Install the Proxima CE package

Prerequisites

Make sure you have completed the environment preparation.

Download the Proxima CE package

Click Proxima CE package to download the installation package.

The installation package contains the executable JAR file for Proxima CE. Upload this file to your MaxCompute project as a resource. You can then call the JAR file to run Proxima CE tasks.

Upload as a MaxCompute resource

You can upload the downloaded package to your MaxCompute project by using either the MaxCompute client (odpscmd) or DataWorks. This topic uses DataWorks as an example to show how to upload and publish the resource. For information about how to upload a resource by using odpscmd, see Add resources.

1. On the Data Development page in DataWorks, visually upload the installation package as a JAR resource.

   Note

   When you create or upload a resource using the DataWorks console, note the following:

   • If the resource has not been uploaded to MaxCompute, select Upload to MaxCompute. If the resource has already been uploaded to MaxCompute, deselect Upload to MaxCompute. Otherwise, the upload will fail.

   • If you select Upload to MaxCompute during the upload, the resource is stored in both DataWorks and MaxCompute. If you later delete the resource from MaxCompute by using a command, the resource still exists in DataWorks and remains visible.

   • The resource name does not need to match the name of the uploaded file.

   In the Create Resource dialog box, set File Source to Local, and click Click to upload to upload the package file. If the resource type is JAR, you must add the .jar suffix to the resource name.

2. Commit and publish the resource.

   After you create the resource, click the icon in the toolbar of the resource editor to commit the resource to the scheduling development server.

   Note

   If a production task needs to use this resource, you must also deploy the resource to the production environment. For more information, see Deploy tasks.

Prepare input tables

Before running the task, you must prepare two input tables:

• doc table: The table that contains the base vectors to search.

• query table: The table containing the query vectors used to find the nearest neighbors.

-- Create a doc table
CREATE TABLE doc_table_float_smoke(pk STRING,vector STRING <,category BIGINT>) PARTITIONED BY (pt STRING);
-- Create a query table
CREATE TABLE query_table_float_smoke(pk STRING,vector STRING <,category BIGINT>) PARTITIONED BY (pt STRING);

Next steps: Use vector search

After a vector search task runs, an output table is automatically generated and stored in MaxCompute. You do not need to create this table. Specify the table name by using the -output_table parameter in your Proxima CE code. For more information about the output table format, see Output table format.

Output table format

After a vector search task runs, an output table is automatically generated and stored in MaxCompute. The format of the output table is described as follows.

• Table name: The name of the output table that you specified in the Proxima CE code.

  • The output table name cannot contain a period (.). This is a special character in MaxCompute and causes MaxCompute to fail to parse the table name.

  • The output table name cannot contain the tmp_ string. Otherwise, the task fails.

  • An output table name and its partition name cannot exceed 64 characters in length. Otherwise, the task fails.

• Fields

  Field	Description	Type
  pk	The pk value for each query in the query table.	STRING by default. Values in the pk column can be numbers or strings, such as string values 1.nid,2.nid,3.nid,... or INT64 numeric values 123,456,789,.... If the pk column stores only INT64 numbers, you can set the column type to BIGINT. If you also set the -pk_type startup parameter to INT64, you can improve performance.
  knn_result	The pk value of the matched record in the doc table.	STRING by default.
  score	The similarity score of the retrieved doc.	The default data type is STRING. In Proxima CE, results are uniformly sorted in descending order by similarity score. Note The score field's meaning depends on the distance algorithm. However, Proxima CE unifies the sorting so that results are always returned in descending order of similarity. For inner_product and mips_squared_euclidean distance, the score represents similarity, where a higher value indicates greater similarity. For other distance algorithms, the calculation is based on distance, where a lower value indicates greater similarity.
  category	The category field for multi-category search. This field is required only for multi-category search.	BIGINT by default.
  pt	The partition field.	STRING by default.