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调整对应的调用

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UnCLAS-Prommer
2025-07-30 17:07:55 +08:00
parent 3c40ceda4c
commit 6c0edd0ad7
40 changed files with 580 additions and 1236 deletions
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38
src/chat/memory_system/Hippocampus.py
View File

@@ -5,25 +5,27 @@ import random
import time
import re
import json
from itertools import combinations
import jieba
import networkx as nx
import numpy as np
from itertools import combinations
from typing import List, Tuple, Coroutine, Any, Dict, Set
from collections import Counter
from ...llm_models.utils_model import LLMRequest
from rich.traceback import install
from src.llm_models.utils_model import LLMRequest
from src.config.config import global_config, model_config
from src.common.database.database_model import Messages, GraphNodes, GraphEdges # Peewee Models导入
from src.common.logger import get_logger
from src.chat.memory_system.sample_distribution import MemoryBuildScheduler # 分布生成器
from ..utils.chat_message_builder import (
from src.chat.utils.chat_message_builder import (
get_raw_msg_by_timestamp,
build_readable_messages,
get_raw_msg_by_timestamp_with_chat,
) # 导入 build_readable_messages
from ..utils.utils import translate_timestamp_to_human_readable
from rich.traceback import install
from src.chat.utils.utils import translate_timestamp_to_human_readable
from ...config.config import global_config
from src.common.database.database_model import Messages, GraphNodes, GraphEdges # Peewee Models导入
install(extra_lines=3)
@@ -198,8 +200,7 @@ class Hippocampus:
self.parahippocampal_gyrus = ParahippocampalGyrus(self)
# 从数据库加载记忆图
self.entorhinal_cortex.sync_memory_from_db()
# TODO: API-Adapter修改标记
self.model_summary = LLMRequest(global_config.model.memory, request_type="memory.builder")
self.model_summary = LLMRequest(model_set=model_config.model_task_config.memory, request_type="memory.builder")
def get_all_node_names(self) -> list:
"""获取记忆图中所有节点的名字列表"""
@@ -339,9 +340,7 @@ class Hippocampus:
else:
topic_num = 5 # 51+字符: 5个关键词 (其余长文本)
topics_response, (reasoning_content, model_name) = await self.model_summary.generate_response_async(
self.find_topic_llm(text, topic_num)
)
topics_response, _ = await self.model_summary.generate_response_async(self.find_topic_llm(text, topic_num))
# 提取关键词
keywords = re.findall(r"<([^>]+)>", topics_response)
@@ -353,12 +352,11 @@ class Hippocampus:
for keyword in ",".join(keywords).replace("，", ",").replace("、", ",").replace(" ", ",").split(",")
if keyword.strip()
]
if keywords:
logger.info(f"提取关键词: {keywords}")
return keywords
return keywords
async def get_memory_from_text(
self,
@@ -1245,7 +1243,7 @@ class ParahippocampalGyrus:
# 2. 使用LLM提取关键主题
topic_num = self.hippocampus.calculate_topic_num(input_text, compress_rate)
topics_response, (reasoning_content, model_name) = await self.hippocampus.model_summary.generate_response_async(
topics_response, _ = await self.hippocampus.model_summary.generate_response_async(
self.hippocampus.find_topic_llm(input_text, topic_num)
)
@@ -1269,7 +1267,7 @@ class ParahippocampalGyrus:
logger.debug(f"过滤后话题: {filtered_topics}")
# 4. 创建所有话题的摘要生成任务
tasks = []
tasks: List[Tuple[str, Coroutine[Any, Any, Tuple[str, Tuple[str, str, List[Dict[str, Any]] | None]]]]] = []
for topic in filtered_topics:
# 调用修改后的 topic_what，不再需要 time_info
topic_what_prompt = self.hippocampus.topic_what(input_text, topic)
@@ -1281,7 +1279,7 @@ class ParahippocampalGyrus:
continue
# 等待所有任务完成
compressed_memory = set()
compressed_memory: Set[Tuple[str, str]] = set()
similar_topics_dict = {}
for topic, task in tasks:

40
src/chat/memory_system/instant_memory.py
View File

@@ -3,13 +3,16 @@ import time
import re
import json
import ast
from json_repair import repair_json
from src.llm_models.utils_model import LLMRequest
from src.common.logger import get_logger
import traceback
from src.config.config import global_config
from json_repair import repair_json
from datetime import datetime, timedelta
from src.llm_models.utils_model import LLMRequest
from src.common.logger import get_logger
from src.common.database.database_model import Memory # Peewee Models导入
from src.config.config import model_config
logger = get_logger(__name__)
@@ -35,8 +38,7 @@ class InstantMemory:
self.chat_id = chat_id
self.last_view_time = time.time()
self.summary_model = LLMRequest(
model=global_config.model.memory,
temperature=0.5,
model_set=model_config.model_task_config.memory,
request_type="memory.summary",
)
@@ -48,14 +50,11 @@ class InstantMemory:
"""
try:
response, _ = await self.summary_model.generate_response_async(prompt)
response, _ = await self.summary_model.generate_response_async(prompt, temperature=0.5)
print(prompt)
print(response)
if "1" in response:
return True
else:
return False
return "1" in response
except Exception as e:
logger.error(f"判断是否需要记忆出现错误：{str(e)} {traceback.format_exc()}")
return False
@@ -71,9 +70,9 @@ class InstantMemory:
}}
"""
try:
response, _ = await self.summary_model.generate_response_async(prompt)
print(prompt)
print(response)
response, _ = await self.summary_model.generate_response_async(prompt, temperature=0.5)
# print(prompt)
# print(response)
if not response:
return None
try:
@@ -142,7 +141,7 @@ class InstantMemory:
请只输出json格式，不要输出其他多余内容
"""
try:
response, _ = await self.summary_model.generate_response_async(prompt)
response, _ = await self.summary_model.generate_response_async(prompt, temperature=0.5)
print(prompt)
print(response)
if not response:
@@ -177,7 +176,7 @@ class InstantMemory:
for mem in query:
# 对每条记忆
mem_keywords = mem.keywords or []
mem_keywords = mem.keywords or ""
parsed = ast.literal_eval(mem_keywords)
if isinstance(parsed, list):
mem_keywords = [str(k).strip() for k in parsed if str(k).strip()]
@@ -201,6 +200,7 @@ class InstantMemory:
return None
def _parse_time_range(self, time_str):
# sourcery skip: extract-duplicate-method, use-contextlib-suppress
"""
支持解析如下格式：
- 具体日期时间：YYYY-MM-DD HH:MM:SS
@@ -208,8 +208,6 @@ class InstantMemory:
- 相对时间：今天，昨天，前天，N天前，N个月前
- 空字符串：返回(None, None)
"""
from datetime import datetime, timedelta
now = datetime.now()
if not time_str:
return 0, now
@@ -239,14 +237,12 @@ class InstantMemory:
start = (now - timedelta(days=2)).replace(hour=0, minute=0, second=0, microsecond=0)
end = start + timedelta(days=1)
return start, end
m = re.match(r"(\d+)天前", time_str)
if m:
if m := re.match(r"(\d+)天前", time_str):
days = int(m.group(1))
start = (now - timedelta(days=days)).replace(hour=0, minute=0, second=0, microsecond=0)
end = start + timedelta(days=1)
return start, end
m = re.match(r"(\d+)个月前", time_str)
if m:
if m := re.match(r"(\d+)个月前", time_str):
months = int(m.group(1))
# 近似每月30天
start = (now - timedelta(days=months * 30)).replace(hour=0, minute=0, second=0, microsecond=0)

25
src/chat/memory_system/memory_activator.py
View File

@@ -1,13 +1,15 @@
from src.llm_models.utils_model import LLMRequest
from src.config.config import global_config
from src.common.logger import get_logger
from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
from datetime import datetime
from src.chat.memory_system.Hippocampus import hippocampus_manager
from typing import List, Dict
import difflib
import json
from json_repair import repair_json
from typing import List, Dict
from datetime import datetime
from src.llm_models.utils_model import LLMRequest
from src.config.config import global_config, model_config
from src.common.logger import get_logger
from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
from src.chat.memory_system.Hippocampus import hippocampus_manager
logger = get_logger("memory_activator")
@@ -61,11 +63,8 @@ def init_prompt():
class MemoryActivator:
def __init__(self):
# TODO: API-Adapter修改标记
self.key_words_model = LLMRequest(
model=global_config.model.utils_small,
temperature=0.5,
model_set=model_config.model_task_config.utils_small,
request_type="memory.activator",
)
@@ -92,7 +91,9 @@ class MemoryActivator:
# logger.debug(f"prompt: {prompt}")
response, (reasoning_content, model_name) = await self.key_words_model.generate_response_async(prompt)
response, (reasoning_content, model_name, _) = await self.key_words_model.generate_response_async(
prompt, temperature=0.5
)
keywords = list(get_keywords_from_json(response))