search_vectors async ¶

async def search_vectors(
    self,
    table_name: "capo_dynamodb.types.table_arn.TableArn",
    index_name: "capo_dynamodb.types.index_name.IndexName",
    search_vector: "capo_dynamodb.types.search_vector_list.SearchVectorList",
    top_k: "capo_dynamodb.types.top_k_integer.TopKInteger",
    *,
    config_overrides: Optional[AsyncDynamoDBClientConfig] = None,
    return_consumed_capacity: Optional[
        "capo_dynamodb.types.return_consumed_capacity.ReturnConsumedCapacity"
    ] = None,
    expression_attribute_names: Optional[
        "capo_dynamodb.types.expression_attribute_name_map.ExpressionAttributeNameMap"
    ] = None,
    expression_attribute_values: Optional[
        "capo_dynamodb.types.expression_attribute_value_map.ExpressionAttributeValueMap"
    ] = None,
    projection_expression: Optional[
        "capo_dynamodb.types.projection_expression.ProjectionExpression"
    ] = None,
    search_condition_expression: Optional[
        "capo_dynamodb.types.string.String"
    ] = None,
) -> "capo_dynamodb.types.search_vectors_output.SearchVectorsOutput":
    """<p>Performs a vector similarity search on a vector index associated with an Amazon DynamoDB table, and returns the most similar items sorted by similarity score based on the distance function configured for the index.</p> <p>Score interpretation depends on the distance function:</p> <ul> <li> <p> <code>COSINE</code> - Returns the items with the <i>k smallest</i> scores. Scores range from 0 (identical) to 2 (opposite). Lower scores indicate higher similarity.</p> </li> <li> <p> <code>EUCLIDEAN</code> - Returns the items with the <i>k smallest</i> scores. Scores represent the Euclidean distance between vectors. Lower scores indicate higher similarity.</p> </li> <li> <p> <code>DOT_PRODUCT</code> - Returns the items with the <i>k highest</i> scores. Higher scores indicate higher similarity.</p> </li> </ul>

    Args:
        table_name: <p>The name or Amazon Resource Name (ARN) of the table containing the vector index.</p>
        index_name: <p>The name of the vector index to search. The index must be in the <code>ACTIVE</code> state.</p>
        expression_attribute_names: <p>One or more substitution tokens for attribute names in an expression. Use the <code>#</code> character in an expression to dereference an attribute name.</p>
        expression_attribute_values: <p>One or more values that can be substituted in an expression. Use the <code>:</code> character in an expression to dereference an attribute value.</p>
        projection_expression: <p>A string that identifies one or more attributes to retrieve from the index. Separate attribute names with commas. If not specified, the operation returns all attributes projected into the vector index.</p> <p>Only attributes projected into the vector index can be retrieved.</p>
        search_vector: <p>The search vector to compare against the indexed vectors. Each element is a 32-bit IEEE-754 floating point number, provided in DynamoDB list format.</p> <p>The number of dimensions must match the number of dimensions configured for the vector index.</p>
        search_condition_expression: <p>A condition expression used to filter the vector search results. The expression can reference attributes defined in the vector index search schema, including <code>HASH</code> and <code>INLINE_FILTER</code> key elements.</p> <p>Only the equality operator (<code>=</code>) is supported for <code>HASH</code> attributes. Comparison and range operators are supported for <code>INLINE_FILTER</code> attributes. Only top-level attributes from the search schema can be referenced.</p>
        top_k: <p>The number of most similar results to return.</p>

    Raises:
        capo_dynamodb.errors.internal_server_error.InternalServerError: <p>An error occurred on the server side.</p>
        capo_dynamodb.errors.request_limit_exceeded.RequestLimitExceeded: <p>Throughput exceeds the current throughput quota for your account. For detailed information about why the request was throttled and the ARN of the impacted resource, find the <a href="https://docs.aws.amazon.com/amazondynamodb/latest/APIReference/API_ThrottlingReason.html">ThrottlingReason</a> field in the returned exception. Contact <a href="https://aws.amazon.com/support">Amazon Web Services Support</a> to request a quota increase.</p>
        capo_dynamodb.errors.resource_not_found_exception.ResourceNotFoundException: <p>The operation tried to access a nonexistent table or index. The resource might not be specified correctly, or its status might not be <code>ACTIVE</code>.</p>
        capo_dynamodb.errors.throttling_exception.ThrottlingException: <p>The request was denied due to request throttling. For detailed information about why the request was throttled and the ARN of the impacted resource, find the <a href="https://docs.aws.amazon.com/amazondynamodb/latest/APIReference/API_ThrottlingReason.html">ThrottlingReason</a> field in the returned exception.</p>
        capo_dynamodb.errors.UnknownServiceError: The service returned an error code this client does not model.

    Examples:
        To search for similar vectors
        This example searches the Products table for the top 3 items most similar to a provided vector, using the 'cosine-product-idx' vector index. The SearchConditionExpression filters results to the 'Electronics' category. The operation returns only the ProductName and Price attributes.

        >>> await client.search_vectors(table_name='Products', index_name='cosine-product-idx', search_vector=[{'N': '0.12'}, {'N': '0.85'}, {'N': '0.44'}, {'N': '0.67'}], top_k=3, search_condition_expression='Category = :cat', projection_expression='ProductName, Price', expression_attribute_values={':cat': {'S': 'Electronics'}}, return_consumed_capacity='INDEXES')
    """

    async def _handler(
        req: "AsyncOperationRequest[capo_dynamodb.types.search_vectors_input.SearchVectorsInput]",
    ) -> AsyncOperationResponse[
        "capo_dynamodb.types.search_vectors_output.SearchVectorsOutput"
    ]:
        import capo_dynamodb._operations.dynamo_db_20120810.search_vectors

        (
            output,
            http_response,
        ) = await capo_dynamodb._operations.dynamo_db_20120810.search_vectors.async_search_vectors(
            req.options, req.input
        )
        return AsyncOperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("SearchVectors")
    )
    input_: capo_dynamodb.types.search_vectors_input.SearchVectorsInput = {
        "table_name": table_name,
        "index_name": index_name,
        "search_vector": search_vector,
        "top_k": top_k,
    }
    if return_consumed_capacity is not None:
        input_["return_consumed_capacity"] = return_consumed_capacity
    if expression_attribute_names is not None:
        input_["expression_attribute_names"] = expression_attribute_names
    if expression_attribute_values is not None:
        input_["expression_attribute_values"] = expression_attribute_values
    if projection_expression is not None:
        input_["projection_expression"] = projection_expression
    if search_condition_expression is not None:
        input_["search_condition_expression"] = search_condition_expression

    response = await aexecute_pipeline(
        AsyncOperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    await response.response.aclose()
    return response.output