From 1e5db5d7e1f379d10f6166388bdb70a137dcad3b Mon Sep 17 00:00:00 2001 From: UnCLAS-Prommer Date: Sun, 3 Aug 2025 19:52:31 +0800 Subject: [PATCH] =?UTF-8?q?=E6=AD=A3=E7=A1=AE=E4=BD=BF=E7=94=A8lpmm?= =?UTF-8?q?=E6=9E=84=E5=BB=BAprompt?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/chat/knowledge/embedding_store.py | 2 - src/chat/knowledge/llm_client.py | 45 ------ src/chat/knowledge/qa_manager.py | 4 - src/chat/planner_actions/planner.py | 2 - src/chat/replyer/default_generator.py | 109 ++++++++++----- src/common/database/database_model.py | 16 --- src/llm_models/utils_model.py | 3 +- src/plugin_system/apis/llm_api.py | 55 +++++++- src/plugin_system/base/base_plugin.py | 4 +- src/plugin_system/core/tool_use.py | 17 +-- .../built_in/knowledge/get_knowledge.py | 131 ------------------ .../built_in/knowledge/lpmm_get_knowledge.py | 2 + 12 files changed, 141 insertions(+), 249 deletions(-) delete mode 100644 src/chat/knowledge/llm_client.py delete mode 100644 src/plugins/built_in/knowledge/get_knowledge.py diff --git a/src/chat/knowledge/embedding_store.py b/src/chat/knowledge/embedding_store.py index 447ef8e7e..d0f6e7744 100644 --- a/src/chat/knowledge/embedding_store.py +++ b/src/chat/knowledge/embedding_store.py @@ -12,8 +12,6 @@ import pandas as pd # import tqdm import faiss -# from .llm_client import LLMClient -# from .lpmmconfig import global_config from .utils.hash import get_sha256 from .global_logger import logger from rich.traceback import install diff --git a/src/chat/knowledge/llm_client.py b/src/chat/knowledge/llm_client.py deleted file mode 100644 index 52d0dca06..000000000 --- a/src/chat/knowledge/llm_client.py +++ /dev/null @@ -1,45 +0,0 @@ -from openai import OpenAI - - -class LLMMessage: - def __init__(self, role, content): - self.role = role - self.content = content - - def to_dict(self): - return {"role": self.role, "content": self.content} - - -class LLMClient: - """LLM客户端，对应一个API服务商""" - - def __init__(self, url, api_key): - self.client = OpenAI( - base_url=url, - api_key=api_key, - ) - - def send_chat_request(self, model, messages): - """发送对话请求，等待返回结果""" - response = self.client.chat.completions.create(model=model, messages=messages, stream=False) - if hasattr(response.choices[0].message, "reasoning_content"): - # 有单独的推理内容块 - reasoning_content = response.choices[0].message.reasoning_content - content = response.choices[0].message.content - else: - # 无单独的推理内容块 - response = response.choices[0].message.content.split("")[-1].split("") - # 如果有推理内容，则分割推理内容和内容 - if len(response) == 2: - reasoning_content = response[0] - content = response[1] - else: - reasoning_content = None - content = response[0] - - return reasoning_content, content - - def send_embedding_request(self, model, text): - """发送嵌入请求，等待返回结果""" - text = text.replace("\n", " ") - return self.client.embeddings.create(input=[text], model=model).data[0].embedding diff --git a/src/chat/knowledge/qa_manager.py b/src/chat/knowledge/qa_manager.py index 1a47767cb..5354447af 100644 --- a/src/chat/knowledge/qa_manager.py +++ b/src/chat/knowledge/qa_manager.py @@ -2,11 +2,7 @@ import time from typing import Tuple, List, Dict, Optional from .global_logger import logger - -# from . import prompt_template from .embedding_store import EmbeddingManager - -# from .llm_client import LLMClient from .kg_manager import KGManager # from .lpmmconfig import global_config diff --git a/src/chat/planner_actions/planner.py b/src/chat/planner_actions/planner.py index 04e17ad6e..85dd5e637 100644 --- a/src/chat/planner_actions/planner.py +++ b/src/chat/planner_actions/planner.py @@ -36,8 +36,6 @@ def init_prompt(): {chat_context_description}，以下是具体的聊天内容 {chat_content_block} - - {moderation_prompt} 现在请你根据{by_what}选择合适的action和触发action的消息: diff --git a/src/chat/replyer/default_generator.py b/src/chat/replyer/default_generator.py index 3c8a54922..c2b6e1cb9 100644 --- a/src/chat/replyer/default_generator.py +++ b/src/chat/replyer/default_generator.py @@ -24,13 +24,13 @@ from src.chat.utils.chat_message_builder import ( replace_user_references_sync, ) from src.chat.express.expression_selector import expression_selector -from src.chat.knowledge.knowledge_lib import qa_manager from src.chat.memory_system.memory_activator import MemoryActivator from src.chat.memory_system.instant_memory import InstantMemory from src.mood.mood_manager import mood_manager from src.person_info.relationship_fetcher import relationship_fetcher_manager from src.person_info.person_info import get_person_info_manager from src.plugin_system.base.component_types import ActionInfo +from src.plugin_system.apis import llm_api logger = get_logger("replyer") @@ -102,6 +102,22 @@ def init_prompt(): "s4u_style_prompt", ) + Prompt( + """ +你是一个专门获取知识的助手。你的名字是{bot_name}。现在是{time_now}。 +群里正在进行的聊天内容： +{chat_history} + +现在，{sender}发送了内容:{target_message},你想要回复ta。 +请仔细分析聊天内容，考虑以下几点： +1. 内容中是否包含需要查询信息的问题 +2. 是否有明确的知识获取指令 + +If you need to use the search tool, please directly call the function "lpmm_search_knowledge". If you do not need to use any tool, simply output "No tool needed". +""", + name="lpmm_get_knowledge_prompt", + ) + class DefaultReplyer: def __init__( @@ -698,7 +714,7 @@ class DefaultReplyer: self._time_and_run_task( self.build_tool_info(chat_talking_prompt_short, reply_to, enable_tool=enable_tool), "tool_info" ), - self._time_and_run_task(get_prompt_info(target, threshold=0.38), "prompt_info"), + self._time_and_run_task(self.get_prompt_info(chat_talking_prompt_short, reply_to), "prompt_info"), ) # 任务名称中英文映射 @@ -1000,6 +1016,63 @@ class DefaultReplyer: logger.debug(f"replyer生成内容: {content}") return content, reasoning_content, model_name, tool_calls + async def get_prompt_info(self, message: str, reply_to: str): + related_info = "" + start_time = time.time() + from src.plugins.built_in.knowledge.lpmm_get_knowledge import SearchKnowledgeFromLPMMTool + if not reply_to: + logger.debug("没有回复对象，跳过获取知识库内容") + return "" + sender, content = self._parse_reply_target(reply_to) + if not content: + logger.debug("回复对象内容为空，跳过获取知识库内容") + return "" + logger.debug(f"获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}") + # 从LPMM知识库获取知识 + try: + # 检查LPMM知识库是否启用 + if not global_config.lpmm_knowledge.enable: + logger.debug("LPMM知识库未启用，跳过获取知识库内容") + return "" + time_now = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) + + bot_name = global_config.bot.nickname + + prompt = await global_prompt_manager.format_prompt( + "lpmm_get_knowledge_prompt", + bot_name=bot_name, + time_now=time_now, + chat_history=message, + sender=sender, + target_message=content, + ) + _, _, _, _, tool_calls = await llm_api.generate_with_model_with_tools( + prompt, + model_config=model_config.model_task_config.tool_use, + tool_options=[SearchKnowledgeFromLPMMTool.get_tool_definition()], + ) + if tool_calls: + result = await self.tool_executor.execute_tool_call(tool_calls[0], SearchKnowledgeFromLPMMTool()) + end_time = time.time() + if not result or not result.get("content"): + logger.debug("从LPMM知识库获取知识失败，返回空知识...") + return "" + found_knowledge_from_lpmm = result.get("content", "") + logger.debug( + f"从LPMM知识库获取知识，相关信息：{found_knowledge_from_lpmm[:100]}...，信息长度: {len(found_knowledge_from_lpmm)}" + ) + related_info += found_knowledge_from_lpmm + logger.debug(f"获取知识库内容耗时: {(end_time - start_time):.3f}秒") + logger.debug(f"获取知识库内容，相关信息：{related_info[:100]}...，信息长度: {len(related_info)}") + + return f"你有以下这些**知识**：\n{related_info}\n请你**记住上面的知识**，之后可能会用到。\n" + else: + logger.debug("从LPMM知识库获取知识失败，可能是从未导入过知识，返回空知识...") + return "" + except Exception as e: + logger.error(f"获取知识库内容时发生异常: {str(e)}") + return "" + def weighted_sample_no_replacement(items, weights, k) -> list: """ @@ -1035,36 +1108,4 @@ def weighted_sample_no_replacement(items, weights, k) -> list: return selected -async def get_prompt_info(message: str, threshold: float): - related_info = "" - start_time = time.time() - - logger.debug(f"获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}") - # 从LPMM知识库获取知识 - try: - # 检查LPMM知识库是否启用 - if qa_manager is None: - logger.debug("LPMM知识库已禁用，跳过知识获取") - return "" - - found_knowledge_from_lpmm = await qa_manager.get_knowledge(message) - - end_time = time.time() - if found_knowledge_from_lpmm is not None: - logger.debug( - f"从LPMM知识库获取知识，相关信息：{found_knowledge_from_lpmm[:100]}...，信息长度: {len(found_knowledge_from_lpmm)}" - ) - related_info += found_knowledge_from_lpmm - logger.debug(f"获取知识库内容耗时: {(end_time - start_time):.3f}秒") - logger.debug(f"获取知识库内容，相关信息：{related_info[:100]}...，信息长度: {len(related_info)}") - - return f"你有以下这些**知识**：\n{related_info}\n请你**记住上面的知识**，之后可能会用到。\n" - else: - logger.debug("从LPMM知识库获取知识失败，可能是从未导入过知识，返回空知识...") - return "" - except Exception as e: - logger.error(f"获取知识库内容时发生异常: {str(e)}") - return "" - - init_prompt() diff --git a/src/common/database/database_model.py b/src/common/database/database_model.py index 1d0b8a397..d2b3acce7 100644 --- a/src/common/database/database_model.py +++ b/src/common/database/database_model.py @@ -281,20 +281,6 @@ class Memory(BaseModel): table_name = "memory" -class Knowledges(BaseModel): - """ - 用于存储知识库条目的模型。 - """ - - content = TextField() # 知识内容的文本 - embedding = TextField() # 知识内容的嵌入向量，存储为 JSON 字符串的浮点数列表 - # 可以添加其他元数据字段，如 source, create_time 等 - - class Meta: - # database = db # 继承自 BaseModel - table_name = "knowledges" - - class Expression(BaseModel): """ 用于存储表达风格的模型。 @@ -382,7 +368,6 @@ def create_tables(): ImageDescriptions, OnlineTime, PersonInfo, - Knowledges, Expression, ThinkingLog, GraphNodes, # 添加图节点表 @@ -408,7 +393,6 @@ def initialize_database(): ImageDescriptions, OnlineTime, PersonInfo, - Knowledges, Expression, Memory, ThinkingLog, diff --git a/src/llm_models/utils_model.py b/src/llm_models/utils_model.py index d2a960f1d..b67640641 100644 --- a/src/llm_models/utils_model.py +++ b/src/llm_models/utils_model.py @@ -181,7 +181,8 @@ class LLMRequest: endpoint="/chat/completions", ) if not content: - raise RuntimeError("获取LLM生成内容失败") + logger.warning("生成的响应为空") + content = "生成的响应为空，请检查模型配置或输入内容是否正确" return content, (reasoning_content, model_info.name, tool_calls) diff --git a/src/plugin_system/apis/llm_api.py b/src/plugin_system/apis/llm_api.py index eaf48556b..9d37a8e34 100644 --- a/src/plugin_system/apis/llm_api.py +++ b/src/plugin_system/apis/llm_api.py @@ -7,8 +7,9 @@ success, response, reasoning, model_name = await llm_api.generate_with_model(prompt, model_config) """ -from typing import Tuple, Dict +from typing import Tuple, Dict, List, Any, Optional from src.common.logger import get_logger +from src.llm_models.payload_content.tool_option import ToolCall from src.llm_models.utils_model import LLMRequest from src.config.config import global_config, model_config from src.config.api_ada_configs import TaskConfig @@ -52,7 +53,11 @@ def get_available_models() -> Dict[str, TaskConfig]: async def generate_with_model( - prompt: str, model_config: TaskConfig, request_type: str = "plugin.generate", **kwargs + prompt: str, + model_config: TaskConfig, + request_type: str = "plugin.generate", + temperature: Optional[float] = None, + max_tokens: Optional[int] = None, ) -> Tuple[bool, str, str, str]: """使用指定模型生成内容 @@ -60,7 +65,6 @@ async def generate_with_model( prompt: 提示词 model_config: 模型配置（从 get_available_models 获取的模型配置） request_type: 请求类型标识 - **kwargs: 其他模型特定参数，如temperature、max_tokens等 Returns: Tuple[bool, str, str, str]: (是否成功, 生成的内容, 推理过程, 模型名称) @@ -70,12 +74,53 @@ async def generate_with_model( logger.info(f"[LLMAPI] 使用模型集合 {model_name_list} 生成内容") logger.debug(f"[LLMAPI] 完整提示词: {prompt}") - llm_request = LLMRequest(model_set=model_config, request_type=request_type, **kwargs) + llm_request = LLMRequest(model_set=model_config, request_type=request_type) - response, (reasoning_content, model_name, _) = await llm_request.generate_response_async(prompt) + response, (reasoning_content, model_name, _) = await llm_request.generate_response_async(prompt, temperature=temperature, max_tokens=max_tokens) return True, response, reasoning_content, model_name except Exception as e: error_msg = f"生成内容时出错: {str(e)}" logger.error(f"[LLMAPI] {error_msg}") return False, error_msg, "", "" + +async def generate_with_model_with_tools( + prompt: str, + model_config: TaskConfig, + tool_options: List[Dict[str, Any]] | None = None, + request_type: str = "plugin.generate", + temperature: Optional[float] = None, + max_tokens: Optional[int] = None, +) -> Tuple[bool, str, str, str, List[ToolCall] | None]: + """使用指定模型和工具生成内容 + + Args: + prompt: 提示词 + model_config: 模型配置（从 get_available_models 获取的模型配置） + tool_options: 工具选项列表 + request_type: 请求类型标识 + temperature: 温度参数 + max_tokens: 最大token数 + + Returns: + Tuple[bool, str, str, str]: (是否成功, 生成的内容, 推理过程, 模型名称) + """ + try: + model_name_list = model_config.model_list + logger.info(f"[LLMAPI] 使用模型集合 {model_name_list} 生成内容") + logger.debug(f"[LLMAPI] 完整提示词: {prompt}") + + llm_request = LLMRequest(model_set=model_config, request_type=request_type) + + response, (reasoning_content, model_name, tool_call) = await llm_request.generate_response_async( + prompt, + tools=tool_options, + temperature=temperature, + max_tokens=max_tokens + ) + return True, response, reasoning_content, model_name, tool_call + + except Exception as e: + error_msg = f"生成内容时出错: {str(e)}" + logger.error(f"[LLMAPI] {error_msg}") + return False, error_msg, "", "", None diff --git a/src/plugin_system/base/base_plugin.py b/src/plugin_system/base/base_plugin.py index 3cf82390e..ea28c5143 100644 --- a/src/plugin_system/base/base_plugin.py +++ b/src/plugin_system/base/base_plugin.py @@ -3,10 +3,11 @@ from typing import List, Type, Tuple, Union from .plugin_base import PluginBase from src.common.logger import get_logger -from src.plugin_system.base.component_types import ActionInfo, CommandInfo, EventHandlerInfo +from src.plugin_system.base.component_types import ActionInfo, CommandInfo, EventHandlerInfo, ToolInfo from .base_action import BaseAction from .base_command import BaseCommand from .base_events_handler import BaseEventHandler +from .base_tool import BaseTool logger = get_logger("base_plugin") @@ -31,6 +32,7 @@ class BasePlugin(PluginBase): Tuple[ActionInfo, Type[BaseAction]], Tuple[CommandInfo, Type[BaseCommand]], Tuple[EventHandlerInfo, Type[BaseEventHandler]], + Tuple[ToolInfo, Type[BaseTool]], ] ]: """获取插件包含的组件列表 diff --git a/src/plugin_system/core/tool_use.py b/src/plugin_system/core/tool_use.py index 65cceb006..7a5eee311 100644 --- a/src/plugin_system/core/tool_use.py +++ b/src/plugin_system/core/tool_use.py @@ -1,6 +1,7 @@ import time from typing import List, Dict, Tuple, Optional, Any from src.plugin_system.apis.tool_api import get_llm_available_tool_definitions, get_tool_instance +from src.plugin_system.base.base_tool import BaseTool from src.plugin_system.core.global_announcement_manager import global_announcement_manager from src.llm_models.utils_model import LLMRequest from src.llm_models.payload_content import ToolCall @@ -114,7 +115,7 @@ class ToolExecutor: ) # 执行工具调用 - tool_results, used_tools = await self._execute_tool_calls(tool_calls) + tool_results, used_tools = await self.execute_tool_calls(tool_calls) # 缓存结果 if tool_results: @@ -133,7 +134,7 @@ class ToolExecutor: user_disabled_tools = global_announcement_manager.get_disabled_chat_tools(self.chat_id) return [definition for name, definition in all_tools if name not in user_disabled_tools] - async def _execute_tool_calls(self, tool_calls: Optional[List[ToolCall]]) -> Tuple[List[Dict[str, Any]], List[str]]: + async def execute_tool_calls(self, tool_calls: Optional[List[ToolCall]]) -> Tuple[List[Dict[str, Any]], List[str]]: """执行工具调用 Args: @@ -158,7 +159,7 @@ class ToolExecutor: logger.debug(f"{self.log_prefix}执行工具: {tool_name}") # 执行工具 - result = await self._execute_tool_call(tool_call) + result = await self.execute_tool_call(tool_call) if result: tool_info = { @@ -191,7 +192,7 @@ class ToolExecutor: return tool_results, used_tools - async def _execute_tool_call(self, tool_call: ToolCall) -> Optional[Dict[str, Any]]: + async def execute_tool_call(self, tool_call: ToolCall, tool_instance: Optional[BaseTool] = None) -> Optional[Dict[str, Any]]: # sourcery skip: use-assigned-variable """执行单个工具调用 @@ -207,7 +208,7 @@ class ToolExecutor: function_args["llm_called"] = True # 标记为LLM调用 # 获取对应工具实例 - tool_instance = get_tool_instance(function_name) + tool_instance = tool_instance or get_tool_instance(function_name) if not tool_instance: logger.warning(f"未知工具名称: {function_name}") return None @@ -294,7 +295,7 @@ class ToolExecutor: if expired_keys: logger.debug(f"{self.log_prefix}清理了{len(expired_keys)}个过期缓存") - async def execute_specific_tool(self, tool_name: str, tool_args: Dict) -> Optional[Dict]: + async def execute_specific_tool_simple(self, tool_name: str, tool_args: Dict) -> Optional[Dict]: """直接执行指定工具 Args: @@ -314,7 +315,7 @@ class ToolExecutor: logger.info(f"{self.log_prefix}直接执行工具: {tool_name}") - result = await self._execute_tool_call(tool_call) + result = await self.execute_tool_call(tool_call) if result: tool_info = { @@ -405,7 +406,7 @@ results, used_tools, prompt = await executor.execute_from_chat_message( ) # 5. 直接执行特定工具 -result = await executor.execute_specific_tool( +result = await executor.execute_specific_tool_simple( tool_name="get_knowledge", tool_args={"query": "机器学习"} ) diff --git a/src/plugins/built_in/knowledge/get_knowledge.py b/src/plugins/built_in/knowledge/get_knowledge.py deleted file mode 100644 index ce90cb680..000000000 --- a/src/plugins/built_in/knowledge/get_knowledge.py +++ /dev/null @@ -1,131 +0,0 @@ -import json # Added for parsing embedding -import math # Added for cosine similarity -from typing import Any, Union, List # Added List - -from src.chat.utils.utils import get_embedding -from src.common.database.database_model import Knowledges # Updated import -from src.common.logger import get_logger -from src.plugin_system import BaseTool, ToolParamType - - -logger = get_logger("get_knowledge_tool") - - -class SearchKnowledgeTool(BaseTool): - """从知识库中搜索相关信息的工具""" - - name = "search_knowledge" - description = "使用工具从知识库中搜索相关信息" - parameters = [ - ("query", ToolParamType.STRING, "搜索查询关键词", True, None), - ("threshold", ToolParamType.FLOAT, "相似度阈值，0.0到1.0之间", False, None), - ] - - async def execute(self, function_args: dict[str, Any]) -> dict[str, Any]: - """执行知识库搜索 - - Args: - function_args: 工具参数 - - Returns: - dict: 工具执行结果 - """ - query = "" # Initialize query to ensure it's defined in except block - try: - query = function_args.get("query") - threshold = function_args.get("threshold", 0.4) - - # 调用知识库搜索 - embedding = await get_embedding(query, request_type="info_retrieval") - if embedding: - knowledge_info = self.get_info_from_db(embedding, limit=3, threshold=threshold) - if knowledge_info: - content = f"你知道这些知识: {knowledge_info}" - else: - content = f"你不太了解有关{query}的知识" - return {"type": "knowledge", "id": query, "content": content} - return {"type": "info", "id": query, "content": f"无法获取关于'{query}'的嵌入向量，你知识库炸了"} - except Exception as e: - logger.error(f"知识库搜索工具执行失败: {str(e)}") - return {"type": "info", "id": query, "content": f"知识库搜索失败，炸了: {str(e)}"} - - @staticmethod - def _cosine_similarity(vec1: List[float], vec2: List[float]) -> float: - """计算两个向量之间的余弦相似度""" - dot_product = sum(p * q for p, q in zip(vec1, vec2, strict=False)) - magnitude1 = math.sqrt(sum(p * p for p in vec1)) - magnitude2 = math.sqrt(sum(q * q for q in vec2)) - if magnitude1 == 0 or magnitude2 == 0: - return 0.0 - return dot_product / (magnitude1 * magnitude2) - - @staticmethod - def get_info_from_db( - query_embedding: list[float], limit: int = 1, threshold: float = 0.5, return_raw: bool = False - ) -> Union[str, list]: - """从数据库中获取相关信息 - - Args: - query_embedding: 查询的嵌入向量 - limit: 最大返回结果数 - threshold: 相似度阈值 - return_raw: 是否返回原始结果 - - Returns: - Union[str, list]: 格式化的信息字符串或原始结果列表 - """ - if not query_embedding: - return [] if return_raw else "" - - similar_items = [] - try: - all_knowledges = Knowledges.select() - for item in all_knowledges: - try: - item_embedding_str = item.embedding - if not item_embedding_str: - logger.warning(f"Knowledge item ID {item.id} has empty embedding string.") - continue - item_embedding = json.loads(item_embedding_str) - if not isinstance(item_embedding, list) or not all( - isinstance(x, (int, float)) for x in item_embedding - ): - logger.warning(f"Knowledge item ID {item.id} has invalid embedding format after JSON parsing.") - continue - except json.JSONDecodeError: - logger.warning(f"Failed to parse embedding for knowledge item ID {item.id}") - continue - except AttributeError: - logger.warning(f"Knowledge item ID {item.id} missing 'embedding' attribute or it's not a string.") - continue - - similarity = SearchKnowledgeTool._cosine_similarity(query_embedding, item_embedding) - - if similarity >= threshold: - similar_items.append({"content": item.content, "similarity": similarity, "raw_item": item}) - - # 按相似度降序排序 - similar_items.sort(key=lambda x: x["similarity"], reverse=True) - - # 应用限制 - results = similar_items[:limit] - logger.debug(f"知识库查询后，符合条件的结果数量: {len(results)}") - - except Exception as e: - logger.error(f"从 Peewee 数据库获取知识信息失败: {str(e)}") - return [] if return_raw else "" - - if not results: - return [] if return_raw else "" - - if return_raw: - # Peewee 模型实例不能直接序列化为 JSON，如果需要原始模型，调用者需要处理 - # 这里返回包含内容和相似度的字典列表 - return [{"content": r["content"], "similarity": r["similarity"]} for r in 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) - - -# 注册工具 -# register_tool(SearchKnowledgeTool) diff --git a/src/plugins/built_in/knowledge/lpmm_get_knowledge.py b/src/plugins/built_in/knowledge/lpmm_get_knowledge.py index da20c348b..fd3d811b2 100644 --- a/src/plugins/built_in/knowledge/lpmm_get_knowledge.py +++ b/src/plugins/built_in/knowledge/lpmm_get_knowledge.py @@ -1,6 +1,7 @@ from typing import Dict, Any from src.common.logger import get_logger +from src.config.config import global_config from src.chat.knowledge.knowledge_lib import qa_manager from src.plugin_system import BaseTool, ToolParamType @@ -16,6 +17,7 @@ class SearchKnowledgeFromLPMMTool(BaseTool): ("query", ToolParamType.STRING, "搜索查询关键词", True, None), ("threshold", ToolParamType.FLOAT, "相似度阈值，0.0到1.0之间", False, None), ] + available_for_llm = global_config.lpmm_knowledge.enable async def execute(self, function_args: Dict[str, Any]) -> Dict[str, Any]: """执行知识库搜索