README.md

---
title: Document Insights - Extractive & Generative Methods
emoji: 👑
colorFrom: indigo
colorTo: indigo
sdk: streamlit
sdk_version: 1.23.0
app_file: app.py
pinned: false
---

# Document Insights - Extractive & Generative Methods using Haystack

This template [Streamlit](https://docs.streamlit.io/) app set up for
simple [Haystack search applications](https://docs.haystack.deepset.ai/docs/semantic_search). 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 🤗](#pushing-to-hugging-face-spaces-).

## Installation and Running

### Local development

To run the bare application which does _nothing_:

1. Install requirements: `pip install -r requirements.txt`
2. 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:

1. Build the Docker image: `docker build -t haystack-streamlit .`
2. Run the Docker container: `docker run -p 8501:8501 haystack-streamlit` (make sure to bind any other ports you need)
3. 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](https://github.com/TuanaCelik/should-i-follow/blob/main/utils/config.py).
    - `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](https://prompthub.deepset.ai) or create your own `PromptTemplate`

### Using local LLM models

To use the `local LLM` mode you can use [LM Studio](https://lmstudio.ai/) or [Ollama](https://ollama.com/).
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:

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](https://github.com/TuanaCelik/should-i-follow/blob/main/.github/workflows/hf_sync.yml)
   working for the [Should I Follow demo](https://huggingface.co/spaces/deepset/should-i-follow)

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