -> str:
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"""
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url = str(base_url or "").strip().rstrip("/")
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if url.endswith("/anthropic"):
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# ZAI (open.bigmodel.cn) uses /api/anthropic for Anthropic wire
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# but /api/paas/v4 for OpenAI wire — the generic /v1 rewrite is wrong.
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if "open.bigmodel.cn" in url or "bigmodel" in url:
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rewritten = url[: -len("/anthropic")] + "/paas/v4"
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logger.debug("Auxiliary client: rewrote ZAI base URL %s → %s", url, rewritten)
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return rewritten
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rewritten = url[: -len("/anthropic")] + "/v1"
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logger.debug("Auxiliary client: rewrote base URL %s → %s", url, rewritten)
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return rewritten
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@ -828,7 +834,14 @@ class _AnthropicCompletionsAdapter:
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model = kwargs.get("model", self._model)
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tools = kwargs.get("tools")
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tool_choice = kwargs.get("tool_choice")
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max_tokens = kwargs.get("max_tokens") or kwargs.get("max_completion_tokens") or 2000
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# ZAI's Anthropic-compatible endpoint rejects max_tokens on vision
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# models (glm-4v-flash etc.) with error code 1210. When the caller
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# signals this by setting _skip_zai_max_tokens in kwargs, omit it.
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_skip_mt = kwargs.pop("_skip_zai_max_tokens", False)
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if _skip_mt:
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max_tokens = None
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else:
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max_tokens = kwargs.get("max_tokens") or kwargs.get("max_completion_tokens") or 2000
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temperature = kwargs.get("temperature")
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normalized_tool_choice = None
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@ -2835,6 +2848,33 @@ def resolve_vision_provider_client(
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)
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return _finalize(requested, sync_client, default_model)
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# ZAI vision models must use the OpenAI-compatible endpoint, not the
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# Anthropic-compatible one (which may be the main-runtime default).
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# The Anthropic wire rejects max_tokens on multimodal calls (error 1210),
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# while the OpenAI wire handles it correctly.
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if requested == "zai" and not resolved_base_url:
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zai_openai_urls = [
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"https://open.bigmodel.cn/api/paas/v4",
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"https://api.z.ai/api/paas/v4",
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]
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for _zai_url in zai_openai_urls:
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client, final_model = _get_cached_client(
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requested, resolved_model, async_mode,
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base_url=_zai_url,
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api_key=resolved_api_key or None,
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api_mode="chat_completions",
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is_vision=True,
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)
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if client is not None:
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return _finalize(requested, client, final_model)
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# Fallback: try without explicit base_url (old behavior)
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client, final_model = _get_cached_client(requested, resolved_model, async_mode,
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api_mode=resolved_api_mode,
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is_vision=True)
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if client is None:
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return requested, None, None
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return requested, client, final_model
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client, final_model = _get_cached_client(requested, resolved_model, async_mode,
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api_mode=resolved_api_mode,
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is_vision=True)
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@ -3394,7 +3434,16 @@ def _build_call_kwargs(
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if max_tokens is not None:
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# Codex adapter handles max_tokens internally; OpenRouter/Nous use max_tokens.
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# Direct OpenAI api.openai.com with newer models needs max_completion_tokens.
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if provider == "custom":
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# ZAI vision models (glm-4v-flash, glm-4v-plus, etc.) reject max_tokens with
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# error code 1210 ("API 调用参数有误") on multimodal requests — skip it.
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_model_lower = (model or "").lower()
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_skip_max_tokens = (
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provider == "zai"
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and ("4v" in _model_lower or "5v" in _model_lower or "-v" in _model_lower)
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)
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if _skip_max_tokens:
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pass # ZAI vision models do not accept max_tokens
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elif provider == "custom":
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custom_base = base_url or _current_custom_base_url()
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if base_url_hostname(custom_base) == "api.openai.com":
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kwargs["max_completion_tokens"] = max_tokens
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@ -3625,13 +3674,23 @@ def call_llm(
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kwargs = retry_kwargs
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err_str = str(first_err)
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# ZAI vision models (glm-4v-flash etc.) return error code 1210
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# ("API 调用参数有误") when max_tokens is passed on multimodal
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# calls. The error message does NOT contain "max_tokens" so the
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# generic retry below never fires. Detect the ZAI-specific error
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# and strip max_tokens before retrying.
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_is_zai_param_error = (
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"1210" in err_str
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and "bigmodel" in str(getattr(client, "base_url", ""))
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)
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if max_tokens is not None and (
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"max_tokens" in err_str
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or "unsupported_parameter" in err_str
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or _is_unsupported_parameter_error(first_err, "max_tokens")
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or _is_zai_param_error
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):
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kwargs.pop("max_tokens", None)
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kwargs["max_completion_tokens"] = max_tokens
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kwargs.pop("max_completion_tokens", None)
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try:
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return _validate_llm_response(
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client.chat.completions.create(**kwargs), task)
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@ -3931,13 +3990,23 @@ async def async_call_llm(
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kwargs = retry_kwargs
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err_str = str(first_err)
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# ZAI vision models (glm-4v-flash etc.) return error code 1210
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# ("API 调用参数有误") when max_tokens is passed on multimodal
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# calls. The error message does NOT contain "max_tokens" so the
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# generic retry below never fires. Detect the ZAI-specific error
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# and strip max_tokens before retrying.
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_is_zai_param_error = (
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"1210" in err_str
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and "bigmodel" in str(getattr(client, "base_url", ""))
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)
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if max_tokens is not None and (
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"max_tokens" in err_str
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or "unsupported_parameter" in err_str
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or _is_unsupported_parameter_error(first_err, "max_tokens")
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or _is_zai_param_error
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):
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kwargs.pop("max_tokens", None)
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kwargs["max_completion_tokens"] = max_tokens
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kwargs.pop("max_completion_tokens", None)
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try:
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return _validate_llm_response(
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await client.chat.completions.create(**kwargs), task)
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