Index and search e docs

ALLOW_ORIGINS="*"

ES_HOST="localhost"

ES_PORT=9200

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__pycache__/

.env

venv

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# When using dind, it's wise to use the overlayfs driver for

# improved performance.

variables:

    DOCKER_DRIVER: overlay2

stages:

  - build

  - deploy

default:

  image: docker:20.10

# Build stage

.build:docker:

  stage: build

  tags:

    - generic_privileged

  services:

    - docker:20.10-dind

  before_script:

    - docker login -u "$CI_REGISTRY_USER" -p "$CI_REGISTRY_PASSWORD" $CI_REGISTRY

  script:

    - docker build . --pull -t "$CI_REGISTRY_IMAGE:$IMAGE_TAG" -f Dockerfile

    - docker push "$CI_REGISTRY_IMAGE:$IMAGE_TAG"

build:branch:

  extends: .build:docker

  rules:

    - if: '$CI_COMMIT_TAG'

      when: never

    - if: '$CI_PIPELINE_SOURCE == "merge_request_event"'

      when: never

    - if: '$CI_PIPELINE_SOURCE =~ /schedule|web|api|trigger/ && $IMAGE_TAG != $CI_COMMIT_REF_SLUG'

      when: never

    - when: on_success

  variables:

    IMAGE_TAG: $CI_COMMIT_REF_SLUG

FROM python:3.9.6-alpine3.13 AS py

EXPOSE 5000

WORKDIR /usr/src/app

COPY requirements.txt ./

RUN pip install --no-cache-dir -r requirements.txt

COPY src .

CMD [ "flask", "run" ]

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Full-text search for your Jekyll blog with ElasticSearch.

## Installation

1. Clone the project

1. run `python3 -m venv venv` to create the virtual env

1. run `. venv/bin/activate` to be able to use the python packages

1. run `pip install -r requirements.txt` to install all packages

To be able to use the linting inside the virtual environment, it is recomended to use the python inside the virtual env folder. Take a look at [here](https://code.visualstudio.com/docs/python/environments#_select-and-activate-an-environment) for vscode

## Development

1. You need a running copy of elasticsearch:

```bash

docker run \

--name elasticsearch \

--rm \

-p 9200:9200 \

-p 9300:9300 \

-e "discovery.type=single-node" \

elasticsearch:7.13.3

```

1. Make sure you are at the virtual env (run `. venv/bin/activate`)

1. To index your content, run: `python3 src/main.py "PATH_TO_YOUR_CONTENT"`

1. run `export FLASK_ENV=development`

1. run `export FLASK_APP=src/app`

1. run `flask run`

## Features

 - Parses the html from your Jekyll `_site` directory using BeautifulSoup to get more accurate content instead of using the raw Markdown.

### Indexing:

 - Make sure you have an ElasticSearch server running. If not local, change the config in `indexer.py` to reflect your location.

 - Run the command `python main.py <path_to_blog>`, running without an argument will assume your compiled blog is located at `~/blog/_site`.

 - Run the command `python src/main.py <path_to_blog>`, running without an argument will assume your compiled blog is located at `~/blog/_site`.

 - If the library cannot find your content correctly, modify `indexer.py` to point to the correct HTML elements for title, post content etc (assuming you have unique CSS classes for these).

### Searching: