title: Haystack Application with Streamlit emoji: 👑 colorFrom: indigo colorTo: indigo sdk: streamlit sdkversion: 1.41.1 appfile: app.py

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Template for Haystack Apps with Streamlit

This template Streamlit app is set up for simple Haystack applications. The template is ready to do Retrievel Augmented Generation on example files.

See the 'How to use this template' instructions below to create a simple UI for your own Haystack search pipelines.

Below you will also find instructions on how you could push this to Hugging Face Spaces 🤗.

Installation and Running

To run the bare application: 1. Install requirements: pip install -r requirements.txt 2. Include all environment variable in a .env file Example .env WEAVIATE_API_KEY="YOUR_KEY" MISTRAL_API_KEY="YOUR_KEY" # this demo uses Mistral models by default 3. Decide on the files and the method to populate your database (Check out instructions in haystack.py) 4. Run the streamlit app: streamlit run app.py

This will start up the app on localhost:8501 where you will find a simple search bar.

How to use this template

1. Create a new repository from this template or simply open it in a codespace to start playing around 💙
2. Make sure your requirements.txt file includes the Haystack (haystack-ai) and Streamlit versions you would like to use.
3. Change the code in utils/haystack.py if you would like a different pipeline.
4. Create a .env file with all of your configuration settings.
5. Make any UI edits if you'd like to.
6. Run the app as show in installation and running

Repo structure

• ./utils: This is where we have 2 files:
  • haystack.py: Here you will find some functions already set up for you to start creating your Haystack search pipeline. It includes 2 main functions called start_haystack_pipeline() which is what we use to create a pipeline and cache it, and query() which is the function called by app.py once a user query is received.
  • ui.py: Use this file for any UI and initial value setups.
• app.py: This is the main Streamlit application file that we will run. In its current state it has a sidebar, a simple search bar, a 'Run' button, and a response.
• ./files: You can use this folder to store files to be indexed.

What to edit?

There are default pipelines both in start_document_store() and start_haystack_pipeline(). Change the pipelines to use different document stores, embedding and generative models or update the pipelines as you need. Check out 📚 Useful Resources section for details.

📚 Useful Resources

• Get Started
• Docs
  • Creating Custom Components
• Tutorials
• Integrations
  • Mistral
  • Weaviate

Pushing to Hugging Face Spaces 🤗

Below is an example GitHub action that will let you push your Streamlit app straight to the Hugging Face Hub as a Space.

A few things to pay attention to:

1. Create a New Space on Hugging Face with the Streamlit SDK.
2. Create a Hugging Face token on your HF account.
3. Create a secret on your GitHub repo called HF_TOKEN and put your Hugging Face token here.
4. If you're using DocumentStores or APIs that require some keys/tokens, make sure these are provided as a secret for your HF Space too!
5. This readme is set up to tell HF spaces that it's using streamlit and that the app is running on app.py, make any changes to the frontmatter of this readme to display the title, emoji etc you desire.
6. Create a file in .github/workflows/hf_sync.yml. Here's an example that you can change with your own information, and an example workflow working for the Should I Follow demo

```yaml name: Sync to Hugging Face hub on: push: branches: [main]

# to run this workflow manually from the Actions tab workflow_dispatch:

jobs: sync-to-hub: runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 with: fetch-depth: 0 lfs: true - name: Push to hub env: HFTOKEN: ${{ secrets.HFTOKEN }} run: git push --force https://{YOURHFUSERNAME}:$HFTOKEN@{YOURHFSPACEREPO} main ```