
DeepClaude
Unleash Next-Level AI! 🚀 💻 Code Generation: DeepSeek r1 + Claude 3.5 Sonnet - Unparalleled Performance! 📝 Content Creation: DeepSeek r1 + Gemini 2.0 - Superior Quality! 🔌 OpenAI-Compatible. 🌊 Streaming & Non-Streaming Support. ✨ Experience the Future of AI – Today! Click to Try Now! ✨
Stars: 1469
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DeepClaude is an open-source project inspired by the DeepSeek R1 model, aiming to provide the best results in various tasks by combining different models. It supports OpenAI-compatible input and output formats, integrates with DeepSeek and Claude APIs, and offers special support for other OpenAI-compatible models. Users can run the project locally or deploy it on a server to access a powerful language model service. The project also provides guidance on obtaining necessary APIs and running the project, including using Docker for deployment.
README:
特别说明:
1.编程:推荐 DeepSeek r1 + Claude 3.5 Sonnet 组合,效果最好;
2.内容创作:推荐 DeepSeek r1 + Gemini 2.0 Flash 或 Gemini 2.0 Pro 组合,效果最好,并且可以完全免费使用。
对于不太会部署,只是希望使用上最强组合模型的朋友,可以直接访问 Erlich 个人网站自助购买按量付费的 API:https://erlich.fun/deepclaude-pricing
也可以直接联系 Erlich(微信号:erlichliu1)
赞助商:问小白 https://www.wenxiaobai.com (丝滑使用 DeepSeek r1 满血版, 支持联网、上传文件、图片、AI 创作 PPT 等)
更新日志:
2025-02-21.1: 添加 Claude 这段的详细数据结构安全检查。
2025-02-16.1: 支持 claude 侧采用请求体中的自定义模型名称。(如果你采用 oneapi 等中转方,那么现在可以通过配置环境变量或在 API 请求中采用任何 Gemini 等模型完成后半部分。接下来将重构代码,更清晰地支持不同的思考模型组合。)
2025-02-08.2: 支持非流式请求,支持 OpenAI 兼容的 models 接口返回。(
2025-02-08.1: 添加 Github Actions,支持 fork 自动同步、支持自动构建 Docker 最新镜像、支持 docker-compose 部署
2025-02-07.2: 修复 Claude temperature 参数可能会超过范围导致的请求失败的 bug
2025-02-07.1: 支持 Claude temputerature 等参数;添加更详细的 .env.example 说明
2025-02-06.1:修复非原生推理模型无法获得到推理内容的 bug
2025-02-05.1: 支持通过环境变量配置是否是原生支持推理字段的模型,满血版本通常支持
2025-02-04.2: 支持跨域配置,可在 .env 中配置
2025-02-04.1: 支持 Openrouter 以及 OneAPI 等中转服务商作为 Claude 部分的供应商
2025-02-03.3: 支持 OpenRouter 作为 Claude 的供应商,详见 .env.example 说明
2025-02-03.2: 由于 deepseek r1 在某种程度上已经开启了一个规范,所以我们也遵循推理标注的这种规范,更好适配支持的更好的 Cherry Studio 等软件。
2025-02-03.1: Siliconflow 的 DeepSeek R1 返回结构变更,支持新的返回结构
最近 DeepSeek 推出了 DeepSeek R1 模型,在推理能力上已经达到了第一梯队。但是 DeepSeek R1 在一些日常任务的输出上可能仍然无法匹敌 Claude 3.5 Sonnet。Aider 团队最近有一篇研究,表示通过采用 DeepSeek R1 + Claude 3.5 Sonnet 可以实现最好的效果。
R1 as architect with Sonnet as editor has set a new SOTA of 64.0% on the aider polyglot benchmark. They achieve this at 14X less cost compared to the previous o1 SOTA result.
本项目受到该项目的启发,通过 fastAPI 完全重写,经过 15 天大量社区用户的真实测试,我们创作了一些新的组合使用方案。
1.编程:推荐使用 deepclaude = deepseek r1 + claude 3.5 sonnet; 2.内容创作:推荐使用 deepgeminipro = deepseek r1 + gemini 2.0 pro (该方案可以完全免费使用); 3.日常实验:推荐 deepgeminiflash = deepseek r1 + gemini 2.0 flash (该方案可以完全免费使用)。
项目支持 OpenAI 兼容格式的输入输出,支持 DeepSeek 官方 API 以及第三方托管的 API、生成部分也支持 Claude 官方 API 以及中转 API,并对 OpenAI 兼容格式的其他 Model 做了特别支持。
🔥推荐使用方法: 1.用户可以自行运行在自己的服务器,并对外提供开放 API 接口,接入 OneAPI 等实现统一分发。
2.接入你的日常大语言模型对话聊天使用。
项目支持本地运行和服务器运行,推荐使用服务器部署,实现随时随处可访问的最强大语言模型服务,甚至可以完全免费使用。
- 获取 DeepSeek API,因为最近 DeepSeek 官方的供应能里不足,所以经常无法使用,
推荐使用 Siliconflow 的效果更好(也可以本地 Ollama 的): https://cloud.siliconflow.cn/i/RXikvHE2 (点击此链接可以获得到 2000 万免费 tokens)目前仅推荐字节的火山引擎,可以做到 100% 回复率,速度也非常不错。可以扫码走我的邀请码,一起获得奖励额度。 - 获取 Claude 的 API KEY:https://console.anthropic.com。(也可采用其他中转服务,如 Openrouter 以及其他服务商的 API KEY)
- 获取 Gemini 的 API KEY:https://aistudio.google.com/apikey (有免费的额度,日常够用)
Step 1. 克隆本项目到适合的文件夹并进入项目
git clone https://github.com/ErlichLiu/DeepClaude.git
cd DeepClaude
Step 2. 通过 uv 安装依赖(如果你还没有安装 uv,请看下方注解)
# 通过 uv 在本地创建虚拟环境,并安装依赖
uv sync
# macOS 激活虚拟环境
source .venv/bin/activate
# Windows 激活虚拟环境
.venv\Scripts\activate
Step 3. 配置环境变量
# 复制 .env 环境变量到本地
cp .env.example .env
Step 4. 按照环境变量当中的注释依次填写配置信息
# 此处为各个环境变量的解释
ALLOW_API_KEY=你允许向你本地或服务器发起请求所需的 API 密钥,可随意设置
DEEPSEEK_API_KEY=deepseek r1 所需的 API 密钥,可在👆上面步骤 1 处获取
DEEPSEEK_API_URL=请求 deepseek r1 所需的请求地址,根据你的供应商说明进行填写
DEEPSEEK_MODEL=不同供应商的 deepseek r1 模型名称不同,根据你的供应商说明进行填写
IS_ORIGIN_REASONING=是否原生支持推理,只有满血版 671B 的 deepseek r1 支持,其余蒸馏模型不支持
CLAUDE_API_KEY=Claude 3.5 Sonnet 的 API 密钥,可在👆上面步骤 1 处获取
CLAUDE_MODEL=Claude 3.5 Sonnet 的模型名称,不同供应商的名称不同,根据你的供应商说明进行填写
CLAUDE_PROVIDER=支持 anthropic (官方) 以及 oneapi(其他中转服务商)两种模式,根据你的供应商填写
CLAUDE_API_URL=请求 Claude 3.5 Sonnet 所需的请求地址,根据你的供应商说明进行填写
OPENAI_COMPOSITE_API_KEY=通常推荐配置为 Gemini 的 API 密钥,可在👆上面步骤 1 处获取
OPENAI_COMPOSITE_API_URL=请求 Gemini 所需的请求地址,默认地址为 https://generativelanguage.googleapis.com/v1beta/openai/chat/completions
OPENAI_COMPOSITE_MODEL=通常推荐配置为 Gemini 的模型名称,可配置为 gemini-2.0-flash 或 gemini-2.0-pro-exp(pro 版本当前为实验模型)
Step 5. 通过命令行启动
# 本地运行
uvicorn app.main:app
Step 6. 配置程序到你的 Chatbox(推荐 Cherry Studio NextChat、ChatBox、LobeChat)
# 如果你的客户端是 Cherry Studio、Chatbox(OpenAI API 模式,注意不是 OpenAI 兼容模式)
# API 地址为 http://127.0.0.1:8000
# API 密钥为你在 ENV 环境变量内设置的 ALLOW_API_KEY
# 需要手动配置两个模型,模型名为 deepclaude 和 deepgemini
# 如果你的客户端是 LobeChat
# API 地址为:http://127.0.0.1:8000/v1
# API 密钥为你在 ENV 环境变量内设置的 ALLOW_API_KEY
# 支持获取模型列表,可以同时获取到 deepclaude 模型和 deepgemini 模型
注:本项目采用 uv 作为包管理器,这是一个更快速更现代的管理方式,用于替代 pip,你可以在此了解更多
项目支持 Docker 服务器部署,可自行调用接入常用的 Chatbox,也可以作为渠道一直,将其视为一个特殊的
DeepClaude
模型接入到 OneAPI 等产品使用。
一键部署到 Railway
-
点击打开 Railway 主页:https://railway.com?referralCode=RNTGCA
-
部署完成后,点击
Settings
按钮,然后向下查看到Networking
区域,然后选择Generate Domain
,并输入8000
作为端口号
注:模型名称为 deepclaude 和 deepgemini
推荐可以使用 docker-compose.yml
文件进行部署,更加方便快捷。
-
确保已安装 Docker Compose。
-
复制
docker-compose.yml
文件到项目根目录。 -
修改
docker-compose.yml
文件中的环境变量配置,将your_allow_api_key
,your_allow_origins
,your_deepseek_api_key
和your_claude_api_key
等值替换为你的实际配置。 -
在项目根目录下运行 Docker Compose 命令启动服务:
docker-compose up -d
服务启动后,DeepClaude API 将在 http://宿主机IP:8000/v1/chat/completions
上进行访问。
5. 模型名称为 deepclaude 和 deepgemini
-
构建 Docker 镜像
在项目根目录下,使用 Dockerfile 构建镜像。请确保已经安装 Docker 环境。
docker build -t deepclaude:latest .
-
运行 Docker 容器
运行构建好的 Docker 镜像,将容器的 8000 端口映射到宿主机的 8000 端口。同时,通过
-e
参数设置必要的环境变量,包括 API 密钥、允许的域名等。请根据.env.example
文件中的说明配置环境变量。docker run -d \ -p 8000:8000 \ -e ALLOW_API_KEY=your_allow_api_key \ -e ALLOW_ORIGINS="*" \ -e DEEPSEEK_API_KEY=your_deepseek_api_key \ -e DEEPSEEK_API_URL=https://api.deepseek.com/v1/chat/completions \ -e DEEPSEEK_MODEL=deepseek-reasoner \ -e IS_ORIGIN_REASONING=true \ -e CLAUDE_API_KEY=your_claude_api_key \ -e CLAUDE_MODEL=claude-3-5-sonnet-20241022 \ -e CLAUDE_PROVIDER=anthropic \ -e CLAUDE_API_URL=https://api.anthropic.com/v1/messages \ -e OPENAI_COMPOSITE_API_KEY=your_gemini_api_key -e OPENAI_COMPOSITE_API_URL=https://generativelanguage.googleapis.com/v1beta/openai/chat/completions -e OPENAI_COMPOSITE_MODEL=gemini-2.0-flash -e LOG_LEVEL=INFO \ --restart always \ deepclaude:latest
请替换上述命令中的
your_allow_api_key
,your_allow_origins
,your_deepseek_api_key
和your_claude_api_key
为你实际的 API 密钥和配置。ALLOW_ORIGINS
请设置为允许访问的域名,如"http://localhost:3000,https://chat.example.com"
或"*"
表示允许所有来源。
项目已经支持 Github Actions 自动更新 fork 项目的代码,保持你的 fork 版本与当前 main 分支保持一致。如需开启,请 frok 后在 Settings 中开启 Actions 权限即可。
- Email: [email protected]
- Website: Erlichliu
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album-ai
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LLMStack
LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.
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LocalAI
LocalAI is a free and open-source OpenAI alternative that acts as a drop-in replacement REST API compatible with OpenAI (Elevenlabs, Anthropic, etc.) API specifications for local AI inferencing. It allows users to run LLMs, generate images, audio, and more locally or on-premises with consumer-grade hardware, supporting multiple model families and not requiring a GPU. LocalAI offers features such as text generation with GPTs, text-to-audio, audio-to-text transcription, image generation with stable diffusion, OpenAI functions, embeddings generation for vector databases, constrained grammars, downloading models directly from Huggingface, and a Vision API. It provides a detailed step-by-step introduction in its Getting Started guide and supports community integrations such as custom containers, WebUIs, model galleries, and various bots for Discord, Slack, and Telegram. LocalAI also offers resources like an LLM fine-tuning guide, instructions for local building and Kubernetes installation, projects integrating LocalAI, and a how-tos section curated by the community. It encourages users to cite the repository when utilizing it in downstream projects and acknowledges the contributions of various software from the community.
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AiTreasureBox
AiTreasureBox is a versatile AI tool that provides a collection of pre-trained models and algorithms for various machine learning tasks. It simplifies the process of implementing AI solutions by offering ready-to-use components that can be easily integrated into projects. With AiTreasureBox, users can quickly prototype and deploy AI applications without the need for extensive knowledge in machine learning or deep learning. The tool covers a wide range of tasks such as image classification, text generation, sentiment analysis, object detection, and more. It is designed to be user-friendly and accessible to both beginners and experienced developers, making AI development more efficient and accessible to a wider audience.
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glide
Glide is a cloud-native LLM gateway that provides a unified REST API for accessing various large language models (LLMs) from different providers. It handles LLMOps tasks such as model failover, caching, key management, and more, making it easy to integrate LLMs into applications. Glide supports popular LLM providers like OpenAI, Anthropic, Azure OpenAI, AWS Bedrock (Titan), Cohere, Google Gemini, OctoML, and Ollama. It offers high availability, performance, and observability, and provides SDKs for Python and NodeJS to simplify integration.
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jupyter-ai
Jupyter AI connects generative AI with Jupyter notebooks. It provides a user-friendly and powerful way to explore generative AI models in notebooks and improve your productivity in JupyterLab and the Jupyter Notebook. Specifically, Jupyter AI offers: * An `%%ai` magic that turns the Jupyter notebook into a reproducible generative AI playground. This works anywhere the IPython kernel runs (JupyterLab, Jupyter Notebook, Google Colab, Kaggle, VSCode, etc.). * A native chat UI in JupyterLab that enables you to work with generative AI as a conversational assistant. * Support for a wide range of generative model providers, including AI21, Anthropic, AWS, Cohere, Gemini, Hugging Face, NVIDIA, and OpenAI. * Local model support through GPT4All, enabling use of generative AI models on consumer grade machines with ease and privacy.
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langchain_dart
LangChain.dart is a Dart port of the popular LangChain Python framework created by Harrison Chase. LangChain provides a set of ready-to-use components for working with language models and a standard interface for chaining them together to formulate more advanced use cases (e.g. chatbots, Q&A with RAG, agents, summarization, extraction, etc.). The components can be grouped into a few core modules: * **Model I/O:** LangChain offers a unified API for interacting with various LLM providers (e.g. OpenAI, Google, Mistral, Ollama, etc.), allowing developers to switch between them with ease. Additionally, it provides tools for managing model inputs (prompt templates and example selectors) and parsing the resulting model outputs (output parsers). * **Retrieval:** assists in loading user data (via document loaders), transforming it (with text splitters), extracting its meaning (using embedding models), storing (in vector stores) and retrieving it (through retrievers) so that it can be used to ground the model's responses (i.e. Retrieval-Augmented Generation or RAG). * **Agents:** "bots" that leverage LLMs to make informed decisions about which available tools (such as web search, calculators, database lookup, etc.) to use to accomplish the designated task. The different components can be composed together using the LangChain Expression Language (LCEL).
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sweep
Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.
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teams-ai
The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.
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ai-guide
This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.
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classifai
Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.
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chatbot-ui
Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.
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BricksLLM
BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students
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uAgents
uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.
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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.