feat: raise configurable chat tool call limit
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@@ -13,3 +13,4 @@ OCR_MODEL_DIR=/models/ocr
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MODEL_TIMEOUT_SECONDS=120
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MODEL_CONCURRENCY=2
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MODEL_RETRIES=2
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CHAT_MAX_TOOL_CALLS=32
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@@ -218,8 +218,9 @@ def test_sse_persists_messages_and_tool_failure(session_factory, monkeypatch):
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def tool_call(self, messages, tools):
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calls.append(messages)
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if len(calls) == 1:
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return {"role": "assistant", "content": None, "tool_calls": [{"id": "call1", "type": "function",
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"function": {"name": "list_courses", "arguments": "{}"}}]}
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return {"role": "assistant", "content": None, "tool_calls": [
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{"id": f"call{index}", "type": "function",
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"function": {"name": "list_courses", "arguments": "{}"}} for index in range(9)]}
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return {"role": "assistant", "content": "已列出课程版本。", "tool_calls": []}
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monkeypatch.setattr(chat_api, "client_for_chat", lambda db: FakeClient())
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def override_db():
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@@ -231,9 +232,10 @@ def test_sse_persists_messages_and_tool_failure(session_factory, monkeypatch):
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created = client.post("/api/conversations", json={"title": "测试对话"}).json()
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with client.stream("POST", f"/api/conversations/{created['id']}/messages", json={"content": "列出课程"}) as response:
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body = "".join(response.iter_text())
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assert "event: tool" in body and "event: message" in body
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assert body.count("event: tool") == 9 and "event: message" in body
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restored = client.get(f"/api/conversations/{created['id']}").json()
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assert [m["role"] for m in restored["messages"]] == ["user", "assistant"]
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assert len(restored["messages"][1]["tool_calls"]) == 9
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assert restored["runs"][0]["status"] == "completed"
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monkeypatch.setattr(chat_api, "client_for_chat", lambda db: (_ for _ in ()).throw(ModelError("不支持工具调用")))
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with client.stream("POST", f"/api/conversations/{created['id']}/messages", json={"content": "再试"}) as response:
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@@ -251,6 +253,35 @@ def test_sse_persists_messages_and_tool_failure(session_factory, monkeypatch):
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app.dependency_overrides.clear()
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def test_failed_tool_calls_count_toward_limit(monkeypatch):
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class RepeatingClient:
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def tool_call(self, messages, tools):
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return {"role": "assistant", "content": None, "tool_calls": [
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{"id": f"call{len(messages)}", "type": "function",
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"function": {"name": "unknown_tool", "arguments": "{}"}}]}
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conversation = SimpleNamespace(id=1, course_version_id=None, batch_id=None, project_id=None, updated_at=None)
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run = SimpleNamespace(id=1, status="running", error=None, finished_at=None)
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class FakeSession:
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def __enter__(self):
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return self
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def __exit__(self, *_args):
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pass
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def get(self, model, _id):
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return conversation if model is Conversation else run
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def scalars(self, _query):
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return SimpleNamespace(all=lambda: [])
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def commit(self):
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pass
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monkeypatch.setattr(chat_api, "client_for_chat", lambda db: RepeatingClient())
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monkeypatch.setattr(chat_api, "get_settings", lambda: SimpleNamespace(chat_max_tool_calls=9))
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body = "".join(chat_api._run_agent(FakeSession, 1, 1, "循环调用工具"))
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assert body.count("event: tool") == 9
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assert "本次消息工具调用超过上限 9 次" in body
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assert run.status == "failed"
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def test_native_tool_calls_unsupported_endpoint():
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transport = httpx.MockTransport(lambda request: httpx.Response(400, json={"error": "unsupported"}))
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client = DeepSeekClient(api_key="test-key", transport=transport)
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@@ -10,6 +10,7 @@ from sqlalchemy.orm import Session, sessionmaker
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from .actions import action_dict, confirm, prepare, require_scope
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from .agent_tools import execute, tool_specs
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from .config import get_settings
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from .db import get_db
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from .llm import ModelError
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from .models import AgentRun, ChatMessage, Conversation, PendingAction, Project, ScoringBatch, now
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@@ -113,7 +114,10 @@ def _run_agent(factory, conversation_id, run_id, user_text):
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"project_id": conversation.project_id}, ensure_ascii=False)}]
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messages.extend({"role": m.role, "content": m.content[:4000]} for m in history)
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yield _event("run", {"id": run.id, "status": "running"})
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for round_number in range(4):
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max_tool_calls = get_settings().chat_max_tool_calls
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attempted_calls = 0
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# One extra model response lets the agent answer after its last allowed tool call.
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for _ in range(max_tool_calls + 1):
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reply = client.tool_call(messages, tool_specs())
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calls = reply.get("tool_calls") or []
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if not calls:
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@@ -121,16 +125,17 @@ def _run_agent(factory, conversation_id, run_id, user_text):
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if not isinstance(content, str) or not content.strip():
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raise ModelError("对话模型未返回文字或工具调用")
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db.add(ChatMessage(conversation_id=conversation_id, role="assistant", content=content,
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citations=citations[:20], tool_calls=used[:8]))
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citations=citations[:20], tool_calls=used))
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run.status, run.finished_at = "completed", now()
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conversation.updated_at = now()
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db.commit()
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completed = True
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yield _event("message", {"content": content, "citations": citations[:20], "tool_calls": used[:8]})
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yield _event("message", {"content": content, "citations": citations[:20], "tool_calls": used})
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yield _event("done", {"run_id": run.id})
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return
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if len(calls) + len(used) > 8:
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raise ModelError("单轮工具调用超过上限 8 次")
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if attempted_calls + len(calls) > max_tool_calls:
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raise ModelError(f"本次消息工具调用超过上限 {max_tool_calls} 次")
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attempted_calls += len(calls)
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messages.append({"role": "assistant", "content": reply.get("content"), "tool_calls": calls})
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for call in calls:
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name = call.get("function", {}).get("name", "")
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@@ -155,7 +160,7 @@ def _run_agent(factory, conversation_id, run_id, user_text):
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messages.append({"role": "tool", "tool_call_id": call_id,
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"content": json.dumps({"error": error[:500]}, ensure_ascii=False)})
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yield _event("tool", {"name": name, "status": "failed", "error": error[:500]})
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raise ModelError("模型工具调用轮数超过上限 4 次")
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raise ModelError(f"本次消息模型调用超过上限 {max_tool_calls + 1} 轮")
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except Exception as exc:
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run.status, run.error, run.finished_at = "failed", str(exc)[:500], now()
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db.commit()
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@@ -1,6 +1,7 @@
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from functools import lru_cache
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from pathlib import Path
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from pydantic import Field
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from pydantic_settings import BaseSettings, SettingsConfigDict
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@@ -23,6 +24,7 @@ class Settings(BaseSettings):
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model_timeout_seconds: int = 120
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model_concurrency: int = 2
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model_retries: int = 2
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chat_max_tool_calls: int = Field(default=32, ge=1, le=64)
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@lru_cache
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+1
-1
@@ -26,7 +26,7 @@
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对话使用模型设置页的 OpenAI 兼容 `/chat/completions` 地址、模型和后端 API Key。适配层按 [DeepSeek 原生工具调用文档](https://api-docs.deepseek.com/guides/tool_calls) 发送 `tools`，在每轮前用不含学生资料的 `wisegrade_ping` 最小探针验证端点会返回 `tool_calls`。当前 `deepseek-flash` 端点支持自动工具选择，但拒绝强制 `tool_choice="required"`，因此探针采用 `auto` 并检查返回结构。模型不支持时会显示明确配置错误，不会伪装工具已执行。既有评分 `json_call` 保持原有 JSON 行为。
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工具输入由 Pydantic 校验，后端再校验课程、批次和项目关系。每轮最多 8 次工具调用、4 轮模型调用；工具输出和会话上下文限量。工具没有任意 SQL、任意文件路径、HTTP 请求、代码执行或确认能力。检索片段只作为数据，不作为工具指令。模型只收到当前问题所需的规则摘要、检索片段或紧凑统计；API Key 不写入日志或会话表。
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工具输入由 Pydantic 校验，后端再校验课程、批次和项目关系。每条教师消息默认最多调用工具 32 次，成功与失败的尝试均计数；模型最多请求 33 轮，以便用完工具次数后还能生成回答。可在 `.env` 中通过 `CHAT_MAX_TOOL_CALLS` 调整工具上限（1–64），修改后重新创建 API 容器。工具输出和会话上下文限量。工具没有任意 SQL、任意文件路径、HTTP 请求、代码执行或确认能力。检索片段只作为数据，不作为工具指令。模型只收到当前问题所需的规则摘要、检索片段或紧凑统计；API Key 不写入日志或会话表。
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## API 与故障恢复
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