Hayhooks

# RAG example: indexing and querying with Elasticsearch This example will show how you can deploy with Hayhooks an index pipeline and a query pipeline, using Elasticsearch as the document store. ## Prerequisites - [Docker](https://docs.docker.com/get-docker/) ## 1. Create a virtual environment It's always a good idea to create a virtual environment to install the dependencies in. ```bash python -m venv .venv source .venv/bin/activate ``` ## 2. Install the dependencies We'll need to install the Hayhooks package and the other dependencies needed for the pipelines. ```bash pip install -r requirements.txt ``` ## 3a. Launch Hayhooks For simplicity, we'll use the Hayhooks CLI to launch the Hayhooks server. ```bash hayhooks run ``` You can achieve the same result by running [a Docker image with Hayhooks already installed](https://github.com/deepset-ai/hayhooks-open-webui-docker-compose). ## 3b. Launch Elasticsearch ```bash docker compose up ``` This will start an [Elasticsearch](https://www.elastic.co/elasticsearch) instance on port 9200. ## 4. Deploy the pipelines ```bash hayhooks pipeline deploy-files -n indexing indexing_pipeline hayhooks pipeline deploy-files -n query query_pipeline ``` Let's also check on <http://localhost:1416/docs> if the pipelines are deployed correctly. ## 5. Test the indexing pipeline We'll use `hayhooks pipeline run` to run the indexing pipeline. We will index the files in the `files_to_index` directory, then launch a query to check if the indexing was successful. ```bash hayhooks pipeline run indexing --dir files_to_index ``` ## 6. Test the query pipeline ```bash hayhooks pipeline run query --param 'question="is this recipe vegan?"' ```