Add embeddings and vector_io tests

[jgarciao]

• Add test to verify embedding model functionality and vector generation.
• Add tests to verify registering, inserting and retrieving information from a milvus vector db database
• Based on the example available at: https://llama-stack.readthedocs.io/en/latest/building_applications/rag.html

[coderabbitai]

📝 Walkthrough

Summary by CodeRabbit

• Tests
  • Added new tests for embedding model inference and vector database operations.
  • Renamed an existing test for improved clarity.
• Chores
  • Updated ignored files to exclude the results/ directory from version control.

Walkthrough

The .gitignore file was updated to exclude the results/ directory from version control. In addition, the RAG test suite was expanded with two new tests covering embedding inference and vector database operations, and an existing test was renamed for clarity without modifying its logic.

Changes

Cohort / File(s)	Change Summary
Git Ignore Update .gitignore	Added results/ to the ignore list, preventing tracked changes to this directory or its contents.
RAG Test Enhancements tests/rag/test_rag.py	Added two tests for embedding inference and vector database ingestion/retrieval; renamed an existing test method.

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~8 minutes

Suggested labels

size/xl, lgtm-by-lugi0

Suggested reviewers

• jiripetrlik
• ChristianZaccaria

Note

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📜 Recent review details

Configuration used: .coderabbit.yaml
Review profile: CHILL
Plan: Pro

📥 Commits

Reviewing files that changed from the base of the PR and between 059bb71 and 79d9103.

📒 Files selected for processing (2)

• .gitignore (1 hunks)
• tests/rag/test_rag.py (3 hunks)

🧰 Additional context used

🧠 Learnings (2)

📓 Common learnings

Learnt from: dbasunag
PR: opendatahub-io/opendatahub-tests#338
File: tests/model_registry/rbac/test_mr_rbac.py:24-53
Timestamp: 2025-06-06T12:22:57.057Z
Learning: In the opendatahub-tests repository, prefer keeping test parameterization configurations inline rather than extracting them to separate variables/constants, as it makes triaging easier by avoiding the need to jump between different parts of the file to understand the test setup.

📚 Learning: in model registry rbac tests, client instantiation tests are designed to verify the ability to creat...

Learnt from: dbasunag
PR: opendatahub-io/opendatahub-tests#354
File: tests/model_registry/rbac/test_mr_rbac.py:64-77
Timestamp: 2025-06-16T11:26:53.789Z
Learning: In Model Registry RBAC tests, client instantiation tests are designed to verify the ability to create and use the MR python client, with actual API functionality testing covered by separate existing tests.

Applied to files:

• tests/rag/test_rag.py

🧬 Code Graph Analysis (1)

tests/rag/test_rag.py (1)

tests/rag/conftest.py (1)

• rag_lls_client (122-155)

🔇 Additional comments (5)

.gitignore (1)

53-53: LGTM! Appropriate exclusion of test artifacts.

Adding results/ to .gitignore is a sensible change that prevents test output and intermediate data from being committed to version control, which aligns with the new RAG tests being added.

tests/rag/test_rag.py (4)

6-7: LGTM! Appropriate type imports for new tests.

The additional imports for EmbeddingsResponse, QueryChunksResponse, and Chunk are necessary for the new embedding and vector I/O tests and follow proper typing practices.

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55-55: LGTM! Improved test method naming.

Renaming to test_rag_chat_completions makes the test purpose more specific and descriptive compared to the generic test_rag_basic_inference.

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82-103: LGTM! Well-structured embedding functionality test.

The new test method effectively validates embedding generation with:

• Clear documentation of test purpose
• Proper model retrieval and dimension extraction
• Comprehensive assertions for response structure and data types
• Appropriate use of type ignore for the output_dimension parameter

The test follows established patterns and provides good coverage for embedding model functionality.

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104-162: LGTM! Comprehensive vector I/O integration test.

This test method excellently validates the complete vector database workflow:

• Proper resource management with unique ID generation and cleanup
• Complete test flow: register → insert → query → cleanup
• Robust error handling with logging for cleanup failures
• Comprehensive assertions validating retrieved data integrity
• Clear documentation referencing the llama-stack documentation

The try/finally structure ensures proper cleanup even on test failures, which is a best practice for integration tests that create external resources.

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[jiripetrlik]

Looks good to me. I'm concerned, though, that we might have too many smoke tests. Perhaps we can move some of them to a higher tier.

[jgarciao]

Looks good to me. I'm concerned, though, that we might have too many smoke tests. Perhaps we can move some of them to a higher tier.

@jiripetrlik , it is a valid concern. Currently, all rag tests (including these 2 new) run in 3m 30s in my machine. In my opinion, we can merge this PR and move some tests to sanity when we have more or they require more time

[jiripetrlik]

Yes @jgarciao , it is true. Let's merge this PR and decide about moving tests to sanity in the future.

[github-actions]

Status of building tag latest: success.
Status of pushing tag latest to image registry: success.