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reActLLMDemo
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old-tom 7 months ago
parent 7793ee0ce0
commit 7a0e526eff
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35
env.toml
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[base]
# 向量库相似度阈值
similarity_threshold = 0.93
# 模型供应商
model_form = 'siliconflow'
[siliconflow]
# 硅基流动
# 密钥
api_key = 'sk-rdoyeoxcyvqjynufqjmewmipwtvrhjjzerqlinpqxiodyafp'
# 模型名称
model = 'Qwen/QwQ-32B'
# API地址
base_url = 'https://api.siliconflow.cn/v1/'
# 最大token数
max_tokens = 4096
# 温度系数
temperature = 0.6
# 是否流式返回
streaming = true
[ark]
# 火山引擎
# 密钥
api_key = '4eefc827-187f-4756-9637-7e0153c93d81'
# 模型名称
model = 'deepseek-r1-250120'
# API地址
base_url = 'https://ark.cn-beijing.volces.com/api/v3/'
# 最大token数
max_tokens = 4096
# 温度系数
temperature = 0.6
# 是否流式返回
streaming = true

83
llmagent/llm_agent.py
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@ -5,4 +5,87 @@
# @File : llm_agent
# @Project : reActLLMDemo
# @Desc : 代理
from typing import Annotated
from langgraph.checkpoint.memory import MemorySaver
from langchain_openai import ChatOpenAI
from llmagent.llm_config import LLMConfigLoader
from llmagent.llm_config import base_conf
from llmtools.tool_impl import tools, tool_node
from langchain_core.messages import AnyMessage
from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages
from langgraph.prebuilt import tools_condition
# 内存记忆
memory = MemorySaver()
# 初始化LLM模型
llm_conf = LLMConfigLoader.load(item_name=base_conf.model_form)
llm = ChatOpenAI(
model=llm_conf.model, api_key=llm_conf.api_key,
base_url=llm_conf.base_url, max_tokens=llm_conf.max_tokens,
temperature=llm_conf.temperature,
streaming=llm_conf.streaming
)
# 绑定工具
llm_with_tools = llm.bind_tools(tools)
class AgentState(TypedDict):
"""
状态机
add_messages 函数会自动合并message到一个list中，例如HumanMessage\AIMessage
"""
messages: Annotated[list[AnyMessage], add_messages]
graph_builder = StateGraph(AgentState)
def chat(state: AgentState):
"""
LLM单轮对话
:param state: 状态机
LLM需要从状态机获取message
:return:
"""
return {"messages": [llm_with_tools.invoke(state["messages"])]}
# LLM节点
graph_builder.add_node("chat_llm", chat)
# 工具节点
graph_builder.add_node("tools", tool_node)
graph_builder.add_edge(START, "chat_llm")
graph_builder.add_edge("chat_llm", END)
# 添加条件边，tools_condition 是官方实现的函数，用于判断是否应该调用tool或者直接结束
graph_builder.add_conditional_edges("chat_llm", tools_condition)
graph_builder.add_edge("tools", "chat_llm")
# checkpointer 是检查点设置
graph = graph_builder.compile(name='语音助手', checkpointer=memory)
def stream_graph_updates(user_input: str):
config = {"configurable": {"thread_id": "1"}}
for chunk, metadata in graph.stream({"messages": [{"role": "user", "content": user_input}]}, config,
stream_mode='messages'):
if chunk.content:
print(chunk.content, end='', flush=True)
print('\n')
while True:
try:
user_input = input("User: ")
if user_input.lower() in ["quit", "exit", "q"]:
print("Goodbye!")
break
stream_graph_updates(user_input)
except:
user_input = "What do you know about LangGraph?"
print("User: " + user_input)
stream_graph_updates(user_input)
break

56
llmagent/llm_config.py
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@ -5,3 +5,59 @@
# @File : llm_config
# @Project : reActLLMDemo
# @Desc : llm配置文件解析
import os
from pydantic import BaseModel
import toml
# 默认配置文件名
DEFAULT_CONF_NAME = 'env.toml'
path = os.path.dirname(__file__)
path = os.path.dirname(path)
# 默认配置文件位置
DEFAULT_CONF_PATH = os.path.join(path, DEFAULT_CONF_NAME)
class ConfigNotFoundError(Exception):
"""
配置不存在异常
"""
def __init__(self, msg):
Exception.__init__(self, msg)
def load_env():
if not os.path.isfile(DEFAULT_CONF_PATH):
raise ConfigNotFoundError(f'模型配置文件{DEFAULT_CONF_NAME}不存在')
return toml.load(DEFAULT_CONF_PATH)
conf = load_env()
class LLMConf(BaseModel):
api_key: str
model: str
base_url: str
max_tokens: int
temperature: float
streaming: bool = True
class LLMConfigLoader(object):
@staticmethod
def load(item_name) -> LLMConf:
"""
校验并加载配置
:return:
"""
return LLMConf(**conf[item_name])
class BaseConf(BaseModel):
similarity_threshold: float
model_form: str
base_conf = BaseConf(**conf['base'])

7
llmtools/__init__.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2025/4/17 15:40
# @Author : old-tom
# @File : __init__.py
# @Project : reActLLMDemo
# @Desc :

89
llmtools/tool_impl.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2025/4/12 14:55
# @Author : old-tom
# @File : tool_impl
# @Project : sttFunctionCallBackend
# @Desc : 工具定义
from typing import Annotated
from langchain_core.tools import tool
from vector_db import query_vector_db
from log_conf import log
from llmagent.llm_config import base_conf
from langgraph.prebuilt import ToolNode
@tool("play_video", description="播放、查看、打开实时视频")
def play_video(camera_name: Annotated[str, "相机名称,例如：南卡口1号相机"]) -> str:
camera_info = query_camera_from_db(camera_name)
log.info('【function】play_video 输入 [{}],向量库返回{}', camera_name, camera_info)
if camera_info:
if len(camera_info) > 1:
hit_camera_names = [x['carme_name'] for x in camera_info]
return f"找到以下相机，请选择一个：{hit_camera_names}"
else:
# TODO 调用业务系统
return f"正在打开{camera_name},请等待操作完成"
else:
return "未找到该相机,请尝试其他名称"
@tool("split_screen", description="切换分屏")
def split_screen(split_n: Annotated[int, "要切换的分屏数量,整数并且大于0，例如：1分屏、2分屏"]) -> str:
return f"正在切换到{split_n}分屏,请等待操作完成"
@tool("play_video_record", description="播放、打开录像")
def play_video_record(camera_name: Annotated[str, "相机名称,例如：南卡口1号相机"],
start_time: Annotated[str, "录像开始时间,格式为yyyy-MM-dd hh:mm:ss，例 2025-03-16 01:00:00"],
end_time: Annotated[str, "录像结束时间,格式为yyyy-MM-dd hh:mm:ss，例 2025-03-16 02:09:31"]) -> str:
log.info('【function】play_video_record 输入 [{},{},{}]', camera_name, start_time, end_time)
return f"正在打开{camera_name}的录像,请等待操作完成"
@tool("switch_page", description="打开、跳转页面")
def switch_page(page_name: Annotated[str, "页面中文名称或者缩写，例：人员核查、系统日志、设备管理、首页"]) -> str:
log.info('【function】switch_page 输入 [{}]', page_name)
return f"正在打开{page_name},请等待操作完成"
@tool("zoom_in", description="放大电子地图")
def zoom_in(level_n: Annotated[int, "放大等级,整数并且大于0小于5，例如：放大1级、放大2级"]) -> str:
log.info('【function】zoom_in 输入 [{}]', level_n)
return f"正在放大电子地图至{level_n}级,请等待操作完成"
@tool("view_flight_details", description="查询指定机场指定航班及时间的出入境人员明细")
def view_flight_details(
airport_name: Annotated[str, "机场名称，简体中文，可以是缩写，例如：成都天府机场、天府机场、长水机场、上海浦东机场"],
flight_code: Annotated[
str, "航班编号,由字母+数字组成的完整编号,若编号包含多余字符（如标点符号），需过滤后保留有效部分"],
flight_date: Annotated[str, "提取完整日期（年月日），自动补零至标准格式 yyyy-MM-dd, 例：2025-03-16"],
ie_type: Annotated[str, "出入境类型，仅识别'入境'或'出境'两种类型"]) -> str:
log.info('【function】view_flight_details 输入 [{},{},{},{}]', airport_name, flight_code, flight_date, ie_type)
return f"{airport_name}航班号{flight_code}{flight_date}{ie_type}数据,共100人乘机,起飞准时,晚点降落"
def query_camera_from_db(camera_name: str, top_n: int = 3) -> str:
"""
相机名称查询向量库,根据相似度阈值取top_one或者top_n
:param camera_name: 相机名称
:param top_n: 返回前N个
:return:
"""
rt = query_vector_db(camera_name)
if rt:
log.info('【function】相机相似度检索查询[{}],返回 {}', camera_name, rt)
# 判断相似度最高的相机是否超过阈值
top_one = rt['hits'][0]
# 相似度评分
score = top_one['_score']
if score > base_conf.similarity_threshold:
return rt['hits'][0:1]
else:
return rt['hits'][0:top_n]
tools = [play_video, split_screen, play_video_record, switch_page, zoom_in, view_flight_details]
tool_node = ToolNode(tools=tools)

44
log_conf.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2025/4/12 14:51
# @Author : old-tom
# @File : log_conf
# @Project : sttFunctionCallBackend
# @Desc :
import sys
import os
from loguru import logger
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
# 日志输出路径
LOG_PATH = os.path.join(BASE_DIR, r'logout/logout.log')
class Logger(object):
def __init__(self):
self.logger = logger
self.logger.remove()
self.logger.add(sys.stdout,
format="<green>{time:YYYY-MM-DD HH:mm:ss}</green> | " # 颜色>时间
"{process.name} | " # 进程名
"{thread.name} | " # 进程名
"<cyan>{module}</cyan>.<cyan>{function}</cyan>" # 模块名.方法名
":<cyan>{line}</cyan> | " # 行号
"<level>{level}</level>: " # 等级
"<level>{message}</level>", # 日志内容
)
# 输出到文件的格式,注释下面的add',则关闭日志写入
self.logger.add(LOG_PATH, level='DEBUG',
format='{time:YYYY-MM-DD HH:mm:ss} - ' # 时间
"{process.name} | " # 进程名
"{thread.name} | " # 进程名
'{module}.{function}:{line} - {level} -{message}', # 模块名.方法名:行号
rotation="10 MB")
def get_logger(self):
return self.logger
log = Logger().get_logger()

3
pyproject.toml
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@ -8,6 +8,9 @@ dependencies = [
"langchain-community>=0.3.21",
"langchain-openai>=0.3.13",
"langgraph>=0.3.30",
"loguru>=0.7.3",
"marqo>=3.12.0",
"toml>=0.10.2",
]
[[tool.uv.index]]

52
uv.lock
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@ -558,6 +558,34 @@ wheels = [
{ url = "https://mirrors.aliyun.com/pypi/packages/64/c3/5db3d0977bb53e16eab834f2eea6e1c68d327e2f5c25b88f6506ef06e692/langsmith-0.3.31-py3-none-any.whl", hash = "sha256:ee780ae3eac69998c336817c0b9f5ccfecaaaa3e67d94b7ef726b58ab3e72a25" },
]
[[package]]
name = "loguru"
version = "0.7.3"
source = { registry = "https://mirrors.aliyun.com/pypi/simple/" }
dependencies = [
{ name = "colorama", marker = "sys_platform == 'win32'" },
{ name = "win32-setctime", marker = "sys_platform == 'win32'" },
]
sdist = { url = "https://mirrors.aliyun.com/pypi/packages/3a/05/a1dae3dffd1116099471c643b8924f5aa6524411dc6c63fdae648c4f1aca/loguru-0.7.3.tar.gz", hash = "sha256:19480589e77d47b8d85b2c827ad95d49bf31b0dcde16593892eb51dd18706eb6" }
wheels = [
{ url = "https://mirrors.aliyun.com/pypi/packages/0c/29/0348de65b8cc732daa3e33e67806420b2ae89bdce2b04af740289c5c6c8c/loguru-0.7.3-py3-none-any.whl", hash = "sha256:31a33c10c8e1e10422bfd431aeb5d351c7cf7fa671e3c4df004162264b28220c" },
]
[[package]]
name = "marqo"
version = "3.12.0"
source = { registry = "https://mirrors.aliyun.com/pypi/simple/" }
dependencies = [
{ name = "packaging" },
{ name = "pydantic" },
{ name = "requests" },
{ name = "urllib3" },
]
sdist = { url = "https://mirrors.aliyun.com/pypi/packages/b2/36/3c5678593df50ff8795056cd46224b32febe414f5900af8a569b8aac4192/marqo-3.12.0.tar.gz", hash = "sha256:5c2e0a01fc3a7be5a7db8a4630f2f7d7164b09cf8d72480a5f6db68550f24afb" }
wheels = [
{ url = "https://mirrors.aliyun.com/pypi/packages/b1/6f/b478951b8e3b3ff32f2af36bbf9cd28b0cf232894f25ec93294f6ace3aa7/marqo-3.12.0-py3-none-any.whl", hash = "sha256:e30e33308465629ff5b245d1072cb5a94606891fceb84d8cd873995066bd4b17" },
]
[[package]]
name = "marshmallow"
version = "3.26.1"
@ -944,6 +972,9 @@ dependencies = [
{ name = "langchain-community" },
{ name = "langchain-openai" },
{ name = "langgraph" },
{ name = "loguru" },
{ name = "marqo" },
{ name = "toml" },
]
[package.metadata]
@ -951,6 +982,9 @@ requires-dist = [
{ name = "langchain-community", specifier = ">=0.3.21" },
{ name = "langchain-openai", specifier = ">=0.3.13" },
{ name = "langgraph", specifier = ">=0.3.30" },
{ name = "loguru", specifier = ">=0.7.3" },
{ name = "marqo", specifier = ">=3.12.0" },
{ name = "toml", specifier = ">=0.10.2" },
]
[[package]]
@ -1089,6 +1123,15 @@ wheels = [
{ url = "https://mirrors.aliyun.com/pypi/packages/de/a8/8f499c179ec900783ffe133e9aab10044481679bb9aad78436d239eee716/tiktoken-0.9.0-cp313-cp313-win_amd64.whl", hash = "sha256:5ea0edb6f83dc56d794723286215918c1cde03712cbbafa0348b33448faf5b95" },
]
[[package]]
name = "toml"
version = "0.10.2"
source = { registry = "https://mirrors.aliyun.com/pypi/simple/" }
sdist = { url = "https://mirrors.aliyun.com/pypi/packages/be/ba/1f744cdc819428fc6b5084ec34d9b30660f6f9daaf70eead706e3203ec3c/toml-0.10.2.tar.gz", hash = "sha256:b3bda1d108d5dd99f4a20d24d9c348e91c4db7ab1b749200bded2f839ccbe68f" }
wheels = [
{ url = "https://mirrors.aliyun.com/pypi/packages/44/6f/7120676b6d73228c96e17f1f794d8ab046fc910d781c8d151120c3f1569e/toml-0.10.2-py2.py3-none-any.whl", hash = "sha256:806143ae5bfb6a3c6e736a764057db0e6a0e05e338b5630894a5f779cabb4f9b" },
]
[[package]]
name = "tqdm"
version = "4.67.1"
@ -1144,6 +1187,15 @@ wheels = [
{ url = "https://mirrors.aliyun.com/pypi/packages/6b/11/cc635220681e93a0183390e26485430ca2c7b5f9d33b15c74c2861cb8091/urllib3-2.4.0-py3-none-any.whl", hash = "sha256:4e16665048960a0900c702d4a66415956a584919c03361cac9f1df5c5dd7e813" },
]
[[package]]
name = "win32-setctime"
version = "1.2.0"
source = { registry = "https://mirrors.aliyun.com/pypi/simple/" }
sdist = { url = "https://mirrors.aliyun.com/pypi/packages/b3/8f/705086c9d734d3b663af0e9bb3d4de6578d08f46b1b101c2442fd9aecaa2/win32_setctime-1.2.0.tar.gz", hash = "sha256:ae1fdf948f5640aae05c511ade119313fb6a30d7eabe25fef9764dca5873c4c0" }
wheels = [
{ url = "https://mirrors.aliyun.com/pypi/packages/e1/07/c6fe3ad3e685340704d314d765b7912993bcb8dc198f0e7a89382d37974b/win32_setctime-1.2.0-py3-none-any.whl", hash = "sha256:95d644c4e708aba81dc3704a116d8cbc974d70b3bdb8be1d150e36be6e9d1390" },
]
[[package]]
name = "xxhash"
version = "3.5.0"

632
vector_db.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2025/3/16 20:09
# @Author : old-tom
# @File : vector_agent
# @Project : llmFunctionCallDemo
# @Desc : 向量数据库,解决相似度查询,例如 相机名称
import marqo
# 索引名称
INDEX_NAME = 'test_index'
# 初始化marqo
mq = marqo.Client(url='http://171.92.0.3:8882')
# mq.delete_index(INDEX_NAME)
# settings = {
# "treatUrlsAndPointersAsImages": False,
# "model": "hf/bge-large-zh-v1.5",
# "normalizeEmbeddings": True,
# }
def create_and_set_index():
"""
全局只能调用一次
:return:
"""
mq.create_index(INDEX_NAME, model='hf/e5-base-v2')
# 添加文档(测试用)
mq.index(INDEX_NAME).add_documents([
{
"carme_name": "中方国门AI算法",
"ip": "192.168.10.80",
"location": "国门顶部"
},
{
"carme_name": "中方国门AI算法0102",
"ip": "192.168.10.80",
"location": "国门顶部"
},
{
"carme_name": "中方国门入境1",
"ip": "192.168.10.77",
"location": "国门通道"
},
{
"carme_name": "中方国门入境2",
"ip": "192.168.10.79",
"location": "国门通道"
},
{
"carme_name": "中方国门入境摄像头中",
"ip": "192.168.10.72",
"location": "登临检验"
},
{
"carme_name": "中方国门入境摄像头右",
"ip": "192.168.10.74",
"location": "登临检验"
},
{
"carme_name": "中方国门入境摄像头左",
"ip": "192.168.10.75",
"location": "登临检验"
},
{
"carme_name": "中方国门出境1",
"ip": "192.168.10.76",
"location": "国门通道"
},
{
"carme_name": "中方国门出境2",
"ip": "192.168.10.78",
"location": "国门通道"
},
{
"carme_name": "中方国门出境摄像头右",
"ip": "192.168.10.71",
"location": "登临检验"
},
{
"carme_name": "中方国门出境摄像头左",
"ip": "192.168.10.73",
"location": "登临检验"
},
{
"carme_name": "中方国门-面向国门球机",
"ip": "192.168.10.215",
"location": "国门通道拐角"
},
{
"carme_name": "中方国门高点1",
"ip": "192.168.10.70",
"location": "国门顶部"
},
{
"carme_name": "北卡口AI摄像头全景",
"ip": "192.168.10.30",
"location": "北卡口对面"
},
{
"carme_name": "北卡口AI摄像头细节",
"ip": "192.168.10.30",
"location": "北卡口对面"
},
{
"carme_name": "北卡口入境摄像头入场4号通道",
"ip": "192.168.10.112",
"location": "入场通道"
},
{
"carme_name": "北卡口入境摄像头入场5号通道",
"ip": "192.168.10.114",
"location": "入场通道"
},
{
"carme_name": "北卡口入境摄像头入场6号通道",
"ip": "192.168.10.117",
"location": "入场通道"
},
{
"carme_name": "北卡口入境摄像头出场1号通道",
"ip": "192.168.10.115",
"location": "出场通道"
},
{
"carme_name": "北卡口入境摄像头出场2号通道",
"ip": "192.168.10.116",
"location": "出场通道"
},
{
"carme_name": "北卡口入境摄像头出场3号通道",
"ip": "192.168.10.113",
"location": "出场通道"
},
{
"carme_name": "北卡口出口道路监控",
"ip": "192.168.10.153",
"location": "路口"
},
{
"carme_name": "能投大厦高点1",
"ip": "192.168.10.89",
"location": "能投楼顶"
},
{
"carme_name": "南卡口AI算法2",
"ip": "192.168.10.210",
"location": "南卡口顶部"
},
{
"carme_name": "南卡口AI算法20102",
"ip": "192.168.10.210",
"location": "南卡口顶部"
},
{
"carme_name": "南卡口AI算法识别摄像机",
"ip": "192.168.10.91",
"location": "南卡口顶部"
},
{
"carme_name": "南卡口AI算法识别摄像机0102",
"ip": "192.168.10.91",
"location": "南卡口顶部"
},
{
"carme_name": "南卡口出境摄像头1号通道",
"ip": "192.168.10.100",
"location": "出境通道"
},
{
"carme_name": "南卡口出境摄像头2号通道",
"ip": "192.168.10.103",
"location": "出境通道"
},
{
"carme_name": "南卡口出境摄像头3号通道",
"ip": "192.168.10.104",
"location": "出境通道"
},
{
"carme_name": "南卡口出境摄像头4号通道",
"ip": "192.168.10.102",
"location": "出境通道"
},
{
"carme_name": "南卡口出境摄像头5号通道",
"ip": "192.168.10.99",
"location": "出境通道"
},
{
"carme_name": "南卡口出境摄像头6号通道",
"ip": "192.168.10.101",
"location": "出境通道"
},
{
"carme_name": "南卡口入境摄像头7号通道",
"ip": "192.168.10.98",
"location": "入境通道"
},
{
"carme_name": "南卡口入境摄像头8号通道",
"ip": "192.168.10.93",
"location": "入境通道"
},
{
"carme_name": "南卡口入境摄像头9号通道",
"ip": "192.168.10.97",
"location": "入境通道"
},
{
"carme_name": "南卡口入境摄像头10号通道",
"ip": "192.168.10.96",
"location": "入境通道"
},
{
"carme_name": "南卡口入境摄像头11号通道",
"ip": "192.168.10.94",
"location": "入境通道"
},
{
"carme_name": "南卡口入境摄像头12号通道",
"ip": "192.168.10.95",
"location": "入境通道"
},
{
"carme_name": "南卡口高点-1",
"ip": "192.168.10.90",
"location": "南卡口顶部"
},
{
"carme_name": "南卡口高点-2",
"ip": "192.168.10.92",
"location": "南卡口顶部"
},
{
"carme_name": "南卡口高点-3",
"ip": "192.168.3.12",
"location": "南卡口顶部"
},
{
"carme_name": "1.3公里封闭道路入境出场1",
"ip": "192.168.10.82",
"location": "南卡口顶部"
},
{
"carme_name": "1.3公里封闭道路入境出场2",
"ip": "192.168.10.88",
"location": "南卡口顶部"
},
{
"carme_name": "1.3公里封闭道路入境出场20102",
"ip": "192.168.10.88",
"location": "南卡口顶部"
},
{
"carme_name": "1.3公里封闭道路入境摄像头入场2",
"ip": "192.168.10.87",
"location": "南卡口顶部"
},
{
"carme_name": "1.3公里封闭道路入境摄像头入场20102",
"ip": "192.168.10.87",
"location": "南卡口顶部"
},
{
"carme_name": "1.3公里封闭道路入境入场1",
"ip": "192.168.10.81",
"location": "南卡口顶部"
},
{
"carme_name": "H9861号口摄像头入场",
"ip": "192.168.10.107",
"location": "H986停车场出入口"
},
{
"carme_name": "H9862号口摄像头出场",
"ip": "192.168.10.109",
"location": "H987停车场出入口"
},
{
"carme_name": "H9862号口摄像头入场",
"ip": "192.168.10.190",
"location": "H988停车场出入口"
},
{
"carme_name": "车辆定位AI摄像头",
"ip": "192.168.10.29",
"location": "H988停车场山上"
},
{
"carme_name": "车辆定位AI摄像头0102",
"ip": "192.168.10.29",
"location": "H989停车场山上"
},
{
"carme_name": "北山货场森林公园制高点",
"ip": "192.168.10.191",
"location": "H990停车场山上"
},
{
"carme_name": "边民互市入境摄像头出场",
"ip": "192.168.10.119",
"location": "边民互市"
},
{
"carme_name": "边民互市入境摄像头入场",
"ip": "192.168.10.118",
"location": "出入口"
},
{
"carme_name": "车辆缓冲区1号门入口",
"ip": "192.168.10.193",
"location": "出入口"
},
{
"carme_name": "车辆缓冲区2号门入口",
"ip": "192.168.10.195",
"location": "出入口"
},
{
"carme_name": "车辆缓冲区2号门出口",
"ip": "192.168.10.194",
"location": "出入口"
},
{
"carme_name": "车辆缓冲区1号门出口",
"ip": "192.168.10.192",
"location": "出入口"
},
{
"carme_name": "大贸查验场进口入境摄像头入场东口",
"ip": "192.168.10.181",
"location": "出入口"
},
{
"carme_name": "大贸查验场进口入境摄像头出场东口",
"ip": "192.168.10.180",
"location": "出入口"
},
{
"carme_name": "大贸查验场（进口）入境摄像头入场（西口）",
"ip": "192.168.10.106",
"location": "出入口"
},
{
"carme_name": "大贸查验场（进口）高点1",
"ip": "192.168.10.156",
"location": "高点"
},
{
"carme_name": "大贸查验场出口出境摄像头出场2",
"ip": "192.168.10.110",
"location": "出入口"
},
{
"carme_name": "大贸查验场出口出境摄像头入场左",
"ip": "192.168.10.184",
"location": "出入口"
},
{
"carme_name": "大贸查验场出口出境摄像头出场1",
"ip": "192.168.10.111",
"location": "出入口"
},
{
"carme_name": "北山高速收费站入境摄像头入场出方向",
"ip": "192.168.10.170",
"location": "道路"
},
{
"carme_name": "北山高速收费站入境摄像头入场入方向",
"ip": "192.168.10.169",
"location": "道路"
},
{
"carme_name": "坝洒高点",
"ip": "192.168.10.164",
"location": ""
},
{
"carme_name": "停车场卡口（坝洒）入境摄像头入场",
"ip": "192.168.10.165",
"location": "出入口"
},
{
"carme_name": "停车场卡口（坝洒）入境摄像头出场",
"ip": "192.168.10.166",
"location": "出入口"
},
{
"carme_name": "东西干道卡口入境摄像头入场",
"ip": "192.168.10.136",
"location": "出入口"
},
{
"carme_name": "东西干道卡口入境摄像头出场",
"ip": "192.168.10.135",
"location": "出入口"
},
{
"carme_name": "主卡口（临时）货场大门-高点",
"ip": "192.168.10.146",
"location": ""
},
{
"carme_name": "利丰酒店往边民互市高点",
"ip": "192.168.10.211",
"location": "利丰酒店楼顶"
},
{
"carme_name": "利丰货场近御峰货场入境摄像头出场2",
"ip": "192.168.10.132",
"location": "出入口"
},
{
"carme_name": "利丰酒店往边民互市高点2",
"ip": "192.168.10.212",
"location": "利丰酒店楼顶"
},
{
"carme_name": "利丰货场近御峰货场入境摄像头入场2",
"ip": "192.168.10.139",
"location": "出入口"
},
{
"carme_name": "利丰货场入境摄像头出场左",
"ip": "192.168.10.127",
"location": "出入口"
},
{
"carme_name": "利丰货场入境摄像头入场左",
"ip": "192.168.10.128",
"location": "出入口"
},
{
"carme_name": "利丰货场入境摄像头出场右",
"ip": "192.168.10.126",
"location": "出入口"
},
{
"carme_name": "利丰货场-高点",
"ip": "192.168.10.125",
"location": ""
},
{
"carme_name": "利丰货场近御峰货场入境摄像头出场1",
"ip": "192.168.10.131",
"location": "出入口"
},
{
"carme_name": "利丰货场入境摄像头入场右",
"ip": "192.168.10.129",
"location": "出入口"
},
{
"carme_name": "利丰货场近御峰货场入境摄像头入场1",
"ip": "192.168.10.130",
"location": "出入口"
},
{
"carme_name": "南屏高速收费站入境摄像头出场",
"ip": "192.168.10.168",
"location": "道路"
},
{
"carme_name": "南屏高速收费站入境摄像头入场",
"ip": "192.168.10.167",
"location": "道路"
},
{
"carme_name": "停车场卡口老表入境摄像头入场",
"ip": "192.168.10.160",
"location": "出入口"
},
{
"carme_name": "停车场卡口老表右入境摄像头入场",
"ip": "192.168.10.162",
"location": "出入口"
},
{
"carme_name": "停车场卡口老表入境摄像头出场",
"ip": "192.168.10.161",
"location": "出入口"
},
{
"carme_name": "停车场卡口老表右入境摄像头出场",
"ip": "192.168.10.163",
"location": "出入口"
},
{
"carme_name": "槟榔寨站入境摄像头入场",
"ip": "192.168.10.173",
"location": "道路"
},
{
"carme_name": "槟榔寨站入境摄像头出场",
"ip": "192.168.10.174",
"location": "道路"
},
{
"carme_name": "外围冷链停车区入境摄像头出场",
"ip": "192.168.10.158",
"location": "出入口"
},
{
"carme_name": "外围冷链停车区入境摄像头入场",
"ip": "192.168.10.157",
"location": "出入口"
},
{
"carme_name": "清水河隧道出",
"ip": "192.168.10.175",
"location": "道路"
},
{
"carme_name": "清水河隧道入",
"ip": "192.168.10.186",
"location": "道路"
},
{
"carme_name": "商阜路闭环区道路监控-高点",
"ip": "192.168.10.154",
"location": "道路"
},
{
"carme_name": "商阜路闭环区道路监控道路监控高点2",
"ip": "192.168.10.155",
"location": "道路"
},
{
"carme_name": "商阜路闭环区道路监控道路监控-后",
"ip": "192.168.10.152",
"location": "道路"
},
{
"carme_name": "越南城十字路口-东西干道入境摄像头出场",
"ip": "192.168.10.172",
"location": "道路"
},
{
"carme_name": "越南城十字路口-东西干道入境摄像头入场",
"ip": "192.168.10.171",
"location": "道路"
},
{
"carme_name": "停车场卡口能投入境摄像头出场",
"ip": "192.168.10.124",
"location": "出入口"
},
{
"carme_name": "停车场卡口能投入境摄像头入场2",
"ip": "192.168.10.123",
"location": "出入口"
},
{
"carme_name": "停车场卡口能投入境摄像头入场1",
"ip": "192.168.10.122",
"location": "出入口"
},
{
"carme_name": "御峰货场冷链入境摄像头出场出口",
"ip": "192.168.10.145",
"location": "出入口"
},
{
"carme_name": "御峰货场（冷链）-高点",
"ip": "192.168.10.137",
"location": ""
},
{
"carme_name": "御峰货场冷链入境摄像头入场进口",
"ip": "192.168.10.133",
"location": "出入口"
},
{
"carme_name": "御峰货场冷链入境摄像头入场出口",
"ip": "192.168.10.138",
"location": "出入口"
},
{
"carme_name": "御峰货场冷链入境摄像头出场进口",
"ip": "192.168.10.141",
"location": "出入口"
},
{
"carme_name": "御峰货场干货入境摄像头入场",
"ip": "192.168.10.140",
"location": "出入口"
},
{
"carme_name": "御峰货场干货入境摄像头入场2",
"ip": "192.168.10.148",
"location": "出入口"
},
{
"carme_name": "御峰货场干货入境摄像头出场",
"ip": "192.168.10.144",
"location": "出入口"
},
{
"carme_name": "御峰货场（干货）-高点1",
"ip": "192.168.10.142",
"location": ""
},
{
"carme_name": "御峰货场（干货）-高点2",
"ip": "192.168.10.143",
"location": ""
},
{
"carme_name": "御峰货场干货入境摄像头出场2",
"ip": "192.168.10.147",
"location": "出入口"
}],
tensor_fields=["carme_name"]
)
def query_vector_db(query):
return mq.index(INDEX_NAME).search(q=query)
if __name__ == '__main__':
# create_and_set_index()
rt = query_vector_db('利丰高点')
# TODO 根据 _score字段 取出相似度最高的结果
if rt:
for ele in rt['hits']:
print(ele)
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