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feat: 增强 Agent 意图识别和上下文管理

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- 新增 intent_router.py 意图路由模块
- 优化 context.py 上下文管理
- 增强 loop.py Agent 运行循环
- 更新 memory.py 记忆模块
- 修复 builtin.py 工具函数

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
DESKTOP-72TV0V4\caoxiaozhu
2026-03-15 21:44:00 +08:00
parent d72c6a3f25
commit 0e0f988264
5 changed files with 493 additions and 8 deletions
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184
core/agents/agent/loop.py
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@@ -10,6 +10,7 @@ from typing import Any, Callable, Awaitable, AsyncGenerator
from agents.agent.context import ContextBuilder
from agents.agent.memory import AgentMemory
from agents.agent.intent_router import IntentRouter, create_intent_router, IntentType
from agents.llm import LLMProvider, LLMResponse, ProviderFactory
from agents.tools import ToolRegistry
@@ -28,6 +29,7 @@ class AgentLoop:
workspace: Path | None = None,
max_iterations: int = 10,
tools: ToolRegistry | None = None,
enable_intent_routing: bool = True,
):
"""Initialize the agent loop.
@@ -37,16 +39,24 @@ class AgentLoop:
workspace: Workspace directory for memory and configs
max_iterations: Maximum tool call iterations
tools: Tool registry (creates default if None)
enable_intent_routing: Enable intent recognition and routing
"""
self.provider = provider
self.model = model
self.workspace = workspace or Path.cwd()
self.max_iterations = max_iterations
self.tools = tools
self.enable_intent_routing = enable_intent_routing
self.context = ContextBuilder(self.workspace)
self.memory = AgentMemory(self.workspace)
# Initialize intent router
if enable_intent_routing:
self.intent_router = create_intent_router(llm_provider=provider)
else:
self.intent_router = None
async def chat(
self,
message: str,
@@ -79,10 +89,43 @@ class AgentLoop:
"""
history = history or []
# Intent recognition and routing
intent_decision = None
if self.intent_router and not history: # Only for first message in conversation
try:
tool_names = self.tools.tool_names if self.tools else []
intent_decision = self.intent_router.route(
message=message,
available_tools=tool_names,
)
logger.info(f"Intent recognized: {intent_decision['intent']} -> {intent_decision['action']}")
# For simple intent, respond directly without tool loop
if intent_decision["intent"] == IntentType.SIMPLE.value:
# Build messages for direct response
messages = self.context.build_messages(
history=history,
current_message=message,
)
# Call LLM without tools
response = await self.provider.chat_with_retry(
messages=messages,
tools=None, # No tools for simple requests
model=self.model,
)
content = self._strip_think(response.content) or "好的，让我来回答这个问题。"
# Save to history
self._save_history(session_key, messages, len(history))
return content
except Exception as e:
logger.warning(f"Intent routing failed: {e}, continuing with normal flow")
# Load history from session if session_key is provided
if session_key and session_key != "default":
loaded_history = self.memory.get_history(session_key, max_messages=20)
if loaded_history:
# Merge any split assistant messages
loaded_history = self._merge_history_messages(loaded_history)
logger.info(f"Loaded {len(loaded_history)} messages from session history")
# Merge loaded history with provided history (loaded takes precedence if empty)
if not history:
@@ -155,10 +198,43 @@ class AgentLoop:
"""
history = history or []
# Intent recognition and routing
intent_decision = None
if self.intent_router and not history: # Only for first message in conversation
try:
tool_names = self.tools.tool_names if self.tools else []
intent_decision = self.intent_router.route(
message=message,
available_tools=tool_names,
)
logger.info(f"Intent recognized: {intent_decision['intent']} -> {intent_decision['action']}")
# For simple intent, respond directly without tool loop
if intent_decision["intent"] == IntentType.SIMPLE.value:
# Build messages for direct response
messages = self.context.build_messages(
history=history,
current_message=message,
)
# Call LLM without tools
response = await self.provider.chat_with_retry(
messages=messages,
tools=None, # No tools for simple requests
model=self.model,
)
content = self._strip_think(response.content) or "好的，让我来回答这个问题。"
# Save to history
self._save_history(session_key, messages, len(history))
return content
except Exception as e:
logger.warning(f"Intent routing failed: {e}, continuing with normal flow")
# Load history from session if session_key is provided
if session_key and session_key != "default":
loaded_history = self.memory.get_history(session_key, max_messages=20)
if loaded_history:
# Merge any split assistant messages
loaded_history = self._merge_history_messages(loaded_history)
logger.info(f"Loaded {len(loaded_history)} messages from session history")
# Merge loaded history with provided history (loaded takes precedence if empty)
if not history:
@@ -334,6 +410,28 @@ class AgentLoop:
tool_defs = self.tools.get_definitions() if self.tools else []
# Intent recognition - determine if tools are needed before first LLM call
user_message = ""
for msg in messages:
if msg.get("role") == "user":
user_message = msg.get("content", "")
break
# Apply intent recognition on first iteration
if self.enable_intent_routing and self.intent_router and user_message:
available_tools = [t.get("function", {}).get("name", "") for t in tool_defs] if tool_defs else []
routing_decision = self.intent_router.route(
user_message,
available_tools=available_tools,
)
intent = routing_decision.get("intent", "simple")
logger.info(f"Intent recognized: {intent} for message: {user_message[:50]}...")
# If simple intent, don't pass tools to reduce unnecessary tool calls
if intent == "simple":
tool_defs = []
logger.info("Simple intent detected - disabling tool definitions for this request")
while iteration < self.max_iterations:
iteration += 1
@@ -423,6 +521,28 @@ class AgentLoop:
model = model or self.model
tool_defs = self.tools.get_definitions() if self.tools else []
# Intent recognition - determine if tools are needed before first LLM call
user_message = ""
for msg in initial_messages:
if msg.get("role") == "user":
user_message = msg.get("content", "")
break
# Apply intent recognition
if self.enable_intent_routing and self.intent_router and user_message:
available_tools = [t.get("function", {}).get("name", "") for t in tool_defs] if tool_defs else []
routing_decision = self.intent_router.route(
user_message,
available_tools=available_tools,
)
intent = routing_decision.get("intent", "simple")
logger.info(f"[stream] Intent recognized: {intent} for message: {user_message[:50]}...")
# If simple intent, don't pass tools to reduce unnecessary tool calls
if intent == "simple":
tool_defs = []
logger.info("[stream] Simple intent detected - disabling tool definitions")
# First call to check for tool calls
response = await provider.chat_with_retry(
messages=initial_messages,
@@ -490,6 +610,55 @@ class AgentLoop:
return f'{tc.name}("{val[:40]}...")' if len(val) > 40 else f'{tc.name}("{val}")'
return ", ".join(_fmt(tc) for tc in tool_calls)
@staticmethod
def _merge_history_messages(messages: list[dict]) -> list[dict]:
"""Merge adjacent assistant messages that have content and tool_calls separately.
When saving/loading history, assistant messages with both content and tool_calls
might be split into multiple entries. This method merges them back together.
Args:
messages: List of message dictionaries
Returns:
Merged list of messages
"""
if not messages:
return messages
merged = []
i = 0
while i < len(messages):
current = messages[i].copy()
# If current is an assistant message with tool_calls, check if next is
# an assistant message with content (or vice versa)
if current.get("role") == "assistant" and current.get("tool_calls"):
# Look ahead for another assistant message to merge with
j = i + 1
while j < len(messages):
next_msg = messages[j]
if next_msg.get("role") == "assistant":
# Merge content
if next_msg.get("content") and not current.get("content"):
current["content"] = next_msg.get("content")
# Merge tool_calls (should already be in current)
if next_msg.get("tool_calls") and not current.get("tool_calls"):
current["tool_calls"] = next_msg.get("tool_calls")
j += 1
else:
break
# If we merged multiple messages, skip them
if j > i + 1:
logger.debug(f"Merged {j - i} assistant messages")
i = j
else:
merged.append(current)
i += 1
return merged
def _save_history(
self,
session_key: str,
@@ -510,13 +679,18 @@ class AgentLoop:
if role == "user" and content:
self.memory.add_to_history("user", str(content)[:1000], session_key)
elif role == "assistant":
# Save assistant message content
# Build a combined message with content and tool_calls
msg_data = {}
if content:
self.memory.add_to_history("assistant", str(content)[:1000], session_key)
# Save tool_calls if present (needed for multi-turn tool calls)
msg_data["content"] = str(content)[:1000]
if m.get("tool_calls"):
tool_calls_str = json.dumps(m.get("tool_calls", []))
self.memory.add_to_history("assistant", f"[tool_calls]{tool_calls_str}", session_key)
msg_data["tool_calls"] = m.get("tool_calls", [])
# Save as a single JSON message with all data
if msg_data:
msg_str = json.dumps(msg_data)
self.memory.add_to_history("assistant", msg_str, session_key)
# Save tool results (needed for multi-turn conversations)
elif role == "tool":
tool_call_id = m.get("tool_call_id", "")