DocIQ

Free Documentation Assistant

DocIQ is a state-of-the-art open-source tool designed to simplify the search for information in project documentation. By harnessing the capabilities of advanced GPT models, developers can effortlessly query project details and receive precise answers.

Bid farewell to tedious manual searches with DocIQ. Discover information swiftly and effortlessly. Experience the revolution in project documentation. Contribute to its development and shape the future of AI-powered assistance.

Features

Project Structure

• Application - Flask app (main application).

• Extensions - Chrome extension.

• Scripts - Script that creates similarity search index for other libraries.

• Frontend - Frontend uses Vite and React.

QuickStart

Note

Make sure you have Docker installed

On Mac OS or Linux, write:

./setup.sh

It will install all the dependencies and allow you to download the local model, use OpenAI or use our LLM API.

Otherwise, refer to this Guide:

1. Download and open this repository with git clone https://github.com/nakshatra-nahar/DocIQ

2. Create a .env file in your root directory and set the env variables and VITE_API_STREAMING to true or false, depending on whether you want streaming answers or not. It should look like this inside:

   LLM_NAME=[dociq or openai or others] 
   VITE_API_STREAMING=true
   API_KEY=[if LLM_NAME is openai]
   

   See optional environment variables in the /.env-template and /application/.env_sample files.

3. Run ./run-with-docker-compose.sh.

4. Navigate to http://localhost:5173/.

To stop, just run Ctrl + C.

Development Environments

Spin up Mongo and Redis

For development, only two containers are used from docker-compose.yaml (by deleting all services except for Redis and Mongo). See file docker-compose-dev.yaml.

Run

docker compose -f docker-compose-dev.yaml build
docker compose -f docker-compose-dev.yaml up -d

Run the Backend

Note

Make sure you have Python 3.10 or 3.11 installed.

1. Export required environment variables or prepare a .env file in the project folder:
   • Copy .env_sample and create .env.

(check out application/core/settings.py if you want to see more config options.)

2. (optional) Create a Python virtual environment: You can follow the Python official documentation for virtual environments.

a) On Mac OS and Linux

python -m venv venv
. venv/bin/activate

b) On Windows

python -m venv venv
 venv/Scripts/activate

3. Download embedding model and save it in the model/ folder: You can use the script below, or download it manually from here, unzip it and save it in the model/ folder.

wget https://d3dg1063dc54p9.cloudfront.net/models/embeddings/mpnet-base-v2.zip
unzip mpnet-base-v2.zip -d model
rm mpnet-base-v2.zip

4. Install dependencies for the backend:

pip install -r application/requirements.txt

5. Run the app using flask --app application/app.py run --host=0.0.0.0 --port=7091.
6. Start worker with celery -A application.app.celery worker -l INFO.

Start Frontend

Note

Make sure you have Node version 16 or higher.

1. Navigate to the /frontend folder.
2. Install the required packages husky and vite (ignore if already installed).

npm install husky -g
npm install vite -g

3. Install dependencies by running npm install --include=dev.
4. Run the app using npm run dev.