Merge pull request #158 from janhq/improvement/landing-page

web: nitro landing page

Author: tikikun

docs/.gitignore

@@ -18,4 +18,5 @@
 npm-debug.log*
 yarn-debug.log*
 yarn-error.log*
-.env
+yarn.lock
+.env

docs/babel.config.js

@@ -1,3 +1,3 @@
 module.exports = {
-  presets: [require.resolve('@docusaurus/core/lib/babel/preset')],
+  presets: [require.resolve("@docusaurus/core/lib/babel/preset")],
 };

docs/docs/features/chat.md

@@ -24,6 +24,7 @@ curl http://localhost:3928/v1/chat/completions \
   }'
 
 ```
+
 </div>
 
 <div style={{ width: '50%', float: 'right', clear: 'right' }}>
@@ -42,6 +43,7 @@ curl https://api.openai.com/v1/chat/completions \
     ]
   }'
 ```
+
 </div>
 
 This command sends a request to your local LLM, querying about the winner of the 2020 World Series.
@@ -77,6 +79,7 @@ curl http://localhost:3928/v1/chat/completions \
   }'
 
 ```
+
 </div>
 
 <div style={{ width: '50%', float: 'right', clear: 'right' }}>
@@ -106,6 +109,7 @@ curl https://api.openai.com/v1/chat/completions \
     ]
   }'
 ```
+
 </div>
 
 ### Chat Completion Response
@@ -138,6 +142,7 @@ Below are examples of responses from both the Nitro server and OpenAI:
   }
 }
 ```
+
 </div>
 
 <div style={{ width: '50%', float: 'right', clear: 'right' }}>
@@ -166,7 +171,7 @@ Below are examples of responses from both the Nitro server and OpenAI:
   }
 }
 ```
-</div>
 
+</div>
 
-The chat completion feature in Nitro showcases compatibility with OpenAI, making the transition between using OpenAI and local AI models more straightforward. For further details and advanced usage, please refer to the [API reference](https://nitro.jan.ai/api).
+The chat completion feature in Nitro showcases compatibility with OpenAI, making the transition between using OpenAI and local AI models more straightforward. For further details and advanced usage, please refer to the [API reference](https://nitro.jan.ai/api-reference).

docs/docs/features/embed.md

@@ -26,6 +26,7 @@ curl http://localhost:3928/v1/embeddings \
     }'
 
 ```
+
 </div>
 <div style={{ width: '50%', float: 'right', clear: 'right' }}>
 
@@ -39,6 +40,7 @@ curl https://api.openai.com/v1/embeddings \
     "encoding_format": "float"
   }'
 ```
+
 </div>
 
 ## Embedding Reponse
@@ -61,8 +63,9 @@ The example response used the output from model [llama2 Chat 7B Q5 (GGUF)](https
       "object": "embedding"
     }
   ]
-}         
+}
 ```
+
 </div>
 
 <div style={{ width: '50%', float: 'right', clear: 'right' }}>
@@ -83,7 +86,7 @@ The example response used the output from model [llama2 Chat 7B Q5 (GGUF)](https
 
 
 ```
-</div>
 
+</div>
 
-The embedding feature in Nitro demonstrates a high level of compatibility with OpenAI, simplifying the transition between using OpenAI and local AI models. For more detailed information and advanced use cases, refer to the comprehensive [API Reference]((https://nitro.jan.ai/api)).
+The embedding feature in Nitro demonstrates a high level of compatibility with OpenAI, simplifying the transition between using OpenAI and local AI models. For more detailed information and advanced use cases, refer to the comprehensive [API Reference](https://nitro.jan.ai/api-reference)).

docs/docs/new/about.md

@@ -39,8 +39,9 @@ curl http://localhost:3928/v1/chat/completions \
       },
     ]
   }'
-  
+
 ```
+
 </div>
 
 <div style={{ width: '50%', float: 'right', clear: 'right' }}>
@@ -63,6 +64,7 @@ curl https://api.openai.com/v1/chat/completions \
     ]
   }'
 ```
+
 </div>
 
 - **Extends OpenAI's API with helpful model methods:**
@@ -80,7 +82,7 @@ curl https://api.openai.com/v1/chat/completions \
 ### Multi-modal Capabilities
 
 - **Coming Soon**: Expansion to multi-modal functionalities - enabling Nitro to process and generate images, and audio.
-- **Features to Expect**: 
+- **Features to Expect**:
   - Large Language-and-Vision Assistant.
   - Speech recognition and transcription.
 
@@ -90,25 +92,30 @@ curl https://api.openai.com/v1/chat/completions \
 - **Detailed Specifications**: For an in-depth understanding of Nitro's internal workings, components, and design philosophy, refer to our [Architecture Specifications](architecture.md).
 
 ## Support
+
 ### GitHub Issue Tracking
+
 - **Report Problems**: Encounter an issue with Nitro? File a [GitHub issue](https://github.com/janhq/nitro). Please include detailed error logs and steps to reproduce the problem.
 
 ### Discord Community
+
 - **Join the Conversation**: Discuss Nitro development and seek peer support in our [#nitro-dev](https://discord.gg/FTk2MvZwJH) channel on Discord.
 
 ## Contributing
 
 ### How to Contribute
+
 Nitro welcomes contributions in various forms, not just coding. Here are some ways you can get involved:
 
-- **Understand Nitro**: Start with the [Getting Started](nitro/overview) guide. Found an issue or have a suggestion? [Open an issue](https://github.com/janhq/nitro/issues) to let us know.
+- **Understand Nitro**: Start with the [Getting Started](/new/quickstart) guide. Found an issue or have a suggestion? [Open an issue](https://github.com/janhq/nitro/issues) to let us know.
 
 - **Feature Development**: Engage with community feature requests. Bring ideas to life by opening a [pull request](https://github.com/janhq/nitro/pulls) for features that interest you.
 
 ### Links
+
 - [Nitro GitHub Repository](https://github.com/janhq/nitro)
 
 ## Acknowledgements
 
 - [drogon](https://github.com/drogonframework/drogon): The fast C++ web framework
-- [llama.cpp](https://github.com/ggerganov/llama.cpp): Inference of LLaMA model in pure C/C++
+- [llama.cpp](https://github.com/ggerganov/llama.cpp): Inference of LLaMA model in pure C/C++

docs/docs/new/architecture.md

@@ -7,6 +7,7 @@ title: Architecture
 ### Details element example
 
 ## Key Concepts
+
 ## Inference Server
 
 An inference server is a type of server designed to process requests for running large language models and to return predictions. This server acts as the backbone for AI-powered applications, providing real-time execution of models to analyze data and make decisions.
@@ -24,15 +25,6 @@ Parallel processing involves executing multiple computations simultaneously. For
 Drogon is an HTTP application framework based on C++14/17, designed for its speed and simplicity. Utilizing a non-blocking I/O and event-driven architecture, Drogon manages HTTP requests efficiently for high-performance and scalable applications.
 
 - **Event Loop**: Drogon uses an event loop to wait for and dispatch events or messages within a program. This allows for handling many tasks asynchronously, without relying on multi-threading.
-  
 - **Threads**: While the event loop allows for efficient task management, Drogon also employs threads to handle parallel operations. These "drogon threads" process multiple tasks concurrently.
-  
 - **Asynchronous Operations**: The framework supports non-blocking operations, permitting the server to continue processing other tasks while awaiting responses from databases or external services.
-  
 - **Scalability**: Drogon's architecture is built to scale, capable of managing numerous connections at once, suitable for applications with high traffic loads.
-
-
-
-We should only have 1 document
-- [ ] Refactor system/architecture
-- [ ] Refactor system/key-concepts

docs/docs/new/build-source.md

@@ -15,19 +15,21 @@ git clone --recurse https://github.com/janhq/nitro
 If you don't have git, you can download the source code as a file archive from [Nitro GitHub](https://github.com/janhq/nitro). Each [release](https://github.com/caddyserver/caddy/releases) also has source snapshots.
 
 ## Install Dependencies
+
 Next, let's install the necessary dependencies.
 
 - **On MacOS with Apple Silicon:**
-    ```bash
-    ./install_deps.sh
-    ```
+
+  ```bash
+  ./install_deps.sh
+  ```
 
 - **On Windows:**
 
-    ```bash
-    cmake -S ./nitro_deps -B ./build_deps/nitro_deps
-    cmake --build ./build_deps/nitro_deps --config Release
-    ```
+  ```bash
+  cmake -S ./nitro_deps -B ./build_deps/nitro_deps
+  cmake --build ./build_deps/nitro_deps --config Release
+  ```
 
 This creates a `build_deps` folder.
 
@@ -37,66 +39,67 @@ Now, let's generate the build files.
 
 - **On MacOS, Linux, and Windows:**
 
-    ```bash
-    mkdir build && cd build
-    cmake ..
-    ```
+  ```bash
+  mkdir build && cd build
+  cmake ..
+  ```
 
 - **On MacOS with Intel processors:**
 
-    ```bash
-    mkdir build && cd build
-    cmake -DLLAMA_METAL=OFF .. 
-    ```
+  ```bash
+  mkdir build && cd build
+  cmake -DLLAMA_METAL=OFF ..
+  ```
 
 - **On Linux with CUDA:**
 
-    ```bash
-    mkdir build && cd build
-    cmake -DLLAMA_CUBLAS=ON ..
-    ```
+  ```bash
+  mkdir build && cd build
+  cmake -DLLAMA_CUBLAS=ON ..
+  ```
 
 ## Build the Application
 
 Time to build Nitro!
 
 - **On MacOS:**
-    
-    ```bash
-    make -j $(sysctl -n hw.physicalcpu)
-    ```
+
+  ```bash
+  make -j $(sysctl -n hw.physicalcpu)
+  ```
 
 - **On Linux:**
 
-    ```bash
-    make -j $(%NUMBER_OF_PROCESSORS%)
-    ```
+  ```bash
+  make -j $(%NUMBER_OF_PROCESSORS%)
+  ```
 
 - **On Windows:**
 
-    ```bash
-    cmake --build . --config Release
-    ```
+  ```bash
+  cmake --build . --config Release
+  ```
 
 ## Start process
 
 Finally, let's start Nitro.
 
 - **On MacOS and Linux:**
 
-    ```bash
-    ./nitro
-    ```
+  ```bash
+  ./nitro
+  ```
 
 - **On Windows:**
 
-    ```bash
-    cd Release
-    copy ..\..\build_deps\_install\bin\zlib.dll .
-    nitro.exe
-    ```
+  ```bash
+  cd Release
+  copy ..\..\build_deps\_install\bin\zlib.dll .
+  nitro.exe
+  ```
 
 To verify if the build was successful:
+
 ```bash
 curl http://localhost:3928/healthz
-```
+```

docs/docs/new/quickstart.md

@@ -1,19 +1,24 @@
 ---
 title: Quickstart
 ---
+
 ## Step 1: Install Nitro
 
 ### For Linux and MacOS
+
 Open your terminal and enter the following command. This will download and install Nitro on your system.
-  ```bash
-  curl -sfL https://raw.githubusercontent.com/janhq/nitro/main/install.sh -o /tmp/install.sh && chmod +x /tmp/install.sh && sudo bash /tmp/install.sh --gpu && rm /tmp/install.sh
-  ```
+
+```bash
+curl -sfL https://raw.githubusercontent.com/janhq/nitro/main/install.sh -o /tmp/install.sh && chmod +x /tmp/install.sh && sudo bash /tmp/install.sh --gpu && rm /tmp/install.sh
+```
 
 ### For Windows
+
 Open PowerShell and execute the following command. This will perform the same actions as for Linux and MacOS but is tailored for Windows.
-  ```bash
-  powershell -Command "& { Invoke-WebRequest -Uri 'https://raw.githubusercontent.com/janhq/nitro/main/install.bat' -OutFile 'install.bat'; .\install.bat --gpu; Remove-Item -Path 'install.bat' }"
-  ```
+
+```bash
+powershell -Command "& { Invoke-WebRequest -Uri 'https://raw.githubusercontent.com/janhq/nitro/main/install.bat' -OutFile 'install.bat'; .\install.bat --gpu; Remove-Item -Path 'install.bat' }"
+```
 
 > **NOTE:**Installing Nitro will add new files and configurations to your system to enable it to run.
 
@@ -24,6 +29,7 @@ For a manual installation process, see: [Install from Source](install.md)
 Next, we need to download a model. For this example, we'll use the [Llama2 7B chat model](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF/tree/main).
 
 - Create a `/model` and navigate into it:
+
 ```bash
 mkdir model && cd model
 wget -O llama-2-7b-model.gguf https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF/resolve/main/llama-2-7b-chat.Q5_K_M.gguf?download=true
@@ -78,4 +84,4 @@ curl http://localhost:3928/v1/chat/completions \
 
 This command sends a request to Nitro, asking it about the 2020 World Series winner.
 
-- As you can see, A key benefit of Nitro is its alignment with [OpenAI's API structure](https://platform.openai.com/docs/guides/text-generation?lang=curl). Its inference call syntax closely mirrors that of OpenAI's API, facilitating an easier shift for those accustomed to OpenAI's framework.
+- As you can see, A key benefit of Nitro is its alignment with [OpenAI's API structure](https://platform.openai.com/docs/guides/text-generation?lang=curl). Its inference call syntax closely mirrors that of OpenAI's API, facilitating an easier shift for those accustomed to OpenAI's framework.