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feat(workflow): add session context memory support to LLM nodes

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This commit is contained in:
Eternity
2026-01-14 16:35:46 +08:00
parent b5a366ef5e
commit 567624c323
11 changed files with 249 additions and 228 deletions
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37
api/app/core/config.py
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@@ -7,17 +7,18 @@ from dotenv import load_dotenv
load_dotenv()
class Settings:
ENABLE_SINGLE_WORKSPACE: bool = os.getenv("ENABLE_SINGLE_WORKSPACE", "true").lower() == "true"
# API Keys Configuration
OPENAI_API_KEY: str = os.getenv("OPENAI_API_KEY", "")
DASHSCOPE_API_KEY: str = os.getenv("DASHSCOPE_API_KEY", "")
# Neo4j Configuration (记忆系统数据库)
NEO4J_URI: str = os.getenv("NEO4J_URI", "bolt://1.94.111.67:7687")
NEO4J_USERNAME: str = os.getenv("NEO4J_USERNAME", "neo4j")
NEO4J_PASSWORD: str = os.getenv("NEO4J_PASSWORD", "")
# Database configuration (Postgres)
DB_HOST: str = os.getenv("DB_HOST", "127.0.0.1")
DB_PORT: int = int(os.getenv("DB_PORT", "5432"))
@@ -37,7 +38,7 @@ class Settings:
REDIS_PORT: int = int(os.getenv("REDIS_PORT", "6379"))
REDIS_DB: int = int(os.getenv("REDIS_DB", "1"))
REDIS_PASSWORD: str = os.getenv("REDIS_PASSWORD", "")
# ElasticSearch configuration
ELASTICSEARCH_HOST: str = os.getenv("ELASTICSEARCH_HOST", "https://127.0.0.1")
ELASTICSEARCH_PORT: int = int(os.getenv("ELASTICSEARCH_PORT", "9200"))
@@ -48,7 +49,7 @@ class Settings:
ELASTICSEARCH_REQUEST_TIMEOUT: int = int(os.getenv("ELASTICSEARCH_REQUEST_TIMEOUT", "100000"))
ELASTICSEARCH_RETRY_ON_TIMEOUT: bool = os.getenv("ELASTICSEARCH_RETRY_ON_TIMEOUT", "True").lower() == "true"
ELASTICSEARCH_MAX_RETRIES: int = int(os.getenv("ELASTICSEARCH_MAX_RETRIES", "10"))
# Xinference configuration
XINFERENCE_URL: str = os.getenv("XINFERENCE_URL", "http://127.0.0.1")
@@ -57,17 +58,17 @@ class Settings:
LANGCHAIN_TRACING: bool = os.getenv("LANGCHAIN_TRACING", "false").lower() == "true"
LANGCHAIN_API_KEY: str = os.getenv("LANGCHAIN_API_KEY", "")
LANGCHAIN_ENDPOINT: str = os.getenv("LANGCHAIN_ENDPOINT", "")
# LLM Request Configuration
LLM_TIMEOUT: float = float(os.getenv("LLM_TIMEOUT", "120.0"))
LLM_MAX_RETRIES: int = int(os.getenv("LLM_MAX_RETRIES", "2"))
# JWT Token Configuration
SECRET_KEY: str = os.getenv("SECRET_KEY", "a_default_secret_key_that_is_long_and_random")
ALGORITHM: str = "HS256"
ACCESS_TOKEN_EXPIRE_MINUTES: int = int(os.getenv("ACCESS_TOKEN_EXPIRE_MINUTES", "30"))
REFRESH_TOKEN_EXPIRE_DAYS: int = int(os.getenv("REFRESH_TOKEN_EXPIRE_DAYS", "7"))
# Single Sign-On configuration
ENABLE_SINGLE_SESSION: bool = os.getenv("ENABLE_SINGLE_SESSION", "false").lower() == "true"
@@ -86,19 +87,19 @@ class Settings:
LANGFUSE_PUBLIC_KEY: str = os.getenv("LANGFUSE_PUBLIC_KEY", "")
LANGFUSE_SECRET_KEY: str = os.getenv("LANGFUSE_SECRET_KEY", "")
LANGFUSE_HOST: str = os.getenv("LANGFUSE_HOST", "")
# Server Configuration
SERVER_IP: str = os.getenv("SERVER_IP", "127.0.0.1")
# ========================================================================
# Internal Configuration (not in .env, used by application code)
# ========================================================================
# Superuser settings (internal defaults)
FIRST_SUPERUSER_EMAIL: str = os.getenv("FIRST_SUPERUSER_EMAIL", "admin@example.com")
FIRST_SUPERUSER_USERNAME: str = os.getenv("FIRST_SUPERUSER_USERNAME", "admin")
FIRST_SUPERUSER_PASSWORD: str = os.getenv("FIRST_SUPERUSER_PASSWORD", "admin_password")
# Generic File Upload (internal)
GENERIC_FILE_PATH: str = os.getenv("GENERIC_FILE_PATH", "/uploads")
ENABLE_FILE_COMPRESSION: bool = os.getenv("ENABLE_FILE_COMPRESSION", "false").lower() == "true"
@@ -123,7 +124,7 @@ class Settings:
LOG_BACKUP_COUNT: int = int(os.getenv("LOG_BACKUP_COUNT", "5"))
LOG_TO_CONSOLE: bool = os.getenv("LOG_TO_CONSOLE", "true").lower() == "true"
LOG_TO_FILE: bool = os.getenv("LOG_TO_FILE", "true").lower() == "true"
# Sensitive Data Filtering
ENABLE_SENSITIVE_DATA_FILTER: bool = os.getenv("ENABLE_SENSITIVE_DATA_FILTER", "true").lower() == "true"
@@ -142,7 +143,6 @@ class Settings:
LOG_STREAM_BUFFER_SIZE: int = int(os.getenv("LOG_STREAM_BUFFER_SIZE", "8192")) # 8KB
LOG_FILE_MAX_SIZE_MB: int = int(os.getenv("LOG_FILE_MAX_SIZE_MB", "10")) # 10MB
# Celery configuration (internal)
CELERY_BROKER: int = int(os.getenv("CELERY_BROKER", "1"))
CELERY_BACKEND: int = int(os.getenv("CELERY_BACKEND", "2"))
@@ -150,15 +150,15 @@ class Settings:
HEALTH_CHECK_SECONDS: float = float(os.getenv("HEALTH_CHECK_SECONDS", "600"))
MEMORY_INCREMENT_INTERVAL_HOURS: float = float(os.getenv("MEMORY_INCREMENT_INTERVAL_HOURS", "24"))
DEFAULT_WORKSPACE_ID: Optional[str] = os.getenv("DEFAULT_WORKSPACE_ID", None)
REFLECTION_INTERVAL_TIME:Optional[str] = int(os.getenv("REFLECTION_INTERVAL_TIME", 30))
REFLECTION_INTERVAL_TIME: Optional[str] = int(os.getenv("REFLECTION_INTERVAL_TIME", 30))
# Memory Cache Regeneration Configuration
MEMORY_CACHE_REGENERATION_HOURS: int = int(os.getenv("MEMORY_CACHE_REGENERATION_HOURS", "24"))
# Memory Module Configuration (internal)
MEMORY_OUTPUT_DIR: str = os.getenv("MEMORY_OUTPUT_DIR", "logs/memory-output")
MEMORY_CONFIG_DIR: str = os.getenv("MEMORY_CONFIG_DIR", "app/core/memory")
# Tool Management Configuration
TOOL_CONFIG_DIR: str = os.getenv("TOOL_CONFIG_DIR", "app/core/tools")
TOOL_EXECUTION_TIMEOUT: int = int(os.getenv("TOOL_EXECUTION_TIMEOUT", "60"))
@@ -167,7 +167,10 @@ class Settings:
# official environment system version
SYSTEM_VERSION: str = os.getenv("SYSTEM_VERSION", "v0.2.0")
# workflow config
WORKFLOW_NODE_TIMEOUT: int = os.getenv("WORKFLOW_NODE_TIMEOUT", 600)
def get_memory_output_path(self, filename: str = "") -> str:
"""
Get the full path for memory module output files.
@@ -182,7 +185,7 @@ class Settings:
if filename:
return str(base_path / filename)
return str(base_path)
def ensure_memory_output_dir(self) -> None:
"""
Ensure the memory output directory exists.

3
api/app/core/workflow/executor.py
View File

@@ -74,6 +74,7 @@ class WorkflowExecutor:
初始化的工作流状态
"""
user_message = input_data.get("message") or ""
conversation_messages = input_data.get("conv_messages") or []
# 会话变量处理：从配置文件获取变量定义列表，转换为字典（name -> default value）
config_variables_list = self.workflow_config.get("variables") or []
@@ -114,7 +115,7 @@ class WorkflowExecutor:
}
return {
"messages": [('user', user_message)],
"messages": conversation_messages,
"variables": variables,
"node_outputs": {},
"runtime_vars": {}, # 运行时节点变量（简化版，供快速访问）

10
api/app/core/workflow/nodes/base_node.py
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@@ -7,13 +7,13 @@
import asyncio
import logging
from abc import ABC, abstractmethod
from operator import add
from typing import Any
from langchain_core.messages import AnyMessage, AIMessage
from langchain_core.messages import AIMessage
from langgraph.config import get_stream_writer
from typing_extensions import TypedDict, Annotated
from app.core.config import settings
from app.core.workflow.variable_pool import VariablePool
logger = logging.getLogger(__name__)
@@ -25,7 +25,7 @@ class WorkflowState(TypedDict):
The state object passed between nodes in a workflow, containing messages, variables, node outputs, etc.
"""
# List of messages (append mode)
messages: Annotated[list[tuple[str, str]], add]
messages: list[dict[str, str]]
# Set of loop node IDs, used for assigning values in loop nodes
cycle_nodes: list
@@ -154,7 +154,7 @@ class BaseNode(ABC):
Returns:
超时时间
"""
return 60
return settings.WORKFLOW_NODE_TIMEOUT
# return self.error_handling.get("timeout", 60)
async def run(self, state: WorkflowState) -> dict[str, Any]:
@@ -203,6 +203,7 @@ class BaseNode(ABC):
# 返回包装后的输出和运行时变量
return {
**wrapped_output,
"messages": state["messages"],
"variables": state["variables"],
"runtime_vars": {
self.node_id: runtime_var
@@ -356,6 +357,7 @@ class BaseNode(ABC):
# Build complete state update (including node_outputs, runtime_vars, and final streaming buffer)
state_update = {
**final_output,
"messages": state["messages"],
"variables": state["variables"],
"runtime_vars": {
self.node_id: runtime_var

52
api/app/core/workflow/nodes/end/node.py
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@@ -6,7 +6,6 @@ End 节点实现
import logging
import re
import asyncio
from app.core.workflow.nodes.base_node import BaseNode, WorkflowState
from app.core.workflow.nodes.enums import NodeType
@@ -38,7 +37,23 @@ class EndNode(BaseNode):
# 如果配置了输出模板，使用模板渲染；否则使用默认输出
if output_template:
output = self._render_template(output_template, state, strict=False)
state['messages'].extend([
{
"role": "user",
"content": self.get_variable("sys.message", state)
},
{
"role": "assistant",
"content": output
}
])
else:
state['messages'].extend([
{
"role": "user",
"content": self.get_variable("sys.message", state)
},
])
output = "工作流已完成"
# 统计信息（用于日志）
@@ -166,6 +181,12 @@ class EndNode(BaseNode):
"chunk_index": 1,
"is_suffix": False
})
state['messages'].extend([
{
"role": "user",
"content": self.get_variable("sys.message", state)
}
])
yield {"__final__": True, "result": output}
return
@@ -176,7 +197,6 @@ class EndNode(BaseNode):
source_node_id = edge.get("source")
# Check if the source node is an LLM node
for node in self.workflow_config.get("nodes", []):
print("="*50)
logger.info(f"节点 {self.node_id} 的类型 {node.get("type")}")
if node.get("id") == source_node_id and node.get("type") == NodeType.LLM:
direct_upstream_llm_nodes.append(source_node_id)
@@ -216,12 +236,24 @@ class EndNode(BaseNode):
})
logger.info(f"节点 {self.node_id} 已通过 writer 发送完整内容")
state['messages'].extend([
{
"role": "user",
"content": self.get_variable("sys.message", state)
},
{
"role": "assistant",
"content": output
}
])
# yield completion marker
yield {"__final__": True, "result": output}
return
# Has reference to direct upstream LLM node, only output the part after that reference (suffix)
logger.info(f"节点 {self.node_id} 检测到直接上游 LLM 节点引用，只输出后缀部分（从索引 {upstream_llm_ref_index + 1} 开始）")
logger.info(
f"节点 {self.node_id} 检测到直接上游 LLM 节点引用，只输出后缀部分（从索引 {upstream_llm_ref_index + 1} 开始）")
# Collect suffix parts
suffix_parts = []
@@ -258,6 +290,17 @@ class EndNode(BaseNode):
# 构建完整输出（用于返回，包含前缀 + 动态内容 + 后缀）
full_output = self._render_template(output_template, state, strict=False)
state['messages'].extend([
{
"role": "user",
"content": self.get_variable("sys.message", state)
},
{
"role": "assistant",
"content": full_output
}
])
logger.info(f"[后缀调试] 节点 {self.node_id} 后缀部分数量: {len(suffix_parts)}")
logger.info(f"[后缀调试] 后缀内容: '{suffix}'")
logger.info(f"[后缀调试] 后缀长度: {len(suffix)}")
@@ -280,7 +323,8 @@ class EndNode(BaseNode):
})
logger.info(f"节点 {self.node_id} 已通过 writer 发送后缀，full_content 长度: {len(full_output)}")
else:
logger.warning(f"[后缀调试] 节点 {self.node_id} 后缀为空，不发送！upstream_llm_ref_index={upstream_llm_ref_index}, parts数量={len(parts)}")
logger.warning(f"[后缀调试] 节点 {self.node_id} 后缀为空，不发送！"
f"upstream_llm_ref_index={upstream_llm_ref_index}, parts数量={len(parts)}")
# 统计信息
node_outputs = state.get("node_outputs", {})

26
api/app/core/workflow/nodes/llm/config.py
View File

@@ -11,12 +11,12 @@ class MessageConfig(BaseModel):
"""消息配置"""
role: str = Field(
...,
default='user',
description="消息角色：system, user, assistant"
)
content: str = Field(
...,
default="",
description="消息内容，支持模板变量，如：{{ sys.message }}"
)
@@ -30,6 +30,23 @@ class MessageConfig(BaseModel):
return v.lower()
class MemoryWindowSetting(BaseModel):
enable: bool = Field(
default=False,
description="启用记忆"
)
enable_window: bool = Field(
default=False,
description="启用记忆窗口"
)
window_size: int = Field(
default=20,
description="记忆窗口大小"
)
class LLMNodeConfig(BaseNodeConfig):
"""LLM 节点配置
@@ -48,6 +65,11 @@ class LLMNodeConfig(BaseNodeConfig):
description="上下文"
)
memory: MemoryWindowSetting = Field(
...,
description="对话上下文窗口"
)
# 简单模式
prompt: str | None = Field(
default=None,

19
api/app/core/workflow/nodes/llm/node.py
View File

@@ -85,28 +85,31 @@ class LLMNode(BaseNode):
"""
# 1. 处理消息格式（优先使用 messages）
messages_config = self.config.get("messages")
messages_config = self.typed_config.messages
if messages_config:
# 使用 LangChain 消息格式
messages = []
for msg_config in messages_config:
role = msg_config.get("role", "user").lower()
content_template = msg_config.get("content", "")
role = msg_config.role.lower()
content_template = msg_config.content
content_template = self._render_context(content_template, state)
content = self._render_template(content_template, state)
# 根据角色创建对应的消息对象
if role == "system":
messages.append(SystemMessage(content=content))
messages.append({"role": "system", "content": content})
elif role in ["user", "human"]:
messages.append(HumanMessage(content=content))
messages.append({"role": "user", "content": content})
elif role in ["ai", "assistant"]:
messages.append(AIMessage(content=content))
messages.append({"role": "user", "content": content})
else:
logger.warning(f"未知的消息角色: {role}，默认使用 user")
messages.append(HumanMessage(content=content))
messages.append({"role": "user", "content": content})
if self.typed_config.memory.enable:
# if self.typed_config.memory.enable_window:
messages = messages[:-1] + state["messages"][-self.typed_config.memory.window_size:] + messages[-1:]
prompt_or_messages = messages
else:
# 使用简单的 prompt 格式（向后兼容）
@@ -189,7 +192,7 @@ class LLMNode(BaseNode):
return {
"prompt": prompt_or_messages if isinstance(prompt_or_messages, str) else None,
"messages": [
{"role": msg.__class__.__name__.replace("Message", "").lower(), "content": msg.content}
{"role": msg.get("role"), "content": msg.get("content", "")}
for msg in prompt_or_messages
] if isinstance(prompt_or_messages, list) else None,
"config": {