mirror of
https://github.com/Mintplex-Labs/anything-llm.git
synced 2024-11-15 19:00:33 +01:00
63 lines
3.1 KiB
Markdown
63 lines
3.1 KiB
Markdown
# 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
|
|
|
|
|
|
### 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
|
|
- 
|
|
- [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.
|
|
|
|
### Using ther Twitter API
|
|
***required to get data form twitter with tweepy**
|
|
- Go to https://developer.twitter.com/en/portal/dashboard with your twitter account
|
|
- Create a new Project App
|
|
- Get your 4 keys and place them in your `collector.env` file
|
|
* TW_CONSUMER_KEY
|
|
* TW_CONSUMER_SECRET
|
|
* TW_ACCESS_TOKEN
|
|
* TW_ACCESS_TOKEN_SECRET
|
|
populate the .env with the values
|