Add the tanuki_bot model
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?
Related: https://gitlab.com/gitlab-org/enablement-section/tanuki-bot/-/issues/2
- Adds database migration for tanuki_bot_mvc table
- Adds model for new table
- Add new gem
neighborto 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
-
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: 82686Query 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 msAlso, 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
pgvectorfolder - 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.
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I have evaluated the MR acceptance checklist for this MR.
Edited by Dmitry Gruzd