Documentation

You can access this dialog via Search UX and by adding an Elasticsearch instance as search engine.

1. Document index name: this is the name of the document index.

2. Principal index name: this is the name of the principal index. The principal index is where the connector stored user-group relationships and which is used to construct the ACL entries for the user.

3. Elasticsearch base URL: this is the FQDN, with port and protocol which points to the REST endpoint of the Elasticsearch instance.

4. Use basic authentication: if you configured basic authentication for securing direct access to the Elasticsearch instance, check this box.

   1. Username: is the username for basic auth.

   2. Password: is the according password.

5. Public keys for SSL certificates: this configuration is needed, if you run the environment with self-signed certificates, or certificates which are not known to the Java key store.
   We use a straight-forward approach to validate SSL certificates. In order to render a certificate valid, add the modulus of the public key into this text field. You can access this modulus by viewing the certificate within the browser.

6. Response timeout: determines the timeout when waiting for Elasticsearch responses in milliseconds.

7. Connection timeout: determines the timeout when waiting for connections to Elasticsearch in milliseconds.

8. Minimum should match: this determines the minimum should match for the query. Where 100 means that all search terms should be in the search result.

9. Vector field for body. This is the second field, which will be used for vector search, if a vectorized content is found (see the query pipeline documentation at Query Transformers .

10. Vector field for title. This is the first field, which will be used for vector search, if a vectorized content is found (see the query pipeline documentation at Query Transformers .

11. K factor for vector search: this parameter is relevant for vector search and defines how many results will be returned when having a vector search, cf. k-nearest neighbor (kNN) search | Elasticsearch Guide [8.15] | Elastic

12. Num candidates for vector search: this parameter is relevant for vector search and defines how many results will be returned when having a vector search, cf. k-nearest neighbor (kNN) search | Elasticsearch Guide [8.15] | Elastic

When finished with setting these fields, click on validate and save. If you observe any issues, then the validator will let you know or you can find more insights in the log files.

Vector Search Dimensions

When using vector search, you need to make sure that the embeddings you use in the query, as well as in the content transformation pipeline have exactly the same dimension as the index fields vector body and vector title. Otherwise indexing or querying the index will fail.