# How to collect data for vectorizing This process should be run first. This will enable you to collect a ton of data across various sources. Currently the following services are supported: - [x] YouTube Channels - [x] Medium - [x] Substack - [x] Arbitrary Link - [x] Gitbook - [x] Local Files (.txt, .pdf, etc) [See full list](./hotdir/__HOTDIR__.md) _these resources are under development or require PR_ - Twitter ![Choices](../images/choices.png) ### Requirements - [ ] Python 3.8+ - [ ] Google Cloud Account (for YouTube channels) - [ ] `brew install pandoc` [pandoc](https://pandoc.org/installing.html) (for .ODT document processing) ### Setup This example will be using python3.9, but will work with 3.8+. Tested on MacOs. Untested on Windows - install virtualenv for python3.8+ first before any other steps. `python3.9 -m pip install virtualenv` - `cd collector` from root directory - `python3.9 -m virtualenv v-env` - `source v-env/bin/activate` - `pip install -r requirements.txt` - `cp .env.example .env` - `python main.py` for interactive collection or `python watch.py` to process local documents. - Select the option you want and follow follow the prompts - Done! - run `deactivate` to get back to regular shell ### Outputs All JSON file data is cached in the `output/` folder. This is to prevent redundant API calls to services which may have rate limits to quota caps. Clearing out the `output/` folder will execute the script as if there was no cache. As files are processed you will see data being written to both the `collector/outputs` folder as well as the `server/documents` folder. Later in this process, once you boot up the server you will then bulk vectorize this content from a simple UI! If collection fails at any point in the process it will pick up where it last bailed out so you are not reusing credits. ### Running the document processing API locally From the `collector` directory with the `v-env` active run `flask run --host '0.0.0.0' --port 8888`. Now uploads from the frontend will be processed as if you ran the `watch.py` script manually. **Docker**: If you run this application via docker the API is already started for you and no additional action is needed. ### How to get a Google Cloud API Key (YouTube data collection only) **required to fetch YouTube transcripts and data** - Have a google account - [Visit the GCP Cloud Console](https://console.cloud.google.com/welcome) - Click on dropdown in top right > Create new project. Name it whatever you like - ![GCP Project Bar](../images/gcp-project-bar.png) - [Enable YouTube Data APIV3](https://console.cloud.google.com/apis/library/youtube.googleapis.com) - Once enabled generate a Credential key for this API - Paste your key after `GOOGLE_APIS_KEY=` in your `collector/.env` file.