title	emoji	colorFrom	colorTo	sdk	sdk_version	app_file	pinned
Haystack Search Pipeline with Streamlit	👑	indigo	indigo	streamlit	1.23.0	app.py	false

Template Streamlit App for Haystack Search Pipelines

This template Streamlit app set up for simple Haystack search applications. The template is ready to do QA with Retrievel Augmented Generation, or Ectractive QA

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 which does nothing:

Install requirements: pip install -r requirements.txt
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. Before you start editing, you'll notice that the app will only show you instructions on what to edit.

Optional Configurations

You can set optional cofigurations to set the:

--task you want to start the app with: rag or extractive (default: rag)
--store you want to use: inmemory, opensearch, weaviate or milvus (default: inmemory)
--name you want to have for the app. (default: 'My Search App')

E.g.:

streamlit run app.py -- --store opensearch --task extractive --name 'My Opensearch Documentation Search'

In a .env file, include all the config settings that you would like to use based on:

The DocumentStore of your choice
The Extractive/Generative model of your choice

While the /utils/config.py will create default values for some configurations, others have to be set in the .env such as the OPENAI_KEY

Example .env

OPENAI_KEY=YOUR_KEY
EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L12-v2
GENERATIVE_MODEL=text-davinci-003

How to use this template

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

Repo structure

./utils: This is where we have 3 files:
- config.py: This file extracts all of the configuration settings from a .env file. For some config settings, it uses default values. An example of this is in this demo project.
- 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() 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 simple search bar, a 'Run' button, and a response that you can highlight answers with.

What to edit?

There are default pipelines both in start_haystack_extractive() and start_haystack_rag()

Change the pipelines to use the embedding models, extractive or generative models as you need.
If using the rag task, change the default_prompt_template to use one of our available ones on PromptHub or create your own PromptTemplate

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:

Create a New Space on Hugging Face with the Streamlit SDK.
Create a Hugging Face token on your HF account.
Create a secret on your GitHub repo called HF_TOKEN and put your Hugging Face token here.
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!
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.
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

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:
          HF_TOKEN: ${{ secrets.HF_TOKEN }}
        run: git push --force https://{YOUR_HF_USERNAME}:$HF_TOKEN@{YOUR_HF_SPACE_REPO} main

deepset-ai / haystack-search-pipeline-streamlit