This is a web application for creating, managing, and querying MindsDB Knowledge Bases. It provides a user-friendly interface for stress-testing the new MindsDB Knowledge Base release.

• Create Knowledge Base: Create new knowledge bases directly from the web interface.
• Ingest Data: Upload text files to populate your knowledge bases.
• Metadata Support: Add metadata to your data during ingestion.
• Semantic Search: Perform semantic searches on your knowledge bases.
• Metadata Filtering: Filter your search results based on metadata.
• Automated Jobs: Create jobs to periodically update your knowledge bases from a given source.
• AI Table Integration: Create AI tables that leverage your knowledge bases to generate insights.

• Docker
• Python 3
• Node.js and npm

1. Clone the repository:

   git clone https://github.com/gyash1512/knowledge-base-app.git
   cd knowledge-base-app

2. Create a virtual environment and install Python dependencies:

   python3 -m venv venv
   source venv/bin/activate
   pip install -r requirements.txt

3. Install frontend dependencies:

   cd frontend
   npm install

4. Create a .env file in the root of the project and add your MindsDB credentials and other configurations:

   MINDSDB_USER=your-mindsdb-email
   MINDSDB_PASSWORD=your-mindsdb-password
   MINDSDB_TCP_PORT=47334
   MINDSDB_HTTP_PORT=47335
   FLASK_PORT=5000
   MINDSDB_HOST=http://127.0.0.1
   

   You can create a free MindsDB account at https://mindsdb.com/.

5. Set up Google Application Credentials: If you are using a Google-based model like gemini, you will need to provide Google Application Credentials.

   a. Install the Google Cloud CLI: Follow the instructions at https://cloud.google.com/sdk/docs/install to install the gcloud CLI.

   b. Log in to your Google Cloud account:

   gcloud auth login

   c. Create application default credentials:

   gcloud auth application-default login

   This will create a JSON file in your home directory (e.g., ~/.config/gcloud/application_default_credentials.json).

   d. Update the docker-compose.yml file to mount this file into the MindsDB container. Replace ~/.config/gcloud/application_default_credentials.json with the actual path to your credentials file.

1. Start the application with Docker Compose:

   docker-compose up --build

2. Access the application by navigating to http://localhost:5000 in your web browser.

1. Start the MindsDB Docker container:

   docker-compose up -d

2. Build the React application:

   cd frontend
   npm run build
   cd ..

3. Start the Flask server:

   python app.py

4. Access the application by navigating to http://127.0.0.1:5000 in your web browser.

The application is divided into several sections, each corresponding to a specific feature.

The home page serves as a dashboard with links to all the available features.

This page displays a list of all your existing knowledge bases.

To create a new knowledge base, simply enter a name for it and click the "Create" button.

To ingest data into a knowledge base, you need to provide the name of the knowledge base, select a text file to upload, and optionally add metadata in JSON format.

To query a knowledge base, you can provide a search query, a metadata filter, or both.

• Search Query: This performs a semantic search on the content column of your knowledge base. For example, you could search for "gaming laptop".
• Metadata Filter: This allows you to filter the results based on the metadata you provided during ingestion. The filter should be in a SQL WHERE clause format. For example, to find all documents with the product "Laptop", you would enter product = 'Laptop'.
• Combined Query: You can combine a search query and a metadata filter to perform a more specific search. For example, to find all gaming laptops, you could use the search query "gaming laptop" and the metadata filter product = 'Laptop'.

To create a job, you need to provide a name for the job, the name of the knowledge base to ingest data into, a source URL for the data, and a schedule for the job to run.

To create an AI table, you need to provide a name for the table, the name of the knowledge base to use as a source, and the name of the model to use.

To create a workflow, you need to provide a name for the workflow, the name of the knowledge base to use as a source, the column you want to predict, and a list of models to use in the workflow.

To query a workflow, you need to provide the name of the workflow and a query. The query can be provided as text, a file, or from another knowledge base.

.
├── app.py                  # Flask backend
├── docker-compose.yml      # Docker Compose configuration
├── frontend/               # React frontend
│   ├── public/
│   ├── src/
│   │   ├── components/     # React components
│   │   ├── pages/          # React pages
│   │   ├── App.js
│   │   ├── index.css
│   │   └── index.js
│   ├── package.json
│   └── tailwind.config.js
├── README.md               # This file
└── requirements.txt        # Python dependencies