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update：更新脚本

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This commit is contained in:
SengokuCola
2025-06-04 22:24:20 +08:00
parent 20c368ac1a
commit d30f35fe20
3 changed files with 56 additions and 54 deletions
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96
scripts/analyze_group_similarity.py
View File

@@ -48,7 +48,7 @@ def load_group_data(group_dir):
"""加载单个群组的数据"""
json_path = Path(group_dir) / "expressions.json"
if not json_path.exists():
return [], [], []
return [], [], [], 0
with open(json_path, "r", encoding="utf-8") as f:
data = json.load(f)
@@ -56,6 +56,7 @@ def load_group_data(group_dir):
situations = []
styles = []
combined = []
total_count = sum(item["count"] for item in data)
for item in data:
count = item["count"]
@@ -63,41 +64,46 @@ def load_group_data(group_dir):
styles.extend([item["style"]] * count)
combined.extend([f"{item['situation']} {item['style']}"] * count)
return situations, styles, combined
return situations, styles, combined, total_count
def analyze_group_similarity():
# 获取所有群组目录
base_dir = Path("data/expression/learnt_style")
group_dirs = [d for d in base_dir.iterdir() if d.is_dir()]
group_ids = [d.name for d in group_dirs]
# 获取群组名称
group_names = [get_group_name(group_id) for group_id in group_ids]
# 加载所有群组的数据
group_situations = []
group_styles = []
group_combined = []
# 加载所有群组的数据并过滤
valid_groups = []
valid_names = []
valid_situations = []
valid_styles = []
valid_combined = []
for d in group_dirs:
situations, styles, combined = load_group_data(d)
group_situations.append(" ".join(situations))
group_styles.append(" ".join(styles))
group_combined.append(" ".join(combined))
situations, styles, combined, total_count = load_group_data(d)
if total_count >= 50: # 只保留数据量大于等于50的群组
valid_groups.append(d)
valid_names.append(get_group_name(d.name))
valid_situations.append(" ".join(situations))
valid_styles.append(" ".join(styles))
valid_combined.append(" ".join(combined))
if not valid_groups:
print("没有找到数据量大于等于50的群组")
return
# 创建TF-IDF向量化器
vectorizer = TfidfVectorizer()
# 计算三种相似度矩阵
situation_matrix = cosine_similarity(vectorizer.fit_transform(group_situations))
style_matrix = cosine_similarity(vectorizer.fit_transform(group_styles))
combined_matrix = cosine_similarity(vectorizer.fit_transform(group_combined))
situation_matrix = cosine_similarity(vectorizer.fit_transform(valid_situations))
style_matrix = cosine_similarity(vectorizer.fit_transform(valid_styles))
combined_matrix = cosine_similarity(vectorizer.fit_transform(valid_combined))
# 对相似度矩阵进行对数变换
log_situation_matrix = np.log1p(situation_matrix)
log_style_matrix = np.log1p(style_matrix)
log_combined_matrix = np.log1p(combined_matrix)
log_situation_matrix = np.log10(situation_matrix * 100 + 1) * 10 / np.log10(4)
log_style_matrix = np.log10(style_matrix * 100 + 1) * 10 / np.log10(4)
log_combined_matrix = np.log10(combined_matrix * 100 + 1) * 10 / np.log10(4)
# 创建一个大图，包含三个子图
plt.figure(figsize=(45, 12))
@@ -106,45 +112,45 @@ def analyze_group_similarity():
plt.subplot(1, 3, 1)
sns.heatmap(
log_situation_matrix,
xticklabels=group_names,
yticklabels=group_names,
xticklabels=valid_names,
yticklabels=valid_names,
cmap="YlOrRd",
annot=True,
fmt=".2f",
fmt=".1f",
vmin=0,
vmax=np.log1p(0.2),
vmax=30,
)
plt.title("群组场景相似度热力图 (对数变换)")
plt.title("群组场景相似度热力图 (对数百分比)")
plt.xticks(rotation=45, ha="right")
# 表达方式相似度热力图
plt.subplot(1, 3, 2)
sns.heatmap(
log_style_matrix,
xticklabels=group_names,
yticklabels=group_names,
xticklabels=valid_names,
yticklabels=valid_names,
cmap="YlOrRd",
annot=True,
fmt=".2f",
fmt=".1f",
vmin=0,
vmax=np.log1p(0.2),
vmax=30,
)
plt.title("群组表达方式相似度热力图 (对数变换)")
plt.title("群组表达方式相似度热力图 (对数百分比)")
plt.xticks(rotation=45, ha="right")
# 组合相似度热力图
plt.subplot(1, 3, 3)
sns.heatmap(
log_combined_matrix,
xticklabels=group_names,
yticklabels=group_names,
xticklabels=valid_names,
yticklabels=valid_names,
cmap="YlOrRd",
annot=True,
fmt=".2f",
fmt=".1f",
vmin=0,
vmax=np.log1p(0.2),
vmax=30,
)
plt.title("群组场景+表达方式相似度热力图 (对数变换)")
plt.title("群组场景+表达方式相似度热力图 (对数百分比)")
plt.xticks(rotation=45, ha="right")
plt.tight_layout()
@@ -156,18 +162,18 @@ def analyze_group_similarity():
f.write("群组相似度详情\n")
f.write("=" * 50 + "\n\n")
for i in range(len(group_ids)):
for j in range(i + 1, len(group_ids)):
if log_combined_matrix[i][j] > np.log1p(0.05):
f.write(f"群组1: {group_names[i]}\n")
f.write(f"群组2: {group_names[j]}\n")
for i in range(len(valid_names)):
for j in range(i + 1, len(valid_names)):
if log_combined_matrix[i][j] > 50:
f.write(f"群组1: {valid_names[i]}\n")
f.write(f"群组2: {valid_names[j]}\n")
f.write(f"场景相似度: {situation_matrix[i][j]:.4f}\n")
f.write(f"表达方式相似度: {style_matrix[i][j]:.4f}\n")
f.write(f"组合相似度: {combined_matrix[i][j]:.4f}\n")
# 获取两个群组的数据
situations1, styles1, _ = load_group_data(group_dirs[i])
situations2, styles2, _ = load_group_data(group_dirs[j])
situations1, styles1, _ = load_group_data(valid_groups[i])
situations2, styles2, _ = load_group_data(valid_groups[j])
# 找出共同的场景
common_situations = set(situations1) & set(situations2)

4
src/chat/focus_chat/planners/planner_simple.py
View File

@@ -187,10 +187,6 @@ class ActionPlanner(BasePlanner):
prompt = f"{prompt}"
llm_content, (reasoning_content, _) = await self.planner_llm.generate_response_async(prompt=prompt)
# logger.info(
# f"{self.log_prefix}规划器Prompt:\n{prompt}\n\nLLM 原始响应: {llm_content}'"
# )
logger.debug(f"{self.log_prefix}LLM 原始理由响应: {reasoning_content}")
except Exception as req_e:
logger.error(f"{self.log_prefix}LLM 请求执行失败: {req_e}")

10
template/bot_config_template.toml
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@@ -115,19 +115,19 @@ content_filtration = false # 是否启用表情包过滤，只有符合该要
filtration_prompt = "符合公序良俗" # 表情包过滤要求，只有符合该要求的表情包才会被保存
[memory]
memory_build_interval = 2000 # 记忆构建间隔 单位秒 间隔越低，麦麦学习越多，但是冗余信息也会增多
memory_build_interval = 1000 # 记忆构建间隔 单位秒 间隔越低，麦麦学习越多，但是冗余信息也会增多
memory_build_distribution = [6.0, 3.0, 0.6, 32.0, 12.0, 0.4] # 记忆构建分布，参数：分布1均值，标准差，权重，分布2均值，标准差，权重
memory_build_sample_num = 6 # 采样数量，数值越高记忆采样次数越多
memory_build_sample_length = 40 # 采样长度，数值越高一段记忆内容越丰富
memory_build_sample_num = 4 # 采样数量，数值越高记忆采样次数越多
memory_build_sample_length = 30 # 采样长度，数值越高一段记忆内容越丰富
memory_compress_rate = 0.1 # 记忆压缩率 控制记忆精简程度 建议保持默认,调高可以获得更多信息，但是冗余信息也会增多
forget_memory_interval = 1000 # 记忆遗忘间隔 单位秒 间隔越低，麦麦遗忘越频繁，记忆更精简，但更难学习
memory_forget_time = 24 #多长时间后的记忆会被遗忘 单位小时
memory_forget_percentage = 0.01 # 记忆遗忘比例 控制记忆遗忘程度 越大遗忘越多 建议保持默认
consolidate_memory_interval = 2000 # 记忆整合间隔 单位秒 间隔越低，麦麦整合越频繁，记忆更精简
consolidate_memory_interval = 1000 # 记忆整合间隔 单位秒 间隔越低，麦麦整合越频繁，记忆更精简
consolidation_similarity_threshold = 0.7 # 相似度阈值
consolidation_check_percentage = 0.01 # 检查节点比例
consolidation_check_percentage = 0.05 # 检查节点比例
#不希望记忆的词，已经记忆的不会受到影响，需要手动清理
memory_ban_words = [ "表情包", "图片", "回复", "聊天记录" ]