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conf
conf
 
 
controller
controller
 
 
library
library
 
 
log
log
 
 
model
model
 
 
router
router
 
 
static
static
 
 
template
template
 
 
README.md
README.md
 
 
app.py
app.py
 
 
requirements.txt
requirements.txt
 
 

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Ollama LLM API Web Frame

基于Tornado与Ollama的大语言模型能力调用框架（A Ollama LLM API Web Frame Based Tornado！）

[目录]

.
├── app.py               # 项目入口文件
├── conf                 # 配置文件目录
│ └── global.ini         # 基础配置文件
├── controller           # 控制层
│ ├── base.py            # 控制层基类
│ ├── auth.py            # 认证类
│ ├── chat.py            # LLM对话类
│ ├── embeddings.py      # 编码类
│ ├── generate.py        # LLM生成类
│ ├── prompt.py          # 提示工程类
│ ├── rag.py             # LLM检索增强生成类
│ ├── strategy.py        # LLM调用策略类
│ ├── chroma.py          # 向量数据库操作类
│ └── home.py            # 默认首页类
├── library              # 基础类库
│ ├── jwt                # JWT验证基类
│ │ └── jwt.py
│ ├── log                # 日志操作基类
│ │ └── loghelper.py
│ ├── mysql              # MySQL操作基类
│ │ └── mysqlhelper.py
│ ├── redis              # Redis操作基类
│ │ └── redishelper.py
│ ├── oracle             # Oracle操作基类
│ │ └── oraclehelper.py
│ ├── neo4j              # Neo4j操作基类
│ │ └── neo4jhelper.py
│ ├── chromadb           # ChromaDB操作基类
│ │ └── chromahelper.py
│ └── util               # 公共方法工具
│ └── util.py
├── log                  # 日志存放目录
│ └── log.txt
├── model                # 模型层
│ ├── base.py            # 模型基类
│ └── llm.py             # LLM类
├── README.md
├── requirements.txt     # 框架安装包 pip install -r requirements.txt 
├── router               # 路由分发层
│ └── urls.py            # 路由分发文件 
├── static               # 资源层
└── template             # 模板层
 └── 404.html            # 404页面

[准备]

安装依赖包pip install -r requirements.txt

[基础配置]

# APP基础配置
[app]
port = 8282    # 本项目默认端口
debug = True   # True：开启调试模式

# JWT相关配置
[jwt]
secret = q1w2e3r4t5
expire = 7
algorithm = HS256

# MySQL写库
[mysql_w]
host = 192.168.1.1
port = 3306
db = 
user = 
password = 
charset = utf8

# MySQL读库
[mysql_r]
host = 192.168.1.1
port = 3306
db = 
user = 
password = 
charset = utf8

# Redis
[redis]
host = localhost
port = 6379
db = 0
password = 

# Log
[log]
mode = time
when = D
interval = 1

# LLM
[llm]
host = 192.168.16.8:11434 # Ollama服务

# 大模型能力访问策略
[strategy]
# 间隔x秒访问一次
interval = 5
# 在total_time内能够访问times次
times = 30
total_time = 3600
total_son_time = 86400

# 聊天参数
[chat]
temperature = 0.8
num_keep = 5
num_ctx = 1024

# 内容生成参数
[generate]
temperature = 0.8
num_keep = 5
num_ctx = 1024

[启动]

python3 app.py --port=自定义端口号（不加默认8282）

[接口]

请求地址：ip:端口号/接口名称

请求方式：post

请求参数：

Headers

Authorization: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJleHAiOjE3MzU2MjgyOTgsImlhdCI6MTczNTAyMzQ5OCwiaXNzIjoidG9ybmFkbyIsInBheWxvYWQiOnsidWlkIjoiZWI1N2I1NzIzZSIsInVuYW1lIjoiIn19.ubA-P8ehTzOftnS0nDqedJgQYAL3mu3sx2pjJzs3E64

Body

{
  "uid": "用户ID",
  "secret": "用户密码",
  "prompt": "提示工程标识", // 可选
  "model":"gemma2:latest",
  "pid": "项目ID",
  "content": "hello"
}

1. 接口认证(Auth)

请求地址：ip:端口号/auth

请求方式：post

请求参数：json

{
    "uid": "eb57b5723e",
    "secret": "50296e332969d0fe35dfd5a61c016d25"
}

接口返回：json

{
    "code": 0,
    "message": "success",
    "data": "eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJleHAiOjE3MzU2MjgyOTgsImlhdCI6MTczNTAyMzQ5OCwiaXNzIjoidG9ybmFkbyIsInBheWxvYWQiOnsidWlkIjoiZWI1N2I1NzIzZSIsInVuYW1lIjoiIn19.ubA-P8ehTzOftnS0nDqedJgQYAL3mu3sx2pjJzs3E64"
}
CREATE TABLE `t_consumer` (
  `id` int(10) unsigned NOT NULL AUTO_INCREMENT COMMENT 'ID',
  `consumer_no` char(20) NOT NULL DEFAULT '' COMMENT '厂商编号',
  `consumer_name` char(20) NOT NULL DEFAULT '' COMMENT '厂商名称',
  `password` char(32) NOT NULL DEFAULT '' COMMENT '密码',
  `status` tinyint(3) NOT NULL DEFAULT '1' COMMENT '状态(0:无效, 1:有效)',
  `descriptions` varchar(500) NOT NULL DEFAULT '' COMMENT '备注说明',
  `create_time` int(11) NOT NULL DEFAULT '0' COMMENT '创建时间',
  `update_time` int(11) NOT NULL DEFAULT '0' COMMENT '更新时间',
  `last_login_time` int(11) NOT NULL DEFAULT '0' COMMENT '最后登录时间',
  `last_logout_time` int(11) NOT NULL DEFAULT '0' COMMENT '最后登出时间',
  PRIMARY KEY (`id`),
  UNIQUE KEY `consumer_no` (`consumer_no`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8 COMMENT='厂商接入表'

2. 大语言模型生成(Generate)

请求地址：ip:端口号/generate

请求方式：post

请求参数：json

{
    "model":"qwen2.5-coder:14b",
    "stream": 0,
    "content": "hello"
}

接口返回：json

{
     "code": 0,
     "message": "success",
     "data": "Hello! How can I assist you today?"
}

3. 大语言模型对话(Chat)

请求地址：ip:端口号/chat

请求方式：post

请求参数：json

{
    "model":"qwen2.5-coder:14b",
    "is_stream": 0,
    "content": "hello"
}

接口返回：json

{
      "code": 0,
      "message": "success",
      "data": "Hello! How can I assist you today? Feel free to ask any questions or let me if there 's anything specific you'd like help with."
}

4. 大语言模型向量化(Embedding)

请求地址：ip:端口号/embeddings

请求方式：post

请求参数：json

{
    "model": "nomic-embed-text:latest",
    "content": "hello"
}

接口返回：json

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-1.8059077262878418, -0.4663667678833008]]]}

5. 大语言模型提示工程(Prompt)

请求地址：ip:端口号/prompt

请求方式：post

请求参数：json

内部调用，未封装外部调用，可根据实际情况进行调整

接口返回：json

内部调用，未封装外部调用，可根据实际情况进行调整

6. 大语言模型检索增强生成(RAG)

请求地址：ip:端口号/rag

请求方式：post

请求参数：json

{
    "collection": "text2sql-001",                 # 向量数据库集合名称
    "generate_model": "codegemma:7b",             # 生成类大语言模型
    "embedding_model": "nomic-embed-text:latest", # 编码类大语言模型
    "number": 3,                                  # 检索数量（默认1）
    "content": "生成获取用户小明的手机号以及邮箱的sql"  # 问题
}

接口返回：json

{
    "code": 0, 
    "message": "success",
    "data": "SELECT mobile, email FROM t_user WHERE user_name = '小明';"
}
// 向量数据库存储内容
CREATE TABLE `t_user` (
  `id` int(10) unsigned NOT NULL AUTO_INCREMENT COMMENT 'ID',
  `user_no` char(20) NOT NULL DEFAULT '' COMMENT '员工编号',
  `user_name` char(20) NOT NULL DEFAULT '' COMMENT '员工姓名',
  `department_id` int(10) unsigned NOT NULL DEFAULT 0 COMMENT '科室ID',
  `mobile` char(16) NOT NULL DEFAULT '' COMMENT '手机号',
  `email` char(40) NOT NULL DEFAULT '' COMMENT '邮箱',
  `photo` char(100) NOT NULL DEFAULT '' COMMENT '头像',
  `right_id` int(10) unsigned NOT NULL DEFAULT 0 COMMENT '权限ID',
  `title_id` int(10) unsigned NOT NULL DEFAULT 0 COMMENT '职称ID',
  `duty_id` int(10) unsigned NOT NULL DEFAULT 0 COMMENT '职务ID',
  `password` char(32) NOT NULL DEFAULT '' COMMENT '密码',
  `salt` char(8) NOT NULL DEFAULT '' COMMENT '盐',
  `sex` tinyint(3) NOT NULL DEFAULT 1 COMMENT '性别(1:未知, 2:男, 3:女)',
  `status` tinyint(3) NOT NULL DEFAULT 1 COMMENT '状态(0:无效, 1:有效)',
  `descriptions` varchar(500) NOT NULL DEFAULT '' COMMENT '备注说明',
  `create_time` int(11) NOT NULL DEFAULT 0 COMMENT '创建时间',
  `update_time` int(11) NOT NULL DEFAULT 0 COMMENT '更新时间',
  `last_login_time` int(11) NOT NULL DEFAULT 0 COMMENT '最后登录时间',
  `last_logout_time` int(11) NOT NULL DEFAULT 0 COMMENT '最后登出时间',
  PRIMARY KEY (`id`),
  UNIQUE(`user_no`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8 COMMENT='用户表';

7. 创建向量数据库集合

请求地址：ip:端口号/createcollection

请求方式：post

请求参数：json

{
  "collection": "test-001" # 集合名称
}

接口返回：json

{
	"code": 0,
	"message": "Collection 'test-001' create success.",
	"data": []
}

8. 删除向量数据库集合

请求地址：ip:端口号/deletecollection

请求方式：post

请求参数：json

{
  "collection": "test-001" # 集合名称
}

接口返回：json

{
	"code": 0,
	"message": "Collection 'test-001' delete success.",
	"data": []
}

9. 向量数据库添加文档

请求地址：ip:端口号/adddocument

请求方式：post

请求参数：json

{
  "collection": "test-001",     # 集合名称
  "ids": ["001", "002", "003"], # 文档ID
  "model": "nomic-embed-text:latest" # 向量模型（默认：nomic-embed-text:latest）
  "documents": ["xiaoming documents", "xiaohong documents", "xiaoqiang documents"], # 文档数据
  "metadatas": [{"source": "001.txt"}, {"source": "002.txt"}, {"source": "003.txt"}] # 文档元数据
}

接口返回：json

{
	"code": 0,
	"message": "Added 3 documents to 'test-001'.",
	"data": [3]
}

10. 向量数据库查询

请求地址：ip:端口号/querydocument

请求方式：post

请求参数：json

{
  "collection": "test-001",     # 集合名称
  "model": "nomic-embed-text:latest", # 向量模型（默认：nomic-embed-text:latest）
  "number": 3,    # 查询数量（默认：5）
  "query": "hong" # 查询内容
}

接口返回：json

{
	"code": 0,
	"message": "success",
	"data": {
		"ids": [
			["002", "003", "001"]
		],
		"embeddings": null,
		"documents": [
			["xiaohong
				documents ", "
				xiaoqiang documents ", "
				xiaoming documents "]], "
				uris ": null, "
				data ": null, "
				metadatas ": [[{"
				source ":
				"002.txt"
			}, {
				"source": "003.txt"
			}, {
				"source": "001.txt"
			}
		]],
	"distances": [
		[538.5186078395823, 555.5955598005668,
			593.4459312118717
		]
	],
	"included": ["distances", "documents", "metadatas"]
}
}

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基于Tornado的Ollama大语言中模型（LLM）能力调用的api框架

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