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Merge branch 'pfc-memory' of https://github.com/smartmita/MaiBot into G-Test

pull/937/head
Bakadax 2025-05-10 17:28:17 +08:00
parent 899c523317 7ec716f0e1
commit 16b26753fa
5 changed files with 886 additions and 275 deletions
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157
.github/workflows/docker-image.yml vendored
View File

@ -6,20 +6,21 @@ on:
- main
- classical
- dev
- new_knowledge
tags:
- 'v*'
workflow_dispatch:
- "v*.*.*"
- "v*"
jobs:
build-and-push:
build-amd64:
name: Build AMD64 Image
runs-on: ubuntu-latest
env:
DOCKERHUB_USER: ${{ secrets.DOCKERHUB_USERNAME }}
DATE_TAG: $(date -u +'%Y-%m-%dT%H-%M-%S')
steps:
- name: Checkout code
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Clone maim_message
run: git clone https://github.com/MaiM-with-u/maim_message maim_message
@ -29,6 +30,8 @@ jobs:
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
with:
buildkitd-flags: --debug
- name: Login to Docker Hub
uses: docker/login-action@v3
@ -36,31 +39,131 @@ jobs:
username: ${{ secrets.DOCKERHUB_USERNAME }}
password: ${{ secrets.DOCKERHUB_TOKEN }}
- name: Determine Image Tags
id: tags
run: |
if [[ "${{ github.ref }}" == refs/tags/* ]]; then
echo "tags=${{ secrets.DOCKERHUB_USERNAME }}/maimbot:${{ github.ref_name }},${{ secrets.DOCKERHUB_USERNAME }}/maimbot:latest" >> $GITHUB_OUTPUT
elif [ "${{ github.ref }}" == "refs/heads/main" ]; then
echo "tags=${{ secrets.DOCKERHUB_USERNAME }}/maimbot:main,${{ secrets.DOCKERHUB_USERNAME }}/maimbot:main-$(date -u +'%Y%m%d%H%M%S')" >> $GITHUB_OUTPUT
elif [ "${{ github.ref }}" == "refs/heads/classical" ]; then
echo "tags=${{ secrets.DOCKERHUB_USERNAME }}/maimbot:classical,${{ secrets.DOCKERHUB_USERNAME }}/maimbot:classical-$(date -u +'%Y%m%d%H%M%S')" >> $GITHUB_OUTPUT
elif [ "${{ github.ref }}" == "refs/heads/dev" ]; then
echo "tags=${{ secrets.DOCKERHUB_USERNAME }}/maimbot:dev,${{ secrets.DOCKERHUB_USERNAME }}/maimbot:dev-$(date -u +'%Y%m%d%H%M%S')" >> $GITHUB_OUTPUT
elif [ "${{ github.ref }}" == "refs/heads/new_knowledge" ]; then
echo "tags=${{ secrets.DOCKERHUB_USERNAME }}/maimbot:knowledge,${{ secrets.DOCKERHUB_USERNAME }}/maimbot:knowledge-$(date -u +'%Y%m%d%H%M%S')" >> $GITHUB_OUTPUT
fi
- name: Docker meta
id: meta
uses: docker/metadata-action@v5
with:
images: ${{ secrets.DOCKERHUB_USERNAME }}/maibot
tags: |
type=ref,event=branch
type=ref,event=tag
type=semver,pattern={{version}}
type=semver,pattern={{major}}.{{minor}}
type=semver,pattern={{major}}
type=sha
- name: Build and Push Docker Image
- name: Build and Push AMD64 Docker Image
uses: docker/build-push-action@v5
with:
context: .
file: ./Dockerfile
platforms: linux/amd64,linux/arm64
tags: ${{ steps.tags.outputs.tags }}
platforms: linux/amd64
tags: ${{ secrets.DOCKERHUB_USERNAME }}/maibot:amd64-${{ github.sha }}
push: true
cache-from: type=registry,ref=${{ secrets.DOCKERHUB_USERNAME }}/maimbot:buildcache
cache-to: type=registry,ref=${{ secrets.DOCKERHUB_USERNAME }}/maimbot:buildcache,mode=max
labels: |
org.opencontainers.image.created=${{ steps.tags.outputs.date_tag }}
org.opencontainers.image.revision=${{ github.sha }}
cache-from: type=registry,ref=${{ secrets.DOCKERHUB_USERNAME }}/maibot:amd64-buildcache
cache-to: type=registry,ref=${{ secrets.DOCKERHUB_USERNAME }}/maibot:amd64-buildcache,mode=max
labels: ${{ steps.meta.outputs.labels }}
provenance: true
sbom: true
build-args: |
BUILD_DATE=$(date -u +'%Y-%m-%dT%H:%M:%SZ')
VCS_REF=${{ github.sha }}
outputs: type=image,push=true
build-arm64:
name: Build ARM64 Image
runs-on: ubuntu-latest
env:
DOCKERHUB_USER: ${{ secrets.DOCKERHUB_USERNAME }}
steps:
- name: Checkout code
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Set up QEMU
uses: docker/setup-qemu-action@v3
- name: Clone maim_message
run: git clone https://github.com/MaiM-with-u/maim_message maim_message
- name: Clone lpmm
run: git clone https://github.com/MaiM-with-u/MaiMBot-LPMM.git MaiMBot-LPMM
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
with:
buildkitd-flags: --debug
- name: Login to Docker Hub
uses: docker/login-action@v3
with:
username: ${{ secrets.DOCKERHUB_USERNAME }}
password: ${{ secrets.DOCKERHUB_TOKEN }}
- name: Docker meta
id: meta
uses: docker/metadata-action@v5
with:
images: ${{ secrets.DOCKERHUB_USERNAME }}/maibot
tags: |
type=ref,event=branch
type=ref,event=tag
type=semver,pattern={{version}}
type=semver,pattern={{major}}.{{minor}}
type=semver,pattern={{major}}
type=sha
- name: Build and Push ARM64 Docker Image
uses: docker/build-push-action@v5
with:
context: .
file: ./Dockerfile
platforms: linux/arm64
tags: ${{ secrets.DOCKERHUB_USERNAME }}/maibot:arm64-${{ github.sha }}
push: true
cache-from: type=registry,ref=${{ secrets.DOCKERHUB_USERNAME }}/maibot:arm64-buildcache
cache-to: type=registry,ref=${{ secrets.DOCKERHUB_USERNAME }}/maibot:arm64-buildcache,mode=max
labels: ${{ steps.meta.outputs.labels }}
provenance: true
sbom: true
build-args: |
BUILD_DATE=$(date -u +'%Y-%m-%dT%H:%M:%SZ')
VCS_REF=${{ github.sha }}
outputs: type=image,push=true
create-manifest:
name: Create Multi-Arch Manifest
runs-on: ubuntu-latest
needs:
- build-amd64
- build-arm64
steps:
- name: Login to Docker Hub
uses: docker/login-action@v3
with:
username: ${{ secrets.DOCKERHUB_USERNAME }}
password: ${{ secrets.DOCKERHUB_TOKEN }}
- name: Docker meta
id: meta
uses: docker/metadata-action@v5
with:
images: ${{ secrets.DOCKERHUB_USERNAME }}/maibot
tags: |
type=ref,event=branch
type=ref,event=tag
type=semver,pattern={{version}}
type=semver,pattern={{major}}.{{minor}}
type=semver,pattern={{major}}
type=sha
- name: Create and Push Manifest
run: |
# 为每个标签创建多架构镜像
for tag in $(echo "${{ steps.meta.outputs.tags }}" | tr '\n' ' '); do
echo "Creating manifest for $tag"
docker buildx imagetools create -t $tag \
${{ secrets.DOCKERHUB_USERNAME }}/maibot:amd64-${{ github.sha }} \
${{ secrets.DOCKERHUB_USERNAME }}/maibot:arm64-${{ github.sha }}
done

7
docker-compose.yml
View File

@ -16,8 +16,11 @@ services:
- maim_bot
core:
container_name: maim-bot-core
image: sengokucola/maimbot:main
# image: infinitycat/maimbot:main
image: sengokucola/maibot:latest
# image: infinitycat/maibot:latest
# dev
# image: sengokucola/maibot:dev
# image: infinitycat/maibot:dev
environment:
- TZ=Asia/Shanghai
# - EULA_AGREE=35362b6ea30f12891d46ef545122e84a # 同意EULA

175
src/plugins/PFC/pfc_processor.py
View File

@ -1,123 +1,176 @@
import traceback
from maim_message import UserInfo
import re
from typing import Any
from datetime import datetime # 确保导入 datetime
from maim_message import UserInfo # UserInfo 来自 maim_message 包 # 从 maim_message 导入 MessageRecv
from src.plugins.chat.message import MessageRecv # MessageRecv 来自message.py
from src.config.config import global_config
from src.common.logger_manager import get_logger
from ..chat.chat_stream import chat_manager
from typing import Optional, Dict, Any
from ..chat.chat_stream import ChatStream, chat_manager
from src.plugins.chat.utils import get_embedding
from src.common.database import db
from .pfc_manager import PFCManager
from src.plugins.chat.message import MessageRecv
from src.plugins.storage.storage import MessageStorage
from datetime import datetime
logger = get_logger("pfc_processor")
async def _handle_error(error: Exception, context: str, message: Optional[MessageRecv] = None) -> None:
async def _handle_error(
error: Exception, context: str, message: MessageRecv | None = None
) -> None: # 明确 message 类型
"""统一的错误处理函数
Args:
error: 捕获到的异常
context: 错误发生的上下文描述
message: 可选的消息对象，用于记录相关消息内容
# ... (方法注释不变) ...
"""
logger.error(f"{context}: {error}")
logger.error(traceback.format_exc())
if message and hasattr(message, "raw_message"):
# 检查 message 是否 None 以及是否有 raw_message 属性
if (
message and hasattr(message, "message_info") and hasattr(message.message_info, "raw_message")
): # MessageRecv 结构可能没有直接的 raw_message
raw_msg_content = getattr(message.message_info, "raw_message", None) # 安全获取
if raw_msg_content:
logger.error(f"相关消息原始内容: {raw_msg_content}")
elif message and hasattr(message, "raw_message"): # 如果 MessageRecv 直接有 raw_message
logger.error(f"相关消息原始内容: {message.raw_message}")
class PFCProcessor:
"""PFC 处理器，负责处理接收到的信息并计数"""
def __init__(self):
"""初始化 PFC 处理器，创建消息存储实例"""
self.storage = MessageStorage()
# MessageStorage() 的实例化位置和具体类是什么？
# 我们假设它来自 src.plugins.storage.storage
# 但由于我们不能修改那个文件，所以这里的 self.storage 将按原样使用
from src.plugins.storage.storage import MessageStorage # 明确导入，以便类型提示和理解
self.storage: MessageStorage = MessageStorage()
self.pfc_manager = PFCManager.get_instance()
async def process_message(self, message_data: Dict[str, Any]) -> None:
async def process_message(self, message_data: dict[str, Any]) -> None: # 使用 dict[str, Any] 替代 Dict
"""处理接收到的原始消息数据
主要流程:
1. 消息解析与初始化
2. 过滤检查
3. 消息存储
4. 创建 PFC 流
5. 日志记录
Args:
message_data: 原始消息字符串
# ... (方法注释不变) ...
"""
message = None
message_obj: MessageRecv | None = None # 初始化为 None，并明确类型
try:
# 1. 消息解析与初始化
message = MessageRecv(message_data)
groupinfo = message.message_info.group_info
userinfo = message.message_info.user_info
messageinfo = message.message_info
message_obj = MessageRecv(message_data) # 使用你提供的 message.py 中的 MessageRecv
groupinfo = getattr(message_obj.message_info, "group_info", None)
userinfo = getattr(message_obj.message_info, "user_info", None)
logger.trace(f"准备为{userinfo.user_id}创建/获取聊天流")
chat = await chat_manager.get_or_create_stream(
platform=messageinfo.platform,
platform=message_obj.message_info.platform,
user_info=userinfo,
group_info=groupinfo,
)
message.update_chat_stream(chat)
message_obj.update_chat_stream(chat) # message.py 中 MessageRecv 有此方法
# 2. 过滤检查
# 处理消息
await message.process()
# 过滤词/正则表达式过滤
if self._check_ban_words(message.processed_plain_text, userinfo) or self._check_ban_regex(
message.raw_message, userinfo
):
await message_obj.process() # 调用 MessageRecv 的异步 process 方法
if self._check_ban_words(message_obj.processed_plain_text, userinfo) or self._check_ban_regex(
message_obj.raw_message, userinfo
): # MessageRecv 有 raw_message 属性
return
# 3. 消息存储
await self.storage.store_message(message, chat)
logger.trace(f"存储成功: {message.processed_plain_text}")
# 3. 消息存储 (保持原有调用)
# 这里的 self.storage.store_message 来自 src/plugins/storage/storage.py
# 它内部会将 message_obj 转换为字典并存储
await self.storage.store_message(message_obj, chat)
logger.trace(f"存储成功 (初步): {message_obj.processed_plain_text}")
await self._update_embedding_vector(message_obj, chat) # 明确传递 message_obj
# 4. 创建 PFC 聊天流
await self._create_pfc_chat(message)
await self._create_pfc_chat(message_obj)
# 5. 日志记录
# 将时间戳转换为datetime对象
current_time = datetime.fromtimestamp(message.message_info.time).strftime("%H:%M:%S")
logger.info(
f"[{current_time}][私聊]{message.message_info.user_info.user_nickname}: {message.processed_plain_text}"
)
# 确保 message_obj.message_info.time 是 float 类型的时间戳
current_time_display = datetime.fromtimestamp(float(message_obj.message_info.time)).strftime("%H:%M:%S")
# 确保 userinfo.user_nickname 存在
user_nickname_display = getattr(userinfo, "user_nickname", "未知用户")
logger.info(f"[{current_time_display}][私聊]{user_nickname_display}: {message_obj.processed_plain_text}")
except Exception as e:
await _handle_error(e, "消息处理失败", message)
await _handle_error(e, "消息处理失败", message_obj) # 传递 message_obj
async def _create_pfc_chat(self, message: MessageRecv):
async def _create_pfc_chat(self, message: MessageRecv): # 明确 message 类型
try:
chat_id = str(message.chat_stream.stream_id)
private_name = str(message.message_info.user_info.user_nickname)
private_name = str(message.message_info.user_info.user_nickname) # 假设 UserInfo 有 user_nickname
if global_config.enable_pfc_chatting:
await self.pfc_manager.get_or_create_conversation(chat_id, private_name)
except Exception as e:
logger.error(f"创建PFC聊天失败: {e}")
logger.error(f"创建PFC聊天失败: {e}", exc_info=True) # 添加 exc_info=True
@staticmethod
def _check_ban_words(text: str, userinfo: UserInfo) -> bool:
def _check_ban_words(text: str, userinfo: UserInfo) -> bool: # 明确 userinfo 类型
"""检查消息中是否包含过滤词"""
for word in global_config.ban_words:
if word in text:
logger.info(f"[私聊]{userinfo.user_nickname}:{text}")
logger.info(f"[私聊]{userinfo.user_nickname}:{text}") # 假设 UserInfo 有 user_nickname
logger.info(f"[过滤词识别]消息中含有{word}，filtered")
return True
return False
@staticmethod
def _check_ban_regex(text: str, userinfo: UserInfo) -> bool:
def _check_ban_regex(text: str, userinfo: UserInfo) -> bool: # 明确 userinfo 类型
"""检查消息是否匹配过滤正则表达式"""
for pattern in global_config.ban_msgs_regex:
if pattern.search(text):
logger.info(f"[私聊]{userinfo.user_nickname}:{text}")
logger.info(f"[正则表达式过滤]消息匹配到{pattern}，filtered")
if pattern.search(text): # 假设 ban_msgs_regex 中的元素是已编译的正则对象
logger.info(f"[私聊]{userinfo.user_nickname}:{text}") # _nickname
logger.info(f"[正则表达式过滤]消息匹配到{pattern.pattern}，filtered") # .pattern 获取原始表达式字符串
return True
return False
async def _update_embedding_vector(self, message_obj: MessageRecv, chat: ChatStream) -> None:
"""更新消息的嵌入向量"""
# === 新增：为已存储的消息生成嵌入并更新数据库文档 ===
embedding_vector = None
text_for_embedding = message_obj.processed_plain_text # 使用处理后的纯文本
# 在 storage.py 中，会对 processed_plain_text 进行一次过滤
# 为了保持一致，我们也在这里应用相同的过滤逻辑
# 当然，更优的做法是 store_message 返回过滤后的文本，或在 message_obj 中增加一个 filtered_processed_plain_text 属性
# 这里为了简单，我们先重复一次过滤逻辑
pattern = r"<MainRule>.*?</MainRule>|<schedule>.*?</schedule>|<UserMessage>.*?</UserMessage>"
if text_for_embedding:
filtered_text_for_embedding = re.sub(pattern, "", text_for_embedding, flags=re.DOTALL)
else:
filtered_text_for_embedding = ""
if filtered_text_for_embedding and filtered_text_for_embedding.strip():
try:
# request_type 参数根据你的 get_embedding 函数实际需求来定
embedding_vector = await get_embedding(filtered_text_for_embedding, request_type="pfc_private_memory")
if embedding_vector:
logger.debug(f"成功为消息 ID '{message_obj.message_info.message_id}' 生成嵌入向量。")
# 更新数据库中的对应文档
# 确保你有权限访问和操作 db 对象
update_result = db.messages.update_one(
{"message_id": message_obj.message_info.message_id, "chat_id": chat.stream_id},
{"$set": {"embedding_vector": embedding_vector}},
)
if update_result.modified_count > 0:
logger.info(f"成功为消息 ID '{message_obj.message_info.message_id}' 更新嵌入向量到数据库。")
elif update_result.matched_count > 0:
logger.warning(f"消息 ID '{message_obj.message_info.message_id}' 已存在嵌入向量或未作修改。")
else:
logger.error(
f"未能找到消息 ID '{message_obj.message_info.message_id}' (chat_id: {chat.stream_id}) 来更新嵌入向量。可能是存储和更新之间存在延迟或问题。"
)
else:
logger.warning(
f"未能为消息 ID '{message_obj.message_info.message_id}' 的文本 '{filtered_text_for_embedding[:30]}...' 生成嵌入向量。"
)
except Exception as e_embed_update:
logger.error(
f"为消息 ID '{message_obj.message_info.message_id}' 生成嵌入或更新数据库时发生异常: {e_embed_update}",
exc_info=True,
)
else:
logger.debug(f"消息 ID '{message_obj.message_info.message_id}' 的过滤后纯文本为空，不生成或更新嵌入。")
# === 新增结束 ===

675
src/plugins/PFC/pfc_utils.py
View File

@ -1,23 +1,280 @@
import traceback
import json
import re
from typing import Dict, Any, Optional, Tuple, List, Union
from src.common.logger_manager import get_logger # 确认 logger 的导入路径
from src.plugins.memory_system.Hippocampus import HippocampusManager
from src.plugins.heartFC_chat.heartflow_prompt_builder import prompt_builder # 确认 prompt_builder 的导入路径
from src.plugins.chat.chat_stream import ChatStream
from ..person_info.person_info import person_info_manager
import math
from src.plugins.utils.chat_message_builder import build_readable_messages
from .observation_info import ObservationInfo
import time
from datetime import datetime
from typing import Dict, Any, Optional, Tuple, List, Union # 确保导入这些类型
from src.common.logger_manager import get_logger
from src.config.config import global_config
from src.common.database import db # << 确认此路径
# --- 依赖于你项目结构的导入，请务必仔细检查并根据你的实际情况调整 ---
from src.plugins.memory_system.Hippocampus import HippocampusManager # << 确认此路径
from src.plugins.heartFC_chat.heartflow_prompt_builder import prompt_builder # << 确认此路径
from src.plugins.chat.utils import get_embedding # << 确认此路径
from src.plugins.utils.chat_message_builder import build_readable_messages # << 确认此路径
# --- 依赖导入结束 ---
from src.plugins.chat.chat_stream import ChatStream # 来自原始 pfc_utils.py
from ..person_info.person_info import person_info_manager # 来自原始 pfc_utils.py (相对导入)
import math # 来自原始 pfc_utils.py
from .observation_info import ObservationInfo # 来自原始 pfc_utils.py (相对导入)
logger = get_logger("pfc_utils")
async def retrieve_contextual_info(text: str, private_name: str) -> Tuple[str, str]:
# ==============================================================================
# 新增：专门用于检索 PFC 私聊历史对话上下文的函数
# ==============================================================================
async def find_most_relevant_historical_message(
chat_id: str,
query_text: str,
similarity_threshold: float = 0.3, # 相似度阈值，可以根据效果调整
absolute_search_time_limit: Optional[float] = None, # 新增参数：排除最近多少秒内的消息（例如5分钟）
) -> Optional[Dict[str, Any]]:
"""
根据输入文本检索相关的记忆和知识。
根据查询文本，在指定 chat_id 的历史消息中查找最相关的消息。
"""
if not query_text or not query_text.strip():
logger.debug(f"[{chat_id}] (私聊历史)查询文本为空，跳过检索。")
return None
logger.debug(f"[{chat_id}] (私聊历史)开始为查询文本 '{query_text[:50]}...' 检索。")
# 使用你项目中已有的 get_embedding 函数
# request_type 参数需要根据 get_embedding 的实际需求调整
query_embedding = await get_embedding(query_text, request_type="pfc_historical_chat_query")
if not query_embedding:
logger.warning(f"[{chat_id}] (私聊历史)未能为查询文本 '{query_text[:50]}...' 生成嵌入向量。")
return None
effective_search_upper_limit: float
log_source_of_limit: str = ""
if absolute_search_time_limit is not None:
effective_search_upper_limit = absolute_search_time_limit
log_source_of_limit = "传入的绝对时间上限"
else:
# 如果没有传入绝对时间上限，可以设置一个默认的回退逻辑
fallback_exclude_seconds = getattr(global_config, "pfc_historical_fallback_exclude_seconds", 7200) # 默认2小时
effective_search_upper_limit = time.time() - fallback_exclude_seconds
log_source_of_limit = f"回退逻辑 (排除最近 {fallback_exclude_seconds} 秒)"
logger.debug(
f"[{chat_id}] (私聊历史) find_most_relevant_historical_message: "
f"将使用时间上限 {effective_search_upper_limit} "
f"(可读: {datetime.fromtimestamp(effective_search_upper_limit).strftime('%Y-%m-%d %H:%M:%S')}) "
f"进行历史消息锚点搜索。来源: {log_source_of_limit}"
)
# --- [新代码结束] ---
pipeline = [
{
"$match": {
"chat_id": chat_id,
"embedding_vector": {"$exists": True, "$ne": None, "$not": {"$size": 0}},
"time": {"$lt": effective_search_upper_limit}, # <--- 使用新的 effective_search_upper_limit
}
},
{
"$addFields": {
"dotProduct": {
"$reduce": {
"input": {"$range": [0, {"$size": "$embedding_vector"}]},
"initialValue": 0,
"in": {
"$add": [
"$$value",
{
"$multiply": [
{"$arrayElemAt": ["$embedding_vector", "$$this"]},
{"$arrayElemAt": [query_embedding, "$$this"]},
]
},
]
},
}
},
"queryVecMagnitude": {
"$sqrt": {
"$reduce": {
"input": query_embedding,
"initialValue": 0,
"in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]},
}
}
},
"docVecMagnitude": {
"$sqrt": {
"$reduce": {
"input": "$embedding_vector",
"initialValue": 0,
"in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]},
}
}
},
}
},
{
"$addFields": {
"similarity": {
"$cond": [
{"$and": [{"$gt": ["$queryVecMagnitude", 0]}, {"$gt": ["$docVecMagnitude", 0]}]},
{"$divide": ["$dotProduct", {"$multiply": ["$queryVecMagnitude", "$docVecMagnitude"]}]},
0,
]
}
}
},
{"$match": {"similarity": {"$gte": similarity_threshold}}},
{"$sort": {"similarity": -1}},
{"$limit": 1},
{
"$project": {
"_id": 0,
"message_id": 1,
"time": 1,
"chat_id": 1,
"user_info": 1,
"processed_plain_text": 1,
"similarity": 1,
}
}, # 可以不返回 embedding_vector 节省带宽
]
try:
# --- 确定性修改：同步执行聚合和结果转换 ---
cursor = db.messages.aggregate(pipeline) # PyMongo 的 aggregate 返回一个 CommandCursor
results = list(cursor) # 直接将 CommandCursor 转换为列表
if not results:
logger.info(
f"[{chat_id}] (私聊历史) find_most_relevant_historical_message: 在时间点 {effective_search_upper_limit} 之前，未能找到任何与 '{query_text[:30]}...' 相关的历史消息。"
)
else:
logger.info(
f"[{chat_id}] (私聊历史) find_most_relevant_historical_message: 在时间点 {effective_search_upper_limit} 之前，找到了 {len(results)} 条候选历史消息。最相关的一条是："
)
for res_msg in results:
msg_time_readable = datetime.fromtimestamp(res_msg.get("time", 0)).strftime("%Y-%m-%d %H:%M:%S")
logger.info(
f" - MsgID: {res_msg.get('message_id')}, Time: {msg_time_readable} (原始: {res_msg.get('time')}), Sim: {res_msg.get('similarity'):.4f}, Text: '{res_msg.get('processed_plain_text', '')[:50]}...'"
)
# --- [修改结束] ---
# --- 修改结束 ---
if results and len(results) > 0:
most_similar_message = results[0]
logger.info(
f"[{chat_id}] (私聊历史)找到最相关消息 ID: {most_similar_message.get('message_id')}, 相似度: {most_similar_message.get('similarity'):.4f}"
)
return most_similar_message
else:
logger.debug(f"[{chat_id}] (私聊历史)未找到相似度超过 {similarity_threshold} 的相关消息。")
return None
except Exception as e:
logger.error(f"[{chat_id}] (私聊历史)在数据库中检索时出错: {e}", exc_info=True)
return None
async def retrieve_chat_context_window(
chat_id: str,
anchor_message_id: str,
anchor_message_time: float,
excluded_time_threshold_for_window: float,
window_size_before: int = 7,
window_size_after: int = 7,
) -> List[Dict[str, Any]]:
"""
以某条消息为锚点，获取其前后的聊天记录形成一个上下文窗口。
"""
if not anchor_message_id or anchor_message_time is None:
return []
context_messages: List[Dict[str, Any]] = [] # 明确类型
logger.debug(
f"[{chat_id}] (私聊历史)准备以消息 ID '{anchor_message_id}' (时间: {anchor_message_time}) 为锚点，获取上下文窗口..."
)
try:
# --- 同步执行 find_one 和 find ---
anchor_message = db.messages.find_one({"message_id": anchor_message_id, "chat_id": chat_id})
messages_before_cursor = (
db.messages.find({"chat_id": chat_id, "time": {"$lt": anchor_message_time}})
.sort("time", -1)
.limit(window_size_before)
)
messages_before = list(messages_before_cursor)
messages_before.reverse()
# --- 新增日志 ---
logger.debug(
f"[{chat_id}] (私聊历史) retrieve_chat_context_window: Anchor Time: {anchor_message_time}, Excluded Window End Time: {excluded_time_threshold_for_window}"
)
logger.debug(
f"[{chat_id}] (私聊历史) retrieve_chat_context_window: Messages BEFORE anchor ({len(messages_before)}):"
)
for msg_b in messages_before:
logger.debug(
f" - Time: {datetime.fromtimestamp(msg_b.get('time', 0)).strftime('%Y-%m-%d %H:%M:%S')}, Text: '{msg_b.get('processed_plain_text', '')[:30]}...'"
)
messages_after_cursor = (
db.messages.find(
{"chat_id": chat_id, "time": {"$gt": anchor_message_time, "$lt": excluded_time_threshold_for_window}}
)
.sort("time", 1)
.limit(window_size_after)
)
messages_after = list(messages_after_cursor)
# --- 新增日志 ---
logger.debug(
f"[{chat_id}] (私聊历史) retrieve_chat_context_window: Messages AFTER anchor ({len(messages_after)}):"
)
for msg_a in messages_after:
logger.debug(
f" - Time: {datetime.fromtimestamp(msg_a.get('time', 0)).strftime('%Y-%m-%d %H:%M:%S')}, Text: '{msg_a.get('processed_plain_text', '')[:30]}...'"
)
if messages_before:
context_messages.extend(messages_before)
if anchor_message:
anchor_message.pop("_id", None)
context_messages.append(anchor_message)
if messages_after:
context_messages.extend(messages_after)
final_window: List[Dict[str, Any]] = [] # 明确类型
seen_ids: set[str] = set() # 明确类型
for msg in context_messages:
msg_id = msg.get("message_id")
if msg_id and msg_id not in seen_ids: # 确保 msg_id 存在
final_window.append(msg)
seen_ids.add(msg_id)
final_window.sort(key=lambda m: m.get("time", 0))
logger.info(
f"[{chat_id}] (私聊历史)为锚点 '{anchor_message_id}' 构建了包含 {len(final_window)} 条消息的上下文窗口。"
)
return final_window
except Exception as e:
logger.error(f"[{chat_id}] (私聊历史)获取消息 ID '{anchor_message_id}' 的上下文窗口时出错: {e}", exc_info=True)
return []
# ==============================================================================
# 修改后的 retrieve_contextual_info 函数
# ==============================================================================
async def retrieve_contextual_info(
text: str, # 用于全局记忆和知识检索的主查询文本 (通常是短期聊天记录)
private_name: str, # 用于日志
chat_id: str, # 用于特定私聊历史的检索
historical_chat_query_text: Optional[str] = None,
current_short_term_history_earliest_time: Optional[float] = None, # <--- 新增参数
) -> Tuple[str, str, str]: # 返回: 全局记忆, 知识, 私聊历史回忆
"""
检索三种类型的上下文信息：全局压缩记忆、知识库知识、当前私聊的特定历史对话。
Args:
text: 用于检索的上下文文本 (例如聊天记录)。
@ -26,63 +283,183 @@ async def retrieve_contextual_info(text: str, private_name: str) -> Tuple[str, s
Returns:
Tuple[str, str]: (检索到的记忆字符串, 检索到的知识字符串)
"""
retrieved_memory_str = "无相关记忆。"
# 初始化返回值
retrieved_global_memory_str = "无相关全局记忆。"
retrieved_knowledge_str = "无相关知识。"
memory_log_msg = "未自动检索到相关记忆。"
knowledge_log_msg = "未自动检索到相关知识。"
retrieved_historical_chat_str = "无相关私聊历史回忆。"
if not text or text == "还没有聊天记录。" or text == "[构建聊天记录出错]":
logger.debug(f"[私聊][{private_name}] (retrieve_contextual_info) 无有效上下文，跳过检索。")
return retrieved_memory_str, retrieved_knowledge_str
# --- 1. 全局压缩记忆检索 (来自 HippocampusManager) ---
# (保持你原始 pfc_utils.py 中这部分的逻辑基本不变)
global_memory_log_msg = f"开始全局压缩记忆检索 (基于文本: '{text[:30]}...')"
if text and text.strip() and text != "还没有聊天记录。" and text != "[构建聊天记录出错]":
try:
related_memory = await HippocampusManager.get_instance().get_memory_from_text(
text=text,
max_memory_num=2,
max_memory_length=2,
max_depth=3,
fast_retrieval=False,
)
if related_memory:
temp_global_memory_info = ""
for memory_item in related_memory:
if isinstance(memory_item, (list, tuple)) and len(memory_item) > 1:
temp_global_memory_info += str(memory_item[1]) + "\n"
elif isinstance(memory_item, str):
temp_global_memory_info += memory_item + "\n"
# 1. 检索记忆 (逻辑来自原 _get_memory_info)
try:
related_memory = await HippocampusManager.get_instance().get_memory_from_text(
text=text,
max_memory_num=2,
max_memory_length=2,
max_depth=3,
fast_retrieval=False,
)
if related_memory:
related_memory_info = ""
for memory in related_memory:
related_memory_info += memory[1] + "\n"
if related_memory_info:
# 注意：原版提示信息可以根据需要调整
retrieved_memory_str = f"你回忆起：\n{related_memory_info.strip()}\n(以上是你的回忆，供参考)\n"
memory_log_msg = f"自动检索到记忆: {related_memory_info.strip()[:100]}..."
if temp_global_memory_info.strip():
retrieved_global_memory_str = f"你回忆起一些相关的全局记忆：\n{temp_global_memory_info.strip()}\n(以上是你的全局记忆，供参考)\n"
global_memory_log_msg = f"自动检索到全局压缩记忆: {temp_global_memory_info.strip()[:100]}..."
else:
global_memory_log_msg = "全局压缩记忆检索返回为空或格式不符。"
else:
memory_log_msg = "自动检索记忆返回为空。"
logger.debug(f"[私聊][{private_name}] (retrieve_contextual_info) 记忆检索: {memory_log_msg}")
global_memory_log_msg = "全局压缩记忆检索返回为空列表。"
logger.debug(f"[私聊][{private_name}] (retrieve_contextual_info) 全局压缩记忆检索: {global_memory_log_msg}")
except Exception as e:
logger.error(
f"[私聊][{private_name}] (retrieve_contextual_info) 检索全局压缩记忆时出错: {e}\n{traceback.format_exc()}"
)
retrieved_global_memory_str = "[检索全局压缩记忆时出错]\n"
else:
logger.debug(f"[私聊][{private_name}] (retrieve_contextual_info) 无有效主查询文本，跳过全局压缩记忆检索。")
except Exception as e:
logger.error(
f"[私聊][{private_name}] (retrieve_contextual_info) 自动检索记忆时出错: {e}\n{traceback.format_exc()}"
# --- 2. 相关知识检索 (来自 prompt_builder) ---
# (保持你原始 pfc_utils.py 中这部分的逻辑基本不变)
knowledge_log_msg = f"开始知识检索 (基于文本: '{text[:30]}...')"
if text and text.strip() and text != "还没有聊天记录。" and text != "[构建聊天记录出错]":
try:
knowledge_result = await prompt_builder.get_prompt_info(
message=text,
threshold=0.38,
)
if knowledge_result and knowledge_result.strip(): # 确保结果不为空
retrieved_knowledge_str = knowledge_result # 直接使用返回结果，如果需要也可以包装
knowledge_log_msg = f"自动检索到相关知识: {knowledge_result[:100]}..."
else:
knowledge_log_msg = "知识检索返回为空。"
logger.debug(f"[私聊][{private_name}] (retrieve_contextual_info) 知识检索: {knowledge_log_msg}")
except Exception as e:
logger.error(
f"[私聊][{private_name}] (retrieve_contextual_info) 自动检索知识时出错: {e}\n{traceback.format_exc()}"
)
retrieved_knowledge_str = "[检索知识时出错]\n"
else:
logger.debug(f"[私聊][{private_name}] (retrieve_contextual_info) 无有效主查询文本，跳过知识检索。")
# --- 3. 当前私聊的特定历史对话上下文检索 ---
query_for_historical_chat = (
historical_chat_query_text if historical_chat_query_text and historical_chat_query_text.strip() else None
)
# historical_chat_log_msg 的初始化可以移到 try 块之后，根据实际情况赋值
if query_for_historical_chat:
try:
# ---- [新代码] 计算最终的、严格的搜索时间上限 ----
# 1. 设置一个基础的、较大的时间回溯窗口，例如2小时 (7200秒)
# 这个值可以从全局配置读取，如果没配置则使用默认值
default_search_exclude_seconds = getattr(
global_config, "pfc_historical_search_default_exclude_seconds", 7200
) # 默认2小时
base_excluded_time_limit = time.time() - default_search_exclude_seconds
final_search_upper_limit_time = base_excluded_time_limit
if current_short_term_history_earliest_time is not None:
# 我们希望找到的消息严格早于 short_term_history 的开始，减去一个小量确保不包含边界
limit_from_short_term = current_short_term_history_earliest_time - 0.001
final_search_upper_limit_time = min(base_excluded_time_limit, limit_from_short_term)
log_earliest_time_str = "未提供"
if current_short_term_history_earliest_time is not None:
try:
log_earliest_time_str = f"{current_short_term_history_earliest_time} (即 {datetime.fromtimestamp(current_short_term_history_earliest_time).strftime('%Y-%m-%d %H:%M:%S')})"
except Exception:
log_earliest_time_str = str(current_short_term_history_earliest_time)
logger.debug(
f"[{private_name}] (私聊历史) retrieve_contextual_info: "
f"最终用于历史搜索的时间上限: {final_search_upper_limit_time} "
f"(可读: {datetime.fromtimestamp(final_search_upper_limit_time).strftime('%Y-%m-%d %H:%M:%S')}). "
f"基于默认排除 {default_search_exclude_seconds}s 和 '最近记录'片段开始时间: {log_earliest_time_str}"
)
most_relevant_message_doc = await find_most_relevant_historical_message(
chat_id=chat_id,
query_text=query_for_historical_chat,
similarity_threshold=0.5, # 您可以调整这个
# exclude_recent_seconds 不再直接使用，而是传递计算好的绝对时间上限
absolute_search_time_limit=final_search_upper_limit_time, # <--- 传递计算好的绝对时间上限
)
if most_relevant_message_doc:
anchor_id = most_relevant_message_doc.get("message_id")
anchor_time = most_relevant_message_doc.get("time")
# 校验锚点时间是否真的符合我们的硬性上限 (理论上 find_most_relevant_historical_message 内部已保证)
if anchor_time is not None and anchor_time >= final_search_upper_limit_time:
logger.warning(
f"[{private_name}] (私聊历史) find_most_relevant_historical_message 返回的锚点时间 {anchor_time} "
f"并未严格小于最终搜索上限 {final_search_upper_limit_time}。可能导致重叠。跳过构建上下文。"
)
historical_chat_log_msg = "检索到的锚点不符合最终时间要求，可能导致重叠。"
# 直接进入下一个分支 (else)，使得 retrieved_historical_chat_str 保持默认值
elif anchor_id and anchor_time is not None:
# 构建上下文窗口时，其“未来”消息的上限也应该是 final_search_upper_limit_time
# 因为我们不希望历史回忆的上下文窗口延伸到“最近聊天记录”的范围内或更近
time_limit_for_context_window_after = final_search_upper_limit_time
logger.debug(
f"[{private_name}] (私聊历史) 调用 retrieve_chat_context_window "
f"with anchor_time: {anchor_time}, "
f"excluded_time_threshold_for_window: {time_limit_for_context_window_after}"
)
context_window_messages = await retrieve_chat_context_window(
chat_id=chat_id,
anchor_message_id=anchor_id,
anchor_message_time=anchor_time,
excluded_time_threshold_for_window=time_limit_for_context_window_after,
window_size_before=7,
window_size_after=7,
)
if context_window_messages:
formatted_window_str = await build_readable_messages(
context_window_messages,
replace_bot_name=False, # 在回忆中，保留原始发送者名称
merge_messages=False,
timestamp_mode="relative", # 可以选择 'absolute' 或 'none'
read_mark=0.0,
)
if formatted_window_str and formatted_window_str.strip():
retrieved_historical_chat_str = f"你回忆起一段与当前对话相关的历史聊天：\n------\n{formatted_window_str.strip()}\n------\n(以上是针对本次私聊的回忆，供参考)\n"
historical_chat_log_msg = f"自动检索到相关私聊历史片段 (锚点ID: {anchor_id}, 相似度: {most_relevant_message_doc.get('similarity'):.3f})"
return retrieved_global_memory_str, retrieved_knowledge_str, retrieved_historical_chat_str
else:
historical_chat_log_msg = "检索到的私聊历史对话窗口格式化后为空。"
else:
historical_chat_log_msg = f"找到了相关锚点消息 (ID: {anchor_id})，但未能构建其上下文窗口。"
else:
historical_chat_log_msg = "检索到的最相关私聊历史消息文档缺少 message_id 或 time。"
else:
historical_chat_log_msg = "未找到足够相关的私聊历史对话消息。"
logger.debug(
f"[私聊][{private_name}] (retrieve_contextual_info) 私聊历史对话检索: {historical_chat_log_msg}"
)
except Exception as e:
logger.error(
f"[私聊][{private_name}] (retrieve_contextual_info) 检索私聊历史对话时出错: {e}\n{traceback.format_exc()}"
)
retrieved_historical_chat_str = "[检索私聊历史对话时出错]\n"
else:
logger.debug(
f"[私聊][{private_name}] (retrieve_contextual_info) 无专门的私聊历史查询文本，跳过私聊历史对话检索。"
)
retrieved_memory_str = "检索记忆时出错。\n"
# 2. 检索知识 (逻辑来自原 action_planner 和 reply_generator)
try:
# 使用导入的 prompt_builder 实例及其方法
knowledge_result = await prompt_builder.get_prompt_info(
message=text,
threshold=0.38, # threshold 可以根据需要调整
)
if knowledge_result:
retrieved_knowledge_str = knowledge_result # 直接使用返回结果
knowledge_log_msg = "自动检索到相关知识。"
logger.debug(f"[私聊][{private_name}] (retrieve_contextual_info) 知识检索: {knowledge_log_msg}")
except Exception as e:
logger.error(
f"[私聊][{private_name}] (retrieve_contextual_info) 自动检索知识时出错: {e}\n{traceback.format_exc()}"
)
retrieved_knowledge_str = "检索知识时出错。\n"
return retrieved_memory_str, retrieved_knowledge_str
return retrieved_global_memory_str, retrieved_knowledge_str, retrieved_historical_chat_str
# ==============================================================================
# 你原始 pfc_utils.py 中的其他函数保持不变
# ==============================================================================
def get_items_from_json(
content: str,
private_name: str,
@ -105,108 +482,79 @@ def get_items_from_json(
Tuple[bool, Union[Dict[str, Any], List[Dict[str, Any]]]]: (是否成功, 提取的字段字典或字典列表)
"""
cleaned_content = content.strip()
result: Union[Dict[str, Any], List[Dict[str, Any]]] = {} # 初始化类型
# 匹配 ```json ... ``` 或 ``` ... ```
result: Union[Dict[str, Any], List[Dict[str, Any]]] = {}
markdown_match = re.search(r"```(?:json)?\s*([\s\S]*?)\s*```", cleaned_content, re.IGNORECASE)
if markdown_match:
cleaned_content = markdown_match.group(1).strip()
logger.debug(f"[私聊][{private_name}] 已去除 Markdown 标记，剩余内容: {cleaned_content[:100]}...")
# --- 新增结束 ---
# 设置默认值
default_result: Dict[str, Any] = {} # 用于单对象时的默认值
default_result: Dict[str, Any] = {}
if default_values:
default_result.update(default_values)
result = default_result.copy() # 先用默认值初始化
# 首先尝试解析为JSON数组
result = default_result.copy()
if allow_array:
try:
# 尝试直接解析清理后的内容为列表
json_array = json.loads(cleaned_content)
if isinstance(json_array, list):
valid_items_list: List[Dict[str, Any]] = []
for item in json_array:
if not isinstance(item, dict):
logger.warning(f"[私聊][{private_name}] JSON数组中的元素不是字典: {item}")
for item_json in json_array: # Renamed item to item_json to avoid conflict
if not isinstance(item_json, dict):
logger.warning(f"[私聊][{private_name}] JSON数组中的元素不是字典: {item_json}")
continue
current_item_result = default_result.copy() # 每个元素都用默认值初始化
current_item_result = default_result.copy()
valid_item = True
# 提取并验证字段
for field in items:
if field in item:
current_item_result[field] = item[field]
elif field not in default_result: # 如果字段不存在且没有默认值
logger.warning(f"[私聊][{private_name}] JSON数组元素缺少必要字段 '{field}': {item}")
for field in items: # items is args from function signature
if field in item_json:
current_item_result[field] = item_json[field]
elif field not in default_result:
logger.warning(f"[私聊][{private_name}] JSON数组元素缺少必要字段 '{field}': {item_json}")
valid_item = False
break # 这个元素无效
break
if not valid_item:
continue
# 验证类型
if required_types:
for field, expected_type in required_types.items():
# 检查 current_item_result 中是否存在该字段 (可能来自 item 或 default_values)
if field in current_item_result and not isinstance(
current_item_result[field], expected_type
):
logger.warning(
f"[私聊][{private_name}] JSON数组元素字段 '{field}' 类型错误 (应为 {expected_type.__name__}, 实际为 {type(current_item_result[field]).__name__}): {item}"
f"[私聊][{private_name}] JSON数组元素字段 '{field}' 类型错误 (应为 {expected_type.__name__}, 实际为 {type(current_item_result[field]).__name__}): {item_json}"
)
valid_item = False
break
if not valid_item:
continue
# 验证字符串不为空 (只检查 items 中要求的字段)
for field in items:
if (
field in current_item_result
and isinstance(current_item_result[field], str)
and not current_item_result[field].strip()
):
logger.warning(f"[私聊][{private_name}] JSON数组元素字段 '{field}' 不能为空字符串: {item}")
logger.warning(
f"[私聊][{private_name}] JSON数组元素字段 '{field}' 不能为空字符串: {item_json}"
)
valid_item = False
break
if valid_item:
valid_items_list.append(current_item_result) # 只添加完全有效的项
if valid_items_list: # 只有当列表不为空时才认为是成功
valid_items_list.append(current_item_result)
if valid_items_list:
logger.debug(f"[私聊][{private_name}] 成功解析JSON数组，包含 {len(valid_items_list)} 个有效项目。")
return True, valid_items_list
else:
# 如果列表为空（可能所有项都无效），则继续尝试解析为单个对象
logger.debug(f"[私聊][{private_name}] 解析为JSON数组，但未找到有效项目，尝试解析单个JSON对象。")
# result 重置回单个对象的默认值
result = default_result.copy()
except json.JSONDecodeError:
logger.debug(f"[私聊][{private_name}] JSON数组直接解析失败，尝试解析单个JSON对象")
# result 重置回单个对象的默认值
result = default_result.copy()
except Exception as e:
logger.error(f"[私聊][{private_name}] 尝试解析JSON数组时发生未知错误: {str(e)}")
# result 重置回单个对象的默认值
result = default_result.copy()
# 尝试解析为单个JSON对象
try:
# 尝试直接解析清理后的内容
json_data = json.loads(cleaned_content)
if not isinstance(json_data, dict):
logger.error(f"[私聊][{private_name}] 解析为单个对象，但结果不是字典类型: {type(json_data)}")
return False, default_result # 返回失败和默认值
return False, default_result
except json.JSONDecodeError:
# 如果直接解析失败，尝试用正则表达式查找 JSON 对象部分 (作为后备)
# 这个正则比较简单，可能无法处理嵌套或复杂的 JSON
json_pattern = r"\{[\s\S]*?\}" # 使用非贪婪匹配
json_pattern = r"\{[\s\S]*?\}"
json_match = re.search(json_pattern, cleaned_content)
if json_match:
try:
@ -224,25 +572,18 @@ def get_items_from_json(
f"[私聊][{private_name}] 无法在返回内容中找到有效的JSON对象部分。原始内容: {cleaned_content[:100]}..."
)
return False, default_result
# 提取并验证字段 (适用于单个JSON对象)
# 确保 result 是字典类型用于更新
if not isinstance(result, dict):
result = default_result.copy() # 如果之前是列表，重置为字典
result = default_result.copy()
valid_single_object = True
for item in items:
if item in json_data:
result[item] = json_data[item]
elif item not in default_result: # 如果字段不存在且没有默认值
logger.error(f"[私聊][{private_name}] JSON对象缺少必要字段 '{item}'。JSON内容: {json_data}")
for item_field in items: # Renamed item to item_field
if item_field in json_data:
result[item_field] = json_data[item_field]
elif item_field not in default_result:
logger.error(f"[私聊][{private_name}] JSON对象缺少必要字段 '{item_field}'。JSON内容: {json_data}")
valid_single_object = False
break # 这个对象无效
break
if not valid_single_object:
return False, default_result
# 验证类型
if required_types:
for field, expected_type in required_types.items():
if field in result and not isinstance(result[field], expected_type):
@ -251,79 +592,72 @@ def get_items_from_json(
)
valid_single_object = False
break
if not valid_single_object:
return False, default_result
# 验证字符串不为空 (只检查 items 中要求的字段)
for field in items:
if field in result and isinstance(result[field], str) and not result[field].strip():
logger.error(f"[私聊][{private_name}] JSON对象字段 '{field}' 不能为空字符串")
valid_single_object = False
break
if valid_single_object:
logger.debug(f"[私聊][{private_name}] 成功解析并验证了单个JSON对象。")
return True, result # 返回提取并验证后的字典
return True, result
else:
return False, default_result # 验证失败
return False, default_result
async def get_person_id(private_name: str, chat_stream: ChatStream):
"""(保持你原始 pfc_utils.py 中的此函数代码不变)"""
private_user_id_str: Optional[str] = None
private_platform_str: Optional[str] = None
private_nickname_str = private_name
# private_nickname_str = private_name # 这行在你提供的代码中没有被使用，可以考虑移除
if chat_stream.user_info:
private_user_id_str = str(chat_stream.user_info.user_id)
private_platform_str = chat_stream.user_info.platform
logger.debug(
f"[私聊][{private_name}] 从 ChatStream 获取到私聊对象信息: ID={private_user_id_str}, Platform={private_platform_str}, Name={private_nickname_str}"
f"[私聊][{private_name}] 从 ChatStream 获取到私聊对象信息: ID={private_user_id_str}, Platform={private_platform_str}, Name={private_name}" # 使用 private_name
)
elif chat_stream.group_info is None and private_name:
pass
# elif chat_stream.group_info is None and private_name: # 这个 elif 条件体为空，可以移除
# pass
if private_user_id_str and private_platform_str:
try:
private_user_id_int = int(private_user_id_str)
# person_id = person_info_manager.get_person_id( # get_person_id 可能只查询，不创建
# private_platform_str,
# private_user_id_int
# )
# 使用 get_or_create_person 确保用户存在
person_id = await person_info_manager.get_or_create_person(
platform=private_platform_str,
user_id=private_user_id_int,
nickname=private_name, # 使用传入的 private_name 作为昵称
nickname=private_name,
)
if person_id is None: # 如果 get_or_create_person 返回 None，说明创建失败
if person_id is None:
logger.error(f"[私聊][{private_name}] get_or_create_person 未能获取或创建 person_id。")
return None # 返回 None 表示失败
return person_id, private_platform_str, private_user_id_str # 返回获取或创建的 person_id
return None
return person_id, private_platform_str, private_user_id_str
except ValueError:
logger.error(f"[私聊][{private_name}] 无法将 private_user_id_str ('{private_user_id_str}') 转换为整数。")
return None # 返回 None 表示失败
return None
except Exception as e_pid:
logger.error(f"[私聊][{private_name}] 获取或创建 person_id 时出错: {e_pid}")
return None # 返回 None 表示失败
return None
else:
logger.warning(
f"[私聊][{private_name}] 未能确定私聊对象的 user_id 或 platform，无法获取 person_id。将在收到消息后尝试。"
)
return None # 返回 None 表示失败
return None
async def adjust_relationship_value_nonlinear(old_value: float, raw_adjustment: float) -> float:
# 限制 old_value 范围
"""(保持你原始 pfc_utils.py 中的此函数代码不变)"""
old_value = max(-1000, min(1000, old_value))
value = raw_adjustment
if old_value >= 0:
if value >= 0:
value = value * math.cos(math.pi * old_value / 2000)
if old_value > 500:
rdict = await person_info_manager.get_specific_value_list("relationship_value", lambda x: x > 700)
# 确保 person_info_manager.get_specific_value_list 是异步的，如果是同步则需要调整
rdict = await person_info_manager.get_specific_value_list(
"relationship_value", lambda x: x > 700 if isinstance(x, (int, float)) else False
)
high_value_count = len(rdict)
if old_value > 700:
value *= 3 / (high_value_count + 2)
@ -331,22 +665,18 @@ async def adjust_relationship_value_nonlinear(old_value: float, raw_adjustment:
value *= 3 / (high_value_count + 3)
elif value < 0:
value = value * math.exp(old_value / 2000)
else:
value = 0
else:
# else: value = 0 # 你原始代码中没有这句，如果value为0，保持为0
else: # old_value < 0
if value >= 0:
value = value * math.exp(old_value / 2000)
elif value < 0:
value = value * math.cos(math.pi * old_value / 2000)
else:
value = 0
# else: value = 0 # 你原始代码中没有这句
return value
async def build_chat_history_text(observation_info: ObservationInfo, private_name: str) -> str:
"""构建聊天历史记录文本 (包含未处理消息)"""
chat_history_text = ""
try:
if hasattr(observation_info, "chat_history_str") and observation_info.chat_history_str:
@ -358,23 +688,28 @@ async def build_chat_history_text(observation_info: ObservationInfo, private_nam
)
else:
chat_history_text = "还没有聊天记录。\n"
unread_count = getattr(observation_info, "new_messages_count", 0)
unread_messages = getattr(observation_info, "unprocessed_messages", [])
if unread_count > 0 and unread_messages:
bot_qq_str = str(global_config.BOT_QQ)
other_unread_messages = [
msg for msg in unread_messages if msg.get("user_info", {}).get("user_id") != bot_qq_str
]
other_unread_count = len(other_unread_messages)
if other_unread_count > 0:
new_messages_str = await build_readable_messages(
other_unread_messages,
replace_bot_name=True,
merge_messages=False,
timestamp_mode="relative",
read_mark=0.0,
)
chat_history_text += f"\n{new_messages_str}\n------\n"
bot_qq_str = str(global_config.BOT_QQ) if global_config.BOT_QQ else None # 安全获取
if bot_qq_str: # 仅当 bot_qq_str 有效时进行过滤
other_unread_messages = [
msg for msg in unread_messages if msg.get("user_info", {}).get("user_id") != bot_qq_str
]
other_unread_count = len(other_unread_messages)
if other_unread_count > 0:
new_messages_str = await build_readable_messages(
other_unread_messages,
replace_bot_name=True, # 这里是未处理消息，可能不需要替换机器人名字
merge_messages=False,
timestamp_mode="relative",
read_mark=0.0,
)
chat_history_text += f"\n{new_messages_str}\n------\n" # 原始代码是加在末尾的
else:
logger.warning(f"[私聊][{private_name}] BOT_QQ 未配置，无法准确过滤未读消息中的机器人自身消息。")
except AttributeError as e:
logger.warning(f"[私聊][{private_name}] 构建聊天记录文本时属性错误: {e}")
chat_history_text = "[获取聊天记录时出错]\n"

147
src/plugins/PFC/reply_generator.py
View File

@ -1,7 +1,7 @@
import random
from datetime import datetime
from .pfc_utils import retrieve_contextual_info
from typing import Optional
from src.common.logger_manager import get_logger
from ..models.utils_model import LLMRequest
from ...config.config import global_config
@ -60,14 +60,19 @@ PROMPT_DIRECT_REPLY = """
{retrieved_knowledge_str}
请你**记住上面的知识**，在回复中有可能会用到。
你有以下记忆可供参考：
{retrieved_global_memory_str}
你还想到了一些你们之前的聊天记录：
{retrieved_historical_chat_str}
最近的聊天记录：
{chat_history_text}
{retrieved_memory_str}
{last_rejection_info}
请根据上述信息，结合聊天记录，回复对方。该回复应该：
1. 符合对话目标，以"你"的角度发言（不要自己与自己对话！）
2. 符合你的性格特征和身份细节
@ -97,11 +102,15 @@ PROMPT_SEND_NEW_MESSAGE = """
{retrieved_knowledge_str}
请你**记住上面的知识**，在发消息时有可能会用到。
你有以下记忆可供参考：
{retrieved_global_memory_str}
你还想到了一些你们之前的聊天记录：
{retrieved_historical_chat_str}
最近的聊天记录：
{chat_history_text}
{retrieved_memory_str}
{last_rejection_info}
请根据上述信息，判断你是否要继续发一条新消息（例如对之前消息的补充，深入话题，或追问等等）。如果你觉得要发送，该消息应该：
@ -215,6 +224,55 @@ class ReplyGenerator:
else:
goals_str = "- 目前没有明确对话目标\n"
chat_history_for_prompt_builder: list = []
recent_history_start_time_for_exclusion: Optional[float] = None
# 我们需要知道 build_chat_history_text 函数大致会用 observation_info.chat_history 的多少条记录
# 或者 build_chat_history_text 内部的逻辑。
# 假设 build_chat_history_text 主要依赖 observation_info.chat_history_str，
# 而 observation_info.chat_history_str 是基于 observation_info.chat_history 的最后一部分（比如20条）生成的。
# 为了准确，我们应该直接从 observation_info.chat_history 中获取这个片段的起始时间。
# 请确保这里的 MAX_RECENT_HISTORY_FOR_PROMPT 与 observation_info.py 或 build_chat_history_text 中
# 用于生成 chat_history_str 的消息数量逻辑大致吻合。
# 如果 build_chat_history_text 总是用 observation_info.chat_history 的最后 N 条，那么这个 N 就是这里的数字。
# 如果 observation_info.chat_history_str 是由 observation_info.py 中的 update_from_message 等方法维护的，
# 并且总是代表一个固定长度（比如最后30条）的聊天记录字符串，那么我们就需要从 observation_info.chat_history
# 取出这部分原始消息来确定起始时间。
# 我们先做一个合理的假设： “最近聊天记录” 字符串 chat_history_text 是基于
# observation_info.chat_history 的一个有限的尾部片段生成的。
# 假设这个片段的长度由 global_config.pfc_recent_history_display_count 控制，默认为20条。
recent_history_display_count = getattr(global_config, "pfc_recent_history_display_count", 20)
if observation_info and observation_info.chat_history and len(observation_info.chat_history) > 0:
# 获取用于生成“最近聊天记录”的实际消息片段
# 如果 observation_info.chat_history 长度小于 display_count，则取全部
start_index = max(0, len(observation_info.chat_history) - recent_history_display_count)
chat_history_for_prompt_builder = observation_info.chat_history[start_index:]
if chat_history_for_prompt_builder: # 如果片段不为空
try:
first_message_in_display_slice = chat_history_for_prompt_builder[0]
recent_history_start_time_for_exclusion = first_message_in_display_slice.get("time")
if recent_history_start_time_for_exclusion:
# 导入 datetime (如果 reply_generator.py 文件顶部没有的话)
# from datetime import datetime # 通常建议放在文件顶部
logger.debug(
f"[{self.private_name}] (ReplyGenerator) “最近聊天记录”片段(共{len(chat_history_for_prompt_builder)}条)的最早时间戳: "
f"{recent_history_start_time_for_exclusion} "
f"(即 {datetime.fromtimestamp(recent_history_start_time_for_exclusion).strftime('%Y-%m-%d %H:%M:%S')})"
)
else:
logger.warning(f"[{self.private_name}] (ReplyGenerator) “最近聊天记录”片段的首条消息无时间戳。")
except (IndexError, KeyError, TypeError) as e:
logger.warning(f"[{self.private_name}] (ReplyGenerator) 获取“最近聊天记录”起始时间失败: {e}")
recent_history_start_time_for_exclusion = None
else:
logger.debug(
f"[{self.private_name}] (ReplyGenerator) observation_info.chat_history 为空，无法确定“最近聊天记录”起始时间。"
)
# --- [新代码结束] ---
chat_history_text = await build_chat_history_text(observation_info, self.private_name)
sender_name_str = self.private_name
@ -223,12 +281,64 @@ class ReplyGenerator:
current_emotion_text_str = getattr(conversation_info, "current_emotion_text", "心情平静。")
persona_text = f"你的名字是{self.name}，{self.personality_info}。"
retrieval_context = chat_history_text
retrieved_memory_str, retrieved_knowledge_str = await retrieve_contextual_info(
retrieval_context, self.private_name
)
historical_chat_query = ""
num_recent_messages_for_query = 3 # 例如，取最近3条作为查询引子
if observation_info.chat_history and len(observation_info.chat_history) > 0:
# 从 chat_history (已处理并存入 ObservationInfo 的历史) 中取最新N条
# 或者，如果 observation_info.unprocessed_messages 更能代表“当前上下文”，也可以考虑用它
# 我们先用 chat_history，因为它包含了双方的对话历史，可能更稳定
recent_messages_for_query_list = observation_info.chat_history[-num_recent_messages_for_query:]
# 将这些消息的文本内容合并
query_texts_list = []
for msg_dict in recent_messages_for_query_list:
text_content = msg_dict.get("processed_plain_text", "")
if text_content.strip(): # 只添加有内容的文本
# 可以选择是否添加发送者信息到查询文本中，例如：
# sender_nickname = msg_dict.get("user_info", {}).get("user_nickname", "用户")
# query_texts_list.append(f"{sender_nickname}: {text_content}")
query_texts_list.append(text_content) # 简单合并文本内容
if query_texts_list:
historical_chat_query = " ".join(query_texts_list).strip()
logger.debug(
f"[私聊][{self.private_name}] (ReplyGenerator) 生成的私聊历史查询文本 (最近{num_recent_messages_for_query}条): '{historical_chat_query[:100]}...'"
)
else:
logger.debug(
f"[私聊][{self.private_name}] (ReplyGenerator) 最近{num_recent_messages_for_query}条消息无有效文本内容，不进行私聊历史查询。"
)
else:
logger.debug(f"[私聊][{self.private_name}] (ReplyGenerator) 无聊天历史可用于生成私聊历史查询文本。")
current_chat_id = self.chat_observer.stream_id if self.chat_observer else None
if not current_chat_id:
logger.error(f"[私聊][{self.private_name}] (ReplyGenerator) 无法获取 current_chat_id，跳过所有上下文检索！")
retrieved_global_memory_str = "[获取全局记忆出错：chat_id 未知]"
retrieved_knowledge_str = "[获取知识出错：chat_id 未知]"
retrieved_historical_chat_str = "[获取私聊历史回忆出错：chat_id 未知]"
else:
# retrieval_context 之前是用 chat_history_text，现在也用它作为全局记忆和知识的检索上下文
retrieval_context_for_global_and_knowledge = chat_history_text
(
retrieved_global_memory_str,
retrieved_knowledge_str,
retrieved_historical_chat_str, # << 新增接收私聊历史回忆
) = await retrieve_contextual_info(
text=retrieval_context_for_global_and_knowledge, # 用于全局记忆和知识
private_name=self.private_name,
chat_id=current_chat_id, # << 传递 chat_id
historical_chat_query_text=historical_chat_query, # << 传递专门的查询文本
current_short_term_history_earliest_time=recent_history_start_time_for_exclusion, # <--- 新增传递的参数
)
# === 调用修改结束 ===
logger.info(
f"[私聊][{self.private_name}] (ReplyGenerator) 统一检索完成。记忆: {'有' if '回忆起' in retrieved_memory_str else '无'} / 知识: {'有' if '出错' not in retrieved_knowledge_str and '无相关知识' not in retrieved_knowledge_str else '无'}"
f"[私聊][{self.private_name}] (ReplyGenerator) 上下文检索完成。\n"
f" 全局记忆: {'有内容' if '回忆起' in retrieved_global_memory_str else '无或出错'}\n"
f" 知识: {'有内容' if '出错' not in retrieved_knowledge_str and '无相关知识' not in retrieved_knowledge_str and retrieved_knowledge_str.strip() else '无或出错'}\n"
f" 私聊历史回忆: {'有内容' if '回忆起一段相关的历史聊天' in retrieved_historical_chat_str else '无或出错'}"
)
last_rejection_info_str = ""
@ -292,11 +402,18 @@ class ReplyGenerator:
base_format_params = {
"persona_text": persona_text,
"goals_str": goals_str,
"chat_history_text": chat_history_text,
"retrieved_memory_str": retrieved_memory_str if retrieved_memory_str else "无相关记忆。", # 确保已定义
"chat_history_text": chat_history_text
if chat_history_text.strip()
else "还没有聊天记录。", # 当前短期历史
"retrieved_global_memory_str": retrieved_global_memory_str
if retrieved_global_memory_str.strip()
else "无相关全局记忆。",
"retrieved_knowledge_str": retrieved_knowledge_str
if retrieved_knowledge_str
else "无相关知识。", # 确保已定义
if retrieved_knowledge_str.strip()
else "无相关知识。",
"retrieved_historical_chat_str": retrieved_historical_chat_str
if retrieved_historical_chat_str.strip()
else "无相关私聊历史回忆。", # << 新增
"last_rejection_info": last_rejection_info_str,
"current_time_str": current_time_value,
"sender_name": sender_name_str,