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Keyword Search

Vectara disables exact and Boolean text matching by default, which is similar to a traditional, keyword-based search. Exact keyword matching is particularly useful in environments that do not require semantic search where specific phrases and terms are crucial to the desired outcome. This can include information in legal, compliance, technical fields where you might need specific error codes of system specifications.

You enable exact keyword matching, which disables neural retrieval, by specifying the lambda value as 1 at query time, specifically under the corpusKey:

      "corpusKey": [
        {
          "customerId": 123456789,
          "corpusId": 5,
          "semantics": 0,
          "metadataFilter": "",
          "lexicalInterpolationConfig": {
            "lambda": 1.0
          },
          "dim": []
        }
      ]

Enable Exact Keyword Matching in the Console UI​

You can also set this value in the Console UI and experiment with searches and disable the hybrid search option.

Set Lambda to 1.0

The default value of lambda is 0, which disables exact and Boolean text matching.

The following example shows the full query with the lambda value set to 1:


curl -X POST \
-H "Authorization: <Bearer Token>" \
-H "customer-id: 1234567899" \
https://api.vectara.io:443/v1/query \
-d @- <<END;
{
  "query": [
    {
      "query": "What is offsides?",
      "queryContext": "",
      "start": 0,
      "numResults": 10,
      "contextConfig": {
        "charsBefore": 0,
        "charsAfter": 0,
        "sentencesBefore": 2,
        "sentencesAfter": 2,
        "startTag": "%START_SNIPPET%",
        "endTag": "%END_SNIPPET%"
      },
      "corpusKey": [
        {
          "customerId": 123456789,
          "corpusId": 5,
          "semantics": 0,
          "metadataFilter": "",
          "lexicalInterpolationConfig": {
            "lambda": 1.0
          },
          "dim": []
        }
      ],
      "summary": [
        {
          "maxSummarizedResults": 5,
          "responseLang": "eng",
          "summarizerPromptName": "vectara-summary-ext-v1.2.0"
        }
      ]
    }
  ]
}
END

Experimenting with the lambda value is useful if you're trying to evaluate how a keyword system like one based on Elasticsearch or Solr may compare to Vectara.