Thor

None

Stars: 93

Visit

Thor is a powerful AI model management tool designed for unified management and usage of various AI models. It offers features such as user, channel, and token management, data statistics preview, log viewing, system settings, external chat link integration, and Alipay account balance purchase. Thor supports multiple AI models including OpenAI, Kimi, Starfire, Claudia, Zhilu AI, Ollama, Tongyi Qianwen, AzureOpenAI, and Tencent Hybrid models. It also supports various databases like SqlServer, PostgreSql, Sqlite, and MySql, allowing users to choose the appropriate database based on their needs.

README:

Thor(雷神托尔)

Thor(雷神托尔),一个AI大模型管理网关。

Changelog · Report Bug · Request Feature

Thor(雷神托尔)

Thor(雷神托尔) 是一款强大的人工智能模型管理工具，其主要目的是为了实现多种AI模型的统一管理和使用。通过Thor(雷神托尔)，用户可以轻松地管理和使用众多AI模型，而且Thor(雷神托尔)兼容OpenAI的接口格式，使得使用更加方便。

Thor(雷神托尔)提供了丰富的功能：

管理功能：支持用户管理，渠道管理以及token管理，简化了管理流程。
数据统计预览：可以清晰地看到各种数据的统计情况，帮助用户更好地了解使用情况。
日志查看：支持日志查看，方便用户跟踪和解决问题。
系统设置：可以根据需要进行各种系统设置。
外部Chat链接接入：支持接入外部Chat链接，提升交互体验。
支付宝购买账号余额：提供支付宝购买账号余额的功能，方便用户进行充值。

此外，Thor(雷神托尔)还支持多种AI大模型，包括OpenAI、星火大模型、Claudia、智谱AI、Ollama、通义千问（阿里云）、AzureOpenAI以及腾讯混元大模型，满足了用户对各种AI模型的需求。

Thor(雷神托尔)还支持多种数据库，包括SqlServer、PostgreSql、Sqlite以及MySql，用户可以根据自己的需要选择合适的数据库。

功能实现

[x] 支持用户管理
[x] 支持渠道管理
[x] 支持token管理
[x] 提供数据统计预览
[x] 支持日志查看
[x] 支持系统设置
[x] 支持接入外部Chat链接
[x] 支持支付宝购买账号余额
[x] 支持Rabbit消费日志（默认情况下使用本地事件）

AI大模型支持列表

[x] OpenAI （支持function）
[x] Kimi（月之暗面）（支持function）
[x] 星火大模型（支持function）
[x] Claudia (非Stream支持function)
[x] 智谱AI (支持function)
[x] AzureOpenAI（支持function）
[x] Ollama
[x] 通义千问（阿里云）
[x] 腾讯混元大模型
[x] 支持百度大模型(ErnieBot)

支持数据库

[x] SqlServer 配置类型[sqlserver,mssql]
[x] PostgreSql 配置类型[postgresql,pgsql]
[x] Sqlite 配置类型[sqlite,默认]
[x] MySql 配置类型[mysql]

修改appsettings.json的ConnectionStrings:DBType配置项即可切换数据库类型。请注意切换数据库不会迁移数据。

graph LR
    A(用户)
    A --->|使用 Thor 分发的 key 进行请求| B(Thor)
    B -->|中继请求| C(OpenAI)
    B -->|中继请求| D(Azure)
    B -->|中继请求| E(其他 OpenAI API 格式下游渠道)
    B -->|中继并修改请求体和返回体| F(非 OpenAI API 格式下游渠道)

简单使用

默认账号密码 admin admin

注意事项

需要注意的是，如果克隆项目后，项目根目录缺少data时，需要手动创建，docker compose up 时，需要挂载本地目录data。

环境变量

DBType sqlite | [postgresql,pgsql] | [sqlserver,mssql] | mysql
ConnectionString 主数据库连接字符串
LoggerConnectionString 日志数据连接字符串
CACHE_TYPE 缓存类型 Memory|Redis
CACHE_CONNECTION_STRING 缓存连接字符串如果是Redis则为Redis连接字符串，Memory则为空

使用docker compose启动服务：

version: '3.8'

services:
  ai-dotnet-api-service:
    image: registry.token-ai.cn/thor:latest
    ports:
      - 18080:5045
    container_name: ai-dotnet-api-service
    volumes:
      - ./data:/data
    environment:
      - TZ=Asia/Shanghai
      - DBType=sqlite # sqlite | [postgresql,pgsql] | [sqlserver,mssql] | mysql
      - ConnectionString=data source=/data/token.db
      - LoggerConnectionString=data source=/data/logger.db

使用docker run启动服务

docker run -d -p 18080:5045 --name ai-dotnet-api-service --network=gateway -v $PWD/data:/data -e Theme=lobe -e TZ=Asia/Shanghai -e DBType=sqlite -e ConnectionString="data source=/data/token.db" -e LoggerConnectionString="data source=/data/logger.db" registry.token-ai.cn/thor:latest

Sqlite构建

docker compose版本

项目根目录创建docker-compose.yml文件，内容如下：

version: '3.8'

services:
  ai-dotnet-api-service:
    image: registry.token-ai.cn/thor:latest
    container_name: ai-dotnet-api-service
    ports:
      - 18080:5045
    volumes:
      - ./data:/data
    environment:
      - TZ=Asia/Shanghai
      - DBType=sqlite
      - ConnectionString=data source=/data/token.db
      - LoggerConnectionString=data source=/data/logger.db

执行如下命令打包镜像

sudo docker compose build

执行以下命令启动服务

sudo docker compose up -d

docker run版本

docker run -d -p 18080:5045 --name ai-dotnet-api-service -v $(pwd)/data:/data -e Theme=lobe -e TZ=Asia/Shanghai -e DBType=sqlite -e ConnectionString=data source=/data/token.db -e LoggerConnectionString=data source=/data/logger.db registry.token-ai.cn/thor:latest

然后访问 http://localhost:18080 即可看到服务启动成功。

PostgreSql构建

docker compose版本

项目根目录创建docker-compose.yml文件，内容如下：

version: '3.8'

services:
  ai-dotnet-api-service:
    image: registry.token-ai.cn/thor:latest
    container_name: ai-dotnet-api-service
    ports:
      - 18080:5045
    volumes:
      - ./data:/data
    environment:
      - TZ=Asia/Shanghai
      - DBType=postgresql
      - "ConnectionString=Host=127.0.0.1;Port=5432;Database=token;Username=token;Password=dd666666"
      - "ConnectionString=Host=127.0.0.1;Port=5432;Database=logger;Username=token;Password=dd666666"

执行如下命令打包镜像

sudo docker compose build

执行以下命令启动服务

sudo docker compose up -d

docker run版本

docker run -d \
  --name ai-dotnet-api-service \
  -p 18080:5045 \
  -v $(pwd)/data:/data \
  -e TZ=Asia/Shanghai \
  -e DBType=postgresql \
  -e "ConnectionString=Host=127.0.0.1;Port=5432;Database=token;Username=token;Password=dd666666" \
  -e "ConnectionString=Host=127.0.0.1;Port=5432;Database=logger;Username=token;Password=dd666666" \
  registry.token-ai.cn/thor:latest

然后访问 http://localhost:18080 即可看到服务启动成功。

SqlServer构建

docker compose版本

项目根目录创建docker-compose.yml文件，内容如下：

version: '3.8'

services:
  ai-dotnet-api-service:
    image: registry.token-ai.cn/thor:latest
    container_name: ai-dotnet-api-service
    ports:
      - 18080:5045
    volumes:
      - ./data:/data
    environment:
      - TZ=Asia/Shanghai
      - DBType=sqlserver
      - "ConnectionString=Server=127.0.0.1;Database=token;User Id=sa;Password=dd666666;"
      - "ConnectionString=Server=127.0.0.1;Database=logger;User Id=sa;Password=dd666666;"

执行如下命令打包镜像

sudo docker compose build

执行以下命令启动服务

sudo docker compose up -d

docker run版本

docker run -d \
  --name ai-dotnet-api-service \
  -p 18080:5045 \
  -v $(pwd)/data:/data \
  -e TZ=Asia/Shanghai \
  -e DBType=sqlserver \
  -e "ConnectionString=Server=127.0.0.1;Database=token;User Id=sa;Password=dd666666;" \
  -e "ConnectionString=Server=127.0.0.1;Database=logger;User Id=sa;Password=dd666666;" \
  registry.token-ai.cn/thor:latest

然后访问 http://localhost:18080 即可看到服务启动成功。

MySql构建

docker compose版本

项目根目录创建docker-compose.yml文件，内容如下：

version: '3.8'

services:
  ai-dotnet-api-service:
    image: registry.token-ai.cn/thor:latest
    container_name: ai-dotnet-api-service
    ports:
      - 18080:5045
    volumes:
      - ./data:/data
    environment:
      - TZ=Asia/Shanghai
      - DBType=mysql
      - "ConnectionString=mysql://root:dd666666@localhost:3306/token"
      - "ConnectionString=mysql://root:dd666666@localhost:3306/logger"

执行如下命令打包镜像

sudo docker compose build

执行以下命令启动服务

sudo docker compose up -d

docker run版本

docker run -d \
  --name ai-dotnet-api-service \
  -p 18080:5045 \
  -v $(pwd)/data:/data \
  -e TZ=Asia/Shanghai \
  -e DBType=mysql \
  -e "ConnectionString=mysql://root:dd666666@localhost:3306/token" \
  -e "ConnectionString=mysql://root:dd666666@localhost:3306/logger" \
  registry.token-ai.cn/thor:latest

然后访问 http://localhost:18080 即可看到服务启动成功。

For Tasks:

Click tags to check more tools for each tasks

manage ai models view data statistics integrate external chat links purchase account balance switch databases

For Jobs:

ai model manager data scientist machine learning engineer ai solutions architect ai project manager

Alternative AI tools for Thor

Similar Open Source Tools

Thor

github

: 93

AMchat

AMchat is a large language model that integrates advanced math concepts, exercises, and solutions. The model is based on the InternLM2-Math-7B model and is specifically designed to answer advanced math problems. It provides a comprehensive dataset that combines Math and advanced math exercises and solutions. Users can download the model from ModelScope or OpenXLab, deploy it locally or using Docker, and even retrain it using XTuner for fine-tuning. The tool also supports LMDeploy for quantization, OpenCompass for evaluation, and various other features for model deployment and evaluation. The project contributors have provided detailed documentation and guides for users to utilize the tool effectively.

github

: 153

CareGPT

CareGPT is a medical large language model (LLM) that explores medical data, training, and deployment related research work. It integrates resources, open-source models, rich data, and efficient deployment methods. It supports various medical tasks, including patient diagnosis, medical dialogue, and medical knowledge integration. The model has been fine-tuned on diverse medical datasets to enhance its performance in the healthcare domain.

github

: 508

bce-qianfan-sdk

The Qianfan SDK provides best practices for large model toolchains, allowing AI workflows and AI-native applications to access the Qianfan large model platform elegantly and conveniently. The core capabilities of the SDK include three parts: large model reasoning, large model training, and general and extension: * `Large model reasoning`: Implements interface encapsulation for reasoning of Yuyan (ERNIE-Bot) series, open source large models, etc., supporting dialogue, completion, Embedding, etc. * `Large model training`: Based on platform capabilities, it supports end-to-end large model training process, including training data, fine-tuning/pre-training, and model services. * `General and extension`: General capabilities include common AI development tools such as Prompt/Debug/Client. The extension capability is based on the characteristics of Qianfan to adapt to common middleware frameworks.

github

: 307

aiges

AIGES is a core component of the Athena Serving Framework, designed as a universal encapsulation tool for AI developers to deploy AI algorithm models and engines quickly. By integrating AIGES, you can deploy AI algorithm models and engines rapidly and host them on the Athena Serving Framework, utilizing supporting auxiliary systems for networking, distribution strategies, data processing, etc. The Athena Serving Framework aims to accelerate the cloud service of AI algorithm models and engines, providing multiple guarantees for cloud service stability through cloud-native architecture. You can efficiently and securely deploy, upgrade, scale, operate, and monitor models and engines without focusing on underlying infrastructure and service-related development, governance, and operations.

github

: 275

Streamer-Sales

Streamer-Sales is a large model for live streamers that can explain products based on their characteristics and inspire users to make purchases. It is designed to enhance sales efficiency and user experience, whether for online live sales or offline store promotions. The model can deeply understand product features and create tailored explanations in vivid and precise language, sparking user's desire to purchase. It aims to revolutionize the shopping experience by providing detailed and unique product descriptions to engage users effectively.

github

: 2.3k

meet-libai

The 'meet-libai' project aims to promote and popularize the cultural heritage of the Chinese poet Li Bai by constructing a knowledge graph of Li Bai and training a professional AI intelligent body using large models. The project includes features such as data preprocessing, knowledge graph construction, question-answering system development, and visualization exploration of the graph structure. It also provides code implementations for large models and RAG retrieval enhancement.

github

: 1.1k

openlrc

Open-Lyrics is a Python library that transcribes voice files using faster-whisper and translates/polishes the resulting text into `.lrc` files in the desired language using LLM, e.g. OpenAI-GPT, Anthropic-Claude. It offers well preprocessed audio to reduce hallucination and context-aware translation to improve translation quality. Users can install the library from PyPI or GitHub and follow the installation steps to set up the environment. The tool supports GUI usage and provides Python code examples for transcription and translation tasks. It also includes features like utilizing context and glossary for translation enhancement, pricing information for different models, and a list of todo tasks for future improvements.

github

: 381

EduChat

EduChat is a large-scale language model-based chatbot system designed for intelligent education by the EduNLP team at East China Normal University. The project focuses on developing a dialogue-based language model for the education vertical domain, integrating diverse education vertical domain data, and providing functions such as automatic question generation, homework correction, emotional support, course guidance, and college entrance examination consultation. The tool aims to serve teachers, students, and parents to achieve personalized, fair, and warm intelligent education.

github

: 622

ddddocr

ddddocr is a Rust version of a simple OCR API server that provides easy deployment for captcha recognition without relying on the OpenCV library. It offers a user-friendly general-purpose captcha recognition Rust library. The tool supports recognizing various types of captchas, including single-line text, transparent black PNG images, target detection, and slider matching algorithms. Users can also import custom OCR training models and utilize the OCR API server for flexible OCR result control and range limitation. The tool is cross-platform and can be easily deployed.

github

: 80

HivisionIDPhotos

HivisionIDPhoto is a practical algorithm for intelligent ID photo creation. It utilizes a comprehensive model workflow to recognize, cut out, and generate ID photos for various user photo scenarios. The tool offers lightweight cutting, standard ID photo generation based on different size specifications, six-inch layout photo generation, beauty enhancement (waiting), and intelligent outfit swapping (waiting). It aims to solve emergency ID photo creation issues.

github

: 9.5k

metaso-free-api

Metaso AI Free service supports high-speed streaming output, secret tower AI super network search (full network or academic as well as concise, in-depth, research three modes), zero-configuration deployment, multi-token support. Fully compatible with ChatGPT interface. It also has seven other free APIs available for use. The tool provides various deployment options such as Docker, Docker-compose, Render, Vercel, and native deployment. Users can access the tool for chat completions and token live checks. Note: Reverse API is unstable, it is recommended to use the official Metaso AI website to avoid the risk of banning. This project is for research and learning purposes only, not for commercial use.

github

: 372

ERNIE-SDK

ERNIE SDK repository contains two projects: ERNIE Bot Agent and ERNIE Bot. ERNIE Bot Agent is a large model intelligent agent development framework based on the Wenxin large model orchestration capability introduced by Baidu PaddlePaddle, combined with the rich preset platform functions of the PaddlePaddle Star River community. ERNIE Bot provides developers with convenient interfaces to easily call the Wenxin large model for text creation, general conversation, semantic vectors, and AI drawing basic functions.

github

: 321

readme-ai

README-AI is a developer tool that auto-generates README.md files using a combination of data extraction and generative AI. It streamlines documentation creation and maintenance, enhancing developer productivity. This project aims to enable all skill levels, across all domains, to better understand, use, and contribute to open-source software. It offers flexible README generation, supports multiple large language models (LLMs), provides customizable output options, works with various programming languages and project types, and includes an offline mode for generating boilerplate README files without external API calls.

github

: 1.4k

ap-plugin

AP-PLUGIN is an AI drawing plugin for the Yunzai series robot framework, allowing you to have a convenient AI drawing experience in the input box. It uses the open source Stable Diffusion web UI as the backend, deploys it for free, and generates a variety of images with richer functions.

github

: 103

new-api

New API is an open-source project based on One API with additional features and improvements. It offers a new UI interface, supports Midjourney-Proxy(Plus) interface, online recharge functionality, model-based charging, channel weight randomization, data dashboard, token-controlled models, Telegram authorization login, Suno API support, Rerank model integration, and various third-party models. Users can customize models, retry channels, and configure caching settings. The deployment can be done using Docker with SQLite or MySQL databases. The project provides documentation for Midjourney and Suno interfaces, and it is suitable for AI enthusiasts and developers looking to enhance AI capabilities.

github

: 3.0k

For similar tasks

Thor

github

: 93

griptape

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.

github

: 1.9k

tts-generation-webui

TTS Generation WebUI is a comprehensive tool that provides a user-friendly interface for text-to-speech and voice cloning tasks. It integrates various AI models such as Bark, MusicGen, AudioGen, Tortoise, RVC, Vocos, Demucs, SeamlessM4T, and MAGNeT. The tool offers one-click installers, Google Colab demo, videos for guidance, and extra voices for Bark. Users can generate audio outputs, manage models, caches, and system space for AI projects. The project is open-source and emphasizes ethical and responsible use of AI technology.

github

: 1.6k

For similar jobs

Thor

github

: 93

redbox

Redbox is a retrieval augmented generation (RAG) app that uses GenAI to chat with and summarise civil service documents. It increases organisational memory by indexing documents and can summarise reports read months ago, supplement them with current work, and produce a first draft that lets civil servants focus on what they do best. The project uses a microservice architecture with each microservice running in its own container defined by a Dockerfile. Dependencies are managed using Python Poetry. Contributions are welcome, and the project is licensed under the MIT License. Security measures are in place to ensure user data privacy and considerations are being made to make the core-api secure.

github

: 86

WilmerAI

WilmerAI is a middleware system designed to process prompts before sending them to Large Language Models (LLMs). It categorizes prompts, routes them to appropriate workflows, and generates manageable prompts for local models. It acts as an intermediary between the user interface and LLM APIs, supporting multiple backend LLMs simultaneously. WilmerAI provides API endpoints compatible with OpenAI API, supports prompt templates, and offers flexible connections to various LLM APIs. The project is under heavy development and may contain bugs or incomplete code.

github

: 109

MLE-agent

MLE-Agent is an intelligent companion designed for machine learning engineers and researchers. It features autonomous baseline creation, integration with Arxiv and Papers with Code, smart debugging, file system organization, comprehensive tools integration, and an interactive CLI chat interface for seamless AI engineering and research workflows.

github

: 980

LynxHub

LynxHub is a platform that allows users to seamlessly install, configure, launch, and manage all their AI interfaces from a single, intuitive dashboard. It offers features like AI interface management, arguments manager, custom run commands, pre-launch actions, extension management, in-app tools like terminal and web browser, AI information dashboard, Discord integration, and additional features like theme options and favorite interface pinning. The platform supports modular design for custom AI modules and upcoming extensions system for complete customization. LynxHub aims to streamline AI workflow and enhance user experience with a user-friendly interface and comprehensive functionalities.

github

: 62

llmops-promptflow-template

LLMOps with Prompt flow is a template and guidance for building LLM-infused apps using Prompt flow. It provides centralized code hosting, lifecycle management, variant and hyperparameter experimentation, A/B deployment, many-to-many dataset/flow relationships, multiple deployment targets, comprehensive reporting, BYOF capabilities, configuration-based development, local prompt experimentation and evaluation, endpoint testing, and optional Human-in-loop validation. The tool is customizable to suit various application needs.

github

: 222

azure-search-vector-samples

This repository provides code samples in Python, C#, REST, and JavaScript for vector support in Azure AI Search. It includes demos for various languages showcasing vectorization of data, creating indexes, and querying vector data. Additionally, it offers tools like Azure AI Search Lab for experimenting with AI-enabled search scenarios in Azure and templates for deploying custom chat-with-your-data solutions. The repository also features documentation on vector search, hybrid search, creating and querying vector indexes, and REST API references for Azure AI Search and Azure OpenAI Service.

github

: 671

geti-sdk

The Intel® Geti™ SDK is a python package that enables teams to rapidly develop AI models by easing the complexities of model development and enhancing collaboration between teams. It provides tools to interact with an Intel® Geti™ server via the REST API, allowing for project creation, downloading, uploading, deploying for local inference with OpenVINO, setting project and model configuration, launching and monitoring training jobs, and media upload and prediction. The SDK also includes tutorial-style Jupyter notebooks demonstrating its usage.

github

: 71