Add the tanuki_bot model (!118195) · Merge requests · GitLab.org / GitLab

Dmitry Gruzd requested to merge add-tanuki-bot-model into master Apr 20, 2023

Background

This is part of a series of MRs and should be reviewed and merged in this order

MR	status
Add support for the embedding database (!118156 - merged) (db setup)	merged ✅
Add the tanuki_bot model (!118195 - merged) (migration)	merged ⭐
Create initial Tanuki bot api endpoint (!117695 - merged) (api)	in review

What does this MR do and why?

Adds database migration for tanuki_bot_mvc table
Adds model for new table
Add new gem neighbor to allow ActiveRecord to interact with pgvector data structures
Setup pgvector and support for embedding database in CI
- use postgres-pgvector images merged in gitlab-build-images!675 (merged) in CI

Context

We need the pgvector extension installed and enabled for this new database. It is needed for AI experimentation features that require storing and searching embeddings. We don't want to make it a dependency for the main DB on GitLab.com and for self-managed customers.

Gemfile changes

Legal and compliance issue: https://gitlab.com/gitlab-com/legal-and-compliance/-/issues/1479+

gem: https://github.com/ankane/neighbor

From the guidelines

They have an active developer community. At the minimum a maintainer should still be active to merge change requests in case of emergencies.
There are no issues open that we know may impact the availability or performance of GitLab.
The project is tested using some form of test automation. The test suite must be passing using the Ruby version currently used by GitLab. - pipeline: https://github.com/ankane/neighbor/actions/runs/4585609385
CI builds for all supported platforms must succeed using the new dependency. For more information, see how to build a package for testing.
[-] ~~If the project uses a C extension, consider requesting an additional review from a C or MRI domain expert. C extensions can greatly impact GitLab stability and performance.~~

Database

Migrations

The database testing pipeline does not support the new embedding database yet.

Up

➜ be rails db:migrate:up:embedding VERSION=20230420103900 RAILS_ENV=development
embedding: == [advisory_lock_connection] object_id: 273720, pg_backend_pid: 83667
embedding: == 20230420103900 CreateTanukiBotMvc: migrating ===============================
embedding: -- create_table(:tanuki_bot_mvc)
embedding: -- quote_column_name(:url)
embedding:    -> 0.0000s
embedding: -- quote_column_name(:content)
embedding:    -> 0.0000s
embedding: -- quote_column_name(:chroma_id)
embedding:    -> 0.0000s
embedding:    -> 0.0416s
embedding: == 20230420103900 CreateTanukiBotMvc: migrated (0.0423s) ======================

embedding: == [advisory_lock_connection] object_id: 273720, pg_backend_pid: 83667

Down

➜ be rails db:migrate:down:embedding VERSION=20230420103900 RAILS_ENV=development
embedding: == [advisory_lock_connection] object_id: 273760, pg_backend_pid: 82686
embedding: == 20230420103900 CreateTanukiBotMvc: reverting ===============================
embedding: -- drop_table(:tanuki_bot_mvc)
embedding:    -> 0.0065s
embedding: == 20230420103900 CreateTanukiBotMvc: reverted (0.0072s) ======================

embedding: == [advisory_lock_connection] object_id: 273760, pg_backend_pid: 82686

Query plan

This is a plan from the local database, which uses the dataset we're going to use in production for this MVC. We've experimented with indexing, but it is only an approximation and we had very inaccurate results. So currently we need to use sequential scans. The database node will be quite powerful with a lot of cache to handle these sequential reads.

gitlabhq_development_embedding=# SELECT COUNT(*) FROM tanuki_bot_mvc;                                                                                                             count                                                                                                                                                                                                                                        -------
 12742
(1 row)

This is the nearest neighbor search we're going to use:

Click to expand

EXPLAIN (ANALYZE) SELECT "tanuki_bot_mvc"."id" FROM "tanuki_bot_mvc" WHERE "tanuki_bot_mvc"."embedding" IS NOT NULL ORDER BY "tanuki_bot_mvc"."embedding" <#> '[-0.010012891, 0.007275803, -0.0071718628, -0.029491255, -0.033787444, 0.02913093, -0.03581081, -0.03181951, -0.035034724, -0.020746432, 0.020386107, 0.013269679, -0.020289097, -0.007691563, -0.000622341, -0.0044105225, 0.03913689, -0.027038269, 0.011038433, -0.005314801, -0.021882843, -0.000371
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463537, -0.025707837, -0.01994263, -0.034508094, -0.0089873485, -0.00049198285, -0.016034484, -0.0024304646]' LIMIT 7;

Limit  (cost=3427.84..3427.86 rows=7 width=16) (actual time=40.273..40.275 rows=7 loops=1)
   ->  Sort  (cost=3427.84..3459.70 rows=12742 width=16) (actual time=40.272..40.272 rows=7 loops=1)
         Sort Key: ((embedding <#> 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         Sort Method: top-N heapsort  Memory: 25kB
         ->  Seq Scan on tanuki_bot_mvc  (cost=0.00..3185.28 rows=12742 width=16) (actual time=0.053..39.415 rows=12742 loops=1)
               Filter: (embedding IS NOT NULL)
 Planning Time: 0.138 ms
 Execution Time: 40.307 ms

Also, see thread

How to set up and validate locally

Follow instructions for settings up the MR dependency: Add support for the embedding database (!118156 - merged)
Run the migration:
```
rails db:migrate
```
Clone the tanuki-bot repository
cd into the pgvector folder
use asdf to install python (should be python 3.11.3)
```
asdf install python
```
install pip
install requirements
```
pip install -r requirements.txt
```

run the command to copy the chroma db to the gdk postgres database

PG_USER=<Your user> PG_HOST=<Your gdk install directory>/postgresql python chroma_to_pg.py

open rails console
run the following to do a query using the new model

you must have an OPENAI API key to do this test

::Gitlab::CurrentSettings.update!(openai_api_key: '<YOUR_OPENAI_KEY>')

current_user = User.first; client = ::Gitlab::Llm::OpenAi::Client.new(current_user); question = 'What is Advanced Search?'

embeddings_result = client.embeddings(input: question); question_embedding = embeddings_result['data'].first['embedding']; nil

File.open('tmp/question.txt', 'w+'){|f| f.write question_embedding.join(',') }

Embedding::TanukiBot.neighbor_for(question_embedding).limit(5).pluck(:id)

MR acceptance checklist

This checklist encourages us to confirm any changes have been analyzed to reduce risks in quality, performance, reliability, security, and maintainability.

I have evaluated the MR acceptance checklist for this MR.

Edited Apr 25, 2023 by Dmitry Gruzd

Add the tanuki_bot model