Pipelines knowledge base performance tuning

Background

The performance (i.e., throughput of embeddings per second) can be optimized by changing pipeline and model settings. This guide explains the relevant settings and shows how to tune them.

Knowledge base piplines process collections of individual records (rows in a table or objects in a volume). Rather than processing each record individually and sequentially, or processing all of them concurrently, AIDB offers batch processing. All the batches get processed sequentially, one after the other. Within each batch, records get processed concurrently wherever possible.

• Pipeline batch_size determines how many records each batch should have
• Some model providers have configurable internal batch/parallel processing. We recommend leaving these setting at the default values and using the pipeline batch size to control execution.

Testing and tuning performance

We will first set up test data and a knowledge base pipeline, then measure and tune the batch size.

1) Create a table and insert test data

The actual data content length has some impact on model performance. You can use longer text to test that.

CREATE TABLE test_data_10k (id  INT PRIMARY KEY, msg TEXT NOT NULL);

INSERT INTO test_data_10k (id, msg) SELECT generate_series(1, 10000) AS id, 'hello world';

2) Create a knowledge base pipeline

The optimal batch size may be very different for different models. Measure and tune the batch size for each different model you want to use.

SELECT aidb.create_table_knowledge_base(
    name => 'perf_test_b',
    model_name => 'dummy',  -- use the model you want to optimize for
    source_table => 'test_data_10k',
    source_data_column => 'msg',
    source_data_format => 'Text',
    index_type => 'disabled', -- optionally disable vector indexing to include/exclude it from the measurement
    auto_processing => 'Disabled',  -- we want to manually run the pipeline to measure the runtime
    batch_size => 100  -- this is the paramter we will tune during this test
);

INFO:  using vector table: public.perf_test_vector
NOTICE:  index "vdx_perf_test_vector" does not exist, skipping
NOTICE:  auto-processing is set to "Disabled". Manually run "SELECT aidb.bulk_embedding('perf_test');" to compute embeddings.
 create_table_knowledge_base
-----------------------------
 perf_test
(1 row)

3) Run the pipeline, measure the performance

We use psql in this test; the \timing on command is a feature in psql. If you use a different interface, check how it can display timing information.

\timing on

Timing is on.

Now run the pipeline:

SELECT aidb.bulk_embedding('perf_test');

INFO:  perf_test: (re)setting state table to process all data...
INFO:  perf_test: Starting... Batch size 100, unprocessed rows: 10000, count(source records): 10000, count(embeddings): 0
INFO:  perf_test: Batch iteration finished, unprocessed rows: 9900, count(source records): 10000, count(embeddings): 100
INFO:  perf_test: Batch iteration finished, unprocessed rows: 9800, count(source records): 10000, count(embeddings): 200
...
INFO:  perf_test: Batch iteration finished, unprocessed rows: 0, count(source records): 10000, count(embeddings): 10000
INFO:  perf_test: finished, unprocessed rows: 0, count(source records): 10000, count(embeddings): 10000
 bulk_embedding
----------------

(1 row)

Time: 207161,174 ms (03:27,161)

4) Tune the batch size

You can use this call to adjust the batch size of the pipeline. We increase by 10x to 1000 records:

SELECT aidb.set_auto_knowledge_base('perf_test', 'Disabled', batch_size=>1000);

Run the pipeline again.

SELECT aidb.bulk_embedding('perf_test');

INFO:  perf_test: (re)setting state table to process all data...
INFO:  perf_test: Starting... Batch size 1000, unprocessed rows: 10000, count(source records): 10000, count(embeddings): 10000
...
INFO:  perf_test: finished, unprocessed rows: 0, count(source records): 10000, count(embeddings): 10000
 bulk_embedding
----------------

(1 row)

Time: 154276,486 ms (02:34,276)

Conclusion

In this test, the pipeline took 02:34 min with batch size 1000 and 03:27 min with size 100. You can continue testing larger sizes until performance no longer improves, or even declines.