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记忆系统，上下文处理+mem0

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This commit is contained in:
Shin@HOME
2026-04-13 23:37:30 +08:00
parent f6a43302d2
commit d3d41b20d7
13 changed files with 262 additions and 56 deletions
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83
art-agent/backend/app/agent/loop.py
View File

@@ -1,6 +1,8 @@
"""Agent Loop 主循环：LLM 对话 -> 工具调用 -> 结果回传 -> 继续。"""
import asyncio
import json
import logging
import re
import os
from typing import AsyncGenerator, Optional
@@ -8,9 +10,17 @@ from typing import AsyncGenerator, Optional
from openai import AsyncOpenAI
from app.agent.tools import TOOL_DEFINITIONS, execute_tool
from app.config import get_llm_model, get_llm_max_iterations, get_image_model_config
from app.config import (
get_llm_model,
get_llm_max_iterations,
get_image_model_config,
get_max_recent_turns,
)
from app.memory import get_memory
from app.services.image_gen import to_data_uri
logger = logging.getLogger(__name__)
def _get_client() -> AsyncOpenAI:
base_url = os.getenv("OPENAI_BASE_URL")
@@ -59,6 +69,7 @@ async def run_agent_loop(
messages: list[dict],
ref_image_url: Optional[str] = None,
image_model: Optional[str] = None,
session_id: Optional[str] = None,
) -> AsyncGenerator[dict, None]:
"""
运行 Agent Loop，以 SSE 事件流形式 yield 结果。
@@ -67,10 +78,42 @@ async def run_agent_loop(
- text_delta: LLM 文字增量
- tool_start: 开始执行工具
- image_result: 图片生成结果
- memory_warning: 记忆存储异常（不中断对话）
- done: 完成
- error: 错误
"""
# 构建消息列表：将当前生图模型信息注入 system prompt，避免 LLM 猜测模型名
# ── 滑动窗口：截断过早的消息 ──
max_turns = get_max_recent_turns()
if len(messages) > max_turns:
messages = messages[-max_turns:]
# ── 记忆检索：用最新 user 消息语义检索相关记忆 ──
memory_block = ""
try:
memory = get_memory()
last_user_content = ""
for m in reversed(messages):
if m["role"] == "user":
last_user_content = m["content"]
break
if last_user_content:
search_kwargs = {"query": last_user_content, "user_id": "default_user", "limit": 10}
if session_id:
search_kwargs["run_id"] = session_id
relevant = memory.search(**search_kwargs)
results = relevant.get("results", []) if isinstance(relevant, dict) else relevant
if results:
items = "\n".join(f"- {m['memory']}" for m in results if m.get("memory"))
if items:
memory_block = f"\n\n## 用户记忆（来自历史对话）\n{items}"
except Exception as e:
logger.error("Mem0 记忆检索失败: %s", e, exc_info=True)
yield {"type": "error", "data": {"message": f"记忆系统检索失败: {e}"}}
return
# ── 构建 system prompt ──
model_config = get_image_model_config(image_model)
current_model_name = model_config.get('name', '未知')
current_model_id = model_config.get('id', '未知')
@@ -81,7 +124,7 @@ async def run_agent_loop(
f"**注意**：对话历史中可能包含之前使用其他模型的记录，忽略那些旧模型名。"
f"本次生成使用的是 {current_model_name}，在回复中只能使用这个名称。"
)
api_messages = [{"role": "system", "content": SYSTEM_PROMPT + model_hint}]
api_messages = [{"role": "system", "content": SYSTEM_PROMPT + model_hint + memory_block}]
# 检测当前 LLM 是否支持 vision（多模态图片输入）
llm_model = get_llm_model().lower()
@@ -90,7 +133,6 @@ async def run_agent_loop(
for msg in messages:
if msg["role"] == "user" and ref_image_url and msg is messages[-1]:
if vision_capable:
# 支持 vision 的模型：直接发送图片
api_messages.append({
"role": "user",
"content": [
@@ -102,7 +144,6 @@ async def run_agent_loop(
],
})
else:
# 不支持 vision 的模型：以文字提示告知有参考图，风格由 IP-Adapter 处理
hint = (
f"{msg['content']}\n\n"
"【系统提示：用户上传了一张参考图，已自动传递给图片生成工具的 IP-Adapter。"
@@ -181,7 +222,9 @@ async def run_agent_loop(
),
})
continue
yield {"type": "done", "data": {}}
# 对话正常结束，异步存储记忆
async for evt in _store_and_done(messages, session_id):
yield evt
return
# 将 assistant 消息（含 tool_calls）加入历史
@@ -253,6 +296,34 @@ async def run_agent_loop(
# ref_image_url 不清除：消息构建只在循环外执行一次，
# 后续工具调用仍需参考图（InstantStyle 等模型必须有 style_image）
# 迭代次数用尽，存储记忆后结束
async for evt in _store_and_done(messages, session_id):
yield evt
# ─── 异步记忆存储 ─────────────────────────────────────────
async def _store_and_done(
messages: list[dict], session_id: Optional[str]
) -> AsyncGenerator[dict, None]:
"""触发 Mem0 异步存储后 yield done。存储失败时 yield warning 但不中断。"""
try:
memory = get_memory()
recent = messages[-4:] if len(messages) >= 4 else messages
add_kwargs: dict = {"user_id": "default_user"}
if session_id:
add_kwargs["run_id"] = session_id
loop = asyncio.get_running_loop()
await loop.run_in_executor(None, lambda: memory.add(recent, **add_kwargs))
except Exception as e:
logger.error("Mem0 记忆存储失败: %s", e, exc_info=True)
yield {
"type": "memory_warning",
"data": {"message": f"记忆存储失败: {e}"},
}
yield {"type": "done", "data": {}}

7
art-agent/backend/app/api/chat.py
View File

@@ -52,6 +52,7 @@ async def chat(
ref_image: Optional[UploadFile] = File(None),
ref_image_url: Optional[str] = Form(None),
image_model: Optional[str] = Form(None),
session_id: Optional[str] = Form(None),
):
"""
主对话端点。
@@ -61,6 +62,7 @@ async def chat(
- ref_image: 可选的参考图文件（兼容旧方式）
- ref_image_url: 可选，已通过 /upload-ref-image 上传后的服务端路径
- image_model: 可选，指定本次使用的生图模型短 ID
- session_id: 可选，前端会话 ID，用于 Mem0 记忆作用域
"""
parsed_messages = json.loads(messages)
@@ -72,7 +74,10 @@ async def chat(
async def event_generator():
async for event in run_agent_loop(
parsed_messages, resolved_ref_url, image_model=image_model
parsed_messages,
resolved_ref_url,
image_model=image_model,
session_id=session_id,
):
yield {
"event": event["type"],

20
art-agent/backend/app/config.py
View File

@@ -148,6 +148,26 @@ def get_image_models_list() -> list[dict]:
]
# ─── 记忆系统配置 ─────────────────────────────────────
def get_deepseek_api_key() -> str:
return os.getenv("DEEPSEEK_API_KEY", "")
def get_ollama_base_url() -> str:
return os.getenv("OLLAMA_BASE_URL", "http://localhost:11434")
def get_mem0_embedding_model() -> str:
return os.getenv("MEM0_EMBEDDING_MODEL", "nomic-embed-text")
def get_max_recent_turns() -> int:
return int(os.getenv("MAX_RECENT_TURNS", "20"))
# ─── 图像输出配置 ─────────────────────────────────────
def get_image_aspect_ratio() -> str:
return os.getenv("IMAGE_ASPECT_RATIO", "1:1")

99
art-agent/backend/app/memory.py Normal file
View File

@@ -0,0 +1,99 @@
"""
Mem0 记忆层初始化与健康检查。
启动时验证 Ollama 可达和模型可用，失败则抛出 RuntimeError 阻止后端启动。
"""
import logging
import httpx
from mem0 import Memory
from app.config import (
get_deepseek_api_key,
get_ollama_base_url,
get_mem0_embedding_model,
)
logger = logging.getLogger(__name__)
_memory: Memory | None = None
def _check_ollama_health() -> None:
"""检查 Ollama 服务是否可达，以及 embedding 模型是否已安装。"""
base_url = get_ollama_base_url()
model_name = get_mem0_embedding_model()
try:
resp = httpx.get(f"{base_url}/api/tags", timeout=5)
resp.raise_for_status()
except (httpx.ConnectError, httpx.TimeoutException, httpx.HTTPStatusError) as e:
raise RuntimeError(
f"无法连接 Ollama 服务（{base_url}）。"
f"请确认 Ollama 已启动：启动方式参见 https://ollama.com\n"
f"原始错误：{e}"
) from e
models = resp.json().get("models", [])
installed = [m.get("name", "").split(":")[0] for m in models]
if model_name not in installed:
raise RuntimeError(
f"Ollama 已运行，但未找到 embedding 模型 '{model_name}'。\n"
f"已安装的模型：{installed}\n"
f"请运行：ollama pull {model_name}"
)
logger.info("Ollama 健康检查通过：%s 模型可用", model_name)
def init_memory() -> Memory:
"""初始化 Mem0 Memory 单例。首次调用时执行健康检查。"""
global _memory
if _memory is not None:
return _memory
_check_ollama_health()
deepseek_key = get_deepseek_api_key()
if not deepseek_key:
raise RuntimeError(
"DEEPSEEK_API_KEY 未配置。Mem0 需要该 key 进行事实提取。\n"
"请在 .env 中设置 DEEPSEEK_API_KEY"
)
config = {
"llm": {
"provider": "deepseek",
"config": {
"model": "deepseek-chat",
"temperature": 0.1,
"max_tokens": 1500,
"api_key": deepseek_key,
},
},
"embedder": {
"provider": "ollama",
"config": {
"model": get_mem0_embedding_model(),
"ollama_base_url": get_ollama_base_url(),
},
},
"vector_store": {
"provider": "qdrant",
"config": {
"collection_name": "epeekit_memories",
"embedding_model_dims": 768,
"path": "./data/qdrant",
},
},
}
_memory = Memory.from_config(config)
logger.info("Mem0 记忆层初始化成功")
return _memory
def get_memory() -> Memory:
"""获取已初始化的 Memory 实例。未初始化时自动调用 init_memory()。"""
if _memory is None:
return init_memory()
return _memory

2
art-agent/backend/requirements.txt
View File

@@ -7,3 +7,5 @@ python-multipart>=0.0.12
httpx>=0.27.0
python-dotenv>=1.0.0
Pillow>=10.4.0
mem0ai
ollama

7
art-agent/frontend/src/app/page.tsx
View File

@@ -106,7 +106,7 @@ export default function Home() {
let usedModelName = "";
try {
for await (const event of sendChat(apiMessages, finalRefServerUrl, imageModel)) {
for await (const event of sendChat(apiMessages, finalRefServerUrl, imageModel, activeSessionId)) {
switch (event.type) {
case "text_delta":
assistantText += event.data.text as string;
@@ -166,6 +166,11 @@ export default function Home() {
break;
}
case "memory_warning":
console.warn("[记忆系统]", event.data.message);
setStatusText(`⚠ ${event.data.message}`);
break;
case "error":
assistantText += `\n\n[错误: ${event.data.message}]`;
setStreamingText(assistantText);

9
art-agent/frontend/src/lib/api.ts
View File

@@ -72,7 +72,7 @@ export function uploadRefImage(
}
export interface SSEEvent {
type: "text_delta" | "tool_start" | "image_result" | "tool_error" | "done" | "error";
type: "text_delta" | "tool_start" | "image_result" | "tool_error" | "memory_warning" | "done" | "error";
data: Record<string, unknown>;
}
@@ -95,7 +95,8 @@ export async function fetchModels(): Promise<{
export async function* sendChat(
messages: ApiMessage[],
refImageUrl?: string | null,
imageModel?: string | null
imageModel?: string | null,
sessionId?: string | null
): AsyncGenerator<SSEEvent> {
const formData = new FormData();
@@ -113,6 +114,10 @@ export async function* sendChat(
formData.append("image_model", imageModel);
}
if (sessionId) {
formData.append("session_id", sessionId);
}
const response = await fetch(`${API_URL}/api/chat`, {
method: "POST",
body: formData,