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# How We Work
### Open Source
Jan is a startup with an open source business model. We believe in the need for an open source AI ecosystem, and are committed to building it.
- [Jan Framework](https://github.com/janhq/jan) (AGPLv3)
- [Jan Desktop Client & Local server](https://jan.ai) (AGPLv3, built on Jan Framework)
- [Nitro: run Local AI](https://github.com/janhq/nitro) (AGPLv3)
### Build in Public
We use GitHub to build in public and welcome anyone to join in.
- [Jan's Kanban](https://github.com/orgs/janhq/projects/5)
- [Jan's Roadmap](https://github.com/orgs/janhq/projects/5/views/29)
- `coming soon` [Jan's Newsletter](https://newsletter.jan.ai)
### Remote Team
Jan has a fully-remote team. We are mainly based in the APAC timezone. We use [Discord](https://discord.gg/af6SaTdzpx) and [Github](https://github.com/janhq) to work. | https://jan.ai/how-we-work |
# Wall of Love ❤️
## Twitter
Check out our amazing users and what they are saying about Jan!
<div>
<blockquote class="twitter-tweet"><p lang="en" dir="ltr">I can confirm <a href="https://t.co/Hvrfp0iaf9">https://t.co/Hvrfp0iaf9</a> is awesome 👌</p>— Cristian (@cristianmoreno) <a href="https://twitter.com/cristianmoreno/status/1757504717519749292?ref_src=twsrc%5Etfw">February 13, 2024</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8" loading="lazy"></script>
</div>
<div>
<blockquote class="twitter-tweet"><p lang="en" dir="ltr">downloaded this a few weeks ago. amazed by the speed and quality</p>— siddharth (@siddharthd01) <a href="https://twitter.com/siddharthd01/status/1757500111629025788?ref_src=twsrc%5Etfw">February 13, 2024</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8" loading="lazy"></script>
</div>
<div>
<blockquote class="twitter-tweet"><p lang="en" dir="ltr">Anyone else out there running LLMs on steam deck? <a href="https://twitter.com/janframework?ref_src=twsrc%5Etfw">@janframework</a> bringing nerd dreams to life! <a href="https://t.co/7XpnBmc8MN">pic.twitter.com/7XpnBmc8MN</a></p>— crossdefault (@crossdefault) <a href="https://twitter.com/crossdefault/status/1750801065132384302?ref_src=twsrc%5Etfw">January 26, 2024</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8" loading="lazy"></script>
</div>
<div>
<blockquote class="twitter-tweet"><p lang="en" dir="ltr">If you are like me, always wanting your own ChatGPT and have sufficient coding knowledge, you would watch open sourced <a href="https://twitter.com/janframework?ref_src=twsrc%5Etfw">@janframework</a> by <a href="https://twitter.com/0xSage?ref_src=twsrc%5Etfw">@0xSage</a> like a "my-own-ai" hawk<br></br>Still under development, the architecture is really futuristic. The desktop app for Windows, Mac, Linux are… <a href="https://t.co/0HrNquhBsL">pic.twitter.com/0HrNquhBsL</a></p>— Umesh = EG = Educated Guess - NGI doing AI (@trading_indian) <a href="https://twitter.com/trading_indian/status/1745560583548670250?ref_src=twsrc%5Etfw">January 11, 2024</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8" loading="lazy"></script>
</div>
<div>
<blockquote class="twitter-tweet"><p lang="en" dir="ltr">came across <a href="https://twitter.com/janframework?ref_src=twsrc%5Etfw">@janframework</a> yesterday and it's my fav native Apple Silicon LLM app yet. Love that I can switch to GPT 4 API and offline LLM models seamlessly. Looks promising! <a href="https://t.co/gyOX9gHbKQ">https://t.co/gyOX9gHbKQ</a></p>— Keith Hawkins (@kph_practice) <a href="https://twitter.com/kph_practice/status/1744729548074459310?ref_src=twsrc%5Etfw">January 9, 2024</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8" loading="lazy"></script>
</div>
<div>
<blockquote class="twitter-tweet"><p lang="en" dir="ltr">i just ran some ai models locally on my laptop using @janhq_ and can't believe how easy and cool it is. so, now i can have the same experience as with ChatGPT, but offline and without any data concerns</p>— Sergey Kaplich (@sergey_kaplich) <a href="https://twitter.com/sergey_kaplich/status/1742993414986068423?ref_src=twsrc%5Etfw">January 4, 2024</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8" loading="lazy"></script>
</div>
<div>
<blockquote class="twitter-tweet"><p lang="en" dir="ltr"><a href="https://t.co/scBqJ3kIzj">https://t.co/scBqJ3kIzj</a> Great way to try open source all models, like Mixtral8x7b offline. Love to see</p>— Chubby♨️ (@kimmonismus) <a href="https://twitter.com/kimmonismus/status/1742843063938994469?ref_src=twsrc%5Etfw">January 4, 2024</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8" loading="lazy"></script>
</div>
Please share your love for Jan on Twitter and tag us [@janframework](https://twitter.com/janframework)! We would love to hear from you!
## YouTube
Watch these amazing videos to see how Jan is being used and loved by the community!
### Run Any Chatbot FREE Locally on Your Computer
<div>
<iframe width="100%" height="600" src="https://www.youtube.com/embed/zkafOIyQM8s" title="Run Any Chatbot FREE Locally on Your Computer" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
</div>
<br></br>
### Jan AI: Run Open Source LLM 100% Local with OpenAI endpoints
<div>
<iframe width="100%" height="705" src="https://www.youtube.com/embed/9ta2S425Zu8" title="Jan AI: Run Open Source LLM 100% Local with OpenAI endpoints" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
</div>
<br></br>
### Setup Tutorial on Jan.ai. JAN AI: Run open source LLM on local Windows PC. 100% offline LLM and AI.
<div>
<iframe width="100%" height="705" src="https://www.youtube.com/embed/ZCiEQVOjH5U" title="Setup Tutorial on Jan.ai. JAN AI: Run open source LLM on local Windows PC. 100% offline LLM and AI." frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
</div>
<br></br>
### Jan.ai: Like Offline ChatGPT on Your Computer 💡
<div>
<iframe width="100%" height="600" src="https://www.youtube.com/embed/ES021_sY6WQ" title="Jan.ai: Like Offline ChatGPT on Your Computer 💡" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
</div>
<br></br>
### Jan: Bring AI to your Desktop With 100% Offline AI
<div>
<iframe width="100%" height="600" src="https://www.youtube.com/embed/QpMQgJL4AZA" title="Jan: Bring AI to your Desktop With 100% Offline AI" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
</div>
<br></br>
### AI on Your Local PC: Install JanAI (ChatGPT alternative) for Enhanced Privacy
<div>
<iframe width="100%" height="600" src="https://www.youtube.com/embed/CbJGxNmdWws" title="AI on Your Local PC: Install JanAI (ChatGPT alternative) for Enhanced Privacy" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
</div>
<br></br>
### Install Jan to Run LLM Offline and Local First
<div>
<iframe width="100%" height="600" src="https://www.youtube.com/embed/7JpzE-_cKo4" title="Install Jan to Run LLM Offline and Local First" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
</div> | https://jan.ai/wall-of-love |
# Acknowledgements
# Acknowledgements
We would like to express our gratitude to the following third-party libraries that have made the development of Jan possible.
- [llama.cpp](https://github.com/ggerganov/llama.cpp/blob/master/LICENSE)
- [LangChain.js](https://github.com/langchain-ai/langchainjs/blob/main/LICENSE)
- [TensorRT](https://github.com/NVIDIA/TensorRT/blob/main/LICENSE) | https://jan.ai/acknowledgements |
# Hardware Examples
## Add your own example
Add your own examples to this page by creating a new file in the `docs/docs/hardware/examples` directory.
```shell
docs
└── docs
└── hardware
└── examples
└── 3090x1-%40dan-jan.md
└── 3090x1-%40dan-jan.md
// highlight-next-line
└── <YOUR_BUILD_HERE>.md
```
### File and Title Convention
We use a specific naming convention for the file name.
```shell
# Filename
<hardware-type><quantity>-<username>.md
3090x1-@dan-jan.md # Example
# Title | https://jan.ai/hardware/community |
# Motherboard
- PCIe lanes that go to the processor (not Chipset) | https://jan.ai/hardware/concepts/motherboard |
# Anatomy of a Thinking Machine
- Cover the difference between CPU/RAM and GPU/VRAM
- AI can now run on CPU/RAM (llama.cpp)
- AI that runs on Apple Silicon | https://jan.ai/hardware/concepts |
# GPUs and VRAM
## What Is a GPU?
A Graphics Card, or GPU (Graphics Processing Unit), is a fundamental component in modern computing. Think of it as the powerhouse behind rendering the stunning visuals you see on your screen. Similar to the motherboard in your computer, the graphics card is a printed circuit board. However, it's not just a passive piece of hardware; it's a sophisticated device equipped with essential components like fans, onboard RAM, a dedicated memory controller, BIOS, and various other features. If you want to learn more about GPUs then read here to [Understand the architecture of a GPU.](https://medium.com/codex/understanding-the-architecture-of-a-gpu-d5d2d2e8978b)
## What Are GPUs Used For?
Two decades ago, GPUs primarily enhanced real-time 3D graphics in gaming. But as the 21st century dawned, a revelation occurred among computer scientists. They recognized that GPUs held untapped potential to solve some of the world's most intricate computing tasks.
This revelation marked the dawn of the general-purpose GPU era. Today's GPUs have evolved into versatile tools, more adaptable than ever before. They now have the capability to accelerate a diverse range of applications that stretch well beyond their original graphics-focused purpose.
### **Here are some example use cases:**
1. **Gaming**: They make games look good and run smoothly.
2. **Content Creation**: Help with video editing, 3D design, and graphics work.
3. **AI and Machine Learning**: Used for training smart machines.
4. **Science**: Speed up scientific calculations and simulations.
5. **Cryptocurrency Mining**: Mine digital currencies like Bitcoin.
6. **Medical Imaging**: Aid in analyzing medical images.
7. **Self-Driving Cars**: Help cars navigate autonomously.
8. **Simulations**: Create realistic virtual experiences.
9. **Data Analysis**: Speed up data processing and visualization.
10. **Video Streaming**: Improve video quality and streaming efficiency.
## What is VRAM In GPU?
VRAM, or video random-access memory, is a type of high-speed memory that is specifically designed for use with graphics processing units (GPUs). VRAM is used to store the textures, images, and other data that the GPU needs to render graphics. Its allows the GPU to access the data it needs quickly and efficiently. This is essential for rendering complex graphics at high frame rates.
VRAM is different from other types of memory, such as the system RAM that is used by the CPU. VRAM is optimized for high bandwidth and low latency, which means that it can read and write data very quickly. The amount of VRAM that a GPU has is one of the factors that determines its performance. More VRAM allows the GPU to store more data and render more complex graphics. However, VRAM is also one of the most expensive components of a GPU. So when choosing a graphics card, it is important to consider the amount of VRAM that it has. If you are planning on running demanding LLMs or video games, or 3D graphics software, you will need a graphics card with more VRAM.
## What makes VRAM and RAM different from each other?
RAM (Random Access Memory) and VRAM (Video Random Access Memory) are both types of memory used in computers, but they have different functions and characteristics. Here are the differences between RAM and VRAM.
### RAM (Random Access Memory):
- RAM is a general-purpose memory that stores data and instructions that the CPU needs to access quickly.
- RAM is used for short-term data storage and is volatile, meaning that it loses its contents when the computer is turned off.
- RAM is connected to the motherboard and is accessed by the CPU.
- RAM typically has a larger capacity compared to VRAM, which is designed to store smaller amounts of data with faster access times.
- RAM stores data related to the operating system and the various programs that are running, including code, program files, and user data.
### VRAM (Video Random Access Memory):
- VRAM is a type of RAM that is specifically used to store image data for a computer display.
- VRAM is a graphics card component that is connected to the GPU (Graphics Processing Unit).
- VRAM is used exclusively by the GPU and doesn’t need to store as much data as the CPU.
- VRAM is similar to RAM in that it is volatile and loses its contents when the computer is turned off.
- VRAM stores data related specifically to graphics, such as textures, frames, and other graphical data.
- VRAM is designed to store smaller amounts of data with faster access times than RAM.
In summary, RAM is used for general-purpose memory, while VRAM is used for graphics-related tasks. RAM has a larger capacity and is accessed by the CPU, while VRAM has a smaller capacity and is accessed by the GPU.
**Key differences between VRAM and RAM:**
| Characteristic | VRAM | RAM |
| | https://jan.ai/hardware/concepts/gpu-and-vram |
# CPU
- CPU's role vs GPU
- Cooler + Thermal Paste
- RAM | https://jan.ai/hardware/concepts/cpu-and-ram |
# "2 x 4090 Workstation"
Jan uses a 2 x 4090 Workstation to run Codellama for internal use.[^1]
## Component List
| Type | Item | Unit Price | Total Price |
| : | https://jan.ai/hardware/examples/4090x2-@dan-jan |
# Recommended AI Hardware by Model
## Codellama 34b
### System Requirements:
**For example**: If you want to use [Codellama 7B](https://huggingface.co/TheBloke/CodeLlama-7B-Instruct-GPTQ/tree/main) models on your own computer, you can take advantage of your GPU and run this with GPTQ file models.
GPTQ is a format that compresses the model parameters to 4-bit, which reduces the VRAM requirements significantly. You can use the [oobabooga webui](https://github.com/oobabooga/text-generation-webui) or [JanAI](https://jan.ai/), which are simple interfaces that let you interact with different LLMS on your browser. It is pretty easy to set up and run. You can install it on Windows or Linux. (linked it to our installation page)
**For 7B Parameter Models (4-bit Quantization)**
| Format | RAM Requirements | VRAM Requirements | Minimum recommended GPU |
| | https://jan.ai/hardware/recommendations/by-model |
# Selecting AI Hardware
When selecting a GPU for LLMs, remember that it's not just about the GPU itself. Consider the synergy with other components in your PC:
- **CPU**: To ensure efficient processing, pair your GPU with a powerful CPU. LLMs benefit from fast processors, so having a capable CPU is essential.
- **RAM**: Sufficient RAM is crucial for LLMs. They can be memory-intensive, and having enough RAM ensures smooth operation.
- **Cooling System**: LLMs can push your PC's hardware to the limit. A robust cooling system helps maintain optimal temperatures, preventing overheating and performance throttling.
By taking all of these factors into account, you can build a home PC setup that's well-equipped to handle the demands of running LLMs effectively and efficiently.
## GPU Selection
Selecting the optimal GPU for running Large Language Models (LLMs) on your home PC is a decision influenced by your budget and the specific LLMs you intend to work with. Your choice should strike a balance between performance, efficiency, and cost-effectiveness.
### GPU Comparison
| GPU | Price | Cores | VRAM (GB) | Bandwth (T/s) | Power |
| | https://jan.ai/hardware/recommendations/by-hardware |
# Recommended AI Hardware by Use Case
## Which AI Hardware to Choose Based on Your Use Case
Artificial intelligence (AI) is rapidly changing the world, and AI hardware is becoming increasingly important for businesses and individuals alike. Choosing the right hardware for your AI needs is crucial to get the best performance and results. Here are some tips for selecting AI hardware based on your specific use case and requirements.
### Entry-level Experimentation:
**Personal Use:**
When venturing into the world of AI as an individual, your choice of hardware can significantly impact your experience. Here's a more detailed breakdown:
- **Macbook (16GB):** A Macbook equipped with 16GB of RAM and either the M1 or the newer M2 Pro/Max processor is an excellent starting point for AI enthusiasts. These cutting-edge chips leverage Apple's innovative Unified Memory Architecture (UMA), which revolutionizes the way the CPU and GPU interact with memory resources. This advancement plays a pivotal role in enhancing the performance and capabilities of LLMs.
- **Nvidia GeForce RTX 3090:** This powerful graphics card is a solid alternative for AI beginners, offering exceptional performance for basic experiments.
2. **Serious AI Work:**
- **2 x 3090 RTX Card (48GB RAM):** For those committed to more advanced AI projects, this configuration provides the necessary muscle. Its dual Nvidia GeForce RTX 3090 GPUs and ample RAM make it suitable for complex AI tasks and model training.
## Business Use
### For a 10-person Small Business
Run a LLM trained on enterprise data (i.e. RAG)
- Mac Studio M2 Ultra with 192GB unified memory
- Cannot train
- RTX 6000
- Should we recommend 2 x 4090 instead?
### For a 50-person Law Firm
- LLM, PDF Parsing, OCR
- Audit logging and compliance
### For a 1,000-student School
- Llama2 with safeguards
- RAG with textbook data
- Policy engine
## Software Engineering
### Personal Code Assistant
- Llama34b, needs adequate RAM
- Not recommended to run on local device due to RAM
### For a 10 person Software Team
Run Codellama with RAG on existing codebase
- Codellama34b
- RTX 6000s (48gb)
## Enterprise
### For a 1000-person Enterprise
### For a 10,000-person Enterprise
- 8 x H100s
- NVAIE with vGPUs | https://jan.ai/hardware/recommendations/by-usecase |
# Recommended AI Hardware by Budget
> :warning: **Warning:** Do your own research before any purchase. Jan is not liable for compatibility, performance or other issues. Products can become outdated quickly.
## Entry-level PC Build at $1000
| Type | Item | Price |
| : | https://jan.ai/hardware/recommendations/by-budget |
# GPU vs CPU What's the Difference?
## CPU vs. GPU
| | CPU | GPU |
| | https://jan.ai/hardware/overview/cpu-vs-gpu |
# Cloud vs. Self-hosting Your AI
The choice of how to run your AI - on GPU cloud services, on-prem, or just using an API provider - involves various trade-offs. The following is a naive exploration of the pros and cons of renting vs self-hosting.
## Cost Comparison
The following estimations use these general assumptions:
| | Self-Hosted | GPT 4.0 | GPU Rental |
| | https://jan.ai/hardware/overview/cloud-vs-self-hosting |
# Who we are
What's Jan the company about?
We aim to build the cognitive framework for future robots
### Open Source
Jan is a startup with an open source business model. We believe in the need for an open source AI ecosystem, and are committed to building it.
- [Jan Framework](https://github.com/janhq/jan) (AGPLv3)
- [Jan Desktop Client & Local server](https://jan.ai) (AGPLv3, built on Jan Framework)
- [Nitro: run Local AI](https://github.com/janhq/nitro) (AGPLv3)
### Bootstrapped
Jan is currently a bootstrapped startup.
We balance technical invention with the search for a sustainable business model. Thus, we appreciate any business inquiries that can balance growth with cashflow.
**We invite you to join us on our journey to find PMF**. Join our [Discord here](https://discord.gg/BnHRr3Q7Ms)
## Our Team
- Contributors
- Core Team | https://jan.ai/team/team |
# Join us
- [ ] Explain Core Team, Contributors and Open Source approach
[Careers on Bamboo](https://janai.bamboohr.com/careers)
### Careers
Jan has a culture of ownership, independent thought, and lightning fast execution. If you'd like to join us, we have open positions on our [careers page](https://janai.bamboohr.com/careers). | https://jan.ai/team/join-us |
# Regression test
**Release Version:** v0.4.7
**Operating System:** MacOS
---
## A. Installation, Update, and Uninstallation
### 1. Users install app
- [ ] Check that the installation package is not corrupted and passes all security checks.
- [ ] :key: Confirm that the app launches successfully after installation.
### 2. Users update app
- [ ] :key: Validate that the update does not corrupt user data or settings.
- [ ] :key: Confirm that the app restarts or prompts the user to restart after an update.
- [ ] When updating the app, check if the `/models` directory has any JSON files that change according to the update.
- [ ] Verify if updating the app also updates extensions correctly (test functionality changes, support notifications for necessary tests with each version related to extensions update).
### 3. Users uninstall / close app
- [ ] :key: Ensure that after closing the app, all models are unloaded.
- [ ] :key::warning: Check that the uninstallation process removes the app successfully from the system.
- [ ] Clean the Jan root directory and open the app to check if it creates all the necessary folders, especially models and extensions.
## B. Overview
### 1. Shortcut key, memory usage / CPU usage
- [ ] :key: Test each shortcut key to confirm it works as described (My models, navigating, opening, closing, etc.).
- [ ] :key: Ensure that the interface presents the correct numbers for memory and CPU usage.
### 2. Users check the `active model`
- [ ] :key: Verify that the app correctly displays the state of the loading model (e.g., loading, ready, error).
- [ ] :key: Confirm that the app allows users to switch between models if multiple are available.
- [ ] Check that the app provides feedback or instructions if the model fails to load.
- [ ] Verify the troubleshooting assistant correctly capture hardware / log info #1784
## C. Thread
### 1. Users can chat with Jan, the default assistant
- [ ] :key: Verify sending a message enables users to receive responses from model.
- [ ] :key: Ensure that the conversation thread is maintained without any loss of data upon sending multiple messages.
- [ ] Users should be able to edit msg and the assistant will re-generate the answer based on the edited version of the message.
- [ ] Test for the ability to send different types of messages (e.g., text, emojis, code blocks).
- [ ] Check the output format of the AI (code blocks, JSON, markdown, ...).
- [ ] :key: Validate the scroll functionality in the chat window for lengthy conversations.
- [ ] Check if the user can copy / delete the response.
- [ ] :key: Check the `clear message` / `delete entire chat` button works.
- [ ] Check if deleting all the chat retains the system prompt.
- [ ] :key: Validate that there is appropriate error handling and messaging if the assistant fails to respond.
- [ ] Test assistant's ability to maintain context over multiple exchanges.
- [ ] :key: Check the `create new chat` button, and new conversation will have an automatically generated thread title based on users msg.
- [ ] Confirm that by changing `models` mid-thread the app can still handle it.
- [ ] Check the `regenerate` button renews the response (single / multiple times).
- [ ] Check the `Instructions` update correctly after the user updates it midway (mid-thread).
### 2. Users can customize chat settings like model parameters via both the GUI & thread.json
- [ ] Test the functionality to adjust model parameters (e.g., Temperature, Top K, Top P) from the GUI and verify they are reflected in the chat behavior.
- [ ] :key: Ensure that changes can be saved and persisted between sessions.
- [ ] Validate that users can access and modify the thread.json file.
- [ ] :key: Check that changes made in thread.json are correctly applied to the chat session upon reload or restart.
- [ ] Check the maximum and minimum limits of the adjustable parameters and how they affect the assistant's responses.
- [ ] :key: Ensure that users switch between threads with different models, the app can handle it.
### 3. Model dropdown
- [ ] :key: Model list should highlight recommended based on user RAM
- [ ] Model size should display (for both installed and imported models)
### 4. Users can click on a history thread
- [ ] Confirm that the chat window displays the entire conversation from the selected history thread without any missing messages.
- [ ] :key: Check the performance and accuracy of the history feature when dealing with a large number of threads.
- [ ] Validate that historical threads reflect the exact state of the chat at that time, including settings.
- [ ] :key: Verify the ability to delete or clean old threads.
- [ ] Confirm that changing the title of the thread updates correctly.
### 5. Users can config instructions for the assistant.
- [ ] Test if the instructions set by the user are being followed by the assistant in subsequent conversations.
- [ ] :key: Validate that changes to instructions are updated in real time and do not require a restart of the application or session.
- [ ] :key: Check for the ability to reset instructions to default or clear them completely.
- [ ] :key: RAG - Users can import documents and the system should process queries about the uploaded file, providing accurate and appropriate responses in the conversation thread.
## D. Hub
### 1. Users can discover recommended models (Jan ships with a few preconfigured model.json files)
- [ ] :key: Ensure that each model's recommendations are consistent with the user’s activity and preferences.
- [ ] Test the functionality of any filters that refine model recommendations.
### 2. Users can download models suitable for their devices, e.g. compatible with their RAM
- [ ] Display the best model for their RAM at the top.
- [ ] :key: Ensure that models are labeled with RAM requirements and compatibility.
- [ ] :key: Check the download model functionality and validate if the cancel download feature works correctly.
### 3. Users can download models via a HuggingFace URL (coming soon)
- [ ] :key: Have the warning/status when the user enters the URL.
- [ ] :key: Check the progress bar reflects the right process.
- [ ] Validate the error handling for invalid or inaccessible URLs.
### 4. Users can import new models to the Hub
- [ ] :key: Ensure import successfully via drag / drop or upload GGUF.
- [ ] :key: Verify Move model binary file / Keep Original Files & Symlink option are working
- [ ] :warning: Ensure it raises clear errors for users to fix the problem while adding a new model.
- [ ] Users can add more info to the imported model / edit name
- [ ] :key: Ensure the new model updates after restarting the app.
### 5. Users can use the model as they want
- [ ] :key: Check `start` / `stop` / `delete` button response exactly what it does.
- [ ] Check if starting another model stops the other model entirely.
- [x] :rocket: Check the `Explore models` navigate correctly to the model panel.
- [ ] :key: Check when deleting a model it will delete all the files on the user's computer.
- [ ] :warning:The recommended tags should present right for the user's hardware.
### 6. Users can Integrate With a Remote Server
- [ ] :key: Import openAI GPT model https://jan.ai/guides/using-models/integrate-with-remote-server/ and the model displayed in Hub / Thread dropdown
- [ ] Users can use the remote model properly
## E. System Monitor
### 1. Users can see disk and RAM utilization
- [ ] :key: Verify that the RAM and VRAM utilization graphs display accurate information.
- [ ] :key: Check that the CPU usage is accurately reported in real time.
- [ ] :key: Validate that the utilization percentages reflect the actual usage compared to the system's total available resources.
- [ ] :key: Ensure that the system monitors updates dynamically as the models run and stop.
### 2. Users can start and stop models based on system health
- [ ] :key: Test the 'Start' action for a model to ensure it initiates and the system resource usage reflects this change.
- [ ] :key: Verify the 'Stop' action for a model to confirm it ceases operation and frees up the system resources accordingly.
- [ ] Confirm that any changes in model status (start/stop) are logged or reported to the user for transparency.
## F. Settings
### 1. Appearance
- [ ] :key: Test the `Light`, `Dark`, and `System` theme settings to ensure they are functioning as expected.
- [ ] Confirm that the application saves the theme preference and persists it across sessions.
- [ ] Validate that all elements of the UI are compatible with the theme changes and maintain legibility and contrast.
### 2. Extensions [TBU]
- [ ] Confirm that the `Extensions` tab lists all available plugins.
- [x] :key: Test the toggle switch for each plugin to ensure it enables or disables the plugin correctly.
- [x] Verify that plugin changes take effect without needing to restart the application unless specified.
- [x] :key: Check that the plugin's status (`Installed the latest version`) updates accurately after any changes.
- [x] Validate the `Manual Installation` process by selecting and installing a plugin file.
- [x] Test for proper error handling and user feedback when a plugin installation fails.
### 3. Users can add custom plugins via manual installation [TBU]
- [x] Verify that the `Manual Installation` option is clearly visible and accessible in the `Extensions` section.
- [x] Test the functionality of the `Select` button within the `Manual Installation` area.
- [x] :warning: Check that the file picker dialog allows for the correct plugin file types (e.g., .tgz).
- [x] :key: Validate that the selected plugin file installs correctly and the plugin becomes functional.
- [x] Ensure that there is a progress indicator or confirmation message once the installation is complete.
- [x] Confirm that if the installation is interrupted or fails, the user is given a clear error message.
- [x] :key: Test that the application prevents the installation of incompatible or corrupt plugin files.
- [x] :key: Check that the user can uninstall or disable custom plugins as easily as pre-installed ones.
- [x] Verify that the application's performance remains stable after the installation of custom plugins.
### 4. Advanced settings
- [ ] :key: Test the `Experimental Mode` toggle to confirm it enables or disables experimental features as intended.
- [ ] :key: Check the functionality of `Open App Directory` to ensure it opens the correct folder in the system file explorer.
- [ ] Users can move **Jan data folder**
- [ ] Validate that changes in advanced settings are applied immediately or provide appropriate instructions if a restart is needed.
- [ ] Attemp to test downloading model from hub using **HTTP Proxy** [guideline](https://github.com/janhq/jan/pull/1562)
- [ ] Logs that are older than 7 days or exceed 1MB in size will be automatically cleared upon starting the application.
- [ ] Users can click on Reset button to **factory reset** app settings to its original state & delete all usage data.
## G. Local API server
### 1. Local Server Usage with Server Options
- [ ] :key: Explore API Reference: Swagger API for sending/receiving requests
- [ ] Use default server option
- [ ] Configure and use custom server options
- [ ] Test starting/stopping the local API server with different Model/Model settings
- [ ] Server logs captured with correct Server Options provided
- [ ] Verify functionality of Open logs/Clear feature
- [ ] Ensure that threads and other functions impacting the model are disabled while the local server is running
| QA_script.md |
# Jan Enterprise
# Customize and run AI across your organization
Jan can professional backend to create, customize and run AIs at scale, for production-grade data centers.
:::warning
The server suite is actively under development and lacking documentation.
You can find the source code [here](https://github.com/janhq/jan/tree/dev/server) and [here](https://github.com/janhq/jan/blob/dev/docker-compose.yml).
It is free to use. Your feedback is appreciated 🙏.
:::
## Own your AI. Own your data. Own your IP.
Over time, we expect more teams and organizations to turn to running their own AIs on-prem.
**Why?**
- Prevent shadow data
- Avoid vendor lock-in
- Keep your IP in house
- Uptime and support predictability
- Eliminate monthly API bills - use your existing hardware
- Full control over your AI - you can open it up and see what's going on
## Why Jan Enterprise
### Fast deployment
- **1 click deployment**. Immediately serve, customize, and scale models and assistants across your org. Scale your AI team so they can focus on the IP instead of fixing plumbing across every computer.
- **Scale across infrastructures**: on premise, with cloud providers, or as a hybrid deployment. Run Jan in completely air-gapped environments.
- **Optimized for datacenter-grade GPUs**: Can run on Nvidia, AMD Hardware, or even normal CPUs. Use TensorRT-LLM for more speedups on A6000s and above.
### Full customization
- Runs custom models or popular LLMs like Llama2, Mistral at production scale
- API that is fully OpenAI-compatible, i.e. can be a drop-in migration
- Powerful Agent framework to customize LLMs using RAG or Enterprise Data integrations.
:::tip
Not a Jan fan but convinced about local AI? No worries, here's a list of [awesome local ai](https://github.com/janhq/awesome-local-ai) alternatives that you can use in your team.
:::
## Supported Extensions
The SDK and current implemention accomodate the following potential extensions.
### Admin console
Integrate SAML, OAUTH, OIDC
<!-- link to that page -->
### Identity access management
Grant roles, groups and general ACL
<!-- link to that page -->
### Audit compliance
Plug in Guardrails, LLMGuard, your custom rules engine and more
<!-- Link to that page -->
### Observability
Plug in Langfuse, Langsmith, Openllmetry and more
<!-- Link to this page -->
## Enterprise support SLA
Our core team and AI solutions partners are to help.
Email us at: `inquiries@jan.ai` for:
- Priority case routing
- Proactive case monitoring
- 24-hour support response | https://jan.ai/enterprise |
# Jan Home Server
# Customize and run AI across all of your devices
Self-host and access your AI from anywhere with Jan server suite.
:::warning
Jan's server suite is actively under development and lacking documentation.
You can find the source code [here](https://github.com/janhq/jan/tree/dev/server) and [here](https://github.com/janhq/jan/blob/dev/docker-compose.yml).
It is free to use. Your feedback is appreciated 🙏.
:::
## Why Home Servers
We built [Jan Desktop](/desktop) for our personal use. We're now building Server Suite, for our team & community use.
Our goal is to help teams, like ours, move past cobbling together demo apps to use AI at work. We should be able to customize and collaborate with AIs that are usable on a daily basis.
**Check out [Server Suite](https://github.com/janhq/jan/tree/dev/server) if you need to:**
- Self-host Jan, with multi client sync
- Customize it with Personal Data Connectors
- Simple Authentication (username / pw)
- Scales across Consumer-grade Hardware, including GPUs
- Everyone has admin level visibility and can see all conversations
- Create assistants that has access to the same knowledge base
:::tip
Not a Jan fan but convinced about running AI locally? No worries, here's a list of [awesome local ai](https://github.com/janhq/awesome-local-ai) alternatives that you can use in your home server.
::: | https://jan.ai/home-server |
# Pre-configured Models
## Overview
Jan provides various pre-configured AI models with different capabilities. Please see the following list for details.
| Model | Description |
| | https://jan.ai/guides/models-list |
# Quickstart
import installImageURL from './assets/jan-ai-quickstart.png';
import flow from './assets/quick.png';
# Quickstart
{/* After finish installing, here are steps for using Jan
## Run Jan
<Tabs>
<TabItem value="mac" label="MacOS" default>
1. Search Jan in the Dock and run the program.
</TabItem>
<TabItem value="windows" label="Windows" default>
1. Search Jan in the Start menu and run the program.
</TabItem>
<TabItem value="linux" label="Linux" default>
1. Go to the Jan directory and run the program.
</TabItem>
</Tabs>
2. After you run Jan, the program will take you to the Threads window, with list of threads and each thread is a chatting box between you and the AI model.
3. Go to the **Hub** under the **Thread** section and select the AI model that you want to use. For more info, go to the [Using Models](category/using-models) section.
4. A new thread will be added. You can use Jan in the thread with the AI model that you selected before. */}
To get started quickly with Jan, follow the steps below:
### Step 1: Install Jan
Go to [Jan.ai](https://jan.ai/) > Select your operating system > Install the program.
:::note
To learn more about system requirements for your operating system, go to [Installation guide](/guides/install).
:::
### Step 2: Select AI Model
Before using Jan, you need to select an AI model that based on your hardware capabilities and specifications. Each model has their purposes, capabilities, and different requirements. To select AI models:
Go to the **Hub** > select the models that you would like to install.
:::note
For more info, go to [list of supported models](/guides/models-list/).
:::
### Step 3: Use the AI Model
After you install the AI model, you use it immediately under **Thread** tab. | https://jan.ai/guides |
# Best Practices
Jan is a versatile platform offering solutions for integrating AI locally across various platforms. This guide outlines best practices for developers, analysts, and AI enthusiasts to enhance their experience with Jan when adding AI locally to their computers. Implementing these practices will optimize the performance of AI models.
## Follow the Quickstart Guide
The [quickstart guide](quickstart.mdx) is designed to facilitate a quick setup process. It provides a clear instruction and simple steps to get you up and running with Jan.ai quickly. Even, if you are inexperienced in AI, the quickstart can offer valuable insights and tips to help you get started quickly.
## Setting up the Right Models
Jan offers a range of pre-configured AI models that is tailored to different tasks and industries. You should indentify which on that aligns with your objectives. There are model's factors to be considered:
- Capabilities
- Accuracy
- Processing Speed
:::note
Some of these factors also depends on your hardware, please see [Hardware Requirement](hardware-requiement.mdx).
:::
Choosing the right model is important to achieve the best performance.
## Setting up Jan
Ensure that you familiarize yourself with the Jan application. Jan offers advanced settings that you can adjust. These settings may influence how your AI behaves locally. Please see the [Advanced Settings](./advanced-settings/advanced-settings.mdx) article for a complete list of Jan's configurations and instructions on how to configure them.
## Integrations
One of Jan.ai key features is its ability to integrate with many FileSystemWritableFileStream. Whether you are incorporating Jan.ai with any open-source LLM provider or other WebTransportBidirectionalStream, it is important to understand the integration capabilities and limitations.
## Mastering the Prompt Engineering
Prompt engineering is an important aspect when dealing with AI models to generate the desired outputs. Mastering this skill can significantly enhance the performance and the responses of the AI. Below are some tips that you can do for promptengineering:
- Ask the model to adopt a persona
- Be specific and details get a more specific answers
- Provide examples or preference text or context at the beginning
- Use a clear and concise language
- Use a certain keywords and phrases | https://jan.ai/guides/best-practices |
# Installation
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
import installImageURL from './assets/jan-ai-download.png';
<Tabs>
<TabItem value="mac" label = "Mac" default>
### Pre-requisites
Ensure that your MacOS version is 13 or higher to run Jan.
### Stable Releases
To download stable releases, go to [Jan.ai](https://jan.ai/) > select **Download for Mac**.
The download should be available as a `.dmg`.
### Nightly Releases
We provide the Nightly Release so that you can test new features and see what might be coming in a future stable release. Please be aware that there might be bugs!
You can download it from [Jan's Discord](https://discord.gg/FTk2MvZwJH) in the [`#nightly-builds`](https://discord.gg/q8szebnxZ7) channel.
### Experimental Model
To enable the experimental mode, go to **Settings** > **Advanced Settings** and toggle the **Experimental Mode**
:::warning
If you are stuck in a broken build, go to the [Broken Build](/guides/common-error/broken-build) section of Common Errors.
:::
</TabItem>
<TabItem value = "windows" label = "Windows">
### Pre-requisites
Ensure that your system meets the following requirements:
- Windows 10 or higher is required to run Jan.
To enable GPU support, you will need:
- NVIDIA GPU with CUDA Toolkit 11.7 or higher
- NVIDIA driver 470.63.01 or higher
### Stable Releases
To download stable releases, go to [Jan.ai](https://jan.ai/) > select **Download for Windows**.
The download should be available as a `.exe` file.
### Nightly Releases
We provide the Nightly Release so that you can test new features and see what might be coming in a future stable release. Please be aware that there might be bugs!
You can download it from [Jan's Discord](https://discord.gg/FTk2MvZwJH) in the [`#nightly-builds`](https://discord.gg/q8szebnxZ7) channel.
### Experimental Model
To enable the experimental mode, go to **Settings** > **Advanced Settings** and toggle the **Experimental Mode**
### Default Installation Directory
By default, Jan is installed in the following directory:
```sh
# Default installation directory
C:\Users\{username}\AppData\Local\Programs\Jan
```
:::warning
If you are stuck in a broken build, go to the [Broken Build](/guides/common-error/broken-build) section of Common Errors.
:::
</TabItem>
<TabItem value = "linux" label = "Linux">
### Pre-requisites
Ensure that your system meets the following requirements:
- glibc 2.27 or higher (check with `ldd --version`)
- gcc 11, g++ 11, cpp 11, or higher, refer to this link for more information.
To enable GPU support, you will need:
- NVIDIA GPU with CUDA Toolkit 11.7 or higher
- NVIDIA driver 470.63.01 or higher
### Stable Releases
To download stable releases, go to [Jan.ai](https://jan.ai/) > select **Download for Linux**.
The download should be available as a `.AppImage` file or a `.deb` file.
### Nightly Releases
We provide the Nightly Release so that you can test new features and see what might be coming in a future stable release. Please be aware that there might be bugs!
You can download it from [Jan's Discord](https://discord.gg/FTk2MvZwJH) in the [`#nightly-builds`](https://discord.gg/q8szebnxZ7) channel.
### Experimental Model
To enable the experimental mode, go to **Settings** > **Advanced Settings** and toggle the **Experimental Mode**
<Tabs groupId = "linux_type">
<TabItem value="linux_main" label = "Linux">
To install Jan, you should use your package manager's install or `dpkg`.
</TabItem>
<TabItem value = "deb_ub" label = "Debian / Ubuntu">
To install Jan, run the following command:
```sh
# Install Jan using dpkg
sudo dpkg -i jan-linux-amd64-{version}.deb
# Install Jan using apt-get
sudo apt-get install ./jan-linux-amd64-{version}.deb
# where jan-linux-amd64-{version}.deb is path to the Jan package
```
</TabItem>
<TabItem value = "other" label = "Others">
To install Jan, run the following commands:
```sh
# Install Jan using AppImage
chmod +x jan-linux-x86_64-{version}.AppImage
./jan-linux-x86_64-{version}.AppImage
# where jan-linux-x86_64-{version}.AppImage is path to the Jan package
```
</TabItem>
</Tabs>
:::warning
If you are stuck in a broken build, go to the [Broken Build](/guides/common-error/broken-build) section of Common Errors.
:::
</TabItem>
<TabItem value="docker" label = "Docker" default>
### Pre-requisites
Ensure that your system meets the following requirements:
- Linux or WSL2 Docker
- Latest Docker Engine and Docker Compose
To enable GPU support, you will need:
- `nvidia-driver`
- `nvidia-docker2`
:::note
- If you have not installed Docker, follow the instructions [here](https://docs.docker.com/engine/install/ubuntu/).
- If you have not installed the required file for GPU support, follow the instructions [here](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html).
:::
### Run Jan in Docker
You can run Jan in Docker with two methods:
1. Run Jan in CPU mode
2. Run Jan in GPU mode
<Tabs groupId = "ldocker_type">
<TabItem value="docker_cpu" label = "CPU">
To run Jan in Docker CPU mode, by using the following code:
```bash
# cpu mode with default file system
docker compose --profile cpu-fs up -d
# cpu mode with S3 file system
docker compose --profile cpu-s3fs up -d
```
</TabItem>
<TabItem value="docker_gpu" label = "GPU">
To run Jan in Docker CPU mode, follow the steps below:
1. Check CUDA compatibility with your NVIDIA driver by running nvidia-smi and check the CUDA version in the output as shown below:
```sh
nvidia-smi
# Output
+ | https://jan.ai/guides/install |
# Local Server
Jan provides a built-in API server that can be used as a drop-in for OpenAI's API local replacement. This guide will walk you through on how to start the local server and use it to make request to the local server.
## Step 1: Set the Local Server
To start the local server, follow the steps below:
1. Navigate to the Jan main menu dashboard.
2. Click the corresponding icon on the bottom left side of your screen.
3. Select the model you want to use under the Model Settings screen to set the LLM for your local server.
4. Configure the server settings as follows:
| Feature | Description | Default Setting |
| | https://jan.ai/guides/start-server |
# Thread Management
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
Jan provides a straightforward and private solution for managing your threads with AI on your own device. As you interact with AI using Jan, you'll accumulate a history of threads.
Jan offers easy tools to organize, delete, or review your past threads with AI. This guide will show you how to keep your threads private and well-organized.
<Tabs>
<TabItem value="view" label = "View Thread History" default>
### View Thread History
To view your threads history, follow the steps below:
1. Navigate to the main dashboard.
2. Locate the list of threads screen on the left side.
3. To view a specific thread, simply choose the one you're interested in and then scroll up or down to explore the entire conversation.
</TabItem>
<TabItem value = "manage" label = "Managing Threads">
### Manage the Threads via Folder
To manage your threads history and configurations, follow the steps below:
1. Navigate to the Thread that you want to manage via the list of threads on the left side of the dashboard.
2. Click on the **three dots (⋮)** on the Thread section.
3. There are two available options to select:
- **Reveal in Finder**: Opens the folder containing the thread history and configurations.
- **View as JSON**: Opens the thread.json file in your default browser.
</TabItem>
<TabItem value = "clean" label = "Clean Threads">
### Clean Threads History
To clean all the messages from a thread, follow the steps below:
1. Navigate to the Thread that you want to clean.
2. Click on the **three dots (⋮)** on the Thread section.
3. Sleect the **Clean Thread** button.
:::note
This will delete all messages in the thread, while keeping the thread settings.
:::
</TabItem>
<TabItem value="delete" label = "Delete Thread" default>
### Delete Threads History
To delete a thread, follow the steps below:
1. Navigate to the Thread that you want to delete.
2. Click on the **three dots (⋮)** on the Thread section.
3. Sleect the **Delete Thread** button.
:::note
This will delete all messages and the thread settings.
:::
</TabItem>
</Tabs> | https://jan.ai/guides/thread |
# Remote Server Integration
This guide will show you how to configure Jan as a client and point it to any remote & local (self-hosted) API server.
## OpenAI Platform Configuration
### 1. Create a Model JSON
1. In `~/jan/models`, create a folder named `gpt-3.5-turbo-16k`.
2. In this folder, add a `model.json` file with Filename as `model.json`, `id` matching folder name, `Format` as `api`, `Engine` as `openai`, and `State` as `ready`.
```json title="~/jan/models/gpt-3.5-turbo-16k/model.json"
{
"sources": [
{
"filename": "openai",
"url": "https://openai.com"
}
],
"id": "gpt-3.5-turbo-16k",
"object": "model",
"name": "OpenAI GPT 3.5 Turbo 16k",
"version": "1.0",
"description": "OpenAI GPT 3.5 Turbo 16k model is extremely good",
"format": "api",
"settings": {},
"parameters": {},
"metadata": {
"author": "OpenAI",
"tags": ["General", "Big Context Length"]
},
"engine": "openai"
}
```
### `model.json`
The `model.json` file is used to set up your local models.
:::note
- If you've set up your model's configuration in `nitro.json`, please note that `model.json` can overwrite the settings.
- When using OpenAI models like GPT-3.5 and GPT-4, you can use the default settings in `model.json` file.
:::
There are two important fields in model.json that you need to setup:
#### Settings
This is the field where to set your engine configurations, there are two imporant field that you need to define for your local models:
| Term | Description |
| | https://jan.ai/guides/models/integrate-remote |
# Manual Import
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
import janModel from './assets/jan-model-hub.png';
This guide will show you how to perform manual import. In this guide, we are using a GGUF model from [HuggingFace](https://huggingface.co/) and our latest model, [Trinity](https://huggingface.co/janhq/trinity-v1-GGUF), as an example.
## Newer versions - nightly versions and v0.4.7+
Starting from version 0.4.7, Jan has introduced the capability to import models using an absolute file path. It allows you to import models from any directory on your computer.
### 1. Get the Absolute Filepath of the Model
After downloading the model from HuggingFace, get the absolute filepath of the model.
### 2. Configure the Model JSON
1. Navigate to the `~/jan/models` folder.
2. Create a folder named `<modelname>`, for example, `tinyllama`.
3. Create a `model.json` file inside the folder, including the following configurations:
- Ensure the `id` property matches the folder name you created.
- Ensure the `url` property is the direct binary download link ending in `.gguf`. Now, you can use the absolute filepath of the model file.
- Ensure the `engine` property is set to `nitro`.
```json
{
"sources": [
{
"filename": "tinyllama.gguf",
// highlight-next-line
"url": "<absolute-filepath-of-the-model-file>"
}
],
"id": "tinyllama-1.1b",
"object": "model",
"name": "(Absolute Path) TinyLlama Chat 1.1B Q4",
"version": "1.0",
"description": "TinyLlama is a tiny model with only 1.1B. It's a good model for less powerful computers.",
"format": "gguf",
"settings": {
"ctx_len": 4096,
"prompt_template": "<|system|>\n{system_message}<|user|>\n{prompt}<|assistant|>",
"llama_model_path": "tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf"
},
"parameters": {
"temperature": 0.7,
"top_p": 0.95,
"stream": true,
"max_tokens": 2048,
"stop": [],
"frequency_penalty": 0,
"presence_penalty": 0
},
"metadata": {
"author": "TinyLlama",
"tags": ["Tiny", "Foundation Model"],
"size": 669000000
},
"engine": "nitro"
}
```
:::warning
- If you are using Windows, you need to use double backslashes in the url property, for example: `C:\\Users\\username\\filename.gguf`.
:::
### 3. Done!
If your model doesn't show up in the **Model Selector** in conversations, **restart the app** or contact us via our [Discord community](https://discord.gg/Dt7MxDyNNZ).
## Newer versions - nightly versions and v0.4.4+
### 1. Create a Model Folder
1. Navigate to the `App Settings` > `Advanced` > `Open App Directory` > `~/jan/models` folder.
<Tabs groupId = "operating-systems" >
<TabItem value="mac" label = "MacOS" default>
```sh
cd ~/jan/models
```
</TabItem>
<TabItem value = "windows" label = "Windows" default>
```sh
C:/Users/<your_user_name>/jan/models
```
</TabItem>
<TabItem value = "linux" label = "Linux" default>
```sh
cd ~/jan/models
```
</TabItem>
</Tabs>
2. In the `models` folder, create a folder with the name of the model.
```sh
mkdir trinity-v1-7b
```
### 2. Drag & Drop the Model
Drag and drop your model binary into this folder, ensuring the `modelname.gguf` is the same name as the folder name, e.g. `models/modelname`.
### 3. Done!
If your model doesn't show up in the **Model Selector** in conversations, **restart the app** or contact us via our [Discord community](https://discord.gg/Dt7MxDyNNZ).
## Older versions - before v0.44
### 1. Create a Model Folder
1. Navigate to the `App Settings` > `Advanced` > `Open App Directory` > `~/jan/models` folder.
<Tabs groupId = "operating-systems" >
<TabItem value="mac" label = "MacOS" default>
```sh
cd ~/jan/models
```
</TabItem>
<TabItem value = "windows" label = "Windows" default>
```sh
C:/Users/<your_user_name>/jan/models
```
</TabItem>
<TabItem value = "linux" label = "Linux" default>
```sh
cd ~/jan/models
```
</TabItem>
</Tabs>
2. In the `models` folder, create a folder with the name of the model.
```sh
mkdir trinity-v1-7b
```
### 2. Create a Model JSON
Jan follows a folder-based, [standard model template](https://jan.ai/docs/engineering/models/) called a `model.json` to persist the model configurations on your local filesystem.
This means that you can easily reconfigure your models, export them, and share your preferences transparently.
<Tabs groupId = "operating-systems" >
<TabItem value="mac" label = "MacOS" default>
```sh
cd trinity-v1-7b
touch model.json
```
</TabItem>
<TabItem value = "windows" label = "Windows" default>
```sh
cd trinity-v1-7b
echo {} > model.json
```
</TabItem>
<TabItem value = "linux" label = "Linux" default>
```sh
cd trinity-v1-7b
touch model.json
```
</TabItem>
</Tabs>
To update `model.json`:
- Match `id` with folder name.
- Ensure GGUF filename matches `id`.
- Set `source.url` to direct download link ending in `.gguf`. In HuggingFace, you can find the direct links in the `Files and versions` tab.
- Verify that you are using the correct `prompt_template`. This is usually provided in the HuggingFace model's description page.
```json title="model.json"
{
"sources": [
{
"filename": "trinity-v1.Q4_K_M.gguf",
"url": "https://huggingface.co/janhq/trinity-v1-GGUF/resolve/main/trinity-v1.Q4_K_M.gguf"
}
],
"id": "trinity-v1-7b",
"object": "model",
"name": "Trinity-v1 7B Q4",
"version": "1.0",
"description": "Trinity is an experimental model merge of GreenNodeLM & LeoScorpius using the Slerp method. Recommended for daily assistance purposes.",
"format": "gguf",
"settings": {
"ctx_len": 4096,
"prompt_template": "{system_message}\n### Instruction:\n{prompt}\n### Response:",
"llama_model_path": "trinity-v1.Q4_K_M.gguf"
},
"parameters": {
"max_tokens": 4096
},
"metadata": {
"author": "Jan",
"tags": ["7B", "Merged"],
"size": 4370000000
},
"engine": "nitro"
}
```
:::note
For more details regarding the `model.json` settings and parameters fields, please see [here](/docs/guides/models/integrate-remote.mdx#modeljson).
:::
### 3. Download the Model
1. Restart Jan and navigate to the Hub.
2. Locate your model.
3. Click **Download** button to download the model binary.
:::info[Assistance and Support]
If you have questions, please join our [Discord community](https://discord.gg/Dt7MxDyNNZ) for support, updates, and discussions.
::: | https://jan.ai/guides/models/import-models |
# Customize Engine Settings
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
In this guide, we'll walk you through the process of customizing your engine settings by configuring the `nitro.json` file
1. Navigate to the `App Settings` > `Advanced` > `Open App Directory` > `~/jan/engine` folder.
<Tabs>
<TabItem value="mac" label="MacOS" default>
```sh
cd ~/jan/engines
```
</TabItem>
<TabItem value="windows" label="Windows" default>
```sh
C:/Users/<your_user_name>/jan/engines
```
</TabItem>
<TabItem value="linux" label="Linux" default>
```sh
cd ~/jan/engines
```
</TabItem>
</Tabs>
2. Modify the `nitro.json` file based on your needs. The default settings are shown below.
```json title="~/jan/engines/nitro.json"
{
"ctx_len": 2048,
"ngl": 100,
"cpu_threads": 1,
"cont_batching": false,
"embedding": false
}
```
The table below describes the parameters in the `nitro.json` file.
| Parameter | Type | Description |
| | https://jan.ai/guides/models/customize-engine |
# Import Extensions
Besides default extensions, you can import extensions into Jan by following the steps below:
1. Navigate to **Settings** > **Extensions** > Click Select under **Manual Installation**.
2. Then, the ~/jan/extensions/extensions.json file will be updated automatically.
:::caution
You need to prepare the extension file in .tgz format to install the **non-default** extension.
:::
:::info[Assistance and Support]
If you have questions, please join our [Discord community](https://discord.gg/Dt7MxDyNNZ) for support, updates, and discussions.
::: | https://jan.ai/guides/extensions/import-ext |
# Extension Setup
The current Jan Desktop Client has some default extensions built on top of this framework to enhance the user experience. In this guide, we will show you the list of default extensions and how to configure extension settings.
## Default Extensions
You can find the default extensions in the `Settings` > `Extensions`.
## List of Default Extensions
| Extension Name | Version | Description | Source Code Link |
| | https://jan.ai/guides/extensions/setup-ext |
# HTTPS Proxy
## Why HTTPS Proxy?
HTTPS Proxy encrypts data between your browser and the internet, making it hard for outsiders to intercept or read. It also helps you to maintain your privacy and security while being able to bypass regional restrictions on internet.
:::note
When configuring Jan using an HTTPS proxy, the speed of the downloading model may be affected due to the encryption and decryption process. It also depends on the networking of the cloud service provider.
:::
## Setting Up Your Own HTTPS Proxy Server
This guide provides a simple overview of setting up an HTTPS proxy server using **Squid**, a widely used open-source proxy software.
:::note
Other software options are also available depending on your requirements.
:::
### Step 1: Choosing a Server
1. Firstly, you need to choose a server to host your proxy server.
:::note
We recommend using a well-known cloud provider service like:
- Amazon AWS
- Google Cloud
- Microsoft Azure
- Digital Ocean
:::
2. Ensure that your server has a public IP address and is accessible from the internet.
### Step 2: Installing Squid
Instal **Squid** using the following command:
```bash
sudo apt-get update
sudo apt-get install squid
```
### Step 3: Configure Squid for HTTPS
To enable HTTPS, you will need to configure Squid with SSL support.
1. Squid requires an SSL certificate to be able to handle HTTPS traffic. You can generate a self-signed certificate or obtain one from a Certificate Authority (CA). For a self-signed certificate, you can use OpenSSL:
```bash
openssl req -new -newkey rsa:2048 -days 365 -nodes -x509 -keyout squid-proxy.pem -out squid-proxy.pem
```
2. Edit the Squid configuration file `/etc/squid/squid.conf` to include the path to your SSL certificate and enable the HTTPS port:
```bash
http_port 3128 ssl-bump cert=/path/to/your/squid-proxy.pem
ssl_bump server-first all
ssl_bump bump all
```
3. To intercept HTTPS traffic, Squid uses a process called SSL Bumping. This process allows Squid to decrypt and re-encrypt HTTPS traffic. To enable SSL Bumping, ensure the `ssl_bump` directives are configured correctly in your `squid.conf` file.
### Step 4 (Optional): Configure ACLs and Authentication
1. You can define rules to control who can access your proxy. This is done by editing the squid.conf file and defining ACLs:
```bash
acl allowed_ips src "/etc/squid/allowed_ips.txt"
http_access allow allowed_ips
```
2. If you want to add an authentication layer, Squid supports several authentication schemes. Basic authentication setup might look like this:
```bash
auth_param basic program /usr/lib/squid/basic_ncsa_auth /etc/squid/passwords
acl authenticated proxy_auth REQUIRED
http_access allow authenticated
```
### Step 5: Restart and Test Your Proxy
1. After configuring, restart Squid to apply the changes:
```bash
sudo systemctl restart squid
```
2. To test, configure your browser or another client to use the proxy server with its IP address and port (default is 3128).
3. Check if you can access the internet through your proxy.
:::tip
Tips for Secure Your Proxy:
- **Firewall rules**: Ensure that only intended users or IP addresses can connect to your proxy server. This can be achieved by setting up appropriate firewall rules.
- **Regular updates**: Keep your server and proxy software updated to ensure that you are protected against known vulnerabilities.
- **Monitoring and logging**: Monitor your proxy server for unusual activity and enable logging to keep track of the traffic passing through your proxy.
:::
## Setting Up Jan to Use Your HTTPS Proxy
Once you have your HTTPS proxy server set up, you can configure Jan to use it.
1. Navigate to `Settings` > `Advanced Settings` and specify the HTTPS proxy (proxy auto-configuration and SOCKS not supported).
2. You can turn on the feature `Ignore SSL Certificates` if you are using a self-signed certificate. This feature allows self-signed or unverified certificates. | https://jan.ai/guides/advanced-settings/http-proxy |
# Advanced Settings
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
This guide will show you how to use the advanced settings in Jan.
## Access the Advanced Settings
To access the Jan's advanced settings, follow the steps below:
1. Navigate to the main dashboard.
2. Click the **gear icon (⚙️)** on the bottom left of your screen.
3. Under the **Settings screen**, click the **Advanced Settings**.
4. You can configure the following settings:
| Feature | Description |
| | https://jan.ai/guides/advanced-settings/advanced-settings |
# Undefined Issue
Encountering an `undefined issue` in Jan is caused by errors related to the Nitro tool or other internal processes. It can be resolved through the following steps:
1. Clearing the Jan folder and then reopen the application to determine if the problem persists
2. Manually run the nitro tool located at `~/jan/extensions/@janhq/inference-nitro-extensions/dist/bin/(your-os)/nitro` to check for error messages.
3. Address any nitro error messages that are identified and reassess the persistence of the issue.
4. Reopen Jan to determine if the problem has been resolved after addressing any identified errors.
5. If the issue persists, please share the [app logs](https://jan.ai/troubleshooting/how-to-get-error-logs/) via [Jan Discord](https://discord.gg/mY69SZaMaC) for further assistance and troubleshooting. | https://jan.ai/guides/error-codes/undefined-issue |
# Permission Denied
When running Jan, you might encounter the following error message:
```
Uncaught (in promise) Error: Error invoking layout-480796bff433a3a3.js:538 remote method 'installExtension':
Error Package /Applications/Jan.app/Contents/Resources/app.asar.unpacked/pre-install/janhq-assistant-extension-1.0.0.tgz does not contain a valid manifest:
Error EACCES: permission denied, mkdtemp '/Users/username/.npm/_cacache/tmp/ueCMn4'
```
This error mainly caused by permission problem during installation. To resolve this issue, follow these steps:
1. Open your terminal.
2. Execute the following command to change ownership of the `~/.npm` directory to the current user:
```sh
sudo chown -R $(whoami) ~/.npm
```
:::note
This command ensures that the necessary permissions are granted for Jan installation, resolving the encountered error.
::: | https://jan.ai/guides/error-codes/permission-denied |
# Unexpected Token
Encountering the `Unexpected token` error when initiating a chat with OpenAI models mainly caused by either your OpenAI key or where you access your OpenAI from. This issue can be solved through the following steps:
1. Obtain an OpenAI API key from [OpenAI's developer platform](https://platform.openai.com/) and integrate it into your application.
2. Trying a VPN could potentially solve the issue, especially if it's related to region locking for accessing OpenAI services. By connecting through a VPN, you may bypass such restrictions and successfully initiate chats with OpenAI models. | https://jan.ai/guides/error-codes/unexpected-token |
# Something's Amiss
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
When you start a chat with a model and encounter with a Something's Amiss error, here's how to resolve it:
1. Ensure your OS is up to date.
2. Choose a model smaller than 80% of your hardware's V/RAM. For example, on an 8GB machine, opt for models smaller than 6GB.
3. Install the latest [Nightly release](https://jan.ai/install/nightly/) or [clear the application cache](https://jan.ai/troubleshooting/stuck-on-broken-build/) when reinstalling Jan.
4. Confirm your V/RAM accessibility, particularly if using virtual RAM.
5. Nvidia GPU users should download [CUDA](https://developer.nvidia.com/cuda-downloads).
6. Linux users, ensure your system meets the requirements of gcc 11, g++ 11, cpp 11, or higher. Refer to this [link](https://jan.ai/guides/troubleshooting/gpu-not-used/#specific-requirements-for-linux) for details.
7. You might use the wrong port when you [check the app logs](https://jan.ai/troubleshooting/how-to-get-error-logs/) and encounter the Bind address failed at 127.0.0.1:3928 error. To check the port status, try use the `netstat` command, like the following:
<Tabs>
<TabItem value="mac" label="MacOS" default>
```sh
netstat -an | grep 3928
```
</TabItem>
<TabItem value="windows" label="Windows" default>
```sh
netstat -ano | find "3928"
tasklist /fi "PID eq 3928"
```
</TabItem>
<TabItem value="linux" label="Linux" default>
```sh
netstat -anpe | grep "3928"
```
</TabItem>
</Tabs>
:::note
`Netstat` displays the contents of various network-related data structures for active connections
:::
:::tip
Jan uses the following ports:
- Nitro: `3928`
- Jan API Server: `1337`
- Jan Documentation: `3001`
::: | https://jan.ai/guides/error-codes/something-amiss |
# v0.3.0
For more details, [GitHub Issues](https://github.com/janhq/jan/releases/tag/v0.3.0)
Highlighted Issue: [Issue #482: fix: hide preferences section if empty](https://github.com/janhq/jan/pull/482)
## Changes
- fix: hide preferences section if empty @louis-jan (#482)
- chore: fix conversation summary @louis-jan (#480)
- chore: missing create conversation button when there is no conversation is selected @louis-jan (#478)
- fix: download now change state immediately @namchuai (#475)
- chore: add required app version to edge release plugin @louis-jan (#471)
## 🐛 Bug Fixes
- add rebuild for mac x64 @hiento09 (#473)
## 🧰 Maintenance
- Add build deps for data-plugin in CI @hiento09 (#472)
## Contributor
@hiento09, @hientominh, @jan-service-account, @louis-jan and @namchuai | https://jan.ai/guides/changelog/changelog-v0.3.0 |
# v0.4.0
For more details, [GitHub Issues](https://github.com/janhq/jan/releases/tag/v0.4.0)
Highlighted Issue: [Issue #878: bug: fix tag description showing a title and fix card right panel](https://github.com/janhq/jan/pull/878)
## Changes
- bug: fix tag description showing a title and fix card right panel @urmauur (#878)
- fix/no-assistant-available-fresh-install @louis-jan (#876)
- Model.json update @hahuyhoang411 (#870)
- Hotfix desc for openhermes @hahuyhoang411 (#864)
- Openhermes update v1 @hahuyhoang411 (#863)
- update deepseek 1.3b @hahuyhoang411 (#858)
- Update tags @hahuyhoang411 (#857)
- Update model hub @hahuyhoang411 (#829)
- hotfix: fix typo @tikikun (#836)
- chore: pre-populate Jan's /models folder with model.jsons @hahuyhoang411 (#775)
- chore: clarification changes to the model settings and model parameters @tikikun (#742)
## 🚀 Features
- feat: revamp landing page @urmauur (#745)
- feat : add cover image model hub screen @urmauur (#872)
- feat: boilerplate for express server localhost 1337 @linhtran174 (#803)
- enhancement: revamp hub screen @urmauur (#825)
- feat: revamp thread screen @urmauur (#802)
- docs/update-api-reference @hieu-jan (#739)
- refactor: model plugin to follow new specs @namchuai (#682)
## 🐛 Fixes
- fix: Nitro interface update to prevent warning @vuonghoainam (#877)
- fix: delete message break the entire thread @louis-jan (#869)
- fix: can not download multiple models at once @louis-jan (#867)
- fix: production CI workflow does not populate models @louis-jan (#862)
- fix: update wrong main view state when use a model @namchuai (#861)
- fix: handle crash issue on hljs highlighting @louis-jan (#859)
- fix: empty assistant instruction by default @louis-jan (#855)
- bug: fix broken banner position hub screen @urmauur (#846)
- fix: not update active model when using resend button @namchuai (#834)
- Hotfix jan windows download nitro failed @hiento09 (#838)
- Switch to download nitro .tar.gz file instead of .zip file on windows @hiento09 (#832)
- fix/docusaurus-seo @hieu-jan (#818)
- fix: CI script - reorder copy models action @louis-jan (#819)
- fix: messages sync is not threadsafe @louis-jan (#784)
- Fix Makefile Indentation Issue @hiento09 (#788)
## 🧰 Maintenance
- chore: update model ranking @louis-jan (#874)
- Bump nitro version to 0.1.21 - nitro has windows codesign @hiento09 (#843)
- Hotfix jan windows download nitro failed @hiento09 (#838)
- 810 docs add modeljson and revamp models specs page @tikikun (#816)
- Add document for nightly build and update message for manual build @hiento09 (#831)
- chore: Bump nitro to 0.1.20 @vuonghoainam (#830)
- Refactor build:extension command @hiento09 (#822)
- feat: pre-populate Jan's /models folder @namchuai (#796)
- chore: fix pr auto labeling @0xSage (#812)
- chore: add gi automations @0xSage (#809)
- refactor: jan extensions @louis-jan (#799)
- Remove .zip in artifact name @hiento09 (#800)
- docs/update-api-reference @hieu-jan (#739)
- Add nightly build ci @hiento09 (#794)
- Fix Makefile Indentation Issue @hiento09 (#788)
- Switch from .zip to .tar.gz for nitro url inference plugin @hiento09 (#781)
## Contributor
@0xSage, @hahuyhoang411, @hiento09, @hieu-jan, @linhtran174, @louis-jan, @namchuai, @tikikun, @urmauur and @vuonghoainam | https://jan.ai/guides/changelog/changelog-v0.4.0 |
# v0.2.1
For more details, [GitHub Issues](https://github.com/janhq/jan/releases/tag/v0.2.1)
Highlighted Issue: [Issue #446: fix: model is started but the indicator is not stopped loading](https://github.com/janhq/jan/pull/446)
## Changes
- fix: model is started but the indicator is not stopped loading @louis-jan (#446)
- fix: bring back install plugin manually function @louis-jan (#448)
- fix: duplicated messages when user switch between conversations @namchuai (#441)
- chore: added loader starting and stopping model @urmauur (#438)
- chore: Change license to AGPL @dan-jan (#442)
- fix: plugin \& model catalog import cache are not cleared properly @louis-jan (#437)
- fix error codesign @hiento09 (#439)
- fix: app version and cleanup unused code @urmauur (#434)
- chore: update core service - get plugin manifest @louis-jan (#432)
- ui: interface revamp @urmauur (#429)
- fix: scroll on explore models does not work @namchuai (#427)
- feat: adding create bot functionality @namchuai (#368)
- chore: install or update a plugin should not interrupt dev process @louis-jan (#420)
- chore: Update nitro 0.1.2 windows/ linux @vuonghoainam (#421)
- chore: update core service enums @louis-jan (#414)
- feat: chat with documents plugin @louis-jan (#417)
- misc: setup prettier @urmauur (#412)
- adr: 007 - Jan Plugin Catalog @louis-jan (#408)
- adr: 006 - Jan Core Module @louis-jan (#404)
- feat: Support for nitro release 0.1.2 @vuonghoainam (#409)
- feat: explore plugins from the npm repository and install them remotely @louis-jan (#399)
- feat: fix event description @dan-jan (#400)
- fix: high cpu usage @louis-jan (#401)
- docs: model installation ADR @0xSage (#390)
- chore: update core events module @louis-jan (#394)
- feat: Update Social OG Image and Meta Description @dan-jan (#387)
- misc: UI home @urmauur (#392)
- Update hcmc-oct23.md @0xSage (#389)
- chore: remove deprecated extension functions @louis-jan (#388)
- Fix bugs image overlap dropdown button download @urmauur (#384)
- chore: resolve fetch models api limit rate @louis-jan (#383)
- chore: update convo summary @louis-jan (#378)
- Update interface landing page @urmauur (#381)
- Add simple copywriting changes @dan-jan (#382)
- chore: update core services and module export @louis-jan (#376)
- chore: #371 - reference to plugin name and module path as variables @louis-jan (#372)
- feat: Edit event details, hide all unnecessary website sections @dan-jan (#369)
- docs: UI Service ADR @0xSage (#318)
- Feat/issue 255 adr 001 jand cloud native @nam-john-ho (#262)
- Move plugins folder from electron to root folder @hiento09 (#366)
- feature: core plugin support events \& preferences modules @louis-jan (#365)
- Fix/250 @namchuai (#349)
- Change License and update README @dan-jan (#356)
- Jan 339 @dan-jan (#348)
- feat: Jan 339 @dan-jan (#347)
- Add social og:image @dan-jan (#346)
- feat(ard): Add adr 002 @vuonghoainam (#261)
## 🚀 Features
- #357 plugin \& app can subscribe and emit events @louis-jan (#358)
- feature: @janhq/plugin-core module \& usage @louis-jan (#321)
## 🐛 Bug Fixes
- Change to load nitron on windows and linux from bash/shell script @hiento09 (#451)
- Fix data-plugin install failed on mac silicon from npm @hiento09 (#413)
- Correct version of plugins @hiento09 (#374)
## 🧰 Maintenance
- upgrade leveldown to newest version @hiento09 (#447)
- Update auto-sign plugin by search file permission 664 @hiento09 (#445)
- Change codesign plugin folder in ci @hiento09 (#440)
- Add continue on error for import cert @hiento09 (#436)
- Update code siging for new data plugin @hiento09 (#433)
- Add readme inference plugin @hiento09 (#426)
- Add username to remote origin @hiento09 (#425)
- Add auto create PR to plugin-catalog when a new version of plugin is … @hiento09 (#416)
- Fix data-plugin install failed on mac silicon from npm @hiento09 (#413)
- Chore/remove package lock @hiento09 (#398)
- Refactor cicd @hiento09 (#397)
- Correct version of plugins @hiento09 (#374)
- Rename plugin-core to core @hiento09 (#370)
- Fix error check change in plugins folder @hiento09 (#367)
- chore: jan.ai nits @0xSage (#354)
## Contributor
@0xSage, @dan-jan, @hiento09, @jan-service-account, @louis-jan, @nam-john-ho, @namchuai, @tikikun, @urmauur, @vuonghoainam and Hien To | https://jan.ai/guides/changelog/changelog-v0.2.1 |
# v0.4.6
For more details, [GitHub Issues](https://github.com/janhq/jan/releases/tag/v0.4.6)
Highlighted Issue: [Issue #1918: Regression fix assistant extension codesign](https://github.com/janhq/jan/pull/1918)
## Changes
- Regression fix assitant extension codesign @hiento09 (#1918)
- Release cut 0.4.6 @louis-jan (#1888)
- feat: add factory reset feature @namchuai (#1750)
- chore: add react developer tools to electron @Helloyunho (#1858)
- Sync Release 0.4.5 to dev @louis-jan (#1830)
## 🚀 Features
- feat: integrate umami @hieu-jan (#1809)
- feat: Add default value for ngl @hiro-v (#1886)
- feat: add start/stop model via http api @namchuai (#1862)
- feat: add snackbar component and update style side banner @urmauur (#1874)
- feat: move open app directory into icon folder @urmauur (#1879)
- chore: Bump nitro to 0.3.3 @hiro-v (#1877)
- feat: put timestamp under thread name in left panel @urmauur (#1820)
- perf: remove unnecessary rerender when user typing input @namchuai (#1818)
## 🐛 Fixes
- fix: umami analytics send app loaded event @louis-jan (#1928)
- fix: migration loading indicator @louis-jan (#1913)
- fix: broken manual import model with NA fields @louis-jan (#1912)
- fix: openAIEmbedding now requires top level API Key configuration @louis-jan (#1902)
- fix: load model fail overlays thread message error @louis-jan (#1901)
- fix: show generate response on message send @louis-jan (#1895)
- fix: display error message on model load fail @louis-jan (#1894)
- fix: the selected model auto revert back to previous used model with setting mismatch @louis-jan (#1883)
- fix: add dialog confirm when move folder and next dest isn't empty @urmauur (#1880)
- Increase timeout for explore.e2e.spec test @hiento09 (#1844)
- chore: Bump nitro to 0.3.3 @hiro-v (#1877)
- fix: auto collapse retrieval setting while update config @urmauur (#1866)
- fix: loader show while error global when change folder @urmauur (#1870)
- fix: retrieval always ask for api key @louis-jan (#1856)
- fix: all input text box are disabled @namchuai (#1855)
- fix: add loader when user change folder @urmauur (#1850)
- Add code sign step for darwin assistant extension @hiento09 (#1841)
- fix: preserve focused thread when navigating in jan app @namchuai (#1814)
- fix: highlight menu dropdown server options @urmauur (#1831)
## 🧰 Maintenance
- chore: mark RAG as experimental feature @louis-jan (#1882)
- Increase timeout for explore.e2e.spec test @hiento09 (#1844)
- chore: Bump nitro to 0.3.3 @hiro-v (#1877)
- chore: Jan Data Folder setting is no longer an experimental feature @louis-jan (#1847)
- chore: resolve main conflict @louis-jan (#1833)
- Update release url on README to default branch instead of main branch @hiento09 (#1832)
## Contributor
@Helloyunho, @hiento09, @hieu-jan, @hiro-v, @jan-service-account, @louis-jan, @namchuai, @urmauur and James | https://jan.ai/guides/changelog/changelog-v0.4.6 |
# v0.2.0
For more details, [GitHub Issues](https://github.com/janhq/jan/releases/tag/v0.2.0)
Highlighted Issue: [Issue #342: feat: Add Jan Hacker House event page to Docs](https://github.com/janhq/jan/pull/342)
## Changes
- feat: Add Jan Hacker House event page to Docs @dan-jan (#342)
- feat: Hide incomplete Hardware section from Docs site @dan-jan (#341)
- style: better chatbox ui @0xSage (#338)
- feat: allowing user to fetch models from github @namchuai (#319)
- fixes: #247 - inference plugin should check nitro service available @louis-jan (#313)
- Fix icon error for linux app @hiento09 (#316)
- docs: initial hardware content @Its-Alamin-H (#240)
- fixes #277 - bug: memory utilization always at 99% @louis-jan (#309)
- Docusaurus parser string from githubapi to get latest release @hiento09 (#312)
- Footer background, CTA \& Highlight colors @drakehere (#288)
- Fix CI Test run failed on ubuntu and change release file app name @hiento09 (#307)
- Add docusaurus test build pipeline @hiento09 (#302)
- fix: #271 Cannot read properties of undefined (reading 'map') @louis-jan (#300)
- Fix Docusaurus server side render error @hiento09 (#301)
- fix #283: small ui fixes @namchuai (#299)
## 🐛 Bug Fixes
- Fix #290: Add description in package.json and rename to jan @hiento09 (#333)
## 🧰 Maintenance
- Add Documentation category to release note template @hiento09 (#332)
- Chore/release note template @hiento09 (#323)
- Add release note template @hiento09 (#322)
## 📖 Documentaion
- Add auto update app download url on jan.ai @hiento09 (#311)
- docs: update per v0.1.3 @0xSage (#280)
## Contributor
@0xSage, @Its-Alamin-H, @dan-jan, @drakehere, @hiento09, @hientominh, @louis-jan, @namchuai, Hien To and James | https://jan.ai/guides/changelog/changelog-v0.2.0 |
# v0.2.3
For more details, [GitHub Issues](https://github.com/janhq/jan/releases/tag/v0.2.3)
Highlighted Issue: [Issue #482: fix: hide preferences section if empty](https://github.com/janhq/jan/pull/482)
## Changes
- fix: hide preferences section if empty @louis-jan (#482)
- chore: fix conversation summary @louis-jan (#480)
- chore: missing create conversation button when there is no conversation is selected @louis-jan (#478)
- fix: download now change state immediately @namchuai (#475)
- chore: add required app version to edge release plugin @louis-jan (#471)
## 🐛 Bug Fixes
- add rebuild for mac x64 @hiento09 (#473)
## 🧰 Maintenance
- Add build deps for data-plugin in CI @hiento09 (#472)
## Contributor
@hiento09, @hientominh, @jan-service-account, @louis-jan and @namchuai | https://jan.ai/guides/changelog/changelog-v0.2.3 |
# v0.4.5
For more details, [GitHub Issues](https://github.com/janhq/jan/releases/tag/v0.4.5)
Highlighted Issue: [Issue #1758: bug: Correct text for Windows](https://github.com/janhq/jan/issues/1758)
## Changes
- fix(Wording): #1758 correct text for windows @namchuai (#1768)
- fix(Log): server log is not display in windows @namchuai (#1764)
- Release Cut v0.4.5 @louis-jan (#1752)
- chore(nitro): 0.2.11 -> 0.2.12 @hiro-v (#1754)
- fix: Nitro CPU threads with correct physical/ performance CPU count @hiro-v (#1726)
- fix(Model): #1662 imported model does not use gpu @namchuai (#1723)
- fix(API): #1720 host/port provided in the local API server does not fully applied @namchuai (#1721)
- fix: server API reference @hiro-v (#1670)
- fix(Model): refactor model label @namchuai (#1596)
- docs/postmortem v 0.4.4 @hieu-jan (#1617)
- chore(ShortcutModal): clean up shortcut modal @namchuai (#1614)
- chore(Dependencies): upgrade node-fetch to fix vulnerable issue @namchuai (#1598)
## 🚀 Features
- feat: update UI allow user change folder @urmauur (#1738)
- feat: error message when not enough RAM @urmauur (#1706)
- feat: improvement ux for local api server @urmauur (#1704)
- feat: allow user to move jan folder @namchuai (#1649)
- feat: HTTP proxy support @markmehere (#1562)
- Feature add schedule clean cloudflare page and r2 @hiento09 (#1653)
- feat: relayout left panel setting page @urmauur (#1648)
- Update CI follow git flow @hiento09 (#1625)
- feat: Implement UI page API server dashboard @urmauur (#1636)
- fix: #1545 long thread title @lucido-simon (#1605)
## 🐛 Fixes
- fix: model selection does not show in API settings page @louis-jan (#1828)
- fix: user can't view model setting in local api server @namchuai (#1807)
- fix: cannot change jan data folder @namchuai (#1805)
- fix: model selection does not show in API settings page @louis-jan (#1802)
- fix: user can't use a model in model hub @namchuai (#1801)
- fix: stop openai inference raises something amiss @louis-jan (#1799)
- regression fix: input disabled darkmode @urmauur (#1800)
- fix: clean last message when user clean thread message @namchuai (#1793)
- fix: app log not being printed @namchuai (#1790)
- fix: api settings are not applied on changes @louis-jan (#1789)
- fix: could not delete model @louis-jan (#1779)
- fix: can not start model when server is not enabled from model settings page @louis-jan (#1774)
- regression fix: input port not accept alphabets @urmauur (#1772)
- Correct bash script syntax in ci @hiento09 (#1769)
- Hotfix CI pre-release not trigger @hiento09 (#1757)
- fix: bring back open app directory @louis-jan (#1756)
- fix: input port have range validation @urmauur (#1741)
- Fix error nightly build schedule run failed @hiento09 (#1736)
- fix: active model when start server @urmauur (#1719)
- fix: Change to fixed `localhost` instead of using host variable @hiro-v (#1729)
- Fix autoupdater nightly build error @hiento09 (#1727)
- Correct download url readme @hiento09 (#1724)
- fix: API chat/completion is blocked by CORS @louis-jan (#1705)
- fix: Jan server - v1/chat/completions is throwing ERR\_REQUIRE\_ESM @louis-jan (#1703)
- fix: Jan server is showing blank page @louis-jan (#1702)
- fix: switching loader from remote to local model from thread right panel @urmauur (#1692)
- fix: hot-fix algolia search @hieu-jan (#1700)
- fix: disable api key field while server is running @urmauur (#1694)
- fix: stoping model show starting model @urmauur (#1693)
- fix bug #1650 hogging resources @hiento09 (#1663)
- fix: auto select text when collapse panel @urmauur (#1645)
- fix: wrong selected model ref @louis-jan (#1638)
- fix: enable check for update on all supported platforms @louis-jan (#1626)
- fix: correct footer @hieu-jan (#1628)
## 🧰 Maintenance
- Docs publish to github page trigger on push to docs branch @hiento09 (#1783)
- Correct bash script syntax in ci @hiento09 (#1769)
- Combine 2 ci pipeline pre-release and nightly into one @hiento09 (#1767)
- Hotfix CI pre-release not trigger @hiento09 (#1757)
- Fix error nightly build schedule run failed @hiento09 (#1736)
- docs: add troubleshoot unexpected token @hieu-jan (#1711)
- docs: fix about pages @0xSage (#1699)
- refactor: deprecate extension type implementation @louis-jan (#1677)
- refactor: file prefix replace utils \& add unit test @louis-jan (#1676)
- Correct ref branch for update url on README.md file @hiento09 (#1672)
- docs: update 02-somethings-amiss @hieu-jan (#1668)
- Cherrypick cicd to main branch to apply new gitflow @hiento09 (#1665)
- docs: add user and developer guides for extensions @hieu-jan (#1657)
- docs: add QA Script @hieu-jan (#1660)
- chore: Bump nitro to 0.2.11 @hiro-v (#1655)
- chore: Bump version nitro to 0.2.10 @hiro-v (#1644)
- docs: add antivirus compatibility testing @hieu-jan (#1641)
- refactor: introduce node module in nitro extension @louis-jan (#1630)
- Update 02-somethings-amiss.mdx @Ssstars (#1634)
- docs: add integration AzureOpenAI @hieu-jan (#1632)
- docs: add troubleshooting permission denied @hieu-jan (#1631)
## Contributor
@0xSage, @Ssstars, @hiento09, @hientominh, @hieu-jan, @hiro-v, @jan-service-account, @louis-jan, @lucido-simon, @markmehere, @namchuai and @urmauur | https://jan.ai/guides/changelog/changelog-v0.4.5 |
# v0.2.2
For more details, [GitHub Issues](https://github.com/janhq/jan/releases/tag/v0.2.2)
Highlighted Issue: [Issue #469: chore: plugin and app version dependency](https://github.com/janhq/jan/pull/469)
## Changes
- chore: plugin and app version dependency @louis-jan (#469)
- bug: showing a modal when user start conf but model not active @urmauur (#466)
- fix: duplicated modal and loading state @louis-jan (#465)
- bug: fix overflow scroll horizontal message @urmauur (#464)
- bug: avoid chat body scroll horizontal @urmauur (#462)
- bug: fix logic plugin update plugin and show installed version @urmauur (#459)
- bug: chat view drops enumeration @urmauur (#456)
- fix: allow switching models when switch between conversations @namchuai (#458)
- fix: CI run fails on windows @louis-jan (#463)
- fix: failed to build electron app @louis-jan (#461)
- fix: correct app version display @louis-jan (#452)
- fix: enable link color blue on docusaurus markdown @urmauur (#449)
## 🚀 Features
- feat: Add ADR-008 for extensible Jan @vuonghoainam (#431)
## 🐛 Bug Fixes
- data-plugin force leveldown to 6.1.1 @hiento09 (#453)
## 🧰 Maintenance
- Use electron-rebuild to build leveldown@5.6.0 for darwin arm64 @hiento09 (#455)
- data-plugin force leveldown back to 5.6.0 and rebuild for darwin arm64 @hiento09 (#454)
- data-plugin force leveldown to 6.1.1 @hiento09 (#453)
## Contributor
@hiento09, @jan-service-account, @louis-jan, @namchuai, @urmauur and @vuonghoainam | https://jan.ai/guides/changelog/changelog-v0.2.2 |
# v0.4.1
For more details, [GitHub Issues](https://github.com/janhq/jan/releases/tag/v0.4.1)
Highlighted Issue: [Issue #903: Update README.md](https://github.com/janhq/jan/pull/903)
## Changes
- Update README.md @imtuyethan (#903)
## 🚀 Features
- feat: Kill nitro process with API - nitro 0.1.27 @vuonghoainam (#975)
- feat: Inference Nitro with Prompt Template @hahuyhoang411 (#952)
- feat: Add NVIDIA triton trt-llm extension @vuonghoainam (#888)
- feat: Hotfit for Nitro loading on CPU with hyper-threading support @vuonghoainam (#931)
- feat: adding model params @namchuai (#886)
- feat: Multiple inference engines for nitro and openai @vuonghoainam (#814)
- docs: add json schema for engine and model parameters @tikikun (#840)
- feat: improve SEO keywords @hieu-jan (#894)
- enhancement: fix spacing landing page responsive @urmauur (#891)
- bug: added label coming soon for windows and linux @urmauur (#881)
## 🐛 Fixes
- fix: 963 can not run openai models on windows @louis-jan (#974)
- fix: Inference engine Nitro with Windows with/ without CUDA @vuonghoainam (#950)
- Fix error Jan app linux crash @hiento09 (#958)
- fix: windows bug - control buttons close,max,min hidden @linhtran174 (#949)
- bug: fix ui landing page @urmauur (#937)
- fix: model parameters for inference extensions @vuonghoainam (#935)
- [bug] Fix floating border outside card right panel @urmauur (#934)
- fix: import\_typescript.default.isTokenKind is not a function @louis-jan (#923)
- bug: fix syntax formatting @urmauur (#899)
- bug: update metadata title and desc @urmauur (#884)
- fix: download button text color is blending into the background @louis-jan (#883)
## 🧰 Maintenance
- chore: add desktop app analytics @louis-jan (#978)
- refactor: clean types and interfaces @0xSage (#966)
- docs: scaffold dev docs @0xSage (#856)
- chore: Bump nitro to 0.1.26 @vuonghoainam (#960)
- Update update-release-url.yml @hiento09 (#951)
- Fix update release url pipeline run failed @hiento09 (#947)
- chore: Bumpt nitro bin version to version 0.1.23 @vuonghoainam (#942)
- Fix update release url pipeline @hiento09 (#941)
- CI automatically update Update README with Nightly Build Information and stable download URL @hiento09 (#940)
- refactor: deprecate invokers - auto proxying apis - strict types @louis-jan (#924)
- docs: standardize yaml files @hieu-jan (#933)
- chore: universal module definition @louis-jan (#902)
- docs: add assistants api reference @hieu-jan (#801)
- docs: add json schema for engine and model parameters @tikikun (#840)
## Contributor
@0xSage, @hahuyhoang411, @hiento09, @hieu-jan, @imtuyethan, @jan-service-account, @linhtran174, @louis-jan, @namchuai, @tikikun, @urmauur and @vuonghoainam | https://jan.ai/guides/changelog/changelog-v0.4.1 |
# v0.3.1
For more details, [GitHub Issues](https://github.com/janhq/jan/releases/tag/v0.3.1)
Highlighted Issue: [Issue #580: fix: preformatted text indents the first line strangely](https://github.com/janhq/jan/pull/580)
## Changes
- fix: preformatted text indents the first line strangely @louis-jan (#580)
- fix: failed to package app since core and uikit are not being built @louis-jan (#575)
- cleanup: remove component folder and cleanup conversation screen @urmauur (#574)
- bug: update convo state when user change model @urmauur (#571)
- fix(#566): jan cannot retrieve the conversations @namchuai (#570)
- bug: Toast messages shows [object object] @urmauur (#569)
- ui: improve state of welcome screen @urmauur (#563)
- chore: fixed an issue where app does not yield message result @louis-jan (#561)
- Update readme @urmauur (#560)
- ui: standalone UIKit and refactor @urmauur (#557)
- Small description changes @dan-jan (#558)
- add 'change download button based on OS' feature @Vikram-2004 (#551)
- feat: revamp plugin architecture @louis-jan (#535)
- Fix mobile padding @imtuyethan (#550)
- chore: Update Readme @dan-jan (#549)
- Update Homepage and README with 1-line pitch @dan-jan (#548)
- docs: Add About, Events, Blog @dan-jan (#546)
- Ashley/update website content @imtuyethan (#545)
- Add guides @hahuyhoang411 (#488)
- Structure Docs @dan-jan (#536)
- Update README.md @imtuyethan (#533)
- Chore: Setup "Jan Improvements Proposal" workflow @dan-jan (#534)
- Update website tag line @imtuyethan (#527)
- fix: #396 - allow user to cancel a model download @louis-jan (#530)
- fix: #479 - Toggle plugin is now experimental feature @louis-jan (#531)
- chore: disable app update on test @louis-jan (#521)
- bug: chat UI is not consistent @urmauur (#520)
- refactor: plugin manager and execution as ts @louis-jan (#504)
- fix: app toolbar is gone on windows @louis-jan (#503)
- Chore: refactor code, hide plugin menu in web @ghost (#502)
- fix: dest.end is not a function @louis-jan (#501)
- #255: Jan cloud native @ghost (#320)
- bug: download new version should show in status bar @urmauur (#500)
- feat: add New Conversation button on the conversation sidebar @urmauur (#499)
- chore: update plugin readme @louis-jan (#497)
- chore: update plugins license @louis-jan (#496)
- #255: Read plugins manifest from CDN @ghost (#495)
- chore: update plugin sdk - add appDataPath @louis-jan (#492)
- chore: enable back bot function for edge-release @louis-jan (#474)
- chore: attempt to kill Nitro subprocesses @louis-jan (#484)
- docs: new dev hub @0xSage (#450)
## 🚀 Features
- feat: Experimental Feature Toggle @louis-jan (#525)
## 🐛 Bug Fixes
- Add rebuild leveldown for arm on mac intel @hiento09 (#487)
## 🧰 Maintenance
- Bump nitro version from 0.1.4 to 0.1.6 @hiento09 (#581)
- Add set yarn network timeout for uikit @hiento09 (#579)
- Fix error CI e2e run failed on windows @hiento09 (#578)
- Fix build plugins macos codesiging error @hiento09 (#576)
- Add install nitro mac intel inference plugin build locally @hiento09 (#542)
- Bump nitro version to 0.1.4 @hiento09 (#532)
- Chore/update yarn dev script @hiento09 (#529)
- Inference Plugin pull nitro binary from release @hiento09 (#524)
- Correct version and license @hiento09 (#498)
## Contributor
@0xSage, @Vikram-2004, @dan-jan, @hahuyhoang411, @hiento09, @imtuyethan, @jan-service-account, @louis-jan, @namchuai, @tikikun, @urmauur, Han, James, John and nam-john-ho | https://jan.ai/guides/changelog/changelog-v0.3.1 |
# v0.3.3
For more details, [GitHub Issues](https://github.com/janhq/jan/releases/tag/v0.3.3)
Highlighted Issue: [Issue #719: docs: cleanup](https://github.com/janhq/jan/pull/719)
## Changes
- docs: cleanup @0xSage (#719)
- docs: threads and messages @0xSage (#681)
- Updating Onboarding Kit @Diane0111 (#675)
- Update issue templates @0xSage (#685)
- docs: polish models spec @0xSage (#680)
- Feature: Preview URL for each PR and add pre-release.jan.ai as staging of Jan Docs @hiento09 (#669)
- Migrate Model definitions to Swagger/OpenAPI @dan-jan (#659)
- [docs] Add Introduction and refactor Models Spec @dan-jan (#657)
- docs: Add model methods to swagger @0xSage (#660)
- Models Spec: Delete broken Markdown links @dan-jan (#648)
- docs: assistants and threads specs @0xSage (#646)
## 🚀 Features
- improvement: styling message action toolbar @urmauur (#737)
- experimental: allow user to give instruction for the conversation @louis-jan (#714)
- docs/enable-seo-docusaurus @hieu-jan (#725)
- Add windows code sign to CI @hiento09 (#712)
- docs: update installation guide @hieu-jan (#664)
- chore: Update based on team discussion on Nov 20 @vuonghoainam (#673)
- docs: add OpenAI swagger file @hieu-jan (#623)
- Update landing page Jan @urmauur (#638)
## 🐛 Bug Fixes
- chore: open app data should lead user to jan root @louis-jan (#749)
- fix: cancel download does not work @louis-jan (#746)
- fix: error when switching between threads @louis-jan (#736)
- chore: app raises error when attempting to start a model that is already starting @louis-jan (#721)
- bug: fix filter list menu from command base on search type and make a symbol base on OS @urmauur (#723)
- bug: fix clickable small download button on chat screen @urmauur (#722)
- fix: incorrect update progress bar visibility check @louis-jan (#713)
- fix: app shows wrong performance tag, all say not enough ram on windows @louis-jan (#699)
- bug: fix padding quotations and numbering list @urmauur (#695)
- fix: local npm module update does not reflect web app @louis-jan (#677)
- [bug] fix markdown todo items shifted to the left and remove the dots @urmauur (#694)
- bug: fix footer and section spacing landing page @urmauur (#683)
- bug: fix anchor link sidebar openapi @urmauur (#668)
- refactor: remove unused hooks and resolve no-explicit-any @louis-jan (#647)
- bug: fix titles should have spaces in between @urmauur (#652)
- bug: fix compatibility content not fully display @urmauur (#653)
## 🧰 Maintenance
- chore: fix app grammar @0xSage (#750)
- chore: bumb nitro version @louis-jan (#740)
- chore: fs module should not cover app logic @louis-jan (#720)
- API Reference for Models, Messages, Threads @hahuyhoang411 (#679)
- docs: upgrade mdx-js package @hieu-jan (#705)
- [docs] Update Docusaurus to 3.0 and fix closing tag issue in Handbook @dan-jan (#704)
- Fix error docs pipeline run failed @hiento09 (#702)
- Revert docs CICD trigger on push to main instead of tag-based @hiento09 (#698)
- fix: local npm module update does not reflect web app @louis-jan (#677)
- Chore: refactor to makefile @hiento09 (#691)
- Add Instruction to publish docs @hiento09 (#687)
- chore/add-mermaid @hieu-jan (#672)
- chore/update package docs @hieu-jan (#670)
- Enhance Cross-Platform Argument Handling for Nitro Startup Scripts @hiento09 (#674)
- refactor: remove unused hooks and resolve no-explicit-any @louis-jan (#647)
- docs: add OpenAI swagger file @hieu-jan (#623)
- Preliminary Restructure of Docs @dan-jan (#655)
- Model specs @vuonghoainam (#641)
- refactor: refactor app entities @louis-jan (#626)
- refactor: move file to jan root @namchuai (#598)
- Add run-script-os @linhtran174 (#620)
- Refactor Jan Documentation @dan-jan (#625)
## 📖 Documentaion
- docs: update specs/product @0xSage (#744)
- docs/enable-seo-docusaurus @hieu-jan (#725)
- docs: assistant spec @vuonghoainam (#707)
- docs: Refactor Jan Site Structure @dan-jan (#706)
- docs/improve install docs @hieu-jan (#708)
- API Reference for Models, Messages, Threads @hahuyhoang411 (#679)
- [docs] Update Docusaurus to 3.0 and fix closing tag issue in Handbook @dan-jan (#704)
- docs: update installation guide @hieu-jan (#664)
- chore: Update based on team discussion on Nov 20 @vuonghoainam (#673)
- docs: add OpenAI swagger file @hieu-jan (#623)
- Preliminary Restructure of Docs @dan-jan (#655)
- Fix: specs revision @vuonghoainam (#649)
- Model specs @vuonghoainam (#641)
- Update README.md @imtuyethan (#629)
- Refactor Jan Documentation @dan-jan (#625)
## Contributor
@0xSage, @Diane0111, @dan-jan, @hahuyhoang411, @hiento09, @hieu-jan, @imtuyethan, @linhtran174, @louis-jan, @namchuai, @urmauur, @vuonghoainam and Le Tra Mi | https://jan.ai/guides/changelog/changelog-v0.3.3 |
# v0.3.2
For more details, [GitHub Issues](https://github.com/janhq/jan/releases/tag/v0.3.2)
Highlighted Issue: [Issue #612: fix: disabled required env](https://github.com/janhq/jan/pull/612)
## Changes
- fix: disabled required env @urmauur (#612)
- Install Posthog snippet @imtuyethan (#573)
- web: google tag manager @urmauur (#562)
- docs: fix syntax highlighting @0xSage (#602)
- chore: remove past event @0xSage (#600)
- docs: new docs @0xSage (#599)
- [chore]: Update docs @dan-jan (#597)
## 🚀 Features
- refactor: main electron with managers and handlers @louis-jan (#610)
## 🐛 Bug Fixes
- Fix: Failed to load model - unload model nitro @louis-jan (#616)
- Restore cpx nitro step in yarn script @hiento09 (#617)
- fix(#591): prevent duplicate message id issue @namchuai (#595)
- bug: cancelling a model download should be delete the model file on user data @urmauur (#613)
- bug: fix weird padding vertical snippet code @urmauur (#608)
- bug: Fix button download detect intel or apple silicon @urmauur (#609)
- bug: enable delete conversation after deleted model @urmauur (#594)
- bug: download modal should truncate model name @urmauur (#592)
- bug: support multiple line input chat using Textarea instead @urmauur (#593)
## 🧰 Maintenance
- refactor: main electron with managers and handlers @louis-jan (#610)
- Chore/refactor yarn script @hiento09 (#615)
- fix: line height and update typography component @urmauur (#611)
## Contributor
@0xSage, @dan-jan, @hiento09, @imtuyethan, @jan-service-account, @louis-jan, @namchuai and @urmauur | https://jan.ai/guides/changelog/changelog-v0.3.2 |
# v0.4.3
For more details, [GitHub Issues](https://github.com/janhq/jan/releases/tag/v0.4.3)
Highlighted Issue: [Issue #1159: Hotfix Prompt template for models on the Hub](https://github.com/janhq/jan/pull/1159)
## Changes
- Hotfix Prompt template for models on the Hub @hahuyhoang411 (#1159)
- Update model list for new release @hahuyhoang411 (#1143)
- fix(Thread): #1119 focus on the first thread to prevent blank chat screen @namchuai (#1127)
- fix(Thread): #1064 message being added to wrong thread if switching thread @namchuai (#1108)
- fix(Thread): #1042 allow create new thread by clicking Use in Jan Hub @namchuai (#1103)
- feat(ModelSetting): #1065 update state of model setting between threads @namchuai (#1090)
- Update model version @hahuyhoang411 (#1086)
- fix: cache hallucinations and failed to load model due to race condition @louis-jan (#1071)
- fix(thread): #1043 default model to prefer active model @namchuai (#1070)
- Update issue templates @0xSage (#1058)
- Update ctx\_len and max\_tokens @hahuyhoang411 (#1035)
## 🚀 Features
- feat: Add codeQL analysis for push main and pr main @hiro-v (#1128)
- Feature autoupdater for nightly build @hiento09 (#1068)
- feat: copy button for code block @urmauur (#1062)
- Enhancements to Dependency Installation and App Testing @hiento09 (#965)
## 🐛 Fixes
- fix: error road map url @hieu-jan (#1153)
- Fix token speed slow in machine has multi gpus @hiento09 (#1157)
- fix: added dialog confirmation clean thread @urmauur (#1142)
- fix: remove remote model from shortcut models dialog @urmauur (#1124)
- fix: ui issue - all models are activated @louis-jan (#1120)
- fix: should not hide empty message away @louis-jan (#1116)
- fix: added tooltip for user cannot change model after starting thread @urmauur (#1115)
- fix: remote model always active badges @urmauur (#1113)
- fix: handle chat completion state with enter button @louis-jan (#1114)
- fix: model active indicator only show when model activated @urmauur (#1110)
- fix: #1096 yield error message upon thread switching @louis-jan (#1109)
- fix: toaster success deleted thread showing id instead of active model @urmauur (#1111)
- fix: update copy setting page @urmauur (#1105)
- fix: search recommended model @urmauur (#1106)
- fix: #1097 streaming response is replaced by error message @louis-jan (#1099)
- Fix auto update windows Bug @hiento09 (#1102)
- fix: added dialog confirmation when delete thread @urmauur (#1093)
- fix: system monitor broken layout when responsive @urmauur (#1085)
- bug: chatbox doesn't resize back down @urmauur (#1084)
- fix: thread is broken after deleted first generated message @louis-jan (#1061)
## 🧰 Maintenance
- feat: Add codeQL analysis for push main and pr main @hiro-v (#1128)
- docs: refactor dev docs, guides and specs @0xSage (#1092)
- Correct jq command cause ci nightly build run failed @hiento09 (#1104)
- Fix nightly build autoupdater @hiento09 (#1073)
- Feature autoupdater for nightly build @hiento09 (#1068)
- docs: Update product.md @0xSage (#1066)
- Posthog disable click event and increase timeout for nitro load model… @hiento09 (#1060)
- docs: improve quickstart docs @0xSage (#1047)
## Contributor
@0xSage, @hahuyhoang411, @hiento09, @hieu-jan, @hiro-v, @jan-service-account, @louis-jan, @namchuai and @urmauur | https://jan.ai/guides/changelog/changelog-v0.4.3 |
# v0.4.4
For more details, [GitHub Issues](https://github.com/janhq/jan/releases/tag/v0.4.4)
Highlighted Issue: [Issue #1587: Update 2023-11-05-hello-world.md](https://github.com/janhq/jan/pull/1587)
## Changes
- Update 2023-11-05-hello-world.md @Ssstars (#1587)
- fix(API): #1511 update swagger page @namchuai (#1572)
- fix(Thread): #1212 thread.json not created when user change thread settings @namchuai (#1570)
- fix(Thread): #1336 not allow creating too many unfinished thread @namchuai (#1538)
- Update 01-how-to-get-involved-and-FAQ.mdx @Ssstars (#1555)
- Update 01-how-to-get-involved-and-FAQ.mdx @Ssstars (#1553)
- Update 02-embracing-pod-structure.mdx @Ssstars (#1550)
- Update 01-how-we-hire.mdx @Ssstars (#1551)
- Update 01-how-we-hire.mdx @Ssstars (#1524)
- fix(InferenceExtension): #1067 sync the nitro process state @namchuai (#1493)
- fix(Messages): #1434 create message via api does not display on app correctly @namchuai (#1479)
- Docs for the Integration of Continue and Jan in VSCode @0xgokuz (#1467)
- Chore: Update model.json for UI @hahuyhoang411 (#1448)
- Docs for Installing Models from Hub @0xgokuz (#1450)
- Update about.md @Ssstars (#1436)
- feat(UI): #1404 make left side bar collapsible by hot key @namchuai (#1420)
- docs: Typo in 06-hardware.md @akaMrNagar (#1408)
- fix(API): #1409 fix wrong prefix for threads api @namchuai (#1410)
- Update model hub @hahuyhoang411 (#1383)
- fix(Model): remove unsupported default model setting params @namchuai (#1382)
- fix(trinity): update cover path for trinity v1.2 @hahuyhoang411 (#1380)
- Chore/update model hub @hahuyhoang411 (#1342)
- Update about.md @Ssstars (#1359)
- fix(JanHub): #1158 sort model list @namchuai (#1257)
- fix(Message): open link with external browser @namchuai (#1339)
- feat(Model): #1028 made model.json optional @namchuai (#1314)
- docs: Update onboarding.md @Diane0111 (#1293)
- fix: clean resource on exit @louis-jan (#1290)
- fix: posthog configuration @hieu-jan (#1283)
- docs: update README.md @eltociear (#1277)
- Enable scrolling in the message chat box @Gri-ffin (#1280)
- chore: Update README.md @sr-albert (#1263)
- Adding new model to the Hub @hahuyhoang411 (#1213)
- Feature GPU detection for Jan on Windows and Linux @hiento09 (#1242)
- fix(Thread): #1168 fix newly created thread cannot select model after restart @namchuai (#1176)
## 🚀 Features
- feat: add compatibility tag to model selection in right panel @urmauur (#1552)
- Feature integrate antivirus scanner to ci @hiento09 (#1529)
- feat: [hub] update compatibility tags colors @urmauur (#1516)
- feat: hub recommendation labels @urmauur (#1440)
- Feature linux support app image format @hiento09 (#1442)
- fix: render external links @urmauur (#1441)
- fix: add icon collapsible left panel and update keyboard shortcut page @urmauur (#1439)
- feat(UI): update UI footer @urmauur (#1424)
- Fix Bug for Chat Reply Goes off Screen @mishrababhishek (#1393)
- feat: move social media from left panel into footer @urmauur (#1325)
- feat: implementation new UI thread settings @urmauur (#1301)
- Bring social media links @Gri-ffin (#1295)
- feat: added keyboard shortcut list in setting page @urmauur (#1275)
- feat: add swagger /docs to localhost:1337 @louis-jan (#1268)
- feat: update posthog configuration @hieu-jan (#1258)
- feat: Deprecate model.json ready state in favor of .download ext @louis-jan (#1238)
- feat: add engine settings @namchuai (#1199)
- feat: users should be able to switch models mid-thread @louis-jan (#1226)
- feat: temporary link how to import model @urmauur (#1209)
## 🐛 Fixes
- fix: #1594 - Model settings - change thread model - go back does not see according settings @louis-jan (#1595)
- fix: #1548 - duplicate command shortcut instruction @louis-jan (#1600)
- fix: switch model caused app crash @louis-jan (#1597)
- fix: #1559 Inference Parameters displayed on new thread with Openai GPT Model @louis-jan (#1588)
- fix: enable user set value manually model setting from input @urmauur (#1585)
- fix: #1569 - Does not apply thread settings when loading model @louis-jan (#1576)
- fix: could not change model params settings @louis-jan (#1547)
- fix: gpu check module export does not work in extension @louis-jan (#1536)
- fix: adjust calculation hub labels using total RAM instead remaining RAM @urmauur (#1522)
- Feature integrate antivirus scanner to ci @hiento09 (#1529)
- fix: allow users to set max tokens variably @urmauur (#1513)
- fix: stop word update @louis-jan (#1457)
- Revert nitro to 0.2.6 @hiento09 (#1491)
- fix: enable text selection codeblock @urmauur (#1466)
- fix: suppress all main node JS error message dialog @louis-jan (#1460)
- Correct AppImage path @hiento09 (#1446)
- fix: render external links @urmauur (#1441)
- fix: add icon collapsible left panel and update keyboard shortcut page @urmauur (#1439)
- fix: GET /models does not work due to new default model dir @louis-jan (#1392)
- fix: model migration stopped working @louis-jan (#1378)
- fix: wrong condition for displaying error message @louis-jan (#1376)
- fix: show hide section engine params @urmauur (#1374)
- fix: copy stream tooltip and hide section when no params setting @urmauur (#1373)
- bugs: fix stop streaming when user delete or clean thread @urmauur (#1347)
- fix: show a proper error message on download failure @louis-jan (#1345)
- Add detect cuda version jan app @hiento09 (#1351)
- fix: Error occurred: Unexpected token "d", "data: ..." is not a valid JSON @louis-jan (#1332)
- fix: app getting stuck at downloading 99% while downloading model @louis-jan (#1320)
- correct type utf-8 @hiento09 (#1311)
- Fix memory on mac included cached and swap @hiento09 (#1298)
- fix: should check app dir before spawning log @louis-jan (#1297)
- fix: disable process logging from server @louis-jan (#1296)
- fix: user should be able to access Swagger docs from localhost:1337 @louis-jan (#1292)
- Switch from Gigabyte to Gibibyte - System monitor @hiento09 (#1286)
- Switch from systeminformation to os-utils to resolve Bitdefender false positive and memory leak issue @hiento09 (#1282)
- fix: swagger CSP issue @louis-jan (#1284)
- fix: support markdown break line @urmauur (#1274)
- fix ci test run failed @hiento09 (#1267)
- Fix wrong linux nitro path @hiento09 (#1266)
- fix: enable command enter on dialog confirmation clean thread @urmauur (#1261)
- fix: input message duplicated due with some input sources @louis-jan (#1259)
- fix: mac users should not see GPU settings @louis-jan (#1255)
- fix: remove redundant gpu detection prompt event @louis-jan (#1254)
- fix: engine settings GUI - feature toggle @louis-jan (#1252)
- Fix bug #1178 high ram on windows @hiento09 (#1241)
- fix: #1183 Reveal in finder does not work on windows @namchuai (#1239)
- fix: remove delay tooltip and click event @urmauur (#1217)
- fix: enable enter command on dialog confirmation delete thread @urmauur (#1218)
- fix: Cleared thread last message is not updated @louis-jan (#1225)
- Fix switch thread crash nitro windows linux @hiento09 (#1214)
- fix: darkmode broken color @urmauur (#1186)
## 🧰 Maintenance
- chore: typo model.json @louis-jan (#1599)
- docs: add 04-how-to-get-error-logs.mdx @hieu-jan (#1580)
- chore: teach how to attach logs @0xSage (#1578)
- chore: issues should auto close with PRs through template @0xSage (#1577)
- chore: Update issue templates @0xSage (#1568)
- docs: fix x handles @0xSage (#1532)
- Docs to integrate OpenRouter with Jan without UI/UX @0xgokuz (#1495)
- chore: fix darkmode docs @hieu-jan (#1520)
- docs: fix algolia configuration @hieu-jan (#1518)
- docs: fix algolia configuration @hieu-jan (#1517)
- Revert URL release in readme to version 0.4.3 @hiento09 (#1502)
- refactor: add app and nitro log - resolve dependencies issue @louis-jan (#1447)
- chore: enable agolia @hieu-jan (#1497)
- docs: update troubleshooting and redirects old pages @hieu-jan (#1492)
- docs: minor fix @hieu-jan (#1478)
- docs: initial handbook structure @hieu-jan (#1477)
- Bump nitro to 0.2.8 and change Jan App to support cuda >= 11.7 @hiento09 (#1476)
- Chore update docs jan - add AppImage instruction to docusaurus @hiento09 (#1480)
- Bump nitro to 0.2.7 @hiento09 (#1474)
- chore: error message update @louis-jan (#1473)
- docs: Update 02-import-manually.mdx @0xSage (#1469)
- docs: Update about.md @0xSage (#1465)
- Bump nitro version to 0.2.6 @hiento09 (#1458)
- docs: adding customize engine settings @hieu-jan (#1455)
- docs: add-missing-path @hieu-jan (#1454)
- docs: resize gif @hieu-jan (#1453)
- docs: revenue philosophy @0xSage (#1443)
- docs: jan framework principles @0xSage (#1438)
- docs: fix typo in docs @hieu-jan (#1419)
- chore: clean up use os hook @namchuai (#1418)
- docs: explain each docs page intent @0xSage (#1417)
- docs: Update 01-server.md @0xSage (#1416)
- Update warning url from github md file to jan.ai docs site @hiento09 (#1414)
- docs: improve gpu not used guide @hieu-jan (#1405)
- chore: update README.md @eltociear (#1406)
- Update USAGE docs for linux @hiento09 (#1401)
- docs: gpu not detected @0xSage (#1399)
- docs: Troubleshoot Failed To Fetch @gabrielle-ong (#1398)
- docs: improve docs syntax @hieu-jan (#1394)
- docs: add-install-nightly-guide @hieu-jan (#1390)
- docs: correct href link @hieu-jan (#1338)
- docs: fix chat payload and cURL @hieu-jan (#1360)
- docs: add Chatting Guide @hieu-jan (#1184)
- Chore add docs usage how to switch run mode jan app @hiento09 (#1353)
- docs: configure index page @hieu-jan (#1330)
- docs: Update product.md @0xSage (#1326)
- docs: Update 01-server.md @0xSage (#1327)
- refactor: deprecate the appVersion IPC and use the predefined VERSION @louis-jan (#1309)
- docs: update using models documentation @hieu-jan (#1288)
- docs: update pm handbook @0xSage (#1307)
- docs: contributor docs overview @0xSage (#1305)
- chore: github PR template @0xSage (#1304)
- Fix memory on mac included cached and swap @hiento09 (#1298)
- Enrich discord message for nightly build url @hiento09 (#1294)
- Refactor CI by create shared jobs output @hiento09 (#1287)
- docs: update README.md @hieu-jan (#1281)
- docs: Update README.md @0xSage (#1248)
- feat: Jan Server, API and decoupled clients @louis-jan (#948)
- docs: improve 02-import-manually @hieu-jan (#1222)
- chore: Update issue templates @0xSage (#1229)
- docs: Update 02-import-manually.mdx @0xSage (#1197)
- add sleep 500ms if platform is windows before starting nitro process @hiento09 (#1215)
- docs: improve troubleshoot documentation @hieu-jan (#1173)
- docs: update bug report template @hieu-jan (#1180)
- docs: add troubleshooting @hieu-jan (#1169)
- chore: copy fixes @0xSage (#1167)
- docs: Update 01-start-thread.md @0xSage (#1122)
## Contributor
@0xSage, @0xgokuz, @Diane0111, @Gri-ffin, @Ssstars, @akaMrNagar, @eltociear, @gabrielle-ong, @hahuyhoang411, @hiento09, @hieu-jan, @jan-service-account, @louis-jan, @mishrababhishek, @namchuai, @sr-albert, @urmauur and Abhishek Mishra | https://jan.ai/guides/changelog/changelog-v0.4.4 |
# v0.4.2
For more details, [GitHub Issues](https://github.com/janhq/jan/releases/tag/v0.4.2)
Highlighted Issue: [Issue #1033: Hotfix model hub](https://github.com/janhq/jan/pull/1033)
## Changes
- Hotfix model hub @hahuyhoang411 (#1033)
- Update Model.json @hahuyhoang411 (#1005)
## 🚀 Features
- feat: app theme depend on local storage instead native theme electron @urmauur (#1014)
- feat: move stop inference button into the send button @urmauur (#1011)
- feat: loader when starting model @urmauur (#945)
- fix: enable download app linux @urmauur (#993)
- fix: remove coming soon windows @urmauur (#986)
## 🐛 Fixes
- fix: migrate new models @louis-jan (#1034)
- fix: add input for api key remote model @urmauur (#1031)
- fix bug #1013, enable posthog for release app version only @hiento09 (#1019)
- fix: delete first message then regenerate again will break the thread @louis-jan (#1015)
- fix: #995 - Fix onboarding state and model sorting @louis-jan (#1009)
- fix: limit analytics events capture @louis-jan (#1012)
- fix: wrong selected model right panel @urmauur (#1001)
- fix: review finder and view as json @louis-jan (#1000)
- fix: enable download app linux @urmauur (#993)
## 🧰 Maintenance
- chore: remigrate if there is no models dir @louis-jan (#1038)
- bump nitro version to 0.1.30 @hiento09 (#1036)
- chore: in app copy fixes @0xSage (#1032)
- Separate posthog project for jan app and docs @hiento09 (#1029)
- Update posthog capture url list @hiento09 (#1022)
- docs: second half of "import model docs" PR @0xSage (#1021)
- docs: how to import models @0xSage (#1020)
- fix bug #1013, enable posthog for release app version only @hiento09 (#1019)
## Contributor
@0xSage, @hahuyhoang411, @hiento09, @jan-service-account, @louis-jan and @urmauur | https://jan.ai/guides/changelog/changelog-v0.4.2 |
# v0.4.7
For more details, [GitHub Issues](https://github.com/janhq/jan/releases/tag/v0.4.7)
Highlighted Issue: [Issue #2121: Release cut v0.4.7](https://github.com/janhq/jan/pull/2121)
## Changes
- Release cut v0.4.7 @louis-jan (#2121)
- chore: update models @hahuyhoang411 (#1829)
- add docs for entire advanced settings @hieu-jan (#2063)
- docs: Fix #2040 : added /v1 path to apiBase @ldebs (#2041)
- fix: ui for disabled state of gpu acceleration @namchuai (#2034)
- feat: Initialize POM structure with fixtures on Playwright @Van-QA (#2015)
- Alternative solution for `Thread titles should auto-summarize Topic` @0xgokuz (#1976)
- Update authors.yml Rex @hahuyhoang411 (#1956)
- Update authors.yml Louis @louis-jan (#1955)
- Change env Dockerfile.gpu and update README @hiento09 (#1963)
- chore: Update authors.yml for Van Pham @Van-QA (#1954)
- Sync dev branch to docs branch @hieu-jan (#1948)
- sync current docs branch to dev branch @hieu-jan (#1947)
- feat: Playwright capture screenshot of Electron desktop app (Jan) on failures @Van-QA (#1934)
- Sync main to dev after release 0.4.6 @hiento09 (#1929)
## 🚀 Features
- feat: Add nitro vulkan to support AMD GPU/ APU and Intel Arc GPU @hiro-v (#2056)
- fix: flow edit message @urmauur (#2113)
- Feature helmchart and ci jan server @hiento09 (#2106)
- feat: improvementUI GPU acceleration @urmauur (#1990)
- feat: add edit messages users @urmauur (#1974)
- feat: revamp ui dropdown list model option @urmauur (#1977)
- feat: add modal troubleshooting guideline @urmauur (#1968)
- feat: integrate umami script locally @hieu-jan (#1958)
- feat: User Selectable GPUs and GPU-based Model Recommendations @hiento09 (#1730)
## 🐛 Fixes
- fix: correct vulkan settings @louis-jan (#2128)
- fix: chore UI @louis-jan (#2125)
- Regression: bump nitro to 0.3.13 @hiento09 (#2124)
- Regression: Linux vulkan binary path @hiento09 (#2123)
- fix: revert back menu actions @louis-jan (#2120)
- fix: mismatching between nightly build and version - jan about @louis-jan (#2114)
- fix: flow edit message @urmauur (#2113)
- fix: tools section should be expanded by default @louis-jan (#2110)
- fix: failed to bind port - nitro error message copy @louis-jan (#2101)
- fix: remove caret down icon when tab selected into remote model @urmauur (#2102)
- fix: openai client sdk compatible @louis-jan (#2096)
- Fix bug #2005 docker blank website @hiento09 (#2093)
- fix: check if port is occupied before start local server @namchuai (#2098)
- fix: broken model.json update @louis-jan (#2099)
- fix: make text input scrollable @urmauur (#2083)
- fix: failed to send message blocks thread creation @louis-jan (#2091)
- fix: server crashes on missing module @louis-jan (#2089)
- fix: expand assistant and model settings by default @louis-jan (#2081)
- fix: move jan data folder - error handling for no write permission granted @louis-jan (#2077)
- fix: check for updates should show no update are available on the latest build @louis-jan (#2075)
- fix: infinity showed when haven't get total size @namchuai (#2066)
- fix: should stop running the model when GPU settings are changed @louis-jan (#2067)
- fix: settings page state loop and dark theme @louis-jan (#2065)
- fix: Fix Nitro windows with error 3221225781 @hiro-v (#2057)
- fix: message should only be interrupted when i start another thread @louis-jan (#2053)
- fix: local server start error should not change to started state @louis-jan (#2052)
- fix: update copy of message queue @louis-jan (#2051)
- fix: download mutilple binaries @namchuai (#2043)
- fix: disable gpu drop down box if there's no GPU ready @namchuai (#2046)
- fix: app should generate thread title with length restriction @louis-jan (#2037)
- fix: factory reset not remove jan data folder @namchuai (#2027)
- fix: content setting right panel default to collapse @urmauur (#2026)
- fix: local server blank parameters if there is no thread selected @louis-jan (#2028)
- fix: model path backward compatible @louis-jan (#2018)
- fix: resolve state update loop infinitive rerendering @louis-jan (#2017)
- fix: lack of auto-cleaning mechanism for logs @louis-jan (#2003)
- fix: app stuck regenerating assistant response @louis-jan (#2001)
- fix: decouple thread summary update @louis-jan (#1994)
- fix: app fails gracefully with clear error messages @louis-jan (#1993)
- fix: retrieval stuck at generating response @louis-jan (#1988)
- Fix macos auto update failed on nightly build @hiento09 (#1991)
- fix: model downloads broken on nightly @louis-jan (#1984)
- fix: RAG enhancements @urmauur (#1965)
- Update docs run Jan Server in Docker mode @hiento09 (#1960)
- fix: update conditional check last status message @urmauur (#1951)
- fix: markdown render for chat completion role user @urmauur (#1944)
- fix: avoid users to create so many threads at the same time @urmauur (#1930)
- fix: download model will close panel item hub @urmauur (#1923)
## 🧰 Maintenance
- docs: improve integrations guide \& import model using absolute path @hieu-jan (#2076)
- chore: add app version into log @namchuai (#2116)
- docs: add integration docs Mistral AI API @hieu-jan (#2070)
- docs:add-advanced-settings-https-proxy @hieu-jan (#2054)
- chore: refactor watch system resource hook @louis-jan (#2048)
- docs: Updates Guide Using the Local Server @SamPatt (#1924)
- server install core using link instead of file @hiento09 (#2025)
- chore: prettier fix @louis-jan (#2019)
- chore: bump nitro 0.3.9 @louis-jan (#2016)
- refactor: reduce IPC \& API handlers - shared node logics @louis-jan (#2011)
- docs: update 03-gpu-not-used with RTX issues @hieu-jan (#1992)
- docs: add Jan installation using Docker @hieu-jan (#1981)
- chore: reduce bundle size @louis-jan (#1970)
- docs: add author.yml @hieu-jan (#1973)
- Update authors.yml hien @hiento09 (#1953)
- chore: server download progress + S3 @louis-jan (#1925)
- chore: add author james @namchuai (#1952)
- chore: Add author - Ashley @imtuyethan (#1950)
- chore: Add Author - Hiro @hiro-v (#1949)
- docs: adding new feature for v0.4.6 to release checklist @Van-QA (#1927)
## Contributor
@0xSage, @0xgokuz, @SamPatt, @Van-QA, @hahuyhoang411, @hiento09, @hieu-jan, @hiro-v, @imtuyethan, @jan-service-account, @ldebs, @louis-jan, @namchuai, @urmauur and James | https://jan.ai/guides/changelog/changelog-v0.4.7 |
# Troubleshooting NVIDIA GPU
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
This guide provides steps to troubleshoot and resolve issues when the Jan app does not utilize the NVIDIA GPU on Windows and Linux systems.
### 1. Ensure GPU Mode Requirements
<Tabs>
<TabItem value="windows" label="Windows">
#### NVIDIA Driver
- Install an [NVIDIA Driver](https://www.nvidia.com/Download/index.aspx) supporting CUDA 11.7 or higher.
- Use the following command to verify the installation:
```sh
nvidia-smi
```
#### CUDA Toolkit
- Install a [CUDA toolkit](https://developer.nvidia.com/cuda-downloads) compatible with your NVIDIA driver.
- Use the following command to verify the installation:
```sh
nvcc --version
```
</TabItem>
<TabItem value="linux" label="Linux">
#### NVIDIA Driver
- Install an [NVIDIA Driver](https://www.nvidia.com/Download/index.aspx) supporting CUDA 11.7 or higher.
- Use the following command to verify the installation:
```sh
nvidia-smi
```
#### CUDA Toolkit
- Install a [CUDA toolkit](https://developer.nvidia.com/cuda-downloads) compatible with your NVIDIA driver.
- Use the following command to verify the installation:
```sh
nvcc --version
```
#### Linux Specifics
- Ensure that `gcc-11`, `g++-11`, `cpp-11`, or higher is installed.
- See [instructions](https://gcc.gnu.org/projects/cxx-status.html#cxx17) for Ubuntu installation.
- **Post-Installation Actions**: Add CUDA libraries to `LD_LIBRARY_PATH`.
- Follow the [Post-installation Actions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions) instructions.
</TabItem>
</Tabs>
### 2. Switch to GPU Mode
Jan defaults to CPU mode but automatically switches to GPU mode if your system supports it, selecting the GPU with the highest VRAM. Check this setting in `Settings` > `Advanced Settings`.
#### Troubleshooting Tips
If GPU mode isn't enabled by default:
1. Confirm that you have installed an NVIDIA driver supporting CUDA 11.7 or higher. Refer to [CUDA compatibility](https://docs.nvidia.com/deploy/cuda-compatibility/index.html#binary-compatibility__table-toolkit-driver).
2. Ensure compatibility of the CUDA toolkit with your NVIDIA driver. Refer to [CUDA compatibility](https://docs.nvidia.com/deploy/cuda-compatibility/index.html#binary-compatibility__table-toolkit-driver).
3. For Linux, add CUDA's `.so` libraries to the `LD_LIBRARY_PATH`. For Windows, ensure that CUDA's `.dll` libraries are in the PATH. Refer to [Windows setup](https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html#environment-setup).
### 3. Check GPU Settings
1. Navigate to `Settings` > `Advanced Settings` > `Jan Data Folder` to access GPU settings.
2. Open the `settings.json` file in the `settings` folder. Here's an example:
```json title="~/jan/settings/settings.json"
{
"notify": true,
"run_mode": "gpu",
"nvidia_driver": {
"exist": true,
"version": "531.18"
},
"cuda": {
"exist": true,
"version": "12"
},
"gpus": [
{
"id": "0",
"vram": "12282"
},
{
"id": "1",
"vram": "6144"
},
{
"id": "2",
"vram": "6144"
}
],
"gpu_highest_vram": "0"
}
```
### 4. Restart Jan
Restart Jan application to make sure it works.
#### Troubleshooting Tips
- Ensure `nvidia_driver` and `cuda` fields indicate installed software.
- If `gpus` field is empty or lacks your GPU, check NVIDIA driver and CUDA toolkit installations.
- For further assistance, share the `settings.json` file.
### Tested Configurations
- **Windows 11 Pro 64-bit:**
- GPU: NVIDIA GeForce RTX 4070ti
- CUDA: 12.2
- NVIDIA driver: 531.18 (Bare metal)
- **Ubuntu 22.04 LTS:**
- GPU: NVIDIA GeForce RTX 4070ti
- CUDA: 12.2
- NVIDIA driver: 545 (Bare metal)
- **Ubuntu 20.04 LTS:**
- GPU: NVIDIA GeForce GTX 1660ti
- CUDA: 12.1
- NVIDIA driver: 535 (Proxmox VM passthrough GPU)
- **Ubuntu 18.04 LTS:**
- GPU: NVIDIA GeForce GTX 1660ti
- CUDA: 12.1
- NVIDIA driver: 535 (Proxmox VM passthrough GPU)
### Common Issues and Solutions
1. If the issue persists, try installing the [Nightly version](https://jan.ai/install/nightly/).
2. Ensure your (V)RAM is accessible; some users with virtual RAM may require additional configuration.
3. Seek assistance in [Jan Discord](https://discord.gg/mY69SZaMaC). | https://jan.ai/guides/common-error/not-using-gpu |
# Broken Build
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
This guide provides you steps to troubleshoot and to resolve the issue where your Jan is stuck in a broken build after installation.
<Tabs>
<TabItem value="mac" label="Mac" default>
### 1. Uninstall Jan
Delete Jan from your `/Applications` folder.
### 2. Delete Application Data, Cache, and User Data
```zsh
# Step 1: Delete the application data
## Newer versions
rm -rf ~/Library/Application\ Support/jan
## Versions 0.2.0 and older
rm -rf ~/Library/Application\ Support/jan-electron
# Step 2: Clear application cache
rm -rf ~/Library/Caches/jan*
# Step 3: Remove all user data
rm -rf ~/jan
```
### 3. Additional Step for Versions Before 0.4.2
If you are using a version before `0.4.2`, you need to run the following commands:
```zsh
ps aux | grep nitro
# Looks for processes like `nitro` and `nitro_arm_64`, and kill them one by one by process ID
kill -9 <PID>
```
### 4. Download the Latest Version
Download the latest version of Jan from our [homepage](https://jan.ai/).
</TabItem>
<TabItem value="windows" label="Windows">
### 1. Uninstall Jan
To uninstall Jan on Windows, use the [Windows Control Panel](https://support.microsoft.com/en-us/windows/uninstall-or-remove-apps-and-programs-in-windows-4b55f974-2cc6-2d2b-d092-5905080eaf98).
### 2. Delete Application Data, Cache, and User Data
```sh
# You can delete the `/Jan` directory in Windows's AppData Directory by visiting the following path `%APPDATA%\Jan`
cd C:\Users\%USERNAME%\AppData\Roaming
rmdir /S jan
```
### 3. Additional Step for Versions Before 0.4.2
If you are using a version before `0.4.2`, you need to run the following commands:
```sh
# Find the process ID (PID) of the nitro process by filtering the list by process name
tasklist | findstr "nitro"
# Once you have the PID of the process you want to terminate, run the `taskkill`
taskkill /F /PID <PID>
```
### 4. Download the Latest Version
Download the latest version of Jan from our [homepage](https://jan.ai/).
</TabItem>
<TabItem value="linux" label="Linux">
### 1. Uninstall Jan
<Tabs groupId = "linux_type">
<TabItem value="linux_main" label = "Linux">
To uninstall Jan, you should use your package manager's uninstall or remove option.
This will return your system to its state before the installation of Jan.
This method can also reset all settings if you are experiencing any issues with Jan.
</TabItem>
<TabItem value = "deb_ub" label = "Debian / Ubuntu">
To uninstall Jan, run the following command.MDXContent
```sh
sudo apt-get remove jan
# where jan is the name of Jan package
```
This will return your system to its state before the installation of Jan.
This method can also be used to reset all settings if you are experiencing any issues with Jan.
</TabItem>
<TabItem value = "other" label = "Others">
To uninstall Jan, you can uninstall Jan by deleting the `.AppImage` file.
If you wish to completely remove all user data associated with Jan after uninstallation, you can delete the user data at `~/jan`.
This method can also reset all settings if you are experiencing any issues with Jan.
</TabItem>
</Tabs>
### 2. Delete Application Data, Cache, and User Data
```sh
# You can delete the user data folders located at the following `~/jan`
rm -rf ~/jan
```
### 3. Additional Step for Versions Before 0.4.2
If you are using a version before `0.4.2`, you need to run the following commands:
```zsh
ps aux | grep nitro
# Looks for processes like `nitro` and `nitro_arm_64`, and kill them one by one by process ID
kill -9 <PID>
```
### 4. Download the Latest Version
Download the latest version of Jan from our [homepage](https://jan.ai/).
</TabItem>
</Tabs>
By following these steps, you can cleanly uninstall and reinstall Jan, ensuring a smooth and error-free experience with the latest version.
:::note
Before reinstalling Jan, ensure it's completely removed from all shared spaces if it's installed on multiple user accounts on your device.
::: | https://jan.ai/guides/common-error/broken-build |
# Mistral AI
## How to Integrate Mistral AI with Jan
[Mistral AI](https://docs.mistral.ai/) provides two ways to use their Large Language Models (LLM):
1. API
2. Open-source models on Hugging Face.
To integrate Jan with Mistral AI, follow the steps below:
:::note
This tutorial demonstrates integrating Mistral AI with Jan using the API.
:::
### Step 1: Configure Mistral API Key
1. Obtain Mistral API keys from your [Mistral](https://console.mistral.ai/user/api-keys/) dashboard.
2. Insert the Mistral AI API key into `~/jan/engines/openai.json`.
```json title="~/jan/engines/openai.json"
{
"full_url": "https://api.mistral.ai/v1/chat/completions",
"api_key": "<your-mistral-ai-api-key>"
}
```
### Step 2: Model Configuration
1. Navigate to `~/jan/models`.
2. Create a folder named `mistral-(modelname)` (e.g., `mistral-tiny`).
3. Inside, create a `model.json` file with these settings:
- Set `id` to the Mistral AI model ID.
- Set `format` to `api`.
- Set `engine` to `openai`.
- Set `state` to `ready`.
```json title="~/jan/models/mistral-tiny/model.json"
{
"sources": [
{
"filename": "mistral-tiny",
"url": "https://mistral.ai/"
}
],
"id": "mistral-tiny",
"object": "model",
"name": "Mistral-7B-v0.2 (Tiny Endpoint)",
"version": "1.0",
"description": "Currently powered by Mistral-7B-v0.2, a better fine-tuning of the initial Mistral-7B released, inspired by the fantastic work of the community.",
"format": "api",
"settings": {},
"parameters": {},
"metadata": {
"author": "Mistral AI",
"tags": ["General", "Big Context Length"]
},
"engine": "openai"
}
```
:::note
- For more details regarding the `model.json` settings and parameters fields, please see [here](../models/integrate-remote.mdx#modeljson).
- Mistral AI offers various endpoints. Refer to their [endpoint documentation](https://docs.mistral.ai/platform/endpoints/) to select the one that fits your requirements. Here, we use the `mistral-tiny` model as an example.
:::
### Step 3: Start the Model
1. Restart Jan and navigate to the **Hub**.
2. Locate your model and click the **Use** button. | https://jan.ai/guides/integration/mistral |
# Raycast
## How to Integrate Raycast
[Raycast](https://www.raycast.com/) is a productivity tool designed for macOS that enhances workflow efficiency by providing quick access to various tasks and functionalities through a keyboard-driven interface. To integrate Raycast with Jan, follow the steps below:
### Step 1: Download the TinyLlama Model
1. Go to the **Hub** and download the TinyLlama model.
2. The model will be available at `~jan/models/tinyllama-1.1b`.
### Step 2: Clone and Run the Program
1. Clone this [GitHub repository](https://github.com/InNoobWeTrust/nitro-raycast).
2. Execute the project using the following command:
```sh title="Node.js"
npm i && npm run dev
```
### Step 3: Search for Nitro and Run the Model
Search for `Nitro` using the program and you can use the models from Jan in RayCast. | https://jan.ai/guides/integration/raycast |
# Ollama
## How to Integrate Ollama with Jan
Ollama provides you with largen language that you can run locally. There are two methods to integrate Ollama with Jan:
1. Integrate Ollama server with Jan.
2. Migrate the downloaded model from Ollama to Jan.
To integrate Ollama with Jan, follow the steps below:
:::note
In this tutorial, we'll show how to integrate Ollama with Jan using the first method. We will use the [llama2](https://ollama.com/library/llama2) model as an example.
:::
### Step 1: Start the Ollama Server
1. Choose your model from the [Ollama library](https://ollama.com/library).
2. Run your model with this command:
```sh
ollama run <model-name>
```
3. According to the [Ollama documentation on OpenAI compatibility](https://github.com/ollama/ollama/blob/main/docs/openai.md), you can connect to the Ollama server using the web address `http://localhost:11434/v1/chat/completions`. To do this, change the `openai.json` file in the `~/jan/engines` folder to add the Ollama server's full web address:
```json title="~/jan/engines/openai.json"
{
"full_url": "http://localhost:11434/v1/chat/completions"
}
```
### Step 2: Model Configuration
1. Navigate to the `~/jan/models` folder.
2. Create a folder named `(ollam-modelname)`, for example, `lmstudio-phi-2`.
3. Create a `model.json` file inside the folder including the following configurations:
- Set the `id` property to the model name as Ollama model name.
- Set the `format` property to `api`.
- Set the `engine` property to `openai`.
- Set the `state` property to `ready`.
```json title="~/jan/models/llama2/model.json"
{
"sources": [
{
"filename": "llama2",
"url": "https://ollama.com/library/llama2"
}
],
"id": "llama2",
"object": "model",
"name": "Ollama - Llama2",
"version": "1.0",
"description": "Llama 2 is a collection of foundation language models ranging from 7B to 70B parameters.",
"format": "api",
"settings": {},
"parameters": {},
"metadata": {
"author": "Meta",
"tags": ["General", "Big Context Length"]
},
"engine": "openai"
}
```
:::note
For more details regarding the `model.json` settings and parameters fields, please see [here](../models/integrate-remote.mdx#modeljson).
:::
### Step 3: Start the Model
1. Restart Jan and navigate to the **Hub**.
2. Locate your model and click the **Use** button. | https://jan.ai/guides/integration/ollama |
# Azure OpenAI
## How to Integrate Azure OpenAI with Jan
The [Azure OpenAI Service](https://learn.microsoft.com/en-us/azure/ai-services/openai/overview?source=docs) offers robust APIs, making it simple for you to incorporate OpenAI's language models into your applications. You can integrate Azure OpenAI with Jan by following the steps below:
### Step 1: Configure Azure OpenAI Service API Key
1. Set up and deploy the Azure OpenAI Service.
2. Once you've set up and deployed Azure OpenAI Service, you can find the endpoint and API key in [Azure OpenAI Studio](https://oai.azure.com/) under `Chat` > `View code`.
3. Set up the endpoint and API key for Azure OpenAI Service in the `~/jan/engines/openai.json` file.
```json title="~/jan/engines/openai.json"
{
// https://hieujan.openai.azure.com/openai/deployments/gpt-35-hieu-jan/chat/completions?api-version=2023-07-01-preview
"full_url": "https://<your-resource-name>.openai.azure.com/openai/deployments/<your-deployment-name>/chat/completions?api-version=<api-version>",
"api_key": "<your-api-key>"
}
```
### Step 2: Model Configuration
1. Go to the `~/jan/models` directory.
2. Make a new folder called `(your-deployment-name)`, for example `gpt-35-hieu-jan`.
3. Create a `model.json` file inside the folder with the specified configurations:
- Match the `id` property with both the folder name and your deployment name.
- Set the `format` property as `api`.
- Choose `openai` for the `engine` property.
- Set the `state` property as `ready`.
```json title="~/jan/models/gpt-35-hieu-jan/model.json"
{
"sources": [
{
"filename": "azure_openai",
"url": "https://hieujan.openai.azure.com"
}
],
"id": "gpt-35-hieu-jan",
"object": "model",
"name": "Azure OpenAI GPT 3.5",
"version": "1.0",
"description": "Azure Open AI GPT 3.5 model is extremely good",
"format": "api",
"settings": {},
"parameters": {},
"metadata": {
"author": "OpenAI",
"tags": ["General", "Big Context Length"]
},
"engine": "openai"
}
```
:::note
For more details regarding the `model.json` settings and parameters fields, please see [here](../models/integrate-remote.mdx#modeljson).
:::
### Step 3: Start the Model
1. Restart Jan and go to the Hub.
2. Find your model in Jan application and click on the Use button. | https://jan.ai/guides/integration/azure |
# Discord
## How to Integrate Discord Bot with Jan
Discord bot can enhances your discord server interactions. By integrating Jan with it, you can significantly boost responsiveness and user engaggement in your discord server.
To integrate Jan with a Discord bot, follow the steps below:
### Step 1: Clone the repository
To make this integration successful, it is necessary to clone the discord bot's [repository](https://github.com/jakobdylanc/discord-llm-chatbot).
### Step 2: Install the Required Libraries
After cloning the repository, run the following command:
```sh
pip install -r requirements.txt
```
### Step 3: Set the Environment
1. Create a copy of `.env.example`.
2. Change the name to `.env`.
3. Set the environment with the following options:
| Setting | Instructions |
| | https://jan.ai/guides/integration/discord |
# LM Studio
## How to Integrate LM Studio with Jan
[LM Studio](https://lmstudio.ai/) enables you to explore, download, and run local Large Language Models (LLMs). You can integrate Jan with LM Studio using two methods:
1. Integrate the LM Studio server with Jan UI
2. Migrate your downloaded model from LM Studio to Jan.
To integrate LM Studio with Jan follow the steps below:
:::note
In this guide, we're going to show you how to connect Jan to [LM Studio](https://lmstudio.ai/) using the second method. We'll use the [Phi 2 - GGUF](https://huggingface.co/TheBloke/phi-2-GGUF) model from Hugging Face as our example.
:::
### Step 1: Server Setup
1. Access the `Local Inference Server` within LM Studio.
2. Select your desired model.
3. Start the server after configuring the port and options.
4. Update the `openai.json` file in `~/jan/engines` to include the LM Studio server's full URL.
```json title="~/jan/engines/openai.json"
{
"full_url": "http://localhost:<port>/v1/chat/completions"
}
```
:::tip
Replace `(port)` with your chosen port number. The default is 1234.
:::
### Step 2: Model Configuration
1. Navigate to `~/jan/models`.
2. Create a folder named `lmstudio-(modelname)`, like `lmstudio-phi-2`.
3. Inside, create a `model.json` file with these options:
- Set `format` to `api`.
- Specify `engine` as `openai`.
- Set `state` to `ready`.
```json title="~/jan/models/lmstudio-phi-2/model.json"
{
"sources": [
{
"filename": "phi-2-GGUF",
"url": "https://huggingface.co/TheBloke/phi-2-GGUF"
}
],
"id": "lmstudio-phi-2",
"object": "model",
"name": "LM Studio - Phi 2 - GGUF",
"version": "1.0",
"description": "TheBloke/phi-2-GGUF",
"format": "api",
"settings": {},
"parameters": {},
"metadata": {
"author": "Microsoft",
"tags": ["General", "Big Context Length"]
},
"engine": "openai"
}
```
:::note
For more details regarding the `model.json` settings and parameters fields, please see [here](../models/integrate-remote.mdx#modeljson).
:::
### Step 3: Starting the Model
1. Restart Jan and proceed to the **Hub**.
2. Locate your model and click **Use** to activate it.
## Migrating Models from LM Studio to Jan (version 0.4.6 and older)
### Step 1: Model Migration
1. In LM Studio, navigate to `My Models` and access your model folder.
2. Copy the model folder to `~/jan/models`.
3. Ensure the folder name matches the model name in the `.gguf` filename. Rename as necessary.
### Step 2: Activating the Model
1. Restart Jan and navigate to the **Hub**, where the model will be automatically detected.
2. Click **Use** to use the model.
## Direct Access to LM Studio Models from Jan (version 0.4.7+)
Starting from version 0.4.7, Jan enables direct import of LM Studio models using absolute file paths.
### Step 1: Locating the Model Path
1. Access `My Models` in LM Studio and locate your model folder.
2. Obtain the absolute path of your model.
### Step 2: Model Configuration
1. Go to `~/jan/models`.
2. Create a folder named `(modelname)` (e.g., `phi-2.Q4_K_S`).
3. Inside, craft a `model.json` file:
- Set `id` to match the folder name.
- Use the direct binary download link ending in `.gguf` as the `url`. You can now use the absolute filepath of the model file.
- Set `engine` as `nitro`.
```json
{
"object": "model",
"version": 1,
"format": "gguf",
"sources": [
{
"filename": "phi-2.Q4_K_S.gguf",
"url": "<absolute-path-of-model-file>"
}
],
"id": "phi-2.Q4_K_S",
"name": "phi-2.Q4_K_S",
"created": 1708308111506,
"description": "phi-2.Q4_K_S - user self import model",
"settings": {
"ctx_len": 4096,
"embedding": false,
"prompt_template": "{system_message}\n### Instruction: {prompt}\n### Response:",
"llama_model_path": "phi-2.Q4_K_S.gguf"
},
"parameters": {
"temperature": 0.7,
"top_p": 0.95,
"stream": true,
"max_tokens": 2048,
"stop": ["<endofstring>"],
"frequency_penalty": 0,
"presence_penalty": 0
},
"metadata": {
"size": 1615568736,
"author": "User",
"tags": []
},
"engine": "nitro"
}
```
:::warning
For Windows users, ensure to include double backslashes in the URL property, such as `C:\\Users\\username\\filename.gguf`.
:::
### Step 3: Starting the Model
1. Restart Jan and proceed to the **Hub**.
2. Locate your model and click **Use** to activate it. | https://jan.ai/guides/integration/lmstudio |
# Open Interpreter
## How to Integrate Open Interpreter with Jan
[Open Interpreter](https://github.com/KillianLucas/open-interpreter/) lets LLMs run code (Python, Javascript, Shell, and more) locally. You can chat with Open Interpreter through a ChatGPT-like interface in your terminal by running `interpreter` after installing. To integrate Open Interpreter with Jan, follow the steps below:
### Step 1: Install Open Interpreter
1. Install Open Interpreter by running:
```sh
pip install open-interpreter
```
2. A Rust compiler is required to install Open Interpreter. If not already installed, run the following command or go to [this page](https://rustup.rs/) if you are running on windows:
```zsh
sudo apt install rustc
```
### Step 2: Configure Jan's Local API Server
Before using Open Interpreter, configure the model in `Settings` > `My Model` for Jan and activate its local API server.
#### Enabling Jan API Server
1. Click the `<>` button to access the **Local API Server** section in Jan.
2. Configure the server settings, including **IP Port**, **Cross-Origin-Resource-Sharing (CORS)**, and **Verbose Server Logs**.
3. Click **Start Server**.
### Step 3: Set the Open Interpreter Environment
1. For integration, provide the API Base (`http://localhost:1337/v1`) and the model ID (e.g., `mistral-ins-7b-q4`) when running Open Interpreter. For example see the code below:
```zsh
interpreter --api_base http://localhost:1337/v1 --model mistral-ins-7b-q4
```
> **Open Interpreter is now ready for use!** | https://jan.ai/guides/integration/openinterpreter |
# OpenRouter
## How to Integrate OpenRouter with Jan
[OpenRouter](https://openrouter.ai/docs#quick-start) is a tool that gathers AI models. Developers can utilize its API to engage with diverse large language models, generative image models, and generative 3D object models.
To connect Jan with OpenRouter for accessing remote Large Language Models (LLMs) through OpenRouter, you can follow the steps below:
### Step 1: Configure OpenRouter API key
1. Find your API keys in the [OpenRouter API Key](https://openrouter.ai/keys).
2. Set the OpenRouter API key in `~/jan/engines/openai.json` file.
### Step 2: MModel Configuration
1. Go to the directory `~/jan/models`.
2. Make a new folder called `openrouter-(modelname)`, like `openrouter-dolphin-mixtral-8x7b`.
3. Inside the folder, create a `model.json` file with the following settings:
- Set the `id` property to the model id obtained from OpenRouter.
- Set the `format` property to `api`.
- Set the `engine` property to `openai`.
- Ensure the `state` property is set to `ready`.
```json title="~/jan/models/openrouter-dolphin-mixtral-8x7b/model.json"
{
"sources": [
{
"filename": "openrouter",
"url": "https://openrouter.ai/"
}
],
"id": "cognitivecomputations/dolphin-mixtral-8x7b",
"object": "model",
"name": "Dolphin 2.6 Mixtral 8x7B",
"version": "1.0",
"description": "This is a 16k context fine-tune of Mixtral-8x7b. It excels in coding tasks due to extensive training with coding data and is known for its obedience, although it lacks DPO tuning. The model is uncensored and is stripped of alignment and bias. It requires an external alignment layer for ethical use. Users are cautioned to use this highly compliant model responsibly, as detailed in a blog post about uncensored models at erichartford.com/uncensored-models.",
"format": "api",
"settings": {},
"parameters": {},
"metadata": {
"tags": ["General", "Big Context Length"]
},
"engine": "openai"
}
```
:::note
For more details regarding the `model.json` settings and parameters fields, please see [here](../models/integrate-remote.mdx#modeljson).
:::
### Step 3 : Start the Model
1. Restart Jan and go to the **Hub**.
2. Find your model and click on the **Use** button. | https://jan.ai/guides/integration/openrouter |
# Continue
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
## How to Integrate with Continue VS Code
[Continue](https://continue.dev/docs/intro) is an open-source autopilot compatible with Visual Studio Code and JetBrains, offering the simplest method to code with any LLM (Local Language Model).
To integrate Jan with a local AI language model, follow the steps below:
### Step 1: Installing Continue on Visal Studio Code
Follow this [guide to install the Continue extension on Visual Studio Code](https://continue.dev/docs/quickstart)
### Step 2: Enable the Jan API Server
To set up Continue for use with Jan's Local Server, you must activate the Jan API Server with your chosen model.
1. Press the `<>` button. Jan will take you to the **Local API Server** section.
2. Setup the server, which includes the **IP Port**, **Cross-Origin-Resource-Sharing (CORS)** and **Verbose Server Logs**.
3. Press the **Start Server** button
### Step 3: Configure Continue to Use Jan's Local Server
1. Go to the `~/.continue` directory.
<Tabs>
<TabItem value="mac" label="Mac" default>
```sh
cd ~/.continue
```
</TabItem>
<TabItem value="windows" label="Windows">
```sh
C:/Users/<your_user_name>/.continue
```
</TabItem>
<TabItem value="linux" label="Linux">
```sh
cd ~/.continue
```
</TabItem>
</Tabs>
```json title="~/.continue/config.json"
{
"models": [
{
"title": "Jan",
"provider": "openai",
"model": "mistral-ins-7b-q4",
"apiKey": "EMPTY",
"apiBase": "http://localhost:1337"
}
]
}
```
2. Ensure the file has the following configurations:
- Ensure `openai` is selected as the `provider`.
- Match the `model` with the one enabled in the Jan API Server.
- Set `apiBase` to `http://localhost:1337`.
- Leave the `apiKey` field to `EMPTY`.
### Step 4: Ensure the Using Model Is Activated in Jan
1. Navigate to `Settings` > `Models`.
2. Activate the model you want to use in Jan by clicking the **three dots (⋮)** and select **Start Model**.
## Try out Jan integration with Continue in Visual Studio Code
### 1. Asking questions about the code
1. Highlight a code snippet and press `Command + Shift + M` to open the Left Panel.
2. Select Jan at the bottom and ask a question about the code, for example, `Explain this code`.
### 2. Editing the code with the help of a large language model
1. Select a code snippet and use `Command + Shift + L`.
2. Enter your editing request, such as `Add comments to this code`. | https://jan.ai/guides/integration/vscode |
# llama.cpp
## Quicklinks
- Jan Framework [Extension Code](https://github.com/janhq/jan/tree/main/extensions/inference-nitro-extension)
- ggerganov/llama.pp [Source URL](https://github.com/ggerganov/llama.cpp)
- [Productized Wrapper](https://nitro.jan.ai/): a bit lower effort to use out of the box | https://jan.ai/integrations/llamacpp |
# TensorRT-LLM
## Quicklinks
- Jan Framework [Extension Code](https://github.com/janhq/jan/tree/main/extensions/inference-triton-trtllm-extension)
- TensorRT [Source URL](https://github.com/NVIDIA/TensorRT-LLM) | https://jan.ai/integrations/tensorrt |
# OpenAI
## Quicklinks
- Jan Framework [Extension Code](https://github.com/janhq/jan/tree/main/extensions/inference-openai-extension)
- OpenAI API [Reference Docs](https://platform.openai.com/docs/api-reference) | https://jan.ai/integrations/openai |
# Jan's Community
## Socials
- [Discord](https://discord.gg/SH3DGmUs6b)
- [X](https://twitter.com/janframework)
- [HuggingFace](https://huggingface.co/janhq)
- [LinkedIn](https://www.linkedin.com/company/janframework/)
## Community Run
- [Reddit](https://www.reddit.com/r/janframework/)
## Careers
- [Jobs](https://janai.bamboohr.com/careers)
## Newsletter
<iframe
width="100%"
height="600px"
src="https://c0c7c086.sibforms.com/serve/MUIFAEWm49nC1OONIibGnlV44yxPMw6Fu1Yc8pK7nP3jp7rZ6rvrb5uOmCD8IIhrRj6-h-_AYrw-sz7JNpcUZ8LAAZoUIOjGmSvNWHwoFhxX5lb-38-fxXj933yIdGzEMBZJv4Nu2BqC2A4uThDGmjM-n_DZBV1v_mKbTcVUWVUE7VutWhRqrDr69IWI4SgbuIMACkcTiWX8ZNLw"
frameborder="0"
scrolling="auto"
allowfullscreen
style={{
margin: 'auto',
maxWidth: '100%',
}}
></iframe> | https://jan.ai/community |
# "Nov 23: Nvidia GenAI Day"
## Nvidia GenAI Innovation Day
Jan will be at Nvidia's GenAI Innovation Day in Nov '23, focusing on Enterprise use-cases of LLMs.
### Location
- JW Marriott Hanoi Hotel
- 8:30am November 8th 2023
- Registration: [https://gmcgroup.com.vn/nvidia-genai-event/](https://gmcgroup.com.vn/nvidia-genai-event/)
### Programme
 | https://jan.ai/events/nvidia-llm-day-nov-23 |
# "Jan's AI Hacker House (Ho Chi Minh City)"
🎉 Join us at our Friday Launch Party for an evening of AI talks from other builders! [(RSVP here)](https://jan-launch-party.eventbrite.sg/) 🎉
## Ho Chi Minh City
[Jan's Hacker House](https://jan.ai) is a 4-day event where we host an open AI Hacker House and invite the local AI community to join us. There is fast wifi, free snacks, drinks and pizza.
We also host a series of talks, workshops and social events at night. We usually start off the week with a "Intro to LLMs" that targets local university students, and then progress to more in-depth technical and research areas.
Jan is a fully remote team. We use the money we save from not having an office, to hold Hack Weeks where we meet in a city, eat pizza and work to ship major releases.
### Date & Time
- 24-27 October 2023
### Location
- Districts 1 & 3, Ho Chi Minh City
- Exact location in Evenbrite (see below)
## Agenda
To help us manage RSVPs, please use the Eventbrite links below to RSVP for each event.
| Day | Eventbrite Link | Signups |
| | https://jan.ai/events/hcmc-oct23 |
# About Jan
Jan is a [open-source](https://en.wikipedia.org/wiki/Open_source), [local-first](https://www.inkandswitch.com/local-first/) tool to [create, customize and use AI](https://www.gatesnotes.com/AI-agents) for everyday tasks.
You can:
- Run locally using [open-source LLMs](https://huggingface.co/models?pipeline_tag=text-generation) or connect to cloud AIs like [ChatGPT](https://openai.com/blog/openai-api) or [Google](https://ai.google.dev/)
- Fine-tune AI with specific knowledge
- Search the web and other databases
- Connect AI to your everyday tools and (with your permission) do work on your behalf
Longer-term, Jan is building a cognitive framework for future robots. We envision a world where we have personal or company robots that we continually improve and customize, growing together with us.
## Why do we exist
At Jan, our mission is to advance human-machine collaboration. We achieve this through delivering the best open-source, local-first tools to allow users to run, customize and tinker with AI.
## What's different about it?
| | Status Quo | Jan |
| | https://jan.ai/about |
# Roadmap
- [ ] [Immediate Roadmap on Github](https://github.com/orgs/janhq/projects/5/views/16)
- [ ] [Longer-term Roadmap on Discord](https://discord.gg/Ey62mynnYr) | https://jan.ai/about/roadmap |
# Jan's Vision for 2035
[Jan 2035: A Robotics Company](https://hackmd.io/QIWyYbNNQVWVbupuI3kjAA)
We only have 2 planning parameters:
- 10 year vision
- 2 week sprint
And we measure our success on Quarterly OKRs | https://jan.ai/about/2035 |
# Pricing
| $0 | $1 | Enterprise |
| | https://jan.ai/pricing |
# Support
- Bugs & requests: file a GitHub ticket [here](https://github.com/janhq/jan/issues)
- For discussion: join our Discord [here](https://discord.gg/FTk2MvZwJH)
- For business inquiries: email hello@jan.ai
- For jobs: please email hr@jan.ai
| support.md |
# Privacy Policy
Jan is committed to protecting your privacy and ensuring that your personal information is handled in a safe and responsible way. This policy outlines how we collect, store, and use your personal information when you use our mobile application.
## Data Collection and Usage
When you use Jan, we may collect certain information about you, including your name, email address, and other personal information that you provide to us. We use this information to provide you with the best possible experience when using our app.
We may also collect certain non-personal information, such as your device type, operating system, and app usage data. This information is used to improve our app and to provide you with a better user experience.
## Data Sharing
We do not share your personal information with third parties except as required by law or as necessary to provide you with the services you have requested. We may share non-personal information with third parties for the purpose of improving our app and providing you with a better user experience.
## Data Security
We take the security of your personal information seriously and have implemented appropriate technical and organizational measures to protect your personal information from unauthorized access, disclosure, or misuse.
## Your Choices
You have the right to access, update, and delete your personal information at any time. You may also opt-out of receiving marketing communications from us by following the unsubscribe link included in our emails.
## Contact Us
If you have any questions or concerns about our privacy policy, please contact us at hello@jan.ai.
| privacy.md |
# Architecture
:::warning
This page is still under construction, and should be read as a scratchpad
:::
## Overview
- Jan has a modular architecture and is largely built on top of its own modules.
- Jan uses a local [file-based approach](/developer/file-based) for data persistence.
- Jan provides an Electron-based [Desktop UI](https://github.com/janhq/jan).
- Jan provides an embeddable inference engine, written in C++, called [Nitro](https://nitro.jan.ai/docs/).
## Extensions
Jan has an Extensions API inspired by VSCode. In fact, most of Jan's core services are built as extensions.
Jan supports the following OpenAI compatible extensions:
| Jan Module | Description | API Docs |
| | https://jan.ai/developer/architecture |
# User Interface
:::warning
This page is still under construction, and should be read as a scratchpad
:::
Jan provides a UI Kit for customize the UI for your use case. This means you can personalize the entire application according to your own brand and visual styles.
This page gives you an overview of how to customize the UI.
You can see some of the user interface components when you first open Jan.
To Link:
- Ribbon
- LeftSidebar
- Main
- RightSidebar
- StatusBar
## Views
TODO: add a better image.
### Ribbon
Assistants shortcuts and Modules settings show up here.
```js
import .. from "@jan"
sample code here
```
### LeftSidebar
Conversation threads show up here. This is customizable, so custom assistants can add additional menu items here.
```js
import .. from "@jan"
sample code here
```
### Main
The main view for interacting with assistants. This is customizable, so custom assistants can add in additional UI components. By default, this is a chat thread with assistants.
```js
import .. from "@jan"
sample code here
```
### RightSidebar
A "settings" view for each thread. Users should be able to edit settings or other configs to customize the assistant experience within each thread.
```js
import .. from "@jan"
sample code here
```
### StatusBar
A global status bar that shows processes, hardware/disk utilization and more.
```js
import .. from "@jan"
sample code here
``` | https://jan.ai/developer/ui |
# Installation and Prerequisites
## Requirements
### Hardware Requirements
Ensure your system meets the following specifications to guarantee a smooth development experience:
- Hardware Requirements
### System Requirements
Make sure your operating system meets the specific requirements for Jan development:
- [Windows](../../install/windows/#system-requirements)
- [MacOS](../../install/mac/#system-requirements)
- [Linux](../../install/linux/#system-requirements)
## Prerequisites
- [Node.js](https://nodejs.org/en/) (version 20.0.0 or higher)
- [yarn](https://yarnpkg.com/) (version 1.22.0 or higher)
- [make](https://www.gnu.org/software/make/) (version 3.81 or higher)
## Instructions
1. **Clone the Repository:**
```bash
git clone https://github.com/janhq/jan
cd jan
git checkout -b DESIRED_BRANCH
```
2. **Install Dependencies**
```bash
yarn install
```
3. **Run Development and Use Jan Desktop**
```bash
make dev
```
This command starts the development server and opens the Jan Desktop app.
## For Production Build
```bash
# Do steps 1 and 2 in the previous section
# Build the app
make build
```
This will build the app MacOS (M1/M2/M3) for production (with code signing already done) and place the result in `/electron/dist` folder.
## Troubleshooting
If you run into any issues due to a broken build, please check the [Stuck on a Broken Build](../../troubleshooting/stuck-on-broken-build) guide. | https://jan.ai/developer/prereq |
# File-based Approach
:::warning
This page is still under construction, and should be read as a scratchpad
:::
Jan use the local filesystem for data persistence, similar to VSCode. This allows for composability and tinkerability.
```yaml
janroot/ # Jan's root folder (e.g. ~/jan)
models/ # For raw AI models
threads/ # For conversation history
assistants/ # For AI assistants' configs, knowledge, etc.
```
```yaml
/models
/modelA
model.json # Default model settings
llama-7b-q4.gguf # Model binaries
/threads
/jan-unixstamp
thread.json # thread metadata (e.g. subject)
messages.jsonl # messages
files/ # RAG
/assistants
/jan # A default assistant that can use all models
assistant.json # Assistant configs (see below)
package.json # Import npm modules, e.g. Langchain, Llamaindex
/src # For custom code
index.js # Entrypoint
# `/threads` at root level
# `/models` at root level
/shakespeare # Example of a custom assistant
assistant.json
package.json
/threads # Assistants remember conversations in the future
/models # Users can upload custom models
```
## Data Dependencies
```mermaid
graph LR
A1[("A User Integrators")] -->|uses| B1[assistant]
B1 -->|persist conversational history| C1[("thread A")]
B1 -->|executes| D1[("built-in tools as module")]
B1 -.->|uses| E1[model]
E1 -.->|model.json| D1
D1 --> F1[retrieval]
F1 -->|belongs to| G1[("web browsing")]
G1 --> H1[Google]
G1 --> H2[Duckduckgo]
F1 -->|belongs to| I1[("API calling")]
F1 --> J1[("knowledge files")]
```
- User/ Integrator
- Assistant object
- Model object
- Thread object
- Built-in tool object | https://jan.ai/developer/file-based |
# Overview
The following docs are aimed at developers who want to build extensions on top of the Jan Framework.
:::tip
If you are interested to **contribute to the framework's Core SDK itself**, like adding new drivers, runtimes, and infrastructure level support, please refer to [framework docs](/developer/framework) instead.
:::
## Extensions
Jan an **extensible framework** (like VSCode or Obsidian) that lets you build, customize and run AI applications everywhere, with an emphasis on local first.
Extensions are automatically available across Mac, Windows, Linux Desktops.
Extensions can also be made available in local API server-mode, which can be deployed on any VM.
### Building Extensions
This framework is packaged and regularly published as an SDK through [npm](https://www.npmjs.com/org/janhq) and [pip](https://pypi.org/).
The framework provides built-in support for the following:
- Native OS integrations with Electron and Chromium
- Native server integrations with Nodejs
- Native mobile integrations with Capacitor (coming soon)
:::tip
Build once, deploy everywhere
:::
## Jan in Action
The [Jan Desktop client](https://github.com/janhq/jan/releases) is built with Jan SDK. This means you can customize any part of the application from the branding to the features, and truly make it your own.
[Gif: show desktop & server side by side] | https://jan.ai/developer |
# Your First Extension
:::caution
This is currently under development.
:::
In this guide, we'll walk you through the process of building your first extension and integrating it into Jan.
## Steps to Create Your First Extension
To create your own extension, you can follow the steps below:
1. Click the **Use this template** button at the top of the [extension-template repository](https://github.com/janhq/extension-template).
2. Select **Create a new repository**.
3. Choose an owner and name for your new repository.
4. Click **Create repository**.
5. Clone your new repository to your local machine.
## Initial Setup
After you have cloned the repository to your local machine or codespace, you will need to perform some initial setup steps before you can develop your extension.
:::info
You will need to have a reasonably modern version of [Node.js](https://nodejs.org) handy. If you are using a version manager like [`nodenv`](https://github.com/nodenv/nodenv) or [`nvm`](https://github.com/nvm-sh/nvm), you can run `nodenv install` in the root of your repository to install the version specified in
[`package.json`](https://github.com/janhq/extension-template/blob/main/package.json). Otherwise, 20.x or later should work!
:::
1. :hammer_and_wrench: Install the dependencies
```bash
npm install
```
2. :building_construction: Package the TypeScript for distribution
```bash
npm run bundle
```
3. :white_check_mark: Check your artifact
There will be a `.tgz` file in your extension directory now. This is the file you will need to import into Jan. You can import this file into Jan by following the instructions in the [Import Extension](https://jan.ai/guides/using-extensions/import-extensions/) guide.
## Update the Extension Metadata
The [`package.json`](https://github.com/janhq/extension-template/blob/main/package.json) file defines metadata about your extension, such as extension name, main entry, description and version.
When you copy this repository, update `package.json` with the name, and description for your extension.
## Update the Extension Code
The [`src/`](https://github.com/janhq/extension-template/tree/main/src) directory is the heart of your extension! This contains the source code that will be run when your extension extension functions are invoked. You can replace the contents of this directory with your own code.
There are a few things to keep in mind when writing your extension code:
- Most Jan Extension functions are processed asynchronously.
In `index.ts`, you will see that the extension function will return a `Promise<any>`.
```typescript
import { core } from "@janhq/core";
function onStart(): Promise<any> {
return core.invokePluginFunc(MODULE_PATH, "run", 0);
}
```
For more information about the Jan Extension Core module, see the [documentation](https://github.com/janhq/jan/blob/main/core/README.md).
Now, go ahead and start customizing your extension! Happy coding! | https://jan.ai/developer/build-extension/your-first-extension/ |
# Your First Engine
:::caution
This is currently under development.
:::
A quickstart on how to integrate tensorrt llm | https://jan.ai/developer/build-engine/your-first-engine/ |
# Framework
The following low-level docs are aimed at core contributors.
We cover how to contribute to the core framework (aka the `Core SDK`).
:::tip
If you are interested to **build on top of the framework**, like creating assistants or adding app level extensions, please refer to [developer docs](/developer) instead.
:::
## Jan Framework
At its core, Jan is a **cross-platform, local-first and AI native framework** that can be used to build anything.
### Extensions
Ultimately, we aim for a `VSCode` or `Obsidian` like SDK that allows **devs to build and customize complex and ethical AI applications for any use case**, in less than 30 minutes.
In fact, the current Jan [Desktop Client](https://jan.ai/) is actually just a specific set of extensions & integrations built on top of this framework.
:::tip
We encourage devs to fork, customize, and open source your improvements for the greater community.
:::
### Cross Platform
Jan follows [Clean Architecture](https://blog.cleancoder.com/uncle-bob/2012/08/13/the-clean-architecture.html) to the best of our ability. Though leaky abstractions remain (we're a fast moving, open source codebase), we do our best to build an SDK that allows devs to **build once, deploy everywhere.**
**Supported Runtimes:**
- `Node Native Runtime`, good for server side apps
- `Electron Chromium`, good for Desktop Native apps
- `Capacitor`, good for Mobile apps (planned, not built yet)
- `Python Runtime`, good for MLOps workflows (planned, not built yet)
**Supported OS & Architectures:**
- Mac Intel & Silicon
- Windows
- Linux (through AppImage)
- Nvidia GPUs
- AMD ROCm (coming soon)
Read more:
- [Code Entrypoint](https://github.com/janhq/jan/tree/main/core)
- [Dependency Inversion](https://en.wikipedia.org/wiki/Dependency_inversion_principle)
### Local First
Jan's data persistence happens on the user's local filesystem.
We implemented abstractions on top of `fs` and other core modules in an opinionated way, s.t. user data is saved in a folder-based framework that lets users easily package, export, and manage their data.
Future endeavors on this front include cross device syncing, multi user experience, and more.
Long term, we want to integrate with folks working on [CRDTs](https://www.inkandswitch.com/local-first/), e.g. [Socket Runtime](https://www.theregister.com/2023/04/11/socket_runtime/) to deeply enable local-first software.
Read more:
- [Folder-based wrappers entrypoint](https://github.com/janhq/jan/blob/main/core/src/fs.ts)
- [Piping Node modules across infrastructures](https://github.com/janhq/jan/tree/main/core/src/node)
:::caution
Our local first approach at the moment needs a lot of work. Please don't hesitate to refactor as you make your way through the codebase.
:::
### AI Native
We believe all software applications can be natively supercharged with AI primitives and embedded AI servers.
Including:
- OpenAI Compatible AI [types](https://github.com/janhq/jan/tree/main/core/src/types) and [core extensions](https://github.com/janhq/jan/tree/main/core/src/extensions) to support common functionality like making an inference call.
- Multiple inference engines through [extensions, integrations & wrappers](https://github.com/janhq/jan/tree/main/extensions/inference-nitro-extension) _On this, we'd like to appreciate the folks at [llamacpp](https://github.com/ggerganov/llama.cpp) and [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) for. To which we'll continue to make commits & fixes back upstream._
- [Code Entrypoint](https://github.com/janhq/jan/tree/main/core/src/api)
## Fun Project Ideas
Beyond the current Jan client and UX, the Core SDK can be used to build many other AI-powered and privacy preserving applications.
- `Game engine`: For AI enabled character games, procedural generation games
- `Health app`: For a personal healthcare app that improves habits
- Got ideas? Make a PR into this docs page!
If you are interested to tackle these issues, or have suggestions for integrations and other OSS tools we can use, please hit us up in [Discord](https://discord.gg/5rQ2zTv3be).
:::caution
Our open source license is copy left, which means we encourage forks to stay open source, and allow core contributors to merge things upstream.
::: | https://jan.ai/developer/framework/ |
# Chats
:::caution
This is currently under development.
:::
## Overview
In Jan, `chats` are LLM responses in the form of OpenAI compatible `chat completion objects`.
- Models take a list of messages and return a model-generated response as output.
- An [OpenAI Chat API](https://platform.openai.com/docs/api-reference/chat) compatible endpoint at `localhost:1337/v1/chats`.
## Folder Structure
Chats are stateless, thus are not saved in `janroot`. Any content and relevant metadata from calling this endpoint is extracted and persisted through [Messages](/docs/engineering/messages).
## API Reference
Jan's Chat API is compatible with [OpenAI's Chat API](https://platform.openai.com/docs/api-reference/chat).
See [Jan Chat API](https://jan.ai/api-reference/#tag/Chat-Completion)
## Implementation
Under the hood, the `/chat` endpoint simply reroutes an existing endpoint from [Nitro server](https://nitro.jan.ai). Nitro is a lightweight & local inference server, written in C++ and embedded into the Jan app. See [Nitro documentation](https://nitro.jan.ai/docs). | https://jan.ai/developer/framework/engineering/chats |
# Engine
:::caution
Currently Under Development
:::
## Overview
In the Jan application, engines serve as primary entities with the following capabilities:
- Engine will be installed through `inference-extensions`.
- Models will depend on engines to do [inference](https://en.wikipedia.org/wiki/Inference_engine).
- Engine configuration and required metadata will be stored in a json file.
## Folder Structure
- Default parameters for engines are stored in JSON files located in the `/engines` folder.
- These parameter files are named uniquely with `engine_id`.
- Engines are referenced directly using `engine_id` in the `model.json` file.
```yaml
jan/
engines/
nitro.json
openai.json
.....
```
## Engine Default Parameter Files
- Each inference engine requires default parameters to function in cases where user-provided parameters are absent.
- These parameters are stored in JSON files, structured as simple key-value pairs.
### Example
Here is an example of an engine file for `engine_id` `nitro`:
```js
{
"ctx_len": 512,
"ngl": 100,
"embedding": false,
"n_parallel": 1,
"cont_batching": false
"prompt_template": "<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant"
}
```
For detailed engine parameters, refer to: [Nitro's Model Settings](https://nitro.jan.ai/features/load-unload#table-of-parameters)
## Adding an Engine
- Engine parameter files are automatically generated upon installing an `inference-extension` in the Jan application. | https://jan.ai/developer/framework/engineering/engine |
# Engineering Specs
import DocCardList from "@theme/DocCardList";
<DocCardList className="DocCardList--no-description" />
Talk about CoreSDK here | https://jan.ai/developer/engineering |
# Prompts
- [ ] /prompts folder
- [ ] How to add to prompts
- [ ] Assistants can have suggested Prompts | https://jan.ai/developer/framework/engineering/prompts |
# Threads
:::caution
This is currently under development.
:::
## Overview
`Threads` are conversations between an `assistant` and the user:
- Users can tweak `model` params and `assistant` behavior within each thread.
- Users can import and export threads.
- An [OpenAI Thread API](https://platform.openai.com/docs/api-reference/threads) compatible endpoint at `localhost:1337/v1/threads`.
## Folder Structure
- Threads are saved in the `/threads` folder.
- Threads are organized by folders, one for each thread, and can be easily zipped, exported, and cleared.
- Thread folders follow the naming: `assistant_id` + `thread_created_at`.
- Thread folders also contain `messages.jsonl` files. See [messages](/docs/engineering/messages).
```yaml
janroot/
threads/
assistant_name_unix_timestamp/ # Thread `ID`
thread.json
```
## `thread.json`
- Each `thread` folder contains a `thread.json` file, which is a representation of a thread.
- `thread.json` contains metadata and model parameter overrides.
- There are no required fields.
### Example
Here's a standard example `thread.json` for a conversation between the user and the default Jan assistant.
```js
"id": "thread_....", // Defaults to foldername
"object": "thread", // Defaults to "thread"
"title": "funny physics joke", // Defaults to ""
"assistants": [
{
"assistant_id": "jan", // Defaults to "jan"
"model": { // Defaults to the currently active model (can be changed before thread is begun)
"id": "...",
"settings": {}, // Defaults to and overrides assistant.json's "settings" (and if none, then model.json "settings")
"parameters": {}, // Defaults to and overrides assistant.json's "parameters" (and if none, then model.json "parameters")
}
},
],
"created": 1231231 // Defaults to file creation time
"metadata": {}, // Defaults to {}
```
## API Reference
Jan's Threads API is compatible with [OpenAI's Threads API](https://platform.openai.com/docs/api-reference/threads), with additional methods for managing threads locally.
See [Jan Threads API](https://jan.ai/api-reference#tag/Threads). | https://jan.ai/developer/framework/engineering/threads |
# "Files"
:::warning
Draft Specification: functionality has not been implemented yet.
:::
Files can be used by `threads`, `assistants` and `fine-tuning`
> Equivalent to: https://platform.openai.com/docs/api-reference/files
## Files Object
- Equivalent to: https://platform.openai.com/docs/api-reference/files
- Note: OAI's struct doesn't seem very well designed
- `files.json`
```js
{
// Public properties (OpenAI Compatible: https://platform.openai.com/docs/api-reference/files/object)
"id": "file-BK7bzQj3FfZFXr7DbL6xJwfo",
"object": "file",
"bytes": 120000,
"created_at": 1677610602,
"filename": "salesOverview.pdf",
"purpose": "assistants"
}
```
## File API
### List Files
> OpenAI Equivalent: https://platform.openai.com/docs/api-reference/files/list
### Upload file
> OpenAI Equivalent: https://platform.openai.com/docs/api-reference/files/create
### Delete file
> OpenAI Equivalent: https://platform.openai.com/docs/api-reference/files/delete
### Retrieve file
> OpenAI Equivalent: https://platform.openai.com/docs/api-reference/files/retrieve
### Retrieve file content
> OpenAI Equivalent: https://platform.openai.com/docs/api-reference/files/retrieve-contents
## Files Filesystem
- Files can exist in several parts of Jan's filesystem
- TODO: are files hard copied into these folders? Or do we define a `files.json` and only record the relative filepath?
```sh
/files # root `/files` for finetuning, etc
/assistants
/jan
/files # assistant-specific files
/threads
/jan-12938912
/files # thread-specific files
``` | https://jan.ai/developer/framework/engineering/files |
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