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: image ### /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.