Update doc formatting and fix some spelling.

docs/pages/Deploying/Hosting-the-app.md

@@ -4,7 +4,7 @@ Here's a step-by-step guide on how to setup an Amazon Lightsail instance to host
 
 ## Configuring your instance
 
-(If you know how to create a Lightsail instance, you can skip to the recommended configuration part by clicking here)
+(If you know how to create a Lightsail instance, you can skip to the recommended configuration part by clicking here).
 
 ### 1. Create an account or login to https://lightsail.aws.amazon.com
 
@@ -36,7 +36,7 @@ Your instance will be ready for use a few minutes after being created. To access
 
 #### Clone the repository
 
-A terminal window will pop up, and the first step will be to clone the DocsGPT git repository.
+A terminal window will pop up, and the first step will be to clone the DocsGPT git repository:
 
 `git clone https://github.com/arc53/DocsGPT.git`
 
@@ -64,11 +64,11 @@ 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`
 
-Make sure your .env file looks like this:
+Make sure your `.env` file looks like this:
 
 ```
 OPENAI_API_KEY=(Your OpenAI API key)
@@ -103,10 +103,10 @@ Before you are able to access your live instance, you must first enable the port
 
 Open your Lightsail instance and head to "Networking".
 
-Then click on "Add rule" under "IPv4 Firewall", enter 5173 as your port, and hit "Create". 
+Then click on "Add rule" under "IPv4 Firewall", enter `5173` as your port, and hit "Create". 
-Repeat the process for port 7091.
+Repeat the process for port `7091`.
 
 #### Access your instance
 
-Your instance will now be available under your Public IP Address and port 5173. Enjoy!
+Your instance will now be available under your Public IP Address and port `5173`. Enjoy!
 

docs/pages/Deploying/Quickstart.md

@@ -9,23 +9,23 @@ 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`
+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
+2. Create a `.env` file in your root directory and set your `API_KEY` with your openai api key.
-3. Run `docker-compose build && docker-compose up`
+3. Run `docker-compose build && docker-compose up`.
-4. Navigate to `http://localhost:5173/`
+4. Navigate to `http://localhost:5173/`.
 
-To stop just run Ctrl + C
+To stop just run `Ctrl + C`.
 
 ### Chrome Extension
 
 To install the Chrome extension:
 
-1. In the DocsGPT GitHub repository, click on the "Code" button and select Download ZIP
+1. In the DocsGPT GitHub repository, click on the "Code" button and select "Download ZIP".
-2. Unzip the downloaded file to a location you can easily access
+2. Unzip the downloaded file to a location you can easily access.
-3. Open the Google Chrome browser and click on the three dots menu (upper right corner)
+3. Open the Google Chrome browser and click on the three dots menu (upper right corner).
-4. Select "More Tools" and then "Extensions"
+4. Select "More Tools" and then "Extensions".
-5. Turn on the "Developer mode" switch in the top right corner of the Extensions page
+5. Turn on the "Developer mode" switch in the top right corner of the Extensions page.
-6. Click on the "Load unpacked" button
+6. Click on the "Load unpacked" button.
-7. Select the "Chrome" folder where the DocsGPT files have been unzipped (docsgpt-main > extensions > chrome)
+7. Select the "Chrome" folder where the DocsGPT files have been unzipped (docsgpt-main > extensions > chrome).
-8. The extension should now be added to Google Chrome and can be managed on the Extensions page
+8. The extension should now be added to Google Chrome and can be managed on the Extensions page.
 9. To disable or remove the extension, simply turn off the toggle switch on the extension card or click the "Remove" button.

docs/pages/Developing/API-docs.md

@@ -1,8 +1,8 @@
-App currently has two main api endpoints:
+Currently, the application provides the following main API endpoints:
 
 ### /api/answer 
-Its a POST request that sends a JSON in body with 4 values. Here is a JavaScript fetch example
+It's a POST request that sends a JSON in body with 4 values. It will receive an answer for a user provided question.
-It will receive an answer for a user provided question
+Here is a JavaScript fetch example:
 
 ```js
 // answer (POST http://127.0.0.1:5000/api/answer)
@@ -29,8 +29,8 @@ 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 will make sure documentation is loaded on a server (just run it every time user is switching between libraries (documentations)).
-Its 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 body with 1 value. Here is a JavaScript fetch example:
 
 ```js
 // answer (POST http://127.0.0.1:5000/api/docs_check)
@@ -54,10 +54,10 @@ 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.
 
-Respsonse will include:
+Response will include:
-date, description, docLink, fullName, language, location (local or docshub), model, name, version
+`date`, `description`, `docLink`, `fullName`, `language`, `location` (local or docshub), `model`, `name`, `version`.
 
 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">
@@ -73,7 +73,6 @@ HTML example:
     <input type="text" name="user" value="local" hidden>
     <input type="text" name="name" placeholder="Name:">
     
-
     <button type="submit" class="py-2 px-4 text-white bg-blue-500 rounded-md hover:bg-blue-600 focus:outline-none focus:ring-2 focus:ring-offset-2 focus:ring-blue-500">
         Upload
     </button>
@@ -90,7 +89,7 @@ Response:
 ```
 
 ### /api/task_status
-Gets task status (task_id) from /api/upload
+Gets task status (`task_id`) from `/api/upload`:
 ```js
 // Task status (Get http://127.0.0.1:5000/api/task_status)
 fetch("http://localhost:5001/api/task_status?task_id=b2d2a0f4-387c-44fd-a443-e4fe2e7454d1", {
@@ -105,7 +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 - 100
+1. while task it still running, where "current" will show progress from 0 to 100
 ```json
 {
   "result": {
@@ -134,7 +133,7 @@ There are two types of responses:
 ```
 
 ### /api/delete_old
-deletes old vecotstores
+Deletes old vecotstores:
 ```js
 // Task status (GET http://127.0.0.1:5000/api/docs_check)
 fetch("http://localhost:5001/api/task_status?task_id=b2d2a0f4-387c-44fd-a443-e4fe2e7454d1", {
@@ -146,7 +145,8 @@ fetch("http://localhost:5001/api/task_status?task_id=b2d2a0f4-387c-44fd-a443-e4f
 .then((res) => res.text())
 .then(console.log.bind(console))
 ```
-response:
+
+Response:
 
 ```json
 { "status": "ok" }

docs/pages/Extensions/Chatwoot-extension.md

@@ -1,9 +1,8 @@
 ### To start chatwoot extension:
-1. Prepare and start the DocsGPT itself (load your documentation too) 
+1. Prepare and start the DocsGPT itself (load your documentation too). Follow our [wiki](https://github.com/arc53/DocsGPT/wiki) to start it and to [ingest](https://github.com/arc53/DocsGPT/wiki/How-to-train-on-other-documentation) data.
-Follow our [wiki](https://github.com/arc53/DocsGPT/wiki) to start it and to [ingest](https://github.com/arc53/DocsGPT/wiki/How-to-train-on-other-documentation) data
+2. Go to chatwoot, **Navigate** to your profile (bottom left), click on profile settings, scroll to the bottom and copy **Access Token**.
-2. Go to chatwoot, Navigate to your profile (bottom left), click on profile settings, scroll to the bottom and copy Access Token 
+3. Navigate to `/extensions/chatwoot`. Copy `.env_sample` and create `.env` file.
-2. Navigate to `/extensions/chatwoot`. Copy .env_sample and create .env file
+4. Fill in the values.
-3. Fill in the values
 
 ```
 docsgpt_url=<docsgpt_api_url>
@@ -12,18 +11,19 @@ docsgpt_key=<openai_api_key or other llm key>
 chatwoot_token=<from part 2>
 ```
 
-4. start with `flask run` command
+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 label `human-requested` in your conversation.
 
 
 ### Optional (extra validation)
-In app.py uncomment lines 12-13 and 71-75
+In `app.py` uncomment lines 12-13 and 71-75
 
-in your .env file add:
+in your `.env` file add:
 
-`account_id=(optional) 1 `
+```
+account_id=(optional) 1
+assignee_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

docs/pages/Extensions/react-widget.md

@@ -1,7 +1,7 @@
 ### How to set up react docsGPT widget on your website:
 
 ### Installation
-Got to your project and install a new dependency: `npm install docsgpt`
+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: 
@@ -14,9 +14,9 @@ import "docsgpt/dist/style.css";
 Then you can use it like this: `<DocsGPTWidget />`
 
 DocsGPTWidget takes 3 props:
-- `apiHost` - url of your DocsGPT API
+- `apiHost` — url of your DocsGPT API.
-- `selectDocs` - documentation that you want to use for your widget (eg. `default` or `local/docs1.zip`)
+- `selectDocs` — documentation that you want to use for your widget (eg. `default` or `local/docs1.zip`).
-- `apiKey` - usually its empty
+- `apiKey` — usually its 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:

docs/pages/Guides/Customising-prompts.md

@@ -1,4 +1,4 @@
 ## To customise a main prompt navigate to `/application/prompt/combine_prompt.txt`
 
-You can try editing it to see how the model responds.
+You can try editing it to see how the model responses.
 

docs/pages/Guides/How-to-train-on-other-documentation.md

@@ -3,14 +3,13 @@ This AI can use any documentation, but first it needs to be prepared for similar
 
 ![video-example-of-how-to-do-it](https://d3dg1063dc54p9.cloudfront.net/videos/how-to-vectorise.gif)
 
-Start by going to 
+Start by going to `/scripts/` folder.
-`/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`. 
 
-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 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/`  

docs/pages/Guides/How-to-use-different-LLM.md

@@ -1,10 +1,10 @@
 Fortunately there are many providers for LLM's and some of them can even be ran locally
 
 There are two models used in the app:
-1. Embeddings
+1. Embeddings.
-2. Text generation
+2. Text generation.
 
-By default we use OpenAI's models but if you want to change it or even run it locally, its very simple!
+By default, we use OpenAI's models but if you want to change it or even run it locally, it's very simple!
 
 ### Go to .env file or set environment variables:
 
@@ -18,7 +18,7 @@ By default we use OpenAI's models but if you want to change it or even run it lo
 
 `VITE_API_STREAMING=<true or false (true if using openai, false for all others)>`
 
-You dont need to provide keys if you are happy with users providing theirs, so make sure you set LLM_NAME and EMBEDDINGS_NAME
+You don't need to provide keys if you are happy with users providing theirs, so make sure you set `LLM_NAME` and `EMBEDDINGS_NAME`.
 
 Options:  
 LLM_NAME (openai, manifest, cohere, Arc53/docsgpt-14b, Arc53/docsgpt-7b-falcon)  
@@ -27,6 +27,6 @@ EMBEDDINGS_NAME (openai_text-embedding-ada-002, huggingface_sentence-transformer
 That's it!
 
 ### Hosting everything locally and privately (for using our optimised open-source models)
-If you are working with important data and dont want anything to leave your premises.
+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 you `.env` variable and for your `LLM_NAME` you can use anything that's on Hugging Face.