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https://github.com/meta-llama/llama-stack.git
synced 2025-12-12 12:06:04 +00:00
pre-commit and unit test fixes
This commit is contained in:
Ashwin Bharambe
parent
e5a1cdf554
commit
eaa91aa408
2 changed files with 37 additions and 22 deletions
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@ -327,7 +327,7 @@ async def test_create_vector_store_file_batch(vector_io_adapter):
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vector_io_adapter._process_file_batch_async = AsyncMock()
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batch = await vector_io_adapter.openai_create_vector_store_file_batch(
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vector_store_id=store_id, params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(file_ids=file_ids)
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params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(vector_store_id=store_id, file_ids=file_ids)
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)
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assert batch.vector_store_id == store_id
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@ -354,7 +354,7 @@ async def test_retrieve_vector_store_file_batch(vector_io_adapter):
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# Create batch first
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created_batch = await vector_io_adapter.openai_create_vector_store_file_batch(
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vector_store_id=store_id, params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(file_ids=file_ids)
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params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(vector_store_id=store_id, file_ids=file_ids)
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)
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# Retrieve batch
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@ -387,7 +387,7 @@ async def test_cancel_vector_store_file_batch(vector_io_adapter):
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# Create batch
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batch = await vector_io_adapter.openai_create_vector_store_file_batch(
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vector_store_id=store_id, params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(file_ids=file_ids)
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params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(vector_store_id=store_id, file_ids=file_ids)
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)
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# Cancel batch
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@ -432,7 +432,7 @@ async def test_list_files_in_vector_store_file_batch(vector_io_adapter):
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# Create batch
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batch = await vector_io_adapter.openai_create_vector_store_file_batch(
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vector_store_id=store_id, params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(file_ids=file_ids)
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params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(vector_store_id=store_id, file_ids=file_ids)
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)
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# List files
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@ -451,7 +451,9 @@ async def test_file_batch_validation_errors(vector_io_adapter):
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# Test nonexistent vector store
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with pytest.raises(VectorStoreNotFoundError):
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await vector_io_adapter.openai_create_vector_store_file_batch(
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vector_store_id="nonexistent", params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(file_ids=["file_1"])
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params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(
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vector_store_id="nonexistent", file_ids=["file_1"]
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),
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)
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# Setup store for remaining tests
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@ -468,7 +470,7 @@ async def test_file_batch_validation_errors(vector_io_adapter):
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# Test wrong vector store for batch
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vector_io_adapter.openai_attach_file_to_vector_store = AsyncMock()
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batch = await vector_io_adapter.openai_create_vector_store_file_batch(
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vector_store_id=store_id, params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(file_ids=["file_1"])
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params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(vector_store_id=store_id, file_ids=["file_1"])
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)
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# Create wrong_store so it exists but the batch doesn't belong to it
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@ -515,7 +517,7 @@ async def test_file_batch_pagination(vector_io_adapter):
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# Create batch
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batch = await vector_io_adapter.openai_create_vector_store_file_batch(
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vector_store_id=store_id, params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(file_ids=file_ids)
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params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(vector_store_id=store_id, file_ids=file_ids)
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)
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# Test pagination with limit
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@ -587,7 +589,7 @@ async def test_file_batch_status_filtering(vector_io_adapter):
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# Create batch
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batch = await vector_io_adapter.openai_create_vector_store_file_batch(
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vector_store_id=store_id, params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(file_ids=file_ids)
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params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(vector_store_id=store_id, file_ids=file_ids)
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)
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# Test filtering by completed status
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@ -629,7 +631,7 @@ async def test_cancel_completed_batch_fails(vector_io_adapter):
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# Create batch
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batch = await vector_io_adapter.openai_create_vector_store_file_batch(
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vector_store_id=store_id, params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(file_ids=file_ids)
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params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(vector_store_id=store_id, file_ids=file_ids)
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)
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# Manually update status to completed
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@ -663,7 +665,7 @@ async def test_file_batch_persistence_across_restarts(vector_io_adapter):
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# Create batch
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batch = await vector_io_adapter.openai_create_vector_store_file_batch(
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vector_store_id=store_id, params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(file_ids=file_ids)
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params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(vector_store_id=store_id, file_ids=file_ids)
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)
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batch_id = batch.id
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@ -718,7 +720,7 @@ async def test_cancelled_batch_persists_in_storage(vector_io_adapter):
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# Create batch
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batch = await vector_io_adapter.openai_create_vector_store_file_batch(
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vector_store_id=store_id, params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(file_ids=file_ids)
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params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(vector_store_id=store_id, file_ids=file_ids)
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)
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batch_id = batch.id
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@ -765,10 +767,10 @@ async def test_only_in_progress_batches_resumed(vector_io_adapter):
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# Create multiple batches
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batch1 = await vector_io_adapter.openai_create_vector_store_file_batch(
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vector_store_id=store_id, params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(file_ids=["file_1"])
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params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(vector_store_id=store_id, file_ids=["file_1"])
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)
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batch2 = await vector_io_adapter.openai_create_vector_store_file_batch(
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vector_store_id=store_id, params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(file_ids=["file_2"])
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params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(vector_store_id=store_id, file_ids=["file_2"])
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)
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# Complete one batch (should persist with completed status)
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@ -781,7 +783,7 @@ async def test_only_in_progress_batches_resumed(vector_io_adapter):
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# Create a third batch that stays in progress
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batch3 = await vector_io_adapter.openai_create_vector_store_file_batch(
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vector_store_id=store_id, params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(file_ids=["file_3"])
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params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(vector_store_id=store_id, file_ids=["file_3"])
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)
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# Simulate restart - clear memory and reload from persistence
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@ -942,7 +944,7 @@ async def test_max_concurrent_files_per_batch(vector_io_adapter):
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file_ids = [f"file_{i}" for i in range(8)] # 8 files, but limit should be 5
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batch = await vector_io_adapter.openai_create_vector_store_file_batch(
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vector_store_id=store_id, params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(file_ids=file_ids)
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params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(vector_store_id=store_id, file_ids=file_ids)
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)
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# Give time for the semaphore logic to start processing files
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@ -13,7 +13,10 @@ from unittest.mock import AsyncMock, MagicMock
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import numpy as np
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import pytest
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from llama_stack.apis.inference.inference import OpenAIEmbeddingData
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from llama_stack.apis.inference.inference import (
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OpenAIEmbeddingData,
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OpenAIEmbeddingsRequestWithExtraBody,
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)
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from llama_stack.apis.tools import RAGDocument
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from llama_stack.apis.vector_io import Chunk
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from llama_stack.providers.utils.memory.vector_store import (
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@ -226,9 +229,14 @@ class TestVectorDBWithIndex:
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await vector_db_with_index.insert_chunks(chunks)
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mock_inference_api.openai_embeddings.assert_called_once_with(
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"test-model without embeddings", ["Test 1", "Test 2"]
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)
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# Verify openai_embeddings was called with correct params
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mock_inference_api.openai_embeddings.assert_called_once()
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call_args = mock_inference_api.openai_embeddings.call_args[0]
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assert len(call_args) == 1
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params = call_args[0]
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assert isinstance(params, OpenAIEmbeddingsRequestWithExtraBody)
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assert params.model == "test-model without embeddings"
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assert params.input == ["Test 1", "Test 2"]
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mock_index.add_chunks.assert_called_once()
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args = mock_index.add_chunks.call_args[0]
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assert args[0] == chunks
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@ -321,9 +329,14 @@ class TestVectorDBWithIndex:
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await vector_db_with_index.insert_chunks(chunks)
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mock_inference_api.openai_embeddings.assert_called_once_with(
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"test-model with partial embeddings", ["Test 1", "Test 3"]
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)
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# Verify openai_embeddings was called with correct params
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mock_inference_api.openai_embeddings.assert_called_once()
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call_args = mock_inference_api.openai_embeddings.call_args[0]
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assert len(call_args) == 1
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params = call_args[0]
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assert isinstance(params, OpenAIEmbeddingsRequestWithExtraBody)
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assert params.model == "test-model with partial embeddings"
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assert params.input == ["Test 1", "Test 3"]
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mock_index.add_chunks.assert_called_once()
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args = mock_index.add_chunks.call_args[0]
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assert len(args[0]) == 3
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