add convert_to_openai_tool

langgraph_qwen/chat_model.py

@@ -2,6 +2,7 @@ from __future__ import annotations
 
 import json
 import os
+from copy import deepcopy
 from dataclasses import dataclass, field
 from typing import Any, Dict, Iterable, Iterator, List, Optional, Tuple
 from langchain_core.language_models.chat_models import BaseChatModel
@@ -13,6 +14,8 @@ except Exception:  # pragma: no cover
     # pydantic v1 fallback
     from pydantic.fields import PrivateAttr  # type: ignore
 
+from langchain_core.utils.function_calling import convert_to_openai_tool
+
 
 def _env(name: str, default: Optional[str] = None) -> Optional[str]:
     v = os.getenv(name)
@@ -84,25 +87,46 @@ def _to_openai_messages(messages: List["BaseMessage"]) -> List[Dict[str, Any]]:
     return out
 
 
+def _normalize_params(schema: Any) -> Dict[str, Any]:
+    """Normalize various schema-like inputs into a valid OpenAI JSON Schema."""
+    if not isinstance(schema, dict):
+        return {"type": "object", "properties": {}, "required": []}
+    if schema.get("type") == "object" and isinstance(schema.get("properties"), dict):
+        out = dict(schema)
+    elif "properties" in schema and isinstance(schema["properties"], dict):
+        out = {"type": "object", **schema}
+    elif all(isinstance(v, dict) for v in schema.values()):
+        out = {"type": "object", "properties": schema}
+    else:
+        out = {"type": "object", "properties": {}}
+    for k in ("$schema", "$defs", "definitions", "title"):
+        out.pop(k, None)
+    req = out.get("required", [])
+    if not isinstance(req, list):
+        req = list(req) if req else []
+    out["required"] = req
+    return out
+
+
 def _convert_tool(tool: Any) -> Dict[str, Any]:
-    """Best-effort conversion to OpenAI tool schema."""
+    """Convert LangChain/MCP tool to OpenAI JSON via official utility, with fallback normalization."""
-    # Prefer built-in conversion if available
-    if hasattr(tool, "to_openai_tool"):
     try:
-            return tool.to_openai_tool()  # type: ignore[attr-defined]
+        t = convert_to_openai_tool(tool)
+        fn = t.get("function", {})
+        fn["description"] = (fn.get("description") or "")[:1000]
+        fn["parameters"] = _normalize_params(fn.get("parameters"))
+        return {"type": "function", "function": fn}
     except Exception:
         pass
     name = getattr(tool, "name", None) or getattr(tool, "__name__", "tool")
-    description = getattr(tool, "description", "")
+    desc = getattr(tool, "description", "") or ""
-    # Attempt to get a JSON schema
-    params: Dict[str, Any] = {"type": "object", "properties": {}, "required": []}
     schema = None
-    if hasattr(tool, "args_schema") and getattr(tool, "args_schema") is not None:
+    if getattr(tool, "args_schema", None) is not None:
         try:
-            schema = tool.args_schema.schema()  # pydantic v1
+            schema = tool.args_schema.model_json_schema()
         except Exception:
             try:
-                schema = tool.args_schema.model_json_schema()  # pydantic v2
+                schema = tool.args_schema.schema()
             except Exception:
                 schema = None
     if schema is None and hasattr(tool, "args"):
@@ -110,16 +134,8 @@ def _convert_tool(tool: Any) -> Dict[str, Any]:
             schema = tool.args
         except Exception:
             schema = None
-    if isinstance(schema, dict):
+    params = _normalize_params(schema)
-        params = schema
+    return {"type": "function", "function": {"name": name, "description": desc[:1000], "parameters": params}}
-    return {
-        "type": "function",
-        "function": {
-            "name": name,
-            "description": description,
-            "parameters": params,
-        },
-    }
 
 
 def _convert_tools(tools: Optional[List[Any]]) -> Optional[List[Dict[str, Any]]]:
@@ -272,6 +288,7 @@ class ChatQwenOpenAICompat(BaseChatModel):
     def _request(self, payload: Dict[str, Any], *, stream: bool = False) -> Any:
         try:
             import httpx
+            import sys as _sys
         except Exception as e:
             raise RuntimeError("httpx is required for ChatQwenOpenAICompat. Install via `pip install httpx`. ") from e
 
@@ -280,24 +297,69 @@ class ChatQwenOpenAICompat(BaseChatModel):
         auth_scheme = _env("QWEN_AUTH_SCHEME", "Bearer")
         auth_value = f"{auth_scheme} {self._api_key}" if auth_scheme else self._api_key
         headers = {auth_header: auth_value, "Content-Type": "application/json"}
+
         base = self._base_url.rstrip("/")
-        # Accept either a root base_url (ending with /v1) or a full endpoint that already includes /chat/completions
+        url = base if "chat/completions" in base else base + "/chat/completions"
-        if "chat/completions" in base:
+
-            url = base
+        # honor system proxy by default; disable via QWEN_HTTP_TRUST_ENV=0
-        else:
-            url = base + "/chat/completions"
-        # Proxy behavior: honor env by default; allow disabling via QWEN_HTTP_TRUST_ENV=0
         trust_env = _env("QWEN_HTTP_TRUST_ENV", "1") != "0"
-        client = httpx.Client(timeout=self._timeout, trust_env=trust_env)
+
         if _env("QWEN_DEBUG") == "1":
-            import sys as _sys
             print(f"[qwen] POST {url} timeout={self._timeout} trust_env={trust_env}", file=_sys.stderr)
+
+        # ===== DEBUG: dump payload & headers (API key redacted) =====
+        if _env("QWEN_DEBUG_BODY") == "1":
+            try:
+                # 打印请求体
+                print("[qwen] payload =\n" + json.dumps(payload, ensure_ascii=False, indent=2), file=_sys.stderr)
+            except Exception:
+                print("[qwen] payload = <non-serializable>", file=_sys.stderr)
+            try:
+                # 打印打码后的 headers
+                redacted = dict(headers)
+                if auth_header in redacted and isinstance(redacted[auth_header], str):
+                    # 只保留认证方案，密钥打码
+                    val = redacted[auth_header]
+                    if " " in val:
+                        scheme, _token = val.split(" ", 1)
+                        redacted[auth_header] = f"{scheme} ***REDACTED***"
+                    else:
+                        redacted[auth_header] = "***REDACTED***"
+                print("[qwen] headers = " + json.dumps(redacted, ensure_ascii=False), file=_sys.stderr)
+            except Exception:
+                pass
+        # ===========================================================
+
+        client = httpx.Client(timeout=self._timeout, trust_env=trust_env)
+
         if stream:
+            # 流式时也会先把 payload 打出来（上面已做），这里直接返回流
             return client.stream("POST", url, headers=headers, json=payload)
+
         resp = client.post(url, headers=headers, json=payload)
+
+        # ===== DEBUG: dump response =====
+        if _env("QWEN_DEBUG_RESP") == "1":
+            print(f"[qwen] status={resp.status_code}", file=_sys.stderr)
+            try:
+                print("[qwen] resp.text =\n" + resp.text, file=_sys.stderr)
+            except Exception:
+                print("[qwen] resp.text = <binary or non-text>", file=_sys.stderr)
+        # =================================
+
+        try:
             resp.raise_for_status()
+        except httpx.HTTPStatusError as e:
+            # 打印错误体，帮助定位 4xx/5xx 的具体原因
+            if _env("QWEN_DEBUG_RESP", "1") == "1":
+                try:
+                    print(f"[qwen] ERROR status={e.response.status_code}\n{e.response.text}", file=_sys.stderr)
+                except Exception:
+                    pass
+            raise
         return resp.json()
 
+
     def _build_payload(self, messages: List["BaseMessage"], **kwargs: Any) -> Dict[str, Any]:
         payload: Dict[str, Any] = {
             "model": self._model,
@@ -308,13 +370,27 @@ class ChatQwenOpenAICompat(BaseChatModel):
         if self._max_tokens is not None:
             payload["max_tokens"] = self._max_tokens
         if self._tools:
-            payload["tools"] = self._tools
+            safe_tools: List[Dict[str, Any]] = []
+            for t in self._tools:
+                if isinstance(t, dict) and t.get("type") == "function" and isinstance(t.get("function"), dict):
+                    fn = deepcopy(t["function"])  # type: ignore
+                    fn["description"] = (fn.get("description") or "")[:1000]
+                    fn["parameters"] = _normalize_params(fn.get("parameters"))
+                    safe_tools.append({"type": "function", "function": fn})
+                else:
+                    safe_tools.append(_convert_tool(t))
+            payload["tools"] = safe_tools
         if self._tool_choice is not None:
             payload["tool_choice"] = self._tool_choice
         if self._extra_body:
             payload.update(self._extra_body)
         if kwargs:
-            payload.update(kwargs)
+            allowed = {
+                "tool_choice","temperature","top_p","max_tokens","stop","stream","n","logprobs","logit_bias","user","presence_penalty","frequency_penalty","seed","parallel_tool_calls","response_format"
+            }
+            for k, v in kwargs.items():
+                if k in allowed and v is not None:
+                    payload[k] = v
         return payload
 
     # -- Non-streaming generate --