Build a Hotel Search Application with RAGStack and Astra DB Serverless

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This page demonstrates using RAGStack and an vector-enabled Astra DB Serverless database to build a Hotels Search application.

The application uses an vector-enabled Astra DB Serverless database to store hotel data, and RAGStack to search for hotels and generate summaries.


  1. Clone the Git repository and change to that directory.

    git clone
    cd langchain-astrapy-hotels-app
  2. You will need an vector-enabled Astra DB Serverless database.

    1. Create an Astra vector database.

    2. Within your database, create an Astra DB Access Token with Database Administrator permissions.

    3. Copy your Astra DB Serverless API Endpoint for the vector-enabled Astra DB Serverless database, as displayed in Astra Portal.

  3. Set the following environment variables in a .env file in langchain-astrapy-hotels-app:

  4. Install the following dependencies:

    pip install ragstack-ai fastapi python-dotenv uvicorn

The installed dependencies differ from the requirements.txt file in the repo because RAGStack includes many of them already.

See the Prerequisites page for more details on finding these values.

Load the data

  1. From the root folder, run four Python scripts to populate your database with data collections.

    • Python

    • Result

    python -m setup.2-populate-review-vector-collection
    python -m setup.3-populate-hotels-and-cities-collections
    python -m setup.4-create-users-collection
    python -m setup.5-populate-reviews-collection
    [] Finished. 10000 rows written.
    [] Inserted 1433 hotels
    [] Inserted 842 cities
    [] Inserted 10000 reviews
  2. Each script populates a different collection in your vector-enabled Astra DB Serverless database, including a collection of precalculated embeddings for vector search.

The application will use these collections to deliver valuable, personalized results to users.

Run the application

Now that your vector database is populated, run the application frontend to see the results.

  1. Open a new terminal and start the API server.

    uvicorn api:app --reload
  2. Open a new terminal and change directory to the client folder. Install the node dependencies and start the application.

    npm install
    npm start
  3. Open http://localhost:3000 to view the application in your browser. Click "Login" in the upper right corner, enter any values for the username and password, and click Login.

  4. Enter US for the country and a US city for the location, and click Search.

  5. The application lists hotels, including an OpenAI-generated summary of reviews from the reviews collection.

  6. Selecting "Details" will show more information about the hotel, including a summary based on your Preferences, stored in the users collection.


If your results summaries aren’t displaying and you’re getting openai errors from pydantic, run pip install pydantic~=1.10.10 to downgrade pydantic.


  1. Use ctrl+c to stop the API server and the application.

  2. Use the Data API command below or see delete a collection to delete the created collections and make room in your vector-enabled Astra DB Serverless database.

    curl -v -s --location \
    --request POST "" \
    --header "X-Cassandra-Token: AstraCS:..." \
    --header "Content-Type: application/json" \
    --header "Accept: application/json" \
    --data '{
      "deleteCollection": {
        "name": "hotels"

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