in devel
This commit is contained in:
parent
169666443f
commit
3640f3c216
|
|
@ -1,9 +1,11 @@
|
|||
"""
|
||||
client/agent_client.py
|
||||
Agent 客户端：驱动完整 OpenAI Function Calling + Tool Chain 执行流程
|
||||
新增: OpenAI 格式消息序列管理，支持多轮工具调用上下文传递
|
||||
修复：OpenAI 消息序列必须满足
|
||||
user → assistant(tool_calls) → tool(s) → assistant(reply)
|
||||
每个 tool 消息的 tool_call_id 必须与同一 assistant 消息中的 tool_calls[].id 完全匹配
|
||||
"""
|
||||
|
||||
import json
|
||||
import uuid
|
||||
from dataclasses import dataclass, field
|
||||
|
||||
|
|
@ -39,23 +41,31 @@ class AgentClient:
|
|||
"""
|
||||
Agent 客户端：OpenAI Function Calling + Multi-Step Tool Chain
|
||||
|
||||
执行流程:
|
||||
1. [CLIENT] 接收用户输入，写入 Memory
|
||||
2. [LLM] plan_tool_chain() → OpenAI Function Calling → ChainPlan
|
||||
3. [CHAIN] 串行执行每个 ToolStep:
|
||||
a. 构造 MCPRequest → MCPServer 执行工具
|
||||
b. 将工具结果追加到 OpenAI 消息序列（tool role）
|
||||
c. 记录 StepResult，更新链路上下文（占位符替换）
|
||||
4. [LLM] generate_chain_reply() → OpenAI 整合结果 → 最终回复
|
||||
5. [MEMORY] 写入完整调用链记录
|
||||
✅ 修复后的 OpenAI 消息序列规范:
|
||||
|
||||
OpenAI 消息序列示例（多步骤）:
|
||||
{"role": "system", "content": "规划器提示"}
|
||||
【单步工具调用】
|
||||
{"role": "system", "content": "..."}
|
||||
{"role": "user", "content": "计算 1+1"}
|
||||
{"role": "assistant", "content": null,
|
||||
"tool_calls": [{"id": "call_abc", "type": "function",
|
||||
"function": {"name": "calculator", "arguments": "{...}"}}]}
|
||||
{"role": "tool", "content": "结果: 2", "tool_call_id": "call_abc"}
|
||||
→ generate_reply() → 最终回复
|
||||
|
||||
【多步工具调用（并行声明，串行执行）】
|
||||
{"role": "system", "content": "..."}
|
||||
{"role": "user", "content": "搜索天气然后计算..."}
|
||||
{"role": "assistant", "tool_calls": [{web_search}, {calculator}]}
|
||||
{"role": "tool", "content": "web_search 结果", "tool_call_id": "call_1"}
|
||||
{"role": "tool", "content": "calculator 结果", "tool_call_id": "call_2"}
|
||||
→ generate_reply() → 最终自然语言回复
|
||||
{"role": "assistant", "content": null,
|
||||
"tool_calls": [{"id": "call_001", ...web_search...},
|
||||
{"id": "call_002", ...calculator...}]}
|
||||
{"role": "tool", "content": "搜索结果", "tool_call_id": "call_001"}
|
||||
{"role": "tool", "content": "计算结果", "tool_call_id": "call_002"}
|
||||
→ generate_reply() → 最终回复
|
||||
|
||||
⚠️ 关键约束:
|
||||
1. tool 消息必须紧跟在含 tool_calls 的 assistant 消息之后
|
||||
2. 每个 tool 的 tool_call_id 必须与 assistant.tool_calls[i].id 完全一致
|
||||
3. tool_calls 中声明了几个工具，就必须有几条对应的 tool 消息
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
|
|
@ -68,7 +78,6 @@ class AgentClient:
|
|||
self.mcp_server = mcp_server
|
||||
self.memory = memory
|
||||
self.logger = get_logger("CLIENT")
|
||||
# OpenAI 格式的结构化对话历史（跨轮次保持上下文）
|
||||
self._openai_history: list[dict] = []
|
||||
self.logger.info("💻 Agent Client 初始化完成（OpenAI Function Calling 模式）")
|
||||
|
||||
|
|
@ -83,12 +92,11 @@ class AgentClient:
|
|||
self.logger.info(f"📨 收到用户输入: {user_input}")
|
||||
self.logger.info(sep)
|
||||
|
||||
# Step 1: 记录用户消息
|
||||
self.memory.add_user_message(user_input)
|
||||
context = self.memory.get_context_summary()
|
||||
|
||||
# Step 2: LLM 规划工具调用链
|
||||
self.logger.info("🗺 Step 2 [LLM] 规划工具调用链...")
|
||||
# Step 1: LLM 规划工具调用链
|
||||
self.logger.info("🗺 [LLM] 规划工具调用链...")
|
||||
tool_schemas = self.mcp_server.get_tool_schemas()
|
||||
plan: ChainPlan = self.llm.plan_tool_chain(
|
||||
user_input=user_input,
|
||||
|
|
@ -101,11 +109,13 @@ class AgentClient:
|
|||
if not plan.steps:
|
||||
return self._handle_direct_reply(user_input, context)
|
||||
|
||||
# Step 3~4: 执行工具调用链，构造 OpenAI 消息序列
|
||||
chain_result, tool_messages = self._execute_chain(plan, user_input)
|
||||
# Step 2: 执行工具调用链，构造合规的 OpenAI 消息序列
|
||||
chain_result, openai_tool_block = self._execute_chain(plan, user_input)
|
||||
|
||||
# Step 5: 调用 OpenAI 整合结果，生成最终回复
|
||||
return self._generate_response(user_input, chain_result, tool_messages, context)
|
||||
# Step 3: 调用 OpenAI 整合结果，生成最终回复
|
||||
return self._generate_response(
|
||||
user_input, chain_result, openai_tool_block, context
|
||||
)
|
||||
|
||||
# ════════════════════════════════════════════════════════════
|
||||
# 串行执行引擎
|
||||
|
|
@ -117,12 +127,20 @@ class AgentClient:
|
|||
user_input: str,
|
||||
) -> tuple[ChainResult, list[dict]]:
|
||||
"""
|
||||
串行执行工具调用链，同步构造 OpenAI 消息序列
|
||||
串行执行工具调用链，构造符合 OpenAI 协议的消息块
|
||||
|
||||
Returns:
|
||||
(ChainResult, tool_messages)
|
||||
tool_messages 为 OpenAI 格式的工具调用消息列表，
|
||||
用于后续 generate_reply() 调用
|
||||
chain_result: 执行结果汇总
|
||||
openai_tool_block: 合规的 OpenAI 消息块，结构为:
|
||||
[
|
||||
assistant(tool_calls=[call_001, call_002, ...]),
|
||||
tool(tool_call_id=call_001, content=...),
|
||||
tool(tool_call_id=call_002, content=...),
|
||||
...
|
||||
]
|
||||
|
||||
⚠️ 关键：所有 tool_calls 在 assistant 消息中一次性声明，
|
||||
然后逐条追加对应的 tool 结果消息，保证 id 完全匹配。
|
||||
"""
|
||||
self.logger.info(
|
||||
f"\n{'─' * 60}\n"
|
||||
|
|
@ -132,21 +150,26 @@ class AgentClient:
|
|||
f"{'─' * 60}"
|
||||
)
|
||||
|
||||
# ── 1. 预先为每个步骤生成稳定的 tool_call_id ──────────
|
||||
# 必须在执行前全部生成，assistant 消息和 tool 消息共享同一批 id
|
||||
step_call_ids: dict[int, str] = {
|
||||
step.step_id: f"call_{uuid.uuid4().hex[:12]}"
|
||||
for step in plan.steps
|
||||
}
|
||||
self.logger.debug(f"🔑 预生成 tool_call_ids: {step_call_ids}")
|
||||
|
||||
# ── 2. 构造 assistant 消息（一次性声明全部 tool_calls）──
|
||||
assistant_msg = self._build_assistant_message(plan, step_call_ids)
|
||||
|
||||
# ── 3. 串行执行每个步骤，收集 tool 结果消息 ────────────
|
||||
step_results: list[StepResult] = []
|
||||
tool_result_msgs: list[dict] = []
|
||||
chain_context: dict[str, str] = {}
|
||||
tool_messages: list[dict] = []
|
||||
failed_step: int | None = None
|
||||
|
||||
# 构造 assistant 消息（含 tool_calls 声明）
|
||||
assistant_tool_calls = self._build_assistant_tool_calls(plan)
|
||||
tool_messages.append({
|
||||
"role": "assistant",
|
||||
"content": None,
|
||||
"tool_calls": assistant_tool_calls,
|
||||
})
|
||||
|
||||
for step in plan.steps:
|
||||
# 检查前置依赖
|
||||
call_id = step_call_ids[step.step_id]
|
||||
|
||||
if self._has_failed_dependency(step, failed_step):
|
||||
self.logger.warning(
|
||||
f"⏭ Step {step.step_id} [{step.tool_name}] 跳过"
|
||||
|
|
@ -159,25 +182,22 @@ class AgentClient:
|
|||
output="",
|
||||
error=f"跳过：依赖步骤 {failed_step} 失败",
|
||||
))
|
||||
# 向 OpenAI 消息序列写入失败占位
|
||||
tool_messages.append({
|
||||
# ⚠️ 即使跳过，也必须补一条 tool 消息，保持 id 完全匹配
|
||||
tool_result_msgs.append({
|
||||
"role": "tool",
|
||||
"content": f"步骤跳过：依赖步骤 {failed_step} 执行失败",
|
||||
"tool_call_id": assistant_tool_calls[step.step_id - 1]["id"],
|
||||
"tool_call_id": call_id,
|
||||
})
|
||||
continue
|
||||
|
||||
# 执行单步
|
||||
result, tool_call_id = self._execute_single_step(
|
||||
step, chain_context, assistant_tool_calls
|
||||
)
|
||||
result = self._execute_single_step(step, chain_context, call_id)
|
||||
step_results.append(result)
|
||||
|
||||
# 追加 tool 消息到 OpenAI 序列
|
||||
tool_messages.append({
|
||||
# 追加 tool 结果消息（tool_call_id 与 assistant 声明完全一致）
|
||||
tool_result_msgs.append({
|
||||
"role": "tool",
|
||||
"content": result.output if result.success else f"执行失败: {result.error}",
|
||||
"tool_call_id": tool_call_id,
|
||||
"tool_call_id": call_id,
|
||||
})
|
||||
|
||||
if result.success:
|
||||
|
|
@ -186,6 +206,22 @@ class AgentClient:
|
|||
else:
|
||||
failed_step = step.step_id
|
||||
|
||||
# ── 4. 组装合规的 OpenAI 消息块 ─────────────────────────
|
||||
# 格式: [assistant(tool_calls), tool, tool, ...]
|
||||
openai_tool_block = [assistant_msg] + tool_result_msgs
|
||||
|
||||
self.logger.debug("📦 OpenAI 消息块结构:")
|
||||
for i, msg in enumerate(openai_tool_block):
|
||||
role = msg["role"]
|
||||
if role == "assistant":
|
||||
ids = [tc["id"] for tc in (msg.get("tool_calls") or [])]
|
||||
self.logger.debug(f" [{i}] assistant tool_calls.ids = {ids}")
|
||||
else:
|
||||
self.logger.debug(
|
||||
f" [{i}] tool tool_call_id = {msg.get('tool_call_id')}"
|
||||
f" content = {str(msg.get('content', ''))[:50]}..."
|
||||
)
|
||||
|
||||
overall_success = failed_step is None
|
||||
chain_result = ChainResult(
|
||||
goal=plan.goal,
|
||||
|
|
@ -200,35 +236,26 @@ class AgentClient:
|
|||
f" 完成: {chain_result.completed_steps}/{chain_result.total_steps} 步\n"
|
||||
f"{'─' * 60}"
|
||||
)
|
||||
return chain_result, tool_messages
|
||||
return chain_result, openai_tool_block
|
||||
|
||||
def _execute_single_step(
|
||||
self,
|
||||
step: ToolStep,
|
||||
chain_context: dict[str, str],
|
||||
assistant_tool_calls: list[dict],
|
||||
) -> tuple[StepResult, str]:
|
||||
"""
|
||||
执行单个步骤，返回 (StepResult, tool_call_id)
|
||||
|
||||
Returns:
|
||||
StepResult: 步骤执行结果
|
||||
tool_call_id: 对应的 OpenAI tool_call_id（用于消息序列关联）
|
||||
"""
|
||||
# 注入前步上下文（占位符替换）
|
||||
resolved_step = step.inject_context(chain_context)
|
||||
tool_call_id = assistant_tool_calls[step.step_id - 1]["id"]
|
||||
step: ToolStep,
|
||||
chain_context: dict[str, str],
|
||||
call_id: str,
|
||||
) -> StepResult:
|
||||
"""执行单个步骤，返回 StepResult"""
|
||||
resolved_step = step.inject_context(chain_context)
|
||||
|
||||
self.logger.info(
|
||||
f"\n ▶ Step {step.step_id} 执行中\n"
|
||||
f" 工具: [{resolved_step.tool_name}]\n"
|
||||
f" 说明: {resolved_step.description}\n"
|
||||
f" 参数: {resolved_step.arguments}\n"
|
||||
f" call_id: {tool_call_id}"
|
||||
f" 工具 : [{resolved_step.tool_name}]\n"
|
||||
f" 说明 : {resolved_step.description}\n"
|
||||
f" 参数 : {resolved_step.arguments}\n"
|
||||
f" call_id : {call_id}"
|
||||
)
|
||||
|
||||
# 构造并发送 MCP 请求
|
||||
mcp_request: MCPRequest = resolved_step.to_mcp_request()
|
||||
mcp_request: MCPRequest = resolved_step.to_mcp_request()
|
||||
mcp_response: MCPResponse = self.mcp_server.handle_request(mcp_request)
|
||||
|
||||
if mcp_response.success:
|
||||
|
|
@ -239,7 +266,7 @@ class AgentClient:
|
|||
tool_name=step.tool_name,
|
||||
success=True,
|
||||
output=output,
|
||||
), tool_call_id
|
||||
)
|
||||
else:
|
||||
error_msg = mcp_response.error.get("message", "未知错误")
|
||||
self.logger.error(f" ❌ Step {step.step_id} 失败: {error_msg}")
|
||||
|
|
@ -249,7 +276,7 @@ class AgentClient:
|
|||
success=False,
|
||||
output="",
|
||||
error=error_msg,
|
||||
), tool_call_id
|
||||
)
|
||||
|
||||
# ════════════════════════════════════════════════════════════
|
||||
# 回复生成
|
||||
|
|
@ -257,57 +284,46 @@ class AgentClient:
|
|||
|
||||
def _generate_response(
|
||||
self,
|
||||
user_input: str,
|
||||
chain_result: ChainResult,
|
||||
tool_messages: list[dict],
|
||||
context: str,
|
||||
user_input: str,
|
||||
chain_result: ChainResult,
|
||||
openai_tool_block: list[dict],
|
||||
context: str,
|
||||
) -> AgentResponse:
|
||||
"""调用 OpenAI 整合工具结果，生成最终 AgentResponse"""
|
||||
self.logger.info("✍️ Step 5 [LLM] 调用 OpenAI 生成最终回复...")
|
||||
self.logger.info("✍️ [LLM] 调用 OpenAI 生成最终回复...")
|
||||
|
||||
chain_summary = self._build_chain_summary(chain_result)
|
||||
|
||||
# 单步走简洁路径
|
||||
if chain_result.total_steps == 1:
|
||||
r = chain_result.step_results[0]
|
||||
final_reply = self.llm.generate_final_reply(
|
||||
user_input=user_input,
|
||||
tool_name=r.tool_name,
|
||||
tool_output=r.output,
|
||||
context=context,
|
||||
tool_call_id=tool_messages[-1].get("tool_call_id", "") if tool_messages else "",
|
||||
)
|
||||
else:
|
||||
# 多步走完整 OpenAI 消息序列路径
|
||||
final_reply = self.llm.generate_chain_reply(
|
||||
user_input=user_input,
|
||||
chain_summary=chain_summary,
|
||||
context=context,
|
||||
tool_messages=tool_messages,
|
||||
)
|
||||
# 将合规的消息块传给 LLMEngine
|
||||
final_reply = self.llm.generate_chain_reply(
|
||||
user_input=user_input,
|
||||
chain_summary=chain_summary,
|
||||
context=context,
|
||||
openai_tool_block=openai_tool_block, # ← 传递完整合规消息块
|
||||
)
|
||||
|
||||
chain_result.final_reply = final_reply
|
||||
|
||||
# 更新 OpenAI 结构化历史（供下一轮使用）
|
||||
# 更新跨轮次 OpenAI 历史（只保留 user/assistant 摘要，不含 tool 消息）
|
||||
self._openai_history.append({"role": "user", "content": user_input})
|
||||
self._openai_history.append({"role": "assistant", "content": final_reply})
|
||||
# 保留最近 10 轮
|
||||
if len(self._openai_history) > 20:
|
||||
self._openai_history = self._openai_history[-20:]
|
||||
|
||||
# 写入 Memory
|
||||
if chain_result.total_steps > 1:
|
||||
self.memory.add_chain_result(chain_result)
|
||||
else:
|
||||
self.memory.add_assistant_message(final_reply)
|
||||
|
||||
self.logger.info("🎉 [CLIENT] 流程完成，回复已返回")
|
||||
self.logger.info("🎉 流程完成，回复已返回")
|
||||
return AgentResponse(
|
||||
user_input=user_input,
|
||||
final_reply=final_reply,
|
||||
chain_result=chain_result,
|
||||
tool_used=chain_result.step_results[0].tool_name if chain_result.total_steps == 1 else None,
|
||||
tool_output=chain_result.step_results[0].output if chain_result.total_steps == 1 else None,
|
||||
tool_used=(chain_result.step_results[0].tool_name
|
||||
if chain_result.total_steps == 1 else None),
|
||||
tool_output=(chain_result.step_results[0].output
|
||||
if chain_result.total_steps == 1 else None),
|
||||
success=chain_result.success,
|
||||
)
|
||||
|
||||
|
|
@ -321,42 +337,52 @@ class AgentClient:
|
|||
return AgentResponse(user_input=user_input, final_reply=reply)
|
||||
|
||||
# ════════════════════════════════════════════════════════════
|
||||
# 工具方法
|
||||
# 消息构造工具方法
|
||||
# ════════════════════════════════════════════════════════════
|
||||
|
||||
@staticmethod
|
||||
def _build_assistant_tool_calls(plan: ChainPlan) -> list[dict]:
|
||||
def _build_assistant_message(
|
||||
plan: ChainPlan,
|
||||
step_call_ids: dict[int, str],
|
||||
) -> dict:
|
||||
"""
|
||||
构造 OpenAI assistant 消息中的 tool_calls 字段
|
||||
构造 assistant 消息，一次性声明全部 tool_calls
|
||||
|
||||
格式:
|
||||
[
|
||||
{
|
||||
"id": "call_abc123",
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "calculator",
|
||||
"arguments": '{"expression": "1+2"}'
|
||||
✅ 正确格式:
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": null,
|
||||
"tool_calls": [
|
||||
{
|
||||
"id": "call_abc123", ← 与后续 tool 消息的 tool_call_id 完全一致
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "calculator",
|
||||
"arguments": "{\"expression\": \"1+2\"}" ← 必须是 JSON 字符串
|
||||
}
|
||||
}
|
||||
}
|
||||
]
|
||||
]
|
||||
}
|
||||
"""
|
||||
import json
|
||||
tool_calls = []
|
||||
for step in plan.steps:
|
||||
tool_calls.append({
|
||||
"id": f"call_{uuid.uuid4().hex[:8]}",
|
||||
"id": step_call_ids[step.step_id],
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": step.tool_name,
|
||||
"arguments": json.dumps(step.arguments, ensure_ascii=False),
|
||||
},
|
||||
})
|
||||
return tool_calls
|
||||
return {
|
||||
"role": "assistant",
|
||||
"content": None,
|
||||
"tool_calls": tool_calls,
|
||||
}
|
||||
|
||||
@staticmethod
|
||||
def _build_chain_summary(chain_result: ChainResult) -> str:
|
||||
"""将调用链结果格式化为 LLM 可读的摘要"""
|
||||
"""将调用链结果格式化为 LLM 可读的摘要（降级时使用）"""
|
||||
lines = []
|
||||
for r in chain_result.step_results:
|
||||
if r.success:
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
"""
|
||||
llm/llm_engine.py
|
||||
LLM 引擎：通过 Provider 接口调用真实 OpenAI API
|
||||
支持 Function Calling 多步骤规划 + 工具结果整合回复
|
||||
修复：generate_chain_reply / generate_final_reply 消息序列构造
|
||||
确保 tool 消息始终紧跟在含 tool_calls 的 assistant 消息之后
|
||||
"""
|
||||
|
||||
import re
|
||||
|
|
@ -35,25 +35,26 @@ class ToolDecision:
|
|||
|
||||
class LLMEngine:
|
||||
"""
|
||||
LLM 推理引擎（Provider 模式）
|
||||
LLM 推理引擎
|
||||
|
||||
核心流程:
|
||||
1. plan_tool_chain()
|
||||
构造 OpenAI 格式消息 + tools
|
||||
→ Provider.plan_with_tools()
|
||||
→ 解析 tool_calls → ChainPlan
|
||||
✅ 修复后的消息序列规范:
|
||||
|
||||
2. generate_chain_reply()
|
||||
构造含工具结果的完整消息历史
|
||||
→ Provider.generate_reply()
|
||||
→ 最终自然语言回复
|
||||
generate_chain_reply() 构造的完整消息:
|
||||
[
|
||||
{"role": "system", "content": REPLY_SYSTEM_PROMPT},
|
||||
{"role": "user", "content": "用户输入"},
|
||||
# ↓ openai_tool_block（来自 AgentClient._execute_chain）
|
||||
{"role": "assistant", "content": null,
|
||||
"tool_calls": [{"id":"call_001","type":"function","function":{...}},
|
||||
{"id":"call_002","type":"function","function":{...}}]},
|
||||
{"role": "tool", "content": "结果1", "tool_call_id": "call_001"},
|
||||
{"role": "tool", "content": "结果2", "tool_call_id": "call_002"},
|
||||
]
|
||||
→ provider.generate_reply() → 最终回复
|
||||
|
||||
降级策略:
|
||||
API 调用失败 且 fallback_to_rules=true
|
||||
→ 自动切换到规则引擎（保证系统可用性）
|
||||
⚠️ 绝对不能在 tool 消息前插入任何其他消息（尤其是 system/user）
|
||||
"""
|
||||
|
||||
# 规则引擎关键词（降级时使用）
|
||||
_MULTI_STEP_KEYWORDS = [
|
||||
"然后", "接着", "再", "并且", "同时", "之后",
|
||||
"先.*再", "首先.*然后", "搜索.*计算", "读取.*执行",
|
||||
|
|
@ -75,13 +76,12 @@ class LLMEngine:
|
|||
self.logger.info(f" fallback_rules = {settings.agent.fallback_to_rules}")
|
||||
|
||||
def reconfigure(self, cfg: LLMConfig) -> None:
|
||||
"""热更新配置并重建 Provider"""
|
||||
self.cfg = cfg
|
||||
self.provider = create_provider(cfg)
|
||||
self.logger.info(f"🔄 LLM 配置已更新: model={cfg.model_name}")
|
||||
|
||||
# ════════════════════════════════════════════════════════════
|
||||
# 核心接口
|
||||
# 工具规划
|
||||
# ════════════════════════════════════════════════════════════
|
||||
|
||||
def plan_tool_chain(
|
||||
|
|
@ -91,35 +91,16 @@ class LLMEngine:
|
|||
context: str = "",
|
||||
history: list[dict] | None = None,
|
||||
) -> ChainPlan:
|
||||
"""
|
||||
使用 OpenAI Function Calling 规划工具调用链
|
||||
|
||||
消息构造策略:
|
||||
system → 规划器系统提示
|
||||
history → 历史对话（可选）
|
||||
user → 当前用户输入
|
||||
|
||||
Args:
|
||||
user_input: 用户输入文本
|
||||
tool_schemas: 可用工具列表
|
||||
context: 对话历史摘要（文本格式，用于无 history 时）
|
||||
history: 结构化对话历史（OpenAI 消息格式，优先使用）
|
||||
|
||||
Returns:
|
||||
ChainPlan 实例
|
||||
"""
|
||||
"""使用 OpenAI Function Calling 规划工具调用链"""
|
||||
self.logger.info(f"🗺 规划工具调用链: {user_input[:60]}...")
|
||||
|
||||
# 构造消息列表
|
||||
messages = self._build_plan_messages(user_input, context, history)
|
||||
|
||||
if self.cfg.function_calling:
|
||||
# ── 真实 OpenAI Function Calling ──────────────────
|
||||
result = self.provider.plan_with_tools(messages, tool_schemas)
|
||||
|
||||
if result.success and result.plan is not None:
|
||||
plan = result.plan
|
||||
# 补充 goal 字段
|
||||
if not plan.goal:
|
||||
plan.goal = user_input
|
||||
self.logger.info(f"📋 OpenAI 规划完成: {plan.step_count} 步")
|
||||
|
|
@ -130,15 +111,12 @@ class LLMEngine:
|
|||
)
|
||||
return plan
|
||||
|
||||
# API 失败处理
|
||||
self.logger.warning(f"⚠️ OpenAI 规划失败: {result.error}")
|
||||
if settings.agent.fallback_to_rules:
|
||||
self.logger.info("🔄 降级到规则引擎...")
|
||||
return self._rule_based_plan(user_input)
|
||||
return ChainPlan(goal=user_input, steps=[])
|
||||
|
||||
else:
|
||||
# function_calling=false 时直接使用规则引擎
|
||||
self.logger.info("⚙️ function_calling=false，使用规则引擎")
|
||||
return self._rule_based_plan(user_input)
|
||||
|
||||
|
|
@ -148,7 +126,6 @@ class LLMEngine:
|
|||
tool_schemas: list[ToolSchema],
|
||||
context: str = "",
|
||||
) -> ToolDecision:
|
||||
"""单步工具决策（代理到 plan_tool_chain）"""
|
||||
plan = self.plan_tool_chain(user_input, tool_schemas, context)
|
||||
if not plan.steps:
|
||||
return ToolDecision(need_tool=False, reasoning="无需工具，直接回复")
|
||||
|
|
@ -160,114 +137,191 @@ class LLMEngine:
|
|||
reasoning=first.description,
|
||||
)
|
||||
|
||||
# ════════════════════════════════════════════════════════════
|
||||
# 回复生成（核心修复区域）
|
||||
# ════════════════════════════════════════════════════════════
|
||||
|
||||
def generate_chain_reply(
|
||||
self,
|
||||
user_input: str,
|
||||
chain_summary: str,
|
||||
context: str = "",
|
||||
tool_messages: list[dict] | None = None,
|
||||
user_input: str,
|
||||
chain_summary: str,
|
||||
context: str = "",
|
||||
openai_tool_block: list[dict] | None = None,
|
||||
) -> str:
|
||||
"""
|
||||
整合多步骤执行结果，调用 OpenAI 生成最终自然语言回复
|
||||
整合工具执行结果，调用 OpenAI 生成最终自然语言回复
|
||||
|
||||
消息构造（含工具执行结果）:
|
||||
system → 回复生成系统提示
|
||||
user → 原始用户输入
|
||||
assistant → 工具调用决策（tool_calls）
|
||||
tool → 工具执行结果
|
||||
...（多轮工具调用）
|
||||
✅ 修复后消息序列:
|
||||
system → 回复生成提示
|
||||
user → 原始用户输入
|
||||
← openai_tool_block 直接追加（已包含 assistant+tool 消息）→
|
||||
assistant(tool_calls=[...])
|
||||
tool(tool_call_id=..., content=...)
|
||||
tool(tool_call_id=..., content=...)
|
||||
...
|
||||
|
||||
❌ 修复前的错误（导致 400）:
|
||||
system → user → tool → tool ← tool 前缺少 assistant(tool_calls)
|
||||
|
||||
Args:
|
||||
user_input: 原始用户输入
|
||||
chain_summary: 步骤摘要（API 失败时的降级内容）
|
||||
context: 对话历史
|
||||
tool_messages: 完整的工具调用消息序列（OpenAI 格式）
|
||||
|
||||
Returns:
|
||||
最终回复字符串
|
||||
user_input: 原始用户输入
|
||||
chain_summary: 步骤摘要（API 失败时的降级内容）
|
||||
context: 对话历史（仅规划阶段使用，回复阶段不注入）
|
||||
openai_tool_block: 由 AgentClient 构造的合规消息块
|
||||
格式: [assistant(tool_calls), tool, tool, ...]
|
||||
"""
|
||||
self.logger.info("✍️ 生成最终回复...")
|
||||
self.logger.info("✍️ 生成最终回复（工具调用链模式）...")
|
||||
|
||||
if tool_messages:
|
||||
# 构造含工具结果的完整消息历史
|
||||
messages = self._build_reply_messages(user_input, tool_messages)
|
||||
result = self.provider.generate_reply(messages)
|
||||
if not openai_tool_block:
|
||||
self.logger.warning("⚠️ openai_tool_block 为空，降级到摘要模板")
|
||||
return self._fallback_chain_reply(user_input, chain_summary)
|
||||
|
||||
if result.success and result.content:
|
||||
self.logger.info(
|
||||
f"✅ OpenAI 回复生成成功 ({len(result.content)} chars)"
|
||||
)
|
||||
return result.content
|
||||
# 验证消息块合规性
|
||||
if not self._validate_tool_block(openai_tool_block):
|
||||
self.logger.warning("⚠️ 消息块验证失败，降级到摘要模板")
|
||||
return self._fallback_chain_reply(user_input, chain_summary)
|
||||
|
||||
self.logger.warning(f"⚠️ OpenAI 回复生成失败: {result.error}")
|
||||
# ✅ 正确的消息序列构造
|
||||
messages = self._build_reply_messages_with_block(user_input, openai_tool_block)
|
||||
|
||||
# 降级：使用模板回复
|
||||
self.logger.debug(f"📤 发送回复请求，消息数: {len(messages)}")
|
||||
self._log_messages_structure(messages)
|
||||
|
||||
result = self.provider.generate_reply(messages)
|
||||
|
||||
if result.success and result.content:
|
||||
self.logger.info(
|
||||
f"✅ OpenAI 回复生成成功 ({len(result.content)} chars)"
|
||||
)
|
||||
return result.content
|
||||
|
||||
self.logger.warning(f"⚠️ OpenAI 回复生成失败: {result.error}")
|
||||
return self._fallback_chain_reply(user_input, chain_summary)
|
||||
|
||||
def generate_final_reply(
|
||||
self,
|
||||
user_input: str,
|
||||
tool_name: str,
|
||||
tool_output: str,
|
||||
context: str = "",
|
||||
user_input: str,
|
||||
tool_name: str,
|
||||
tool_output: str,
|
||||
context: str = "",
|
||||
tool_call_id: str = "",
|
||||
) -> str:
|
||||
"""单步工具结果整合（调用 OpenAI 生成自然语言回复）"""
|
||||
"""
|
||||
单步工具结果整合（调用 OpenAI 生成自然语言回复）
|
||||
|
||||
✅ 修复后消息序列:
|
||||
system → 回复生成提示
|
||||
user → 原始用户输入
|
||||
assistant → tool_calls=[{id: tool_call_id, function: {name, arguments}}]
|
||||
tool → content=tool_output, tool_call_id=tool_call_id
|
||||
|
||||
❌ 修复前的错误:
|
||||
system → user → tool ← 缺少 assistant(tool_calls) 前置消息
|
||||
"""
|
||||
self.logger.info(f"✍️ 整合单步工具结果 [{tool_name}]...")
|
||||
|
||||
# 构造单步工具消息
|
||||
tool_messages = []
|
||||
if tool_call_id:
|
||||
tool_messages = [
|
||||
{
|
||||
"role": "tool",
|
||||
"content": tool_output,
|
||||
"tool_call_id": tool_call_id,
|
||||
}
|
||||
]
|
||||
if not tool_call_id:
|
||||
# 无 tool_call_id 时降级到直接回复模式
|
||||
self.logger.warning("⚠️ tool_call_id 为空，使用直接回复模式")
|
||||
return self._generate_simple_reply(user_input, tool_name, tool_output)
|
||||
|
||||
import json
|
||||
# 构造单步合规消息块
|
||||
single_tool_block = [
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": None,
|
||||
"tool_calls": [{
|
||||
"id": tool_call_id,
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": tool_name,
|
||||
"arguments": json.dumps({"result": tool_output[:100]},
|
||||
ensure_ascii=False),
|
||||
},
|
||||
}],
|
||||
},
|
||||
{
|
||||
"role": "tool",
|
||||
"content": tool_output,
|
||||
"tool_call_id": tool_call_id,
|
||||
},
|
||||
]
|
||||
|
||||
return self.generate_chain_reply(
|
||||
user_input=user_input,
|
||||
chain_summary=tool_output,
|
||||
context=context,
|
||||
tool_messages=tool_messages,
|
||||
openai_tool_block=single_tool_block,
|
||||
)
|
||||
|
||||
def generate_direct_reply(self, user_input: str, context: str = "") -> str:
|
||||
"""无需工具时直接调用 OpenAI 生成回复"""
|
||||
"""无需工具时直接调用 OpenAI 生成回复（不涉及 tool 消息，无需修复）"""
|
||||
self.logger.info("💬 直接生成回复（无需工具）...")
|
||||
messages = [
|
||||
{"role": "system", "content": "你是一个友好、专业的 AI 助手，请简洁准确地回答用户问题。"},
|
||||
{"role": "user", "content": user_input},
|
||||
{"role": "system", "content": "你是一个友好、专业的 AI 助手，请简洁准确地回答用户问题。"},
|
||||
{"role": "user", "content": user_input},
|
||||
]
|
||||
result = self.provider.generate_reply(messages)
|
||||
if result.success and result.content:
|
||||
return result.content
|
||||
# 降级
|
||||
return (
|
||||
f"[{self.cfg.model_name}] 您好！\n"
|
||||
f"关于「{user_input}」，我已收到您的问题。\n"
|
||||
f"您好！关于「{user_input}」，我已收到您的问题。\n"
|
||||
f"（API 暂时不可用，请检查 API Key 配置）"
|
||||
)
|
||||
|
||||
# ════════════════════════════════════════════════════════════
|
||||
# 消息构造
|
||||
# 消息构造（修复核心）
|
||||
# ════════════════════════════════════════════════════════════
|
||||
|
||||
@staticmethod
|
||||
def _build_reply_messages_with_block(
|
||||
user_input: str,
|
||||
openai_tool_block: list[dict],
|
||||
) -> list[dict]:
|
||||
"""
|
||||
构造回复生成阶段的完整消息列表
|
||||
|
||||
✅ 正确结构:
|
||||
[
|
||||
{"role": "system", "content": REPLY_SYSTEM_PROMPT},
|
||||
{"role": "user", "content": user_input},
|
||||
{"role": "assistant", "content": null, "tool_calls": [...]}, ← tool_block[0]
|
||||
{"role": "tool", "content": "...", "tool_call_id": "..."}, ← tool_block[1]
|
||||
{"role": "tool", "content": "...", "tool_call_id": "..."}, ← tool_block[2]
|
||||
...
|
||||
]
|
||||
|
||||
⚠️ 注意: openai_tool_block 必须整体追加，不能拆分或重排
|
||||
"""
|
||||
from llm.providers.openai_provider import OpenAIProvider
|
||||
messages: list[dict] = [
|
||||
{"role": "system", "content": OpenAIProvider._REPLY_SYSTEM_PROMPT},
|
||||
{"role": "user", "content": user_input},
|
||||
]
|
||||
# 整体追加工具消息块（assistant + tool(s)）
|
||||
messages.extend(openai_tool_block)
|
||||
return messages
|
||||
|
||||
@staticmethod
|
||||
def _build_plan_messages(
|
||||
user_input: str,
|
||||
context: str,
|
||||
history: list[dict] | None,
|
||||
) -> list[dict]:
|
||||
"""构造规划阶段的消息列表"""
|
||||
"""构造规划阶段的消息列表（不含 tool 消息，无需修复）"""
|
||||
from llm.providers.openai_provider import OpenAIProvider
|
||||
messages: list[dict] = [
|
||||
{"role": "system", "content": OpenAIProvider._PLANNER_SYSTEM_PROMPT},
|
||||
]
|
||||
# 注入结构化历史（优先）或文本摘要
|
||||
if history:
|
||||
messages.extend(history[-6:]) # 最近 3 轮
|
||||
# 过滤掉 tool 消息，只保留 user/assistant 对话历史
|
||||
clean_history = [
|
||||
m for m in history[-6:]
|
||||
if m.get("role") in ("user", "assistant")
|
||||
and not m.get("tool_calls") # 排除含 tool_calls 的 assistant 消息
|
||||
]
|
||||
messages.extend(clean_history)
|
||||
elif context and context != "（暂无对话历史）":
|
||||
messages.append({
|
||||
"role": "system",
|
||||
|
|
@ -277,29 +331,87 @@ class LLMEngine:
|
|||
return messages
|
||||
|
||||
@staticmethod
|
||||
def _build_reply_messages(
|
||||
user_input: str,
|
||||
tool_messages: list[dict],
|
||||
) -> list[dict]:
|
||||
"""构造回复生成阶段的消息列表（含工具执行结果）"""
|
||||
from llm.providers.openai_provider import OpenAIProvider
|
||||
messages: list[dict] = [
|
||||
{"role": "system", "content": OpenAIProvider._REPLY_SYSTEM_PROMPT},
|
||||
{"role": "user", "content": user_input},
|
||||
def _validate_tool_block(openai_tool_block: list[dict]) -> bool:
|
||||
"""
|
||||
验证 openai_tool_block 消息块合规性
|
||||
|
||||
规则:
|
||||
1. 第一条必须是 role=assistant 且含 tool_calls
|
||||
2. 后续每条必须是 role=tool 且含 tool_call_id
|
||||
3. tool 消息数量 == assistant.tool_calls 数量
|
||||
4. 所有 tool_call_id 必须在 assistant.tool_calls[].id 中存在
|
||||
"""
|
||||
if not openai_tool_block:
|
||||
return False
|
||||
|
||||
first = openai_tool_block[0]
|
||||
if first.get("role") != "assistant" or not first.get("tool_calls"):
|
||||
return False
|
||||
|
||||
declared_ids = {tc["id"] for tc in first["tool_calls"]}
|
||||
tool_msgs = openai_tool_block[1:]
|
||||
|
||||
if len(tool_msgs) != len(declared_ids):
|
||||
return False
|
||||
|
||||
for msg in tool_msgs:
|
||||
if msg.get("role") != "tool":
|
||||
return False
|
||||
if msg.get("tool_call_id") not in declared_ids:
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
def _generate_simple_reply(
|
||||
self,
|
||||
user_input: str,
|
||||
tool_name: str,
|
||||
tool_output: str,
|
||||
) -> str:
|
||||
"""无 tool_call_id 时的降级回复（不走 Function Calling 协议）"""
|
||||
messages = [
|
||||
{"role": "system",
|
||||
"content": "你是一个友好的 AI 助手，请基于工具执行结果回答用户问题。"},
|
||||
{"role": "user",
|
||||
"content": (
|
||||
f"用户问题: {user_input}\n\n"
|
||||
f"工具 [{tool_name}] 执行结果:\n{tool_output}\n\n"
|
||||
f"请基于以上结果给出清晰的回答。"
|
||||
)},
|
||||
]
|
||||
messages.extend(tool_messages)
|
||||
return messages
|
||||
result = self.provider.generate_reply(messages)
|
||||
return result.content if result.success else self._fallback_chain_reply(
|
||||
user_input, tool_output
|
||||
)
|
||||
|
||||
def _log_messages_structure(self, messages: list[dict]) -> None:
|
||||
"""调试：打印消息序列结构（不打印完整内容）"""
|
||||
self.logger.debug("📋 消息序列结构:")
|
||||
for i, msg in enumerate(messages):
|
||||
role = msg.get("role", "?")
|
||||
if role == "assistant" and msg.get("tool_calls"):
|
||||
ids = [tc["id"] for tc in msg["tool_calls"]]
|
||||
names = [tc["function"]["name"] for tc in msg["tool_calls"]]
|
||||
self.logger.debug(
|
||||
f" [{i}] {role:10s} tool_calls={names} ids={ids}"
|
||||
)
|
||||
elif role == "tool":
|
||||
self.logger.debug(
|
||||
f" [{i}] {role:10s} tool_call_id={msg.get('tool_call_id')} "
|
||||
f"content={str(msg.get('content',''))[:40]}..."
|
||||
)
|
||||
else:
|
||||
content_preview = str(msg.get("content", ""))[:50]
|
||||
self.logger.debug(f" [{i}] {role:10s} {content_preview}...")
|
||||
|
||||
# ════════════════════════════════════════════════════════════
|
||||
# 降级规则引擎
|
||||
# ════════════════════════════════════════════════════════════
|
||||
|
||||
def _rule_based_plan(self, user_input: str) -> ChainPlan:
|
||||
"""规则引擎（API 不可用时的降级方案）"""
|
||||
self.logger.info("⚙️ 使用规则引擎规划...")
|
||||
text = user_input.lower()
|
||||
|
||||
# 搜索 + 计算
|
||||
if (any(k in text for k in ["搜索", "查询", "查一下"]) and
|
||||
any(k in text for k in ["计算", "算", "等于", "结果"])):
|
||||
return ChainPlan(
|
||||
|
|
@ -314,7 +426,6 @@ class LLMEngine:
|
|||
"进行计算", [1]),
|
||||
],
|
||||
)
|
||||
# 读取文件 + 执行代码
|
||||
if (any(k in text for k in ["读取", "文件", "file"]) and
|
||||
any(k in text for k in ["执行", "运行", "run"])):
|
||||
fname = re.search(r"[\w\-\.]+\.\w+", user_input)
|
||||
|
|
@ -333,7 +444,6 @@ class LLMEngine:
|
|||
return self._rule_single_step(user_input)
|
||||
|
||||
def _rule_single_step(self, user_input: str) -> ChainPlan:
|
||||
"""单步规则匹配"""
|
||||
text = user_input.lower()
|
||||
if any(k in text for k in ["计算", "等于", "×", "÷", "+", "-", "*", "/"]):
|
||||
expr = self._extract_expression(user_input)
|
||||
|
|
@ -364,7 +474,6 @@ class LLMEngine:
|
|||
|
||||
@staticmethod
|
||||
def _fallback_chain_reply(user_input: str, chain_summary: str) -> str:
|
||||
"""API 不可用时的模板回复"""
|
||||
return (
|
||||
f"✅ **任务已完成**\n\n"
|
||||
f"针对您的需求「{user_input}」，执行结果如下：\n\n"
|
||||
|
|
|
|||
Loading…
Reference in New Issue