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fix：加入工具调用能力

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This commit is contained in:
SengokuCola
2025-04-10 22:13:17 +08:00
parent de061024c1
commit 110f94353f
6 changed files with 627 additions and 265 deletions
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271
src/heart_flow/sub_heartflow.py
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@@ -16,6 +16,8 @@ import random
from src.plugins.chat.chat_stream import ChatStream
from src.plugins.person_info.relationship_manager import relationship_manager
from src.plugins.chat.utils import get_recent_group_speaker
import json
from src.heart_flow.tool_use import ToolUser
subheartflow_config = LogConfig(
# 使用海马体专用样式
@@ -47,6 +49,7 @@ class SubHeartflow:
self.llm_model = LLM_request(
model=global_config.llm_sub_heartflow, temperature=0.2, max_tokens=600, request_type="sub_heart_flow"
)
self.main_heartflow_info = ""
@@ -63,6 +66,8 @@ class SubHeartflow:
self.running_knowledges = []
self.bot_name = global_config.BOT_NICKNAME
self.tool_user = ToolUser()
def add_observation(self, observation: Observation):
"""添加一个新的observation对象到列表中，如果已存在相同id的observation则不添加"""
@@ -115,6 +120,7 @@ class SubHeartflow:
observation = self.observations[0]
await observation.observe()
async def do_thinking_before_reply(self, message_txt:str, sender_name:str, chat_stream:ChatStream):
current_thinking_info = self.current_mind
mood_info = self.current_state.mood
@@ -123,6 +129,19 @@ class SubHeartflow:
chat_observe_info = observation.observe_info
# print(f"chat_observe_info：{chat_observe_info}")
# 首先尝试使用工具获取更多信息
tool_result = await self.tool_user.use_tool(message_txt, sender_name, chat_stream)
# 如果工具被使用且获得了结果，将收集到的信息合并到思考中
if tool_result.get("used_tools", False):
logger.info("使用工具收集了信息")
# 如果有收集到的信息，将其添加到当前思考中
if "collected_info" in tool_result:
collected_info = tool_result["collected_info"]
# 开始构建prompt
prompt_personality = f"你的名字是{self.bot_name},你"
# person
@@ -158,38 +177,11 @@ class SubHeartflow:
f"根据你和说话者{sender_name}的关系和态度进行回复，明确你的立场和情感。"
)
# 调取记忆
related_memory = await HippocampusManager.get_instance().get_memory_from_text(
text=chat_observe_info, 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]
else:
related_memory_info = ""
related_info, grouped_results = await self.get_prompt_info(chat_observe_info + message_txt, 0.4)
# print(related_info)
for _topic, results in grouped_results.items():
for result in results:
# print(result)
self.running_knowledges.append(result)
# print(f"相关记忆：{related_memory_info}")
schedule_info = bot_schedule.get_current_num_task(num=1, time_info=False)
prompt = ""
# prompt += f"麦麦的总体想法是：{self.main_heartflow_info}\n\n"
if tool_result.get("used_tools", False):
prompt += f"{collected_info}\n"
prompt += f"{relation_prompt_all}\n"
prompt += f"{prompt_personality}\n"
# prompt += f"你刚刚在做的事情是：{schedule_info}\n"
# if related_memory_info:
# prompt += f"你想起来你之前见过的回忆：{related_memory_info}。\n以上是你的回忆，不一定是目前聊天里的人说的，也不一定是现在发生的事情，请记住。\n"
# if related_info:
# prompt += f"你想起你知道：{related_info}\n"
prompt += f"刚刚你的想法是{current_thinking_info}。如果有新的内容，记得转换话题\n"
prompt += "-----------------------------------\n"
prompt += f"现在你正在上网，和qq群里的网友们聊天，群里正在聊的话题是：{chat_observe_info}\n"
@@ -211,7 +203,7 @@ class SubHeartflow:
logger.info(f"prompt:\n{prompt}\n")
logger.info(f"麦麦的思考前脑内状态：{self.current_mind}")
return self.current_mind ,self.past_mind
return self.current_mind, self.past_mind
async def do_thinking_after_reply(self, reply_content, chat_talking_prompt):
# print("麦麦回复之后脑袋转起来了")
@@ -310,224 +302,5 @@ class SubHeartflow:
self.past_mind.append(self.current_mind)
self.current_mind = response
async def get_prompt_info(self, message: str, threshold: float):
start_time = time.time()
related_info = ""
logger.debug(f"获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}")
# 1. 先从LLM获取主题，类似于记忆系统的做法
topics = []
# try:
# # 先尝试使用记忆系统的方法获取主题
# hippocampus = HippocampusManager.get_instance()._hippocampus
# topic_num = min(5, max(1, int(len(message) * 0.1)))
# topics_response = await hippocampus.llm_topic_judge.generate_response(hippocampus.find_topic_llm(message, topic_num))
# # 提取关键词
# topics = re.findall(r"<([^>]+)>", topics_response[0])
# if not topics:
# topics = []
# else:
# topics = [
# topic.strip()
# for topic in ",".join(topics).replace("，", ",").replace("、", ",").replace(" ", ",").split(",")
# if topic.strip()
# ]
# logger.info(f"从LLM提取的主题: {', '.join(topics)}")
# except Exception as e:
# logger.error(f"从LLM提取主题失败: {str(e)}")
# # 如果LLM提取失败，使用jieba分词提取关键词作为备选
# words = jieba.cut(message)
# topics = [word for word in words if len(word) > 1][:5]
# logger.info(f"使用jieba提取的主题: {', '.join(topics)}")
# 如果无法提取到主题，直接使用整个消息
if not topics:
logger.debug("未能提取到任何主题，使用整个消息进行查询")
embedding = await get_embedding(message, request_type="info_retrieval")
if not embedding:
logger.error("获取消息嵌入向量失败")
return ""
related_info = self.get_info_from_db(embedding, limit=3, threshold=threshold)
logger.info(f"知识库检索完成，总耗时: {time.time() - start_time:.3f}秒")
return related_info, {}
# 2. 对每个主题进行知识库查询
logger.info(f"开始处理{len(topics)}个主题的知识库查询")
# 优化：批量获取嵌入向量，减少API调用
embeddings = {}
topics_batch = [topic for topic in topics if len(topic) > 0]
if message: # 确保消息非空
topics_batch.append(message)
# 批量获取嵌入向量
embed_start_time = time.time()
for text in topics_batch:
if not text or len(text.strip()) == 0:
continue
try:
embedding = await get_embedding(text, request_type="info_retrieval")
if embedding:
embeddings[text] = embedding
else:
logger.warning(f"获取'{text}'的嵌入向量失败")
except Exception as e:
logger.error(f"获取'{text}'的嵌入向量时发生错误: {str(e)}")
logger.info(f"批量获取嵌入向量完成，耗时: {time.time() - embed_start_time:.3f}秒")
if not embeddings:
logger.error("所有嵌入向量获取失败")
return ""
# 3. 对每个主题进行知识库查询
all_results = []
query_start_time = time.time()
# 首先添加原始消息的查询结果
if message in embeddings:
original_results = self.get_info_from_db(embeddings[message], limit=3, threshold=threshold, return_raw=True)
if original_results:
for result in original_results:
result["topic"] = "原始消息"
all_results.extend(original_results)
logger.info(f"原始消息查询到{len(original_results)}条结果")
# 然后添加每个主题的查询结果
for topic in topics:
if not topic or topic not in embeddings:
continue
try:
topic_results = self.get_info_from_db(embeddings[topic], limit=3, threshold=threshold, return_raw=True)
if topic_results:
# 添加主题标记
for result in topic_results:
result["topic"] = topic
all_results.extend(topic_results)
logger.info(f"主题'{topic}'查询到{len(topic_results)}条结果")
except Exception as e:
logger.error(f"查询主题'{topic}'时发生错误: {str(e)}")
logger.info(f"知识库查询完成，耗时: {time.time() - query_start_time:.3f}秒，共获取{len(all_results)}条结果")
# 4. 去重和过滤
process_start_time = time.time()
unique_contents = set()
filtered_results = []
for result in all_results:
content = result["content"]
if content not in unique_contents:
unique_contents.add(content)
filtered_results.append(result)
# 5. 按相似度排序
filtered_results.sort(key=lambda x: x["similarity"], reverse=True)
# 6. 限制总数量（最多10条）
filtered_results = filtered_results[:10]
logger.info(
f"结果处理完成，耗时: {time.time() - process_start_time:.3f}秒，过滤后剩余{len(filtered_results)}条结果"
)
# 7. 格式化输出
if filtered_results:
format_start_time = time.time()
grouped_results = {}
for result in filtered_results:
topic = result["topic"]
if topic not in grouped_results:
grouped_results[topic] = []
grouped_results[topic].append(result)
# 按主题组织输出
for topic, results in grouped_results.items():
related_info += f"【主题: {topic}】\n"
for _i, result in enumerate(results, 1):
_similarity = result["similarity"]
content = result["content"].strip()
# 调试：为内容添加序号和相似度信息
# related_info += f"{i}. [{similarity:.2f}] {content}\n"
related_info += f"{content}\n"
related_info += "\n"
logger.info(f"格式化输出完成，耗时: {time.time() - format_start_time:.3f}秒")
logger.info(f"知识库检索总耗时: {time.time() - start_time:.3f}秒")
return related_info, grouped_results
def get_info_from_db(
self, query_embedding: list, limit: int = 1, threshold: float = 0.5, return_raw: bool = False
) -> Union[str, list]:
if not query_embedding:
return "" if not return_raw else []
# 使用余弦相似度计算
pipeline = [
{
"$addFields": {
"dotProduct": {
"$reduce": {
"input": {"$range": [0, {"$size": "$embedding"}]},
"initialValue": 0,
"in": {
"$add": [
"$$value",
{
"$multiply": [
{"$arrayElemAt": ["$embedding", "$$this"]},
{"$arrayElemAt": [query_embedding, "$$this"]},
]
},
]
},
}
},
"magnitude1": {
"$sqrt": {
"$reduce": {
"input": "$embedding",
"initialValue": 0,
"in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]},
}
}
},
"magnitude2": {
"$sqrt": {
"$reduce": {
"input": query_embedding,
"initialValue": 0,
"in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]},
}
}
},
}
},
{"$addFields": {"similarity": {"$divide": ["$dotProduct", {"$multiply": ["$magnitude1", "$magnitude2"]}]}}},
{
"$match": {
"similarity": {"$gte": threshold} # 只保留相似度大于等于阈值的结果
}
},
{"$sort": {"similarity": -1}},
{"$limit": limit},
{"$project": {"content": 1, "similarity": 1}},
]
results = list(db.knowledges.aggregate(pipeline))
logger.debug(f"知识库查询结果数量: {len(results)}")
if not results:
return "" if not return_raw else []
if return_raw:
return results
else:
# 返回所有找到的内容，用换行分隔
return "\n".join(str(result["content"]) for result in results)
# subheartflow = SubHeartflow()