e2b-dev / fragments
- понедельник, 21 октября 2024 г. в 00:00:03
Open-source Next.js template for building apps that are fully generated by AI. By E2B.
This is an open-source version of Anthropic's Claude Artifacts, Vercel v0 and GPT Engineer.
Powered by the E2B SDK.
Make sure to give us a star!
In your terminal:
git clone https://github.com/e2b-dev/fragments.git
Enter the repository:
cd fragments
Run the following to install the required dependencies:
npm i
Create a .env.local
file and set the following:
# Get your API key here - https://e2b.dev/
E2B_API_KEY="your-e2b-api-key"
# OpenAI API Key
OPENAI_API_KEY=
# Other providers
ANTHROPIC_API_KEY=
GROQ_API_KEY=
FIREWORKS_API_KEY=
TOGETHER_AI_API_KEY=
GOOGLE_AI_API_KEY=
MISTRAL_API_KEY=
### Optional env vars
# Domain of the site
NEXT_PUBLIC_SITE_URL=
# Disabling API key and base URL input in the chat
NEXT_PUBLIC_NO_API_KEY_INPUT=
NEXT_PUBLIC_NO_BASE_URL_INPUT=
# Rate limit
RATE_LIMIT_MAX_REQUESTS=
RATE_LIMIT_WINDOW=
# Vercel/Upstash KV (short URLs, rate limiting)
KV_REST_API_URL=
KV_REST_API_TOKEN=
# Supabase (auth)
SUPABASE_URL=
SUPABASE_ANON_KEY=
# PostHog (analytics)
NEXT_PUBLIC_POSTHOG_KEY=
NEXT_PUBLIC_POSTHOG_HOST=
npm run dev
npm run build
Make sure E2B CLI is installed and you're logged in.
Add a new folder under sandbox-templates/
Initialize a new template using E2B CLI:
e2b template init
This will create a new file called e2b.Dockerfile
.
Adjust the e2b.Dockerfile
Here's an example streamlit template:
# You can use most Debian-based base images
FROM python:3.19-slim
RUN pip3 install --no-cache-dir streamlit pandas numpy matplotlib requests seaborn plotly
# Copy the code to the container
WORKDIR /home/user
COPY . /home/user
Specify a custom start command in e2b.toml
:
start_cmd = "cd /home/user && streamlit run app.py"
Deploy the template with the E2B CLI
e2b template build --name <template-name>
After the build has finished, you should get the following message:
✅ Building sandbox template <template-id> <template-name> finished.
Open lib/templates.json in your code editor.
Add your new template to the list. Here's an example for Streamlit:
"streamlit-developer": {
"name": "Streamlit developer",
"lib": [
"streamlit",
"pandas",
"numpy",
"matplotlib",
"request",
"seaborn",
"plotly"
],
"file": "app.py",
"instructions": "A streamlit app that reloads automatically.",
"port": 8501 // can be null
},
Provide a template id (as key), name, list of dependencies, entrypoint and a port (optional). You can also add additional instructions that will be given to the LLM.
Optionally, add a new logo under public/thirdparty/templates
Open lib/models.json in your code editor.
Add a new entry to the models list:
{
"id": "mistral-large",
"name": "Mistral Large",
"provider": "Ollama",
"providerId": "ollama"
}
Where id is the model id, name is the model name (visible in the UI), provider is the provider name and providerId is the provider tag (see adding providers below).
Open lib/models.ts in your code editor.
Add a new entry to the providerConfigs
list:
Example for fireworks:
fireworks: () => createOpenAI({ apiKey: apiKey || process.env.FIREWORKS_API_KEY, baseURL: baseURL || 'https://api.fireworks.ai/inference/v1' })(modelNameString),
Optionally, adjust the default structured output mode in the getDefaultMode
function:
if (providerId === 'fireworks') {
return 'json'
}
Optionally, add a new logo under public/thirdparty/logos
As an open-source project, we welcome contributions from the community. If you are experiencing any bugs or want to add some improvements, please feel free to open an issue or pull request.