diff --git a/docs/pages/Deploying/Hosting-the-app.md b/docs/pages/Deploying/Hosting-the-app.md
index 7505f60..13296b4 100644
--- a/docs/pages/Deploying/Hosting-the-app.md
+++ b/docs/pages/Deploying/Hosting-the-app.md
@@ -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
diff --git a/docs/pages/Deploying/Quickstart.md b/docs/pages/Deploying/Quickstart.md
index 2cc03c5..5ed37a5 100644
--- a/docs/pages/Deploying/Quickstart.md
+++ b/docs/pages/Deploying/Quickstart.md
@@ -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
diff --git a/docs/pages/Developing/API-docs.md b/docs/pages/Developing/API-docs.md
index eabd29c..1b908a8 100644
--- a/docs/pages/Developing/API-docs.md
+++ b/docs/pages/Developing/API-docs.md
@@ -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:
### /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": {
diff --git a/docs/pages/Extensions/Chatwoot-extension.md b/docs/pages/Extensions/Chatwoot-extension.md
index 4dd5782..e95891a 100644
--- a/docs/pages/Extensions/Chatwoot-extension.md
+++ b/docs/pages/Extensions/Chatwoot-extension.md
@@ -13,7 +13,7 @@ chatwoot_token=
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.
\ No newline at end of file
+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.
diff --git a/docs/pages/Extensions/react-widget.md b/docs/pages/Extensions/react-widget.md
index be4d6bd..1cc1132 100644
--- a/docs/pages/Extensions/react-widget.md
+++ b/docs/pages/Extensions/react-widget.md
@@ -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 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";
diff --git a/docs/pages/Guides/Customising-prompts.md b/docs/pages/Guides/Customising-prompts.md
index 1d3a7d4..19dcdef 100644
--- a/docs/pages/Guides/Customising-prompts.md
+++ b/docs/pages/Guides/Customising-prompts.md
@@ -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.
diff --git a/docs/pages/Guides/How-to-train-on-other-documentation.md b/docs/pages/Guides/How-to-train-on-other-documentation.md
index 64ee103..2e8e4af 100644
--- a/docs/pages/Guides/How-to-train-on-other-documentation.md
+++ b/docs/pages/Guides/How-to-train-on-other-documentation.md
@@ -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] |
diff --git a/docs/pages/Guides/How-to-use-different-LLM.md b/docs/pages/Guides/How-to-use-different-LLM.md
index aa5815f..c0245a1 100644
--- a/docs/pages/Guides/How-to-use-different-LLM.md
+++ b/docs/pages/Guides/How-to-use-different-LLM.md
@@ -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.
diff --git a/docs/pages/Guides/My-AI-answers-questions-using-external-knowledge.md b/docs/pages/Guides/My-AI-answers-questions-using-external-knowledge.md
index be1bffa..fb15835 100644
--- a/docs/pages/Guides/My-AI-answers-questions-using-external-knowledge.md
+++ b/docs/pages/Guides/My-AI-answers-questions-using-external-knowledge.md
@@ -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.