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feat(agent, memory): add agent-perceived memory writing

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This commit is contained in:
Eternity
2026-03-30 11:47:58 +08:00
parent a5bce221bd
commit 7acb7045f0
12 changed files with 304 additions and 530 deletions
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90
api/app/core/agent/langchain_agent.py
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@@ -11,18 +11,14 @@ LangChain Agent 封装
import time
from typing import Any, AsyncGenerator, Dict, List, Optional, Sequence
from app.core.memory.agent.langgraph_graph.write_graph import write_long_term
from app.db import get_db
from app.core.logging_config import get_business_logger
from app.core.models import RedBearLLM, RedBearModelConfig
from app.models.models_model import ModelType, ModelProvider
from app.services.memory_agent_service import (
get_end_user_connected_config,
)
from langchain.agents import create_agent
from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, SystemMessage
from langchain_core.tools import BaseTool
from app.core.logging_config import get_business_logger
from app.core.models import RedBearLLM, RedBearModelConfig
from app.models.models_model import ModelType
logger = get_business_logger()
@@ -226,10 +222,9 @@ class LangChainAgent:
Returns:
List[BaseMessage]: 消息列表
"""
messages = []
messages:list = [SystemMessage(content=self.system_prompt)]
# 添加系统提示词
messages.append(SystemMessage(content=self.system_prompt))
# 添加历史消息
if history:
@@ -293,12 +288,7 @@ class LangChainAgent:
message: str,
history: Optional[List[Dict[str, str]]] = None,
context: Optional[str] = None,
end_user_id: Optional[str] = None,
config_id: Optional[str] = None, # 添加这个参数
storage_type: Optional[str] = None,
user_rag_memory_id: Optional[str] = None,
memory_flag: Optional[bool] = True,
files: Optional[List[Dict[str, Any]]] = None # 新增：多模态文件
files: Optional[List[Dict[str, Any]]] = None
) -> Dict[str, Any]:
"""执行对话
@@ -306,32 +296,12 @@ class LangChainAgent:
message: 用户消息
history: 历史消息列表 [{"role": "user/assistant", "content": "..."}]
context: 上下文信息（如知识库检索结果）
files: 多模态文件
Returns:
Dict: 包含 content 和元数据的字典
"""
message_chat = message
start_time = time.time()
actual_config_id = config_id
# If config_id is None, try to get from end_user's connected config
if actual_config_id is None and end_user_id:
try:
from app.services.memory_agent_service import (
get_end_user_connected_config,
)
db = next(get_db())
try:
connected_config = get_end_user_connected_config(end_user_id, db)
actual_config_id = connected_config.get("memory_config_id")
except Exception as e:
logger.warning(f"Failed to get connected config for end_user {end_user_id}: {e}")
finally:
db.close()
except Exception as e:
logger.warning(f"Failed to get db session: {e}")
actual_end_user_id = end_user_id if end_user_id is not None else "unknown"
logger.info(f'写入类型{storage_type, str(end_user_id), message, str(user_rag_memory_id)}')
print(f'写入类型{storage_type, str(end_user_id), message, str(user_rag_memory_id)}')
try:
# 准备消息列表（支持多模态）
messages = self._prepare_messages(message, history, context, files)
@@ -419,9 +389,6 @@ class LangChainAgent:
logger.info(f"最终提取的内容长度: {len(content)}")
elapsed_time = time.time() - start_time
if memory_flag:
await write_long_term(storage_type, end_user_id, message_chat, content, user_rag_memory_id,
actual_config_id)
response = {
"content": content,
"model": self.model_name,
@@ -452,12 +419,7 @@ class LangChainAgent:
message: str,
history: Optional[List[Dict[str, str]]] = None,
context: Optional[str] = None,
end_user_id: Optional[str] = None,
config_id: Optional[str] = None,
storage_type: Optional[str] = None,
user_rag_memory_id: Optional[str] = None,
memory_flag: Optional[bool] = True,
files: Optional[List[Dict[str, Any]]] = None # 新增：多模态文件
files: Optional[List[Dict[str, Any]]] = None
) -> AsyncGenerator[str, None]:
"""执行流式对话
@@ -465,6 +427,7 @@ class LangChainAgent:
message: 用户消息
history: 历史消息列表
context: 上下文信息
files: 多模态文件
Yields:
str: 消息内容块
@@ -475,23 +438,6 @@ class LangChainAgent:
logger.info(f" Has tools: {bool(self.tools)}")
logger.info(f" Tool count: {len(self.tools) if self.tools else 0}")
logger.info("=" * 80)
message_chat = message
actual_config_id = config_id
# If config_id is None, try to get from end_user's connected config
if actual_config_id is None and end_user_id:
try:
db = next(get_db())
try:
connected_config = get_end_user_connected_config(end_user_id, db)
actual_config_id = connected_config.get("memory_config_id")
except Exception as e:
logger.warning(f"Failed to get connected config for end_user {end_user_id}: {e}")
finally:
db.close()
except Exception as e:
logger.warning(f"Failed to get db session: {e}")
# 注意：不在这里写入用户消息，等 AI 回复后一起写入
try:
# 准备消息列表（支持多模态）
messages = self._prepare_messages(message, history, context, files)
@@ -501,17 +447,18 @@ class LangChainAgent:
)
chunk_count = 0
yielded_content = False
# 统一使用 agent 的 astream_events 实现流式输出
logger.debug("使用 Agent astream_events 实现流式输出")
full_content = ''
try:
last_event = {}
async for event in self.agent.astream_events(
{"messages": messages},
version="v2",
config={"recursion_limit": self.max_iterations}
):
last_event = event
chunk_count += 1
kind = event.get("event")
@@ -525,7 +472,6 @@ class LangChainAgent:
if isinstance(chunk_content, str) and chunk_content:
full_content += chunk_content
yield chunk_content
yielded_content = True
elif isinstance(chunk_content, list):
# 多模态响应：提取文本部分
for item in chunk_content:
@@ -536,18 +482,15 @@ class LangChainAgent:
if text:
full_content += text
yield text
yielded_content = True
# OpenAI 格式: {"type": "text", "text": "..."}
elif item.get("type") == "text":
text = item.get("text", "")
if text:
full_content += text
yield text
yielded_content = True
elif isinstance(item, str):
full_content += item
yield item
yielded_content = True
elif kind == "on_llm_stream":
# 另一种 LLM 流式事件
@@ -558,7 +501,6 @@ class LangChainAgent:
if isinstance(chunk_content, str) and chunk_content:
full_content += chunk_content
yield chunk_content
yielded_content = True
elif isinstance(chunk_content, list):
# 多模态响应：提取文本部分
for item in chunk_content:
@@ -569,22 +511,18 @@ class LangChainAgent:
if text:
full_content += text
yield text
yielded_content = True
# OpenAI 格式: {"type": "text", "text": "..."}
elif item.get("type") == "text":
text = item.get("text", "")
if text:
full_content += text
yield text
yielded_content = True
elif isinstance(item, str):
full_content += item
yield item
yielded_content = True
elif isinstance(chunk, str):
full_content += chunk
yield chunk
yielded_content = True
# 记录工具调用（可选）
elif kind == "on_tool_start":
@@ -594,7 +532,7 @@ class LangChainAgent:
logger.debug(f"Agent 流式完成，共 {chunk_count} 个事件")
# 统计token消耗
output_messages = event.get("data", {}).get("output", {}).get("messages", [])
output_messages = last_event.get("data", {}).get("output", {}).get("messages", [])
for msg in reversed(output_messages):
if isinstance(msg, AIMessage):
response_meta = msg.response_metadata if hasattr(msg, 'response_metadata') else None
@@ -604,9 +542,7 @@ class LangChainAgent:
) if response_meta else 0
yield total_tokens
break
if memory_flag:
await write_long_term(storage_type, end_user_id, message_chat, full_content, user_rag_memory_id,
actual_config_id)
except Exception as e:
logger.error(f"Agent astream_events 失败: {str(e)}", exc_info=True)
raise