}

    /**
     * Registers a data store instance with the factory using the specified name.
     * The data store will be accessible by both the provided name and its class simple name,
     * both converted to lowercase for case-insensitive lookup.
     *
     * @param name the name to register the data store under, must not be null

          echo "DB_HOST=$(echo ${PERFORMANCE_DB_URL} | cut -d/ -f3 | cut -d: -f1)" >> $GITHUB_ENV
      
      - name: Clean up stale data and verify results
        run: |
          echo "Cleaning up stale data from both databases..."
          docker run --rm mysql:latest mysql \
            -u${{ env.PERFORMANCE_DB_USERNAME }} \
            -p${{ env.PERFORMANCE_DB_PASSWORD_TCAGENT }} \
            -h${{ env.DB_HOST }} \
            -e '

This way, you can get the best of both worlds: type annotations with **tooling support** and **data filtering**.

## See it in the docs { #see-it-in-the-docs }

When you see the automatic docs, you can check that the input model and output model will both have their own JSON Schema:

<img src="/img/tutorial/response-model/image01.png">

And both models will be used for the interactive API documentation:

 * text during indexing and search operations.
 *
 * <p>Each mapping item consists of one or more input character sequences that are mapped to a single output
 * character sequence. The mapping supports both original values and new values for update operations.</p>
 */
public class CharMappingItem extends DictionaryItem {
    /**
     * Array of input character sequences that will be mapped to the output sequence.

     *
     * @since 4.0.0
     */
    enum Nature {
        /**
         * Query only release repositories to discover versions.
         */
        RELEASE,
        /**
         * Query only snapshot repositories to discover versions.
         */
        SNAPSHOT,
        /**
         * Query both release and snapshot repositories to discover versions.

  public void testRoundToDouble_twoToThe54PlusOne() {
    double twoToThe54 = Math.pow(2, 54);
    // the representable doubles are 2^54 and 2^54 + 4
    // 2^54+1 is less than halfway between, so HALF_DOWN and HALF_UP will both go down.
    new RoundToDoubleTester(BigDecimal.valueOf((1L << 54) + 1))
        .setExpectation(twoToThe54, DOWN, FLOOR, HALF_DOWN, HALF_UP, HALF_EVEN)
        .setExpectation(Math.nextUp(twoToThe54), CEILING, UP)

        assertEquals("testValue", bean2.getName());

        // Second call should be faster (though this is not guaranteed in all environments)
        // We mainly verify that both calls work correctly
        assertTrue(time2 >= 0, "Expected " + time2 + " to be >= " + 0); // Just verify it completed
    }

    @Test
    void testCacheClearance() throws Exception {

                new String[] { SuggestConstants.DEFAULT_ROLE }, null, SuggestItem.Kind.QUERY);

        // Both Document and Query (simulated by setting both frequencies)
        String[][] readings3 = new String[1][];
        readings3[0] = new String[] { "both" };
        items[2] = new SuggestItem(new String[] { "両方" }, readings3, new String[] { "content" }, 1L, 1L, -1.0f, new String[] { "tag1" },

     */
    public void add(final String name, final ScriptEngine scriptEngine) {
        if (name == null || scriptEngine == null) {
            throw new IllegalArgumentException(
                    "Both name and scriptEngine parameters are required. name: " + name + ", scriptEngine: " + scriptEngine);
        }
        if (logger.isDebugEnabled()) {
            logger.debug("Loaded ScriptEngine: {}", name);
        }

        end
    end

    style V2 fill:#f9fff3
    style V1 fill:#fff6f0
    style V1Field fill:#fff6f0
    style V2Field fill:#f9fff3
```

In some cases, it's even possible to have both Pydantic v1 and v2 models in the same **path operation** in your FastAPI app:

{* ../../docs_src/pydantic_v1_in_v2/tutorial003_an_py310.py hl[2:3,6,12,21:22] *}