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This is the repository for all the material of the OpenSearch Neural Search Tutorial. Here you can find everything you need to deploy a simple OpenSearch system to do neural queries.

Requirements

To directly use the existing material, without generating documents and models by yourself, you only need:

  • OpenSearch 2.11.0

To create documents by yourself you also need:

  • python 3.10

Repository content

Installation

Set up your Docker host environment:

  • macOS & Windows: In Docker Preferences > Resources, set RAM to at least 4 GB.
  • Linux: Ensure vm.max_map_count is set to at least 262144 as per the documentation.

Verify to meet all the installation requirements: https://opensearch.org/docs/latest/install-and-configure/install-opensearch/index/

To generate the documents

You can skip this step if you want to use the already provided material.

To generate documents:

python convert_msmarco_data_to_opensearch_format.py

To start OpenSearch

From the folder containing the docker-compose.yml file, start OpenSearch with:

docker-compose up

OpenSearch will be available at https://localhost:9200/

Usage

Approximate Nearest Neighbor Search

{
  "_source": [
      "general_text"
  ],
  "query": {
    "neural": {
      "general_text_vector": {
        "query_text": "what is a bank transit number",
        "model_id": "loaded_neural_model_id",
        "k": 3
      }
    }
  }
}

Approximate Nearest Neighbor with Query Filter

{
  "query": {
    "bool": {
      "filter": {
        "term": {
          "color": "white"
        }
      },
      "must": {
        "neural": {
          "general_text_vector": {
            "query_text": "what is a bank transit number",
            "model_id": "loaded_neural_model_id",
            "k": 3
          }
        }
      }
    }
  }
}

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