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Available Parameters

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search_query
string
required
The main search query string.
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num_batches
integer
default:"1"
The number of synthetic data batches to generate for the search.
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top_k
integer
default:"10"
The maximum number of results to return.

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Configuring Parameters in the Playground

1

Synthetic Dataset Size

Adjust the ‘Synthetic Dataset Size’ input to control how much synthetic data is used for each search request. This correlates to the num_batches parameter.
2

Proba Threshold

Set the ‘Proba Threshold’ to filter and keep only results above a certain probability. This helps in refining the quality of your search results.
3

Top K Threshold

Use the ‘Top K Threshold’ to limit the number of results returned. This corresponds to the top_k parameter.

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Using Parameters in API Requests

When making API requests, you can include these parameters in your query string: 
curl -X GET "https://api.search-artemis.com/search?search_query=HIPAA%20compliant%20companies&num_batches=2&top_k=5" \
  -H "Authorization: Bearer YOUR_API_KEY"
Remember to replace YOUR_API_KEY with your actual Artemis Search API key.

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Advanced Search Parameters

We also provide advanced search parameters that you can use to further refine your search results.

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Filter Query

If your dataset has filter columns, you can use the filter_query parameter to filter the search results using the filter columns in the dataset. Your filter query should be included in the form { filter_query: "YOUR_FILTER_QUERY" } in the body of your search request. The filter query you provide may be any valid Pandas query string and assume the ‘python’ engine is used to evaluate the query. Learn more about valid filter queries here.
ChatGPT is a fantastic resource for generating filter queries.

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Example 1:

If you had a dataset of companies with a filter column called company_size with the values small, medium, and large, you could filter the search results to only include search results with the company_size of ‘large’ by setting the filter_query parameter to company_size == 'large'. Your search body would look like this: 
{
  ...
  "filter_query": "company_size == 'large'"
}

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Example 2:

If you had a dataset of companies with a filter column called country which contained the names of countries associated with each search result, you could filter the search results to only include search results with a country that started with United by setting the filter_query parameter to country.str.contains('United', case=False, na=False). Your search body would look like this: 
{
  ...
  "filter_query": "country.str.contains('United', case=False, na=False)"
}
You must have at least one filter column in your dataset to use the filter_query parameter.

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Best Practices

  • Start with a larger top_k value and gradually decrease it as you refine your search.
  • Experiment with different num_batches values to balance between search depth and response time.

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Next Steps