metadata

title: NLP Q&A Tool
emoji: 👑
colorFrom: indigo
colorTo: indigo
sdk: streamlit
sdk_version: 1.32.2
app_file: app.py
pinned: false

Document Insights - Extractive & Generative Methods using Haystack

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

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

Installation and Running

Local development

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.

Docker

To run the app in a Docker container:

Build the Docker image: docker build -t haystack-streamlit .
Run the Docker container: docker run -p 8501:8501 haystack-streamlit (make sure to bind any other ports you need)
Open your browser and go to http://localhost:8501

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.
requirements.txt: This file includes the required libraries to run the Streamlit app.
document_qa_engine.py: This file includes the QA pipeline with Haystack.

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

Using local LLM models

To use the local LLM mode you can use LM Studio or Ollama. For more info on how to run the app with a local LLM model please refer to the documentation of the tool you are using. The local_llm mode expects an API available at http://localhost:1234/v1.

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