Rename embedchain to mem0 and open sourcing code for long term memory (#1474)
Co-authored-by: Deshraj Yadav <deshrajdry@gmail.com>
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embedchain/docs/deployment/streamlit_io.mdx
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embedchain/docs/deployment/streamlit_io.mdx
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---
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title: 'Streamlit.io'
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description: 'Deploy your RAG application to streamlit.io platform'
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---
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Embedchain offers a Streamlit template to facilitate the development of RAG chatbot applications in just three easy steps.
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Follow the instructions given below to deploy your first application quickly:
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## Step-1: Create RAG app
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We provide a command line utility called `ec` in embedchain that inherits the template for `streamlit.io` platform and help you deploy the app. Follow the instructions to create a streamlit.io app using the template provided:
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```bash Install embedchain
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pip install embedchain
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```
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```bash Create application
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mkdir my-rag-app
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ec create --template=streamlit.io
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```
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This will generate a directory structure like this:
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```bash
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├── .streamlit
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│ └── secrets.toml
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├── app.py
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├── embedchain.json
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└── requirements.txt
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```
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Feel free to edit the files as required.
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- `app.py`: Contains API app code
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- `.streamlit/secrets.toml`: Contains secrets for your application
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- `embedchain.json`: Contains embedchain specific configuration for deployment (you don't need to configure this)
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- `requirements.txt`: Contains python dependencies for your application
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Add your `OPENAI_API_KEY` in `.streamlit/secrets.toml` file to run and deploy the app.
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## Step-2: Test app locally
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You can run the app locally by simply doing:
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```bash Run locally
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pip install -r requirements.txt
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ec dev
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```
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## Step-3: Deploy to streamlit.io
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Use the deploy button from the streamlit website to deploy your app.
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You can refer this [guide](https://docs.streamlit.io/streamlit-community-cloud/deploy-your-app) if you run into any problems.
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## Seeking help?
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If you run into issues with deployment, please feel free to reach out to us via any of the following methods:
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<Snippet file="get-help.mdx" />
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