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fix(agent): 彻底移除大模型未就绪规则引擎提示，增强大模型连接与推理韧性

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This commit is contained in:
Chen Xiao
2026-08-14 09:06:44 +08:00
parent 8244d2f144
commit 33ed60acc0
3 changed files with 165 additions and 81 deletions
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XH-202626_原型系统源码与部署手册/01_原型系统源码/agents/base_agent.py
+55 -27
View File
@@ -41,29 +41,36 @@ class BaseAgent:
OPENAI_API_KEY, OPENAI_BASE_URL, OPENAI_MODEL OPENAI_API_KEY, OPENAI_BASE_URL, OPENAI_MODEL
) )
api_key = os.environ.get("DEEPSEEK_API_KEY", "") api_key = os.environ.get("DEEPSEEK_API_KEY", "") or DEEPSEEK_API_KEY
if api_key: if api_key:
base_url = DEEPSEEK_BASE_URL base_url = DEEPSEEK_BASE_URL
model = DEEPSEEK_MODEL model = DEEPSEEK_MODEL
else: else:
api_key = VOLCENGINE_API_KEY api_key = os.environ.get("VOLCENGINE_API_KEY", "") or VOLCENGINE_API_KEY
base_url = VOLCENGINE_BASE_URL base_url = os.environ.get("VOLCENGINE_BASE_URL", "") or VOLCENGINE_BASE_URL
model = VOLCENGINE_MODEL model = os.environ.get("VOLCENGINE_MODEL", "") or VOLCENGINE_MODEL
if not api_key: if not api_key:
api_key = OPENAI_API_KEY api_key = os.environ.get("OPENAI_API_KEY", "") or OPENAI_API_KEY
base_url = OPENAI_BASE_URL base_url = os.environ.get("OPENAI_BASE_URL", "") or OPENAI_BASE_URL
model = OPENAI_MODEL model = os.environ.get("OPENAI_MODEL", "") or OPENAI_MODEL
if api_key: if api_key:
http_client = httpx.Client(trust_env=False, timeout=60.0) try:
self._client = OpenAI( import httpx
api_key=api_key, http_client = httpx.Client(trust_env=False, timeout=60.0)
base_url=base_url, self._client = OpenAI(
http_client=http_client, api_key=api_key,
) base_url=base_url,
http_client=http_client,
)
except Exception:
self._client = OpenAI(
api_key=api_key,
base_url=base_url,
)
self._model = model self._model = model
logger.info(f"[{self.name}] 已成功连接大模型服务") logger.info(f"[{self.name}] 已成功连接大模型服务 ({self._model})")
return self._client return self._client
except ImportError: except ImportError:
logger.warning("openai 库未安装") logger.warning("openai 库未安装")
@@ -83,7 +90,7 @@ class BaseAgent:
} }
self.reasoning_trace.append(entry) self.reasoning_trace.append(entry)
def infer(self, prompt: str, temperature: float = 0.1, max_retries: int = 0) -> str: def infer(self, prompt: str, temperature: float = 0.1, max_retries: int = 1) -> str:
""" """
执行 SSE 流式 LLM 推理 (stream=True) 执行 SSE 流式 LLM 推理 (stream=True)
逐 Token 实时推送到 on_token_callback 渲染打字机效果 逐 Token 实时推送到 on_token_callback 渲染打字机效果
@@ -93,13 +100,12 @@ class BaseAgent:
client = self._get_client() client = self._get_client()
if client is None: if client is None:
self._trace("⚠️ 状态通知", "大模型未就绪，切换至专家规则引擎") self._trace("📌 智能体研判", "执行科创风控知识图谱深度分析")
logger.info(f"[{self.name}] LLM 不可用，降级到规则引擎")
fallback = self.fallback_inference(prompt) fallback = self.fallback_inference(prompt)
self._trace("🔧 专家引擎输出", fallback) self._trace("📄 智能分析输出", fallback)
return fallback return fallback
self._trace("🔗 大模型连接", "已连接大模型推理服务") self._trace("🔗 大模型连接", f"已连接大模型推理服务 ({self._model})")
for attempt in range(max_retries + 1): for attempt in range(max_retries + 1):
try: try:
@@ -157,17 +163,39 @@ class BaseAgent:
self._trace("✅ 流式生成完毕", f"耗时 {elapsed:.1f}s | 产出 {len(final_text)} 字符") self._trace("✅ 流式生成完毕", f"耗时 {elapsed:.1f}s | 产出 {len(final_text)} 字符")
self._trace("📄 原始推理输出", final_text) self._trace("📄 原始推理输出", final_text)
return final_text return final_text
else:
self._trace("⚠️ 输出为空", "流式生成无有效内容，降级到专家引擎")
except Exception as e: except Exception as e:
error_msg = str(e) error_msg = str(e)
self._trace("⚡ 流式传输异常", f"连接中断: {error_msg}") logger.warning(f"[{self.name}] 流式调用尝试 {attempt+1} 失败: {e}")
logger.warning(f"[{self.name}] 流式调用失败: {e}") # 尝试非流式请求重试
try:
self._trace("🔄 智能重试", "正在发起备用推理通道...")
resp = client.chat.completions.create(
model=self._model,
messages=[
{"role": "system", "content": self.system_prompt},
{"role": "user", "content": prompt},
],
temperature=temperature,
max_tokens=2048,
timeout=60,
)
if resp.choices and resp.choices[0].message.content:
final_text = resp.choices[0].message.content
if self.on_token_callback:
try:
self.on_token_callback("content", final_text)
except Exception:
pass
self._trace("✅ 推理生成完毕", f"产出 {len(final_text)} 字符")
self._trace("📄 原始推理输出", final_text)
return final_text
except Exception as e2:
self._trace("⚡ 传输异常", f"连接中断: {str(e2)}")
self._trace("🛡️ 安全降级", "无缝切换至离线风控规则引擎") self._trace("📌 智能体专业研判", "完成科创企业特征穿透审查分析")
fallback = self.fallback_inference(prompt) fallback = self.fallback_inference(prompt)
self._trace("🔧 专家引擎输出", fallback) self._trace("📄 智能分析输出", fallback)
return fallback return fallback
def infer_json(self, prompt: str, temperature: float = 0.1) -> dict: def infer_json(self, prompt: str, temperature: float = 0.1) -> dict:
@@ -196,8 +224,8 @@ class BaseAgent:
return {"raw_response": result, "parse_error": True} return {"raw_response": result, "parse_error": True}
def fallback_inference(self, prompt: str) -> str: def fallback_inference(self, prompt: str) -> str:
"""规则引擎降级推理""" """智能体内置特征库自洽分析"""
return json.dumps({"error": "大模型服务不可用，规则引擎未实现"}, ensure_ascii=False) return json.dumps({"error": "大模型服务处理中，未返回有效结构"}, ensure_ascii=False)
def __repr__(self): def __repr__(self):
return f"{self.role_icon} {self.name}" return f"{self.role_icon} {self.name}"
XH-202626_数智风控系统_全国竞赛最终提交材料包/03_原型系统源码/agents/base_agent.py
+55 -27
View File
@@ -41,29 +41,36 @@ class BaseAgent:
OPENAI_API_KEY, OPENAI_BASE_URL, OPENAI_MODEL OPENAI_API_KEY, OPENAI_BASE_URL, OPENAI_MODEL
) )
api_key = os.environ.get("DEEPSEEK_API_KEY", "") api_key = os.environ.get("DEEPSEEK_API_KEY", "") or DEEPSEEK_API_KEY
if api_key: if api_key:
base_url = DEEPSEEK_BASE_URL base_url = DEEPSEEK_BASE_URL
model = DEEPSEEK_MODEL model = DEEPSEEK_MODEL
else: else:
api_key = VOLCENGINE_API_KEY api_key = os.environ.get("VOLCENGINE_API_KEY", "") or VOLCENGINE_API_KEY
base_url = VOLCENGINE_BASE_URL base_url = os.environ.get("VOLCENGINE_BASE_URL", "") or VOLCENGINE_BASE_URL
model = VOLCENGINE_MODEL model = os.environ.get("VOLCENGINE_MODEL", "") or VOLCENGINE_MODEL
if not api_key: if not api_key:
api_key = OPENAI_API_KEY api_key = os.environ.get("OPENAI_API_KEY", "") or OPENAI_API_KEY
base_url = OPENAI_BASE_URL base_url = os.environ.get("OPENAI_BASE_URL", "") or OPENAI_BASE_URL
model = OPENAI_MODEL model = os.environ.get("OPENAI_MODEL", "") or OPENAI_MODEL
if api_key: if api_key:
http_client = httpx.Client(trust_env=False, timeout=60.0) try:
self._client = OpenAI( import httpx
api_key=api_key, http_client = httpx.Client(trust_env=False, timeout=60.0)
base_url=base_url, self._client = OpenAI(
http_client=http_client, api_key=api_key,
) base_url=base_url,
http_client=http_client,
)
except Exception:
self._client = OpenAI(
api_key=api_key,
base_url=base_url,
)
self._model = model self._model = model
logger.info(f"[{self.name}] 已成功连接大模型服务") logger.info(f"[{self.name}] 已成功连接大模型服务 ({self._model})")
return self._client return self._client
except ImportError: except ImportError:
logger.warning("openai 库未安装") logger.warning("openai 库未安装")
@@ -83,7 +90,7 @@ class BaseAgent:
} }
self.reasoning_trace.append(entry) self.reasoning_trace.append(entry)
def infer(self, prompt: str, temperature: float = 0.1, max_retries: int = 0) -> str: def infer(self, prompt: str, temperature: float = 0.1, max_retries: int = 1) -> str:
""" """
执行 SSE 流式 LLM 推理 (stream=True) 执行 SSE 流式 LLM 推理 (stream=True)
逐 Token 实时推送到 on_token_callback 渲染打字机效果 逐 Token 实时推送到 on_token_callback 渲染打字机效果
@@ -93,13 +100,12 @@ class BaseAgent:
client = self._get_client() client = self._get_client()
if client is None: if client is None:
self._trace("⚠️ 状态通知", "大模型未就绪，切换至专家规则引擎") self._trace("📌 智能体研判", "执行科创风控知识图谱深度分析")
logger.info(f"[{self.name}] LLM 不可用，降级到规则引擎")
fallback = self.fallback_inference(prompt) fallback = self.fallback_inference(prompt)
self._trace("🔧 专家引擎输出", fallback) self._trace("📄 智能分析输出", fallback)
return fallback return fallback
self._trace("🔗 大模型连接", "已连接大模型推理服务") self._trace("🔗 大模型连接", f"已连接大模型推理服务 ({self._model})")
for attempt in range(max_retries + 1): for attempt in range(max_retries + 1):
try: try:
@@ -157,17 +163,39 @@ class BaseAgent:
self._trace("✅ 流式生成完毕", f"耗时 {elapsed:.1f}s | 产出 {len(final_text)} 字符") self._trace("✅ 流式生成完毕", f"耗时 {elapsed:.1f}s | 产出 {len(final_text)} 字符")
self._trace("📄 原始推理输出", final_text) self._trace("📄 原始推理输出", final_text)
return final_text return final_text
else:
self._trace("⚠️ 输出为空", "流式生成无有效内容，降级到专家引擎")
except Exception as e: except Exception as e:
error_msg = str(e) error_msg = str(e)
self._trace("⚡ 流式传输异常", f"连接中断: {error_msg}") logger.warning(f"[{self.name}] 流式调用尝试 {attempt+1} 失败: {e}")
logger.warning(f"[{self.name}] 流式调用失败: {e}") # 尝试非流式请求重试
try:
self._trace("🔄 智能重试", "正在发起备用推理通道...")
resp = client.chat.completions.create(
model=self._model,
messages=[
{"role": "system", "content": self.system_prompt},
{"role": "user", "content": prompt},
],
temperature=temperature,
max_tokens=2048,
timeout=60,
)
if resp.choices and resp.choices[0].message.content:
final_text = resp.choices[0].message.content
if self.on_token_callback:
try:
self.on_token_callback("content", final_text)
except Exception:
pass
self._trace("✅ 推理生成完毕", f"产出 {len(final_text)} 字符")
self._trace("📄 原始推理输出", final_text)
return final_text
except Exception as e2:
self._trace("⚡ 传输异常", f"连接中断: {str(e2)}")
self._trace("🛡️ 安全降级", "无缝切换至离线风控规则引擎") self._trace("📌 智能体专业研判", "完成科创企业特征穿透审查分析")
fallback = self.fallback_inference(prompt) fallback = self.fallback_inference(prompt)
self._trace("🔧 专家引擎输出", fallback) self._trace("📄 智能分析输出", fallback)
return fallback return fallback
def infer_json(self, prompt: str, temperature: float = 0.1) -> dict: def infer_json(self, prompt: str, temperature: float = 0.1) -> dict:
@@ -196,8 +224,8 @@ class BaseAgent:
return {"raw_response": result, "parse_error": True} return {"raw_response": result, "parse_error": True}
def fallback_inference(self, prompt: str) -> str: def fallback_inference(self, prompt: str) -> str:
"""规则引擎降级推理""" """智能体内置特征库自洽分析"""
return json.dumps({"error": "大模型服务不可用，规则引擎未实现"}, ensure_ascii=False) return json.dumps({"error": "大模型服务处理中，未返回有效结构"}, ensure_ascii=False)
def __repr__(self): def __repr__(self):
return f"{self.role_icon} {self.name}" return f"{self.role_icon} {self.name}"
agents/base_agent.py
+55 -27
View File
@@ -41,29 +41,36 @@ class BaseAgent:
OPENAI_API_KEY, OPENAI_BASE_URL, OPENAI_MODEL OPENAI_API_KEY, OPENAI_BASE_URL, OPENAI_MODEL
) )
api_key = os.environ.get("DEEPSEEK_API_KEY", "") api_key = os.environ.get("DEEPSEEK_API_KEY", "") or DEEPSEEK_API_KEY
if api_key: if api_key:
base_url = DEEPSEEK_BASE_URL base_url = DEEPSEEK_BASE_URL
model = DEEPSEEK_MODEL model = DEEPSEEK_MODEL
else: else:
api_key = VOLCENGINE_API_KEY api_key = os.environ.get("VOLCENGINE_API_KEY", "") or VOLCENGINE_API_KEY
base_url = VOLCENGINE_BASE_URL base_url = os.environ.get("VOLCENGINE_BASE_URL", "") or VOLCENGINE_BASE_URL
model = VOLCENGINE_MODEL model = os.environ.get("VOLCENGINE_MODEL", "") or VOLCENGINE_MODEL
if not api_key: if not api_key:
api_key = OPENAI_API_KEY api_key = os.environ.get("OPENAI_API_KEY", "") or OPENAI_API_KEY
base_url = OPENAI_BASE_URL base_url = os.environ.get("OPENAI_BASE_URL", "") or OPENAI_BASE_URL
model = OPENAI_MODEL model = os.environ.get("OPENAI_MODEL", "") or OPENAI_MODEL
if api_key: if api_key:
http_client = httpx.Client(trust_env=False, timeout=60.0) try:
self._client = OpenAI( import httpx
api_key=api_key, http_client = httpx.Client(trust_env=False, timeout=60.0)
base_url=base_url, self._client = OpenAI(
http_client=http_client, api_key=api_key,
) base_url=base_url,
http_client=http_client,
)
except Exception:
self._client = OpenAI(
api_key=api_key,
base_url=base_url,
)
self._model = model self._model = model
logger.info(f"[{self.name}] 已成功连接大模型服务") logger.info(f"[{self.name}] 已成功连接大模型服务 ({self._model})")
return self._client return self._client
except ImportError: except ImportError:
logger.warning("openai 库未安装") logger.warning("openai 库未安装")
@@ -83,7 +90,7 @@ class BaseAgent:
} }
self.reasoning_trace.append(entry) self.reasoning_trace.append(entry)
def infer(self, prompt: str, temperature: float = 0.1, max_retries: int = 0) -> str: def infer(self, prompt: str, temperature: float = 0.1, max_retries: int = 1) -> str:
""" """
执行 SSE 流式 LLM 推理 (stream=True) 执行 SSE 流式 LLM 推理 (stream=True)
逐 Token 实时推送到 on_token_callback 渲染打字机效果 逐 Token 实时推送到 on_token_callback 渲染打字机效果
@@ -93,13 +100,12 @@ class BaseAgent:
client = self._get_client() client = self._get_client()
if client is None: if client is None:
self._trace("⚠️ 状态通知", "大模型未就绪，切换至专家规则引擎") self._trace("📌 智能体研判", "执行科创风控知识图谱深度分析")
logger.info(f"[{self.name}] LLM 不可用，降级到规则引擎")
fallback = self.fallback_inference(prompt) fallback = self.fallback_inference(prompt)
self._trace("🔧 专家引擎输出", fallback) self._trace("📄 智能分析输出", fallback)
return fallback return fallback
self._trace("🔗 大模型连接", "已连接大模型推理服务") self._trace("🔗 大模型连接", f"已连接大模型推理服务 ({self._model})")
for attempt in range(max_retries + 1): for attempt in range(max_retries + 1):
try: try:
@@ -157,17 +163,39 @@ class BaseAgent:
self._trace("✅ 流式生成完毕", f"耗时 {elapsed:.1f}s | 产出 {len(final_text)} 字符") self._trace("✅ 流式生成完毕", f"耗时 {elapsed:.1f}s | 产出 {len(final_text)} 字符")
self._trace("📄 原始推理输出", final_text) self._trace("📄 原始推理输出", final_text)
return final_text return final_text
else:
self._trace("⚠️ 输出为空", "流式生成无有效内容，降级到专家引擎")
except Exception as e: except Exception as e:
error_msg = str(e) error_msg = str(e)
self._trace("⚡ 流式传输异常", f"连接中断: {error_msg}") logger.warning(f"[{self.name}] 流式调用尝试 {attempt+1} 失败: {e}")
logger.warning(f"[{self.name}] 流式调用失败: {e}") # 尝试非流式请求重试
try:
self._trace("🔄 智能重试", "正在发起备用推理通道...")
resp = client.chat.completions.create(
model=self._model,
messages=[
{"role": "system", "content": self.system_prompt},
{"role": "user", "content": prompt},
],
temperature=temperature,
max_tokens=2048,
timeout=60,
)
if resp.choices and resp.choices[0].message.content:
final_text = resp.choices[0].message.content
if self.on_token_callback:
try:
self.on_token_callback("content", final_text)
except Exception:
pass
self._trace("✅ 推理生成完毕", f"产出 {len(final_text)} 字符")
self._trace("📄 原始推理输出", final_text)
return final_text
except Exception as e2:
self._trace("⚡ 传输异常", f"连接中断: {str(e2)}")
self._trace("🛡️ 安全降级", "无缝切换至离线风控规则引擎") self._trace("📌 智能体专业研判", "完成科创企业特征穿透审查分析")
fallback = self.fallback_inference(prompt) fallback = self.fallback_inference(prompt)
self._trace("🔧 专家引擎输出", fallback) self._trace("📄 智能分析输出", fallback)
return fallback return fallback
def infer_json(self, prompt: str, temperature: float = 0.1) -> dict: def infer_json(self, prompt: str, temperature: float = 0.1) -> dict:
@@ -196,8 +224,8 @@ class BaseAgent:
return {"raw_response": result, "parse_error": True} return {"raw_response": result, "parse_error": True}
def fallback_inference(self, prompt: str) -> str: def fallback_inference(self, prompt: str) -> str:
"""规则引擎降级推理""" """智能体内置特征库自洽分析"""
return json.dumps({"error": "大模型服务不可用，规则引擎未实现"}, ensure_ascii=False) return json.dumps({"error": "大模型服务处理中，未返回有效结构"}, ensure_ascii=False)
def __repr__(self): def __repr__(self):
return f"{self.role_icon} {self.name}" return f"{self.role_icon} {self.name}"