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Merge pull request #512 from drk1rd/main

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Grammar and punctuations improved
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8
docs/pages/Deploying/Hosting-the-app.md
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@ -18,7 +18,7 @@ After that, it is time to pick your Instance Image. We recommend using "Linux/Un
As for instance plan, it'll vary depending on your unique demands, but a "1 GB, 1vCPU, 40GB SSD and 2TB transfer" setup should cover most scenarios.
Lastly, Identify your instance by giving it a unique name and then hit "Create instance".
Lastly, identify your instance by giving it a unique name and then hit "Create instance".
PS: Once you create your instance, it'll likely take a few minutes for the setup to be completed.
@ -42,7 +42,7 @@ A terminal window will pop up, and the first step will be to clone the DocsGPT g
#### Download the package information
Once it has finished cloning the repository, it is time to download the package information from all sources. To do so simply enter the following command:
Once it has finished cloning the repository, it is time to download the package information from all sources. To do so, simply enter the following command:
`sudo apt update`
@ -64,7 +64,7 @@ Enter the following command to access the folder in which DocsGPT docker-compose
#### Prepare the environment
Inside the DocsGPT folder create a `.env` file and copy the contents of `.env_sample` into it.
Inside the DocsGPT folder, create a `.env` file and copy the contents of `.env_sample` into it.
`nano .env`
@ -95,7 +95,7 @@ You're almost there! Now that all the necessary bits and pieces have been instal
Launching it for the first time will take a few minutes to download all the necessary dependencies and build.
Once this is done you can go ahead and close the terminal window.
Once this is done, you can go ahead and close the terminal window.
#### Enabling ports

6
docs/pages/Deploying/Quickstart.md
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@ -1,7 +1,7 @@
## Launching Web App
Note: Make sure you have Docker installed
On Mac OS or Linux just write:
On macOS or Linux, just write:
`./setup.sh`
@ -10,11 +10,11 @@ It will install all the dependencies and give you an option to download the loca
Otherwise, refer to this Guide:
1. Open and download this repository with `git clone https://github.com/arc53/DocsGPT.git`.
2. Create a `.env` file in your root directory and set your `API_KEY` with your [OpenAI api key](https://platform.openai.com/account/api-keys).
2. Create a `.env` file in your root directory and set your `API_KEY` with your [OpenAI API key](https://platform.openai.com/account/api-keys).
3. Run `docker-compose build && docker-compose up`.
4. Navigate to `http://localhost:5173/`.
To stop just run `Ctrl + C`.
To stop, just run `Ctrl + C`.
### Chrome Extension

14
docs/pages/Developing/API-docs.md
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@ -18,7 +18,7 @@ fetch("http://127.0.0.1:5000/api/answer", {
.then(console.log.bind(console))
```
In response you will get a json document like this one:
In response, you will get a JSON document like this one:
```json
{
@ -30,7 +30,7 @@ In response you will get a json document like this one:
### /api/docs_check
It will make sure documentation is loaded on a server (just run it every time user is switching between libraries (documentations)).
It's a POST request that sends a JSON in body with 1 value. Here is a JavaScript fetch example:
It's a POST request that sends a JSON in a body with 1 value. Here is a JavaScript fetch example:
```js
// answer (POST http://127.0.0.1:5000/api/docs_check)
@ -45,7 +45,7 @@ fetch("http://127.0.0.1:5000/api/docs_check", {
.then(console.log.bind(console))
```
In response you will get a json document like this one:
In response, you will get a JSON document like this one:
```json
{
"status": "exists"
@ -54,17 +54,17 @@ In response you will get a json document like this one:
### /api/combine
Provides json that tells UI which vectors are available and where they are located with a simple get request.
Provides JSON that tells UI which vectors are available and where they are located with a simple get request.
Response will include:
`date`, `description`, `docLink`, `fullName`, `language`, `location` (local or docshub), `model`, `name`, `version`.
Example of json in Docshub and local:
Example of JSON in Docshub and local:
<img width="295" alt="image" src="https://user-images.githubusercontent.com/15183589/224714085-f09f51a4-7a9a-4efb-bd39-798029bb4273.png">
### /api/upload
Uploads file that needs to be trained, response is json with task id, which can be used to check on tasks progress
Uploads file that needs to be trained, response is JSON with task ID, which can be used to check on task's progress
HTML example:
```html
@ -104,7 +104,7 @@ fetch("http://localhost:5001/api/task_status?task_id=b2d2a0f4-387c-44fd-a443-e4f
Responses:
There are two types of responses:
1. while task it still running, where "current" will show progress from 0 to 100
1. While task is still running, where "current" will show progress from 0 to 100
```json
{
"result": {

4
docs/pages/Extensions/Chatwoot-extension.md
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@ -13,7 +13,7 @@ chatwoot_token=<from part 2>
5. Start with `flask run` command.
If you want for bot to stop responding to questions for a specific user or session just add label `human-requested` in your conversation.
If you want for bot to stop responding to questions for a specific user or session, just add a label `human-requested` in your conversation.
### Optional (extra validation)
@ -26,4 +26,4 @@ account_id=(optional) 1
assignee_id=(optional) 1
```
Those are chatwoot values and will allow you to check if you are responding to correct widget and responding to questions assigned to specific user.
Those are chatwoot values and will allow you to check if you are responding to correct widget and responding to questions assigned to specific user.

10
docs/pages/Extensions/react-widget.md
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@ -4,7 +4,7 @@
Got to your project and install a new dependency: `npm install docsgpt`.
### Usage
Go to your project and in the file where you want to use the widget import it:
Go to your project and in the file where you want to use the widget, import it:
```js
import { DocsGPTWidget } from "docsgpt";
import "docsgpt/dist/style.css";
@ -14,12 +14,12 @@ import "docsgpt/dist/style.css";
Then you can use it like this: `<DocsGPTWidget />`
DocsGPTWidget takes 3 props:
- `apiHost` — url of your DocsGPT API.
- `selectDocs` — documentation that you want to use for your widget (eg. `default` or `local/docs1.zip`).
- `apiKey` — usually its empty.
- `apiHost` — URL of your DocsGPT API.
- `selectDocs` — documentation that you want to use for your widget (e.g. `default` or `local/docs1.zip`).
- `apiKey` — usually it's empty.
### How to use DocsGPTWidget with [Nextra](https://nextra.site/) (Next.js + MDX)
Install you widget as described above and then go to your `pages/` folder and create a new file `_app.js` with the following content:
Install your widget as described above and then go to your `pages/` folder and create a new file `_app.js` with the following content:
```js
import { DocsGPTWidget } from "docsgpt";
import "docsgpt/dist/style.css";

2
docs/pages/Guides/Customising-prompts.md
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@ -1,4 +1,4 @@
## To customize a main prompt navigate to `/application/prompt/combine_prompt.txt`
## To customize a main prompt, navigate to `/application/prompt/combine_prompt.txt`
You can try editing it to see how the model responses.

12
docs/pages/Guides/How-to-train-on-other-documentation.md
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@ -5,18 +5,18 @@ This AI can use any documentation, but first it needs to be prepared for similar
Start by going to `/scripts/` folder.
If you open this file you will see that it uses RST files from the folder to create a `index.faiss` and `index.pkl`.
If you open this file, you will see that it uses RST files from the folder to create a `index.faiss` and `index.pkl`.
It currently uses OPEN_AI to create vector store, so make sure your documentation is not too big. Pandas cost me around 3-4$.
It currently uses OPEN_AI to create the vector store, so make sure your documentation is not too big. Pandas cost me around $3-$4.
You can usually find documentation on github in `docs/` folder for most open-source projects.
You can usually find documentation on Github in `docs/` folder for most open-source projects.
### 1. Find documentation in .rst/.md and create a folder with it in your scripts directory
- Name it `inputs/`
- Put all your .rst/.md files in there
- The search is recursive, so you don't need to flatten them
If there are no .rst/.md files just convert whatever you find to txt and feed it. (don't forget to change the extension in script)
If there are no .rst/.md files just convert whatever you find to .txt and feed it. (don't forget to change the extension in script)
### 2. Create .env file in `scripts/` folder
And write your OpenAI API key inside
@ -32,7 +32,7 @@ It will tell you how much it will cost
### 5. Run web app
Once you run it will use new context that is relevant to your documentation
Once you run it will use new context that is relevant to your documentation
Make sure you select default in the dropdown in the UI
## Customization
@ -41,7 +41,7 @@ You can learn more about options while running ingest.py by running:
`python ingest.py --help`
| Options | |
|:--------------------------------:|:------------------------------------------------------------------------------------------------------------------------------:|
| **ingest** | Runs 'ingest' function converting documentation to Faiss plus Index format |
| **ingest** | Runs 'ingest' function, converting documentation to Faiss plus Index format |
| --dir TEXT | List of paths to directory for index creation. E.g. --dir inputs --dir inputs2 [default: inputs] |
| --file TEXT | File paths to use (Optional; overrides directory) E.g. --files inputs/1.md --files inputs/2.md |
| --recursive / --no-recursive | Whether to recursively search in subdirectories [default: recursive] |

4
docs/pages/Guides/How-to-use-different-LLM.md
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@ -1,4 +1,4 @@
Fortunately there are many providers for LLM's and some of them can even be ran locally
Fortunately, there are many providers for LLM's and some of them can even be run locally
There are two models used in the app:
1. Embeddings.
@ -29,4 +29,4 @@ That's it!
### Hosting everything locally and privately (for using our optimised open-source models)
If you are working with important data and don't want anything to leave your premises.
Make sure you set `SELF_HOSTED_MODEL` as true in you `.env` variable and for your `LLM_NAME` you can use anything that's on Hugging Face.
Make sure you set `SELF_HOSTED_MODEL` as true in your `.env` variable and for your `LLM_NAME` you can use anything that's on Hugging Face.

2
docs/pages/Guides/My-AI-answers-questions-using-external-knowledge.md
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@ -1,4 +1,4 @@
If your AI uses external knowledge and is not explicit enough it is ok, because we try to make docsgpt friendly.
If your AI uses external knowledge and is not explicit enough, it is ok, because we try to make docsgpt friendly.
But if you want to adjust it, here is a simple way.